Explaining Socio-economic Causes of Urban Unemployment
and Policy Responses in Ethiopia
Tesfaye Chofana and Tegegn Gebeyaw
firstname.lastname@example.org and email@example.com
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.
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
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.
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
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
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
188.8.131.52. Characteristics of Micro and Small Scale Enterprises .................................................................. 101
184.108.40.206. Employment Contribution of MSEs.............................................................................................. 103
220.127.116.11. Startup Motives of MSEs .............................................................................................................. 105
18.104.22.168. Constraints of Micro and Small Scale Enterprises ........................................................................ 105
22.214.171.124. Market and Other Constraints to Expand Business ................................................................... 105
126.96.36.199. Source of Startup Capital and Capital Growth .............................................................................. 107
188.8.131.52. Cause of Job Interruption .............................................................................................................. 108
184.108.40.206. Assistance Needed from Government ........................................................................................... 109
220.127.116.11. Determinants Urban Employment Growth within MSEs .............................................................. 110
CONCLUSIONS AND RECOMMENDATIONS ........................................................... 112
5.1. Conclusions .................................................................................................................................................... 112
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
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
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.
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.
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
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
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
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
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.
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,
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?
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
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
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.
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
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.
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
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
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.
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
Population above Specified Age
Currently active population
Source: Prakash (2001)
(the labor force)
Population Not Current
ly Active (NILF)
age), or other
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
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,
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
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
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.
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
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
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.
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
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,
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
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
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
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
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
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
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
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
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
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
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.
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).
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
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
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.
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
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
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
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
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
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
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
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
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
and statistically significant. A person who received some sort of training is 10.9 percent and
8.7 percent less likely to be unemployed in 1999 and 2005, respectively compared with a
person who did not. Similarly, the finding of Seife (2006) confirms “very high returns to higher
education, at least in terms of the probability of getting employment” (pp. 193). People with
vocational, college or university education have higher exit rates from unemployment than
secondary school graduates. Although less significant, the coefficients of primary education
imply shorter unemployment durations than secondary education. On the other hand, the
findings of Guracello, Lyon and Rosati (2008a) indicate that the probability of employment
decreases as the level of education increases, implying a positive relationship between
unemployment and level of education. Also as indicated in Serneels (2007), the probability of
being unemployed increases with education up to senior secondary level. Longer duration of
unemployment is associated with junior secondary education while it is shorter for senior
2.7. Policy Responses to Address Unemployment in Ethiopia
There have been a number of policy responses since the early 1970s introduced to create
employment and increase employability in Ethiopia. Nevertheless, for the purpose of this
study, among the various policy interventions made in recent years, only two - one from the
supply side and the other one from the demand side - are chosen for discussion. The supply
side policy response focuses on the expansion of Technical and Vocational Education and
Training (TVET) programs and the demand side policy response focuses on the development
of Micro and Small Scale Enterprises (MSEs).
Expansion of TVET programs and
development of MSEs are among the major development strategies and policy priorities of the
current government of Ethiopia. Indeed, as a large body of the literature argues, such policies
are subcategories of ALMPs often said to have significant impact on employment opportunities
of working age population in general and of women and the youth in particular.
2.7.1. Expansion of Technical and Vocational Education and Training Programs
Expansion of education and training is among the active labor market policies that
governments adopt to increase the employability of the labor force. TVET is generally a kind
of education and training program mainly aimed at leading participants to acquire practical
skills, knowhow and understanding, and necessary for employment in a particular occupation,
trade or group of occupations (Atchoerena, 2002). The multidisciplinary nature of TVET and
its supposedly close links to the world of work make it one of the education sectors that
contributes greatly to the training of skilled labor (Rena, 2006). In many sub-Saharan African
countries, TVET is going through a stage of transition and reorientation in the region, as efforts
are being made to give students some basic skills and knowledge, as well as the tools they need
to play an active role in the production system (Atchoerena, 2002)
In 1994, a new education and training policy has been carried out considering the drawbacks of
the so far educational systems of Ethiopia. The new policy has given emphasis to education
and training that offer specific learning skills related to the specific needs or gainfully tradable
skills based on demand driven and in response to the country’s development approach. The
government considered Technical and Vocational Education and Training (TVET) as an
instrument for producing medium level technicians equipped with practical knowledge who
can create job rather than expecting employment opportunities to be offered by public. Also as
noted in the new GTP document, greater emphasis is given to TVET institutions so as to make
them serve as centers for technology transfer and accumulation for MSEs as well as to provide
employable skill to the youth.
TVET is expected to play a key role in this strategy by building the required motivated and
competent workforce. In the early strategic plan document, PASDEP, TVET is envisaged to
provide the necessary “relevant and demand-driven education and training that corresponds to
the needs of economic and social sectors for employment and self-employment” (MoE, 2008).
In reaction to the reform in the educational sector, enrollment has increased considerably in all
levels including TVET and higher education. Particularly, Technical and Vocational Education
and Training (TVET) enrolment reached 371,347 in 2010/11, showing a 5.1 percent increase
relative to 2009/10 and a 94.3 percent compared to 2006/07. In the same way, the number of
TVET institutions increased to 505 in 2010/11 from 388 in 2006/07 (NBE, 2011).
2.7.2. Micro and Small Scale Enterprises (MSEs) Development
The informal sector accounts for the majority of employment in Ethiopia. According to the
2005 LFS, it represented 71 percent of urban employment overall and 81 percent of youth
employment. Several sectors are almost exclusively informal. These include domestic work,
wholesale and retail trade, hotels and restaurants, and primary production. Overall,
manufacturing accounts for about 45 percent and trade/ hotels/ restaurants for about 38 percent
of informal firms (WB, 2007). Due to the growing labor supply and limited formal
employment opportunities, there is a lot of interest in building the capacity of the informal
economy that employs a significant portion of the labor force. On the other hand, small and
medium enterprises (SMEs) comprise the largest share of enterprises and employment in the
non-agricultural sector in Ethiopia. Therefore, SMEs have been a special focus of the
government and the promotion and development of SMEs was emphasized as one of the most
effective means for achieving faster development and creating job opportunities, especially for
women and the youth (MoLSA, 2009).
The Federal Micro and Small Enterprise Development Strategy Agency (FeMSEDA) oversees
the promotion of micro and small enterprises development, while the direct support and
promotional activities are carried out by institutions established at the Regional States
(ReMSEDA). According to Birhanu, Abraham, and van der Dejil (2005), the Regional
Governments have been promoting MSEs by providing training and counseling, finance and
credit facilities, organizational support, production and marketing space, market facilities and
raw material supplies. The results of the support provided to MSEs have been encouraging.
About 72,577 new jobs were created in micro and small enterprises, nearly 63 per cent in
Addis Ababa, in 2004.
MSEs constitute nearly 90 per cent of industrial employment. In recognition of their role the
Ministry of Trade and Industry (MoTI) formulated the micro and small enterprises
development strategy in 2004 with a major objective of creating long-term employment
opportunities. The strategy gives priority to enterprises operated by women. It also favors
enterprises operated by school dropouts, people with disabilities, and previously unemployed
youth. It outlines key limitations faced by micro- and small enterprises and sets out the goal of
providing comprehensive support (Guarcello, 2008a).
Many people are earning their livelihood from MSEs. However, these sectors are performing
below capacities and their growth has been severely constrained by many factors. Lack of
purchasing power of local people, lack of access to financial services, poor partnership and
networking, and inadequate entrepreneurial capacity are among the bottlenecks of the sector
(Mulat, 2006). Although measures are being taken to support them, most of the challenges that
MSEs face are yet to be tackled. Some of these challenges include: (i) unfavorable legal and
regulatory environments and, in some cases, discriminatory regulatory practices; (ii) lack of
access to markets, finance, business information; (iii) lack of business premises (at affordable
rent); (iv) low ability to acquire skills and managerial expertise; (v) Low access to appropriate
technology; and (vi) Poor access to quality business infrastructure (MoLSA, 2009).
Access to credit is one of the major constraints for the expansion and growth of MSEs mainly
due to the collateral requirements by commercial banks. In Ethiopia, beyond appreciating the
problem, the government’s effort to help MSEs get access to credit services through
microfinance institutes has been considerable. For instance, in 2004 Addis Ababa, Amhara,
Tigray, Oromia, South, and Dire Dawa Regions together provided more than 110 million ETB
loan to small and micro enterprises in 2004. Addis Ababa and Tigray States extended more
than Birr 44 million and Birr 33 million, respectively in the same period (Birhanu, 2005). This
being the case, a study by Rahel and Paul (2010) on the determinants of employment expansion
of women operated MSEs in Nifas Silk-Lafto and Kirkos sub cities of Addis Ababa suggests
that the problem is still a concern. According to them, although there are some efforts to help
women get access to credit, women in the survey area reported that the loan they received is
not enough to expand their enterprises.
Raw material is a fundamental component of inputs for the existence of an enterprise. The
types of raw materials demanded by MSEs and their sources vary with the nature of the
enterprises. The sources of raw materials for most MSEs are agricultural and/or industrial
products, which are domestically produced or imported. As noted in Rahel and Paul (2010), the
fact that the agricultural sector is the main source of raw materials for most MSEs signifies the
potential market and strong backward linkage that the MSEs create for the agricultural sector.
The higher cost of acquiring raw materials is reported to be the key problem pertaining to the
growth of enterprises in the survey area.
Working Premise is another important factor needed for a successful and sustainable growth of
enterprises. In this regard, the Regional States have made encouraging effort by preparing and
arranging working spaces for a number of enterprises. As noted in Birhanu, Abraham, and van der
Dejil (2005), the six Regional States, namely Addis Ababa, Amhara, Tigray, Oromia, South, and
Dire Dawa Regions supplied a total of 1,045,717 m2 of working space to micro and small
enterprises, as of 2004; and more than 62,417 operators of MSEs have benefited from such
arrangements. However, Rahel and Paul (2010) argue that lack of enough working space is still
the main problem for about 54 percent of women-operated MSEs in the survey area (Nifas
Silk-Lafto and Kirkos sub cities of Addis Ababa). At the same time, they pointed out that about
23 percent of the respondents do not face any problems related to the working place.
The five-year Growth and Transformation Plan (GTP, 2011-15) ambitiously targets to create a
total of three million micro and small-scale enterprises (MSE’s) at the end of the plan period.
The development of this sector is believed to be the major source of employment and income
generation for a wider group of the society in general and urban youth in particular. The major
objective of this program, which is creating and promoting MSEs in urban areas, envisages
primarily reducing urban unemployment rate. The government has continued and strengthened
its effort to support and promote MSEs. In recognition to their significant role in the total
economy, the greatest attention given to the development of MSEs as one of the priority policy
agendas can be an indication of the government’s commitment.
Table: 2.1 Number of establishments and jobs created and amount of loan
No. of MSEs
Amount of loan (in
millions of ETB)
Source: NBE, 2009/10 and 2010/11 Annual Reports
According to the annual report of the National Bank of Ethiopia, the total amount of loan
supplied to MSEs from micro finance institutions has been increasing over time. The amount
of loan supplied rose by 22.8 percent in 2009/10 relative to 2008/ 09; and consistently showed
a 20.7 percent increase in 2010/11 relative to 2009/10 fiscal year. With regard to
establishments and job creation, in 2009/10, a total of 176,543 MSEs were established
employing 666,192 people, which is 25.6 and 141.6 percent, respectively, higher compared to
2008/ 09. However, in 2010/11, the number of establishments and total employment
considerably went down by 70.6 percent and 18.7 percent respectively, compared to 2009/10
(NBE (2010); NBE (2011)).
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.
3.1. Research Method
Obviously, any one approach by itself is not complete and perfect. As a research culture,
depending on the nature of data and the research problem, using both quantitative and
qualitative approaches can enhance the validity of the research output (Creswell, 2009). As it is
common in the unemployment literature and related empirical studies such as Serneels (2007)
and Seife (2006), a quantitative approach is frequently employed. Accordingly, given our data
type and research objectives, we mainly adopted a quantitative research method.
3.2. Data Sources
We have used both primary and secondary data to see the trends of unemployment and to
examine socioeconomic causes of unemployment and effects of policy interventions. The
primary data were collected from three major urban areas, namely, Addis Ababa, Bahir Dar
and Hawassa. The data included information on labor market outcomes of individuals,
characteristics of MSEs and their employment generation capacity, and opinion of stakeholders
on the effectiveness of the TVET program.
The secondary data were taken from Urban Employment Unemployment Surveys (2003 to
2011) and National Labor Force Surveys (1999 and 2005) conducted by the Central Statistical
Agency (CSA) of Ethiopia. We constructed data set known as pooled cross-section from the
UEUS data collected for five years. The labor force surveys and urban employment
unemployment surveys consist of important variables to the study such as demographic
variables, educational qualification, employment status, training received and training type,
migration, duration of unemployment, hours of work in a week and other important variables.
In addition, to see the patterns and relationships between unemployment and some important
macroeconomic variables, we made use of the World Bank dataset.
3.3. Sampling Techniques and Procedures
The primary data collection was begun with identification of the study sites. We employed
purposive sampling to identify the three main urban areas (viz. Addis Ababa, Bahir Dar, and
Hawassa). Some of the features considered in selecting the three representative urban centers
and the respective sample sizes include the geographical location, the number of Technical and
Vocational Training institutions and size of TVET graduates, the level of unemployment rate
and the concentration of MSEs. Since the distribution of TVET institutions and MSEs are more
concentrated in Addis Ababa, a larger sample size was drawn from Addis Ababa relative to
Bahir Dar or Hawassa.
Taking into account the budget and time constraints, we employed some other sampling
strategies to finally arrive at the selection of representative sampling units. The desired
sampling units for the survey are an individual in the labor force aged between 15-64 years and
an enterprise in the MSEs category. In doing so, we first took list of sub cities and woredas in
each of the three cities. In collaboration with the concerned department of the respective city
administration, we stratified each of the three cities in to three strata, i.e., residential, business
and industrial zones. In Addis Ababa, one sub city per a stratum and from each sub city two
woredas and thus a total of six woredas were randomly selected. In the case of Bahir Dar and
Hawassa, one woreda (i.e. local administrative hierarchy one layer higher than keble) per a
stratum and a total of three woredas from each of the two cities were randomly selected. Again,
one kebele (the lowest local administrative tier in Ethiopia) from each woreda was randomly
selected, implying a total of six kebeles in Addis Ababa and three kebeles in each of the two
regional cities, Bahir Dar and Hawassa. Hence, these randomly selected twelve kebeles are the
specific survey areas where from the primary data were collected.
To select the target sample population in each randomly selected kebele, it was mandatory to
have a source list. For the MSEs survey, we made use of the list of MSEs obtained at each
selected woreda. For the survey of individual persons in the labor force, however, there had not
been a readily available list of the population in the desired format. Therefore, the research
team carried out registration of all economically active population in each selected kebele to
produce a source list.
Accordingly, data on labor market outcomes were gathered from 45 persons from each of the
three kebeles in Hawassa, four kebeles in Addis Ababa and one kebele in Bahir Dar as well as
54 and 52 persons, respectively from each of the remaining two kebeles in Addis Ababa and
Bahir Dar. Thus, in relation to labor market outcomes, 135 from Hawassa, 149 from Bahir Dar,
297 from Addis Ababa, and a total of 581 persons were interviewed. Information on MSEs
were collected from 36 randomly selected MSEs from each of the three kebeles in Bahir Dar
and Hawassa. In Addis Ababa, 36 MSEs from each of 5 kebeles as well as 49 MSEs from the
remaining one kebele were interviewed. Therefore, regarding the employment effect of MSEs,
a total of 445 MSEs were involved.
3.4. Data Collection Instruments
To facilitate the data collection, eight interviewers and one supervisor per kebele and a total of
nine enumerators, with educational qualification of at least diploma and who reside in the
sample kebeles, were employed. The enumerators were given a half day training on the study
objective, how to manage the questionnaire, how to friendly communicate with the
interviewees, research ethics, and quality issues. Close monitoring and regular supervision
were among the strategies followed to correct errors on time and ensure the reliability of the
Structured survey questionnaires were used to collect the primary data. Questionnaire-1 for
individual persons and Questionnaire-2 for owners of MSEs, were administered to the selected
interviewees via the trained interviewers. In addition, Questionnaire-3, a kind of Likert scale
was distributed to relevant and concerned groups such as TVET graduates, TVET instructors,
directors of TVET colleges, employers, public officials at regional TVET Bureau and parents
to assess their opinions on the relevance and strategic role of the TVET program in reducing
3.2. Data Analysis
The available quantitative data have been analyzed using descriptive analysis to describe the
characteristics and trend of urban unemployment and econometric analysis to determine
socioeconomic causes of urban unemployment and to examine effect of policy interventionsthrough TVET and MSEs- on unemployment. To this end, the data sets deployed were crosssectional, pooled cross-sectional. The econometric analysis consists of pooled cross-section
data analysis using probit and duration models, and logistic regression.
3.2.1. Pooled Cross-sectional Data Analysis
We used the pooled cross sectional data which is obtained by sampling randomly from two or
more points in time; for example, in our case UEUS was conducted for five periods. Therefore,
independently pooled cross-section is obtained by sampling randomly from a large population
at different points in time. It differs from a single random sample in that sampling from the
population at different points in time likely leads to observations that are not identically
distributed. For example, distributions of wages and unemployment have changed over time in
most countries (Wooldridge, 2000)
A standard regression model applied to a set of pooled cross-sectional and time series data take
the form of NT equations can be written as:
Yit = β 0 + β1 X1it + β 2 X 2it + β1 X 3it + β1 X 4it + ... + β1 X kit + U it i = 1, 2, 3, ..., N1, N1 + 1,..., N1 +,
14243 14 244
N1 + N ,..., N t = 1, 2, 3,....,T − − − − − − − − − − − − − − − − − − − − − (3.1)
Note : We have different number of observatio ( N ) for different periods (T ) when pooled
crosssectional data are used.
Suppose the true error structure does include a year component, but we ignore that and run
OLS on equation (2.1).This introduces serial correlation between observations within the same
time period and violates one of our assumptions for OLS. The OLS coefficient estimate is still
unbiased and consistent. However, the variance-covariance matrix is biased/ inconsistent which
leads to incorrect standard errors and incorrect inferences. This is similar to the problem of
serial correlation in time series models. Thus using pooled cross sections raises only minor
statistical complications. Typically, to reflect the fact that the population may have different
distributions in different time periods, we allow the intercept to differ across periods, years in
our case. This is easily accomplished by including dummy variables for all but one year, where
the earliest year in the sample is usually chosen as the base year. That is 2003 for
unemployment employment data and 1999 for labor force survey in our study.
Thus it makes sense to include time dummies (also known as year effects):
Yit = β 0 + β 1 X 1it + β 1 X 2it + β 1 X 3it + β 1 X 4it + ... + β 1 X kit +
t + ε t − − − − − − − − − (3 .2 )
Yit, refers to the dependent variable for case i in period t
β0, the intercept or the base year and β1, β2, … , βk are slopes
θt, parameter for time dummies and t=2, 3, …..,T is time
εit, error term, εit/X~ (0, δε2) or conditional errors, are usually assumed to be
independent and identically distributed random variables with a mean of zero, a variance of δε2
that is constant across values of the Xs, and, in small samples, a normal distribution.
Each time dummy is the difference in the conditional expected value of dependent variable
between the base year (t=1) and the year t=T
Pooled cross sectional data has some advantages over other data categories (such as cross
sectional data). Pooling can lead to larger sample sizes. This leads to more precise estimators
and test statistics with more power. However, this is only true if the relationship between the
dependent variable and at least some of the explanatory variables remain constant over time
If the explanatory variables are changing over time, it can also provide additional variation in
explanatory variables with which to estimate its effect on dependent variable. Alternatively, a
further benefit of these data is that we can explore changes in the coefficients over time.
In a two-period pooled cross section dataset with k explanatory variables expressed as:
Yit = β 0 + β1 X 1it + β 2 X 2it + β t X 2it D + β 3 X 3it + β 4 X 4it + ... + β k X kit +
t + ε t − − − − − − − − − (3.3)
where : D is the year dummy takes 1 if the the observation comes from year 2 otherwise 0
While changes in the coefficients may be interesting, one has to be very cautious in interpreting
the source of the changes (e.g., as the impact of a policy or changing economic structure).
We used pooled probit model to examine the impact of training and education on urban
unemployment and to determine trends of unemployment because the dependent variable is
dummy. Either logistic or probit regression can be used depending on the distribution assumed
for errors. If the standard normal distribution is assumed, probit analysis would be used. If
instead we assume a logistic distribution for εt (also a distribution symmetric about 0, but with
a variance of П2/3 instead of 1, where in this case П is the constant 3.14159), the analysis
becomes a logistic regression.
The pooled probit model below represents unemployment status where Yit∗ denotes the
dependent variable, dummy for probability of unemployment. The dependent variable,
probability of unemployment is dichotomous such that: Yit∗ = 1 if the worker comes from year t
is unemployed and Yit∗ = 0 otherwise. We are interested in the probability that the labor is
unemployed, p(Yit∗ = 1 / X it Where X it is used to denote the full set of explanatory variables
that come from year t, the factors that increase or decrease probability of involuntary
unemployment and Uit denotes the error term.
The general pooled probit regression model for the probability of being unemployed, using k
regressor variables can be described as:
Yit = β 0 + β1sex + β 2 age + β 3mar1 + .....β 7 mar 5 + β8 exp er +
β 9educ8 + .... + β 6educ12 + β 7TVET + ... + φ0 y 04 + ... +
φ2 y11 + φ3 y 04 • educ8 + φ4 y 06 • educ8 + φ5 y11 • educ8 +
β10tigry + ... + β15 somali + ε it − − − (3.4)
In the above equation, the variable y11 is a dummy variable equal to one if the observation
comes from 2011 and zero if it comes from any other year. The base year is 2003. To show the
trend of unemployment, we analyze whether the coefficients on the year dummy variables
show a significant change or not in unemployment in the 2011.
The intercept for 2003 is β 0 , and the intercept for 2011 is β 0 + φ 2 . Whereas
is the discrete
effect of upper primary education on probability of unemployment in 2003 while β 4 + φ3 is the
discrete effect of upper primary education (educ8) on probability of unemployment in 2011.
φ3 , φ 4, and φ 5
measures how the probability of unemployment to another year of
education has changed over the one, three and eight year period.
The pooled cross-section data analysis is also used for cox-regression. This regression employs
proportional hazard models. The hazard rate for failure at time t is defined as
H (t ) =
Pr obablity of failing between times t and t + Δ t
( Δ t ) ( probablity of failling after time t )
We model this hazard as a function the baseline hazard H 0 (t ) at time t and the effect of one or
more explanatory or X variables. Baseline hazard means the hazard for an observation while all
X variables equal to zero.
H (t ) = H 0(t ) + exp ( β1 X1 + β 2 X 2 + β3 X 3 + β 4 X 4 + ... + β k X k )
ln[ H (t )] = ln[ H 0 (t ) ] + β 1 X 1 + β 2 X 2 + β 3 X 3 + β 4 X 4 + ... + β k X k ....( 3 .5)
H (t ) is a survival time data contain, at a minimum, one variable measuring how much time
elapsed before certain event occurred to each observation. The literature often terms this event
of interest a “failure” a regardless of its substantive meaning. When a failure has not occurred
to an observation by the time when data collection ends, that observation is said to be
“censored”. Duration of unemployment (duration of weeks elapsed) before an individual get
employed or study ended is a time variable. Failure refers to a situation where a person is
employed before the end of the survey period. Censored is when he/she remains unemployed
during the survey period.
For all of the regression analysis we estimated, a log likelihood chi-square is an omnibus test
whether or not the model as a whole is significant. In our finding this test indicates that the
overall model is significant at below 1 percent. This means that the model that includes all the
independent variables included, for example; in annex table 4.22 including the constant fits the
data statistically significantly better than the model without at least one of these variables (or
i.e., a model with only the constant). We have used the link test whether or not the model is
correctly specified; one should be not able to find any additional predictors that are significant
except by chance. After the regression command link test uses the predicted value and the
predicted value squared as the predictors to rebuild the model. The predicted value should be a
significant predictor since it is the predicted value from the model. This will be the case unless
the model is completely miss-specified. On the other hand, if our model is properly specified,
the variable predicted value squared should not have much predictive power other than by
chance. Therefore if predicted value squared is significant, then the link test is significant. That
means either we have omitted relevant variable(s) or our link function is not correctly
specified. The link test result in the case of this study found to be not statistically significant.
To handle, the problems of heteroskedasticity, we estimated robust standard errors.
Transformation of the variables is the best remedy for multicollinearity when it works. We also
used logarithmic transformation of variables with the problem of strong collinearity such as
age. However variables generated through squaring other variables such as age and work
experience cannot be freed from multicollinearity problem. So we removed two variables
experience squared and age squared from our model.
3.2.2 Specification of Study Variables
The specification of study variables included in the regression analysis is summarized in a table
and located in the annex (Table 3.1).
4. RESULTS AND DISCUSSION
4.1. Demographic Characteristics of Respondents
The total sample size of respondents from secondary data for the five years is about 142, 547.
It is fairly distributed across time; nearly 18.9, 16.8, 18, 23, and 23 percent of the respondents
are from 2003, 2004, 2006, 2010, and 2011, respectively. The sex wise distribution is
reasonable; for instance, the proportion of males is estimated about 51.4 percent of the total
sample while that of female is 48.6 percent. Regarding the age wise distribution, adult women
and men account for nearly 28 and 21 percent while youth females and males are about 24 and
27 percent, respectively of the total sample. The literacy rate of urban labor force increased
from 76 percent in 2003 to 80 percent in 2011 and relatively males are more likely literate than
females. Literacy rate is relatively higher for youth than adults, but increasing overtime for
both categories. With regard to marital status, about 49 percent of respondents are found to be
The sample size of respondents in the primary survey is 582 with a sex wise distribution of
fifty-fifty. Age wise adults account for 45 percent of the total sample size while the share of
youth is 55 percent. The share of youth female is 30 percent which makes the largest size of the
sample while the least 20 percent of total sample comes from women. The literacy rate of the
males is around 97 percent and that of females is about 89 percent. Almost half of the
respondents, about 47 and 55 percent of females and males respectively received training. Over
48 percent of the respondents are married and nearly 45 percent are single.
4.2. The Urban Labor Force Participation Trends
Growth of labor force is engine for economic growth when the labor force is gainfully
employed; otherwise it contributes more to social unrest as many working age people remain
unemployed, underemployed or work in the informal sector as working poor. The secondary
data shows that the trend of overall participation rate was decreasing between 2003 and 2006;
but thereafter, it has been slightly increasing (Figure 4.1). Male youth as defined in Ethiopia
youth policy (15-29 years old) expands however the rate of growth lacks uniformity. The
erratic trend of participation maintained for female youth, female and men for the period.
Marginal decline in participation rates for adult male categories of the labor force as compared
with female and female youth. On the other hand, Male youth participation showed
pronounced growth relative to other groups between 2003 and 2011. On the other hand, female
youth labor involvement decreased between 2003 and 2011 at least by one percentage point
while women and men rates are almost constant.
Figure 4.1: urban labor force participation rate (%)
Source: UEUS 2003-2011
The male youth labor force as the share of total increases by 4 percentage points from 2003 to
2011, allowing the overall participation rate to rise at least by 2 percentage points in the period.
The rising trend of youth participation increases with age. The participation rate of male youth
age between 15-19 years over the period decreased from nearly 23-22 percent and by far below
the rate of youth age between 20-24 years old that increased from nearly 71-72 percent and
also adult youth participation rate increased by one percentage point between 2003 and 2011.
The fall in participation rate of teenagers can be taken as positive, if enrollment for the age
increased and responded by better job offer after successful completion of ongoing education
The quality of urban labor supply has been increasing although the rate of its growth is not
attractive (Figure 4.2). By 2003, 87 percent of the urban labor force participants had at least
five years of schooling (above lower primary education), and the size of labor force with the
same level of schooling has increased to 90 percent in 2011. Similarly, the labor force with
more than eight year of schooling increased from 62 to 65 percent over 2003 to 2011.
Figure 4.2: The trend of labor supply by years of schooling (%)
Source: UEUS 2003-11
Labor force size above grade ten educational attainment increased from 37 percent in 2003 to
50 percent in 2011.
4.3. Urban Versus Rural Unemployment
According CSA labor force survey, most urban dwellers aged between 10 and 64 years in
Ethiopian found hard to get job and faced peak unemployment rate of 26.4 percent in 1999
using current status approach, which is five times higher than rural areas (5.1 percent). The rate
is decreased to 20.6 percent in 2005 nevertheless the figure is still considerably above 2.6
percent rate of unemployment for rural population. The rural unemployment is almost threefold
lower than 8 percent rate of unemployment observed for sub-Saharan Africa age groups
between 15 and 64 for the period. The implication is that urban unemployment rate is not only
significantly higher than national unemployment rate but also continental wide average
unemployment rate between 1999 and 2005.
The rate of unemployment also varies throughout sex and age. According to CSA figures, 30
percent of unemployed in the period are women whereas the rate is 18.3 percent for male
workers. Women unemployment rate is higher than male rate for the period 2005 as well. For
instance, the rate is 27.2 and 13.7 percent for females and males respectively. Therefore,
unemployment rates of females are greater than that of males for all age cohorts between1999
4.4. Urban Employment Trends
Urban employment showed an increasing trend over the last eight years; however, the rate of
growth is slow relative to the high rate of urban unemployment. The employed labor force
increased from 74 percent in 2003 to 82 percent in 2011 (Figure 4.3). The growth of
employment varies across gender and age. The employment rate of female workers and young
workers increased by 9 and 12 percentage points, respectively while that of adult males grew
by only 3 percent over the period. However, the employment rates of females and the youth are
still significantly less than that of adult males, signifying their disadvantaged position in the
labor market. Despite the information gap about the quality of employment, the existing
information indicates that between 2003 and 2004 above 64 percent of the employed were
working in the informal sector. For the remaining periods, formal sector employment is at least
above 55 percent for each year and informal employment is decreasing over the time except
marginal increase for the period 2011.
Figure 4.3: Urban employment trends (%)
Source: UEUS 2003-11
When we compare urban employment and unemployment trend urban employment rate is the
mirror image of the total urban unemployment. As indicated in figure 4.3 when the rate of
employment is peak unemployment rate is at its lowest level and vice versa.
Evidences from three cities using primary data strongly oppose the findings from CSA survey
for five years. Female employment rate is as high as twice as male employment rate. Both
women and youth female employment rates are higher than adult male and youth male
employment rates. For instance employment rate of women and youth female are 84.87 and
76.7 percent respectively where as men and youth employment rates are 50.7 and 34 percent
4.5. Urban Employment-to-Population Ratio
Despite the fact that employment-to-population ratio indicator is good measure of quantity than
quality of employment, it indicates the capacity of an economy to create job. Commonly, the
range of the indicator is between 50 and 75 percent, with a higher ratio implies that majority of
the population that could be working does work-involved in income generating activities (ILO,
Table 4.4: Urban employment-to-population ratio (%)
2003 2004 2006 2010 2011
Source: UEUS 2003-11
The total urban employment-to-population ratio in Ethiopia increased from 51 percent in 2003
to 58 percent in 2011 but with significant variations across gender and age (Figure 4.4). It is 40
percent or less for youth females, wile the ratio it is over 80 percent for adult males in the same
period. The implication is that although the strong economic growth since 2003 has generated
more employment opportunity to the entire working age population, it has still been in favor of
the adult male category of the labor force.
4.6. Urban Unemployment Trends
In general, Ethiopia’s urban unemployment rate has been trending downward since 2003
(Figure 4.5), but remained at high level. The composite unemployment rate reached peak of
nearly 26 percent in 2003, and decreased to its lowest level 17 percent in 2006. This might be,
at least partly, attributed to the falling trend in participation rate observed during the same
period. Despite the sound economic growth, the urban unemployment rate is still higher and
stood around 18 percent in 2011. This translates to an average unemployment rate of about 21
percent for almost a decade and a reduction of 8 percentage points between 2003 and 2011.
Despite their lower participation rate and the decrease in unemployment rate over 2003 to 2006
unemployment is concentrated among youth and young females continued to be hardest hit by
unemployment in the period mentioned.
Figure 4.5: urban unemployment rate (%)
The youth and female unemployment rates are considerably higher than adult male rate and
well above the total urban unemployment in each period, which is consistent with the global
experience, and reflecting the relative disadvantaged position of youth and women in job
markets. The gender bias in unemployment is maintained across all cohorts in urban job
markets of Ethiopia. Women and young workers age between 15 and 29 years old (i.e. national
age standard for young workers) are more likely unemployed than adult male workers
consistent with global unemployment incident. The unemployment rate of the youth was more
than triple that of adult males between 2003 and 2006; and the gap increased to fourfold
between 2010 and 2011. Likewise, women unemployment rate was as triple as adult male
unemployment rate between 2003 and 2011 except for the initial period. The situation is the
worst for young females labor force. Nearly one out of every three unemployed persons (42
percent) are young female in 2011 where as one out of six unemployed persons are adult men
in the same year.
Unemployment is more challenge to youth and female workers than adult male for several
reasons. According to ILO (2010), job interruption is high among women due to maternity
leave; low educational qualification of women relative to their male counterparts expose them
to labor market discrimination and prejudice by employers. However our finding indicates that
adult male and female have almost equal job interruption rates. For example, nearly 76 percent
of adult women on average working on their main job daily and 77 percent of adult men
engaged in their main job each day between 2010 and 2011. Nevertheless obviously males
have more educational qualification than females and a labor market prejudice adversely
affects women employment opportunities.
Youth are more likely remain unemployed relative to adult male and consequently suffer more
from unemployment costs. On the supply side, youth extend job search until they secure better
job when they are lucky for family support during spell of unemployment. Family support in
2005 is substitute for unemployment benefit for 75 percent of unemployed youth in Ethiopia
whereas it is means of access to basic needs for 38 percent of adult male in urban areas hence
many youth are more likely to remain unemployed than adult males. The primary data from
three cities indicates that family income is source financial support during unemployment spell
for 72 percent of youth while 2 percent of adult depend on family support during spell of
Furthermore youth lack labor market information and job search experience. In many
developing economies, informal job search methods such as through family, relatives and
friends are dominant methods to find job for youth(ILO, 2010). However this does not hold for
urban higher youth unemployment rate relative to adults in Ethiopia. Annual average of adults
seeks assistance of relatives and friends to seek job are 27 percent and while youth use these
informal job search methods are 22 percent. Young workers who use formal job search options
like search through vacancy advertizing boards, media, direct application to employers and
hold unemployment card are 55.9 percent whereas annually average of male adults use these
options are 38.6 percent.
Youth also switch between job, school enrollment and unemployment as educational
institutions open and close leads to young students more likely enter and exit the labor force.
Not only supply-driven causes but also labor market partiality cause youth are more probably
unemployed than adults. Youth have less work experience, lower job specific training and
firms incur low investment cost to train youth relative to adult. Employment protection
legislation usually requires a minimum duration of employment before it operates and
reparation for job loss often elevate with tenure. The implication is that youth are more
exposed to redundancy than adult. According to the primary survey result from three cites,
average work experience of adult estimated about 8 years was significantly above youth labor
market experience of 2 years in Ethiopia.
However, the primary survey result from three cities indicates that both women and youth
female are experiencing lower rate of unemployment. Women unemployment rate is as low as
15 percent and youth female rate is 23 percent while male youth faced 65.8 percent highest rate
of unemployment and unexpectedly 49 percent of adult males are unemployed during survey
period using current unemployment status approach. Similarly, youth female and women are
more likely to be employed than youth male and adult male. The employment rate of women is
nearly 85 percent while the rate is 50 percent for men. Youth female employment rate is 76.7
percent which more than twice the employment rate of youth male, 34 percent. The significant
variation between the two data sources may be associated with the sample size and the survey
4.7. Regional Unemployment Trends
Understanding spatial distribution of unemployment is important for area specific intervention.
The secondary data cover cities and towns in all the nine regions and two city administrations.
The two city administrations, Dire Dawa and Addis Ababa, are found to experience highest
rate of unemployment over the survey periods. The unemployment rates observed in these
cities are significantly above the national average and all other regions. On the other hand,
Gambella region is relatively lucky for having the lowest unemployment rate in the same
period (table 4.1).
The trend of unemployment across regions is almost synonymous with the national level. The
rate of unemployment decreased from 2003 to 2006 in all regions and increased after 2006; and
it again showed a decreasing trend since 2010. Relatively, the lowest regional unemployment
rates were observed in 2006 for Tigray, Amhara, oromia, Benishangul Gumz and Dire Dawa
regions while for remaining regions the lowest level of unemployment rate occurred either one
of the next two years.
Table 4.1: Regional unemployment distribution (%)
Source: UEUS 2003-11
The urban unemployment disparity observed in terms of gender and age in the regions is
similar to the national one. The unemployment rates for females and youth are significantly
higher than that of adult males in all regions. For example, in Tigray region in 2003, women
and youth unemployment rates were 35 and 39.7 percent, respectively while it was 12 percent
for adult males in the same period. The rates of unemployment decreased to 27percent for both
women and youth in 2011 while it reached down 4.6 percent for adult males. However,
unemployment rate for all categories of the labor force is the highest in Addis Ababa and Dire
Dawa compared with all other regions. For example, in Dire Dawa in 2003, women and youth
unemployment rates were 49 and 52 percent, respectively while that of adult males was 19
percent. The rates decreased to 34 and 29 percent for women and youth, respectively while it
fell to 13 percent for adult males in 2011.
4.8. Urban Unemployment and Education
Education system of Ethiopia divided into lower primary comprises grade one to four, it is four
years of formal education, and upper primary includes education from grade five to eight.
Secondary education that is either from grade nine to ten for those educated in existing
curriculum or grade nine to twelve for those attended the old curriculum. After completing
secondary education, a student can apply either to TVET education or preparatory education
based on results achieved in national examination for leaving secondary general education.
Those who scored above certain point specified for the given year are eligible to apply for
preparatory education (i.e. grade 11 to 12 in the new curriculum), and then compete for
university education. Those who scored below the cut-off point for preparatory education can
compete to enroll in TVET program either at 10 + 1, 10+2, or 10+3 level based on their
Education explains the rate and spell of unemployment. Many international evidences are in
favor of the notion that higher educational qualification boosts employment outcomes such as
better earning and lower unemployment(Garcia and Fares, 2008). The effect of educational
attainment on urban unemployment in Ethiopia is mixed and different from the international
experience. To examine the association between educational achievement and unemployment,
we used lower primary education as a baseline education to compare effects of educational
attainment on unemployment. The overall urban unemployment rate for workers with lower
primary education decreased from 22 percent in 2003 to its lowest level 13 percent in 2006.
The rate for this category of education again increased to nearly 19 percent in 2010 and then
decreased to nearly 15 percent in 2011 (Table 4.2).
Most of higher educational attainments have not resulted in lower rate of unemployment in
Ethiopia. Only degree and above graduates have significantly lower rate of average
unemployment rate than labor force participants with baseline education between 2003 and
2011. Labor force with all other educational qualification such as secondary education,
certificate and diploma and degree not completed including vocational education not
completed face higher average rate of unemployment in the period. The average
unemployment peaks for preparatory education and followed by secondary education despite
preparatory education has short history in Ethiopia. However the average unemployment
variation between primary education and TVET graduates is not significant.
Primary data result also supports the evidences from secondary sources. Unemployment rises
proportionately with more education up the educational ladder including TVET except for
upper primary. Relatively lower unemployment rate is observed for those with at least diploma
education in the old curriculum. For example the unemployment figure is 37. 8 percent for
those with lower primary education and increased to 44 and 39.8 percent for those with grade
nine to ten, and some one completed TVET correspondingly. However it is estimated around
33 percent for those with at least university degree educational qualification.
Table 4.2: Unemployment rate by education
Non formal education
Lower primary education (grade 1-4)
Upper primary education (grade 5-8)
Secondary education (grade 9-10)
TVET completed (TVETc)
TVET not completed (TVETnc)
Degree and diploma not completed
Annual unemployment rate (%)
Source: UEUS 2003-2011
Both at higher and lower educational levels female and youth experience a higher level of
unemployment rate than adult male. Female and youth mean unemployment rate at lower
primary is nearly 25 and 20 percent respectively while the corresponding mean figure for adult
male with the same educational qualification was 8 percent from 2003 to 2011. For workers
with secondary education, the average annual unemployment rate of total youth and female
youth is fourfold the rate of adult male while for adult women it is three times adult male rate
The unemployment rate discrepancy between female, youth and adult is non-declining
overtime along the educational ladder relative to baseline educational qualification. For
example, unemployment rate of female with lower primary qualification was falling but
relatively higher rate between 32 and 23 percent from 2003 to 2011 whereas adult male with
equal educational achievement confronted comparatively low unemployment challenge with
rates between 12 to 4 percent over the same period. The level of unemployment rate for
secondary qualification is varying from 50 percent in 2003 to 34 percent in 2011 for female
while it is slightly falling from 11 percent for year 2003 to 10 percent for 2011 for adult male
with equal educational achievement. The non-declining unemployment ratio along the
educational ladder is attributable to the fact that youth with better education tend to shop
around in the labor market and luck labor market experiences and additional education may fail
to reduce labor market discrimination of females.
The primary data survey results are consistent with urban employment and unemployment
survey. The rate is higher for all educational attainments than lower primary education except
for upper primary, diploma and degree education. For example, the unemployment rate of
degree and diploma graduates are 25 and 33 percent respectively while the rate is estimated
around 38 percent for lower primary education.
4.9. Unemployment Duration
Long unemployment spell permanently weaken an individual’s productive potential and human
capital and hence employment opportunities. Extended unemployment duration is barrier to
enter into employment particularly in the formal sector. One of the features of Ethiopia’s urban
unemployment is the lengthy spell of unemployment. The unemployed remained unemployed
for more than half year was 93 and 95 percent respectively for year 2003 and 2011. Over 90
percent of the unemployed people unable to find work for more than one year in 2003 and the
worst rate of unemployed unable to find job for more than one year sustained in 2011 as well.
The highest mean spell of unemployment figure was estimated around 2.5 years in 2004
followed by 2.4 years in the base period and the lowest level of spell nearly 1 year and six
months observed in 2010. However the average spell of unemployment increased to 2 years in
2011. In average, 37 percent of unemployed persons remain unemployed at least for two years
over the period signals prolonged unemployment permanently impairs the future employment
and productivity to the remarkable size of working age population. The rate of unemployment
in the base year is not only found to be the highest but also ends after long period while we are
linking the rate with mean spell of unemployment.
Figure 4.6: Mean spell of unemployed (in year)
mean spell 1.5
Source: ECSU 2003-11
The average spell of unemployed is not gender impartial over the period and statistically
significant (see figure 4.6). It supports the unpleasant situation of women. The more troubling
is the spells of unemployment increases with years of schooling. Even if, it decreases with
training received close to 90 percent of unemployed with training have more than 24 months
unemployment span between 2003 and 2011.
The primary data survey from three cities is in line with the results of the secondary data. The
average spell of unemployment is estimated about 1.4 years and the higher unemployment
differential across gender and age is statistically significant at 0.001 level. For instance,
average annual spell of unemployment for currently unemployed adult male, female and youth
are 0.6, 1.4 and 2.2 years respectively. Assuming lower primary education as reference line,
average spell of unemployment is significantly higher and inconsistently increasing up the
educational ladder until degree and above educational qualification. Currently unemployed
workers with base line education stay unemployed for half years in average and it is close to
1.6, 0.9 and 1.8 years to someone with upper primary, secondary and preparatory education.
TVET graduates keep unemployed at least for 1.1 years in average; diploma holders may
remain unemployed for 1.4 years however the spell decreased to almost one month for degree
and above educational qualification. The mean spell of unemployment variation along
educational ladder is significant at 1 percent while the variation disappears for those with
training. Furthermore people with training has less annual average spell of unemployment and
significant at 1 percent.
4.10. Urban Unemployment and Training
Labor force participants who have received training are increasing overtime, but the rate of
growth is quite slow. They were 20 percent in 2003, and after eight years increased to 31
percent. Unemployment challenge among labor force with training is lower and they have been
more likely to be employed than those fail to take an opportunity in each of the years since
2003 (figure 4.7). Unemployment among trained workers was 18 percent in 2003 whereas it
was over 28 percent for those without training in the same period. The rate decreased to 13
percent for the former category and 20 percent for those denied training in 2011.
Figure 4.7: The comparison of unemployment rate by training (%)
Generally, the effect of training on labor market outcome is found to be significant over the
period. However, training failed to reduce unemployment differentials between female, youth
and adult male. Over 2003 to 2011 periods, in each year, average unemployment rates of
female and youth with training is approximately fourfold higher than adult male had training.
Figure 4.8: Unemployment differential between female, youth and adult male with training
Source: UEUS 2003-11
The youth and female with training, for example; face unemployment challenge of 29 and 31
percent respectively in 2003 while the adult male with training face unemployment rate of 7
percent which is quite close to natural rate of unemployment. The adult male with training has
unemployment rate of 5 percent which is within the range of natural rate of unemployment
however the female and youth unemployment rate were 21 and 19 percent after eight years.
The rates of unemployment among females without training are more than those with training
at least by 5 percentage points for all periods since 2003 except for 2006. Corresponding
average is 6 percentage points unemployment differential for youth and adult men in the
Among people with the equal level of education except for vocational and technical training
those with vocational training (TVETc2)- who have successfully completed TVET training at
10+1, 10+2 and 10+3 level) are less likely to be unemployed across all the periods. For
example, grade ten graduates more likely to be unemployed than those take technical and
vocational training. The mean unemployment spell of grade ten graduates exceeds average rate
of TVET graduates over the period is statistically significant at 1percent. Labor force with
vocational training are less likely to be unemployed than those with only lower primary
education for some periods. The implication is vocational education and training is successful
in enhancing the employment chance of trainees relative to tenth grade graduates but not vis-àvis lower primary education. Hence effectiveness of vocational education on the labor market
outcome of trainees is not strong.
unemployment rate ( %)
Table 4.9: Unemployment differential between TVET and secondary school graduates
grade 10 graduates
Source: UEUS 2003-11
Primary survey result is consistent with secondary source with regard to association between
training received and the level unemployment. Working age categories with some training are
less likely to be unemployed than those without training. Unemployment rat among trained
workers is 35.5 which is less than 42.4 percent rates among people do not received any
training. Unemployment variation between TVET graduates and secondary school graduates is
almost narrowed dawn to zero, therefore; the finding still questions the effectiveness of TVET
4.11. Training and Self-employment
Government is the dominant training provider for working age population in Ethiopia and the
major objective of the state training is to enhance self-employment. To examine, to what extent
certified trainings promote self-employment in urban Ethiopia, we compared the trend of selfemployment between TVET graduates and secondary school graduates. The share of selfemployment by TVET graduates is less than the comparison groups who completed grade ten
in the new curriculum and grade 12 in the old curriculum. Vocational and technical education
graduate entrepreneurs were 8.1 percent in 2003 and increased to 8.6 percent in 2010 and it
marginally increased to 9.7 percent in 2011. The lowest 6.9 percent self-employment among
vocational graduates observed in 2004. The average self-employment rates of vocational
graduates are 8.6 percent over the period. However the average annual size of entrepreneurs
who completed secondary education is 27 percent over the period. The trend of selfemployment for both comparable groups is falling up to 2006.
Table 4.10: Self-employment by training
grade 10 graduates
w ith training
Source: UEUS 2003-11
Regarding overall training provided, labor force without training are more likely to be selfemployed than those with formal training over 2003-2011 period. The average magnitudes of
entrepreneurs with training and without training are approximately constant at 14 and 50
percent over the period 2003 to 2011. The implication is that training has not increased
entrepreneurship in urban Ethiopia.
The primary survey result neither strongly support secondary source nor oppose it. Selfemployment rate of TVET graduates are marginally higher than secondary school graduates
(such as grade nine to ten completes). However consistent with secondary source workers
without training are more likely involves in to be self-employment business than those missed
4.12. School to Work Transition
School to work transition of urban youth in Ethiopia is long and difficult. The primary data,
from three major cities Addis Ababa, Hawassa and Bahir Dar, indicates that the average time
to find their first job after completion of training or giving up schooling is nearly one year and
three months. The average time to find first job varies significantly by training status. Average
time wasted by those with training decreased to 10 months, while those without training take as
high as two years until they find their first job and statistically significant at 1percent level.
Figure 4.11: Average time from school to work transition by education
Source: Primary survey result
We examined the effect of technical and vocational education on average time wasted to find
first job in order to see whether or not vocational training could ease school to work transition
in urban Ethiopia. We compared average time taken to find first job by TVET graduates and
those who have grade 9 to 10 and grade 11 to 12 educational qualification. Average time spent
by those with technical and vocational training to find the first job is nearly eleven months
where as for those with grade 9-10 educational qualification it takes nearly one year and four
months and the mean school to work transition is significant at 1 percent level. Those who
have educational qualification of grade 11 to 12 need to waste labor hour equals two years to
find their first job after they enter into the labor market. Consistently those with general
training needs less school- to- work transition time relative to those without training and the
mean variation is significant at 1 percent.
4.13. Socioeconomic Causes of Urban Unemployment
We analyzed the relationship between trend of urban unemployment and some important
macroeconomic variables using World Bank database. Ethiopia is now one of the fastest
growing non-oil economies in the continent. However the outstanding economic performance
for a decade has not resulted in equivalent employment creation. For example, according to
World Bank figures in 1999 annual real GDP growth is estimated around 5 percent while the
urban unemployment rate is 21.8 percent for working age population. The rate of annual GDP
growth is negative 2 percent in 2003 while the urban unemployment rate is 26.3 percent. The
real GDP growth had already recovered significantly since 2004, but unemployment remained
at an all-time high. The average real GDP growth is estimated around 10.56 percent, double
digit, for the period between 2004 and 2011 while the average unemployment rate for the
period is 20 percent.
Figure 4.12: Relationship between unemployment rate and GDP
GDP percpita growth
Urban Unemployment rate
Source: UEUS, LFS and WB
This downward rigidity of unemployment, that unemployment rate tend to remain elevated
regardless of promising improvement in real economic growth, raising concerns that
sustainable economic growth is less likely results in the corresponding employment
opportunity. Consistently an empirical study indicated that variation in economic growth has
not result in unemployment reduction in Turkey (Aktar and Ozturk, 2009).
Rapidly growing urban labor force arising from rural-urban migration is a cause of high urban
unemployment in Ethiopia. Annual growth of urban population is at least 1 percentage point
higher than rural population rate of growth for each year between 1999 and 2011. The
significant population growth differential between the urbanized and rural areas primarily
attributed to high net rural-urban migration rather than high net birth rate in urban areas since
CSA figures indicate that rural-urban migration is the second dominant form of migration
between 1984 and 1999. Rural urban migration is often explained in terms of push-full factors.
The push factors comprise the pressure resulting from labor to land ratio in the rural areas and
the existence of serious unemployment arising from the seasonal cycle of climate. Arable land
(hectare per person) marginally increased from its lowest level 0.15 to 0.17 between 2003 and
2010. On the other hand agricultural value added is highly fluctuating from 1999 to 2000. It
ranges from lowest annual growth rate of negative 10.5 in 003 to highest rate of 16 percent in
2004. The average growth rate was 2.72 for the period. For the remaining periods (2003 to
2011) annual agricultural value added is also highly fluctuating from its lowest level 10.5
percent in 2003 to 16.9 in 2004 and decreasing successively for the remaining periods and
stood at 5.2 percent in 2011 and the average growth rate for the period was 7.17 percent. The
undesirably fluctuating growth rate of agricultural value added may lead to high rural-urban
migration. The situation can be further exacerbated by poor infrastructure facilities in rural
areas relative to urban centers. The rural labor force moves to urban areas with the likelihood
of searching lucrative employment in the industries. The industry value added annual growth is
more stable than agriculture and increased from 6.4 percent lowest level in 2003 to 11 percent
in 2011 and the average annual growth rate for the period was 9.6 percent.
Figure 4.13: relationship between participation and employment ratio
employment to population ratio
labor force participation rate
Source: WB, UEUS and LFS
The high population growth results in excess of labor force growth over employment-topopulation growth for the period between 2003 and 2011 can elevate unemployment growth in
urban areas. The national average labor force participation rate is 84 percent for the period
under consideration where as average employment-to-population ratio figure is 72 percent
which is almost 12 percentage points lower than the participation rate. For all the periods
identified the labor force participation rate exceeds employment- to-population ratio. The
implication is that the high population growth rate leads to rapid growth rate of labor force
participation rate, which is far exceeds the supply of jobs. The accelerated growth of
population on Ethiopia’s unemployment problem is multifaceted. It affects the supply side
through high and rapid rise in the labor force relative to the absorptive capacity of the
The industrial and service sectors are not vibrant enough to absorb unemployed and surplus
labor migrating from agricultural sector. The agricultural sector employment share is decreased
by 10 percentage points between 1994 and 2005 while the service and industrial sector
employment share increased by 5.4 and 4.3 percentage point respectively in the decade. The
net growth of employment over the decade is almost zero while the labor force participation
rate increased by 3.2 percentage points. Aggregate non-agricultural employment rates of
growth are negative and high growth rates of urban labor force in Africa have not been
matched by correspondingly high rates of growth of urban labor demand by the modern large
scale establishments(Frank, 1968).
The school curricula are not effective in providing of employable skills. As we discussed
before unemployment is found to increase along educational ladder at least up to college
education relative to primary education. Therefore, general school graduates do not acquire the
skills needed by employers of labor for a formal employment. Moreover low human-capital
that is characterized by low literacy rate over the past two decades and low educational
attainment leaves the labor force especially the young people ill-equipped to the work place
and entrepreneurship. The literacy rate of both youth and adult remain low for a decade. The
literacy rate for adult was 27 percent in 1994 and stood at 39 percent in 2007 while the average
literacy rate for women was 22 percent in the same period. Even if literate youth population is
higher than adults, it is as low as 55 percent for the specified span of time and the gender
disparity is maintained. Tertiary school enrollment increased significantly from its lowest level
0.6 percent in 1994 to 7.6 percent in 2011. Vocational education and training participation of
females increased from 27.3 percent in 1994 to 45.6 percent in 2011 signals commitment of the
government to increase entrepreneurship. However, much is left in both quantity and quality of
TVET to achieve the desired result.
Foreign direct investment (FDI) attraction is effective way to increase employment and FDI
can affect the economy in terms of technological externality, information and management
experience (Song, 2003). Moderate flow of foreign capital relative to other countries and its
extent of employment generation to local labor can be considered as a cause of high
unemployment rate. In 1988 Ethiopia attracted US$ 261 million FDI while in the same period
Song (2003) observed that China’s annual FDI estimated to US$ 45.5 billion and since 1992,
China becomes the largest recipient of FDI next to USA. Ethiopia’s FDI flow increased to US$
627 million in 2011, after two decades. However other evidences indicated FDI attraction may
not contribute remarkably to the reduction of the serious structural unemployment that some
regions faced due to the fall in traditional manufacturing (Driffeld and Taylor, 2000). They
argued that flow of foreign capital generate employment for already employed skilled workers
elsewhere rather than to the structurally unemployed. However unemployed workers substitute
workers who have moved to the modern sector, if they are equipped with the required skills.
Hence, even if FDI flow to Ethiopia is low relative to China but still can result in sound
However we strongly argue for the fact that FDI produce job opportunities for already skilled
workers rather than to the unemployed people. On the other hand, FDI in agriculture results in
employment generation. Chaudhuri and Banerjee (2010) theoretically proved importance of
drawing FDI in agriculture in the developing economies. They find that FDI in agriculture in
these economies curb the unemployment problem of a skilled and unskilled labor. Contrary to
their evidence Aktar and Ozturk (2009) empirically tested FDI do not have significant effect on
unemployment in Turkey. Impact of FDI on job opportunities is not significant in European
union (Seye, 2000). More over we have an important argument on the relationship between two
variables as follows: “An increase in foreign capital investment leaves social welfare intact
and reduces unemployment if foreign capital is specific to foreign firms, and it may increase
social welfare and reduce unemployment if foreign capital is also used in the domestic
manufacturing sector”(Yabuuchi, 2006 P. 360).
4.14. Theories of Unemployment
Previous literatures have identified three main arguments to explain causes of unemployment.
The first one makes a case for “skill mismatch”- that is inequality in the supply and demand
side of the labor market-causes unemployment. The second is the “queuing” hypothesis which
posited unemployment is created when unemployed wait to find better paid and secure jobs
either in the public or formal private sector. The third theory of unemployment argues that
costly labor laws results in job destruction and slow job creation. That is employers find
restrictive labor laws difficult to expand employment. Employers may prefer employment
without written contract between two parties to avoid costs of job dismissal.
The skill mismatch is said to be the cause of unemployment when skills gained from education
system teaches fails to match well the skill demands of the labor market. When graduates face
extended spell of unemployment and lower wages when they find a job suggests existence of
skill mismatch in the labor market. We examined the association between years of schooling
and spell of unemployment as well as years of schooling and likelihood of unemployment to
determine whether skill mismatch is cause of unemployment in urban Ethiopia.
The Cox regression result (annex table 4.19), after controlling for demographic variables,
training received, location dummies, tends to support the skill mismatch theory. The worker
with more years of schooling is less likely to end unemployment spell over a short period. The
implication is that additional years of schooling results in larger total duration of
unemployment in the last six months during the survey period. Similar result was obtained
while we replace explanatory variable years of schooling by dummies of educational levels.
For example, someone with degree and above educational qualification has 84 percent
probability to exit out of unemployment latter than the one with lower primary education.
Similarly, the exit out of spell of unemployment does not improve with educational levels for
the rest of educational achievements up the educational ladder except for non-formal
The result with respect to education variables, contradicts findings by Seife (2006)-college
diploma or degree have far shorter unemployment duration relative to secondary education.
Variation in results may be attributed to he used cross-sectional data for estimation that
collected some 10 years before, referent group used and variations in the local unemployment
levels in two periods. We argue that reservation wage increased with more education and the
ratio of graduates to labor demanded may be significantly increased in recent periods as
compared with the ratio we have ten years before. Equivalently study in US labor market finds
that education remarkably increases employment success of unemployed workers. For
example, graduating from high school increases reemployment probability by around 40
percentage points and an additional year of schooling increases this likelihood by 4.7
percentage points (Riddell and Song, 2011). However impact of continued education and
training for adult on labor market performance in Germany, Denmark and British varies due to
institutional differences, which may influence the quality of training supplied (Dieckhoff,
TVET education increases the spell of unemployment relative to lower primary education
however it has no significant effect on rate of reemployment Seife (2006). On the other hand,
in Western Germany participation in vocational training programs results in adverse effect on
the transition rate into employment (Hujer et al., 2006b). They put forward reasons for negative
effect of participation which seem relevant to the context of Ethiopia. Participants would have
obtained job anyway, the involvement into the program may increase the spell of
unemployment artificially. The content thought to the participants do not match with the
interests of the labor market. However the most important point is there may be unemployment
arise from shortage of labor demand in Ethiopia.
However any training received associated with fast job finding following spell of
unemployment. Subjects with training were 34 percent more likely to exit earlier from
unemployment spell than those without training. Participation in short-term training is effective
in shortening the unemployment duration of job seekers in West Germany (Hujer et al., 2006a).
Age is negatively associated with the probability of reemployment. Male are 42 percent more
likely transferred from being unemployed to employed state than women. Put differently,
female have more unemployment duration than their male counterparts.
More time has been required to terminate spell of unemployment over the years except for year
2010. Relative to the base year, time required for unemployed to escape the spell of
unemployment decreased by 4 percent in 2010. The result suggests that unemployed person in
2010 has less total unemployment duration than the one who had been unemployed in 2003.
We also examined the relationship between unemployment and two closely related variables
years of schooling and education further to test the validity of skill mismatch hypothesis. The
result is consistent with the duration model in both approaches. Additional year of schooling is
measured by years of schooling increases the probability of unemployment and statistically
significant at less than one percent (annex table 4.14). Relative to the reference category of
education (lower primary education), more educational qualification is likely to decrease
unemployment only after at least college diploma education. Educational qualification below
diploma education such as upper primary, secondary, and preparatory education and vocational
education are found to associate with higher probability of unemployment relative to lower
primary education (table 4.5). Therefore, in urban Ethiopia, skill mismatch theory holds up to
diploma education. However Glawwe (1987) and Dicken and Lang (1995) examined the
relationship between education and unemployment to test the validity of skill mismatch
hypothesis. Glawwe find support for the skill mismatch hypothesis among both women and
men in rural and urban sectors while Dicken and Lang fund supportive evidence only for rural
The queuing hypothesis can be either shopping around for better paid job and good work
environment in the public sector or in the formal private sector. To test the validity of this
hypothesis, we examined the association between probability of unemployment and regional
share of both public and formal private sector employment in urban Ethiopia. The probit
regression result supports the queuing hypothesis. There is positive and statistically significant
relationship between the share of both public and the formal private sector job. That means a
person in the labor force more likely to be unemployed when either regional public or formal
private sector employment increases by one percentage point. Indeed the finding supports the
existence of queuing hypothesis as source of unemployment in urban Ethiopia. The result is
consistent with Tan and Chandrasiri (2004 ). They find support for queuing hypothesis by
showing positive correlation between the share of public sector job and probability of being
unemployed. The implication is that unemployed people are more likely to shop around for
public sector job and formal private employment opportunities.
We analyzed the validity of slow job creation hypothesis to determine whether or not job
destruction is cause of unemployment in urban Ethiopia as shown in annex table 4.20. To this
end, first, we examined the relationship between shares of either formal or informal private
sector job to probability of unemployment and second the association between unemployment
and the share of private sector job that has either written or verbal employment agreement
between employer and employee. The probit regression result supports job destruction
hypothesis. There is positive and significant relationship between regional share of private
formal sector employment and probability of unemployment. The likelihood of unemployment
increases by nearly 67 percent when formal private sector employment increases by one
percentage point. When regional share of informal sector employment increase by one
percentage, probability of unemployment increases by nearly 5 percentage point. This may be
attributable to restrictive labor market regulations, for example; high cost of dismissal
discourages employers from making investments that would expand size of the formal sector
job openings. Employers may choose to expand informal sector jobs that decreases
compensation costs when the business going dawn. On the other hand, unemployed people
may shop around rather than simply accepting precarious jobs. The regional share of private
sector employment that involves either written agreement or verbal agreement has no
significant effect on probability of unemployment.
4.15. Effect of Education and Training Polices on Labor Market Outcomes
This section of the study focuses on econometric evidences on the effect of policy
interventions, particularly through the expansion of education and training on labor market
outcomes. To evaluate the effect of the aforementioned policy interventions, we analyzed the
relationship between educational attainment and training against labor market outcomes with
especial emphasis to unemployment.
4.15.1. Effect of Education Polices on Urban Unemployment
Policy makers, almost across the globe, give much emphasis to measure the benefit of
education and training in terms of their labor market outcomes. A common way to look at the
value of education or 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. Much of up to now global evidences in favor of the
notion that more educational qualification results in better employment outcomes such as
higher wages and lower unemployment (Garcia and Fares, 2008). Accordingly, to explore the
effect of education on probability of urban unemployment we deployed probit regression on
pooled cross-sectional data and logistic regression on cross sectional primary data set. We give
more emphasis in this part to answer mainly the question ‘how does education affect likelihood
of urban unemployment?’ and latter the question is extended to training.
The dependent variable unemployment is dichotomous so that it is set to take one if the
individual is unemployed and zero if employed. The analysis of probability of unemployment
uses two alternative measures of unemployment defined by the CSA’s urban employment
unemployment survey. These are the current activity status approach and usual status approach.
Unemployed, measured using current status approach refers to a person who is available and
did not work in the last seven days prior to the day of interview. On the other hand, as per the
usual status approach, “usually unemployed” refers to a person available for work but did not
work during most of the last six months. The second measure, by averaging over a six months
period, may result in robust characterization of economic activity status relative to the
traditional unemployment measure, which has short span of time.
The independent variables included in this specific model are demographic variables such as
age, marital status, sex and education variables categorized in to different levels such as lower
and upper primary, secondary, and preparatory education. Moreover, the probit model includes
the year dummies, to see how probability of being unemployed has been changing overtime,
and urban location, to observe how unemployment differs across regions (see annex table
The regression results obtained from the two approaches, the current status and the usual status,
are found fairly similar whether the length of time is last week or last six months except for the
magnitude of coefficients. For this reason, we indifferently used the current unemployment
status to interpret the result and explained differences. As expected, when the age increases, a
person is less likely to be unemployed. This is in line with the descriptive result of the study as
well as earlier studies in which age and unemployment exhibited an inverse relationship.
Regarding gender gap, being male is likely to reduce probability unemployment. The marginal
effect implies that the probability of women to be unemployed is 16.5 percent higher than that
of male. The gender gap for usual unemployment status appears to have decrease from 2003-11
by about one percentage points while the gender gap in probability of current unemployment
status remained constant even after eight years. It signals existence of some gender
discrimination though it is difficult to single out contribution of all other factors (such as
education gap) to the disparity. The result is consistent with the descriptive finding and with
previous works such as UN (2003), WB (2007), Guarcello, Lyon and Rosati (2008a), and
Controlling for other factors and considering never married labor market participants as a
referent group, the marginal effect indicates for all categories of marital status lower
probability of unemployment and statistically significant at below one percent significance
level. Divorced and widow/widower were 6.4 and 3.3 percent, respectively less likely to be
unemployed than single labor market participants. Separated person but available and ready to
take job offers is found to have 4.3 percent lower probability of unemployment than who is
never married. The implication is that, married, widowed, separated and divorced are more
responsible to the livelihood of family as bread winners so that they do not want to extend time
to shop around for better jobs, rather they would take any available job to discharge their
Regarding education, the marginal effect implies that educational qualification unable to
reduce likelihood of unemployment among the whole urban labor force as compared with the
reference line education, lower primary (grade 1 to 4). For instance, worker with upper primary
education (grade 5 to 8) confront 6.7 percent higher probability of unemployment. Having
secondary and preparatory education associated with 11.7 and 12 percent respectively higher
probability of unemployment. It appears to increase chance of being unemployed by nearly 12
percent relative to lower primary education. Likelihood of unemployment falls with any level
of non-formal and with no education relative to reference category of education.
Tertiary education in Ethiopia commonly concentrates on general long term training dominated
by theory oriented curriculum. The probit regression result indicates that higher education,
except diploma level and above education, including vocational training has no tendency to
bring salutary effect on probability of unemployment estimated using current status approach.
It contradicts Arum and Shavit (1995) that provides evidences in favor of possibility of
vocational education to decrease risk of unemployment and increase chance of students
employment as skilled workers. After eight years of practices TVET education decreases
probability of usual unemployment however it keeps on increasing likelihood of current
unemployment. For example, as indicated in table 4.5 a vocational graduate is 1.9 percent less
likely to fall into usual unemployment status in 2011 relative to 2003. However overtime the
employment effects of general education is promising. TVET drop outs have 7 percent higher
probability of unemployment relative to baseline education. Labor force participants who have
started university degree and diploma education but not completed face 6 percent higher
probability of current unemployment status. The worker with the same educational
qualification may less likely confront usual unemployment. Diploma graduates are 5.7 percent
less likely to be unemployed as compared with referent educational qualification. Other things
being equal, workers with first degree and above qualification have 9.6 percent lower
probability of unemployment relative to baseline education. The implication is that the
employment effect of tertiary education (diploma and above educational qualification) is
relatively highest. For instance, the WB (2007) indicates that general education in urban
Ethiopia, especially among the youth population, is associated with a 78 percent higher
likelihood of being unemployed. Similarly, the finding of Getinet (2003) and Kirishnan,
Gebreselassie and Dercon (1998) confirm that those that completed secondary education are
more likely to be unemployed. Across Europe an academic degree likely to decrease short term
unemployment more effectively than long term unemployment (Nùñez and Livanos, 2010).
This is the effect of employers prefer better educated people to less well-educated people for
positions that were held by less educated workers.
One question of interest is: After controlling for other observable factors, what has happened to
the likelihood of unemployment overtime? The factors we controlled for were sex, age; marital
status and variables related to different educational qualification including tertiary education
and TVET and urban location. The base year is 2003. The variable y04 is a dummy variable
equal to one, if the observation comes from 2004 and zero if it comes from other years. The
coefficient on the year dummy variables showed a sharp drop in probability of unemployment.
That is the likelihood of current unemployment decreases by 2.5 percent in 2004, 7 percent in
2006, and 5.6 percent in 2010 and it falls by 5.2 percent in 2011. The coefficients on year 2004
to 2011 suggest that there are drops in probability of unemployment for reasons that are not
captured in the explanatory variables. Since we controlled, for example, different levels of
educational qualification, this drop was separate from the decline in occurrence of
unemployment that is due to the change in average educational levels. The average years of
education increased from 8.3 in 2003 to 9.3 in 2011. However, probability of unemployment
decreases by 5.2 percent in 2011 relative to 2003 is not attributed to rise of this average
Urban location is statistically important. Probability of unemployment appears to rise
proportionately with the level of urban size except for Jigjiga and other towns in Somali
regional state. Regions with relatively bigger towns have higher probability of unemployment.
We excluded Gambella regional state from this analysis as the region does not have survey for
2004. Relative to Benishangul Gumuz, unemployment probabilities were higher in all regions
and statistically significant at one significance level. The probability of unemployment was
found to be highest in Dire Dawa and Addis Ababa followed by Somali regional state. SNNP
has been experiencing least unemployment probability in the last eight years relative to all
other regions except reference region (Benishangul Gumuz Regional state). Therefore a person,
who lives in Dire Dawa, is about 23.4 percent more likely to experience statistically significant
at 1 percent level of unemployment relative to someone residing in Benishangul Gumuz
regional state. The probability of experiencing unemployment for those who reside in Addis
Ababa, Somali and SNNP regional states is higher by 21.4 percent, 14.8 percent, and 6.2
percent, respectively as compared to a person in Benishangul Gumuz regional state. The probit
regression result is in favor of the unemployment discrepancy among regions identified using
simple descriptive statistics.
To validate the pooled probit model regression result obtained from urban employment
unemployment survey between 2003 and 2011, we made pooled probit regression on different
data set (such as labor force surveys of 1999 and 2005). The results are the same for most of
the variables except for minimal variations due to inclusion/exclusion of few variables to avoid
multicollinearity and specification problems (see annex table 4.23). The dependent variable,
measured using the current status approach for both data sets, is equal to zero for employed and
one for unemployed. Demographic variables such as age, and sex and were likely to decrease
probability of unemployment and statistically significant at 1 percent significance level for
both data sets. Labor market experience is likely to decrease probability of unemployment.
Regarding educational qualifications represented by different dummy variables for most
variables the results are almost the same. For example, for both surveys (urban employment
unemployment surveys and labor force surveys), upper primary, secondary education,
preparatory education, and technical and vocational education have positive and significant
effect on probability of unemployment at below 1 percent significance level (see annex table
4.23). On the other hand, for both data sets diploma and above educational qualification is
likely to decrease probability of unemployment. However the difference was observed for one
year training obtained from teachers training institutions after completion of secondary
education and TVET not completed.
As far as regional unemployment distributions are concerned the same result is obtained for all
regional dummies. Relative to the reference region, Benishangul Gumz, all regions have higher
probability of unemployment. As presented in annex table 4.8 the probability of being
unemployed is highest for Dire Dawa followed by Addis Ababa in both urban employment
unemployment and labor force surveys and statistically significant at below 1 percent level.
Regarding migration variable, migrants with minimum of half year to ten and above are less
likely to be unemployed than non-migrants. This might be attributable to lower wages of rural
areas from where the migrants moved to urban areas for better earning opportunities so that
their reservation wages are low relative to the non- migrants Moreover, migrants may have no
option other than their labor income for their subsistence, so that they are less likely to shop
around for better job than non-migrants. The result is consistent with descriptive analysis
where migrants are less likely to be unemployed. It is also consistent with Tegegn (2011) and
Birhanu, Abraham and van der Dejil (2005).
The result from primary survey using logistic regression is not consistent with the result from
secondary survey. This might be attributed to the variation in spatial coverage of surveys,
sample size, explanatory variables controlled, survey period and other potential reasons. The
paradox is females are less likely to be unemployed than males (see annex table 4.17). The
association between education and probability of unemployment is varied to some extent.
Upper primary, secondary and preparatory schooling do not have any effect on probability of
unemployment relative to lower primary education. TVET, diploma and degree and above
graduates are less likely to be unemployed as compared with lower primary education.
Alternatively after considering years of schooling as explanatory variable we found that more
years of education decreases likelihood of unemployment. Access to any form of credit (formal
and informal) has also negative effect on probability of unemployment. However over 76
percent of respondents borrowed money from formal financial institutions in three cities. It
suggests increasing access to formal financial institutions as sustainability of informal sources
are uncertain and using this sources are relatively costly.
4.15.2. The Effect of Training Polices on Urban Unemployment
The government of Ethiopia has emphasized on technical and vocation education as a strategy
to enhance employment and employability of youth. Labor market participants who received
any training are at most 9.9 percent less likely to be unemployed than those without training
and statistically significant at below one percent. So any training received is preparing people
to meet the skill demands of the labor market overtime.
We also compared the effect of TVET and grade ten completed general education on
unemployment. Surprisingly TVET graduates are more likely to be unemployed in 2011
relative 2003. One additional year of TVET education (10+1, 10+2 and 10+3) has positive
effect on probability of unemployment after eight years. However grade 10 graduates
probability of entering into unemployment is decreasing (annex table 4.14). Suggesting even if
employment effect of TVET is inconsistent it does not have desirable effect on labor market
outcomes after more than eight years of implementation experiences. Hence it calls upon to
reconsider the implementation and the curriculum of technical and vocational education in line
with the demand of industries.
4.15.3. Effect of Education and Training Polices on Self-employment and Schoolto-Work Transition
The entrepreneurial effect of education and training in general and TVET in particular is not
attractive. The regression result suggests that a self-employment condition of workforce is
likely to decrease significantly with access training and better education. For example, degree
and above graduates are 28.8 percent less likely to be self-employed than worker with not more
than lower primary educational qualification. TVET graduates are less likely to be selfemployed than lower primary graduates. One additional years of schooling decreases
probability of self-employment by 1.7 percent (annex table 4.15). However completed grade
ten education and technical and vocational education has salutary effect on self-employment
status of labor force after eight years.
As indicated in annex table 4.16 work experiences, upper primary education, technical and
vocational training, any training received, access to any form of credit received and university
education are associated with shorter school-to-work transition period. The implication is that
TVET, university level education and access to start up capital are important policy variables
to reduce high unemployment rate and extended duration of unemployment in urban areas of
4.16. An Assessment of Strategies to Promote Employment in Ethiopia
Vocational education and training and expansion of micro and small enterprises commonly
used as a remedy to keep youth out of streets and unemployment and to raise the income of the
poor. Furthermore trainings in Ethiopia are mainly provided to increase employability and
encourage self-employment of dropouts and graduates. For example, PASSDEP envisages
TVET to provide relevant and demand driven and training that increases employment and selfemployment. Important reform measures have been introduced after the adoption of the
National TVET Strategy of 2002 and the TVET Proclamation of 2004. However existing
evidences assure that share of self-employment by TVET graduates is less than their
counterparts who are secondary school graduates and employment growth within MSEs is
slow. In the next section, we evaluate the implementation and effectiveness of some important
strategies to increase employment in Ethiopia. Our focus will be on the role of technical and
vocational education and training (TVET) and micro and small scale enterprises (MSEs) in
4.16.1. Strategies to Increase Employment through TVET
Strategies to increase employment through effective implementation of technical and
vocational education and training need to begin from participatory curriculum development to
linking graduates to the labor market. To evaluate the implementation and effectiveness of
technical and vocational education and trainings in Ethiopia, we adapted good training
practices for disadvantaged youth identified in Brewer (2004). The evaluation result helps us to
identify the bottlenecks to job creation potentials of the program and its implementations and to
measure outcomes of the program in relation to 2008 National TVET strategy document. We
collected relevant data to undertake this assessment from different TVET respondents such as
employers, parents, TVET students, TVET teachers and experts who guide and oversee the
implementation of the TVET program. When distributing the questionnaire the sex
composition of respondents is taken in to account. As shown in table 4.3, over 26 percent of the
respondents are either teaching in the government technical and vocational collages or working
as directors, experts and practitioners.
Table 4.3: Distribution of respondents
Government TVET college (teachers, experts and directors)
Private TVET college (teachers, experts and directors)
Employers (both government and private employers)
Parents, above 1 year TVET student and TVET graduates
Source: primary survey
Share of teaching staffs in private colleges are 10.8 percent and the share of employers who are
the major beneficiaries from effective implementation of TVET programs are nearly 18
percent. Parents of the technical and vocational students, students who attended at least one
year course in the college and TVET graduates constitute nearly 44 percent of the respondents.
The good training practices relevant to the current TVET environment of Ethiopia against
responsiveness, relevance, flexibility, upscale and coordination.
Innovativeness refers to the unique quality of the TVET program, which deals with the
drawbacks in other training practices in enabling the disadvantaged youth; appeals to the
interest of all respondents. The Likert scale inquiry is used to gather information from
respondents about innovativeness of the program. We find that nearly three fourth of
respondents (75 percent) of the respondents agree that the training program addresses the
limitations in other training programs. While 17 percent are uncertain about the innovativeness
of the program and the remaining 8 percent at least disagree with the innovativeness of the
Table 4.4: Evaluation of the innovativeness of the program
percentage of respondents
Criteria to measure innovativeness of the program
(number of the respondents= 219)
Disagree and not
The TVET program provides training to women and youth
marginalized by other training programs and prepares them for
Source: Primary survey
The result suggests that majority of the respondents are in favor of the innovativeness of the
program. Specifically, the program addresses training gaps of youth and women who are
disadvantaged in other training programs and prepares these groups for good job relative to
other trainings. Moreover, the outcome is inline with 2008 National TVET strategy developed
by involving various interest groups. The TVET strategy makes every effort for social
inclusion, equal access and opportunity by increasing the access regardless of level of
educational attainment, gender, location, ethnic and religious membership. Gender sensitivity,
particularly promoting TVET institutions to develop gender sensitive polices to boost fair
access and to avoid any bias against female trainees and staffs. This does not mean further
improvement is not required, but the achievement is salutary thus the respondents can achieve
more on fair access to the training and can improve labor market out comes of marginalized.
Feasibility of the training is a standard that measures ‘the program can, realistically, be
implemented; there is sufficient support and funding capacity’. We have set specific criteria to
evaluate the feasibility of the program as indicated in the table 4.8. The average result indicates
that nearly 59 percent of the respondents are uncertain and disagree. They question the
feasibility of the TVET program. The survey result depicts that about 69 percent of the
respondents believe that the program does not have sufficient support and budget; and nearly
67 percent of respondents pointed lack of qualified staff and inadequate laboratory equipments.
Poor access to credit for business startups by technical and vocational graduates is reported by
67 percent of respondents.
Table 4.5: Evaluation of the feasibility of the program
Items to evaluate feasibility of the program
Percentage of respondents
(number of respondents= 221)
The program is realistic and practical
The TVET program has enough support and budget
The program has qualified and experienced teachers, enough lab
TVET graduate access to sufficient credit for business start ups
To fill shortage of experienced teachers the program employees
foreign staffs to facilitate technology transfer
Source: Primary survey
Only 35 percent of the respondents agree that the program employs expatriates to fill shortage
of experienced teachers and to ease the technology transfer. However the strategy document
identified set of alternatives to increase qualified staffs, employment of foreign staffs to closing
teachers and trainers competences, collaborative TVET schemes to decrease cost of training
and increasing income sources of TVET schools ( such as sale of products produced by
students). The policy strategy is silent about availability of credit to graduates for business
The resource available for every economic activity might be scarce but the way we utilize the
resource can matter the implementation of the program. Graduates need soft loan for business
start ups and colleges need money to acquire lab equipments and to employ qualified staffs.
Government may lack sufficient budget for all these activities, however, the labor of graduates
can substitute part of the budget deficit. Similarly, locally available materials, which cost less,
can be used to furnish some of the lab instruments. Nearby private and public organizations can
be source of finance for lab equipments as well as teaching staffs on part time basis. Therefore,
close collaboration of government, private sector and NGOs are important to alleviate these
Responsiveness refers to whether or not the practice of the program is consistent with the
needs identified by young women and men; it has involved a consensus-building approach; it is
responsive to the interests and desires of the participants and others. We identified specific
points summarized in table 4.5 to measure whether or not the program is responsive to the
interests of respondents.
Table 4.5: Evaluation of the TVET program responsiveness
Percent of respondents
Criteria to evaluate responsiveness of the program
(number of respondents= 222)
Agree and strongly
The program enhances entrepreneurship and has played role to reduce
Government polices and strategies have been encouraging business
startups by TVET graduates
TVET program has meaningfully contributing to better productivity of
MSEs through technology transfer
TVET program gives more support and more attention to youth
females relative to other programs
Source: primary survey
Over 68 percent approve that the program enhances entrepreneurship and has played role to
reduce youth unemployment, 55 percent noted that government policies enhances business
startups of TVET graduates, 63 percent of respondents consent that the program contributes to
productivity of MSEs through technology transfer and half of the respondents argue for the
program that it gives more attention to females than other programs. The average value
indicates that 60 percent of respondents believe that the program is responsive while the
remaining 40 percent disagree or uncertain about the responsiveness of the program.
Apparently, the opinion of the respondents suggests, the need to invest more in these institutes
so as to increase their responsiveness to participants. In fact, the opinion of beneficiaries
supports the realization of expectation of TVET strategy. The strategy expects TVET
institutions to replicate and transfer selected technologies to industries and MSEs. It also
underlines importance of entrepreneurial skill development training, internship and connecting
TVET to MSEs.
Relevance of the program refers to the contribution of the program, either directly or
indirectly, to demands of the market and the needs of the participants. We set some points to
evaluate the relevance of the program as indicated in table 4.6. To majority of the respondents,
the program has been student centered and practice oriented relative to other trainings, over 57
percent of respondents’ consensus about vocational training provides skills required by the
However, the progress of the program in terms of enhancing competences of trainees’ overtime
found to be weak and moreover over 50 percent of respondents agreed TVET less likely equip
new labor market entrants and youth with skills required by local and international markets.
Thus, the need assessment efforts of the TVET institutions to identify current skill needs of the
labor markets, and participating employers and trainees in the need assessment are found to be
important areas of focus for further improvement.
Over half of the respondents agree that the program facilitates school to work transition.
Students from higher income families struggle for tertiary education and farmers refused TVET
because they believed that it limits the chance of their off springs to compete for better jobs in
urban areas(Psacharopoulos, 1997). Furthermore, the TVET strategy document (MoE, 2008)
pointed out that poor awareness of the community on the program, for instance, the perception
that those failed to join tertiary education are admitted to the program and earnings for the
graduates is low.
To address such prejudice, the strategy paper suggested various mechanisms such as discussion
with stakeholders on the fact that the program has clear educational qualification and
promotion structure, participating private employers as stakeholders, arrangement of different
competition for entrepreneurs and involving them in international competitions.
Table 4.6: Evaluation of the relevance of the TVET program
Items to evaluate the relevance of the program
Percent of respondents
(number of respondents= 217)
Disagree and not
Agree and strongly
TVET provides skills and knowledge required by local labor market
TVET program enhances the skill and competences of job seekers and
TVET facilitates employability of youth
To enhance local people awareness about TVET sufficient effort was
TVET program ease school to work transition
unemployment spell of youth
It is more student centered and practice oriented relative
entrepreneurs more than before so it becomes more fitting to the skill
needs of the labor market
TVET institutions make need assessment to identify current skill and
technical skill demands of the labor market
TVET allows private and government employers and trainees sufficiently
participate in the need assessment
TVET equip new labor market entrants and youth with skills required by
local and international markets
Source: primary survey
However, the survey result indicated that sufficient effort has not been made to increase
awareness of the beneficiaries. The average measure of the overall relevance of the program
shows that almost half of the stakeholders were uncertain or disagreed with the relevance of the
program. The implication is that concerned bodies need to give more attention to improve the
relevance of the TVET program so as to meet the technical skill demand of the labor market.
Flexibility of the program evaluates the capacity of the program to provide training programs
that meets the changing labor market and international economic environment. As can be seen
in Table 4.7, about 57 percent of the respondents do not agree that TVET institutions are
flexible and capable to make timely adjustment of training programs to meet the changing skill
demands of the labor markets.
Hence, the most likely inference would be that TVET
institutions must be aware of the low flexibility of their programs. Furthermore, the result less
likely meets the 2008 national TVET strategy that advocates flexibility to respond to the
changing occupational requirements.
Table 4.7: Evaluation of the relevance of the TVET program
Items to evaluate flexibility of the program
Share of respondents
(number of respondents= 218)
TVET institutions have enough capacity to make timely adjustment of
training programs to meet changing labor market skill demands
The TVET program make quick adjustment to meet dynamic labor
market needs that vary with international conditions and national
Source: primary survey
Hence, the most likely inference would be that TVET institutions must be aware of the low
flexibility of their programs. Further the result less likely meets the 2008 national TVET
strategy that advocates flexibility to respond to the changing occupational requirements.
Efficiency and Effectiveness refers to use of resources (human, financial, and material) in
such a way that maximizes desired impact. The result in table 4.8 indicates that training
programs are cost efficient.
Table 4.8: Evaluation of the efficiency and effectiveness of the program
Items to evaluate Efficiency and Effectiveness
Share of respondents
(number of respondents=213 )
TVET reduces duration of unemployment and job search time and
TVET program is achieves the desired result at least cost
Source: primary survey
Almost 65 percent of respondents do agree that the TVET program is effective in reducing
duration of unemployment and job search time. Regarding the efficiency of the program, about
half of the respondents agreed that the TVET program is achieving the desired results at least
cost. The remaining 50 percent of the respondents did not believe that the program is efficient,
implying that the program is still required to exert more efforts to improve its cost
effectiveness. Furthermore the figures contradict with the emphasis of strategy document to
increase efficiency of human and financial resource utilization and cost effective TVET
Up scalability of the program evaluates whether or not the practice of the training can be
expanded to operate on a wider level (e.g. from community level to national level). The result
indicates that the practices are not significantly expanded in different areas and circumstances.
Table 4.9: Up Scalability of the Program
Items to evaluate Up scalability
Share of respondents
(number of respondents= 218)
TVET’s best practices were expanded in different areas and
Source: primary survey
Coordination, cooperation and commitment among respondents is vital for effective
implementation of the TVET program (Brewer, 2004). However the evaluation result indicated
that above 56 percent of the respondents are disagree or uncertain about the coordination
between respondents. For instance, participation of respondents in the TVET program from
inception to implementation stage is crucial for successful outcome, but majority of the
respondents agree about lack of involvement of all participants.
Linking vocational trainings to the industries is important to ease school to work transition of
the youth. Nevertheless, the achievement in TVET-industry partnership is not much attractive
and needs further effort to link training programs with industries. Although the private sector is
among the dominant employer of graduates, its participation in curriculum development and
training provision is found to be weak.
Table 4.10: Coordination of the TVET program
Items to evaluate coordination
Share of respondents
(number of respondents= 216)
participates in the evaluation of TVET programs
from inception to implementation phase
TVET program established close link with industries by
participating firms in curriculum development and achieved better
result from labor market outcomes
TVET participates Private sector in curriculum development and
training provision as a major beneficiary from the program
Government, civil society organizations, private sector, local
community and other respondents
has been implementing the
TVET program in collaboration
implementation of TVET programs encourage private sector to
involve in curriculum development and training provision
Source: primary survey
The result does not support 2008 national TVET strategy. The TVET strategy document
stipulates interference of a broad stakeholder groups particularly education sector, the
employers, industry and MSE sectors. It also promotes public private partnership through
facilitation, regulation of the system through proclamation, licensing accreditation, and
4.16.2. Employment Growth within Micro and Small Scale Enterprises
Despite government policy interventions, for example; short term trainings to MSEs and other
business development services, assign appropriate organs and experts to support and to oversee
activities of MSEs, and credit supply through financial institutions to increase employment,
employment contribution of the sector is low. To point out the puzzle to be solved, this section
explains characteristics of micro and small scale enterprises and constraints and opportunities
and factors that determine employment growth within MSEs.
18.104.22.168. Characteristics of Micro and Small Scale Enterprises
Among the surveyed sample MSEs from three cities namely, Addis Ababa, Hawassa, and
Bahir Dar, the average number of male and female operators are nearly four and three,
respectively per an enterprise. It suggests that relatively male are more likely to undertake
small business activities than women in the three survey areas. Majority of the business
operators are non-teenagers; the share of teenagers is only 16 percent. The size of middle levelskilled operators is more than those with non-formal education. Only 6.8 percent of the sample
operators have non-formal education. Those with primary education (grades 1-8) and with
secondary education (grade 9-12) constitute about 39.6 and 30 percent, respectively, of the
sample operators. Those with educational qualification beyond secondary education but below
first degree are 9.9 percent while operators attended first degree and above degree education
are more than 1.6 percent. There are at least 6 literate operators per small business and there is
one illiterate operator per business firm.
Regarding the training status of operators, about 55 percent of them have taken some sort of
training; and at an enterprise level, on average, there are at least four persons who received
training. However, out of those with training only a few of them took long term training
provided by colleges or universities. Nearly 74 percent of the owners with training have short
term training provided by organizations and experts that facilitate and oversee the performance
of MSEs. Among those with training, only 15 percent of owners of MSEs in the sample survey
are diploma holders who graduated from TVET colleges. Among which, about 4 percent of
them earned 10+1, 3 percent of them earned 10+2, and about 8 percent of them earned 10+3
levels of diploma in various technical and vocational education and training institutes.
Out of 55 percent of the sample operators that have attained some sort of training, including
short term training, almost 89.6 percent of them are found using the training they received to
run their current businesses. They are involving in businesses related to their field of training.
This might be because most of the trainings received are short term trainings provided by
experts working for MSE development. These trainings are compulsory to license enterprises
and for business startups. However, many of those with long term training are involved in
businesses other than their field of study. For instance, out of fourteen operators with
agriculture related professional science (bachelor degree), only one person is found engaged in
urban agriculture activity; and out of forty with health training at degree level, nobody is found
involved in businesses related to his/her study. The implication is that the ongoing long term
training programs are less likely contributing to entrepreneurship relative to short term
trainings. Above all, it suggests the mismanagement of scarce public resources and the weak
linkages and coordination between training institutions and the industry; and hence, reminds all
concerned bodies to further evaluate the ongoing efforts and to take appropriate measures.
Type and composition of activities
The major activities undertaken by micro and small scale enterprises in the survey area are
classified in to four categories of enterprises: manufacturing, trade and service, urban
agriculture and construction. Manufacturing related enterprises constitute 33 percent, trade and
service related enterprises constitute 13 percent and 27 percent respectively, urban agriculture
estimated about 11 percent and construction enterprises comprise 15 percent of the sample
MSEs. Commonly, micro and small enterprises are perceived as traders and vendors. As
opposed to the common perception, the survey result suggests that among micro and small
scale enterprises three types of activities are identified as the most important categories. Food
and beverage activities account for 17 percent, metal work and wildering accounts for 15
percent and production and supply of construction materials accounts for 15 of the MSEs
Location and ownership of enterprises
Proportionate to enterprises concentration in Addis Ababa, over half of the MSEs included in
this study are taken from Addis Ababa while the remaining 48 percent of the sample MSEs are
equally shared between Awassa and Bahir Dar. Regarding ownership status, nearly 36 percent
of owners are individuals, 36 percent are owned by proprietors consisting of group of people
between two and ten, and the remaining 28 percent are owned by cooperatives. Most of the
cooperatives are involved in construction and manufacturing activities. Service and
manufacturing activities are dominated by proprietors and individuals. Urban agriculture is
controlled by proprietors while most of the trade activities are run by individual owners.
22.214.171.124. Employment Contribution of MSEs
Micro and small scale enterprises are fundamentals for the emergence of entrepreneurs.
Because they have considerable potential to generate large employment opportunities and to
contribute to the entire economy, they deserve special attention and support for their growth
and development. In this regard, those agencies established at various levels, from federal to
local levels, are expected to play a pivotal role in promoting and strengthening these
Micro and small scale enterprises (MSEs) have been dominant source of employment and
income in many countries of the third world. A quarter of working age people are get
employed in MSE activities. Even in USA, some scholars make a case that eight out of every
ten new jobs opportunities in recent years have been created by small businesses (Mead and
Liedholm, 1998). Employment contribution of MSEs is remarkable in urban Ethiopia while we
consider a firm as MSE when its employment size is not more than. According to CSA survey
result, in 2006, almost 76 percent of the work force had been employed in Micro and small
scale enterprises (MSEs). The employment share of MSEs increased to 83 percent in 2010-11.
Figure 4.14: Employment contribution of MSEs (%)
> 10 workers
Source: UEUS 2003-11
The primary data survey from the three cities (Addis Ababa, Bahir Dar and Hawassa) indicated
that out of the total 4,554 people employed in the sample MSEs in 2011, the employment
opportunities are largely contributed by MSEs engaged in manufacturing related activities
followed by the construction sector. As can be seen from figure 4.14 the employment share of
manufacturing enterprises accounted for 29.6 percent of the total employment followed by 27
percent employment contribution by construction sector while that of MSEs engaged in urban
agriculture accounted only for 12.6 percent. The relative employment contribution rate of trade
Figure 4.15: Employment by type of MSEs (%)
Source: survey data
Growth of employment is measured using the formula proposed by (Evans, 1987) calculated as
the ratio of change in natural logarithm of employment differential between initial and final
period divided by age of a firm. The initial period refers to year of establishment and the final
period is the survey period, October 2011. The result indicates that the employment growth is
positive for nearly 29 percent of MSEs, 21 percent of enterprises have negative employment
growth rate and 50 percent have zero growth rate since establishment. Highest negative
employment growth rate has observed for manufacturing enterprises estimated about 3 percent
followed by service sector accounts for 2.85 percent negative growth rate. However highest
positive growth rate of 4.5 percent observed for manufacturing enterprises followed by trade
accounts for 2.4 percent positive employment growth rate.
126.96.36.199. Startup Motives of MSEs
Majority of business operators, about 76 percent, started their own business because they
believe that self-employment has better financial returns than paid employment. On the other
hand, 18 percent of the operators responded that they started their own enterprise because they
had no another choice to get out of unemployment. Regarding the effect of training on
entrepreneurship, only 14 percent of the operators appreciated that the training they received
inspired them to be entrepreneur. The implication is that trainings provided are almost
ineffective in stimulating trainees, especially young people, to be risk takers and innovators.
188.8.131.52. Constraints of Micro and Small Scale Enterprises
The operators’ entrepreneurial skills and supportive business environment are two important
determinants of success for business enterprises, where success is estimated in terms of
employment size and growth, income and profitability. In this part, we look at different
opportunities and constraints strongly linked to size and growth of employment within MSEs.
The opportunities and constraints comprise: source of startup capital, access to market, lack of
favorable government rules and regulations, lack of work place, shortage of and high prices of
Market and Other Constraints to Expand Business
Constraints to expand business and then employment by micro and small scale enterprises are
indicated in table 4.15. Only 3 percent have responded that they have no problem to expand
their business. However for more than 54 percent of the enterprises lack of market is the major
barrier to increase the scale of their business. Relatively a moderate size of operators, about 29
percent, reported that lack of credit supply is the main challenge to expand their business and
hence employment. As noted earlier, own saving, usually a small amount, is the dominant
source of startup capital. The survey result indicated that only one enterprise in ten has
received loan from informal financial institutions and 3 enterprises in ten has borrowed formal
financial institutions. On the other hand, 45 percent of operators reported lack of capital as a
major barrier to expand their business and employment. In addition to this, 28 percent noted
that lack of equipment is challenge to expand operation. However, there are microfinance
institutions in each woreda that can lend money to new startups and existing ones. The overall
results suggest two important issues. First, the local communities may lack awareness and
skills on possibilities to finance their business through borrowing. Second, the opportunity cost
of borrowing money may be high at ongoing interest rate.
Table 4.11: Problems to expand business (%)
Problems to expand business
Lack of market
Lack of credit
Shortage and high price of inputs
Lack of information
Lack of equipment
Lack of capital
Higher business taxes
Government rules and regulations
Competition with other firms
Lack of work place
Lack of training
Shortage and high wage of labor
Source: Primary survey
Shortage and high prices of inputs is impediment to 46 percent of enterprises to increase scale
of their production.
Most of enterprises supply their products to consumers. Over 82 percent of entrepreneurs sell
their products to consumers, followed by 24.5 percent of small firms supply their product to the
government organizations. Enterprises estimated around 26 percent trade their products with
retailers and whole sellers, manufacturers, and exporters. Hence to overcome the shortage of
market there might be possibilities to increase income of consumers or introducing strategies to
increase demand by manufacturers and traders. Most of the input suppliers are traders. They
supply inputs to 80 percent of entrepreneurs while producers, government organizations and all
others supply inputs to only 36 percent of small firms. A few enterprises buy inputs from
multiple suppliers. Therefore it is important to increase supply share of suppliers other than
traders to realize approximately competitive price to overcome production interruption
associated with shortage and rise of input prices. The source of input is dominated by domestic
industrial products for most producers (52%) followed by agricultural outputs (39%). Over 81
percent of producers depend on domestic agricultural products, imported inputs and natural
resources. The implication is that the row material supply is most likely exposed to weather
shocks and deficits since agricultural export is dominant source for foreign earning. On the
other hand there is an opportunity for strong linkages between agriculture, industry and MSEs.
There is also a possibility to decrease seasonal causes of job interruption by intensifying the
use of domestic industrial outputs as sources of raw-materials.
184.108.40.206. Source of Startup Capital and Capital Growth
The major source of startup capital for most entrepreneurs is personal savings and / or ‘equeb’.
Personal savings is a source of startup capital for more than 69.6 percent of operators, followed
by borrowing from financial institutions, which is used as source of fund by 31 percent. From
each of the remaining sources such as inheritance, donation from government and NGOs,
borrowing from village lenders and others means not more than 10 percent of firms generate
start up capitals. The average initial capital was Birr 55,426.37 while the current capital
significantly increased to Birr 194, 012. 8. The average growth of capital nearly 75 cents per
enterprise is considerably higher than the average growth of employment per enterprise
estimated about 0.04. However, overall growth rate of both capital and employment are not
satisfactory and the mean capital growth rate of firms with positive employment growth is not
significantly different from firms with negative employment growth rate.
Own saving is a source of startup capital to 73 percent of firms that have positive employment
growth rate. Where as borrowing from microfinance institutions is source of finance to 35
percent of micro and small scale enterprises with intended employment expansion rate.
Similarly majority of firms with negative employment growth rate, for example 68 and 29
percent use own saving and borrowing from micro finance institutions respectively to generate
funds. Each of the remaining means of finance are used to raise funds not more than 12 percent
of firms that have either positive or negative employment growth rate. The implication is that
formal financial institutions are almost equally accessible to firms that have positive or
negative employment growth rate and less likely used by both forms of firms to raise startup
fund relative to own saving /equib. This may be attributed to enterprises access to formal
financial institutions may be limited due to collateral and other requirements and hence
220.127.116.11. Cause of Job Interruption
To determine the level and causes of job interruption, we analyzed months that operators were
producing for last 12 months. Over half of the firms operated for the last 12 months, 84 percent
of enterprises operated at least for last six months. Estimated figures of 5 percent of firms
operated only for a month. The mean months of operation of firms with desirable employment
growth rate are not remarkably different from firms with undesirable rate within 5 percent
significance level. The average interruption is nearly 3 months and it needs strong attention to
reduce its adverse impacts on employment. There are ample of causes for disrupting operation
for three months in average in the last 12 months before the start of the survey.
Lack of demand or market is a dominant cause of ceasing operation followed by shortage of
capital. Shortage and/or rise of price of a raw-materials and seasonal nature of business are also
among the causes forcing firms to halt operation. Sufficient supply of Power, water supply and
electricity are crucial for success of MSEs. However a few operators, about 3 enterprises in 50
reported that shortage of electricity is challenge for their success. Polices may
disproportionately affect MSEs. First polices tend to favor larger businesses-export oriented
Table 4.12: causes of job interruption
Causes of interruption
Shortage or a rise of raw material prices
Lack of demand or market
Shortage of electricity and water
Unfavorable government rules and regulations
Seasonal nature of the business
Shortage of capital
Source: Primary survey
Second start up and transaction costs for doing business are high due to complex administrative
systems and often due to corruption. However the survey result has shown that unfavorable
government rules have least impact on job interruption. Only 2 enterprises in the 25 reported
that government policies are reasons for job interruption. It suggests that market creation and
competitive supply of raw materials are important interventions to support micro and small
scale enterprises. Supply of sufficient credit is also worth mentioning to increase employment
and the productivity of labor and capital.
18.104.22.168. Assistance Needed from Government
MSEs are in need of different assistances to expand their capacity and hence employment.
However the survey result identified six major supports required from government. Nearly 75
percent of firms insist on government access to work place and over 74 percent of operators
responded that market creation and networking is vital for expansion.
Table 4.13: Assistance needed from government
Access to working place
Access to building in rent
Access to raw materials
Access to technical training
Better access to bank loans
Favorable government rules
Safety and operation rights
Access to business information, advice
and account keeping
Source: primary survey
Nearly 58 percent requires supports like better access to bank loans, and equal size of
respondents needed supports such as access to business information, advice and account
keeping from government bodies. Supports for instance access to technical training is strongly
demanded by 56 percent of respondents and 53 percent of operators insist on local government
to facilitate access to raw-materials (Table 4:13).
The remaining supports required by operators from the government are favorable government
rules (37 percent), access to building in rent (28 percent), safety and operation rights (31
percent), and other assistances (only 6 percent).
22.214.171.124. Determinants Urban Employment Growth within MSEs
The logistic regression analysis is used to identify factors that determine urban employment
growth within MSEs. The dependent variable is the average annual growth rate of employment
equals one when the rate is positive since startup of the firm and zero otherwise. Estimated
around 29 percent of MSEs have positive average annual employment growth rate. We
employed different regression diagnostic tests such as multicollinearity, specification test and
estimated robust standard errors. We dropped some variables for specification and
multicollinearity problems. However for the remaining controlled explanatory variables we
estimated robust standard errors. The results from logistic regression analysis used to
determine employment growth indicated that business interruption, type and ownership of
MSE, firm size, location, and motive to start up MSE have important effect on employment
growth, however, human capital endowment do not have any significant effect on
The logistic regression result indicates that the model is statistically significant because the p
value is below one percent. While we are interpreting odds ratio of a statistically significant
variable it refers that odds ratios are equal to 1 if there is no effect, smaller than 1 if the effect
is negative and greater than 1 if it is positive. Average annual employment growth of firms
located in Bahir Dar is 2.68 times higher than those located in Addis Ababa. Employment at
start up leads to lower probability of employment growth. Enterprises with relatively more
initial employment size have lower probability of employment growth (annex table 4.18).
Relative to firms involving in trade activities agricultural and manufacturing enterprises have
higher probability of employment growth. For example, the odds of employment growth for
manufacturing firm are 3.4 times higher than trade enterprise. The odds of employment growth
for urban agriculture are 3.9 times higher than trade enterprises. Average magnitude of work
hours has positive effect on the likelihood of employment growth. For every one unit increase
in work hours leads to, an enterprise’s odds of employment growth 1.02 times higher. New
ventures established by cooperatives are more likely to grow faster than those funded by single
owners. Motivations have been found to influence new firm growth. Self-employment
preferred to paid employment as a motivation for starting a business positively affect firm
growth however unemployment and /or training as a motivation to start a business has no
On the other hand, shortage of electricity and water services, lack of good government rules
and lack of capital collectively used as proxy for policy shocks lead to 0.51 times lower odds of
employment growth. The entrepreneurs human capital acquired often considered as good to
his/her likely success. However the index of human capital in the firm’s estimated in terms of
proportion of owners with training, TVET and other different educational qualifications has no
significant effect on probability of employment growth within MSEs. Moreover social ties
(support from families) and access to credit supply are unexpectedly not important for growth
of new ventures.
CONCLUSIONS AND RECOMMENDATIONS
Ethiopia’s urban unemployment rate is significantly higher than national unemployment rate
between 1999 and 2005. These considerable rates of unemployment differential maintained for
youth, females and adults as well over the period. The rate trended downward for all categories
of labor force since 2003, but remained at high level. The composite unemployment rate reach
peak of nearly 26 percent in 2003, but decreased to its lowest level 17 percent in 2006. This
might be attributed to decrease in overall labor force participation rate in the period. Despite
the sound economic growth, the urban unemployment rate is still higher and stood around 18
percent in 2011.
The youth and female unemployment rates in urban Ethiopia are considerably higher than adult
male unemployment rate and well above the total urban unemployment in each period from
2003 to 2011, which is consistent with the global experience and reflecting the relative
disadvantaged position of youth and female in labor markets. The age and sex disparity in
unemployment is maintained across all cohorts in urban job markets of Ethiopia. For example,
youth and female are threefold more likely to be unemployed than adult male between 2003
and 2011. The situation is the wrest for young female labor force.
There are several factors contributed to the high unemployment disparity. High job interruption
of women due to maternity leave and childcare; low educational qualification of women
relative to their male counterparts and labor market discrimination and prejudice are commonly
cited ones. However existing evidences indicate that adult male and female have almost equal
job interruption rates. Nevertheless obviously males have more educational qualification than
females and a labor market prejudice may adversely affect women employment opportunities.
On the supply side, youth extend job search until they secure better job as they are lucky for
family support during spell of unemployment. Family support in 2005 is substitute for
unemployment benefit for 75 percent of unemployed youth in Ethiopia whereas it is means of
access to basic needs for 38 percent of adult male in urban areas hence many youth are more
likely to remain unemployed and shop around for better job than adult males. Contrary to many
developing economies, lack of labor market information and poor job search experience of
youth relative to adult does not result in higher youth unemployment rate in Ethiopia. Youth
also switch between job, school enrollment and unemployment as educational institutions open
and close leads to young students more likely enter and exit the labor force. Not only supplydriven causes, but also labor market partiality causes youth to face higher unemployment rates
than adults. However the primary survey result from three cities contradicts with the forgoing
result. This may be attributed to sample size, coverage of survey and survey period.
As far as regional unemployment differences are concerned the two city administrations Dire
Dawa and Addis Ababa are hit hard by extremely unpleasant rate of unemployment over the
survey period. The unemployment rates observed in these cities are significantly above the
national average and all other regions. Gambella region is relatively lucky for having the
lowest relative unemployment rate in all periods. The trend of unemployment across regions is
almost synonymous with the national trend. Like national rate regional female and youth
unemployment rates are higher than adult male.
Many international facts proof the notion that additional education boosts labor market
outcomes such as better earning and lower unemployment. However the effect of educational
attainment on urban unemployment in Ethiopia is going wrong except for degree and above
educational achievement as opposed to the international experience. Considering lower
primary education (grade 1 to 4) as a baseline only degree and above graduates and non-formal
education have significantly lower rate of average unemployment rate than labor force
participants with baseline education between 2003 and 2011. Labor force with all other
educational qualification such as upper primary, secondary education, certificate and diploma
and degree not completed including vocational education not completed results in higher
average rate of unemployment in the typical period than labor force with baseline educational
qualification. Unemployment positions of TVET graduates are almost as equal as base level
category. Unemployment peaks through secondary to preparatory education.
At all educational levels female and youth experience a higher level of unemployment rate than
adult male. For example, youth females mean annual unemployment rate is estimated to be
more than threefold of adult men. It implies that the unemployment rate discrepancy between
female, youth and adult is non-declining up the educational ladder relative to baseline
Extended unemployment spell permanently weaken an individual’s productive potential and
human capital and hence employment opportunities however long spell of unemployment is
one of the features of Ethiopia’s urban unemployment. The average duration of unemployment
keeps at high level between 2003 and 2011. The unemployed remain jobless for nearly 2.4
years in the initial period and elevated to 2.5 years in 2004 and the lowest level of spell nearly
1 year and six months observed in 2010. The unemployment rate in 2003 is not only found to
be the highest but also ends for long duration relative to other periods while someone linking
the rate with the spell of unemployment. The average spell of unemployment is not gender
impartial over the period. The most challenging fact is that the spells of unemployment
increases with years of schooling and statistically significant. Even if, those with training have
significantly lower rate of spell than those without training considerable size of workers who
has received training also remains unemployed for more than 2 years based on secondary data.
However the mean spell of unemployment variation along educational ladder is significant at 1
percent while the variation disappears for those with training in three cites where primary
survey is conducted.
The effect of training on labor market outcome is significant over the 2003 to 2011 period.
Working age population with training face relatively lower unemployment challenge and more
likely to be employed than those who lacked the opportunity. Nevertheless, training failed to
reduce unemployment differentials between female, youth and adult male.
Combining the results from primary and secondary survey among people with the equal level
of education except for vocational and technical training received we do not have sufficient
evidence to generalize that those with vocational training are less likely to be unemployed in
The share of self-employment by TVET graduates is less than the comparison groups known as
secondary school graduates. Furthermore the trend of self-employment for technical and
vocational graduates is falling overtime while constant for secondary school graduates.
Regarding overall formal training, labor force without training are more likely to be selfemployed than those with training between 2003 and 2011.
School to work transition of urban youth in Ethiopia is long and difficult. The average time to
find first job varies significantly by training status. Mean time wasted to find first job after
quitting schooling by those with training decreased to ten and half months, while it takes
twenty two months to those without training. Average time spent for those with technical and
vocational training is nearly eleven months where as for those with grade 9-10 educational
qualification it takes nearly one year and four months. The mean school-to-work transition
differential is significant at 1 percent level. Those who have educational qualification of grade
11 to 12 need to wait two years to find their first job after they enter into the labor market.
Ethiopia’s fastest growth among non-oil economies for a decade does not result in equivalent
employment creation. Rapidly growing urban population arising from rural-urban migration,
lack of vibrant non-agricultural sector to absorbed surplus labor migrated from agricultural
sector are threats of unemployment. What school system thoughts does not required by labor
market and low level of human capital exacerbate the situation however effects of FDI
attraction on unemployment is mixed.
Existing evidences prove that skill mismatch, job destruction and queuing theories of
unemployment contributes to higher probability of unemployment in urban Ethiopia. The Cox
regression, after controlling for set of explanatory variables, consistent with the skill mismatch
theory. The worker with more years of schooling is less likely to end unemployment spell over
a short period. The implication is that additional years of schooling results in more total
duration of unemployment in the last six months before the survey period. Similar result was
obtained while we replace explanatory variables years of schooling by dummies of educational
levels. We also examined the relationship between probability of unemployment and years of
schooling and education further to test the validity of skill mismatch hypothesis using probit
regression. The result is synonymous with results find from the duration analysis. The probit
regression result supports both the queuing and job destruction hypothesis. The positive and
statistically significant relationship between the share of both public and formal private sector
job and the probability of being unemployed is found to strongly support the queuing
hypothesis. The positive and significant relationship between regional share of private formal
sector employment and probability of unemployment is support for job destruction.
Efforts are made to evaluate the implementation of the current TVET strategies in terms of
innovativeness, feasibility, responsiveness, relevance, flexibility, upscale and coordination
through the opinion of relevant respondents using a Likert scale inquiry. About 75 percent of
the respondents agreed that the TVET program is innovative in that it addresses the limitations
of other training programs. On the other hand, the respondents opinion casts doubt on the
feasibility of the program. The average result indicates that nearly 59 percent of the
respondents are uncertain and disagree regarding the feasibility of the TVET program. Lack of
qualified staff and shortage of budget to supply equipments and furnish laboratories are the
major factors behind the problem.
In terms of responsiveness of the program, the overall evaluation depicts that 60 percent of
respondents agreed that the program is responsive while the remaining 40 percent disagreed or
were uncertain about the responsiveness of the program. Apparently, the opinion of the
respondents suggests, the need to invest more in these institutes so as to increase their
responsiveness to participants that marginalized in other programs. With regard to the overall
relevance, the average measure score shows that more than half of the respondents are
uncertain or disagreed with the relevance of the TVET program. This has also been reflected in
one way or another in the descriptive and regression analysis of the effect of training on
unemployment. Hence, this could be an important message that reminds concerned bodies to
give attention and work hard to increase the relevance of the TVET program in order to meet
the technical skill demand of the labor market.
Concerning the flexibility of the program, about 57 percent of the respondents did not agree
that TVET institutions are flexible and capable to make timely adjustment of training programs
to meet the changing skill demands of the labor market. This suggests that TVET institutions
must be aware of the low flexibility of their programs and should work toward its
improvement. Similarly, the average result regarding the efficiency and effectiveness of the
program suggests that about 43 percent of the respondents do not agree that the TVET program
is efficient and effective. It implies that more effort is needed to improve the situation.
Another important area of concern is coordination among relevant respondents. Linking
vocational training to the market demand is crucial to ease school to work transition of the
youth. However, the evaluation result indicates the disagreement of over 56 percent of the
respondents on this issue; and also the participation of the private sector, which is the dominant
employer of the graduates, is found to be weak.
The probit regression analysis on the effect of education on unemployment shows additional
education starts to decrease the likelihood of unemployment when at least a person has college
degree. That is, education qualifications above upper-primary (grade 5-8) and below first
degree education level are positively related with the probability of unemployment.
Particularly, junior secondary education (grade 9-10) and preparatory education (grade11-12)
appear to increase chance of being unemployed by 11.7 percent and 12 percent, respectively
relative to lower primary education (grade 1-4). The same is true for other categories of
education, above secondary and below university degree, in which the probability
unemployment rate is positive. It is only for those with degree and above that the probability of
unemployment is negative. The implication is that the employment effect of education at
individual level is more pronounced at tertiary level of education. The result is almost similar
to earlier studies on the same area. Probability of unemployment is decreasing overtime
relative to base year and migrants are less likely to be unemployed than non-migrants. Location
is also statistically important determinant of occurrence of unemployment. Access to credit is
likely to decrease unemployment.
The survey result asserts the desirable effect of general training. Labor market participants who
received formal training are 9.9 percent less likely to be unemployed and statistically
significant at one percent level compared with those without training. The employment effect
of TVET is not consistent and decreasing after sufficient experiences of technical and
vocational training implementation. Its contribution to employability is not improving even as
completion of grade ten general schooling.
The entrepreneurial effects of education and training in general and TVET in particular are not
promising. University and TVET graduates are less likely involving in self-employment
activities than people with primary education. Additional years of schooling decreases
likelihood of individual’s involvement in self-employment businesses. However after eight
years of practices entrepreneurial effects of TVET and completed grade ten general schooling
is promising. Accesses to credit, TVET and university education make shorter school to work
Indeed, it is undeniable that training has desirable effects on the individuals’ performance and
productivity. Training would make people more successful and more productive if they apply it
in their day to day business, otherwise it would depreciate and become obsolete. What is
observed from the survey of the three cities is that most of MSE operators take some sort of
short term training, 89.6 percent of them are found to apply it to run their current businesses.
However many of those with long term training are involving in business other than their field
of study. This can be indication of the weak linkages and poor coordination between training
institutions and the industry in general for long term trainings. Besides, regarding the effect of
training on entrepreneurship, the survey result shows that only 14 percent of MSE operators
appreciated that the training they received inspired them to be entrepreneur. This suggests that
training providers are ineffective in inculcating the spirit of entrepreneurship among trainees.
According to CSA’s employment unemployment survey, the employment contributions of
MSEs are significantly larger than employment opportunities created in large-scale enterprises
and in public sector in 2006 while the latter sectors employment contribution exceeds the
contribution of small enterprises for periods 2010 and 2011. This suggests that the relative
employment contributions of MSEs are declining. The sample survey in the three cities
indicates that MSEs involved in manufacturing and construction related activities constitute the
largest share of employment opportunities for the urban labor force. The other types of MSEs
engaged in service, urban agriculture and trade contribute 20.8 percent, 12.6 percent and 10
percent of the total employment, respectively. The overall result indicates that the employment
growth is positive for nearly 29 percent of MSEs, 21 percent of enterprises have negative
employment growth rate and only 50 percent have zero growth rate since establishment. The
average growth of capital nearly 75 cents per enterprise is considerably higher than the average
growth of employment per enterprise estimated about 0.04. However, overall growth rate of
both capital and employment are not satisfactory.
Even if own saving and borrowing from formal financial institutions are dominant source of
startup capital, rate of positive employment growth in these firms are almost as equal as
negative growth. The implication is that access to formal financial institutions does not result
in significant contribution to employment generation. This may be attributed to enterprises
access to formal financial institutions are limited due to collateral and other requirements.
The expansion of scale of production and hence growth of employment in the sample MSEs
has been lower. A number of barriers are identified as impeding the expansion of scale of
production and growth of employment with in MSEs. Insufficient market, shortage and high
prices of raw materials, lack of capital, lack of working place, and lack of credit are among the
major constraints responsible for the poor performance of MSEs in expanding employment.
Furthermore, enterprises did not fully utilize their capacity, and thus experienced job
interruption in the last 12 months just before the survey. They ceased operation on average for
3 months in the last 12 months. About 24 and 15 percent of MSE operators report that lack of
demand and shortage and/ or high prices of inputs are among the major reasons for ceasing
operation and hence job interruption. Interestingly, shortage of electricity and water as well as
unfavorable government rules and regulations are the least frequently cited reasons. Only less
than 6 percent of operators report them as challenges for their business expansion. This may
witness the ongoing effort of the government in supporting and promoting the development of
MSEs through its pro-MSE policies and strategies.
The logistic regression analysis on the determinants of employment growth within MSEs
indicates that employment growth within MSEs is found to vary with location, type and
ownership of enterprise, initial employment size, and average hours of work and motive for
business start up. For example, the probability of employment growth within MSEs located in
Addis Ababa is lower than those located in Bahir Dar. In addition, enterprises with relatively
large initial employment size have lower probability of annual average employment growth.
On the other hand, enterprises with relatively more hours of work have higher likelihood of
experiencing employment growth. Unexpectedly, the human capital endowment of new firms,
social ties and access to credit do not have significant effect on the growth of the firm.
In the ensuing section, policy implications that are supposed to be relevant to addressing the
problem of urban unemployment based on the findings. Ethiopian 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 skill-mismatch, queuing, and job destruction tendencies to increase unemployment can be
addressed by aligning the education and training polices to the needs of the labor market and
also by developing entrepreneurship oriented curriculum.
The effect of education on the labor market outcomes of individuals is not straightforward. The
real employment effect of education at individual level is more pronounced at tertiary level of
education. Therefore we suggest not only introduction of area specific demand driven nation
wide training to unemployed in general and school dropouts in particular that may be financed
by contribution from local community, government and private sector, but also deliberate
employment creation by government in service and industrial sectors. The government needs to
introduce training to school dropouts and unemployed that focus on development of tradable
skills and admission of dropouts to short term training at different levels regardless of
Training in general has desirable effect on some of labor market outcomes of individuals.
However, it makes no significant difference in reducing gender and age disparity of
unemployment and in encouraging self-employment. We suggest expansion of training
programs that targeted the youth and females to facilitate school- to-work transition.
Furthermore, the higher unemployment rate and lower self- employment tendency among
TVET graduates is another issue of concern albeit such trainings result in earlier school-towork transition period. On the other hand, the 2008 national TVET strategy document
identified important limitations and strategies to improve the performance of the sector.
However majority of respondents are blamed the program for its feasibility, flexibility,
coordination, scale up and relevance. Most of the beneficiaries have positive attitude towards
the innovativeness, responsiveness, efficiency and effectiveness of the program; nevertheless
evaluation scores are average for these indicators as well. The commitment of bodies in charge
for effective implementation of strategies identified is sufficiently enough to enhance the
employment effect of TVET. Since the ongoing practices are not sufficiently employing
strategies in place. Increasing access to credit at reasonable interest rate is also important for
new business startups by graduates and school drop outs at any level for unemployment
The employment effect of MSEs is found to be insignificant and only one third of them
registered positive employment growth since start up. As long as the objective of promoting
and supporting the development of MSEs is to make them expand demand for the growing
labor force, therefore their success and performance should be evaluated in terms of the
employment growth they achieve. In this regard, as the finding suggests, support in terms of
market for their products and easy access to supply of raw materials, access to work place and
bank loans are required to help them expand their business and increase their demand for labor.
The government can also devise encouraging mechanisms such as rewarding private firms
based on their size of employment and intensive use of labor intensive technologies.
Furthermore, it could also be possible to consider employment creation of a firm as a criteria
item for increased access to government credits to private firms.
Encouraging ventures established by cooperative are more important than individual
enterprises, and introduction of polices that lead to expansion of manufacturing and urban
agricultural enterprises. Concerned bodies must be cautious about nil employment effects of
human capital endowments acquired by MSEs, social ties and access to credit.
Experiences of active labor market polices in Ethiopia are mostly limited to subsidized training
and occasional government employment services. The Government should introduce official
government employment program and other active labor market polices too.
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Table 3.1: specification of variables (both dependent and independent variables)
Sex: 1 if male ; 0 if female
Age of respondent in years
single: 1 if single; 0 if otherwise
Married: 1 if married; 0 if otherwise
Divorced :1 if divorced; 0 if otherwise
Widow:1 if widow; 0 if otherwise
Separated: 1 if separated; 0 if otherwise
Experience : age minus years of schooling minus 6
Training: 1 if a respondent received formal training
Grade 1-4 formal education: 1 if grade 1-4; 0 otherwise
Grade 5-8 formal education: 1 if grade 5-8; 0 otherwise
Secondary education; 1 if Grade 9-10 (new) and 11-12 (old )
Grade 10 completed : 1 if grade 10 completed (new curr.) and grade 12 completed (old curr.)
Preparatory: 1 if grade 11-12 in new curriculum; 0 otherwise
Non formal education: 1 if non-formal education; 0 otherwise
Certificate ; 1 if had 1 year training after secondary education
TVET completed: 1 if 10+1, 10+2 and 10+3 completed
TVET not completed: 1 if TVET not completed
Diploma, 12 +2, (in old curriculum
Diploma/degree not completed: 1 if diploma/degree not completed
Degree and above completed: 1 if degree and above completed
Tigray region: 1 if the location of respondent is Tigray region
Afar region: 1 if the location of respondent is Afar region
Amhara region: 1 if the location of respondent is Amhara region
Afar region: 1 if the location of respondent is Afar region
Somali region: 1 if the location of respondent is Somali region
SNNP region: 1 if the location of respondent is SNNP region
Harari region: 1 if the location of respondent is Harari region
Addis Ababa City A/min: 1 if the location of respondent is Addis Ababa
Dire Dawa region City A/min: 1 if the location of respondent is Dire Dawa
If the survey data comes from 2004
If the survey data comes from 2006
If the survey data comes from 2010
Data comes from 2011 survey
Share of regional informal sector employment
Share of workers who have written agreement with employer
Share of workers who have oral agreement with employer
Share of regional public sector employment
Share of regional private formal sector employment
Natural logarithm of regional labor supply/labor force
Migration : 1 if a respondent is migrated at most a year before and zero if otherwise, or (0<migr1<1)
Migration : 1 if a respondent is migrated before 2 years and zero if otherwise, or (1<migr1<=2)
Migration : 1 if a respondent is migrated before 3 years and zero if otherwise, or (2<migr1<=3)
Migration : 1 if a respondent is migrated before 4 years and zero if otherwise, or (3<migr1<=4)
Migration : 1 if a respondent is migrated before 5-6 years and zero if otherwise, or (4<migr1<=6)
Migration : 1 if a respondent is migrated before 7-9 years and zero if otherwise, or (6 <migr1<=9)
Migration : 1 if a respondent is migrated before 10 years and above,
or (10> migr1)
Prob > chi2
Source: primary survey
4.17: Effect of education and training on unemployment (primary data result)- dependent
variable is 1if a person is unemployed and 0 if employed
Source: primary survey
Table 4.18: Determinants of annual average employment growth within MSEs
Logarithm of MSE age (year)
1 if service providing MSE; 0 otherwise
1 if Manufacturing MSE; 0 otherwise
1 if Construction MSE; 0 otherwise
1if Urban agriculture type MSE; 0 otherwise
1 If MSE owner is proprietor
1 If MSE owner is cooperatives
1 if the MSEs is located in Hawassa ; 0 or else
1 if the MSEs is located in Bahir- Dar ; 0 or else
Average hours of work (MSE)
Size of MSE (initial employment size)
1 if access to credit
Proportion of owners received training
Average hrs of family labor
Proportion of owners with preparatory education
Proportion of owners with degree/diploma
Proportion of owners with TVET education
Motive to start MSE is it is only option to be
Self-employment is better than paid employment
Training received aspired
Proportion of owners with certificate to degree
Prob > chi2
Source: primary survey