Article Type : Research Article
Authors : Mulatu G*
Keywords : Urban youth; Unemployment; Probit regression; Ethiopia
Youth unemployment has been one of the
most challenging problems in countries with large young and rapidly growing
populations. By taking into account the challenges of unemployment, the major
objective of this study was to identify factors affecting urban youth
unemployment in Ethiopia in the case of Bale-Robe town, bale zone. Both primary
data and secondary data were collected. Primary data was collected using a
structured questionnaire. Probability sampling methods were applied to select
384 sample respondents from the town. Descriptive and econometric models were
used for data analysis. Both logistic and probit regression models were fitted
and compared. Based on the goodness of fit test result by AIC and BIC, probit
regression was used in this research. The result of this study revealed that
social capital variables, educational attainment of the youth, being single,
mother’s education, work experience and family income in etb (000) were among
the significant variables affecting the status of unemployment at less than 5%
significance level. However, being a migrant has a positive effect on the
probability of being unemployed. Based on the findings of the study, government
and all concerned bodies must expand higher education centres which helps
youths to acquire technical skill to create their own jobs, provision of basic
education for youth parents, and create conducive environment to get more job
opportunities are the areas that must be taken into consideration.
Unemployment is a problem for both developed and
developing countries. However, the impact and intensity might differ from place
to place. In Ethiopia, coupled with population growth and increased poverty, it
has a significant impact on growth and development at large. It causes a waste
of economic resources such as the productive labour force and affects the long
run growth potential of an economy. According to the Ethiopian Youth Policy,
youth comprise persons aged between 15 and 29 years. Youth unemployment rate is
high, at 11.8%. Youth unemployment rate is higher in urban (22.9%) than rural
areas (8.1%). In urban areas, youth female unemployment is disproportionately
high, at 28.4%, compared with youth males (11.6%) [1]. While the problem of
unemployment is usually treated as a macroeconomic topic (because unemployment
relates to aggregate production), economists recognize that the decisions made
by individual workers on how long to search for jobs and the
way specific labour markets encourage or impede hiring are also critical in
determining the unemployment rate. The problem of unemployment is high in urban
Ethiopia than in rural Ethiopia. Found that both the incidence and duration of
youth unemployment is higher in urban Ethiopia [2]. According to report, the
rate of youth unemployment in urban areas in 2021 is 23.1 percent and for rural
areas, it was about 12 percent [3]. According to during the COVID-19,
unemployment rose from 3.5 percent in February to a peak of 14.7 percent in
April [4]. This extraordinarily steep increase in unemployment makes the COVID
recession the deepest economic contraction since the Great Depression.
Unemployment has many adverse effects, including increased depression and other
mental health problems, increased crime rates, overall lower economic
productivity and consumption, lower rates of volunteerism, and erosion of
skills. Showed that the probability of being unemployed is high among young
females, youths who are educated up to post-secondary level, vocationally trained youths, and youths with high levels of
language and digital literacy [5]. Results showed that the youths' struggle to
acquire work emanating among others from inadequate skills sets and education
levels whilst lacking the needed labour market information. According to
between 2006 and 2011, the prevalence of urban youth unemployment was high as
compared to the total unemployment rate in Ethiopia [6]. Regarding the
determinants of urban youth, different scholar found mixed results. Using
descriptive and cross tabulation analysis, found that youth unemployment is
highly related with regional location, sex, marital status and education [7].
Applied logistic regression model analysis and showed that saving and supports
from different organizations are important variables that reduces the
probability of being unemployed [8]. However, showed that educational level of
the youth, access to information, income of parents and economic factor of the
parents were significant variables that negatively affect the probability of
being unemployed [9]. Because of its enormous economic and social costs,
policymakers try to limit the amount of unemployment in an economy. In the
Oromia region, 24.6 percent of urban youths and 6.9 percent of rural youths
were reported as unemployed. Therefore, the major objective of this study was
to identify major factors affecting urban youth unemployment in Ethiopia in the
case of Bale-Robe town, Bale zone, Oromia, Ethiopia.
Definition of
unemployment
Employment is the number of people who have a job.
Unemployment is the number of people who do not have a job but are looking for
one. According to a person is unemployed if she/he does not have a job, has
actively looked for work in the prior four weeks, and is currently available
for work. In Ethiopian context labour market situation, relaxed and defined
unemployed as persons who had no work but were available for work. They may be
either seeking work or not seeking /discouraged job seekers. Discouraged job
seekers are those unemployed who want a job but are not taking any active steps
to search for work because they think a job is not available in the labour
market.
Theories of unemployment
There are two important relations among output,
unemployment, and inflation. The first, called Okun’s law is a relation between
output growth and the change in unemployment: High output growth typically
leads to a decrease in the unemployment rate. The second, called the Phillips
curve, is a relation between unemployment and inflation: A lower unemployment
rate typically leads to a higher inflation rate.
Classical theories
The classical theory of employment is based on the
assumption of flexibility of wages, interest and prices. This means that wage
rate, interest rate and price level change in their respective markets
according to the forces of demand and supply. Changes in these variables
automatically adjust the economic system in such a way as to ensure full
employment.
Efficiency Wage Theory
According to this theory, firms operate more
efficiently if wages are above the equilibrium level. Therefore, it may be
profitable for firms to keep wages high even in the presence of a surplus of
labour. High wages make workers more eager to keep their jobs and thus motivate
them to put forward their best effort. If the wage were at the level that
balanced supply and demand, workers would have less reason to work hard because
if they were fired, they could quickly find new jobs at the same wage.
Therefore, firms may raise wages above the equilibrium level to provide an
incentive for workers not to shirk their responsibilities.
Keynesian theory
According to Keynes, the volume of employment in a
country depends on the level of effective demand of the people for goods and
services. Unemployment is attributed to the deficiency of effective demand. It
is to be kept in mind that Keynes’ theory is a short run theory when
population, labour force, technology, etc., do not change. Once Keynes remarked
that since “in the long run we are all dead”, it is of no use to present a long
run theory. In view of this, one can argue that the volume of employment
depends on the level of national income/output.
Description of the study
area
The study was conducted in Robe town, Bale Zone,
Oromia Region at a distance of 430 KM from Addis Ababa. The town was founded in
1930. Robe town is capital of Bale Zone. Robe town is found in to the south
east of the Addis Ababa. The town has three kebeles and bounded with Sinja
farmers’ association in south, Hawusho farmers’ association in south west, Hora
Boka farmers’ association in West, Sanbitu peasant association in north and
Shallo farmers’ association in East direction. The town has located on 2492m
above sea level with mean annual temperature of 15oc which experienced cool
temperature [10].
Research Design
Due to lack of panel and time series data, the
methodological scope of the study was used the cross sectional research design.
Sources and Methods of Data Collection
Both primary and secondary sources of data were used for this study. Primary sources of data was collected using structured questionnaire, whereas the secondary data sources relied on relevant literatures, available official documents and demographic figures accessed from local, zone and district government offices were also used.
The questionnaires have prepared for self-administered
and interviewer administered by using close ended questions to get information
from the respondents.
The sample size of farmers is determined by following:
(????)
is equals to 0.5, (the maximum level of variability taken when the previous
population variability is unknown), the confidence level of 95% which
corresponds to Z -value of 1.96 and an error or precision (e) of 5%.
Methods of data analysis
To achieve the objective the empirical data was analyzed using
descriptive statistics and econometric models. The descriptive analysis were
performed using average and mean difference test to compare the socio economic
and socio demographic characteristics of youths.
Specification of probit
Regression model
Following Gujarati (Gujarati (2004) the probit regression model becomes
Where is not observed. It is commonly called a latent variable. What we observe is a
The Probit and Logit models differ in the specification of the distribution of the error term
For instance, if the observed dummy variable is whether or not a person is employed or not, defined as ‘propensity or ability to find employment. 'Where, F is the cumulative density function of, Xi are independent variables and ????i are the coefficients of the independent variables and the variable e is a random error introduced to accommodate the effect of other predictors which were not included in the model but have relationship with rural-urban migration (Figure 1).
Figure 1: Adapted from
different literatures
Figure 2: Source: computed from
World Bank Data (2022). From the above figure, it can be seen that unemployment
has been rising in the last few years
Figure below shows trends of unemployment rate for Ethiopia. Data was collected from World Bank database. In the last few years the unemployment of Ethiopia has been rising. From the above table it as observed that there is statistically significance difference between unemployed youths and employed youths in terms of family income and educational attainment of youths. This indicates that skill and education is very important to get jobs. In addition, the family background is also helpful to encourage youths during job search. The average monthly income of family was 5.673 thousand birr for employed and 3.242 thousand birr for unemployed youths. Educational attainment of the youth: this variable was statistically significant at less than 5% significance level. The negative coefficient of the variable is negative and it indicates that an increase in year of schooling by one grade decreases the probability of being unemployed by 2.4%. The probable justification is that the more youth achieve more years of schooling, his experience and skill will be improved. Employers usually prefer workers who fit their vacant positions in terms of years of schooling. This result confirms the finding of [11]. However, found that educated young is unemployed due to unavailability of resources to support their full-time job search in Ethiopia.
Table 1: Tabulation of dummy variables
Variables |
Category |
What was your
employment status at this time? |
Chi – square |
|
Employed |
Unemployed |
|||
Sex of the respondent |
Female |
52(80%) |
13(20%) |
1.86 |
Male |
229(71.79%) |
90(28.21%) |
||
Do you participate in local institutions in the community? |
No |
194(69.04%) |
87(30.96%) |
9.14*** |
Yes |
87(84.47%) |
16(15.53%) |
||
Can your mother read and write? |
No |
60(56.60%) |
46(43.40%) |
20.49*** |
Yes |
221(79.50%) |
57(20.50) |
|
|
Have you ever been migrated from rural areas in search of job? |
No |
269(74.3%) |
93(25.69%0 |
4.13** |
Yes |
12(54.55%) |
10(45.45%) |
||
Do you have any work experience? |
No |
105(58.33%) |
75(41.67%) |
38.03 *** |
Yes |
176(86.27%) |
28(13.73%) |
||
Have you ever refused a job that was offered to you? |
No |
220(75.86%) |
70(24.14%) |
4.35** |
Yes |
61(64.89%) |
33(35.11%) |
||
Being single |
No |
23(54.76%) |
19(45.24%) |
8.15*** |
Yes |
258(75.44%) |
84(24.56%) |
||
Source: Survey data (2022) |
Table 2: Descriptive statistics of continuous variables.
Variables |
Employed (n1=281) |
Unemployed (n2=103) |
Dif |
St Err |
T value |
Age |
21.783 |
21.893 |
-.111 |
.335 |
-.35 |
Education attainment of
youths |
13.082 |
12.418 |
.664 |
.254 |
2.6*** |
Father’s educational attainment |
4.192 |
3.457 |
.736 |
.409 |
1.8* |
Family income in ETB (000) |
5.673 |
3.242 |
2.432 |
.377 |
6.45*** |
Source: Computed from Survey data (2022) |
Table 3: Econometric model result
Independent variables |
Logistic regression |
Probit regression |
||||
Coeff. |
St.Err. |
dy/dx |
Coeff. |
St.Err. |
dy/dx |
|
Sex of the youth |
0.346 |
0.396 |
0.049 |
0.177 |
0.229 |
0.047 |
Age of the youth |
0.071 |
0.05 |
0.011 |
0.044 |
0.028 |
0.012 |
Educational attainment of the youth |
-0.137** |
0.059 |
-0.021 |
-0.086** |
0.034 |
-0.024 |
Being single |
-1.591*** |
0.509 |
-0.324 |
-1.007*** |
0.274 |
-0.35 |
Father’s education |
-0.092* |
0.049 |
-0.014 |
-0.05* |
0.026 |
-0.014 |
Job preference |
0.078 |
0.363 |
0.012 |
0.026 |
0.209 |
0.007 |
Work experience |
-1.522*** |
0.297 |
-0.238 |
-0.873*** |
0.163 |
-0.246 |
Being migrant |
1.193** |
0.58 |
0.237 |
0.752** |
0.334 |
0.258 |
Mother’s education |
-1.27*** |
0.338 |
-0.225 |
-0.744*** |
0.196 |
-0.232 |
Social capital |
-0.895** |
0.38 |
-0.119 |
-0.48*** |
0.206 |
-0.121 |
Family income in etb (000) |
-0.267*** |
0.053 |
-0.04 |
-0.152*** |
0.028 |
-0.042 |
Constant |
3.448*** |
1.438 |
2.092*** |
0.803 |
|
|
AIC= 342.1073 |
AIC = 341.8414 |
|||||
BIC = 389.515 |
BIC = 389.2492 |
|||||
Source: Computed from Survey data (2022) |
Being single: This variable has a
negative coefficient and shows that being single reduces the probability of
being unemployed by 35%. The probable justification is that single youths can
easily move from place and apply for more and more jobs. The more they apply
the more they can get jobs. This result corroborates the findings of also
showed that youth that were married had less odds of being unemployed. This
result contradicts the findings of and consistent with the findings of [12].
Parents’
education: Both mother’s and father’s education were important variables affecting
the probability of being unemployed.Work experience:
This variable has a negative coefficient. It is statistically significant. Most
of the time vacancy posted by employing organization usually attached with job
experience. The coefficient of (=0. 246) shows that an increase in experience
by one year reduces the probability of being unemployed by 24.6%.
Being
migrant: this variable was included to indicate whether youths with migration
history are unemployed. The positive sign of the marginal effect result
(=0.258) shows that being migrant from another areas increases the chance of
falling in the unemployed category by 25.8%).
Social
capital: Social network is important in order to get relevant information about
different job opportunities. This study found that social network affects
individual’s unemployment status negatively and significantly at less than
5%. This result corroborates the
findings of [13]. Also found that members of minority groups, may face
discrimination during their job search and unemployed [14-16].
Family
income: parents' income is statistically significant at 1% significance level
and affects the probability of being unemployed by 4.2%. Parents,\ economic
status is very much important to cover expenses related to search of job and
those youths from rich families are usually either self-employed or can easily
get available jobs. This finding is congruent with the findings of who found
that youths from relatively poorer families are most likely unemployed as
compared to youths from richer households.
Youth
unemployment has been one of the most challenging problems in countries with
large young and rapidly growing populations. By taking into account the
challenges of unemployment, the major objective of this study was identify
factors affecting urban youth unemployment in Ethiopia in the case of Bale-Robe
town, bale zone. Both primary data and secondary data were collected. Primary
data was collected using structured questionnaire. Probability sampling methods
was applied to select 384 sample respondents from the town. Descriptive and
econometric models were used for data analysis. Both logistic and probit
regression models were fitted and compared. Base on the goodness of fit test
result indicated by AIC and BIC, probit regression was used in this research.
The result of this study revealed that social capital variable, educational
attainment of the youth, being single, mother’s education, work experience and
family income in etb (000) were among the significant variables affecting the status
of unemployment at less than 5%. However, being migrant has positive effect on
the probability of being unemployed. Based on the findings of the study,
government and all concerned bodies must expand higher education centres which
helps youths to acquire technical skill to create their own jobs, provision of
basic education for youth parents, and creating conducive environment to get
more job opportunities are the areas that must be taken into consideration.