Putting Children First: Identifying solutions and taking action to tackle poverty and inequality in Africa. Addis Ababa, Ethiopia, 23-25 October 2017 This three-day international conference aimed to engage policy makers, practitioners and researchers in identifying solutions for fighting child poverty and inequality in Africa, and in inspiring action towards change. The conference offered a platform for bridging divides across sectors, disciplines and policy, practice and research.

1. Towards Ensuring the Youth Bulge
for
Structural Transformation in
Ethiopia
Alebel Bayrau Weldesilassie (PhD)
Ethiopian Development Research Institute (EDRI)
International Conference ‘Putting Children First: Identifying Solutions and Taking Action to Tackle
Child Poverty and Inequality in Africa’
23 – 25 October 2017, UNCC, Addis Ababa

2. Outline
1. Motivation
2. Policy questions
3. Approach
4. Results
5. Summary of key findings
6. Conclusions and policy implications

3. 1. Motivation
1.5
6.2
10.6
5.2
-1.4
6 5.9
7.4
5
(2.1)
11.7
12.6
11.5 11.8
11.2
9.9
10.5
11.4
8.7
9.8
10.3 10.3
 Trends in the growth rate of GDP: One of the fastest growing country in the world (2003/4 – 2014/15)
Ethiopia’s economic progress & structural transformation

4. 1. Motivation …
 Ethiopia’s Poverty reduced from 69% (1981) to 23% (2015)
69.12
38.96
36.79
52.76
56.22 55.67 56.64
60.85 59.72 59.34
57.11
52.75
49.6648.1946.81
0
10
20
30
40
50
60
70
80
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
Povertyheadcount(%)
Ethiopia Low income Sub-Saharan Africa (developing only)
Poverty Headcount ratio at $1.25/day (PPP) (%)
 Lowest income inequality in East Africa

5. 1. Motivation …
0
10
20
30
40
50
60
1990-95 1995-00 2000-05 2005-10 2010 - 15
Trends in Sectoral share of GDP (1990 - 2010)
Agriculture Industry Services
 The contribution of agriculture to GDP decreased from 59% in the early 1990s to 41% in 2015s, with much of
this movement going to the services sector.
 Limited structural transformation towards manufacturing.
 Structural Transformation: Share of Sectoral output to GDP

6. 1. motivation …
 Mining, Construction, and service sector such as business services and trade service have contributed most
to job creation, with little change in the agriculture and the manufacturing sectors
 Structural Transformation: Growth rate in Employment shares by sector (990 to 2010)

7. 1. Motivation …
Share of the total population (%) (2016)
Age group Country Urban
15 - 29 30 37
0 - 29 70 67
The youth bulg can be a potential for economic development
• Large, economically-productive populations can drive economic gains
• The youth manpower can be a market
• Influence technology and institutions innovation
The youth bulg can be a challenge: civil unrest, political instability, etc
Youth Bulg: Youth population accounts for the highest share in Ethiopia’s population

8. 2. Policy question
 Ethiopia, with a demographic characteristics of Youth Bulg, envisages to transform its
economy towards the manufacturing sector and modernizing its agriculture and
service sectors: GTP – II, Youth policy
 How should Ethiopia capitalize the growing economically productive youth labor force for its structural
transformation?
Research question:
 What are the principal challenges of Ethiopian youth to participate to & benefit from the
structural transformation?
• Has there been change in the structure of youth employment?
• Has there been change in the structure of skill in the economy?
• What determines youth educational and labor outcomes?
 What should be the feasible strategic direction to enhance youth participation to and
benefit from the structural transformation in the country?

9. 3. Approach: Concept
Structural transformation has clear implications for employment growth. Three ways in
which economic growth affects employment:
 The creation of new jobs or the reallocation of workers
 Through creating linkages between the growing sector and the rest of the economy
 Through induced impact:
• Growth in the rest of the economic activities in turn further creates employment, productivity, and
income growth, thereby creating multiplying effects
Youth as a time for transition, characterizes the youth as heterogonous
 From school to Work:
• Searching for employment (entry),
• Searching for better job: secured, interest – based, better income
 From school to Family building: marriage
 Towards citizen contribution: political, sovereignty, etc

10. 3. Approach: data & analyses
Data:
 the National Labor Force surveys conducted by the Central Statistical Agency of Ethiopia in 2005
and 2013.
Descriptive analyses is used to understand the change in the structure of employment
and labour outcomes for the youth for the period 2005 - 2013
• Change in the structure of occupation
• Change in the industry of employment
• Change in the supply of skill and Skill mismatch
• Change in the structure of labor outcome
Econometric analyses is used:
 Determinant of educational outcome among the youth: unemployment: Probit model is estimated
 Determinants of labour market outcome: earning estimated using basic Mincernian earning equation
Different youth cohorts to account for their heterogeneity:
 Young cohort: 15 – 19 years old;
 Middle cohort: 20 – 24 years old; and
 Elderly cohort: 25 - 29 years old

11. 4. Result

12. Change in the structure of occupation
Youth (15 – 29 years old)
Occupational structure 2005 2013
Elementary occupations 41.23 31.84
Skilled manual
Skilled agricultural, forestry and fish 22.53 27.15
Craft and related trades workers 12.31 8.21
Low skilled non manual
Service and sales workers 15.18 17.84
Clerical support workers 2.47 1.98
High skill non manual
Technicians and associate professionals 2.49 5.09
Managers 0.49 0.97
Professionals 1.45 3.74
 the majority of the employed youth are engaging in better occupations in 2013 compared to
in 2005:
 the percentage of the youth engaged in the elementary jobs decreased by 10 percentage points
 High skill non manual increased from 4.5 to 8.7%
 Larger percentage of the youth still engaged in elementary jobs (32%)

13. Change in the structure of occupation by youth cohorts
2005 2013
15-19 20-24 25-29 15-19 20-24 25-29
Elementary occupations 60 38 28 48 30 22
Skilled manual
Skilled agricultural, forestry and fish 15.5 22.8 28.4 30.1 25.8 26.2
Craft and related trades workers 10.12 13.36 13.24 4.88 9.22 9.66
Low skilled non manual
Service and sales workers 12.83 16.19 16.27 14.96 19.39 18.54
Clerical support workers 0.42 2.65 4.09 0.24 2.43 2.80
High skill non manual
Technicians and associate professionals 0.47 2.89 3.88 0.74 5.39 7.83
Managers 0.03 0.41 0.97 0.18 0.71 1.72
Professionals 0.05 1.76 2.37 0.00 3.53 6.48
 Heterogeneity among youth
o The lowest cohort (15-19) are largely involved in the elementary occupations.
o The upper cohorts are largely engaging in the non-elementary occupations
 There is generally an increase in the percentage of the youth engaging in the non-
elementary jobs across the three youth cohorts

14. Change in the industry of employment
(Comparison: Youth & adults)
Sectors 2005 2013
15-29 30-44 45-60 15-29 30-44 45-60
Agriculture 50.66 47.16 56.03 45.16 44.10 53.32
service 41.39 43.2 34.18 41.32 40.69 33.96
Manufacturing 7.68 9.05 9.27 7.78 9.08 8.63
2005 2013
15-29 30-44 45-60 15-29 30-44 45-60
Construction 0.26 0.57 0.51 4.94 5.54 3.54
Mining 0.79 0.60 0.54
Wholesale 3.50 3.77 2.78 10.80 13.21 10.48
Education sector 0.75 1.56 1.01 4.34 4.49 4.12
Transport 12.32 13.03 10.51 2.91 3.19 1.84
Accommodation 5.04 4.32 4.85 3.82 2.49 2.01
ICT 1.83 2.21 1.47 0.53 0.64 0.53
- Generally the employment structure has been
changed in 2013 compared to 2005.
o the share of agriculture sector in youth
employment has declined (6%) over time
BUT agri. still accounts for the largest
share (45%).
o Youth employment increased in wholesale
trade, construction, and education over the
study periods HOWEVER, the share of
employees in sectors such as transport,
accommodation, and ICT has decreased
over the study periods.
o The changes in percentage share of
employees in the manufacturing remain
more or less the same over the two
periods (7.8%).

15. Change in the industry of employment by youth cohorts (age & gender)
 Age – based youth cohort: Modest change in the industry of employment is observed:
 Most of the employed youth in the lowest cohort are engaging in agriculture since 2005
 The percentage of the lowest cohort employed in the manufacturing sector decreased by 1.4%
 The share of employed youth in the service sector increased for the oldest cohort by 1%
Sectors 2005 2013
15-19 20-24 25-29 15-19 20-24 25-29
Agriculture 60.44 47.07 45.50 62.24 41.06 36.80
service 33.29 44.07 45.88 29.98 43.51 47.31
Manufacturing 6.2 8.56 8.19 4.81 8.88 8.91
2005 (15 – 29) 2013 (15 – 29)
Sectors Female Male Female Male
Agriculture 45.8 55.3 38.9 51
Manufacturing 8.8 6.6 7.6 8
Construction 0.1 0.4 2.5 7.2
Wholesale 1.8 5.1 12.8 9
 Gender based youth cohort: Substantial change has been observed in the industry of employment:
 The share of female engaged in agriculture decreased by 7 percentage points
 The share of female youth employed in wholesale increased by 11 percentage points.
 The share of male youth employed in construction increased by 7 percentage points

16. Change in the educational attainment
PERIOD
2005 2013
Literacy (=1) 65.90 76.60
Years of schooling 4.91 6.30
Primary school (=1) 39.80 42.70
Secondary school (=1) 15.20 22.90
Post-secondary education
TVET (Completed) (=1) 3.10 5.30
First degree (completed) (=1) 0.30 2.50
Masters and above (=1) 0.00 0.30
Educational attainment of the youth has substantially improved between 2005 and 2013
o the literacy rate increased from 66.5 in 2005 to 76.6% in 2013
o year of schooling increased from an average of 4.9 in 2005 to 6.3 in 2013
Only small share of the youth have access to post-secondary school
Change in the educational attainment of the youth (15-29)

17. Change in the structure of labor market outcomes
Change in employment structure
Variable 15-29
2005 2013
Unemployed 17.50 11.40
Monthly wage 289.00 464.65
Terms of employment 2005 2013
Permanent 27.50 46.20
Temporary 52.80 37.10
Contractual 9.80 10.50
Casual 7.20 5.80
Other terms 1.30 0.40
Sectors of employment 2005 2013
Informal sector 13.30 22.40
Self-employed (=1) 29.5 28.7
Unpaid family worker (=1) 42.4 40.1
 Unemployment rate is more prevalent among the
youth compared to the adult
 Most of the youth are working under non-permanent basis
THOUGH the share of employed youth under permanent has
increased by 20 percentage points between 2005 and 2013.
 Informal sector, self – employment and unpaid work
still dominates youth employment

18. Skill mismatch among the youth
Occupations Matched Over educated Under educated
2005 2013 2005 2013 2005 2013
National Level 35.0 41.34 4.2 6.84 60.9 51.8
Managers 0.82 1.67 - - 0.61 0.85
Professionals 4.60 9.5 - - 0.57 0.73
Technicians & associate professionals 6.04 9.8 - - 2.17 3.44
Clerical support workers 4.66 1.23 21.65 8.42 0.52 0.22
Service and sales workers 11.47 13.8 6.39 9.90 26.65 26.13
Skilled agricultural, forestry and fish 2.07 6.20 0.93 4.63 46.12 54.94
Craft and related trades workers 8.04 6.50 6.62 5.24 21.72 11.23
Plant & machine operators, and assembly 2.98 4.10 2.41 2.35 1.65 2.43
Elementary occupations 59.31 47.00 61.99 69.46 - -
 Change in Job – education qualification
match:
 Increased from 35% to 41% at national
level
 Decreased for elementary occupation
(59% to 47%)
 Increased for professionals (5% to
10%)
 Change in Job – education qualification mismatch:
 59%: national level mismatch
 Mismatch due to over – education increased from 4% to 7%
 over-education is increased in elementary occupation (62% to 70%)
 Mismatch due to Under - education decreased from 61% to 52%
 under - education is increased for skilled agricultural, forestry &
fish (46% to 55%)

19. Determinants of education outcomes
(Probit estimation of unemployment (=1))
Variables Unemployment
(1) (2)
Age[15-19] 0.0520***
Age[20-24] 0.0556***
Male(=1) -0.0860***
Primary school (grade 1-8) 0.0139*
Secondary School (grade 9-12) 0.0893***
TVET (Completed) 0.00799
BA Degree (graduated) -0.0393***
Masters Degree and above -0.0573**
Household size 0.00585***
Married (=1) 0.0346***
Divorced (=1) -0.0347***
Widowed (=1) -0.0322***
Separated (=1) -0.0150*
Disability (=1) 0.0274**
Year -0.00448***
urban 0.202***
Observations 99,774
 Higher unemployment is positively associated with youth, female, disability, lack of market oriented education
system and urban area
 Lower unemployment is associated with post secondary education

20. Determinants of labor outcomes: OLS estimation of earning (wage)
(1) (3)
Age[15-19] -200.6***
Age[20-24] -147.6***
Male(=1) 182.4***
Primary sch (grade 1-8) -40.76***
Secondary Sch (grade 9-12) 51.99**
TVET (Completed) 235.5***
TVET (currently studying) 69.31***
BA degree (studying) 273.6***
BA Degree (graduated) 1,166***
Masters Degree and above 2,205***
Hhsize -4.509***
Married (=1) 77.35***
Divorced (=1) -21.90
Widowed (=1) 15.24
Separated (=1) -21.18
Disability (=1) -142.5***
year 81.02***
urban 147.3***
Managers 600.6***
Professionals 422.0***
Technicians and associate professionals 321.2***
Clerical support 258.1***
Service and sales -95.68***
Skilled agricultural 34.23
Craft and related 188.2***
Plant and machine operators 274.9***
Other occupations 271.6***
 Low wage is associated with female youth, disability and low skill and elementary occupation.
 Higher wage is positively associated with Male, higher skill (postgraduate education), Non – elementary occupations
and manufacturing, construction, wholesale)
Manufacturing 72.87**
Construction 352.9***
Wholesale 74.35*
Transport 215.1***
ICT 192.3***
Finance and insurance (=1) 357.8***
Scientific and technical 116.0**

21. 5. Summary of key findings
• Occupation: There is generally an increase in the percentage of the youth engaging in the
non-elementary jobs across the three youth cohorts. However
• Larger percentage of the youth still engaged in elementary jobs (32%)
• The lowest cohort (15-19) are largely involved in the elementary occupations.
• The upper cohorts are largely engaging in the non-elementary occupations
• Educational outcome:
• Educational attainment of the youth has substantially improved BUT Only small share of the youth have
access to post-secondary school
• Change in Job – education qualification match Increased from 35% to 41% at national level but mainly for
non – elementary occupation. There is large mismatch at national level. Mismatch due to over – education
increased in elementary occupation while under - education is increased for skilled agricultural, forestry &
fish
• unemployment rate has decreased BUT is still higher for the youth.
• Higher unemployment is positively associated with female youth, disability, low skill and urban area
• Labour outcome:
• the share of agriculture sector in youth employment has declined (6%) over time BUT agri. still accounts for
the largest share (45%).
• Substantial variation is observed in youth industry of employment by gender
• The share of female youth employed in wholesale increased by 11 percentage points whilst the share of male youth
employed in construction increased by 7 percentage points
• Informal sector, self – employment and unpaid family work still dominate youth employment
• Low wage is associated with female youth, younger youth cohort, disability, low skill and elementary
occupation

22. 6. Conclusion and policy implications

23. Conclusion
Educational outcome for the youth is constrained by
 Individual specific factors (gender, age, disability and marriage status)
 Poor skill – developing/enhancing education system
 Lack of access to post high school education
Labor market outcome for the youth is constrained by:
 Individual specific factors (gender, marriage status, age & disability)
 Lack of the required skill as seen from the skill mismatch result
 Low level of educational attainment, which make the youth undereducated for non
elementary occupation
 Low productivity of sector(s) where the majority of the youth engaged in, in which the
majority are over – educated for the job

24. Policy implications
The major strategic direction should be to focus on interventions that take into
consideration the heterogeneity of the youth including gender, age, and residence.
Given youth heterogeneity, interventions that improve the human capital for the youth
should be given high priority for capitalizing the growing youth labour force for
transformation:
 Improve educational attainments (access and learning) for living
 Improve skills that focus on targeted labor market supply, which originated from the
structural transformation efforts of the country, which, in turn, should be
• Employment oriented; and
• Capitalizing the employment - creativity behavior of the youth
Priority should also be given to Interventions that aim to address/improve institutional
constraints:
 Hinder/improve productivity in sectors where the youth engaged in
 Hinder/improve the youth to access opportunities and
 Enhance to materialize the acquired skill

25. Thank you for your attention