1) Off-farm income makes up 18% of total rural income in Ethiopia, with 10% coming from wage income. Wage income is as important as livestock income. 2) Agricultural wages have increased 46% in real terms from 2004-2015, driven by agricultural growth. However, Ethiopian wages remain significantly lower than other countries. 3) Increasing rural wages are pushing the use of herbicides as a labor-saving technology in weeding and could incentivize further mechanization in the future. This may help address future labor shortages.

1. ETHIOPIAN DEVELOPMENT
RESEARCH INSTITUTE
Off-farm income and labor markets in rural
Ethiopia
PRELIMINARY RESULTS
Fantu Bachewe, Guush Berhane, Bart Minten, and Alemayehu S. Taffesse
IFPRI ESSP
Addis Ababa, Ethiopia
1

2. 2
1. Introduction
• Development of well-functioning labor markets crucial for economic
growth and livelihood opportunities, especially for youth
• Rural wages strongly linked with poverty reduction; important to
understand these labor markets
• Ethiopia’s economy is changing fast but unclear how important the
off-farm economy and labor income is in rural areas
• Purpose of the presentation:
- How important is the rural off-farm economy?
- What are the associates with off-farm income and rural income
diversification?
- Are rural wages changing?
- What are drivers and implications of that change?

3. 3
2. Data
• CSA: Wage data collected during monthly price survey (120 markets
covered in the country)
• Agricultural Growth Programme (AGP) survey: fielded in May 2011
(information on 2010/11 agricultural season); collected in 4 major
regions in the country;
• Other datasets used: 1/ large teff (2012) and coffee (2014) surveys;
2/ FTF survey (2015); 3/ ERSS (2014)
• In all surveys, information collected on agricultural crop production,
livestock, agricultural wage and non-wage income, and enterprise
income for 12 months preceding the survey
• Crop income is value of output less variable costs; livestock income
is value of livestock sold and slaughtered and value of livestock
products, minus variable costs; enterprise income is gross enterprise
income minus variable costs

4. 4
3. Off-farm income in rural areas
• Crop income as source of rural income overwhelming (71%)
• Wage income (10%) as important as livestock income
• Agricultural wages twice as important as non-ag. wages
• Enterprise income: 8%
0
20
40
60
80
100
All Tigray Amhara Oromiya SNNP
%
Income sources in rural areas (AGP)
Crop Livestock Agricultural wage Non-ag. wage Enterprise

5. 5
3. Off-farm income in rural areas
• ERSS (national representative) paints a rather similar picture
• Differences: 1/ Livestock more important than in AGP areas; 2/ Wage
and enterprise income less important than in AGP areas;
3/Wage/enterprise income seemingly higher in high potential areas
0
20
40
60
80
100
All Tigray Amhara Oromiya SNNP
%
Income sources in rural areas (ERSS)
Crop Livestock Casual labor Salaried workers Enterprise

6. 6
3. Off-farm income in rural areas
• Look at female/male youth/mature headed household differences
• Youth-headed households depend more on off-farm income
• Female-headed households depend more on off-farm income
0
20
40
60
80
100
Male-youth Female-youth Female-mature Male-mature
%
Income sources inrural areas (AGP)
Crop Livestock Agricultural wage Non-ag. wage Enterprise

7. 7
3. Off-farm income in rural areas
• Off-farm income and wage income especially important for the poor
• Enterprise income shares higher for poorest and richest, reflecting
different enterprises (brewing/liquor versus agricultural trade resp.)
20
30
40
50
60
70
80
90
0
2
4
6
8
10
12
14
Quintile I Quintile II Quintile III Quintile IV Quintile V
Contributionofcropincome(%)
Contributionofnon-cropincome(%)
Livestock Wage, agricultural Wage, non-agricultural Enterprise Crop

8. Crafts
16%
Trade-grains
16%
Trade-livestock
11%
Trade-non
agricultural
10%
Milling
1%
Tella (local ale)
5%
Araqi (local
liquor)
19%
Enjera/Dabbo
(bread)
3%
Other
19%
Type of business enterprise engaged, (%)
3. Off-farm income in rural areas

9. 9
3. Off-farm income in rural areas
• Associates of diversification; run tobit model with share of income in
off-farm as dependent variable; and diversification index
Diversification index
Coeff. St.error
Age of household head (years) -0.003*** 0.000
Education of head 0.018*** 0.005
Household size 0.011*** 0.003
Purchased at least one input on credit 0.026* 0.014
Land quality index -0.006** 0.002
Tropical livestock units -0.012*** 0.002
Travel time to nearest 50K town (mns) -0.0002*** 0.000
Distance from Addis Ababa (00 KMs) -0.014* 0.006

10. 10
3. Off-farm income in rural areas
• Major results on diversification:
1. Younger heads of households are more likely to be associated with
off-farm income (especially agricultural wages and enterprise
income)
2. Education associated with more enterprise income and non-ag.
income
3. Gender link. Women make up 1/3rd of hired labor; men 2/3rds.
4. More and better quality agricultural assets associated with less
diversification
5. Distance to cities an important associate of non-farm income.
Households 100 kms farther from Addis have 11% lower share of off-
farm income.

11. 11
3. Off-farm income in rural areas
• 18% off-farm income in rural Ethiopia:
- Small compared to other African countries
- Small compared to Asia and Latin-America
0
10
20
30
40
50
60
Africa Asia Latin-America
%
Local non-farm Migration income

12. 12
4. Agricultural wage labor
• Hired agricultural wage labor: 7% of all labor
• Relatively more important in Tigray and SNNP
0
20
40
60
80
100
All Tigray Amhara Oromiya SNNP
%
Hired labor in crop production
Family labor Hired labor

13. 13
4. Agricultural wage labor
• Importance of hired labor varies by task
• In teff, less hired labor during production; most at harvest and
afterwards
0
20
40
60
80
100
%
Labor by crop activity
Family Hired Exchange

14. 14
4. Agricultural wage labor
• Associates of agricultural wages:
1. Time of year: compare to land preparation, 13 % lower at planting,
12% higher at weeding; 17% higher at harvesting
2. Gender: men earn 8% more
3. Age: older people earn less (0.2% less per year extra)
4. Remoteness: 100 kms away from Addis reduces wage by 7%
5. Poverty in the district: The higher the poverty, the lower the wage
6. Regions: higher wages in Tigray and Amhara

15. 15
4. Agricultural wage labor
• Associates of hiring in of agricultural wage laborers (tobit):
1. Household characteristics
- Bigger households use less hired labor
- Higher dependency ratio lead to more hired-in labor
- Educated households use more hired labor
2. Characteristics of the farm
- Larger farms use more hired-in labor (1 hectare more; share 12% up)
- Quality land associated with more hired labor
3. Location
- Higher in Tigray than in other regions
- Poorer districts use more hired labor
- Distance to Addis: More used if closer to Addis

16. 16
4. Agricultural wage labor
• Labor arrangement in teff differ by remoteness
• Monetization when less remote; in more remote areas, more reliance
on exchange labor instead of hired labor
0
20406080
0 50 100 150
distance to Addis (Birr/quintal)
95% CI family labor
95% CI wage labor
95% CI exchange labor

17. 17
4. Agricultural wage labor
• Average agricultural wages on average 1.27 USD per day (in AGP
zones); however significant variation
• Compared to other (Asian) countries, wages significantly lower; 0.95
USD higher in Nepal; 1.59 USD higher in Bangladesh; 1 USD higher in
Myanmar
05
101520
0 2 4 6 8
Agricultural wages (USD/day)
0 3 6 9 12
Nepal (2010)
Sri Lanka (2012)
Myanmar (2004)
Malaysia (2012)
Bangladesh (2010)
Ethiopia (2012)
USD/day

18. 18
5. Rural wages - changes
• Rely on CSA price data from 2004 to 2015
• Use different ways of converting/deflation (exchange rate; CPI)
• Wages over times higher in nominal USD in 2015 compared to 2004
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Jul-04
Mar-05
Nov-05
Jul-06
Mar-07
Nov-07
Jul-08
Mar-09
Nov-09
Jul-10
Mar-11
Nov-11
Jul-12
Mar-13
Nov-13
Jul-14
Mar-15
Nov-15
WagesinnominalandrealUSD
Wages in USD
Wages in US CPI deflated real USD

19. 19
5. Rural wages - changes
• Urban and rural wages deflated by the CPI
• Between 2004 and 2015: Real rural wages increased by 46%; real
urban wages increased by 72%
20
25
30
35
40
45
50
Jul-04
Feb-05
Sep-05
Apr-06
Nov-06
Jun-07
Jan-08
Aug-08
Mar-09
Oct-09
May-10
Dec-10
Jul-11
Feb-12
Sep-12
Apr-13
Nov-13
Jun-14
Jan-15
Aug-15
RealwagesinDec.2011prices
GCPI deflated-rural
GCPI deflated-urban

20. 20
5. Rural wages - changes
• Regional differences for rural wages deflated by the CPI
• Least changes in SNNP; biggest improvement in Tigray
15
20
25
30
35
40
45
50
55
Jul-04
Feb-05
Sep-05
Apr-06
Nov-06
Jun-07
Jan-08
Aug-08
Mar-09
Oct-09
May-10
Dec-10
Jul-11
Feb-12
Sep-12
Apr-13
Nov-13
Jun-14
Jan-15
Aug-15
Tigray Amhara
Oromiya SNNP

21. 21
5. Rural wages - changes
• Urban and rural wages deflated by the Poor People’s CPI
• Between 2004 and 2015: Real rural wages increased by 46%; real
urban wages increased by 52%; lower than with other methods
20
25
30
35
40
45
50
Jul-04
Feb-05
Sep-05
Apr-06
Nov-06
Jun-07
Jan-08
Aug-08
Mar-09
Oct-09
May-10
Dec-10
Jul-11
Feb-12
Sep-12
Apr-13
Nov-13
Jun-14
Jan-15
Aug-15
RealwagesinDec.2011prices
PP-GCPI deflated-rural
PP-GCPI deflated-urban

22. 22
5. Rural wages - drivers
Look at the drivers for wage changes:
• Estimate the following regression:
Ln (real wage) = a + b lnY + e
• Growth-unskilled real wage elasticity
Real… Zones where
woreda pop. <
30 k
Zones where
woreda pop. <
50 k
Rural areas Non-urban
regions
GDP 0.22 0.18 0.15 0.12
Agricultural
GDP
0.24 0.20 0.17 0.15
Manufacturing
GDP
0.19 0.16 0.14 0.12
Industry GDP 0.17 0.14 0.12 0.10
Services GDP 0.15 0.12 0.11 0.09

23. 23
5. Rural wages - implications
1. Poverty
Correlation of real wages and poverty head count index0
20406080
10 20 30 40 50 60
Real wages, December 2011 prices
95% confidence interval
Predicted poverty head count index

24. 24
5. Rural wages - implications
1. Poverty
• Estimate the following regression (at zonal level; 4 years):
Ln (Poverty) = a + b ln real wage + e
• A 1% increase in poverty is associated with a 0.26% - 0.35% decrease
in wages
OLS FE
Model 1 Model 2 Model 3 Model 1 Model 2
Log real
wages
0.13 -0.26 -0.49 -0.30 -0.35
Bold: significant at 5% or 10%

25. 25
5. Rural wages - implications
2. Increasing use of herbicides
0
5
10
15
20
Herbicides imports (millions USD)

26. 26
5. Rural wages - implications
2. Increasing use of herbicides
Herbicides is a substitute for weeding; rapidly taking off in Ethiopia
0123
0 2 4 6
Log(weeding labor/ha)
95% interval correlation
0
20406080
100
%offarmers
0 50 100 150
Transport costs to Addis (Birr/quintal)
95% CI herbicides 2012
95% CI herbicides 2002

27. 27
5. Rural wages - implications
3. Mechanization
- Higher wages provide incentives for mechanization in agriculture
- In Ethiopia, mechanization is still rather low; In FTF survey, estimated:
1. 9% of farmers use some form of mechanization
2. 5% in plowing; 3% in harvesting; 2% in threshing
3. Strong threshold effect (Berhane et al., 2016)

28. 28
5. Rural wages - implications
4. Impact of off-farm income on modern input use
- Off-farm income might lead to relaxation of credit constraints in
period of input needs
- Study association of off-farm income and modern inputs with AGP
data through probit model
- Find slightly significant (10%) and positive association between
fertilizer use and off-farm income: households that had off-farm
income were 7% more likely to use chemical fertilizer
- No association with improved seeds and agro-chemicals

29. 29
6. Conclusions
• Major findings:
1/ Off-farm income makes up 18% of total income; Wage income is 10%
of total income, as important as livestock income
2/ Off-farm income (26% of their income) and agricultural wage income
especially important for the poorest (13% of their income); push factors
for diversification still relatively more important
3/ Wages significantly lower than in other countries; however, rural
wages are rapidly increasing; 50% higher in real terms in 2015 compared
to 2004; especially improved agricultural performance has contributed
to that change
4/ Implications of wage changes on poverty and agricultural production
practices (more use of herbicides)

30. 30
6. Conclusions
• Implications:
1/ Low wages have been an asset for the attraction of labor-intensive
industries. That might be changing and Ethiopia might lose that edge.
Ensure that the youth will upgrade skills towards higher labor
productivity
2/ Push for the adoption of labor-saving technologies; important that
Ethiopia implements pro-actively policies that allow appropriate
technologies at low costs
3/ Ensure flexible labor markets so that people can benefit from these
opportunities