Value Addition and Processing by Farmers in Developing Countries:Evidence From the Ethiopian Coffee Sector

1. Value Addition and Processing by Farmers in Developing
Countries:
Evidence From the Ethiopian Coffee Sector
Bart Minten and Seneshaw Tamru

2. Introduction
• Global market shifting towards ‘buyer-driven’ value chains
• with buyers recently embedding complex quality information into widely accepted
standards
• producers must also adhere to the stringent quality and safety standards and
regulations in these markets
• For coffee, value can be added in such ways as:
• washing
• specialty production
• environmental sustainability
• organic production
• produce’s origin and characteristics

3. Problem Identification
• Washed coffee is being sold in
international markets with a
premium of more than 20%
(Minten et.al 2014).
• However, only about 30% of
Ethiopia’s coffee export is
washed
• The small-scale coffee
farmers, processors,
exporters, and the country
are missing out on sizable
opportunity of commanding
higher rewards.
0
.2.4.6.8
1
Density
0 1 2 3 4
US cents/lb)
unwashed washed
Figure 1. KdensityPlot of prices of washed vs unwashed

4. Coffee value (quality) depends importantly on the
type of processing: i.e. ‘wet’ or ‘dry’.
• Washing -wet processing’ fresh red berries are de-pulped,
fermented and washed using wet-mill machines.
• Red cherries delivered to washing stations within 10 -12 hours of
picking
• KEY: Farmers need to sell their coffee in
red-berries
• Dry processing-‘dry processing’, where berries are dried,
often in the house of the farmer, and hulled using hullers
• Mostly very traditional

5. Data
• Both primary and secondary data sources will be used
• Household Survey and Community level survey
• HH level survey covered 1,600 coffee farming households in the largest coffee producing zones of the
country
• Community level survey 80
• The zones were stratified based on the coffee variety produced, as defined in the
classification for export markets
• Sidama, Jimma, Nekempte, Harar, Yirgacheffe

6. …Data…
• Within each strata, woredas (the 3rd highest admin.unit) were ranked from
the highest to the lowest producer.
• Woredas were divided in two, the less productive woredas and the more productive
woredas (each cultivating 50% of the area).
• Two woredas were randomly selected from each group
• A list of all the kebeles (4th & lowest admin.unit) of the selected woredas was then
obtained
• Two kebeles were randomly chosen from each category, the top and the bottom 50% producing
kebeles.
• A total of 20 farmers was then selected:
• 10 from the less productive and 10 from the highly productive ones.
• A total of 16 kebeles times 20 farmers, i.e. 320 farmers were interviewed per stratum.

7. RESULTS:
Descriptive

8. Propositions
• We hypothesize and put forward five challenges related to low level of selling
coffee in red berries and a resulting lower rate of wet processing
• Challenge 1 : Presence washing stations
• Challenge 2 : Volatility in prices and rewards
• Challenge 3 : Quality issues and fear of theft
• Challenge 4 : Lack of savings instruments
• Challenge 5 : Labor requirements (Marketing costs)

9. Challenge 1 : Presence washing stations
0
50
100
0 50100150200 0 50100150200 0 50100150200 0 50100150200 0 50100150200 0 50100150200
Sidama Yirgachefe Jimma Nekemte Harar Total
Fitted values
(mean) time_nearest_wetmill
Graphs by Zone

10. Challenge 2: Beliefs on Rewards

11. ..Challenge 2..price volatility
05
10152025
2006m1 2008m1 2010m1 2012m1 2014m1
period
Jima red Jima dry
Nekemte red Nekemte dry
Rewards of red vs dried berries:2006-2013
Red berries: 5 kg to 1 kg of exportable bean
Dry berries: 2 kg to 1 kg of exportable bean

12. Challenge 3: Theft issues
Table 5 : Theft issues
No of
observation Unit
Mean
(SD)
Harvest coffee beans earlier/unripe -fear of
theft? 1598 %yes 4.13
Harvested coffee beans earlier/unripe fear of
them being eaten by animals? 1,566 %yes 2
Percentage of berries stolen by thieves? 1598 % 1.5(5.8)
Percentage of harvest eaten by monkeys/apes? 1597 % 2.0(6.3)
Source: Authors' calculation based on survey
data

13. ..Challenge 3..Quality issues and other reasons for not selling in red berries

14. Challenge 4: Lack of saving instruments
Unit Yes No I don't know
Local Savings % 86.79 12.77 0.44
Savings & credit assoc. % 31.12 66.06 2.82
Bank/MFI % 11.33 88.23 0.44
mean median sd
Local Savings kms 15 11 12
Savings & credit assoc. kms 17 12 15
Bank/MFI kms 19 15 19
Local Savings %yes 64.81
Savings & credit assoc. %yes 14.4
Bank/MFI %yes 16.91
Beliefs
Yes, I agree % 75.69
No, I disagree % 19.23
It depends % 4.69
I don't know % 0.38
Source: Authors' calculation based on surevy data
Is this form of savings available
in the kebele
If not available, how far is the
closest one-kms
Do you use this saving form
“I prefer selling coffee in dried
form instead of red berries
because I can spread out my
income that way (it is a way of

15. Challenge 5: Labor requirements
T-testdifference
Mean Std.Err. Mean Std.Err. Mean(difference)
Quantitysold per
transaction 478 kgs 53.4 4.2 235.8 13.8 -182***
Harvestingcost(labor) 385 birr 1427.7 87.3 1398.6 87.8 29*
AverageMarketingcosts
(transportcost) 478 birr/kg 0.186 0.017 0.118 0.010 0.068***
Source:Authors'calculationbasedonthesurveydata
***,**,*significantat 1%,5%,and10%significantlevelsrespectively
Laborrequirements
No.of
Observati unit
Red Dry

16. RESULTS:
Econometric

17. Model• Double Hurdle Model
• 1. Red berry sell or not, D is not observed
• 𝐷_𝑖=1 𝑖𝑓 𝑍_𝑖 𝛿+𝑢_𝑖>0
• 𝐷_𝑖=0 𝑖𝑓 𝑍_𝑖 𝛿+𝑢_𝑖≤0
• 2. 〖𝑌_𝑖〗^∗=𝑋_𝑖 𝛽+𝜀_𝑖
• 𝑌_𝑖=〖𝑌_𝑖〗^∗ 𝑖𝑓 𝐷_𝑖=1 𝑎𝑛𝑑 〖𝑌_𝑖〗^∗>0
• 𝑌_𝑖=0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (or 𝐷_𝑖=0 or (〖𝑌_𝑖〗^∗≤0 &𝐷_𝑖=1) )
• 𝑢_𝑖≈𝑁(0,1 )
• 𝜀_𝑖≈𝑁(0,𝜎^2)
• 𝑐𝑜𝑟𝑟(𝑢_𝑖, 𝜀_𝑖)=𝜌 unobserved elements effecting red- berry seller/or not red-
berry seller may affect amount of red-berry sell
• Farmer make decisions in two steps
Decision 1
Sell in Red
Berries or Not?
Coffee Producing
Households
Decision 2
How much coffee
in red berries
farmers sell
Sell Coffee in
Red Berries
Do not Sell
Coffee in Red
Berries
Amount of Sales
283.59974
. display lrtest
. scalar lrtest=2*((lprobit+ltrunc)-ltobit)
• Li(θ)=1[yi=0]log[1- (xiγ)]+1[yi>0]log[ (xiγ)]
• +1[yi>0]{-log [ (xiβ/σ)] +log{φ[(yi – xiβ)/σ]} –log(σ)}
• Conditional: E(y|x, y>0)= xiβ+ σλ(xiβ/σ)
• Unconditional: E(y|x)= (xiγ)[xiβ+ σλ(xiβ/σ)]

18. Results
Average
Partial
Effect Tobit
ape_xj
percentofredberriessale (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)
Savingmechanism yes=1 0.385*** 0.163* 0.522*** 0.120 0.033 -0.166 -20.342*** -22.851*** -15.610*** -22.209*** -17.329*** -24.347*** -16.350*** -18.818***
Distancetosavinginstitutions Km -0.004** 0.000 0.006*** 0.003 0.005 -0.183** 0.021 -0.040 -0.015 0.044 0.044
Perception:drymoreprofitable yes=1 -1.298*** -1.174*** -0.977*** -1.153*** -22.988*** -17.002*** -19.128*** -15.498*** -29.253***
Timetonearestwetmill minutes 0.001** -0.006*** -0.006*** -0.029 -0.031 -0.005 -0.012 -0.116***
Timetonearesthuller minutes 0.001 0.005*** 0.004*** 0.009 0.027* 0.000 -0.016 0.094***
Gov'tobligetosellred yes=1 -0.061 0.119 0.261 4.670* 6.681** 2.291 -1.995 5.281
Gov'tdecidessellingdate yes=1 0.099 -0.197 0.012* 1.794 2.661 2.946 -0.091 3.327
Gov'tsetspricesforred yes=1 0.229** -0.272* -0.488** 4.348** 4.843* 3.701 1.214 -2.407
Fearoftheft yes=1 -0.809*** 1.073 -2.534 -7.290
Lackoflaborforharvest yes=1 0.959*** -8.413 -12.440*** 8.150
Noenoughbuyersofred yes=1 -1.548*** -66.974*** -43.860*** -53.317***
***p<0.01,**p<0.05,*p<0.1
Variables
Unit
Decsiontosellinredberries(mfx)
Coefficient
Quantityofredberrysales(mfx)
Coefficient

19. ….results
Average
PartialEffect
(Cragg) Tobit
ape_xj
percentofredberriessale (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)
religion OrthodoxChristian -1.429
Protestant 0.119 -6.792*** -3.826
Catholic 3.467*** 0.862 4.641
Muslim -1.054*** 8.052 -22.879***
Wakefata 4.946*** -64.136*** -10.960
None 5.375*** 2.406 14.838
Other 3.087*** -37.053*** -24.549
Marital statusMarried 2.627
Widowed 3.026*** -1.331 -2.546
Divorced 3.687*** 36.323*** 33.831**
Separated -5.902*** -153.051
Single 0.808* 1.511 18.501**
***p<0.01,**p<0.05,*p<0.1
Variables
Unit
Decsiontosellinredberriesmfx
Coefficient
Quantityofredberrysalesmfx
Coefficient

20. ….results
Average
Partial Effect
(Cragg) Tobit
ape_xj
percentofred berriessale (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)
gender(head) male=1 -0.739** 10.011*** 5.570 2.432
age(head) 0.075** 1.017*** 0.331*** 1.488***
age2(head) -0.001** -0.007* -0.012**
education(head) -0.030 -0.246 0.165 -0.596
dependents ratio -0.004 0.009 0.037 0.081
totalasset Birr -0.000 -0.001 -0.000 -0.000* -0.003 -0.000
livestock Birr -0.000*** -0.000*** -0.001*** -0.001*** -0.000*** -0.001***
dailywagerate Birr/day -0.052*** -0.043*** -0.489** -0.444** 0.436** -0.926***
mobile own=1 -0.143 -0.138 -1.671 -0.310 1.254 -2.797
sourceinfo - Otherfarmers 2.671***
Traders 0.158 0.213 8.458*** 8.284*** 10.215***
Throughradio 0.542*** 0.586** -7.045** -9.692*** 3.458
Throughmobilephone 0.446* 0.582* 7.243* 16.855*** 13.025***
ThroughTV -0.226 -0.779*** -12.397* 0.189 -14.206*
Zone Sidama -6.079***
Yirgachefe -0.489*** -1.245*** -1.347*** -12.931*** -23.514*** -28.311*** -33.799***
Jima -1.214*** -1.096*** -0.755*** -23.294*** -14.739*** -27.857*** -22.400***
Nekemte -6.245*** -6.478*** -6.725*** -202.916***
***p<0.01,**p<0.05,*p<0.1
Variables
Unit
Decsion to sell in red berries mfx
Coefficient
Quantity ofred berry sales mfx
Coefficient

21. Conclusions
• Lack of access to wet mills (in close proximity)
• Fear of theft
• Government’s action of setting prices for red berries
• Not enough red berry buyers
• Perception of farmers that dry is more profitable
• Considering the dry coffee as a saving mechanism
• Government’s deciding selling date
• Source of information through radio
• Daily wage rates
• Source of info through Mobile phones
• Reduce the likelihood
and/or quantity of red
berries sales
• Increase the likelihood
of selling in red-berries.
• Raise the quantity of
red berries sales.

22. Policy Implications
•The government can further improve the sector by :
• Designing ways to improve access to wet mill (especially
encourage private investors)
• Formal saving institutions (Saving & Credit Assoc. , Microfinance
Inst. and Banks)
• Quality improvement trainings to farmers
• Better price transmission for better incentive
• Better information dissemination mechanisms

23. Thank You!