Value Addition and Processing by Farmers in Developing Countries:

Evidence From the Ethiopian Coffee Sector

Bart Minten and Seneshaw Tamru

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

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

.81

Den

sity

0 1 2 3 4US cents/lb)

unwashed washed

Figure 1. KdensityPlot of prices of washed vs unwashed

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

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

…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.

RESULTS:

Descriptive

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)

Challenge 1 : Presence washing stations0

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

Challenge 2: Beliefs on Rewards

..Challenge 2..price volatility

05

10

15

20

25

real b

irr/

kg

2006m1 2008m1 2010m1 2012m1 2014m1period

Jima red Jima dryNekemte 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

Challenge 3: Theft issuesTable 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

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

Challenge 4: Lack of saving instrumentsUnit Yes No I don't know

Local Savings % 86.79 12.77 0.44Savings & credit assoc. % 31.12 66.06 2.82Bank/MFI % 11.33 88.23 0.44

mean median sdLocal Savings kms 15 11 12Savings & credit assoc. kms 17 12 15Bank/MFI kms 19 15 19Local Savings %yes 64.81Savings & credit assoc. %yes 14.4Bank/MFI %yes 16.91

Beliefs Yes, I agree % 75.69No, I disagree % 19.23It depends % 4.69I 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

Challenge 5: Labor requirementsT-test difference

Mean Std.Err. Mean Std.Err. Mean (difference)Quantity sold per transaction 478 kgs 53.4 4.2 235.8 13.8 -182***Harvesting cost (labor) 385 birr 1427.7 87.3 1398.6 87.8 29*Average Marketing costs (transport cost ) 478 birr/kg 0.186 0.017 0.118 0.010 0.068***Source: Authors' calculation based on the survey data***, **, * significant at 1%, 5%, and 10% significant levels respectively

Labor requirementsNo. of

Observati unitRed Dry

RESULTS:

Econometric

Model• Double Hurdle Model• 1. Red berry sell or not, D is not observed

• 𝐷_ =1 _ + _ >0𝑖 𝑖𝑓 𝑍 𝑖 𝛿 𝑢 𝑖• 𝐷_ =0 _ + _ ≤0 𝑖 𝑖𝑓 𝑍 𝑖 𝛿 𝑢 𝑖• 2. 〖𝑌 _𝑖〗 ^ = _ + _∗ 𝑋 𝑖 𝛽 𝜀 𝑖• 𝑌_ =𝑖 〖𝑌 _𝑖〗 ^ _ =1 ∗ 𝑖𝑓 𝐷 𝑖 𝑎𝑛𝑑 〖𝑌 _𝑖〗 ^ >0∗• 𝑌_ =0 (or _ =0 or (𝑖 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 𝐷 𝑖 〖𝑌 _𝑖〗 ^ ≤0 & _ =1) )∗ 𝐷 𝑖• 𝑢_ ≈ (0,1 )𝑖 𝑁• 𝜀_ ≈ (0, ^2) 𝑖 𝑁 𝜎• 𝑐𝑜𝑟𝑟( _ , _ )= unobserved elements effecting red- berry seller/or not 𝑢 𝑖 𝜀 𝑖 𝜌

red-berry seller may affect amount of red-berry sell

• Farmer make decisions in two steps

Decision 1Sell in Red

Berries or Not?

Coffee Producing Households

Decision 2How 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)]

ResultsAverage Partial Effect Tobit

ape_xj

percent of red berries sale (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

Saving mechanism 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***

Distance to saving institutions Km -0.004** 0.000 0.006*** 0.003 0.005 -0.183** 0.021 -0.040 -0.015 0.044 0.044

Perception: dry more profitable yes=1 -1.298*** -1.174*** -0.977*** -1.153*** -22.988*** -17.002*** -19.128*** -15.498*** -29.253***

Time to nearest wet mill minutes 0.001** -0.006*** -0.006*** -0.029 -0.031 -0.005 -0.012 -0.116***

Time to nearest huller minutes 0.001 0.005*** 0.004*** 0.009 0.027* 0.000 -0.016 0.094***

Gov't oblige to sell red yes=1 -0.061 0.119 0.261 4.670* 6.681** 2.291 -1.995 5.281

Gov't decides selling date yes=1 0.099 -0.197 0.012* 1.794 2.661 2.946 -0.091 3.327

Gov't sets prices for red yes=1 0.229** -0.272* -0.488** 4.348** 4.843* 3.701 1.214 -2.407

Fear of theft yes=1 -0.809*** 1.073 -2.534 -7.290

Lack of labor for harvest yes=1 0.959*** -8.413 -12.440*** 8.150

No enough buyers of red yes=1 -1.548*** -66.974*** -43.860*** -53.317****** p<0.01, ** p<0.05, * p<0.1

Variables

UnitDecsion to sell in red berries (mfx)

Coefficient

Quantity of red berry sales (mfx)Coefficient

….results Average

Partial Effect (Cragg) Tobit

ape_xjpercent of red berries sale (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

religion Orthodox Christian -1.429Protestant 0.119 -6.792*** -3.826

Catholic 3.467*** 0.862 4.641Muslim -1.054*** 8.052 -22.879***

Wakefata 4.946*** -64.136*** -10.960None 5.375*** 2.406 14.838Other 3.087*** -37.053*** -24.549

Marital status Married 2.627Widowed 3.026*** -1.331 -2.546Divorced 3.687*** 36.323*** 33.831**

Separated -5.902*** -153.051Single 0.808* 1.511 18.501**

*** p<0.01, ** p<0.05, * p<0.1

Variables

UnitDecsion to sell in red berries mfx

CoefficientQuantity of red berry sales mfx

Coefficient

….resultsAverage

Partial Effect (Cragg) Tobit

ape_xjpercent of red berries sale (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)gender (head) male=1 -0.739** 10.011*** 5.570 2.432age (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 -0.596dependents ratio -0.004 0.009 0.037 0.081total asset Birr -0.000 -0.001 -0.000 -0.000* -0.003 -0.000livestock Birr -0.000*** -0.000*** -0.001*** -0.001*** -0.000*** -0.001***daily wage rate 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 -2.797source info - Other farmers 2.671***

Traders 0.158 0.213 8.458*** 8.284*** 10.215***Through radio 0.542*** 0.586** -7.045** -9.692*** 3.458

Through mobile phone 0.446* 0.582* 7.243* 16.855*** 13.025***Through TV -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

UnitDecsion to sell in red berries mfx

CoefficientQuantity of red berry sales mfx

Coefficient

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.

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

Thank You!