ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE

Value Addition and Processing by Farmers in Developing Countries:

Evidence From the Ethiopian Coffee SectorSeneshaw Tamru and Bart MintenIFPRI ESSP

14th International Conference on the Ethiopian EconomyEthiopian Economics AssociationJuly, 2016Addis Ababa

1

1. Introduction• Global market shifting towards ‘buyer-driven’ value chains

• complex quality indicators 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 • produce’s origin and characteristics

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

At the export level (2007-2014)

2. Problem Identification

• Washed coffee is being sold in international markets with a premium of more than 20%.

• 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.

Question:

What are the perceived benefits and constraints to the sales of red cherries by farmers?

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

• The zones were stratified based on the coffee variety produced, as defined in the classification for export markets • Sidama, Jimma, Nekempte, Harar, Yirgacheffe

• Community level survey 80

Model-1

1.2- Dose-Response Function- -

-

1.1-Propensity Score Matching:• Nearest Matching• Kernel Matching• Regression Adjustment

Matching

Model-2• 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)]

RESULTS:

3. Propositions

Four factors that might possibly explain low level of selling coffee in red berries by the farmer

• 1 : Presence washing stations• 2 : Time and risk behavior of producers• 3 : Labor requirements (Marketing costs)• 4 : Lack of savings instruments

3.1 : Presence washing stations

3.2: Time and risk preferences

010

2030

perc

ent o

f red

ber

ry sales

Risk taker Risk neutral Risk averse

010

2030

40

perc

ent o

f red

ber

ry s

ales

Time patient Time neutral Time impatient

3.3. Labor and marketing costs?-Larger (overall) costs for red

T-test differenceMean Std.Err. Mean Std.Err. Mean (difference)

Average harvest per hectare 3092 kgs 1979.2 60.7 1614.6 81.4 -69.1***Quantity sold per transaction 5509 kgs 109.3 3.7 309.4 7.4 -200.1***Average number of transaction 5497 number 1.5 0.0 1.3 0.0 0.3***Labor productivity indicators

Harvesting cost (labor time ) 2968 person hours/hectare 78.3 2.3 52.3 2.2 -26.0***Weeding cost (labor time) 3092 person hours/hectare 47.9 1.8 35.8 1.4 -12.1***Compost use cost (labor time) 3092 person hours/hectare 19.4 1.1 7.1 0.6 -12.3***Tilling cost (labor time) 3092 person hours/hectare 34.9 1.4 21.2 1.1 -13.7***Post harvest cost (labor time) 3092 person hours/hectare 17.1 0.7 19.8 1.1 18.1**

Average Marketing costs (transport cost ) 1590 birr/kg 0.2 0.0 0.1 0.0 0.8******, **, * significant at 1%, 5%, and 10% significant levels respectively

Red DryLabor requirements

No. of Obs. Unit

3.4: Lack of savings instrumentsDried cherries can be kept as savings (red cherries have to be sold at once)

For savings bad quality (e.g. picked from the

ground etc.)

late ripening and I could not sell

them anymore as red berries

lack of labor for timely red berry

harvesting

I like to spread out my income over the year

harvest early because of fear

of theft

not enough buyers of red

berries

0

10

20

30

40

50

60

70

80

90

10091.9

16.5 16.2

3.0 2.9 4.8 6.9

Reasons for not selling as red cherries

Yes

Perc

ent

3.4: Lack of saving instrumentsLittle access to formal institutions but those with access, seemingly hesitant to use

Local Savings Savings & credit assoc. Bank/MFI0

102030405060708090

10086.8

31.1

11.3

64.8

14.4 16.9

Access to saving forms

% of farmers having access to saving instruments in the kebele

% of farmers using this saving form

3.4. May (dry) vs November (red) price ratios

.91

1.1

1.2

1.3

May

Dry

vs

Nov

Red

ratio

(low

ess)

2006 2008 2010 2012 2014

year

May (dry) vs November (red) price ratios (lowess)

4.1.Matching Results: Impacts of selling red on:

Average price Yield Total labor/hectare Income per HectareNearest Matching -0.532*** -2.445*** 298.560** -1818.702***Kernel Matching -0.733*** 0.566 381.100*** -1440.950***Regression Adjustment Matching -1.313*** 0.288 31.184 -4080.747***

Matching

ATET sell red (1 vs 0) onCoefficient

4.2.Dose Response Function Results17

17.5

1818

.5

E[a

vera

ge_p

rice(

t)]

0 .2 .4 .6 .8 1Treatment level

Dose Response Low bound

Upper bound

Confidence Bounds at .95 % levelDose response function = Linear prediction

DRF on Average price

2000

4000

6000

8000

1000

0

E[c

offe

e_in

com

e_he

ctar

e(t)]

0 .2 .4 .6 .8 1Treatment level

Dose Response Low bound

Upper bound

Confidence Bounds at .95 % levelDose response function = Linear prediction

DRF Income per hectare

4.2...Dose Response Function Results

150

200

250

300

E[to

t_la

bor_

used

(t)]

0 .2 .4 .6 .8 1Treatment level

Dose Response Low bound

Upper bound

Confidence Bounds at .95 % levelDose response function = Linear prediction

DRF total labor used

68

1012

14

E[y

ield

(t)]

0 .2 .4 .6 .8 1Treatment level

Dose Response Low bound

Upper bound

Confidence Bounds at .95 % levelDose response function = Linear prediction

DRF Yield

4.3.Factors affecting Red berry salesVariables

UnitDecision to sell in red

Quantity of red berry sales (mfx)

Average Partial Effect (Cragg)

percent of red berries sale (share)Distance to nearest saving institution km -0.006 0.100* 0.062*Time to nearest wet mill minutes -0.003 -0.052*** -0.032***Time to all season road minutes -0.008*** -0.065*** -0.040***Time cooperative minutes 0.006*** -0.126*** -0.078***Time patient yes=1 0.048 -1.151 -0.708Time impatient yes=1 -0.174 5.125*** 3.152***Risk taker yes=1 0.819*** 3.769** 2.318**Risk averse yes=1 -1.103*** 1.065 0.655Membership coffee cooperative yes=1 1.030*** 0.263 0.162Lack of labor during harvest yes=1 -1.178*** -13.766*** -8.467***No enough buyers of red yes=1 -2.047*** -35.508*** -21.84***Gov't decides selling date yes=1 0.472*** 6.022*** 3.704***_cons 7.452*** 46.092***Asset indicators included yes yes yesHousehold characteristics included yes yes yesSource of info included yes yes yesRegional dummies included yes yes yessigma _cons 18.669***Log pseudolikelihood -2396.7391No of obs 688*** p<0.01, ** p<0.05, * p<0.1

5. Conclusions • Lack of access to wet mills (in close proximity) • Lack of formal saving institutions • Not enough red berry buyers/• Shortage of labor during harvest• Better (overall) benefits of the dry version• Time and risk preferences

• Government’s deciding selling date • Source of information through radio

• Factors behind low red berry sales

• Increase the likelihood/quantity of selling in red-berries.

6. Policy Implications

Higher sales to wet mills can be achieved by:• Designing ways to improve access to wet mill for farmers (encourage further private investors and cooperatives)

• Encourage formal saving institutions (Saving & Credit Associations, Microfinance Institutions and Banks)

• Ensure quality improvement trainings to farmers• Encourage better price transmission for better incentives

• Better information dissemination mechanisms

Thank You!

…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.

(Poor) Price transmission between export/ecx and producer• Producer and Export

• Producer and ECX

Half Life for adjustment speed of _b[intvarout] is 5.159993 intvarouttr~d -.0055337 .0065961 -0.84 0.406 -.0188188 .0077515 intvarout -.1256994 .2291413 -0.55 0.586 -.5872136 .3358148 dependent Coef. Std. Err. t P>|t| [95% Conf. Interval]

Half Life for adjustment speed of _b[intvarout] is 10.636622 intvarouttr~d -.0147633 .0085019 -1.74 0.089 -.031887 .0023604 intvarout -.0630882 .2677624 -0.24 0.815 -.6023894 .4762131 dependent Coef. Std. Err. t P>|t| [95% Conf. Interval]

Problem identification -2It seems that we might have underused capacity of wet mills (in some areas)

Washed Whole Dr ied

34,

111

25,

415

2,9

04

5,4

83

Processing versus used capacity (Quintals)

Maximum capacity Used capacity

Households that have possibility to sell to wet mills, do not always sell to them: there is a large gap