Evidence from Rwanda’s Coffee Wet Mills

Trust, Efficiency and Organization:

Evidence from Rwanda’s Coffee Wet Mills

Ameet Morjaria Harvard Academy

Rocco Macchiavello Warwick

VIth Summer School in Development Economics

June 24, 2014

Ascea, Italy

Introduction

• Recent empirical studies and attention on persistent

performance differences (PPD) across producers but also

within narrowly defined industries (Syverson 2011).

2

Introduction




A. Large sample profitability studies (control for industry).

B. Large sample productivity studies (control for inputs, prices).

C. Focused-sample productivity studies (detailed dependent

variable and controls).

D. Focused-sample relating management practices to

productivity.

3

Introduction




• This might be particularly relevant in low income countries,

large observed dispersions in PPD (Hsieh and Klenow 2009).

• Heterogeneity in management and relational contract might

account for a large fraction of these PPD’s (Bloom and Van

Reeneen 2007, Gibbons and Henderson 2012).

• I am going to do a measurement exercise using coffee wet

mills in Rwanda as a case study.

4

FOREIGN

BUYER FARMERS

EXPORTER

/ MILLER WET MILL

ORDINARY

Sector: Overview

FOREIGN

BUYER FARMERS

EXPORTER

/ MILLER WET MILL

ORDINARY

Sector: Overview

FOREIGN

BUYER FARMERS

EXPORTER

/ MILLER WET MILL

ORDINARY

Sector: Overview

• Wet mills are “large” [$1m sales, source from 300 farmers, 70

employees, seasonal business, high financial requirements]

• Rare in low-income countries to find a setting with large N

within an industry.

• Cherries to be processed <7 hrs multiple geo markets.

• Intrinsic interest

• Combine “insider econometrics” and “relational

contracts”.

• “Insider econometrics”

– uniform production process practitioner measure of efficiency

– GIS information precise control of conditions that effect cost

– Conversion ratio + unit input prices allows to focus on the

part of cost effected by managers

8

Approach


contracts”.

• Due to other missing markets, well functioning “relational

contracts” between station and farmers might be necessary

for station efficiency.

• These “relational contracts” rely on trust:

– Beliefs about the stations (future) actions,

– Beliefs about the station (future) type,

– Norms and sense of farmer’s affiliation with the station

– …

for now, we do not distinguish

9

Approach


contracts”.

• “Relational contract” (Gibbons-Henderson, HOE, 2012):

– Define “cooperation” and “cheating” measure right practises

– Measure the quality of relations trust (WVS + games)

• Correlate measures of efficiency, quality of relationship and

practises.

10

Approach

• Farmer to Station: farmer sells on credit (or at a lower price to

coop)

Gains from trade: Incentives to cheat:

Station: lower financial requirements

Farmer: demand for smoothing income

Scope of Relational Contracts


coop)


Station: lower financial requirements defaults on loans / 2nd payment




coop)

• Station to Farmer: station provides training/assistance/fertilizer to

farmers





Station: better (stable, vol, quality) supply

Farmer: higher income



coop)


farmers






Farmer: higher income side-sell to other buyers



coop)


farmers

• For trust to matter these interlinked transactions, must be

difficult to enforce.






Farmer: higher income side-sell to other buyers


• Gibbons-Henderson (2013) HOE Chapter

• Bloom, Sadun and Van Reenen (2012):

– Across countries/regions, trust correlates with firm’s size and decentralization

• Firms and Contracts in Developing Countries

- Banerjee and Duflo (2000), McMillan-Woodruff (2000), Banerjee-Munshi

(2004), Fafchamps (2004), Macchiavello (2010), Macchiavello and Morjaria

(2013),

- Blouin and Macchiavello (2013), Dragusano and Nuun (2013), De Janvry et al.

(2013), Casaburi et al. (2014)

- Mullhainathan and Sukhtankar (2013), Banerjee et al. (2014)

• Measuring & Using Trust

- Ferh et al. (2004), Johnson and Mislin (2011), Sapienza et al. (2007) vs.

Glaeser et al. (2000)

- Karlan (2006)

Related Literature

• Census of Coffee Wet Mills in Rwanda

- all wet mills participated (100% compliance)

- one season, May-June 2012

- mill manager (and owner if necessary)

- 4 random workers, 5 farmers in area, main collector

- played games

• Administrative Data

– GIS [location, soil, suitability, roads, water, climate, elevation…]

– Weekly cherry purchases and prices (last 3 years)

– Farmer Coffee Census 2009 (350,000 farmers)

– Export Contracts

– Quality (cupping of lots from wet mill during survey season)

Data

• Capacity and Utilization

• Measure of Efficiency

• Dispersion in Efficiency

• Measuring Quality of Relationship: Trust

• Trust and Efficiency

• Trust and Management Practises: Interlinked Transactions

Plan

Installed Capacity

0.1

.2.3

.4

% o

f S

tatio

ns a

t G

ive

n C

ap

acity

0 500 1000 1500 2000Capacity (Tons of Cherries)

15% < 150 Tons

15% at 150 Tons

30% at 250 Tons

30% at 500 Tons

10% at 1000

Tons

Capacity Utilization

median wet mill

53% capacity U

25% at > 100%

25% at <25%

Aggregate U at

60%

0

.05

.1.1

5.2

% o

f sta

tio

ns a

t ca

pa

city u

tiliz

ation

leve

l

0 50 100 150Capacity Utilization







Plan

Measure of Efficiency

• Focus on unit costs:

– How managers think about it (survey NAEB conducted)

– How much it costs ($) to produce 1 kg of parchment

• Efficiency = - ln(total unit costs)

• Determinants of Unit Costs:

– 60% is cost of cherries:

a) unit prices in “local” markets

b) Conversion ratio: X kg’s of cherries to obtain 1 kg of

parchment

– 40% is “rest”: labour, finance & WK, transport and

procurement

• Unit Costs are (essentially) constant below capacity, which is

measured by (a) pulping machine, (b) water tanks and drying

tables.







Plan

0.2

.4.6

.81

-4 -2 0 2 4Efficiency

Efficiency Dispersion

Dispersion in Efficiency

p(5) 1,400/Kg parchment






0.2

.4.6

.81

-4 -2 0 2 4Efficiency

Efficiency Dispersion Accounting for geography


0.2

.4.6

.81

-4 -2 0 2 4x

Efficiency Dispersion Accounting for geography

Accounting for geography, technology, and cherry costs

Dispersion

Variable: Unit CostsPhysical

EfficiencyCherry Prices

Other Unit

Costs

p75 - p25 Ratio 1.22 1.04 1.11 1.66

p90 - p10 Ratio 1.51 1.1 1.32 2.34

% Variation Explained by:

Geography 27 34 41 30

Technology 8 6 7 4

Physical Efficiency 10 -- [ 4 ] [ 3 ]

Cherry Prices 5 [ 4 ] -- [ 7 ]

% Variation Unexplained: 50 60 52 57

For Cherry costs: R75/25=1.15; R90/10= 1.28. rho(Physical Efficiency, Cherry Prices)=0.33***

Dispersion: Beyond Coffee in Rwanda

Context: P75 - P25 Ratio P90 - P10 Ratio

Unit Costs $ per KG Parchment 1.22 1.51

Physical Efficiency Kg Cherries per Kg parchament 1.04 1.1

Cherry Prices $ per KG cherry 1.11 1.32

Other Unit Costs $ per KG Parchment 1.66 2.34

Across lines & factories 1.95 2.79

Within factories, across lines 1.22 1.64

Syverson Cement VA per HR 1.92 4.02

Hsieh - Klenow U.S. TFP 3.2 --

Hsieh - Klenow China TFP 3.6 --

Hsieh - Klenow India TFP 5 --

For Cherry costs: R75/25=1.15; R90/10= 1.28. rho(Physical Efficiency, Cherry Prices)=0.33***

Minute Output / Minute Labour

Inputs

Measure and Sample

Rwanda Coffee Washing

Stations

Bangladeshi Garments







Plan

Measuring Quality of Relationship: Trust

• Survey questions to managers / workers / collectors / farmers.

• Canonical questions from WVS:

– Do you think most people would try to take advantage of you if they got

a chance, or would they try to be fair? [scale 1- 10]

– Generally speaking, would you say that most people can be trusted or

that you need to be very careful in dealing with people?

• Trust towards groups (adapted):

– I’d like to ask you how much you trust people from various groups.

Could you tell me for each whether you trust people from this group

completely (4), somewhat (3), not very much (2) or not at all (1)?

– family, neighbours, friends, peers

– manager/collectors, people from Kigali, strangers

• Offers in the trust game played against

managers/collector/farmer/workers

Measuring Quality of Relationship: Trust

24

68

10

WVS Farmers Workers Collectors Managers

p90/p10 p50







Plan

• Efficiency Ln ( 1/ Unit Costs)

• Trust Farmers average WVS

trust question towards

collectors and people

from Kigali

• Controls Geographic polynomials

District fixed effects

Station’s type and age

• Inference Clustered at sector level

[Ideally spatial (Conley)]

1108

1044

1029

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1136

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1008

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1100

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1168

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1087

1011

1018

1125

1114

1130

11481174

1123

1077

10691030

1012

1167

1053

11331147

10421089

1121

1051

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105011771078

1071

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1104

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11721096

107611461170

1141

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1001

1007102711101005

1091

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-.4

-.2

0.2

.4

e(

effic

iency | X

)

-2 0 2 4e( TRUST | X )

coef = .034, (robust) se = .0097, t = 3.53

Trust and Efficiency

Trust and Efficiency

Trust enabling firms to sustain interlinked transactions with farmers

Coefficient of trust (PCA)

on various outcomes.

Controls: Location

FE, Station Type,

SE (sector)

Cherries Labour Finance Procurement

I II III IV0.023*** -0.016*** -0.008*** 0.002

[0.004] [0.004] [0.003] [0.002]

Geographic Polyn. yes yes yes yes

Station Controls yes yes yes yes

District FE yes yes yes yes

N. Observations 178 178 178 178

Dep. Variable: Share

of Unit Costs in

Trust

Trust and Unit Cost Shares

Unit Cost Shares: Cherries (60%), Labour (15%), Finance (12%),

Procurement (8%) and Other (5%).

Consistent with trust enabling firms to sustain interlinked

transactions which (i) increase regular capacity utilization and

(ii) lower financial costs.







Plan

Cherries on

Credit

Second

PaymentsInputs PCA

I II III IV0.017** 0.090 0.114* 0.126*

[0.008] [0.059] [0.062] [0.070]





Dep. Variable:

Interlinked

Transactions

Trust

Trust and Interlinked Transactions

• Level of interlinked transactions appear to very low.

• Coefficients cannot be easily given quantitative interpretation.

• TEST 1: trust negatively correlates with unit costs, other than

cherries

• TEST 2: trust positively correlates with use of interlinked

transactions

– More purchases on credit

– More second payments

– More inputs from the station

• TEST 3: trust positively correlates with financial access

– Finance is a lower share of unit costs,

– Lower MPK

– Better main buyer and lower likelihood of borrowing from main buyer

• TEST 4:

A: competition correlates with unit costs, other than cherries

B: competition negatively correlates with trust

Summary of Findings

• Which trust?

• What is trust?

• Competition: Entry of Wet Mills

• Competition: Farmer Outcomes

To Do

Dep. Variable: Efficiency I II III IV

0.031*** 0.028**

[0.009] [0.012]

0.011 -0.001

[0.008] [0.011]

-0.037* -0.001

[0.019] [0.002]





Trust (Generic)

Trust (Kigali, Collectors,

Manager)

Trust (Family, Friends &

Neighboors)

Which trust?

• Trust games (sender to collector / farmer) correlate with

respective individual answers.

• Demographics do not correlate too much with trust:

– Trust [Kigali, Manager, Collector]: Age (+), Skill (-),

Distance (+)

– Trust [Generic] : Female (-), Education (+)

• Generic trust correlates with a measure of intensity of

genocide in the location.

Which trust?

• Trust is co-determined in the equilibrium of the repeated game

between the station and the farmers

– Does trust cause efficiency?

– What can managers do to build trust?

• Trust measures “quality” of these relationships. Quality could be

determined by:

– Better management I (better managers build better relationships)

– Better management II (selection: stations with better access to finance establish

trust)

– Better management III (selection: better managers enter high trust

environments)

– Different “cultural/historical” environment, lucky shocks

• Questions:

– How does it evolve over time? / Which trust? / Does history play a role?

What is trust?

Controlling for Managerial Characteristics

• Empirical challenge on providing evidence on the effect of

competition on efficiency, why?

– precise measurement of both productivity and competition is rare

in low-income countries, we need across firms within a sector.

– entry of competitors is likely to be a result as much as a cause of

firm’s poor management/performance.

Competition: Entry of Wet Mills

• Empirical challenge on providing evidence on the effect of

competition on efficiency, why?

– precise measurement of both productivity and competition is rare

in low-income countries, we need across firms within a sector.

– entry of competitors is likely to be a result as much as a cause of

firm’s poor management/performance.


FARMERS

(Small holders)

FOREIGN

BUYER

EXPORTER

/MILLER

Coffee Wet

Mill A

Coffee Wet

Mill B

< 2002

CWS

Coffee Producing

Sector: No. of Trees

No data

Country boundary

District boundary


2002

CWS

Coffee Producing


No data

Country boundary

District boundary


2003

CWS

Coffee Producing


No data

Country boundary

District boundary


2004

CWS

Coffee Producing


No data

Country boundary

District boundary


2005

CWS

Coffee Producing


No data

Country boundary

District boundary


2006

CWS

Coffee Producing


No data

Country boundary

District boundary


2007

CWS

Coffee Producing


No data

Country boundary

District boundary


2008

CWS

Coffee Producing


No data

Country boundary

District boundary


2009

CWS

Coffee Producing


No data

Country boundary

District boundary


2010

CWS

Coffee Producing


No data

Country boundary

District boundary


2011

CWS

Coffee Producing


No data

Country boundary

District boundary


57


• Placement of stations is not exogenous…

– we build on a detailed engineering model for the optimal

placement of wet mills, using spatial vectors of various GIS

buffers (roads, rivers, elevation and coffee trees).

– Criteria:

• Select trees with tree count > 30,000

• Create buffers ≤ 1km of any type of road in the catchment area

• Create buffers ≤ 3km of from water springs in the catchment area

• Create buffers satisfying the following: the height of water springs to be 10

m above the elevation of the potential wet mill

– The model predicts entry of wet mills

– We than use these predicted placement in areas surrounding

the catchment areas of mills to instrument for the competition

actually experienced by wet mills, conditional on all ingredients of

eng. Model and all other components.

Competition and Efficiency

Coefficient of

competition dummy on

various outcomes.

Controls: Location

FE, Station Type,

SE (sector)

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-4-2

02

4

Tru

st

-1 -.5 0 .5 1Competition

coef = -0.383*, (robust) se = 0.235

Competition and Trust

Controls: Location

FE, Station Type,

SE (sector)

If trust is necessary for wet mill to operate in a given

locality, the figure underestimates how much competition

hinders trust

Competition: Efficiency and Trust

Coefficient of

competition dummy on

various outcomes.

Controls: Location

FE, Station Type,

SE (sector)

• There is a lot to do!!

• Role of competition – instrumenting by an engineering model

• Understand trust

– Farmers’ sense of affiliation with coop

– Past rainfall realizations

– Historical determinants

– Managerial practices: labour

Concluding Remarks

Thank You!

Go Italy!

Documents

Evidence from Rwanda’s Coffee Wet Mills