HIAS-E-110

The Role of Agricultural Market
Information on Farmers' Agricultural
Outcomes: Evidence from Smallholder
Coffee Producers in Ethiopia
Guenwoo Lee (a), Aya Suzuki (b), Yu Ri Kim (c)(a) Hitotsubashi Institute for Advanced Study, Hitotsubashi University(b) The University of Tokyo(c) The University of Tokyo

August 2021

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
tel:+81 42 580 8668 http://hias.hit-u.ac.jp/
HIAS discussion papers can be downloaded without charge from:https://hdl.handle.net/10086/27202
https://ideas.repec.org/s/hit/hiasdp.html

All rights reserved.

The Role of Agricultural Market Information on Farmers' Agricultural Outcomes: Evidence
from Smallholder Coffee Producers in Ethiopia *August, 2021
Guenwoo Lee †, Aya Suzuki ‡, Yu Ri Kim §Abstract
Using data from 466 smallholder coffee farmers in Ethiopia, this paper examines the effect of a public
agricultural market information system (AMIS) on the farmers’ agricultural outcomes. Our findings
confirm that providing market price information via the AMIS is positively related to coffee sales,
the ratio of sales to production, and coffee income. In addition, we consider market heterogeneity bycomparing two zones with different market characteristics. We find that the AMIS is positively
associated with increasing coffee sales, the ratio of sales to production, and coffee income in only
one zone with relatively lower market participation. On the contrary, the sales and income of AMIS
users in the other zone with higher market participation did not increase although their selling priceincreased. While public information provision via ICT is more beneficial to underdeveloped markets,
we suggest correcting other market imperfections is important to maximize the utility of AMISs.
Key words: Agricultural Market Information System; Coffee Farmer; Ethiopia; ICT4D; IPWRA
JEL code: D80; Q12

Financial supports from the JSPS Kakenhi Scientific Research on Innovative Areas Grant Number JP25101003 andthe Japan Securities Scholarship Foundation.
†
Corresponding author: Hitotsubashi Institute for Advanced Study, 510, Faculty Building 2, 2-1 Naka, Kunitachi,
Tokyo 186-8603, JAPAN Phone: +81 42-580-8664 email: guenwoo.lee@r.hit-u.ac.jp‡
Department of International Studies, Graduate School of Frontier Sciences, University of Tokyo email: ayaszk@k.utokyo.ac.jp§
Department of International Studies, Graduate School of Frontier Sciences, University of Tokyo email:yuri.kim@edu.k.u-tokyo.ac.jp
*

1

1

Introduction

The information asymmetry between farmers and buyers is a classic source of market inefficiency(Stigler, 1961) and is more important in rural areas in developing countries than in developed
countries because of high transportation costs, poor infrastructure, and a limited number of buyers(Fafchamps & Hill, 2008; Mérel et al., 2009; Osborne, 2005). To reduce this asymmetry, after theabolishment of marketing boards in the 1980s, many Sub-Saharan African governments introduced
agricultural market information systems (AMISs), which publish price information on thecommodities in various markets (Tollens, 2006; USAID, 2013). Although these donor-funded firstgeneration AMISs did not function effectively because of operational difficulties, the development of
information and communication technology (ICT) and penetration of mobile technologies in the
2000s led to second-generation AMISs (Courtois & Subervie, 2015; USAID, 2013). The new AMISs
were offered in various forms such as through radios, televisions, short messaging services (SMS),
and websites, and private AMIS providers also emerged. The types of information were also expanded
to include not only agricultural prices but also technical advice, input information, and weather
forecasts.
Following the rapid penetration of mobile phones in developing countries in the 2000s, thenumber of studies of the effects of mobile phones, which are considered to reduce information
asymmetry, on agricultural markets expanded (Aker & Mbiti, 2010; Nakasone et al., 2014). The
earlier studies by Jensen (2007) and Aker & Mbiti (2010) showed that the introduction of mobile
phones narrows price dispersions across the markets in India and Niger, respectively. Since then, theimpact of mobile phones on agricultural outcomes has been analyzed, including agricultural prices(Fafchamps & Minten, 2012; Shimamoto et al., 2015; Svensson & Yanagizawa, 2009), market
participation (Aker & Ksoll, 2016; Muto & Yamano, 2009), farmers’ income (Fafchamps & Minten,
2012; Muto & Yamano, 2009), technology adoption (Aker, 2011; Cole & Fernando, 2012), and crop
diversification (Aker & Ksoll, 2016). While most find that the introduction of mobile technology
leads to a decline in price dispersion across markets and thus greater market integration, the findingson the impact on agricultural outcomes are mixed (Nakasone et al., 2014). Positive effects on selling
prices have been found in Cambodia (Shimamoto et al., 2015) and on input use and agricultural
productivity in Kenya (Ogutu et al., 2014). However, heterogeneous effects due to age, farm size, andtypes of crops are also found (Fafchamps & Minten, 2012; Mitra et al., 2018; Muto & Yamano, 2009).
This heterogeneity suggests the need for more studies to clarify what drives the results. In addition,
the AMISs analyzed in existing studies are mostly privately managed ones that charge fees, with thenotable exception of Svensson & Yanagizawa (2009). As a public AMIS has different features than aprivate AMIS in terms of distribution and user fees for farmers, its effects on agricultural outcomes2

must be separately evaluated.
Given this background, we evaluate the effects of a public AMIS on farmers’ agricultural
outcomes using primary data collected from 466 coffee farmers in Ethiopia in 2014. We collect datafrom two zones that differ in market structure to examine the possible heterogeneity in the effects of
the AMIS. We find that while there is no difference in selling price per kilogram between AMIS users
and non-AMIS users, there is a difference in sales volume, the ratio of sales to production, and farmincome. Moreover, the difference in farm income between AMIS users and non-AMIS users is
attributed to an increase in sales volume by AMIS users rather than an increase in their selling prices.
As we expected the major benefit of having more information to be observed in the form of a selling
price increase, this finding was contrary to our expectations.
We further test for the presence of the heterogeneous effects of AMISs and find consistentregional heterogeneity in the impact of AMISs across market structures but find no systematicdifference in the effects of AMIS use on outcomes due to educational background. Such regional
heterogeneity is likely driven by the different degrees of market competition in these areas, which
may be determined by both demand- and supply-side conditions, namely, the number of buyers
accessible and level of farmers’ market participation. Indeed, we find that AMIS use is positively
associated with a price increase in Sidama, where market competition is higher than that in Jimma.
In Jimma, on the contrary, the price is unaffected by AMIS use. However, AMIS use is positively
associated with higher sales volume, the ratio of sales to production, and farm income in Jimma,
which suggests that although farmers cannot obtain higher prices using the AMIS, they can increase
production and sales volume. The results are robust to different estimation methods, including inverse
probability weighting (IPW) in quasi-experimental designs for evaluating the causal treatment effectof AMIS use.
Our study contributes to the emerging literature on the effect of ICT on the information
asymmetry in agricultural markets in developing countries in two ways. First, we evaluate the effects
of using a public AMIS on farmers’ outcomes, while most existing studies analyze the impacts ofprivately managed AMISs or the ownership (or use) of mobile phones. A public AMIS is freely
available to a large pool of farmers through radios, televisions, SMS, and mobile phones, andtherefore it is important to understand whether the implementation of AMISs actually yields positive
results for them. Our findings confirm that providing market price information via the AMIS ispositively related to coffee sales, the ratio of sales to production, and coffee income. In addition, we
consider market heterogeneity by comparing two zones with different market characteristics. We findthat the AMIS is positively associated with increasing coffee sales, the ratio of sales to production,and coffee income in only one zone with relatively lower market participation. On the contrary, thesales and income of AMIS users in the other zone with higher market participation did not increase3

although their selling price increased. These findings indicate the need to improve on other market
imperfections in addition to information asymmetry, consistent with the findings of Aker & Ksoll(2016).
The remainder of this paper is organized as follows. In Section 2, we develop our hypotheses
to be tested based on the relevant literature. Section 3 describes Ethiopia’s coffee industry and its
AMIS. In Section 4, we explain the data used and present summary statistics. Section 5 describes ourempirical strategy, and the results are presented in Section 6. Finally, Section 7 concludes.

2

Hypothesis Development

With the rapid development of ICT allowing farmers in developing countries to obtain market priceinformation more easily than before, there has been growing interest in the impact of providing
market information on farmers’ bargaining power and market participation. First, we focus on therelationship between market information and farmers’ bargaining power. Theoretically, it isstraightforward to predict that removing information asymmetry by providing farmers with moremarket information raises their bargaining power compared with traders (Stigler, 1961). Many
empirical studies have also provided evidence for this. For example, Jensen (2007) found that the useof market information through mobile phones helps fishers choose the fish market with the highest
price. Svensson & Yanagizawa (2009) also found that farmers who are better informed about
agricultural prices have higher bargaining power over traders. Courtois & Subervie (2015) evaluate
the impact of a private AMIS-based program in Ghana on farmers’ marketing performances. They
found that AMIS users sell maize at a price 10% higher than those of non-AMIS users. This implies
that the use of AMIS increases farmers’ bargaining power. However, their target program was
privately operated so that the effect of public AMIS on farmers' bargaining power remains unknown.
Thus, we first test the following:
Hypothesis 1. Providing market information to farmers through a public AMIS increases farmers’
bargaining power so that they can sell at a higher price.
Next, we turn to sales volume. Several studies examine the effect of farmers’ use of market
information on market participation. Muto & Yamano (2009) suggested that as information flow
increases, the cost of crop marketing decreases, which raises the market participation of farmers inremote areas. They interpreted the result as follows: farmers and traders bear the cost and risk oftraveling if both parties are aware of the volume and price of what they can trade in advance. Courtois& Subervie (2015) claimed that asymmetric information about market prices may collapse
4negotiations between farmers and traders. In particular, when the market price falls sharply due toexternal factors, uninformed farmers may refuse to sell their products to traders because the payoffby selling products is lower than expected, even if traders offer prices that minimize their gains. To
deal with this problem, they propose the use of AMISs by farmers and expect transaction successrates to improve when farmers subscribe to an AMIS—even when the market price fluctuates largely
due to unexpected external factors. Hence,
Hypothesis 2. Providing market information through a public AMIS to farmers reduces sales priceand travel cost uncertainty so that farmers can sell in larger quantity or at a higher the ratio of sales
to production.
To provide evidence on the potential benefit of the Ethiopian AMIS, Getnet et al. (2011) used
a quasi-rational expectation formation and found that the AMIS helps farmers make unbiased priceforecasts. The positive impact of the AMIS on farmers’ price predictions may improve their decisionmaking and market behavior related to coffee production and marketing; thus, the AMIS can increase
farmers’ income. However, the study does not estimate the AMIS’s effect on farmers’ income. Hence,empirical evidence of the positive effect of the AMIS on farmers’ income is still lacking. Thus, wetest the following:
Hypothesis 3. Providing market information to farmers through a public AMIS improve farmers’
decision-making and market behavior related to coffee production and marketing so that they cansell at a higher price.
We further assume that depending on the market structure, the impact of AMISs may differ
due to different elasticities of demand and supply. For instance, when the market structure is close toan oligopsony, it may be difficult for farmers to negotiate with traders. Even if farmers are aware of
the prevailing market price, they may not benefit from such information if they lack access to otherbuyers. Farmers can only choose from selling their products at below the market price or not sellingthem at all. By contrast, if there are many buyers in the market, farmers can search for other buyers
willing to pay a price closer to the market price. In such a market, providing market information tofarmers is expected to induce them to sell more in the market. Thus,
Hypothesis 4. The effects of providing market price information to farmers depend on the market
structure.

5

In addition to examining the impact of ICT-based AMISs on farmers’ bargaining power andmarket participation, we examine two types of heterogeneity in this impact, namely, farmers’
individual ability and location. While many previous studies have found that AMISs are effective atchanging farmers’ behavior, it remains unclear if the majority of smallholder farms, especially withless educated farmers, benefit from using such new technologies. In other words, because anindividual’s ability to interpret information depends on his/her cognitive skills, the benefits ofreceiving information are expected to be larger for those with higher levels of education than thosewith lower levels of education. Thus,
Hypothesis 5. The effects of providing market price information to farmers depend on their ability tointerpret information.
We test these five hypotheses using primary data on coffee farmers collected in Ethiopia toverify whether the AMIS is a valid tool for enhancing farmers’ bargaining power and market
participation and how its effects depend on the individual’s level of education and market structure.

3

AMIS in Ethiopia

3.1

Coffee industry

In Ethiopia, there are more than one million coffee-growing households, and the livelihoods of over
15 million people directly and indirectly depend on the coffee industry (Labouisse et al., 2008; Lmc,
2000; Petit, 2007). Since liberalizing its agricultural market in 1990, the Ethiopian government has
striven to improve the productivity, quality, and market efficiency of domestic coffee crops (Petit,
2007). Consequently, in 2018, Ethiopia was ranked the largest coffee exporter in Africa and 10thlargest exporter in the world (ICO, 2020; Minten et al., 2019). Figure 1 shows Ethiopia’s coffee export
volume and value between 1990 and 2018. During this period, Ethiopian coffee exports increased
from 64 thousand tons to 215 thousand tons; in terms of the U.S. dollar value, it rose from $129
million to $759 million. This represents a 340% increase in volume and a 590% increase in value(FAO, 2020).

6

Source: FAO (2020)
Figure 1. Ethiopian coffee export volume and value
In May 2008, to ensure the development of an efficient and modern trading system, theEthiopian government established the Ethiopian Commodity Exchange (ECX) in Addis Ababa with$29 million of funding from international institutes and official development assistance; it alsochanged the hub of its coffee distribution from auction centers to the ECX (Gabre-Madhin, 2012).
Most of the coffee produced by smallholder farms is traded on the ECX because the government hasrevised the laws on coffee trading for export or domestic distribution to ban coffee transactions
outside the ECX (Gelaw et al., 2017). In 2009, the year after its establishment, Ethiopia’s coffee
exports decreased by 60 thousand tons compared with 2008, but they began to recover in 2010 andnearly doubled in 2018, as shown in Figure 1. This increasing trend may be partially due to growing
demand for coffee, as coffee exports from coffee-producing countries rose by nearly 130% from 5.7
million tons to 7.4 million tons between 2009 and 2018 (FAO, 2020). At the same time, themodernized trading system under the ECX has also contributed to the remarkable growth achievedby the Ethiopian coffee industry.

7Source: Based on Tamirat (2013) and Duguma & Van deer Meer (2018)
Figure 2. Ethiopian coffee supply chain after the establishment of the ECX
Figure 2 depicts the Ethiopian coffee supply chain after the ECX system was introduced in2008. Most of the coffee produced by smallholder farms is traded on the ECX because the government
has revised the laws on coffee trading for export or domestic distribution to ban coffee transactions
outside the ECX (Gelaw et al., 2017). Since the Ethiopian government banned unauthorized collectors
and brokers’ business activities with the establishment of the ECX, most smallholder coffee farms
sell their products directly to cooperatives or licensed local suppliers rather than to brokers (Minten
et al., 2019). According to ECX (2020), the coffee collected by cooperatives and suppliers is sent to
the ECX’s warehouses nationally and graded by experts. Coffee with grades 1 to 4 is exported
overseas, while coffee with grade 5 or lower is distributed to domestic markets (Duguma & Van deer
Meer, 2018; ECX, 2020; Tamirat, 2013).
8

3.2

Market information provided by the ECX

Smallholder coffee farms in Ethiopia produce about 95% of the country’s coffee output. However,they have difficulty accessing information on wholesale prices since most are geographically isolated
from central markets (Getnet et al., 2011; Labouisse et al., 2008). Hence, many obtain market
information from unofficial sources such as neighbors, friends, and traders and generally makemarketing decisions such as selling prices and sales volume based on sometimes incorrect andoutdated information (Getnet et al., 2011). Such an environment in which producers rely on unofficial
market price information causes information asymmetries between producers and traders that try tolower selling prices. Since neighbors also obtain market information from traders and other unofficial
sources, the information asymmetry problem persists despite an active exchange among producers(Osborne, 2005).
Since 2008, the ECX has adopted an AMIS and disseminated price information on wholesale
prices to all market actors including smallholder farms through its website, electronic tickers in 250
rural markets, SMS, interactive voice response services, radio broadcasts (three times a day),television programs (twice a day), newspapers, and newsletters (daily, monthly, and half-yearly)(ECX, 2020). Given the Ethiopian coffee industry’s supply chain structure in which most coffee istraded at the ECX, the AMIS is expected to provide more accurate price information than otherchannels.

4

Data and Summary Statistics

To assess the impact of the AMIS on coffee producers’ bargaining power and market participation,
we interviewed the farmers of 466 smallholder coffee farms in 19 kebeles (wards) in the Jimma andSidama zones in 2014. We collected recall data on coffee production and sales in 2012 and 2013 andrestricted respondents to household heads. The kebeles and respondents were chosen randomly using
lists obtained from the Oromia Coffee Farmers Cooperative Union and Sidama Coffee Farmers
Cooperative Union. ** These two zones were chosen, as they are the largest coffee producers andexporters in Ethiopia. In 2013, the Jimma zone exported about 20% of Ethiopia’s coffee exports,
while the Sidama zone exported about 40% (Minten et al., 2014). Another reason for choosing thetwo zones was to compare the effects of the AMIS under different market structures. According toMinten et al. (2015), 80% of farmers in the Sidama zone can choose to whom to sell from multiple
traders. On the contrary, only 63% of farmers in the Jimma zone can select their trading partners.
**
Cooperatives have all smallholder coffee farmers in the Oromia region and the SNNPR region including both
members and non-members.

9While only 2.2% of farmers in the Sidama zone stated that they had no choice, 11.2% in the Jimma
zone did so. These survey results suggest that there are more traders in the Sidama zone than in the
Jimma zone. Owing to the differences in the market structure of the two zones, we thus expect thebenefits of the price information obtained from the AMIS to differ.
Table 1. Number of sample households and AMIS users in each kebele
Zone
Jimma

WoredaGera

Limu Seka
Kersa

SidamaDale

Aleta Wendo

ShebedinoN

Kebele

A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R

House
holds
#
#
245
29
19
37
24
23
39
36
26
12
221
25
34
8
27
27
26
29
23
22
466

AMIS Users
2012
2013
%
#
%
50
20.41
50
20.41
10
34.48
10
34.48
2
10.53
2
10.53
7
18.92
7
18.92
13
54.17
13
54.17
1
4.35
1
4.35
1
2.56
1
2.56
2
5.56
2
5.56
7
26.92
7
26.92
7
58.33
7
58.33
88
39.82
91
41.18
17
68.00
17
68.00
28
82.35
30
88.24
3
37.50
3
37.50
3
11.11
3
11.11
18
66.67
18
66.67
8
30.77
9
34.62
4
13.79
4
13.79
5
21.74
5
21.74
2
9.09
2
9.09
138
29.61
141
30.26

Notes: “AMIS” is an abbreviation for agricultural market information system.
% of AMIS users in each year = # of AMIS users in each year / # of observations in each village

Source: Authors’ survey (2014)
Table 1 presents the number of sample households and AMIS users in each kebele in the two
sample years. Between 2012 and 2013, the number of AMIS users remains the same in all kebeles
except for kebeles K and O, which show increases by two and one, respectively. In the full sample,
the share of AMIS users increases marginally from 29.61% in 2012 to 30.26% in 2013. This stagnant
adoption trend may be attributed to the low awareness of the system among farmers. Farmers mightalso be reluctant to use the AMIS, as its benefits at the time of the survey might not have been visible.

10Table 2. Types of price information sources(Multiple answers are allowed)

From unofficial channels
Family member
Friends and neighbors
Relative

Jimma (n=245)#%

Sidama (n=221)#%

Total (n=466)
#
%

163
226
187

66.53
92.24
76.33

27
109
15

12.22
49.32
6.79

190
335
202

40.77
71.89
43.35

From buyers
BrokerPrivate trader
Collector
Exporter

24
179
46
38

9.80
73.06
18.78
15.51

2
8
23
1

0.90
3.62
10.41
0.45

26
187
69
39

5.58
40.13
14.81
8.37

From official channels
Cooperative
Extension agent
AMIS

21
25
50

8.57
10.20
20.41

193
5
91

87.33
2.26
41.18

214
30
141

45.92
6.44
30.26

Notes: % of users in each channel = # of users in each channel / # of observations in each zone

Source: Authors’ survey (2014)
Table 2 shows the types of information sources used by smallholder coffee farms to obtainprice information in 2013. Altogether, 72% of respondents answered that they obtain informationfrom friends or neighbors, 43% from relatives, and 46% from cooperatives. Only 30% of respondents,
or 141 of the 466 farmers, use the AMIS provided by the ECX. Overall, farmers in the Jimma zone
tend to rely on informal information such as from family members, friends, and private traders, whilemost farmers in the Sidama zone obtain price information from formal information sources such as
cooperatives. In addition, the AMIS is the third most popular information source in Sidama after
cooperatives and friends and neighbors (41.18% of the sample). In turn, the user ratio of the AMIS in
the Jimma zone is only half that of Sidama and the AMIS is only the fifth most popular information
source among the 10 types surveyed.

11Table 3. Types of AMIS channels(Multiple answers are allowed)

Radio
SMS
TV
Electronic ticker
IVR
Newspaper
WebsiteJimma (n=50)
Sidama (n=91)
Total (n=141)
%
#
%
#
%
#
99.29
49
98.00
91
100.00
140
16.31
9
18.00
14
15.38
23
10.64
3
6.00
12
13.19
15
4.96
0
0.00
7
7.69
7
0.71
1
2.00
0
0.00
1
0.71
1
2.00
0
0.00
1
0.00
0
0.00
0
0.00
0

Notes: % of users in each channel = # of users in each channel / # of observations in each zone

Source: Authors’ survey (2014)
Table 3 describes the types of AMIS channels used by coffee farmers in 2013. Virtually all
respondents (157 of 158) who use the AMIS obtain their information from radio broadcasts. Although
85% of farmers in the sample own mobile phones, the usage rates of the costly SMS subscription andinteractive voice response systems are relatively low. Instead, farmers use the radio features on theirmobile phones in addition to a radio receiver, which is owned by 73% of farmers, making radio themost popular AMIS channel. Nobody accessed the ECX website to check price information for two
main reasons: the website targets foreign buyers rather than local coffee farmers and most smallholder
farms do not have a device or network to view the website. Seven farmers in the Sidama zone
answered that they obtain price information through electronic tickers, whereas the number of users
in Jimma is zero.

12

Table 4. Socioeconomic characteristics of respondents
Jimma

Variables

SidamaAMIS

NonAMIS

Mean

Mean

Dif.

Total

AMIS

NonAMIS

Mean

Mean

Dif.

HH’s schooling

years

Household size#

Adults in the household#[1.18][1.22](0.19)[2.11][2.17](0.29)[1.96][1.74](0.18)

birr

55373.72

31317.94

24055.78**

31276.02

31724.18-448.16

39821.30

31480.44

8340.87*[76614.28][56204.60](9649.59)[26948.34][28917.38](3844.10)[51527.78][47164.09](4893.07)

0.48

0.02

0.46***

2.03

1.70

0.33*

1.48

0.69

0.79***[1.16][0.14](0.09)[1.28][1.46](0.19)[1.44][1.24](0.13)

50

46.72[12.81][11.53](1.87)[9.18]

0.98

0.97

0.01

0.98

[0.14][0.17](0.03)

2.84

3.09

-0.25

[2.97][2.88]

5.68

5.96[2.04]
2.84

Dif.

=1 male

N

47.31

Mean

HH’s gender

#

-0.67

Mean

years

AMIS informants

45.55

NonAMIS

HH’s age

Total assets

44.88

AMIS

195

0.58

46.45

46.02

0.43

[11.21](1.43)[10.63][11.40](1.13)

1.00-0.02*

0.98

0.98

-0.00

[0.15][0.00](0.01)[0.14][0.13](0.01)

5.38

4.78

0.61

4.48

3.77

0.72**(0.46)[3.06][3.33](0.44)[3.25][3.18](0.32)

-0.28

5.76

5.81

-0.05

5.73

5.90

-0.17

[1.61](0.27)[2.08][2.05](0.28)[2.06][1.80](0.19)

3.05

-0.21

4.29

4.13

0.15

3.77

3.48

0.29

91

130

141

325

Notes: Standard deviations are in brackets. Standard errors are in parentheses. * p<0.05, ** p<0.01, *** p<0.001.
“HH” and “AMIS” are abbreviations for the head of household and agricultural market information system, respectively.
Total assets = fixed assets + saving - loan

Table 4 outlines respondents’ socioeconomic characteristics from the 2013 data. The AMIS
user and non-AMIS user groups were divided according to the answer of the household head who
participated in the interview. This grouping does not reflect the usage of other household members,
as household heads have the strongest bargaining power in the household. Thus, we only consider thehousehold head’s characteristics in the following analysis. The average age of the household head is
46 years old and 98% are men. There is no significant difference between AMIS and non-AMIS users.
Significant differences are found in the years of schooling, total assets, and number of AMIS
informants (i.e., the number of people to whom respondents mainly talk about market prices, but only
those who use the AMIS), which are higher for AMIS users than non-users. The number of AMIS
informants is the only characteristic that is significantly different between the user and non-usergroups in both Jimma and Sidama as well as for the total sample. The AMIS user groups in both zones
have more informants who use the AMIS around them than the non-user group.

13

Table 5. Coffee production and sales performance of farms
Jimma

Variables

Unit

Coffee farm size

Harvest volume

Sales volume

Ratio of sales to production

SidamaAMIS

NonAMIS

Mean

Mean

Ha

Dif.

Total

AMIS

NonAMIS

Mean

Mean

Dif.

AMIS

NonAMIS

Mean

Mean

Dif.

0.83

0.94

-0.11

0.69

0.61

0.08

0.74

0.81

-0.07

[0.57][0.63](0.10)[0.39][0.36](0.05)[0.46][0.56](0.05)

795.00554.10240.90***1114.18

1011.15103.021000.99736.92264.07***[470.37][404.12](66.31)[544.83][665.58](84.58)[540.14][569.61](56.56)725.00434.74290.26***960.11804.23155.88**876.74582.54294.20***[514.11][360.97](62.87)[532.87][461.00](67.22)[536.46][442.15](47.66)

kg

kg

%

86.81

74.72

12.09***

83.33

82.66

0.67

84.56

77.89

6.67***[17.43][18.11](2.85)[12.49][17.72](2.16)[14.47][18.35](1.74)

12.80-2.41***

14.71

13.10

1.61***

13.18

12.92

0.26

Sales price/kg

birr

10.39
[4.17][4.46](0.70)[4.16][4.45](0.59)[4.63][4.45](0.45)

Total coffee incomebirr

6399.905307.801092.1013848.90

10300.05

3548.85***11207.41

7304.703902.71***[3639.65][4853.12](734.59)[8071.70][6448.60](978.67)[7701.29][6054.69](665.05)

Total coffee costbirr

368.58460.01-91.432230.14966.531263.61***

1570.01

662.62907.40***[1476.41][1480.65](234.58)[1979.71][2492.08](313.73)[2020.20][1961.41](199.60)

22.58

41.44

-18.868.79

0.00

8.79***

13.68

24.86

-11.18[116.35][506.82](72.26)[32.14][0.00](2.82)[73.80][392.70](33.34)

0.00

0.00

0.00

0.66

0.00

0.66

0.43

0.00

0.43

[0.00][0.00](0.00)[6.29][0.00](0.55)[5.05][0.00](0.28)

0.00

14.70

-14.70*

1.47

0.00

1.47**

0.95

8.82-7.87**[0.00][56.59](8.01)[8.25][0.00](0.72)[6.65][44.38](3.76)

346

403.87-57.872219.22966.531252.69***

1554.96628.94926.02***[1419.30][1393.43](221.72)[1968.16][2492.08](313.18)[2001.01][1926.53](196.57)

50

195

91

130

141

325

N

Fertilizer

birr

Pesticidebirr

Herbicide

birr

Labor

birr

245

221

466

Notes: Standard deviations are in brackets. Standard errors are in parentheses. * p<0.05, ** p<0.01, *** p<0.001.
“AMIS” is an abbreviation for agricultural market information system.
Total coffee income = sales volume × sales price/kg
Total coffee cost = fertilizer + pesticide + herbicide + labor

Table 5 shows coffee production, cost, and sales in 2013. The AMIS user group has astatistically higher harvest volume, sales volume, ratio of sales to production, coffee cost, and labor
cost than non-AMIS users. The difference in coffee cost between the two groups is because the AMIS
user group’s labor cost is 926 birr ($24.75) more than that of the non-AMIS group. Since AMIS users
can reduce market price uncertainty by using the AMIS, AMIS users in Sidama appear to haveactively hired workers as an investment strategy to increase revenue. However, in Jimma, thedifference in the labor cost between the groups is not statistically significant. In addition, the AMIS
group harvests about 264 kilograms more and sells about 294 kilograms more than the non-AMIS
group despite the land size differences not reaching statistical significance. Owing to the higher sales
volume among AMIS users, they earn 3,903 birr ($104.29) more coffee income than non-AMIS users.
Such higher income may not result from the higher selling price because the price difference between
the two groups is not statistically significant. We can thus infer that the effect of the AMIS may differ
in the two zones since the difference in the average prices between users and non-users in Jimma
14indicates a negative sign, while that in Sidama shows a positive sign. Further, sample farmers in
Sidama harvest more, sell more, sell at a higher price, earn more, and invest more even though they
have less farmland than farmers in Jimma.

5 Econometric Strategies
Using the novel data set of 466 farmers from the household survey mentioned in Section 4, we
empirically examine the impact of the AMIS on farmers’ bargaining power and market participation.
Although we collected two-year panel data from each farmer, the main analysis of the study is crosssectional because the number of AMIS users was almost the same in 2012 and 2013, as shown in
Table 1. As an alternative, we employ a pooled regression model, a fixed effect model, and a random
effect model using panel data as a robustness check. The following econometric model is estimated:
𝑌𝑌𝑖𝑖𝑖𝑖13 = 𝛼𝛼 + 𝛽𝛽1 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖13 + 𝛽𝛽2 𝑀𝑀𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖13 + 𝑋𝑋𝑖𝑖𝑖𝑖13 𝛽𝛽𝑘𝑘 + 𝛾𝛾𝑌𝑌𝑖𝑖𝑖𝑖12 + 𝛿𝛿𝑘𝑘 + 𝜀𝜀𝑖𝑖𝑖𝑖(1)

where 𝑌𝑌𝑖𝑖𝑖𝑖13 is the outcome (log selling price per kilogram, log sales volume, the ratio of sales to

production, and log farm income) for farmer i in zone or woreda k in 2013; 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖13 is a dummy

variable that indicates whether farmer i is an AMIS user in 2013; and 𝑀𝑀𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖13 is thenumber of informants who use the AMIS and exchange price information with farmer i in 2013. Thisvariable controls for potential treatment spillover effects because, as the price information obtained

from the AMIS can easily spill over to other farmers who do not use the AMIS. 𝑋𝑋𝑖𝑖 accounts forfarmer i’s age and years of schooling, farm size, total coffee costs, and types of coffee priceinformation sources in 2013. A lagged dependent variable 𝑌𝑌𝑖𝑖𝑖𝑖12 is included to estimates to what

extent MIS usage is associated with higher growth of the outcomes. 𝛿𝛿𝑘𝑘 refers to zone or woreda

dummies, and 𝜀𝜀 is an error term. As mentioned in sub-section 3.1, most of the coffee harvested bysmallholders is traded on the regional trading centers managed by the ECX. Since the centers arelocated at the woreda level and the coffee cultivation method varies by zone, the heterogeneity acrossworedas or zones is likely to be greater than that across villages. Thus, to capture unobserved
heterogeneity across zones or woreda, the regression model includes zone or woreda dummies insteadof village dummies.
To test Hypothesis 4, this study employs an interaction term between AMIS usage andresidential area is estimated to test Hypothesis 4. The benefit of the price information obtained fromthe AMIS is expected to differ in the two zones due to differences in their markets, as described in
Section 4.
Another interaction effect between AMIS usage and years of schooling. This interaction term15

is a proxy for revealing the differences in the outcomes among AMIS users depending on their ability
to interpret market price information. If the impact of the AMIS on the outcome variables is higher
for more educated farmers than less educated ones, Hypothesis 5 is supported. In other words, theeffects of the AMIS can be amplified when price information is provided to individuals with a better
understanding and higher cognitive skills.
Because our sample farmers have freely chosen whether to use the AMIS, self-selection bias
may arise in the analyses. Thus, as a quasi-experimental method, this study adopts the inverse
probability-weighted regression adjustment (IPWRA) estimator and estimates the average treatment
effect (ATE) to measure the differences in the average outcomes between the AMIS user and nonAMIS user groups. Since the estimator is a doubly robust estimator that combines a logistic modelfor the treatment (i.e., the IPW component) and a linear model for the outcome (i.e., the RAcomponent), it provides asymptotically unbiased estimates even if one of the models is mis-specified(Bourguignon et al., 2007; Robins et al., 2007; Wooldridge, 2007):
𝑛𝑛

1
𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖 𝑌𝑌𝑖𝑖 (1 − 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖 )𝑌𝑌𝑖𝑖�𝐴𝐴𝐴𝐴𝐴𝐴��−�
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 =𝑛𝑛𝜋𝜋�(𝑍𝑍𝑖𝑖 )1 − 𝜋𝜋�(𝑍𝑍𝑖𝑖 )

(2)

𝑖𝑖=1

where 𝑛𝑛 is the number of individuals in our sample and 𝜋𝜋�(𝑍𝑍𝑖𝑖 ) is the estimated propensity score,

which is the estimated conditional probability of using the AMIS given 𝑍𝑍𝑖𝑖 . Appendices 2 to 4 show
the variables used to estimate the probability.

6 Results
6.1 Effects of the AMIS on farmers’ agricultural outcomesWe first analyze the effect of the AMIS on farmers’ agricultural outcomes using the full sample. In
addition to ordinary least squares (OLS), which may be biased because of the endogenous nature of
AMIS adoption, the IPWRA results are presented. For the matching, we use the household and farmcharacteristics. The matching passes the covariate balance tests (Appendix 1) and overidentification
tests (Table 6).
As shown in Table 6, the effects of the AMIS are consistent under both the OLS and the
IPWRA specifications for all four dependent variables, except for the ratio of sales to production.
First, the AMIS user dummy does not show statistically significant effects on the selling price perkilogram in both OLS and IPWRA. However, the AMIS dummy shows the positive and statisticallysignificant effects on the user’s sales volume, ratio of sales to production, and farm income in either
16the OLS or the IPWRA columns. The sales volume of AMIS users is 10% higher (column (iv)) thanthat of non-AMIS users. The ratio of sales to production is about 3% higher for AMIS users, inferringthat AMIS users increase their sales both in absolute and in relative terms. Consequently, AMIS users
also have statistically higher farm income, as presented in columns (vii) and (viii). In sum, farmers
who use the AMIS enjoy higher income from coffee because of more active market participation, but
not higher selling prices.
Table 6. Effects of the AMIS on farmers’ agricultural outcomesln(sales price/kg)

ln(sales volume)

Ratio of sales to productionln(coffee farm income)

OLS

IPWRA

OLS

IPWRA

OLS

IPWRA

OLS

IPWRA

(i)

(ii)

(iii)

(iv)

(v)

(vi)

(vii)

(viii)= 1 AMIS user

0.002

0.01

0.08**

0.10***

2.17

3.44**

0.12***

0.16***(0.03)(0.02)(0.04)(0.04)(1.33)(1.58)(0.05)(0.04)-0.06-0.10**

0.14*

0.09

1.12

-0.49

0.01

-0.12

(0.04)(0.05)(0.07)(0.08)(2.15)(2.12)(0.09)(0.08)= 1 info. from

0.003

0.07

-0.03

-0.17

0.27

-6.00

-0.04

-0.23

buyers(0.04)(0.09)(0.06)(0.18)(2.16)(3.68)(0.07)(0.22)= 1 info. from-0.09**-0.09*

0.19***

0.06

1.85

-4.21

0.17**

0.04

extension agents(0.04)(0.05)(0.05)(0.07)(1.49)(2.88)(0.07)(0.09)= 1 info. from coop.

-0.00030.15**

-0.01

-0.10

-3.97**

-1.66

0.02

0.08

(0.04)(0.08)(0.06)(0.10)(1.82)(3.35)(0.08)(0.14)-0.02*-0.02

0.06**

0.04**

1.55**

-0.18

0.04

0.01

(0.01)(0.02)(0.02)(0.02)(0.70)(0.89)(0.03)(0.03)

0.67***

0.63***

0.88***

0.93***

0.78***

0.79***

0.74***

0.75***(0.05)(0.06)(0.05)(0.05)(0.06)(0.07)(0.07)(0.07)

Jimma dummy-0.03-0.002

-0.07

0.19

-3.42

7.70*

-0.13

0.28

(=1 live in Jimma)(0.06)(0.13)(0.07)(0.19)(2.40)(4.63)(0.10)(0.21)

1.11***

1.36***

0.62*

0.09

29.96***

23.91

2.24***

1.99***(0.20)(0.35)(0.35)(0.44)(9.16)(18.50)(0.62)(0.75)= 1 info. fromunofficial channels# of AMIS informants

Yt-1

Constant

Over. (Prob>F)

0.33
0.55

R2
N

0.33

466

0.33

0.79
466

466

0.33

0.48
466

466

0.72
466

Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
All regressions include control variables for the household head's age, age^2, schooling, and coffee farm size.

17

466

466

6.2

Heterogeneous effects of the AMIS on farmers’ agricultural outcomes

We investigate the regional heterogeneity between the Jimma and Sidama zones. Based on the
differences in Table 4, we assume that farmers are situated differently in the two zones in terms ofboth coffee production and marketing. Table 7 captures the difference in the effect of the AMIS
between the Jimma and Sidama zones by including the Jimma dummy as well as the interaction term.
While farmers in Jimma sell 5.12% points lower from their production and earn 19% less farm income
than farmers in Sidama on average, the effects of using the AMIS are much greater for farmers in
Jimma. If farmers in Jimma use the AMIS, the ratio of sales to production and farm income increaseby 0.8% points and 0.05%, respectively compared with users in Sidama. The larger effects of the
AMIS among users in Jimma may be attributed to the lower sales volume, ratio of sales to production,and farm income than farmers in Sidama. The effects of the AMIS on selling price remain
insignificant even in Jimma, where selling price per kilogram is lower than that in Sidama.
Table 7. Regional heterogeneity of the AMIS effects: OLS= 1 AMIS user

= 1 info. from unofficial channels= 1 info. from buyers= 1 info. from extension agents= 1 info. from coop.

# of AMIS informants

Yt-1

Jimma dummy (=1 live in Jimma)ln(sales price/kg)

ln(sales volume)

Ratio of sales to
productionln(coffee farmincome)

(i)

(ii)

(iii)

(iv)

0.02

-0.01

-0.40

0.01

(0.04)(0.05)(1.38)(0.06)-0.06

0.16**

1.74

0.04(0.04)(0.07)(2.10)(0.09)

0.01

-0.04

-0.19

-0.06

(0.04)(0.06)(2.16)(0.07)-0.08**

0.16***

1.03

0.13**(0.04)(0.05)(1.63)(0.06)

0.003

-0.01

-4.15**

0.01(0.04)(0.06)(1.79)(0.08)-0.02*

0.06**

1.61**

0.04(0.01)(0.02)(0.71)(0.03)

0.67***

0.87***

0.77***

0.75***(0.05)(0.05)(0.06)(0.07)-0.02-0.14*-5.12**-0.19*(0.06)(0.08)(2.55)(0.10)-0.04

0.22***

5.94**

0.24***(0.05)(0.08)(2.88)(0.09)

1.12***

0.65

30.58***

2.20***(0.20)(0.35)(9.27)(0.61)

Joint. (Prob>F)

0.38

0.004***

0.04**

0.007***

R

2

0.55

0.80

0.48

0.71

N

466

466

466

466

AMIS × Jimma dummy

Constant

Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
All regressions include control variables for the household head's age, age^2, schooling, and coffee farm size.

18

To further distinguish the difference between the effects of the AMIS in Jimma
and Sidama, we divide the sample in half and run separate regressions. Tables 8summarize the results using OLS and IPWRA, respectively. For the matching, we use thehousehold and farm characteristics. The matching passes all the covariate balance tests(Appendices 3 and 4) and overidentification tests. We find, under both OLS and IPWRA,
that the significance level of the four dependent variables varies by region. The AMIS
variable shows the positive and statistically significant effect on the selling price of
farmers in Sidama, but the effects on the other three dependent variables are insignificant.
The opposite case is true for the Jimma zone. The coefficients of the AMIS dummy are
positive and statistically significant for sales volume, the ratio of sales to production, andfarm income, but not selling price (OLS). The results using IPWRA show similar trends
to the OLS results, except for the effect on the selling price of farmers in Jimma. This
contradictory trend between the two zones is likely due to differences in market structure.
As explained in Section 4, farmers in Sidama have relatively more choices of buyers thanfarmers in Jimma, meaning that additional access to the official price information throughthe AMIS could increase their price. Nonetheless, a higher selling price does notcontribute to a statistically significant increase in farm income. In Jimma, the AMIS
encourages farmers in Jimma to sell more coffee to the market, leading to higher farmincome.
Table 8. Effects of the AMIS by region
OLS
Jimma

SidamaJimma

Sidama

(i)

(ii)

(iii)

(iv)

ln(sales price/kg)

ln(sales volume)

Ratio of sales to productionln(coffee farm income)

N

IPWRA

0.03

0.11***

0.08*

0.10***(0.03)(0.04)(0.04)(0.04)

0.22***

0.05

0.21***

0.03(0.07)(0.07)(0.07)(0.07)

8.03***

0.07

8.20***

0.30(2.65)(1.94)(2.73)(1.71)

0.29***

0.11

0.34***

0.10(0.07)(0.10)(0.08)(0.08)

245

221

245

221

Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
All regressions include woreda dummies and same control variables as in Table 6,
except for Jimma dummy.

19

Next, we examine whether the effect of the AMIS differs depending on the user’s
education level using OLS. Table 9 shows that neither the AMIS dummy nor theinteraction term is statistically significant. Hence, the education level of AMIS users does
not affect users’ performance, perhaps because the price information that farmers obtainfrom the AMIS is sufficiently simple that even those with an elementary education canunderstand and process it without much difficulty. Since all the models in this table failthe joint significance test, those without interaction terms seem to be more appropriate
for measuring the effects of the AMIS.
Table 9. Educational heterogeneity of the AMIS effects: OLS
ln(sales price/kg)

ln(sales volume)

Ratio of sales to
productionln(coffee farmincome)

(i)

(ii)

(iii)

(iv)

= 1 AMIS user

= 1 info. from unofficial channels

-0.01

0.08

2.05

0.12

(0.04)(0.06)(2.24)(0.08)

-0.06

0.14*

1.13

0.01

(0.04)(0.07)(2.16)(0.09)

0.004

-0.03

0.27

-0.04

(0.04)(0.06)(2.16)(0.07)-0.09**

0.19***

1.86

0.17**(0.04)(0.05)(1.53)(0.07)-0.00004-0.01-3.97**

0.02(0.04)(0.06)(1.83)(0.08)-0.02*

0.06**

1.55**

0.04(0.01)(0.03)(0.71)(0.03)

0.67***

0.88***

0.78***

0.74***(0.05)(0.05)(0.06)(0.07)

0.01**-0.01-0.50**

0.005(0.01)(0.01)(0.24)(0.01)

0.003

0.00010.03

0.0001(0.01)(0.01)(0.37)(0.01)

Jimma dummy

-0.03

-0.07

-3.42

-0.13

(=1 live in Jimma)(0.06)(0.07)(2.41)(0.10)

1.11***

0.62*

29.99***

2.24***(0.20)(0.35)(9.16)(0.62)

Joint. (Prob>F)

0.42

0.57

0.70

0.47

R2

0.66

0.99

0.94

0.99

= 1 info. from buyers= 1 info. from extension agents= 1 info. from coop.

# of AMIS informants

Yt-1

HH’s schooling

AMIS × schooling

Constant

N

466

466

466

466

Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
All regressions include control variables such as the household head's age, age^2, and coffee farm size.

206.3 Robustness check
As a robustness check, we run a fixed effect model, random effect model, and pooledmodel using the two-year panel data from 2012 and 2013. The results in Table 10 are
consistent with those using OLS and IPWRA. The coefficients of the AMIS dummy are
insignificant for selling price in all the columns, but statistically significant and positive
for sales volume, the ratio of sales to production, and farm income.
Table 10. Robustness check
FE

RE

Pooled

(i)

(ii)

(iii)

ln(sales price/kg)
ln(sales volume)

0.01

0.03

0.03

(0.05)(0.03)(0.03)

0.16** 0.28*** 0.29***(0.08)

Ratio of sales to production

7.1(1.37)(1.11)

0.18*** 0.30*** 0.31***(0.05)

7(0.05)

2.31** 3.71*** 3.76***(1.04)ln(coffee farm income)(0.06)(0.06)(0.05)# of observations

932

932

932

# of groups

466

466

466

Policy Implications and Conclusions
Policy implications

This study evaluates the impact of the AMIS in Ethiopia on smallholder farms’
agricultural outcomes using novel data collected from 446 coffee farmers. Moreover, we
examine whether the AMIS’s effect differs depending on the market structure andfarmer’s level of education. Based on our results, we conclude that AMIS users indeed
obtain higher farm income than non-AMIS users on average. Such differences in farmincome between AMIS and non-AMIS users may be attributed to an increase in sales
volume by AMIS users rather than a rise in their selling prices. Moreover, the ratio ofsales to production is about 3% higher for AMIS users, suggesting that the Ethiopian
AMIS may enhance farmers’ market participation; this finding is consistent with those ofexisting studies (Courtois & Subervie, 2015; Mabota et al., 2003; Muto & Yamano, 2009).
21

However, unlike many studies (Courtois & Subervie, 2015; Jensen, 2007; Muto &Yamano, 2009), the AMIS user dummy does not show statistically significant effects on
farmers’ selling price per kilogram.
One reason for not having an overall impact on selling price is the regional
heterogeneity in our data. By region, the AMIS dummy shows a positive and significant
effect on farmers’ selling price per kilogram in Sidama, whereas the other dependent
variables are statistically insignificant. On the contrary, the AMIS dummy shows the
positive and statistically significant effects on farmers’ sales volume, the ratio of sales to
production, and farm income in Jimma, perhaps because of the difference in market
structure between it and Sidama. According to Minten et al. (2015), coffee farmers in
Sidama have more choices of traders and more options to sell to cooperatives than thosein Jimma. We therefore conclude that in a market dominated by a small number of traders,the AMIS may not affect farmers’ selling price. In addition, in a market in which farmers
sell less actively, the AMIS may enhance their sales-to-production ratio, as shown in Table
5. These findings together suggest that unless other market imperfections such as hightransportation costs that discourage more traders from entering the market and more
farmers from participating are addressed, farmers may not benefit fully from the AMIS.
In addition, we find no difference in outcomes among AMIS users depending on
their years of schooling. There are two possible reasons for this. First, because the average
education level is only four years and variance across farmers is minor, the benefits ofusing the AMIS do not differ markedly. Second, the type of information that farmers canobtain from the AMIS is only simple market prices. These require little knowledge toprocess and do not lead to a statistically significant difference in how farmers use suchinformation in their decision-making processes. Nonetheless, if the information provided
by the AMIS becomes more complex, the difference in AMIS benefits will widen
depending on the individual’s ability to interpret such price data.

7.2

Conclusions

In conclusion, this paper presents the findings relevant to the Ethiopian AMIS will help
to influence policies for public AMISs in developing countries to improve smallholder
farms’ agricultural outcomes. While this study contributes to the literature by examining
the impact of the AMIS on farmers’ agricultural outcomes as well as by considering theheterogeneity among farmers, we acknowledge several limitations. One is that the data
used in this study are not obtained from a random assignment of the treatment. Second,
our sample size is only 466, and we estimate heterogeneous effects across regions using22

data collected in only two zones.
Furthermore, it is beyond the scope of our data to observe the effect of the AMIS
on farmers’ long-term profit maximization decisions. Farmers may use the informationfrom the AMIS to decide on such inputs as farm size, the number of coffee trees, andfertilizers, which may affect the performance of the next cropping year. If farmers use the
AMIS to obtain information on market trends, it may positively and significantly affectusers’ profits in the long run. Therefore, future studies could use data obtained from arandom assignment and from a longer time-span.
Our study contributes to the discussion on the effect of ICT on mitigating
information asymmetry in agricultural markets by providing empirical evidence on theeffects of using a public AMIS on smallholder farms’ agricultural outcomes, while mostexisting studies analyze the impacts of privately managed AMISs or ownership. Ourfindings that the effect of AMIS varies depending on the competitiveness of market
structure suggest the importance of correcting other market imperfections in addition to
information asymmetry to benefit fully from the use of AMISs.

23

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Appendix 1. Covariate balance test: the full sample
Standardized differences

Variance ratio

Raw

Raw

Weighted

Weighted

HH’s age

0.04

-0.01

0.87

0.93

HH’s schooling

0.22

-0.01

1.05

1.02

Adults in the household

0.16

-0.01

1.27

1.02

Coffee farm size

-0.13

-0.04

0.69

0.87

Total assets

0.26

0.02

0.98

1.01

Info. from traders

-0.38

0.01

1.01

1.00

Info. from extension agents

0.19

0.01

1.92

1.03

Village dummies

0.17

0.06

0.71

0.89

Appendix 2. Covariate balance test: Jimma
Standardized differences

Variance ratio

Raw

Raw

Weighted

Weighted

Coffee farm size

-0.19

0.00

0.83

1.05

Total assets

0.37

0.01

1.58

1.22

Info. from unofficial channels

-0.05

0.03

1.31

0.87

Info. from traders

0.36

0.06

0.21

0.83

Info. from extension agents

0.49

0.01

2.98

1.03

Appendix 3. Covariate balance test: Sidama
Standardized differences

Variance ratio

Raw

Raw

Weighted

Weighted

Coffee farm size

0.22

0.01

1.14

0.90

Total assets

0.07

0.01

0.70

0.75

Info. from unofficial channels

0.49

-0.01

0.92

1.00

Info. from traders

-0.19

0.04

0.66

1.08

Info. from extension agents

-0.01

0.00

0.96

0.99

27