Discussion Papers In Economics
And Business

The impact of improved access to market information through mobile
phones usage on selling prices: Evidence from rural areas in Cambodia

Daichi Shimamoto, Hiroyuki Yamada, and Martin Gummert

Discussion Paper 14-06

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe impact of improved access to market information through mobile
phones usage on selling prices: Evidence from rural areas in Cambodia

Daichi Shimamoto, Hiroyuki Yamada, and Martin Gummert

Discussion Paper 14-06

February 2014

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe impact of improved access to market information through mobile
phones usage on selling prices: Evidence from rural areas in Cambodia
Daichi Shimamoto†‡, Hiroyuki Yamada§, and Martin Gummert††

Abstract
Monopsony is often observed in local agricultural markets in developing countries because of the highentry cost to buyers; farmers in such markets therefore sell their agricultural products at a lower price. However, thissituation seems to be changing with the diffusion of mobile phones. This paper investigates how access to market
information through mobile phone usage impacts the selling price of rice in rural areas in Cambodia. We conducted asurvey of farmers’ households concerning agricultural production processes and rice sales in 20 villages in four
provinces (Battambang, Prey Veng, Pursat, and Takeo). We find that farmers who have access to market information
through the use of mobile phones are more likely to sell their rice at a higher price. In addition, we observe that theoffers received by farmers with better access to market information through mobile phone usage tend to affect selling
prices. The results imply that improved access to market information through the use of mobile phones improves
farmers’ bargaining power against traders, enabling them to sell their rice at a higher price.

JEL Classification Codes: O12, D82, D83
Key word: Agricultural Prices, Mobile phone, Cambodia†

Graduate School of Economics, Osaka University, Japan. E-mail: d.shimamoto0407@gmail.com
Research Fellow, Japan Society for the Promotion of Science, Japan.
Osaka School of International Public Policy, Osaka University, Japan. E-mail: hyamada@osipp.osaka-u.ac.jp††
International Rice Research Institution, Philippines. E-mail: m.gummert@irri.org* We are deeply grateful to Tsunehiro Otsuki and Masaru Sasaki, Hiromasa Matsuura, and the participants at the seminars at Osaka
University for their helpful comments and discussions. We would also like to thank Dr, Meas Pyseth and Vichet Sorn for providing
us with useful information on rice farming in Cambodia. All remaining errors are our own.
‡
§

1.

Introduction
Monopsony is widely observed in local agricultural markets in developing countries

because of the high entry cost of buyers such as traders. As a result, farmers tend to sell theiragricultural products at less than the wholesale price in the local agricultural market.

This situation has changed with the diffusion of information communication technologies(ICTs). These technologies allow farmers to acquire information on market outcomes such as selling
prices in other markets, enabling them to sell their agricultural products in other markets at higher
prices. Since one of the widely used ICTs, the mobile phone, is relatively inexpensive and does not
require an urban environment, it is used by many people in rural areas. In fact, 50–60% ofhouseholds in rural areas in countries such as the Dominican Republic, Guyana, and Swaziland own
mobile phones (Jensen, 2010). If the mobile phone contributes to the effective functioning ofmarkets by improving access to market information, the selling prices of agricultural products
should increase.

Previous studies have also examined the impact of ICTs on the selling price ofagricultural products. Svensson and Yanagizawa (2009) investigated the impact of market
information transmitted via FM radio on the farm-gate price of maize in Uganda and found that
informed farmers sell at higher prices. Ochiai and Yamasaki (2013) found that the diffusion ofmobile phones in rural areas in India increased the selling price of wheat for small-scale farmers.
However, SMS-based commercial services that deliver market and weather information had no
impact on selling prices in India (Marcel and Minten, 2012). Lee and Bellemare (2013) showed that
farmers in the Philippines sell agricultural crops at higher prices in cases where either a household’s
father or his spouse owns a mobile phone. However, household ownership of a mobile phone is notcorrelated with selling prices. As described, the impact of the ownership of a mobile phone or
improved access to market information on selling prices is not necessarily positive. Our contribution
in this literature is that we estimate the impact of improved access to market information throughmobile phone usage on the selling price of rice in rural areas in Cambodia. To examine the reasonsfor its effect, we first analyze how improved access to market information affects farmers’
bargaining power against buyers.

We carried out a survey of rice farmers in 20 villages in four provinces (Battambang,
Prey Veng, Pursat, and Takeo) in Cambodia from December 2012 to January 2013 as a part of an1

assessment of the post-harvest technology intervention of the International Rice Research Institution(IRRI). The IRRI has implemented such interventions to improve living standards in rural areas inCambodia since 2005. In this survey, we collected extensive information on the status of mobile
phone ownership, usage of mobile phones to access market information, agricultural activities
during the past year, marketing activities, usage conditions of post-harvest technologies, social anddemographic characteristics, and non-agricultural income sources. The survey allows us to analyze
the impact of improved access to market information through the use of mobile phones on the selling
price of rice while controlling for the effects of other determinants on selling prices such as thequality of sold rice and household characteristics.

Farmers who access market information using mobile phones are likely to sell their rice
at a higher price. However, ownership of mobile phones does not affect the selling price. Thisimplies that it is not the ownership of mobile phones but rather access to market information via
mobile phones that determines the increase of the selling price of rice. In addition, offers of farmers
with access to market information through the use of mobile phones tend to affect selling prices.
Improved access to market information through mobile phone usage increases the farmers’
bargaining power against buyers, and thus, farmers can sell rice at a higher price.

The rest of the paper is organized as follows. In the next section, we review previous
studies regarding the impact of market information on agricultural markets and on the selling pricesof agricultural products in developing countries. Section 3 explains the agricultural situations in
Cambodia and our study’s survey design. Section 4 explains the estimation methods that we apply
and examines the effect of improved access to market information through mobile phone usage on
the selling price of rice and on farmers’ bargaining power against buyers. The last section concludes.

2.

Literature review
Previous studies have primarily focused on the two channels through which market

information affects selling prices.1 We explain the impact of market information on arbitrage.
Suppose there are two markets with one agricultural product. For simplicity, price information does

1

Other papers also examine the impact of mobile phones on farmers’ behavior in developing

countries (e.g., Muto and Yamano, 2010).

2not diffuse over the two markets and transportation between these markets is costly. We consider asituation in which the price of the product differs between the two markets because of the differencein the quantity of supply of the product in the markets. If farmers can obtain the selling price in theother market and if the transportation cost is not higher than the price difference between thesemarkets, farmers in the market where the product’s selling price is lower will go to the other market
to sell the product. As a result, arbitrage occurs between the markets, the difference in the prices
between the markets will decrease, and Pareto efficiency will be achieved.

Several empirical studies examine the important role of mobile phones in market
arbitrage in local agricultural markets in developing countries. Jensen’s (2007) study provided an
excellent example for this mechanism in the market for sardines in Kerala, a southern Indian state.
Before the introduction of mobile phones, arbitrage across markets did not occur because access toprice information for sardines was limited. The introduction of mobile phones stimulated arbitrage
across markets, price dispersion across markets diminished, and the excess supply of sardines waseliminated. As a result, the profits of fishermen increased by 8% and consumer surplus increased by
6%. Aker (2010) investigated the impact of mobile phones in millet markets in Niger. Since mobile
phones facilitate traders’ search activity in markets, the expected impact of mobile phones is almost
the same as in the case of Jensen (2007). Aker found that price dispersion declined by 10–30% andtraders’ profits increased by 29%, leading to an increase in consumer welfare. Goyal (2010)
analyzed the impact of an internet kiosk that provided information on the wholesale price of market
performance in the central Indian state of Madhya Pradesh. Her result supports the proposition thatmarket information improves efficiency in market performance. However, findings by Marcel and
Minten (2012) for Maharashtra, a state in western India, contradicted this proposition. They
conducted a randomized controlled experiment to deliver market information—including price andweather information—to farmers. Their intervention had no impact on the price variation ofagricultural products.

Next, we explain the impact of mobile phone usage on the market power of buyers undermonopsony in the local agricultural market. Because of the high entry cost for buyers (e.g., traders
must bear fixed costs such as for storage facilities and vehicles for transportation as well as runningtransportation costs such as gas or access to credit for the purchase of agricultural products), theentry of buyers is limited in the local agricultural market. Monopsony occurs in local agricultural
product markets, where farmers sell their agricultural products at a lower price (Jensen, 2010).

3

Market information may lower the degree of monopsony in local agricultural markets. Farmers whoare informed of the selling prices in other agricultural markets sell their product in these othermarkets2 if the transportation is not more costly than the difference in the selling prices between themarkets. Thus, buyers in the local agricultural market must set a higher price than what they offeredpreviously, and therefore, buyers’ monopsony power in the local agricultural market declines (Goyal,2010). In addition, mobile phones allow farmers to communicate with traders. Since thiscommunication lowers the search cost of traders, the number of traders who enter the local
agricultural market increases. As a result, the traders’ monopsony power declines in the local
agricultural markets (Jensen, 2010). Hence, mobile phones allow farmers to receive a higher pricefor their output.

A number of empirical papers have investigated the impact of better access to market
information on the selling price. Svensson and Yanagizawa (2009) analyzed the impact of market
information transmitted via local FM radio stations on the farm-gate prices of maize in Uganda.
They found that informed farmers sell at a higher farm-gate price. Ochiai and Yamasaki (2013)
estimated the impact of the diffusion of mobile phones on the selling price of wheat in rural areas inIndia and found a positive impact on the selling price for small-scale farmers. Their interpretationsof these results are that the information gained through FM radio or mobile phone usage improves
farmers’ bargaining power against buyers. Marcel and Minten (2012) investigated the impact ofmarket information through the SMS messaging service of mobile phones on the selling prices ofagricultural products. However, they found SMS messaging to have no impact on the selling prices.
Lee and Bellemare (2013) analyzed how mobile phone ownership affects the prices that farmers
receive for their cash crops in the Philippines. They found that the selling price increases in cases
where a household’s father or his spouse has a mobile phone, while farmer ownership of a mobile
phone did not affect the selling price. Their results imply that the purpose of mobile phone usage isrelated to whether farmers sell their products at a higher price or not.

Our approach differs from the previous studies in two respects. First, in the survey, we
asked not only about farmers’ ownership of mobile phones, but also whether they access market

2

Farmers usually sell their products to traders at the local monopsony market, whereas the traders sell their purchases towholesalers in a competitive market. If farmers can sell their agricultural products directly at the wholesale market, they can sell
their goods at a competitive price.

4

information at the time they sell their rice. Thus, we can directly analyze the impact of improved
access to market information through mobile phone usage on the selling price of agricultural
products. Second, we investigate how improved access to market information through mobile phone
usage improves farmers’ bargaining power against buyers. Previous studies examined therelationship between access to market information and the selling prices of agricultural products.
However, they did not specify in detail the reasons why improved access to market information
increases the selling price of agricultural products.3

3.

Data
Rice farming is an important income source in Cambodia. Rice is a main cereal crop, and

80% of farmers in Cambodia are engaged in rice farming, which covers approximately 80% of
Cambodia’s total cultivated land. According to the World Development Indicators of the World
Bank, 4 the percentage of value added by the agriculture sector to GDP was 37% in 2008.
Small-scale farmers’ production systems and the lack of organized rice farmers associations lower
farmers’ bargaining power against buyers. Therefore, the selling prices of rice in rural areas tend tobe lower than those in urban areas; this creates a profit margin from arbitrage (El-Noush, 2010). In
Cambodia, buyers earn 10–15% of the market price as margin (UNDP, 2004).

Many farmers sell their rice immediately after harvest, while others sell their rice after
drying. This might be related to limited storage facilities (El-Noush, 2010). In addition, they are
under pressure to sell their rice as soon as their crop is harvested because farmers are usuallycontracted to pay back their debt immediately following the harvesting season. In fact, 25.9 % offarmers borrowed money for farming in wet season in our sample. Therefore, they tend to sell theircrop at a price offered by local buyers such as grain traders instead of searching for buyers who
could offer a higher price.

3

Although previous studies have attempted to identify the causal effect of access to market information on selling prices by using

natural experiments (e.g., Svensson and Yanagizawa (2009) applied the difference-in-difference method) or field experiments (e.g.,Marcel and Minten (2012) applied the randomized control trial method), these studies could not identify why access to market
information raises selling prices. It is necessary to collect not only farmer information but also buyer information to identify themechanism.
4

Accessed on December 28, 2013. http://data.worldbank.org/data-catalog/world-development-indicators

5We conducted a survey of farmers in 20 villages in four provinces (Battambang, Prey
Veng, Pursat, and Takeo) from December 2012 to January 2013 to assess the post-harvest
technology interventions that the IRRI implemented to improve living standards in rural areas inCambodia. The survey period corresponds to the one-month period following the wet season
because almost all of the rice farmers in Cambodia are engaged in rice farming during this period.
We collected information on farmers’ ownership of mobile phones, usage of mobile phones toaccess market information, agricultural activities during the past year, marketing activities, usage ofpost-harvest technologies, social and demographic characteristics, and non-agricultural income
sources. In the survey, we selected farmers randomly in each village and invited them to a public
place in the village. Enumerators were assigned to each farmer. Each enumerator started theinterview at a distance from the other enumerators to avoid the influence of other farmers’ responses.
We requested that the selected farmer be the household head or his wife. The enumerators asked thefarmers each question orally to avoid any misunderstanding. Each respondent received a gift at theend of the interview.

Since the primary purpose of the survey is to assess the mid-term impact of these
post-harvest interventions, the surveyed villages were divided into treatment villages and control
villages. Treatment villages received post-harvest technological equipment5 from the IRRI while
control villages did not. We collected the same information from both groups for our analysis.

Two types of rice seed are predominantly used in Cambodia: traditional variety seed andmodern variety seed. The former is an indigenous variety that farmers have used for a long time; thelatter is a hybrid introduced to Cambodia in the 1990s (FAO, 2002). The selling prices of traditionalvariety rice are usually higher than those of modern variety rice—this may reflect Cambodian
people’s taste. Cambodia has three agricultural seasons: dry, early wet, and wet. The seed types havedifferent tolerance levels: farmers can sow the traditional variety only during the wet season, while
the modern variety can be sown in all seasons.

5

The provided post-harvest technologies in the project include combined harvesting services, mechanical dryers, hermetic storage

systems (including 50 kg Super Bags (SB)), granary improvements, rice milling improvements, moisture meters, scales, cleaners,
and thermometers.

6

Our survey asked specifically when farmers sold their rice. According to our data, most
farmers sold their rice immediately after the harvest rather than after drying the rice (285 cases ofsales were immediately after harvest, while 68 cases were after drying). This is consistent with the
anecdotal evidence we mentioned in section 3 (El-Noush, 2010). In our main analysis, we use thesample of farmers who sold their rice immediately after harvest.6

Although we surveyed 349 households, not all farmers sold rice in the past year (somefarmers produce rice for self-consumption). In our sample, 162 farmers sold rice after harvest at least
once in the past year.7 Table 1 shows the descriptive statistics of the farmers. The average age of ahousehold head is 49 years old. Household heads have completed an average of 4.9 years ofeducation. The share of male heads in our sample is 82.1%. The average number of years of rice
farming is 28.1 years. The average total non-agricultural income is 3.2 million riel.

Table 2 displays the descriptive statistics of the variables related to rice sales (including
the characteristics of rice and rice plots). The average selling price of rice is 958.0 riel/kg, and 21.3%
of farmers’ offers affect the selling price. Regarding the usage of mobile phones to access market
information, 80.5% of farmers use mobile phones to acquire market information before selling theirrice. Regarding the types of rice over seasons in our sample, 44.9% of rice was the traditional variety
planted in the wet season, 5.5% was the modern variety planted in the wet season, 21.0% was themodern variety planted in the early wet season, and 28.7% the was modern variety planted in the dry
season. Farmers sold an average of 2,744 kg of rice. Average rice plot size was 1.2 ha. Regarding thesoil type of the plots, the shares of loam, clay, and sand are 86.0%, 14.0% and 0.4%, respectively.

4.

Estimation method and results

4.1

Impact on the selling price of riceFirst, we investigate the effect of improved access to market information through the useof mobile phones. We estimate the following model using ordinary least squares,

6

If we include both types of farmers in the sample—those who sell immediately after harvest and those who sell after drying—wecannot control for the quality of the rice produced according to the soil quality of plots because farmers aggregate rice after drying.
7

230 farmers sold their surplus rice at least once during the past year.

7

𝑝!"# = 𝛽! + 𝛽! 𝑚𝑜𝑏𝑖𝑙𝑒!"# + 𝑧! + 𝑢!"#$ ,(1)

where 𝑝!"#$ is the log of selling price of rice that farmer i grows in plot j at period t; 𝑚𝑜𝑏𝑖𝑙𝑒!"#indicates whether farmer i used a mobile phone to acquire market information before selling rice
grown in plot j at period t; 𝑧! is the village fixed effects; 𝑢!"#$ is the error term. Since common
idiosyncratic

shockssuchasweatherinavillagemayoccur,

we

estimatetheheteroscedasticity-consistent standard error, accounting for clustering within villages. Equation (1)allows us to control for the (time-invariant) characteristics of each village (e.g., accessibility tomarkets in urban areas) and to estimate the impact of the degree of access to market information on
the selling price.

The estimated result of Equation (1) is shown in column 1 of Table 3. The coefficientsfor village fixed effects are not shown in Table 3 to save space. The estimated coefficient for𝑚𝑜𝑏𝑖𝑙𝑒!"# is positive and significant. This implies that farmers who use mobile phones to acquire
market information tend to increase the selling price by 4.4 %. However, other factors are omitted in
Equation (1), resulting in omitted variable bias in the estimated coefficient of 𝑚𝑜𝑏𝑖𝑙𝑒!"# . For
example, the educational level of farmers is omitted in Equation (1). Educated farmers tend to use
mobile phones to acquire market information and sell their rice at a higher price. In this case, theestimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# in Equation (1) is biased upward because of the effect ofeducational level. Another factor may induce bias in the estimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# inEquation (1). Farmers who acquire market information using mobile phones choose a better qualityof rice to sell and thus sell it at a higher price. As a result, the estimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# isbiased upward because of the effect from the better quality of rice.

To remove these biases, we add explanatory variables such as farmers’ characteristics
and rice quality to Equation (1). Equation (1) then becomes𝑝!"#$ = 𝛾! + 𝛾! 𝑚𝑜𝑏𝑖𝑙𝑒!"# + 𝛾!   𝑠𝑒𝑙𝑙𝑖𝑛𝑔!"# + 𝛾!   𝑋! + 𝑧! + 𝑒!"#$ ,(2)

where 𝑠𝑒𝑙𝑙𝑖𝑛𝑔!"# is a vector of variables directly affecting the selling price, such as the variety ofrice, quantity of rice, size of the plot in which the rice was grown, and the soil quality of the plot;  𝑋!
is a vector of farmers’ characteristics (age of household head, education of head, years of rice
farming, sex of head, non-agricultural income in the past year); 𝑒!"#$ is the error term. We also8

estimate the heteroscedasticity-consistent standard error, accounting for clustering within villages.
Equation (2) allows us to control for the characteristics of the quality of rice, farmer characteristics,
and village characteristics.

The estimation results for Equation (2) are shown in column 2 of Table 3. We do notreport the coefficients for the village fixed effects in Table 3 to save space. Along with the estimatedresults of Equation (1), the estimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# is positive and significant, but themagnitude of the estimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# is greater than that for Equation (1). Thisimplies that other uncontrolled determinants of the selling price of rice in Equation (1) are correlatedwith both the usage of mobile phones and the selling price of rice.

The selling prices of traditional variety rice are higher than those of modern variety rice.
The selling prices of rice grown in plots with sandy soil are significantly lower. The quantity of ricesignificantly increases the selling price. This result is consistent with the anecdotal explanation in
El-Noush (2010) that small-scale farmers tend to sell their rice at lower prices. However, it isdifficult to explain why small-scale farmers sell at lower prices. Household characteristics are also
found to affect the selling price: the coefficients for the age of the household head and total
non-agricultural income are positive and significant.

Robustness Check
While most farmers sell their rice to grain traders, some sell to millers or others. Thus far,
we have not distinguished to whom the farmers sold rice8. Here, we limit the sample to farmers who
sold rice to grain traders to estimate Equation (2). Grain traders usually buy rice in villages. The
following estimation corresponds to how improved access to market information through mobile
phone usage affects the selling price in the local agricultural market. The result is shown in column 3
of Table 3. The estimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# is positive, while its significance level is lower
than that in column 2 of Table 3.

Thus far, we have focused on the sale of rice immediately after harvesting, although some
farmers in our sample sold rice after drying. We now check whether our result is robust with theaddition of farmers who sold rice after drying to the sample used in column 2 of Table 3. However,

8 In our sample, 237 buyers are grain traders, 6 buyers are miller and 29 buyers are others.

9we cannot control for the conditions of the plots in which rice grows because farmers aggregate riceafter drying. Thus, we replace plot characteristics such as plot size and soil quality with a dummy
variable indicating whether or not farmers sell rice after drying in Equation (2) and estimate a
modified version of Equation (2). The result of this estimation is shown in column 1 of Table 4. The
estimated coefficient for mobile phone usage is positive and significant. In addition, we limit the
sample used for column 2 in Table 4 to farmers who sold rice to grain traders and estimate themodified version of Equation (2) again. The result of the estimation is presented in column 2 ofTable 4. The estimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# on the selling price is also positive and significant.

Falsification test
As indicated above, improved access to market information through mobile phone usage
is positively correlated with the selling price while controlling for rice quality and farmers’
characteristics. However, an unobservable heterogeneity of farmers may affect both the usage ofmobile phones to access market information and the selling price because whether or not farmers use
mobile phones to acquire market information is the farmers’ choice. Thus, the conditional
independent assumption (CIA) as the necessary condition to identify the hypothesized causality may
not be satisfied (Angrist and Pischke, 2008). If the CIA is violated, the unobservable heterogeneityof farmers results in a bias in the estimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# in our estimation results.

For example, an unobservable difference between mobile phone owners and non-owners
may appear in the estimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# in Equation (2). If the unobservabledifference is correlated with the selling price of rice, we are unable to isolate the effects of improved
access to market information through mobile phone usage from the unobservable differences among
the statuses of mobile phone owners. To test this possibility directly, we replace the variable of𝑚𝑜𝑏𝑖𝑙𝑒!"# with a dummy variable indicating whether the farmer owns a mobile phone or not in
Equation (2) and estimate this modified version of Equation (2). The estimated result is indicated incolumn 1 of Table 5. The estimated coefficient for ownership of a mobile phone is positive but
non-significant. This implies that the unobservable heterogeneity in the differences among thestatuses of mobile phone owners does not cause a bias in the estimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# inour estimation results. This is consistent with the result of Lee and Bellemare (2013), who found thatthe status of farmers’ ownership of mobile phones is not correlated with the selling price in thePhilippines.

10

The effects of other unobservable factors may cause a bias in the estimated coefficientfor 𝑚𝑜𝑏𝑖𝑙𝑒!"# in our estimation results. Farmers who usually take market information into accountmay sell their rice at a high price regardless of their usage of mobile phones to acquire market
information. In this case, the effects of the unobservable characteristics of these farmers cause aselection bias in the estimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# because of a violation of the CIA. Here, weinvestigate whether or not the effects of these farmers affect the selling price of rice irrespective of
the usage of mobile phones to acquire market information. These farmers are expected to ask theirrelatives or other farmers about market information and to sell their rice at a higher price. In this case,farmers’ source of market information should be positively correlated with the selling priceregardless of mobile phone usage for access to market information. To test this possibility, we
replace the variables for 𝑚𝑜𝑏𝑖𝑙𝑒!"# in Equation (2) with a dummy variable indicating the answer tothe question, “What is your source(s) of information on output price?” In particular, we focus on two
sources: relatives and other farmers. The results are shown in columns 2 and 3 of Table 5. The
estimated coefficients of information sources are not significant. These results imply that thesefarmers’ behavior does not cause a bias in the estimated coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# .

4.2

Impact on farmers’ bargaining power against buyers
We now proceed to the second empirical analysis. As we have shown, improved accessto market information through mobile phone usage increases the price of rice; however, at this point,
we still cannot specify why this is. In this subsection, we analyze whether or not access to market
information through the use of mobile phones improves farmers’ bargaining power against buyers.
Previous studies have explained the positive effect of market information on selling price as beingcaused by an improvement of farmers’ bargaining power against buyers (Ochiai and Yamasaki,
2013; Svensson and Yanagizawa, 2009). However, they did not directly analyze the effect of market
information on bargaining power against buyers.

In our survey, we asked whether farmers’ offers affected the selling price of rice at thetime of sale. If farmers’ bargaining power against buyers is high at the time of sale, farmers’ offer
should affect the selling price. We use a dummy variable for whether farmers’ offers affect the11selling price as an indicator of farmers’ bargaining power against buyers.9 We replace the selling
price, the dependent variable in Equation (2), with the dummy variable for the indicator of farmers’
bargaining power against buyers. We estimate the modified version of Equation (2) using the probit
model. The average marginal effect of the coefficient for 𝑚𝑜𝑏𝑖𝑙𝑒!"# is shown in column 1 of Table
6. It is positive and significant. 10 This implies that offers of farmers with access to market
information through mobile phone usage affect the selling price. The result is robust even when welimit the sample to farmers who sold their rice to grain traders (see column 2 in Table 6). Thus,
improved access to market information through mobile phone usage improves farmers’ bargaining
power against grain traders, and consequently, farmers are able to sell their rice at a higher price.

5.

Concluding remarks
In developing countries, monopsony is widely observed in local agricultural markets

because of high entry costs, and thus, farmers sell their agricultural products at lower prices.
However, this trend has changed recently with the diffusion of mobile phones. Mobile phone usage
contributes to the effective functioning of markets by dispersing market information such as theselling prices of agricultural products. As a result, the selling prices of agricultural products increase.
In this paper, we have investigated the impact of improved access to market information throughmobile phone usage on the selling price of rice in rural areas in Cambodia, controlling for
determinants of selling price such as the quality of rice and the characteristics of farmers. Moreover,in order to examine why improved access to market information through the use of mobile phones
increases the selling price of rice, we have analyzed how it affects farmers’ bargaining power againstbuyers regarding the selling price of rice. Our analysis is based on data collected by the authors in 20
villages in four provinces (Battambang, Prey Veng, Pursat, and Takeo) from December 2012 toJanuary 2013.

We found that improved access to market information through mobile phone usage
increases the selling price of rice. However, because of the limitations of our research design, we

9

It is difficult to measure bargaining power between farmers and buyers exactly. We follow an indicator of bargaining power

within households in the context of family economics (e.g., Li and Wu, 2011; Reggio, 2010).
10

The main result does not change even if we use a linear probability model. The results are available from theauthors upon request.

12

were not able to statistically examine the causal relationship between improved access to market
information through mobile phone usage and the selling price of rice. However, our results arerobust even when we limited the sample to farmers who sell rice to grain traders. The falsification
tests show that neither the effect of the unobservable heterogeneity of the ownership of mobile
phones nor the effect of farmers who seek market information affects our results. Thus, our results
are likely to provide the causal effect of the degree of access to market information on the selling
price. In addition, improved access to market information through the use of mobile phones
improves farmers’ bargaining power against grain traders. Therefore, access to market information
through mobile phone usage allows farmers to sell their rice at higher prices.

13

Table 1. Descriptive statistics of households
VariableAge of headSampleMean

Std.Dev

162

48.96311.886

Education of head (year)

162

4.889

3.044

Sex of head (1 = male, 0 = female)

162

0.821

0.385

Years of rice farming
Total nonagricutural income (riel)

162

28.117

12.662

162

31923243479706

Notes
1. As of 31 December 2012, US$1 was equivalent to 3,909.4 Cambodian Riel.

14

Variable
Dependent variable

Table 2. Descriptive statistics of plot level
SampleSelling price (riel/kg)
Did farmer offer affect selling price? (0 = yes, 1 = no)
Independent variables
Whether farmers use mobile phone for gain market
information (1 = yes, 0 = no)
QualityModern Variety in dry season
Modern Variety in early wet season
Modern Variety in wet season
Traditional Variety in wet season
Quantity (kg)Plot size (ha)Type of soil
Clay
Sandy
Loam
Other

15

Mean

Std.Dev272
268

957.960
0.213

194.476
0.410

272

0.805

0.397

272
272
272

0.287
0.210
0.055

0.453
0.408
0.229

272
272
272

0.449
2744.36
1.267

0.498
2840.157
1.086

272

0.140

0.347

272
272
279

0.004
0.860
0.070

0.061
0.347
0.255

16

0.269 ***
0.030 **
-0.010

Traditional Variety in wet season

Quantity (log)Plot size (ha)

0.025

Sand

Other

0.124

Adjusted R squared

0.420

267

0.036

Notes
1. Standard error in parenthesis is clustered at village level.
2. Sample is limited to selling against grain traders in Column (3) of Table 3.
+ significant at the 15% level; * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

272

Total nonagricutural income (log)Sample0.032 **
YES

0.000

Years of rice farming
YES

0.015

-0.025Sex of head (1 = male, 0 = female)

Village fixed effect

0.001-0.002

Education of head (year)

0.003

0.002 *

0.001

0.025

0.025

0.020

0.018

0.013

0.056

0.041
0.044

0.025

Age of head

Characteristics of farmers-0.013-0.092 ***Clay

Type of soil (reference group = loam)

0.059
0.068

0.049 *

Column(2)Coef.
Std. Err

Modern Variety in early wet season
Modern Variety in wet season

Quality

0.044 **

Whether farmers use mobile phone to obtain market information (1 = yes, 0 = no)0.016

Column(1)Coef.
Std. Err

Independent variables

Table 3. Regression result for the impact of mobile phone usage on selling priceDependent variable : selling price (log)

235

YES
0.443

0.022

-0.001-0.031-0.005

0.002

0.030

-0.089 **-0.027-0.0120.034 **

0.267 ***

0.053
0.069

0.045 +

0.018

0.001

0.041

0.004

0.002

0.028

0.038

0.027

0.017

0.013

0.055

0.042
0.064

0.027

Column(3)Coef.
Std. Err

17

292
0.400

0.027
0.011

332
0.383

0.075 **
0.019 *

0.002 +
0.001
-0.005 +0.003-0.0160.031-0.001
0.001
0.018
0.015
YES

0.023
0.008

0.002 +
0.001
-0.0030.003-0.0040.027+-0.0010.001
0.036 *** 0.008
YES

0.079 ***
0.014 +

0.038
0.052
0.049

0.073 *
0.084
0.268 ***

0.071 **
0.085 **
0.262 ***

0.034
0.039
0.047

Column(2)Coef.
Std. Err
0.059 ** 0.025

Column(1)Coef.
Std. Err
0.047 *
0.026

Dependent variable: selling price (log)Adjusted R squaredNotes
1. Standard error in parenthesis is clustered at village level.
2. The sample includes selling of both post-harvesting and post-drying.
3. The sample is limited to selling against grain traders in Column (2) of Table 4.
+ significant at the 15% level; * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

Total nonagricutural income (log)Village fixed effectSampleWhether selling follows drying or not (1 = yes,no = 0)
Quantity (log)Characteristics of farmers
Age of head
Education of head (year)
Sex of head (1 = male, 0 = female)Years of rice farming

Whether farmers use mobile phone to obtain market information (1 = yes, 0 = no)
QualityModern Variety in early wet season
Modern Variety in wet season
Traditional Variety in wet season

Independent variables

Dependent variable : selling price (log)Table 4. Robustness check

18

0.195

0.961
269

Std. Dev

0.032

Mean

0.015

Column (1)Coef.
Std. Err

0.542

Mean

-0.006273

0.4991

Std. Dev

0.018

Column (2)Coef.
Std. Err

0.683

Mean

0.009

273

0.466

Std. Dev

0.021

Column (3)Coef.
Std. Err

Notes
1. Standard error in parenthesis is clustered at village level.
2. control variable is same as Column(2) in Table 3.
+ significant at the 15% level; * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

SampleDescriptive statistics of independent variable

other farmers (1 = yes,0 = no)relative (1 = yes, 0 = no)

Whether farmers own mobile phone or not (1 = yes, 0 = no)
Whats is your source(s) of infromation on output price?

Independent variables

Dependent variable: selling price (log)Table 5 . Falsification test

19

0.363 ***

Averagemarginal
effect0.051

DeltaMethod
Std. Err.

0.442 ***

Averagemarginal
effect0.094

DeltaMethod
Std. Err.

Column (2)Sample
252
212
Notes
1. Standard error in parenthesis are clustered at village level.
2. control variables are same as those in Column(2) in Table 3.
3. Sample is limited to selling against grain traders in Column (2).
+ significant at the 15% level; * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

Whether farmers use mobile phone to obtain market information (1 = yes, 0 = no)

Independent variables

Column (1)Table 6. Regression results for the impact of mobile phone usage on bargaining
Dependent variable: Did your offer affect selling price? (yes = 1,no = 0)