Discussion Papers In Economics
And Business

Does the heterogeneity of project implementers affect the program participation of
beneficiaries? : Evidence from rural Cambodia

Ayako Wakano, Hiroyuki Yamada, and Daichi Shimamoto

Discussion Paper 14-21

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANDoes the heterogeneity of project implementers affect the program participation of
beneficiaries? : Evidence from rural Cambodia

Ayako Wakano, Hiroyuki Yamada, and Daichi Shimamoto

Discussion Paper 14-21

May 2014

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANDoes the heterogeneity of project implementers affect the program participation of
beneficiaries? : Evidence from rural Cambodia
Ayako Wakano☨, Hiroyuki Yamada §, and Daichi Shimamoto☨☨

Abstract
Using the dataset collected for assessment of a post-harvest technology project in rural
Cambodia, we focused on the heterogeneous social preferences of project implementers,
often overlooked in the literature of Randomized Controlled Trials (RCT). Our study
focuses on the “implementer effect” on program participation for the treated farmers. We
show the possibility that heterogeneous program participation of ordinary farmers acrossthe treated villages could be induced due to heterogeneity in the characteristics of project
staff. In particular, we show the altruism of project staff, measured by the dictator game,consistently increases participation and the number of participations in the training
sessions of beneficiaries. This type of heterogeneity in project staffs’ preferences acrosstreatment sites might yield noises in mean effects estimated using RCT methods
conducted at a certain cluster level, which undermines the external validity of theestimated results. While RCT methods are very powerful tools for many program andpolicy evaluations, we cannot emphasize too much the importance of the way how an
actual project is implemented.

JEL code: O22, C93, D03,
Keywords: social preference, program evaluation, heterogeneity in treatment effects

Graduate School of Economics, Osaka University, JAPAN, E-mail address: qgp802wa@student.econ.osaka-u.ac.jp☨§

Osaka School of International Public Policy, Osaka University, JAPAN, Email address: hyamada@osipp.osaka-u.ac.jp☨☨

School of Political Science and Economics, Waseda University, JAPAN, Email address d.shimamoto0407@gmail.com

1. Introduction
In program evaluation studies, empirical researchers often use Randomized
Controlled Trials (RCT) to estimate the treatment effects of development projects. However,one weakness of RCT is its’ exclusive focus on mean impact (Ravallion 2012; Deaton 2010).
Criticisms indicate that positive and significant mean impact does not guarantee that the
program as a whole benefits most of the target population of a project. It may be the case thatthe program has huge benefits for only a small portion of the population. Heckman et al.
(1997) emphasized the importance of looking at the heterogeneous program impacts among
the treated population, pointing out that evaluating a program solely by the mean rests on
the assumption that "the undesirable distributional aspects of programs are either
unimportant or are offset by transfers governed by a social welfare function." In this way, theliterature acknowledges the importance of looking at heterogeneity in program impacts,though the mean impact is the only statistic that we obtain from an RCT on those treated in
the experimental population without posing further assumptions. Secondly, the literature has
extensively studied the case of heterogeneous responses of those targeted by the random
program assignment and hence, full compliancy could not be assured in RCT settings(Heckman et al. 1997, 2006).
In this paper, the sources of heterogeneity that we focus on among the treated are theheterogeneous characteristics of project implementers. Even if program is randomly placed
prior to the project, that ex-ante randomness might not assure homogeneous characteristics ofproject staff. A source of heterogeneity may be what Banerjee and Duflo (2008) term
“implementer effects.” This is realized when implementers of the program, such as local
partners or project staff, are different from each other in the first place. This situation is notuncommon for development projects, and while some development projects seem less affectedby project staffs, others seem directly involve project staffs in implementation stage. This
heterogeneity of project staff could have greater influence especially when those project
implementers are involved intensively in the process of implementation of a development
project. Indeed, project implementers in villages are often not homogeneous in qualifications
and preferences (Ravallion 2012). Hence, the same outcome could not be expected across all
sites even if the other characteristics of the treated sites are very similar.
These “implementer effects” were considered an issue in the discussion of external
validity of the experimental results, since not all counterparts accept randomization as theimplementation manner and not all project implementers are randomly selected. It could be
inferred that randomized programs are only carried out by limited types of organizations. To
eliminate this “specific implementer effect” and to recover the external validity of the result, it
has been noted that replicating randomizations with various implementers is not a1

constructive remedy (Banerjee and Duflo 2008). Nevertheless, there still remains a gap in theliterature as to what extent the ‘implementer effect’ influences the results of projectinterventions. In our study, we quantify the extent to which the characteristics ofimplementers affect the program participation, by testing whether social preferences of"implementers" affect the program participation of those farmers in the treatment groups.
In six provinces in rural Cambodia, a post-harvest project of the International Rice
Research Institute (IRRI) was implemented to improve post-harvest practices andconsequently, raise the income of farmers. One approach used was to diversify extension
sources and collaborate with “key farmers” or farmer leaders who then provide extension
services to other farmers in the village. With its research for development stance, this projectprovides a case for us to examine the effect of the differences in project implementers acrossthe treated groups.
We used household data collected in rural villages in Cambodia between December
2012 and January 2013 where the IRRI had intervened in post-harvest technology extension
programs since 2005. This project has provided post-harvest technology items to rural
farmers in the treatment villages, where two local residents were assigned as the project staff
in each village. They implement the project, encouraging their own villagers to participate in
the training sessions on how to use post-harvest technology items. These local project staffs
are called “key farmers.” Indeed, they play a key role in spreading the post-harvest
technologies among the villagers in the treatment villages. With the experimental measuresfrom the dictator game conducted for the key farmers, our empirical result shows the level ofaltruism of key farmers is highly associated with the beneficiaries’ participation rate and thenumber of participations in the program. Although we do not formally implement the
program evaluation of the post-harvest technology project, this type of heterogeneity acrosstreatment sites might yield noises in mean effects estimated using RCT methods conducted ata certain cluster level. While RCT methods are very powerful tools for many program andpolicy evaluations, we conclude how a project is implemented has great significance andneeded to be considered in program evaluation studies.
The rest of the paper is organized as follows. The next section overviews the related
literature. Section 3 explains the background of the post-harvest technology project. Section 4
provides the conceptual framework, mainly focusing on the social preferences of key farmers.
Section 5 explores the data and the experiment design. Section 6 shows the empirical analysis
and results. The last section provides conclusions and policy implications.
2. Preceding studies2

2.1 Program evaluation studies
In program evaluations, one of the virtues of utilizing RCT methods is that weassume unobserved heterogeneity affecting potential outcomes is very similar (or equal) on
average for the treated and control groups. However, even if the assignment is random, thisdoes not guarantee the same program implementation manner among the treated groups. In
fact, in certain types of programs where project staff are intensively involved in project
implementation, the treatment outcome can be heterogeneous because of the heterogeneousworking attitudes of project staff, their level of commitment, and skills. If the focus is solely toestimate the “average” treatment effects of the project of interest itself, then it should not be a
problem. However, if the degree of heterogeneous impacts is largely influenced by the
program implementation manner across the treated groups, the estimated outcomes might
not be replicable in different settings, which undermines the external validity of theestimated results.
In the literature, many RCT programs are randomized in certain clusters such as at
the school, village, hospital, and districts level, where local project implementers are actively
enrolled. For example, Jensen (2012) report evidence from India where “experienced
recruiters” were employed, while Angrist and Lavy (2009) report a randomized cash incentive
program for low-achievers of high school students in Israel where “teachers and school
administrators” are actively enrolled. Duflo et al. (2012) reported an RCT conducted at theschool level in Kenya, hiring a “local community teacher.” For those studies designed to obtain“project staff” involved in implementation with a greater extent, the different characteristics
of project staff at each cluster could be a crucial source of heterogeneous impacts among thetreated. Our study focuses on heterogeneous project participation that emerged through theimplementation process, which is highly associated with the social preferences of the project
implementers.
2.2 Preceding studies on measured social preferences and real life pro-social behavior
Our study is closely related to preceding empirical studies that tested whether themeasured social preferences in the laboratory or field experiment are associated with subjects’
pro-social behavior in real life (Karlan 2005; Benz and Meier 2008; Carpenter and Cardenas
2008; Carpenter and Myers 2010; Carter and Castillo 2011; Seki and Carpenter 2011; Alberto2013). Although various pro-social behavior in real life as dependent variables were regressedon the experimental measures in the field or laboratory experiment, none of the studies has
tested the relationship between project staffs’ social preference and their real life working
performance. In our study, we show whether measured social preferences of key farmers suchas altruism and reputational concerns can be predictors of their real-life working outcome ofproviding training sessions.
33. Program Background
3.1 Overview of the program,
The project that we studied was implemented by the IRRI funded by the Asia
Development Bank (ADB). According to the IRRI, post-harvest losses in Southeast Asian
countries are not ignorable in scale, typically 15-20% in weight loss and, when quality isfactored in, can result in a 10-30% loss of value in the market. In order to tackle thispost-harvest loss, the project in Cambodia was implemented as one component of a broader
program, covering Vietnam, the Philippines, and Cambodia. In Cambodia, the program hasintervened since 2005 in four villages in Prey Veng Province located in the eastern part andfour villages in Battambang Province, located in the northwestern part of Cambodia. From
2008, this initial project was expanded to the four additional provinces in Cambodia: Pursat,
Kampong Thom, Takeo and Kampot Provinces. As a result, as of 2012, the project has reached13,000 households. We surveyed 14 treated villages scattered in four (Battambang, Prey Veng,
Pursat, and Takeo) provinces in Cambodia between December 2012 and January 20131. Wecollected information of 235 households of rice farmers and 27 key farmers, two in each of 14
villages2.
There were two different types in the provision of post-harvest technology items: one
was provided at the household level, and the other was provided at the village level, such as
weighting scales, moisture meters, thermometers, and cleaners3. The farmers use 150-kg
weighing scales in their transactions with buyers. Moisture meters and thermometers areused to check the quality of dried grains and seeds. Cleaners are used to remove impurities
mixed with grains before broadcasting. For those four items provided at the village level, only
one of each item was provided to each treatment village, and the project staff, called key
farmers, were assigned to maintain and lend items to their own villagers after providing themwith a training session.
3-2. Project implementation: The role and characteristics of “key farmers”
In order to encourage ordinary farmers to use the provided items in the treatment
villages, two local key farmers were selected in each of the treatment villages by the
There are in total 20 treatment villages and 6 control villages under the project. We wereunable to survey two provinces (Kampong Thom and Kampot) due to budget constraints of the
project.
2 At the time of the survey, one key farmer was absent in the village called Por Chery;
therefore, the total number of surveyed key farmers is 27.
3 Other provisions from the IRRI not listed are as follows: for harvesting, mini-combine
harvesters were introduced at the selected villages; 5-ton cube or cocoons and granary
improvements were also provided at selected villages. A market information system where
price data was collected every three days in three different markets (rural, provincial, andPhnom Penh) was also set up to help farmers get better selling prices.
4
1

Provincial Department of Agriculture (PDA), the counterpart of the Ministry of Agriculture in
the central government and IRRI project staff. Locally assigned key farmers have played a
key role in spreading the technologies introduced by the project. Key farmers reported thefeedbacks about the usage of the provided item for periodical visits of PDA and IRRI staff attreatment villages. Key farmers were also asked to show their records on demonstration ortrials and villagers’ history of using the provided items. Key farmers had to submit theirwritten reports to PDA and IRRI project staff when they conducted training sessions forvillagers.
In this way, the selection of key farmers was crucial for the successful
implementation, and hence carefully performed by the PDA based on criteria such as
willingness to work with the project and abilities to communicate with villagers. Out of 27 key
farmers that we surveyed, one quarter of them were an experienced village leader or belonged
to a village leader family. Half of them had worked as a public worker before being selected asa key farmer. Over 35% of them answered that they were interested in other public worker
positions, such as a member of a development committee that exists in each commune, andover 82% of them said they would like to run for election if there were an election to select key
farmers in their village. Hence, we observed that those selected key farmers are in general
highly motivated to work for public issues.
Table 1
Table 1 shows the basic characteristics of key farmers and ordinary farmers. In termsof off farm income, key farmers earn more (an average of 8.4 million riel) than ordinary
villagers (the average is approximately 5 million riel)4. The number of members in a key
farmer’s household is similar to that of ordinary villagers, with an average of 5.4 members.
The average number of years of education of key farmers is 6.7 years, which is higher thanthat of household heads of ordinary farmers. The average age of key farmers is four years
older than that of household heads of ordinary households, implying that more experienced or
senior persons are chosen as key farmers. There were more male than female key farmers.
4. Conceptual framework: role of “key farmers” innate preference and incentive
As mentioned earlier, we assume that the heterogeneity among project staff could be
an important factor determining project participation and outcome. In this section, we explain
why social preferences, that is, altruism and image of social sanction or social reputation
affect the level of pro-social behavior of key farmers.

4

As of 31 December 2012, US$1 is equivalent to 3,909.4 Cambodian Riel.
5

In the literature, the motivations for ones’ pro-social behavior, which is costly to thembut primarily benefit others, was extensively studied. There are two categories of motivations;
one is the intrinsic motivations such as innate preferences and the other is the extrinsic
motivations such as material rewards or benefits associated with acting prosaically (Becker,
1974, Andreoni, 1989, 1990). In our context, the extrinsic incentive is not explicitly embeddedin the project that forces key farmers to commit to their roles. For example, no written official
contract exchanged between key farmers and IRRI/provincial counterparts, nor any financial
incentives are given to accomplish their roles, except a one-off payment of 10 US dollars topurchase phone cards in order to communicate with other villagers.
On the other hand, for key farmers who care about the self-image or reputation fromvillagers, there could be an implicit mechanism to make them work since they are constantly
exposed to monitoring by ordinary farmers in their village. While monitoring by PDA and
IRRI project staff is rare, ordinary villagers would notice the working attitudes of key farmers.
In this sense, their extrinsic motivation is not about the financial benefits, but social
reputation or sense of sanction from their own villagers is at work, for most of them are
greatly concerned about public issues and interested in working as public workers future.
In the literature, “anonymity effect” or “signaling effect” models propose similar typeof motivations to explain one’s pro-social behavior in the presence of others. In the anonymity
effect, models predict that people supply an increased level of labor or provide charitable
donations with the presence of others (Meer (2011), Dellavigna et al. (2012)). The signaling
model (Benabou and Tirole, 2006) specifically explains the pro-social behavior as signaling tobe perceived as an “altruistic” person in the presence or under the observation of others. The“enforced reciprocity model” by Leider et al. (2009, 2007) pays attention to the effect ofsubjects’ expectations of reciprocal favors in future exchanges. Compared to altruism, wename such social consideration as the reputation concerns or sense of sanction, which induces
pro-social behavior in the presence of others. Following the studies of Ligon and Schechter(2012) and Leider et al. (2009), we measure two motivations of altruism and reputation
concerns elicited by the dictator game experiment.
Based on the literature, both altruism and a sense of sanction or reputational
concerns could induce one’s pro-social behavior; however, which one has more explanatory
power for the variation in the level of pro-social behavior or whether they work
complimentarily or work as a substitute for providing pro-social behavior is uncertain.
Therefore, we included reputational concerns as well as altruism of key farmers at the sametime to test which effect has more explanatory power for the variation in the level of pro-social
behavior. Furthermore, we tested whether altruism and reputational concerns can function
6

complimentarily at the level of pro-social behavior by adding the interaction of the two.
5. Data
5-1. Survey design
We conducted the household survey from December 2012 to January 2013 in 14
treatment villages in four provinces, namely Battambang, Prey Veng, Pursat, and Takeo. In
each treatment village, we surveyed ordinary farmers, which amounted to 235 households intotal, and 27 key farmers. The survey was conducted by 10 IRRI recruited Cambodian
enumerators with our field team and IRRI project staff, independently from the projectintervention. To sample households randomly, we worked with PDA officials and IRRI project
staff to draw up a list of 11 to 32 households that had been randomly selected and interviewed
by the previous monitoring and evaluation surveys5. Table 2 shows the household samples
interviewed by our survey in each village.
Table 2
Interviews took place in a public space in each village, and the household head orhead’s wife was asked to complete a household questionnaire. The key farmer interview took
place at the same time as the interviews of household heads or the head’s wife. A pair of aninterviewee and an enumerator was instructed to sit apart from other pairs so that eachother’s interviews could not be heard. We were especially cautious in the interviews with key
farmers so that they could not be heard by anyone attending the household interviews since
the key farmers’ questionnaire contains the experiment section. Both key farmers andrespondent ordinary farmers received a gift at the end of the interview.
5-2. Experiment with key farmers and descriptive statistics of item-carrying key farmers
The field experiment was conducted for every key farmer during the interviews withhim/her. In order to elicit social preferences, we conducted the dictator game virtually. In the
dictator game setting, the first-mover simply makes a transfer to a passive second-mover. If
the game is anonymous, the voluntary transferred amounts are, in general, interpreted asmeasures of altruism, assuming there is no self-interested reason for the first-mover totransfer money. Following the literature of Ligon and Schechter (2012), in our setting, key
Previously, for the purpose of monitoring and evaluation of the project, household surveys
were conducted 4 times from 2005 to 2008. Details of the previous monitoring result areavailable in the report, “Report on technology adoption, lessons learned and impact of the
IRRI ADB-JFPR 9036 Project,” IRRI (2009). We attempted to resurvey all the households inearlier intervention provinces (Battambang and Prey Veng), surveyed lastly in 2008 and all
the households in later intervention provinces (Pursat and Takeo), surveyed lastly in 2009.
However, there was some attrition of households due to migration and other reasons. In suchcases, replacement households were randomly chosen in each village.
7
5

farmers were asked to play the role of dictator in three different settings of the dictator game.
The baseline setting of the dictator game is as follows: the game is played in pairs, ofwhich Player 1 is the dictator (key farmer) and Player 2 is someone specified in each game. At
the start, Player 1 receives 40,000 Cambodian riel (equivalent to 10 US dollars) as anendowment. Player 1 decides how much he/she wants to keep and how much he/she wants tosend to Player 2. Player 1 can send an amount between 0 riel and 40,000 riel to Player 2. Any
money sent to Player 2 will be doubled. Player 2 will receive any money Player 1 sent
multiplied by two, plus an additional contribution of 3,000 riel given by the project staff.
Using the above setting, we asked key farmers to play three different versions of thegame. In the first version, Player 2 (the recipient) is randomly chosen from Cambodia bylottery and the dictator does not know who Player 2 is and Player 2 does not know who the
dictator is. In the second version, Player 2 is randomly chosen from households in the key
farmer’s village by lottery. The dictator again does not know who Player 2 is and Player 2 does
not know who the dictator is, either. Finally, Player 2 is randomly chosen from households inthe village by lottery, which is the same condition as Version 2. However, Player 2 does know
who the dictator is and the amount set to be transferred by the key farmer though the dictator
does not know who Player 2 is.
According to the preceding study of Ligon and Schechter (2012), we defined theamount measured from Version 1 as “general altruism,” which is the amount of voluntarytransfer to the randomly selected person. In contrast, “villager-directed altruism” is defined
based on the amount measured from Version 2. “Reputational concern” or “sense of social
sanction” is defined as the amount measured from Version 3 minus the amount measured in
Version 2. The only difference between Version 2 and 3 is whether Player 2 knows who the
dictator is. Hence the difference in amount between Versions 2 and 3 is defined as the amountfor “social consideration” or “reputational concern” (Ligon and Schechter 2012).
Table 3In the 14 villages that we surveyed, there are two key farmers assigned to work forthe project. In each village, one of the key farmers is identified as the “Item-carrier” key
farmer who mainly keeps those provided items and lends them to ordinary farmers. In ourestimation, we used the social preference measures of 13 item-carrier key farmers, since 1
item-carrier key farmer was absent at the time of the survey. Table 3 shows the descriptive
statistics for 13 item-carrier key farmers. Both altruism and reputational concerns areexperimental measures obtained by the dictator game and have large variation in values6.
6

In the estimation, we take the logarithm for the experimental measures of reputational
8

The mean of general altruism is 4,338 riel with a standard deviation of 8,223 riel and that ofvillage-directed altruism is 5,684 riel with a standard deviation of 10,713 riel. The mean ofreputational concern is 1,846 riel with a standard deviation of 3,078 riel. This shows theminimum value of reputational concern is zero, which implies all key farmers have increased
an amount to transfer in the non-anonymous setting. In the estimation, we used thedemeaned value. The mean (in log) of each variable is subtracted from the log of the altruism
measure or log of reputational concern measure so that, in our estimation, the interpretationof the coefficient of altruism (in log), reputational concern (in log) and the interaction term are
made straightforward. The income level of key farmers and years of education vary greatly,both variables of which are included as control variables.
5-2. Descriptive Statistics
Table 4 shows the descriptive statistics on the ordinary farmers. Due to the missing
values, total effective samples for some variables are not the 235 samples that we surveyed.
Our dependent variables are of two types. One is whether the ordinary farmers have everparticipated in the training sessions conducted by key farmers. The other is how many times
they have participated in the training sessions conducted by key farmers. The mean
participation rate is 0.67, meaning that 67% of ordinary farmers surveyed have participatedin the training sessions at least once. The systematic characteristic difference between
participants and non-participants is not observed, according to the simple mean comparison
test as is shown by Table 5. As for the number of participations, the mean is 3.37 times withgreat variation. This variation cannot be explained only by difference across villages as shownby Figure 1, implying we have to control within village variations such as the characteristicsof ordinary farmers.
Table 4, 5
Explanatory variables are item-carrying key farmers’ characteristics and ordinary
farmers’ characteristics as shown in Tables 3 and 4. For ordinary farmers’ characteristics,
consultation with a key farmer is the binary answer to the question of “Are you able to consult
key farmers when you have farming-related questions?” This is a proxy variable to capture
the subjective psychological distance or level of trust from ordinary farmers to key farmers.
Previous studies have shown that having informal conversations or discussions leads toincreased cooperation and reduced extraction in the experiment (Cardenas and Carpenter2008). Physical distance is the distance between key farmers’ residences to villagers’
residences. Since the training sessions offered by key farmers is mainly held at key farmers’
concern and altruism. The calculation is performed as follows: log altruism =log(altruism+(altruism^2+1)^(1/2)). This is because some key farmers have set their transfer
amount to zero.
9

residences, controlling the distance to key farmers’ residences has great significance, as
distance is considered to be one of the costs for ordinary farmers to participate in training
sessions. The average distance between a key farmers’ house and an ordinary farmers’ house
is 410 meters with a large variance within a village. In order to control for the farmers
harvesting activities, the number of times of rice growing in one year is included. On average,
ordinary farmers grow rice 1.7 times in a year. Our province dummy captures the effect of thelength of the project intervention since four provinces have different intervention periods.
6. Empirical Analysis and Result
6-1. Estimation result from ordinary least squares estimationsFirst, we estimated the following linear regressions regardless of whether the
dependent variables are binary or continuous. A limited dependent variable model yields
nearly identical results as shown in Section 6-2. The estimated linear model is as follows:

𝑌𝑖𝑣 = 𝑋𝑣 𝛼 + 𝑃𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑣 𝛽 + 𝐻𝑖𝑣 𝛾 + 𝛿𝑝 + 𝜇𝑖𝑣
where i denotes ordinary farmer and v denotes village. 𝑌𝑖𝑣 denotes whether the ordinary
farmers have ever participated in the training sessions conducted by key farmers , or thenumber of participations in the training sessions conducted by key farmers. 𝑋𝑣 denotes thecharacteristic variables of an item-carrying key farmer in village v, such as years of educationand log of annual income. 𝑃𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑣 denotes the social preferences of item-carrying key
farmers in village v, that is, the log of altruism, reputational concern, and the interaction termof the two. 𝐻𝑖𝑣 denotes the household characteristics of ordinary farmer i in village v. Thosehousehold characteristics include the physical distance to the item-carrying key farmer, theconsultation dummy, the log of off-farm annual income, the number of rice harvests per year,
age, sex and years of education of household head and the number of household members. 𝛿𝑝denotes the provincial dummies. The standard errors are clustered at the village level.
Table 6
Table 6 shows the estimation result for training participation. The dependent
variable is binary: either zero or one. 𝑌𝑖𝑣 takes 1 if ordinary farmers have ever participatedin the training sessions and zero otherwise. We estimated four different versions of the linear
probability model (LPM): (1) base models, (2) those including key farmers’ characteristics inthe base models, (3) those including the interaction term of altruism and reputational concern
in the base models, and (4) those including both key farmers’ characteristics and interaction
terms in the base models. The base model includes altruism and reputational concern
variables of the key farmers, ordinary farmers’ characteristics and province dummies. The
10base models show that the coefficient of altruism and reputational concern are consistently
positive and statistically significant. A one percent increase in altruism leads to about a three
percent increase in the participation rate of ordinary farmers while a one percent increase in
reputational concern leads to about a one percent increase in participation rate. After adding
the physical distance between key farmers and ordinary farmers’ residences and consultation
dummy, both altruism and reputational concern remain significant. The dummy ofconsultation does not affect the participation. The sign of coefficient for physical distance isnegative, though not statistically significant.
Columns 5 to 8 of Table 6 show the estimation results of the second models including
the key farmers’ characteristics as control variables in the base model. The coefficient ofeducation years for key farmers is consistently positive and significant while the log income of
key farmers is positive, but not statistically significant. The significance of the coefficient of
reputational concern becomes marginal though the sign remains positive once key farmers’
education level and income are included. In contrast, the coefficients of altruism areconsistently positive and statistically significant at the one percent level.
Columns 9 to 12 of Table 6 show the estimation results of the third models including
the interaction term of altruism and reputational concern in the base models. The coefficientsof the interaction term as well as those of altruism are statistically significant at the one
percent level. Furthermore, the coefficients of reputational concern, though the magnitude issmaller than that of the base models, are positive and statistically significant. This suggeststhat when key farmers are altruistic and also consider their social reputation at the sametime, they are likely to be more motivated to encourage ordinary farmers’ participation in the
training sessions. In other words, altruism and reputational concern work complimentarily.
Columns 13 to 16 of Table 6 show the estimation results of the last models including
the key farmers’ characteristics and interaction term in the base models. The altruism isstatistically significant at the one percent level, though the reputational concern is positive
but not significant in some cases. Compared to the third models, after controlling for the key
farmers’ characteristics, the effect of reputational concerns becomes marginal. However, when
we conduct the Wald test for the effect of reputational concern of each key farmer, we reject
the null hypothesis that the effect is not different from zero for 9 out of 13 key farmers at thefive percent level. That is, the statistics of(Coefficient of reputational concern) + (Interaction term of altruism and reputational concern) ∗(A)

altruism𝑣

11

are significantly and positively different from zero for 9 key farmers. This result indicatesthat though the impact on participation of reputational concern on average is marginal, its
full impact is still positively significant for the majority of key farmers.
Table 7The estimation results on the number of participations in training sessions areshown in Columns 1 to 16 of Table 7. We estimate the same specification as LPM (other thanthe dependent variable). The qualitative results are slightly different from those of LPM on
participation. The main difference is that the coefficient of reputational concern is negative
although not statistically significant. In contrast, the altruism and interaction term between
altruism and reputational concern remain positive and statistically significant at the 5
percent level at least. We conducted the same Wald test for the statistics (A) above. We canreject the hypothesis that reputational concern positively impacts the number of training
sessions for the case of 4 key farmers out of 13. This suggests that for some key farmers, thereputational concern still has a significant effect on the number of participations, though on
average, the impact of reputational concern is marginal.
The consultation dummy is consistently positive and significant at the five percent
level. If ordinary farmers have consulted key farmers, that increases the number ofparticipations in training sessions by approximately 1.5 times on average. This is consistent
with the results from the literature, which shows that the more informal the interaction
between neighbors, the more they try to contribute to the community. Lastly, in theestimation of number of participations, the years of education of key farmers became negativeand significant at the 10 percent level in some specifications. This is intuitively
understandable: if key farmers are more educated, ordinary farmers have less need toparticipate in training sessions because such key farmers can provide better training sessions
to ordinary farmers, leading to a reduction in the number of necessary training sessions. In
other words, it can be inferred that key farmers with a higher education level are better atteaching or giving training sessions.
6-2. Type I Tobit model
Samples taking zero participation might be systematically different from non-zerosamples. Therefore, assuming that the data is censored at 𝑌𝑖𝑣 equals zero, we show the
censored regression model to confirm the robustness of the OLS estimation results. We
applied the type I Tobit model.

12

Table 8
Table 8 shows the result of the Type I Tobit model. Similar to both the estimation
results on participation and the number of training sessions, altruism and the interaction
term between altruism and reputational concern remain positive and statistically significant.
The statistical significance of reputational concern does not appear in the Tobit model result.
However, by the Wald test for reputational concern for the statistics (A) above, we can reject
the case of 5 key farmers out of 13 for models 13 to 16. Therefore, though on average the effectof reputational concern is marginal, it has a statistically significant effect on the number ofparticipations in some cases, same as the LPM and OLS models described in Section 6-1.
6-3. General Altruism
As explained in the conceptual framework, we also tested whether “general altruism”
has explanatory power on key farmers’ pro-social behavior. The purpose is to test whether a
key farmer’s activity is truly driven by his/her altruism. Because “villager directed” altruism
is elicited in an anonymous setting (within a village), if key farmers work pro-socially for thereciprocal concerns only toward the villagers, the estimated coefficient of “general” altruism
and that of “villager” directed altruism should largely differ. Put differently, it could be
inferred that the coefficient of “villager directed” and “general” altruism should not largely
differ if key farmers are motivated by “true” altruism.
Table 9, 10
Table 9 shows the result of LPM with the dependent variable of participation and
Table 10 shows the result of ordinary least square estimation with a dependent variable of thenumber of participations. Since we did not conduct an experiment to elicit reputational
concern measures when Player 2 (the recipient) was randomly chosen from Cambodia bylottery, we did not include any reputational concern or interaction term in Tables 9 and 10. In
LPM on participation, general altruism was consistently positive and significant at the 1
percent level. Moreover, the magnitude of the coefficients of altruism in Table 9 are not muchsmaller than those in Table 6, implying key farmers’ “true” altruism, which is measured as“general altruism” in our setting, is important for participation in training sessions. As for thenumber of participations, Table 10 shows similar results to Table 7 although the coefficient ofgeneral altruism is marginally significant in two specifications. However, judging from theseresults shown in Tables 9 and 10, we may conclude that key farmers’ true altruism is highly
associated with better participation and frequency of training sessions.
7. Conclusions and policy discussions13

We focused on the heterogeneous social preferences of project staff, often overlookedin previous RCT, and examined whether they yield heterogeneity among beneficiaries’
participation and the number of participations in training sessions. Our results showed thatthe social preference of project staff measured by the field experiment is highly associatedwith the program participation and the number of participations of project beneficiaries.
Among them, the altruism of project staff (key farmers) consistently increases participationand the number of participations in the training sessions of beneficiaries.
Our empirical results clearly show the possibility that heterogeneous program
participation of ordinary farmers in treated villages could be induced due to heterogeneity inthe characteristics of project staff. Although we did not formally implement the program
evaluation of the post-harvest technology project, this type of heterogeneity across treatmentsites might yield noises in mean effects estimated using RCT methods conducted at a certain
cluster level. If the degree of heterogeneous impacts were largely influenced by a program
implementation manner across the treated groups, the estimated outcomes might not be
replicable in different settings, which undermine the external validity of the estimated results.
While RCT methods are very powerful tools for many program and policy evaluations, wecannot emphasize too much on the importance of project implementation manner.
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16

Table 1: Descriptive statistics of key farmers and ordinary farmers

Variable
Off farm income (1 million Riel)
Number of household members
Years of education
Sex
Age

Obs
27
27
27
27
27

[Key Farmer]
Mean Std. Dev.
Min
8.45
5.41
6.70
0.93
54

9.27
1.62
3.60
0.27
10.13

0.40
2
0
0
33

Max

Obs

[Ordinary Farmers]
Mean Std. Dev. Min

41.00
9
14
1
75

231
235
228
231
231

4.93
5.25
6.09
0.90
49.20

5.84
1.69
3.81
0.31
12.48

T value(H0: Diff=0)Max

0.00 403.00
2
12
0
18
0
1
23
76

2.76
0.47
0.80
0.49
1.92

Table 2: Household samples surveyed from December 2012 to January 2013

Province

Village name
Ansorng
Bantealbos
Prey Veng
Chrey veal
Por Chrey
Pre Kdey Krorm
PursatPre Kdey Leu
Preah Chambann
Svay Russey
Takeo
Veang
Doung
Salatrav
Samrong snor
Battambang
Snapimok
Balat
Total

Obs
16
15
15
16
11
11
32
31
15
14
17
15
12
15
235

Table 3: Descriptive statistics for item-carrying key farmers
Variable
Obs
General altruism (Riel)
13
Village-directed altruism (Riel)
13
Reputational concern (Riel)
13
Years of education
13
Log income (Riel)
13

17

Mean
4,338.46
5,684.62
1,846.15
16.89
7.31

Std. Dev.
8,223.29
10,713.38
3,078.05
0.53
3.95

Min
0
0
0
15.99
0

Max
30,00040,00010,00017.98
14

Table 4: Descriptive statistics for ordinary farmers
Variable

Obs

Mean

Std. Dev.

Min

Max

[Dependent Variable]
Participation
Number of Participation

235
235

0.67
3.37

0.47
3.47

0
0

1
20

[Independent Variable: Beneficiary characteristics]
Consultation to key farmer (=1 if yes, zero otherwise)
Physical distance to key farmer (km)Number of times of rice growing
Log of off-farm income for household
Sex of household head (=1 if male and female otherwise)
Age of household head
Education years for household head
Number of household members
Province Dummy (Prey Veng)
Province Dummy (Pursat)
Province Dummy (Takeo)

230
235
235
231
231
231
228
235
235
235
235

0.91
0.41
1.71
15.33
0.90
49.20
6.09
5.25
0.26
0.23
0.26

0.29
0.46
0.71
2.32
0.31
12.48
3.81
1.69
0.44
0.42
0.44

0
0
1
0
0
23
0
2
0
0
0

1
2.42
3
18.21
1
76
18
12
1
1
1

Table 5: Descriptive statistics for participants and non-participants

Variable
Consultation to key farmer (=1 if yes, zero otherwise)
Physical distance to key farmer (km)Number of times of rice growing
Log of off-farm income for household
Sex of household head (=1 if male and female otherwise)
Age of household head
Education years for household head
Number of household members

Obs

Training Participant
Mean
Std. Dev.
155
0.903
0.297
158
0.423
0.472
158
1.722
0.713
157
15.220
2.420
157
157
155
158

18

0.911
49.834
6.148
5.215

0.286
12.514
3.949
1.739

Non Training Participant
T valueObs
MeanStd. Dev. (H0: Diff=0)
75
0.920
0.273
-0.41
77
0.377
0.440
0.72
77
1.701
0.708
0.20
74
15.548
2.077
-1.00
74
74
73
77

0.865
47.865
5.959
5.312

0.344
12.397
3.514
1.608

1.07
1.12
0.35
-0.41

Table 6: Estimation result for participation by linear probability model
Dependent variable
Participation
Altruism (log)
Reputational concern (log)①Base model
1
2
3
4
0.02980.03040.03150.0321[3.34]*** [2.98]**[3.39]*** [2.93]**0.01160.01280.00890.0102[3.72]*** [3.55]*** [2.86]**[2.89]**

Years of Education of key farmer
Log income of key farmer②Base model + Key farmer characteristics
56
7
80.02730.02830.02940.0304[7.12]*** [6.10]*** [6.42]*** [5.28]***0.00640.00770.00460.006[1.61][1.75][1.05][1.22]
0.01460.01610.01330.0147[4.69]*** [4.06]*** [3.25]*** [3.09]***0.0506
0.04170.04020.0281[1.15][0.87][0.95][0.61]

Interaction term of altruism and Reputational concern③Base model + Interaction term
9
10
11
12
0.027
0.02740.02870.0288[9.45]*** [8.33]*** [9.32]*** [7.90]***0.00880.00990.00590.0069[3.00]**[2.89]**[1.89]*[1.79]*

0.00290.00350.00310.0039[6.51]*** [4.59]*** [6.63]*** [4.65]***
Physical distance to key farmer-0.0536-0.0798-0.0695-0.0934-0.0805-0.1109[-0.98][-1.46][-1.50][-2.05]*[-1.56][-2.18]**Consultation to key farmer-0.066-0.074-0.0597-0.069-0.0576-0.0665[-0.55][-0.63][-0.49][-0.58][-0.48][-0.56]
Number of times of rice growing-0.0354-0.0316-0.0339-0.0246-0.0365-0.034-0.0371-0.0294-0.0404-0.035-0.0396-0.028[-0.66][-0.62][-0.66][-0.52][-0.67][-0.64][-0.68][-0.57][-0.76][-0.69][-0.76][-0.58]Log of off-farm income for household-0.0134-0.0143-0.0129-0.0143-0.0122-0.013-0.0117-0.0129-0.0133-0.0146-0.0129-0.0147[-0.81][-0.89][-0.78][-0.88][-0.73][-0.81][-0.69][-0.79][-0.79][-0.91][-0.77][-0.91]
Sex of household head
0.10470.10540.103
0.10480.0956
0.0931
0.09580.09420.098
0.0984
0.09650.0975[0.80][0.78][0.81][0.80][0.75][0.71][0.77][0.74][0.76][0.74][0.77][0.75]
Age of household head
0.00280.00280.00230.00230.00290.00290.00240.00240.00270.00270.00220.0023[0.93][0.93][0.78][0.81][0.98][0.98][0.83][0.86][0.92][0.92][0.77][0.81]
Education years for household head
0.00430.004
0.00250.002
0.00320.00280.00170.00130.004
0.00340.00220.0015[0.42][0.39][0.23][0.19][0.31][0.27][0.16][0.12][0.39][0.34][0.20][0.13]
Number of household members-0.0097-0.0101-0.0133-0.0138-0.0114-0.0122-0.0151-0.0161-0.0112-0.0121-0.0149-0.0161[-0.59][-0.63][-0.91][-0.98][-0.69][-0.76][-1.02][-1.13][-0.68][-0.75][-1.01][-1.14]
Province Dummy (Prey Veng)
0.15370.15310.16370.16730.11660.12090.13050.14110.12910.12330.13770.136[5.47]*** [4.85]*** [6.73]*** [6.96]***[3.05]**[2.55]**[3.80]*** [3.27]***[3.67]*** [2.98]**[4.55]*** [4.07]***
Province Dummy (Pursat)0.14450.15050.17770.18540.17940.18730.205
0.213
0.16970.18320.20640.2244[3.32]*** [2.71]**[3.61]*** [2.94]**[5.69]*** [4.22]*** [4.40]*** [3.47]***[5.39]*** [3.83]*** [5.41]*** [3.85]***
Province Dummy (Takeo
0.16910.17280.17450.17440.20310.21620.20660.21580.14960.149
0.15420.1488[2.94]**[3.06]*** [3.06]*** [3.08]***[3.54]*** [3.41]*** [3.72]*** [3.63]***[2.76]**[2.70]**[2.94]**[2.83]**
Constant
0.62190.65830.71750.7656-0.3481-0.1604-0.0706
0.18470.64710.70370.73530.8065[1.79]*[1.93]*[1.77][1.92]*[-0.47][-0.20][-0.10][0.24][1.88]*[2.13]*[1.81]*[2.08]*
R-squared
0.06390.06640.06910.07320.07070.07470.07470.08020.06970.07460.07590.0834
Adj-R-squared
0.01160.00410.01060.00450.00880.00270.00620.00150.01280.00780.01270.0103Observation
209
209
204
204
209
209
204
204
209
209
204
204
Standard error is clustered at the village level and the estimated t-values are in parentheses. *, **, *** are indicated to show P-value to reject null hypothesis with P< 0.1, P< 0.05, p<0.01, respectively.

19

④All
13
14
15
16
0.026
0.02670.02730.0277[8.04]*** [7.04]*** [7.64]*** [6.56]***0.00640.00780.00440.006[1.76][1.95]*[1.18][1.41]
0.008
0.00790.0018-0.0003[1.53][1.20][0.29][-0.05]0.05190.04260.04230.029[1.14][0.86][0.93][0.60]
0.002
0.00250.00330.0043[2.07]*[1.80]*[3.15]*** [3.84]***-0.0757-0.108[-1.48][-2.04]*-0.0556-0.0647[-0.45][-0.54]-0.0354-0.0319-0.0341-0.024[-0.64][-0.61][-0.62][-0.47]-0.013-0.0141-0.0132-0.0151[-0.76][-0.87][-0.76][-0.90]0.09940.09810.10120.1016[0.78][0.74][0.81][0.79]
0.00290.00280.00240.0024[0.97][0.96][0.81][0.83]
0.00310.00270.00150.001[0.30][0.26][0.14][0.09]-0.0114-0.0123-0.0148-0.0159[-0.69][-0.76][-0.99][-1.11]0.10160.1026
0.10930.115[2.70]** [2.20]**[3.71]*** [3.11]***0.18640.19630.22120.235[6.31]*** [4.42]*** [5.74]*** [4.18]***0.16630.17
0.14520.1356[2.97]** [2.71]**[2.12]*[2.10]*-0.3041-0.0905-0.0004
0.314[-0.39][-0.11][-0.00][0.36]
0.07160.076
0.07690.08390.0046-0.0010.00320.0002
209
209
204
204

Table 7: Estimation result for number of participations of ordinary farmers
Dependent variable
Number of Participations
Altruism (log)
Reputational concern (log)①Base model
12
3
4
0.15580.16440.16960.1747[2.96]** [2.82]**[2.70]** [2.64]**-0.0037-0.00180.00060[-0.08][-0.04][0.01][-0.00]Years of Education of key farmer
Log income of key farmer②Base model + Key farmer characteristics
56
7
80.13970.14770.15250.157[2.71]** [2.70]** [2.75]**[2.70]**-0.0236-0.0241-0.0223-0.0242[-0.47][-0.44][-0.45][-0.46]0.03570.04020.04880.0503[0.71][0.77][0.85][0.87]
0.52250.52770.52580.5388[1.10][1.04][1.14][1.08]

Interaction term of altruism and Reputational concern③Base model + Interaction term
9
10
11
12
0.14070.14860.15210.1572[4.98]*** [4.75]*** [5.04]*** [4.75]***-0.0196-0.0167-0.0182-0.0173[-0.45][-0.38][-0.39][-0.38]0.01610.01810.01940.0207[3.70]*** [2.47]**[4.32]*** [2.72]**
Physical distance to key farmer
0.0696
0.12470.11470.1659-0.0683-0.0399[0.10][0.19][0.15][0.22][-0.10][-0.06]
Consultation to key farmer
1.5674
1.53291.59771.5667
1.62
1.5726[2.63]** [2.55]**[2.64]**[2.56]**[2.76]** [2.62]**
Number of times of rice growing-0.3016-0.3667-0.3121-0.3661-0.2385-0.3117-0.2599-0.3187-0.3295-0.3842-0.3479-0.3845[-0.76][-0.85][-0.76][-0.82][-0.60][-0.73][-0.63][-0.71][-0.85][-0.92][-0.86][-0.88]Log of off-farm income for household-0.112-0.1094-0.115-0.1122-0.1145-0.1106-0.1159-0.1123-0.1115-0.111-0.1146-0.1144[-1.09][-1.12][-1.10][-1.11][-1.09][-1.12][-1.08][-1.10][-1.08][-1.14][-1.09][-1.14]Sex of household head
0.69480.63220.84030.78190.741
0.67030.87550.81510.65770.59640.79990.7432[1.00][0.91][1.18][1.14][1.06][0.97][1.22][1.18][0.94][0.85][1.11][1.07]
Age of household head
-0.0088-0.01-0.0078-0.0092-0.0076-0.0089-0.0065-0.0078-0.0091-0.0102-0.008-0.0093[-0.42][-0.47][-0.36][-0.41][-0.37][-0.42][-0.30][-0.35][-0.43][-0.48][-0.36][-0.41]
Education years for household head
-0.0726-0.0765-0.0658-0.0691-0.0811-0.085-0.0741-0.0775-0.0746-0.0792-0.0678-0.0722[-1.08][-1.11][-0.93][-0.94][-1.24][-1.27][-1.06][-1.07][-1.11][-1.15][-0.95][-0.98]
Number of household members
0.10960.1073
0.0931
0.09470.10950.10690.0912
0.093
0.10130.09710.08280.0826[0.98][1.02][0.83][0.90][0.96][1.02][0.80][0.88][0.91][0.95][0.75][0.81]
Province Dummy (Prey Veng)
1.24661.203
1.1825
1.14640.90570.85580.83050.78281.10991.051
1.01930.9806[2.08]*[1.92]*[1.98]*[1.79]*[1.63][1.45][1.43][1.24][1.76][1.63][1.62][1.50]
Province Dummy (Pursat)-0.0409
0.03850.10670.15290.13920.22570.30370.35290.09960.20550.28670.3599[-0.09][0.09][0.20][0.28][0.30][0.48][0.56][0.65][0.26][0.53][0.59][0.70]
Province Dummy (Takeo-0.5781-0.436-0.672-0.5677-0.6312-0.4715-0.6799-0.5716-0.6863-0.5576-0.7991-0.7036[-1.01][-0.72][-1.21][-0.96][-1.05][-0.77][-1.15][-0.93][-1.36][-1.01][-1.68][-1.34]
Constant
5.26965.507
3.81574.0103-3.8909-3.7921-5.5314-5.5879
5.40975.73873.9275
4.227[2.11]*[2.18]*[1.35][1.38][-0.47][-0.43][-0.67][-0.62][2.20]** [2.33]**[1.40][1.48]
R-squared
0.094
0.09770.1112
0.1133
0.09660.10040.114
0.1162
0.09710.10140.11570.1181
Adj-R-squared
0.04340.0376
0.05540.0476
0.03630.03050.04840.0406
0.04180.0366
0.05520.0477
Observation
209
209
204
204
209
209
204
204
209
209
204
204
Standard error is clustered at the village level and the estimated t-values are in parentheses. *, **, *** are indicated to show P-value to reject null hypothesis with P< 0.1, P< 0.05, p<0.01, respectively.

20

④All
13
14
15
16
0.10590.11450.11570.1211[2.78]** [2.95]** [3.09]*** [3.12]***-0.0233-0.0216-0.025-0.024[-0.60][-0.50][-0.66][-0.59]-0.1297-0.1239-0.1497-0.1478[-1.87]*[-1.67][-1.94]*[-1.81]*0.55450.54490.56240.5515[1.04][0.96][1.07][0.98]
0.04890.0491
0.05650.0571[3.70]*** [3.16]*** [4.11]*** [3.43]***-0.0094-0.0277[-0.01][-0.04]
1.66861.6238[2.77]**[2.64]**-0.2105-0.2713-0.2092-0.2472[-0.54][-0.67][-0.51][-0.58]-0.1349-0.1327-0.1425-0.1418[-1.23][-1.29][-1.26][-1.31]0.83580.76950.96950.9132[1.14][1.07][1.29][1.27]-0.0089-0.0101-0.0076-0.0088[-0.44][-0.49][-0.35][-0.40]-0.0828-0.0869-0.0777-0.0816[-1.26][-1.30][-1.11][-1.13]0.109
0.10530.09550.0952[0.94][0.99][0.82][0.88]
0.53070.4917
0.46290.4363[1.05][0.90][0.98][0.84]
0.31410.40440.58210.6444[1.37][1.53][1.89]*[1.78]*-1.5529-1.3935-1.7389-1.635[-2.24]** [-1.88]*[-2.45]** [-2.15]*-2.7894-2.3966-4.32-3.8746[-0.30][-0.24][-0.45][-0.37]0.1056
0.10940.12510.1271
0.04110.03520.05520.0474
209
209
204
204

Table 8: Estimation result by type I Tobit model
Dependent variable
Number of Participations
Altruism (log)
Reputational concern (log)①Base model
12
3
4
0.28420.29190.30580.3108[3.05]*** [2.91]*** [2.97]*** [2.85]***0.02630.0301
0.02120.0232[0.46][0.50][0.36][0.39]

Years of Education of key farmer
Log income of key farmer②Base model + Key farmer characteristics
56
7
80.2587
0.26640.28090.286[3.59]*** [3.54]*** [3.61]*** [3.50]***-0.0137-0.0126-0.017-0.0164[-0.21][-0.18][-0.26][-0.24]0.09050.10050.09540.1015[1.42][1.52][1.26][1.33]
0.71480.70830.65740.6472[1.14][1.08][1.11][1.04]

Interaction term of altruism and Reputational concern
Physical distance to key farmer
Consultation to key farmer
Number of times of rice growing
Log of off-farm income for household
Sex of household headAge of household head
Education years for household head
Number of household members
Constant-0.0461[-0.06]-0.0684[-0.09]1.52251.4766[1.68]*[1.64]-0.4784-0.5415-0.4784-0.514[-0.79][-0.86][-0.79][-0.82]-0.1622-0.1599-0.1635-0.1629[-1.16][-1.20][-1.19][-1.23]1.168
1.08481.31091.2418[0.96][0.90][1.08][1.05]-0.0009-0.0023-0.0015-0.0029[-0.03][-0.07][-0.05][-0.09]-0.0603-0.0669-0.0575-0.0635[-0.62][-0.67][-0.55][-0.59]0.08930.08230.05720.0548[0.57][0.55][0.38][0.39]
4.17684.54242.89643.2319[1.21][1.32][0.74][0.82]-0.046[-0.06]-0.0614[-0.08]1.54591.4977[1.71]*[1.66]*-0.4256-0.5016-0.4453-0.4921[-0.70][-0.80][-0.72][-0.77]-0.1607-0.1568-0.16-0.1582[-1.11][-1.15][-1.12][-1.16]
1.1952
1.09791.33871.2589[1.00][0.93][1.12][1.08]
0.0011-0.00030.0006-0.0009[0.04][-0.01][0.02][-0.03]-0.0724-0.0797-0.068-0.0744[-0.75][-0.80][-0.65][-0.70]0.08380.07470.04950.0458[0.54][0.51][0.33][0.33]-8.6792-8.2572-9.0448-8.562[-0.82][-0.75][-0.87][-0.79]sigma③Base model + Interaction term
9
10
11
12
0.25790.26340.27620.2795[5.92]*** [5.71]*** [6.12]*** [5.81]***-0.00390.0012-0.0124-0.0092[-0.08][0.02][-0.22][-0.16]0.02930.033
0.033
0.0366[3.92]*** [3.13]*** [4.30]*** [3.30]***-0.2802-0.3393[-0.38][-0.46]
1.59181.5284[1.76]*[1.69]*-0.5348-0.5834-0.5442-0.557[-0.90][-0.96][-0.90][-0.90]-0.1608-0.1618-0.1622-0.1657[-1.14][-1.22][-1.17][-1.26]
1.16291.07811.30991.2352[0.95][0.88][1.07][1.03]-0.0009-0.0022-0.0013-0.0025[-0.03][-0.07][-0.04][-0.08]-0.0636-0.072-0.0607-0.0691[-0.65][-0.72][-0.58][-0.64]0.0752
0.06360.04080.0337[0.49][0.44][0.28][0.24]
4.34884.88043.01543.5422[1.29][1.47][0.78][0.92]

④All
13
14
15
16
0.21550.22310.23130.2359[4.14]*** [4.24]*** [4.59]*** [4.51]***-0.016-0.0125-0.0237-0.0197[-0.31][-0.22][-0.47][-0.37]-0.1111-0.1026-0.1618-0.1637[-1.28][-1.14][-1.64][-1.68]*0.77410.75060.725
0.6848[1.12][1.04][1.08][0.97]
0.06130.06230.07470.0777[3.53]*** [3.27]*** [4.14]*** [3.99]***-0.1782-0.2978[-0.22][-0.37]1.66041.602[1.82]*[1.75]*-0.3939-0.4565-0.3795-0.3997[-0.66][-0.76][-0.62][-0.66]-0.1866-0.1848-0.1962-0.1989[-1.26][-1.33][-1.32][-1.41]
1.32731.23391.47081.3955[1.08][1.02][1.20][1.17]
0.0002-0.0011-0.0001-0.0013[0.01][-0.04][-0.00][-0.04]-0.0729-0.0803-0.0706-0.0779[-0.76][-0.82][-0.69][-0.74]0.08450.07380.05710.0505[0.53][0.50][0.37][0.35]-7.7164-6.9307-7.8806-6.6944[-0.65][-0.56][-0.66][-0.54]4.63194.62284.61294.60844.62544.616
4.60614.60134.62174.611
4.59894.59344.60094.59164.57454.5696[11.11]*** [11.14]*** [10.59]*** [10.67]***[11.16]*** [11.20]*** [10.60]*** [10.70]***[11.10]*** [11.07]*** [10.55]*** [10.57]***[11.22]*** [11.21]*** [10.61]*** [10.63]***Observation
209
209
204
204
209
209
204
204
209
209
204
204
209
209
204
204
Standard error is clustered at the village level and the estimated t-values are in parentheses. *, **, *** are indicated to show P-value to reject null hypothesis with P< 0.1, P< 0.05, p<0.01, respectively. All regressions include Province fixed effect.

21

Table 9: Estimation result by general altruism and participation by linear probability model
Dependent variable
Participation
Altruism (log)①Base model
1
2
3
4
0.02250.02320.02130.0222[4.04]*** [3.96]*** [3.58]*** [3.49]***Years of education of key farmer
Log income of key farmer
Physical distance to key farmer
Consultation to key farmer
Number of times of rice growing
Log of off-farm income for household
Sex of household headAge of household head
Education years for household head
Number of household members
Province Dummy (Prey Veng)
Province Dummy (Pursat)
Province Dummy (Takeo)
Constant
R-squared
Adj-R-squared
Observation-0.0461[-0.70]-0.0763[-1.14]-0.0707-0.0781[-0.57][-0.65]0.00560.01040.00860.0193[0.11][0.22][0.17][0.44]-0.0149-0.0156-0.0146-0.0159[-0.91][-0.97][-0.90][-0.98]0.11220.113
0.11090.113[0.84][0.83][0.84][0.85]
0.00230.00230.00180.0019[0.78][0.80][0.60][0.65]
0.001
0.0005-0.0007-0.0012[0.09][0.05][-0.06][-0.11]-0.0069-0.0072-0.0094-0.0099[-0.42][-0.44][-0.62][-0.68]0.08040.07780.0961
0.0975[1.74][1.72][2.06]*[2.29]**
0.08870.094
0.11130.1195[1.53][1.37][1.71][1.56]
0.04190.0382
0.05380.0461[0.59][0.57][0.71][0.64]
0.6632
0.69290.75790.801[1.92]*[2.03]*[1.85]*[1.99]*
0.05630.05790.05880.06240.00860.00020.0049-0.0017
209
209
204
204

②Base model + Key farmer characteristics
56
7
80.02250.02460.02230.0247[5.11]*** [5.82]*** [4.56]*** [5.49]***0.01380.01560.012
0.0135[2.52]**[2.53]**[1.88]*[2.10]*-0.0196-0.0366-0.03-0.052[-0.32][-0.57][-0.49][-0.82]-0.0843-0.1097[-1.37][-1.83]*-0.063-0.0732[-0.51][-0.61]-0.0075-0.0021-0.0059
0.0049[-0.14][-0.04][-0.11][0.10]-0.0131-0.0141-0.0128-0.0142[-0.80][-0.89][-0.78][-0.89]0.0963
0.09380.098
0.0962[0.75][0.71][0.77][0.74]
0.00230.00230.00170.0018[0.77][0.77][0.60][0.63]
0.00140.00090-0.0006[0.14][0.08][0.00][-0.06]-0.0095-0.0107-0.0123-0.0137[-0.57][-0.66][-0.81][-0.95]0.0911
0.09480.10610.1172[2.43]**[2.88]**[2.29]**[2.94]**
0.09750.10410.11220.1194[2.54]**[2.22]**[2.33]**[2.12]*
0.11490.12120.11690.1195[1.60][1.62][1.54][1.63]
0.895
1.2252
1.17571.6061[0.86][1.13][1.12][1.49]
0.06640.07130.06730.07440.00920.00430.00340.0006
209
209
204
204

Standard error is clustered at the village level and the estimated t-values are in parentheses. *, **, *** are indicated to show P-value to reject null
hypothesis with P< 0.1, P< 0.05, p<0.01, respectively.

22

Table 10: Estimation result by general altruism and participation by linear probability model
Dependent variable
Number of Participations
Altruism (log)①Base model
12
3
4
0.10480.1096
0.10970.1117[2.71]**[2.53]**[2.66]**[2.54]**

Years of education of key farmer
Log income of key farmer
Physical distance to key farmer
Consultation to key farmer
Number of times of rice growing
Log of off-farm income for household
Sex of household headAge of household head
Education years for household head
Number of household members
Province Dummy (Prey Veng)
Province Dummy (Pursat)
Province Dummy (Takeo
Constant
R-squared
Adj-R-squared
Observation

0.0009[0.00]

0.0715[0.11]
1.58091.5463[2.57]**[2.49]**-0.0956-0.1372-0.0939-0.1309[-0.27][-0.37][-0.25][-0.33]-0.1238-0.1227-0.1271-0.1255[-1.17][-1.21][-1.17][-1.19]0.794
0.741
0.93760.8889[1.10][1.04][1.26][1.24]-0.0119-0.0131-0.0109-0.0121[-0.58][-0.63][-0.51][-0.55]-0.0815-0.0862-0.0757-0.0792[-1.24][-1.27][-1.08][-1.08]0.123
0.12170.11190.1137[1.08][1.12][0.97][1.05]
0.94250.88650.85530.8184[1.47][1.36][1.34][1.21]-0.4936-0.4334-0.3763-0.3452[-1.33][-1.07][-0.83][-0.70]-0.986-0.8941-1.138-1.061[-2.16]*[-1.74][-2.40]** [-2.05]*
5.44485.70173.9682
4.1655[2.18]*[2.26]**[1.40][1.43]
0.09270.096
0.10840.11
0.04690.0406
0.05730.0491
209
209
204
204

②Base model + Key farmer characteristics
56
7
8
0.09810.10430.10080.1036[1.91]*[1.78][1.95]*[1.74]
0.00940.01540.02560.0279[0.17][0.27][0.39][0.41]
0.1
0.07180.11920.1094[0.15][0.10][0.18][0.15]-0.0132
0.0496[-0.02][0.07]
1.597
1.5609[2.57]**[2.45]**-0.0912-0.1394-0.1024-0.14[-0.25][-0.37][-0.27][-0.35]-0.1236-0.1218-0.1251-0.1234[-1.15][-1.19][-1.15][-1.17]0.79850.73610.93350.8816[1.11][1.04][1.26][1.23]-0.0116-0.0129-0.0105-0.0118[-0.59][-0.64][-0.50][-0.54]-0.0824-0.0868-0.0762-0.0797[-1.28][-1.31][-1.10][-1.11]0.12310.12020.109
0.1101[1.07][1.14][0.94][1.03]
0.89590.85290.79230.759[1.32][1.19][1.11][0.99]-0.4561-0.3957-0.3221-0.289[-1.15][-0.94][-0.69][-0.60]-0.964-0.8362-1.0464-0.9574[-2.28]** [-1.84]*[-2.47]** [-2.05]*
3.65924.3636
1.71842.082[0.32][0.35][0.14][0.16]
0.09280.09610.10880.11050.03720.03090.04780.0395
209
209
204
204

Standard error is clustered at the village level and the estimated t-values are in parentheses. *, **, *** are indicated to show P-value to rejectnull hypothesis with P< 0.1, P< 0.05, p<0.01, respectively.

23

1015

Ansorng

0 5

4

1 1 1 1 1 2

0

5

Bantealbos

5

5

10

15

20

2 1

0

1015

4 5 3

0 5

3

0

1

5

1 1

10

15

20

0

1015
0 5

0

1

5

10

1

15

20

Pre Kdey Leu
4

10

15

20

1 1 2 1 1 1

0

5

10

Svay Russey

15

20

15

20

15

20

Veang

13

0

3

5

10

2

15

20

0

5
1

3 3

2

0

5

1

1 1

3

5

2 1

15

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0

20

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3
1 2

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Salatrav
8

1 1

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4

1

5

1

10

15

20

15

20

0

3

1 1

5

Snapimok

1

10

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Samrong Snor
6

10

Doung

1

10

2 2 2

1

5

6
1

8

7

6
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Balat
1015

20

1

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9
3 3

0 5

15

2 3 3

8

Preah Chambann

1015

10

2 3

1

Pre Kdey Krorm

1

5

11

0

Chrey veal

5

Por Chrey

0 5Frequency

Figure 1: Variation of beneficiaries’ number of participations in the training sessions within village

1 1

10

Number of Participation histogram by village

24

2

1

10