00074918.2015.1110851
Bulletin of Indonesian Economic Studies
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Determinants and Effects of Small Chilli Farmers’
Participation in Supermarket Channels in
Indonesia
Sahara Sahara, Nicholas Minot, Randy Stringer & Wendy J. Umberger
To cite this article: Sahara Sahara, Nicholas Minot, Randy Stringer & Wendy J. Umberger
(2015) Determinants and Effects of Small Chilli Farmers’ Participation in Supermarket
Channels in Indonesia, Bulletin of Indonesian Economic Studies, 51:3, 445-460, DOI:
10.1080/00074918.2015.1110851
To link to this article: http://dx.doi.org/10.1080/00074918.2015.1110851
Published online: 29 Nov 2015.
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Date: 17 January 2016, At: 23:18
Bulletin of Indonesian Economic Studies, Vol. 51, No. 3, 2015: 445–60
DETERMINANTS AND EFFECTS OF SMALL
CHILLI FARMERS’ PARTICIPATION IN
SUPERMARKET CHANNELS IN INDONESIA
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Sahara Sahara*
Department of Economics and InterCAFE, Bogor Agricultural University
Nicholas Minot*
International Food Policy Research Institute
Randy Stringer*
University of Adelaide
Wendy J. Umberger*
University of Adelaide
The number of supermarkets in Indonesia is increasing, but small-scale farmers
may be at risk of being excluded from these emerging marketing channels. Drawing
on data from our survey of 600 small-scale chilli farmers in West Java, we examine
the factors that inluence farmers’ decisions to participate in supermarket channels.
We use a treatment-effect model to examine the effect of supermarket participation on income, while controlling for the possibility of selectivity bias. Our results
indicate that participation in the supermarket channel signiicantly increases farm
income, even after we controlled for differences in education, chilli-farming experience, storage-house ownership, and the distance from the farmer’s house to a bitumen road.
Keywords: supermarkets, chilli
JEL classiication: Q12, Q13
INTRODUCTION
The dramatic growth of supermarket chains in developing countries over the past
10–15 years has been well documented (Reardon et al. 2003; Reardon, Timmer,
* InterCAFE = International Centre for Applied Finance and Economics. We wish to
thank David Shearer, from the Australian Centre for International Agricultural Research
(ACIAR), for his helpful comments and suggestions during our research. We acknowledge
and thank our research partner—the Indonesian Center for Agricultural Socio Economic
and Policy Studies (ICASEPS)—as well as Wahida, Ashari, and Nur Khoiriyah Agustin for
survey and research assistance. This article was made possible by inancial support from
ACIAR. All views, interpretations, and conclusions expressed are those of the authors and
not necessarily those of the supporting or cooperating institutions.
ISSN 0007-4918 print/ISSN 1472-7234 online/15/000445-16
http://dx.doi.org/10.1080/00074918.2015.1110851
© 2015 Indonesia Project ANU
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446
Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
and Minten 2012). In Indonesia, which has followed this trend, supermarkets rose
in number from 1 in 1970 to 284 in 2004 (Suryadarma et al. 2010), and, according
to the World Bank (2007), accounted for about 30% of retail food sales in 2006,
a three-fold increase in market share since 1999. It is expected that the share of
supermarkets in food retail will increase along with the rapid growth of per capita
income, urbanisation, and liberalisation of foreign investment.
The proliferation of supermarkets has created a new market opportunity for
farmers. As supermarket chains look to differentiate their products from those
sold in traditional markets and improve food safety, they develop and impose
private grades and standards, particularly for fresh fruits and vegetables. They
offer higher net prices than traditional markets to farmers who can meet these
requirements, which potentially increases farmers’ incomes (Reardon et al. 2009).
For small-scale farmers, however, who have limited access to information and
credit, meeting the grades and standards imposed by supermarkets can be dificult (Moustier et al. 2010). In addition, some supermarket chains try to guarantee
a certain quality of supply by using vertical coordination mechanisms such as
formal or informal contracts with farmers. Each contract has a transaction cost, so
it is cheaper for a supermarket to work with a small number of large farmers than
a large number of small farmers (Dries et al. 2009).
For these reasons, there is widespread concern among researchers and policymakers that small-scale farmers will be excluded from this emerging market. This
raises two important research questions: whether small farmers can participate
in supermarket channels, and whether participation in these channels increases
their net income.
In spite of the rapid growth in the number of supermarkets in developing countries, few empirical studies examine the determinants of small-farmer participation
in supermarket channels or the effects of this participation on economic aspects.
Rao and Qaim’s (2011) study examined factors associated with participating in
supermarket supply chains for fresh fruits and vegetables in Kenya. They found
that older farmers with larger farms who are members of farmer organisations are
more likely to participate in supermarket supply channels. Hernández, Reardon,
and Berdegué (2007), in contrast, found that participants in supermarket supply
channels for tomatoes in Guatemala are younger and less likely to be members of
farmer organisations than other farmers. Rao and Qaim concluded that larger farmers are more likely to participate in supermarket channels, as did Neven et al. (2009),
whereas Hernández, Reardon, and Berdegué did not ind farm size signiicant.
Furthermore, only a few studies—including Rao and Qaim’s—comprehensively assess the impact of supermarket participation on household income.
Other studies consider the income effects by comparing the gross margin of farm
crops between farmers selling to supermarket channels and farmers selling to
traditional channels, but, as Rao and Qaim note, this method is limited, because it
does not measure other variables that may inluence household income.
This article attempts to contribute to the emerging literature by focusing on
an important high-value agricultural product in Indonesia: chillies. More speciically, it examines the factors that inluence the participation of chilli growers in
the supermarket channel and investigates the effects of this participation on their
incomes. We focus on chillies for several reasons. It is a main ingredient in the
Indonesian daily diet, despite being native to Mexico and thus a non-traditional
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Determinants and Effects of Small Chilli Farmers’ Participation
447
food in Indonesia. Chillies are also produced by small farmers and, unlike other
fresh vegetable products that are harvested just once, at the end of the season,
they can be harvested weekly or fortnightly and can therefore provide a steady
cash low for small farmers. Although only small amounts of chillies end up in
supermarkets, they are a main item in the their fresh-produce sections. We surmise that their sales in supermarkets in Indonesia will increase along with the
rapid rise of supermarkets themselves, owing to an rising trend in the contribution of fresh-food products to supermarket food sales in Indonesia. The World
Bank (2007) reported that the share of fresh fruit and vegetables (FFVs) increased
from virtually zero to 8% of supermarket retail sales between 1998 and 2007.
Further, our interviews with key informants from the three leading supermarkets
in Indonesia (Carrefour, Giant, and Hypermart) revealed that the contribution of
FFVs to supermarket sales was about 15% in 2014, a two-fold increase in market
share since 2007. One informant stated that chillies are an important FFV product
in their chain’s stores.
INDONESIA’S CHILLI INDUSTRY
Chillies, both large and small varieties, are one of Indonesia’s most important
food crops, and most households consume fresh chillies daily. In 2012, the average consumption per capita per week of large chillies and small chillies was 0.317
grams and 0.269 grams, respectively (BPS 2012). Indonesia’s production of chillies
increased by about 8.7%, on average, between 2008 and 2013. Chilli production
increased from about 1.2 million tonnes in 2005 to more than 1.7 million tonnes
in 2013 (table 1), by which time the value of chillies in the Indonesian economy
was Rp 55 trillion—up from Rp 24 trillion in 2008—or 4.2% of the value of the
agricultural sector.1
An estimated 463,000 small producers grow chillies, planting and harvesting year-round (Mustafa, Ali, and Kuswanty 2006). Any increase in production
would potentially generate employment opportunities, especially in rural areas,
since chilli cultivation is labour-intensive. It requires more labour, for example,
than staple crops such as rice and maize; it is estimated that chilli cultivation
needs around 2.6 times more labour days than rice (Mustafa, Ali, and Kuswanty
2006). Chilli planting and harvesting require the most labour.
Java and Sumatra are the major producing areas of chillies in Indonesia, having
accounted for about 80% of national chilli production in 2008 and 87% in 2013.
West Java contributed around 20% of the country’s chilli production during 2008–
13, making it largest chilli-farming area in Indonesia, followed by East Java and
Central Java (table 1).
Around 95% of fresh chillies are sold through traditional wet markets, with the
balance going to supermarkets, small and large food processors, and exporters
(White et al. 2007). The traditional channel includes traders, collectors, and other
buyers who purchase chillies directly from farmers and sell to wholesale markets,
where chillies are sorted by size, variety, and colour. Compared with supermarket
channels, the traditional marketing channels of chillies involve many intermediaries (Chowdhury, Gulati, and Gumbira-Said. 2005). In most traditional channels,
1. In 2013, the total value of Indonesia’s agricultural sector was Rp 1,310 trillion.
448
Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
TABLE 1 Indonesia’s Chilli Production, Selected Provinces, 2008–13 (tonnes)
West Java
East Java
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Central Java
North Sumatra
Aceh
Bengkulu
West Nusa Tenggara
Indonesia
2008
2009
2010
2011
2012
2013
241,362
(20.9)
193,522
(16.8)
150,747
(13.1)
136,419
(11.8)
41,010
(3.6)
50,994
(4.4)
45,014
(3.9)
315,574
(22.9)
243,562
(17.7)
220,929
(16.0)
154,802
(11.2)
34,826
(2.5)
47,700
(3.5)
39,339
(2.9)
245,597
(18.5)
213,674
(16.1)
194,971
(14.7)
196,347
(14.8)
64,149
(4.8)
58,529
(4.4)
18,870
(1.4)
300,620
(20.3)
255,483
(17.2)
184,358
(12.4)
233,258
(15.7)
49,525
(3.3)
41,495
(2.8)
26,128
(1.8)
291,907
(17.6)
343,714
(20.7)
215,129
(13.0)
245,773
(14.8)
90,030
(5.4)
41,618
(2.5)
36,883
(2.2)
374,669
(21.7)
329,177
(19.1)
230,398
(13.3)
198,878
(11.5)
79,139
(4.6)
52,928
(3.1)
35,325
(2.0)
1,153,144 1,378,911 1,328,864 1,483,079 1,656,615 1,726,381
Source: Data from Badan Pusat Statistik (BPS), Indonesia’s central statistics agency.
Note: The values in parentheses refer to the share of national chilli production in each province.
no speciic farm-gate standards are imposed; farmers decide when to plant, which
varieties to plant, and how much to plant, without any input from buyers.
Most chilli farmers selling to supermarket channels, in contrast, are required
to meet speciic product standards, including of colour, variety, and length. The
reward for this effort is a premium from supermarkets that varies with the traditional market price (supermarkets tend to renegotiate prices fortnightly, using
prices in traditional wholesale markets as a reference).
The supermarket channel consists of farmers, traders and farmer groups, specialised wholesalers, and supermarkets (White et al. 2007). The transaction costs
associated with organising exchanges with thousands of small farmers create
opportunities for specialised wholesalers to act as intermediaries. Saung Mirwan
and Bimandiri are examples of specialised wholesalers in West Java (Chowdhury
et al. 2005; World Bank 2007). Similar to trends in other countries, in Indonesia
specialised wholesalers play a central role in organising emerging modern-market
channels, including for chillies. These wholesalers supply a range of supermarket
chains by organising hundreds of small farmers and giving them all the necessary product-speciic guidelines. They communicate intensively with individual
farmers via traders, ‘lead farmers’, or farmer associations, conveying supermarket requirements as well as helping to source quality seeds, providing technical
assistance, and offering related technical support. They are responsible for delivering clean, sorted, and packaged chillies to supermarkets. Farmers in supermarket channels know that they have to meet speciic standards. If they cannot do
so, traders and farmer groups may reject their products, forcing them to sell their
products through traditional channels.
Determinants and Effects of Small Chilli Farmers’ Participation
449
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The cost of entering the supermarket channel includes the necessary time to
learn and use different growing and handling techniques. Investment costs include
new equipment (especially for irrigation and pesticide spraying) and storage
space. Meeting these investment costs often proves challenging for chilli producers, since most are small-scale farmers with limited access to capital (Chowdhury
et al. 2005). Among the potential net beneits of selling through modern market
channels are higher proits and new relationships (with input suppliers and traders) that could increase productivity-enhancing knowledge and assets useful for
other horticultural crops.
ECONOMETRIC MODEL
Model Speciication
Our original research design aimed to gather information from individual households on the quantity of chillies sold to supermarkets and to traditional markets. Our
initial ieldwork suggested that many households sold through traditional markets
only, and that some households sold through the supermarket channel while also
selling a share of their production into the traditional market. We therefore used the
Tobit model to capture the range of chillies sold per household into the supermarket channel, from zero to 100%. Collecting speciic data on the share of chillies sold
through each channel was problematic, however, for a number of reasons.
Pretesting our household questionnaire made it clear that producers could not
give even a rough estimate of how much of their production was sold through
the supermarket and traditional channels, although they were quite conident in
their estimates of the yields per plant and the total quantity sold. Most producers harvest each chilli plant multiple times in one season, up to 12 times in some
cases. Thus, for our sample of farmers, we learned that the households sold chillies every two weeks, on average. Our survey conirmed that only a tiny fraction
of households keep records of any kind.
Chilli traders and wholesale market managers stated that even though they
pass on information to their client farmers on varieties, sizes, and other attributes
required by the supermarkets, they do much of the grading and sorting themselves. In other words, farmers often do not know what share of their production
goes to supermarkets.
To counter this problem, we used a probit model to test whether producers
have any participation in supermarket channels, and how those households differ from those with no participation in supermarket channels. Previous literature
demonstrates that the probit model can be used to examine the farm and household characteristics of farmers who sell any portion of their products through
modern channels, compared with those who do not (Reardon et al. 2009; Rao
and Qaim 2011; Neven et al. 2009; Hernández, Reardon, and Berdegué 2007). We
remain conident that testing the effects of modern-market participation on producers makes an important contribution to the literature, the policy debate, and
the design of development projects.2
2. At the same time, we acknowledge the reviewers’ point that it would have been preferable to be able to collect information on shares for a Tobit analysis.
450
Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
Reardon et al. (2009) equate a farmer’s decision to switch from the traditional to
the modern channel with a farmer’s decision to adopt new technologies. We can
thus use a probit model in which the dependent variable, z, takes the value of one
for farmers selling through a supermarket channel and a value of zero for farmers
selling through a traditional channel (see Rao and Qaim 2011; Neven et al. 2009;
Hernández, Reardon, and Berdegué 2007). It is assumed that z is a linear function
of explanatory variables, w, and an error tem, u:
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zj = wj + γ + uj
(1)
where subscript j indicates the farmer, and u has a zero mean and a variance of
σ2. As in farmer adoption decisions, here the explanatory variables, w, can be
divided into those representing farmers’ incentives and those representing farmers’ capacities.
Incentive variables include the net price of the product (controlling for product
quality), the cost of production and marketing (including transaction costs), and
risk factors. These are usually measured in relative terms, with the supermarket
value in the numerator and the traditional value in the denominator. Capacity
variables include assets that potentially inluence access to supermarket channels: farm assets, including land and non-land assets; collective capital, such as
public infrastructure; and access to credit, quality inputs, technical assistance, and
related information. Each capacity variable is expected to increase the probability
of a farmer’s ‘adopting’ a supermarket channel. For the incentive variables, relative net price is expected to have a positive effect on supermarket-channel adoption, while the relative costs and risks have negative effects.
Farmer participation in supermarket channels is expected to have important
economic impacts on net farm income, productivity, and total output. Hernández,
Reardon, and Berdegué (2007) found that tomato farmers selling through supermarket channels have higher input use (labour, land, pesticides, and fungicides)
and greater output than those selling through traditional channels. Likewise, kale
farmers selling through supermarket channels in Kenya use more inputs (land
and fertiliser), produce higher yields, and earn higher net incomes than those selling through traditional channels (Neven et al. 2009). Rao and Qaim (2011) found
that vegetable farmers participating in supermarket channels in Kenya are associated with higher per capita income, which helps to reduce poverty. This study
focuses on the impact of supermarket-channel adoption on net household income.
Following the literature (Rao and Qaim 2011; Miyata, Minot, and Hu 2009), we
express the income equation as follows:
yj = x jβ + δ z j + ε j
(2)
where y is per capita net household income, x is a vector of exogenous variables that may inluence household income, and z is a dummy variable indicating
supermarket participation. Ordinary least squares (OLS) does not provide a satisfactory solution for how to measure the effect of participation on income, unless
we can guarantee that there is no selectivity bias (Maddala 1983).
In practice, the decision to participate in a speciic market channel may depend
not only on observable variables but also on unobservable variables (such as
farmer self-initiative, entrepreneurial skills, and network relationships). Farmers
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Determinants and Effects of Small Chilli Farmers’ Participation
451
in supermarket channels may have higher individual abilities than farmers in traditional channels (Rao and Qaim 2011). In this case, farmers who chose to sell
through supermarket channels would have higher incomes regardless of whether
they participated in those channels. Hence, OLS would overestimate the impact
of participation (δ) because it contains both the effect of participating in the supermarket channel and the effect of unobservable variables (Greene 2008). For this
reason, previous studies use Heckman selection procedures, and one variation of
these procedures in the empirical literature is a treatment-effects model, which
consists of two equations: the outcome equation (the income equation, as in equation [2]) and the selection equation, containing the unobserved or latent variable
z* (whether the farmers participate in the supermarket channel). Speciically,
yj = x jβ + δ z j + ε j
z*j = w jγ + u j
(3)
where the observed decision in the selection equation is
⎧1, if z* > 0
⎪
j
zj = ⎨
⎪ 0, o herwise
⎩
The error term in the outcome equation, ε, and the selection equation, u, are
bivariate normal with a mean of zero.
Empirical Model
The design of this study involved multiple stages. We irst selected variables that
potentially inluence supermarket participation among small chilli farmers in
Indonesia. We then focused on the impact of channel selection on net household
income—chilli farmers may reallocate labour and land from other activities to
participate in supermarket channels.3 Focusing only on the income from chillies
may overstate the impact on household well-being (Miyata, Minot, and Hu 2009;
Rao and Qaim 2011). Additionally, as this study is interested in whether supermarket participants are better-off, (net) per capita income is a better measure of
household welfare.
We did not directly enter input and output prices into the empirical model,
because it was dificult to control for quality, location of sale, packaging, and other
endogenous farm-level decisions (Neven et al. [2009] excluded prices for similar
reasons). The incentives variables entered into the empirical model refer to the
opportunity to reduce transaction costs. The transaction-costs variables include
the distance, in kilometres, from a farmer’s house to a bitumen road (a proxy of
3. Net income is calculated from farming and off-farm activities. Household income from
farming includes the value of chilli income minus the cost of purchased inputs, income
from other agricultural production, livestock and animal-product sales, aquaculture, agricultural trading, rice-milling business and agricultural wage labour minus the cost of
purchased inputs. Off-farm household income includes wages and transfers. Among the
activities, income from chillies contributes about 34% of household income.
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452
Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
distance to output and input markets)) and a farmer’s access to communication
assets (dummy variable for mobile-phone ownership). Respondents who live farther from a bitumen road spend more time and money selling their produce or
buying inputs than those who live closer to a bitumen road. Hence, this distance
is expected to reduce supermarket-channel participation. Mobile-phone ownership may increase access to either input- or output-market information, and, in
contrast, is expected to increase supermarket-channel participation.
The capacity variables are the following farm assets: land size owned (hectares); irrigated land (a dummy variable that takes the value of one if the land
owned is irrigated, and zero otherwise); motorbike ownership (units); waterpump ownership (units); mist-blower ownership (units); power-tiller ownership
(units); and storage-house ownership (units). Our analysis uses the ownership of
assets in 2005, ive years before the survey was carried out. In this study, all the
supermarket-channel suppliers became entered this channel after 2005. By using
the 2005 assets, we avoid the endogeneity problem of supermarket participation
inluencing asset ownership rather than the reverse (Neven et al. 2009).
We also incorporate in our analysis the potential household labour supply for
chilli production by including, as capacity variables, the proportion of productive
adults (that is, the proportion of household members between 15 and 65 years) and
non-productive adults (the proportion of members over 65 years) in the household
and the number of household members. We do so because participation in modern
markets is more labour-intensive, given the higher quality standards that farmers
are expected to meet compared with those selling through traditional channels
(Miyata et al. 2009). We hypothesise that each of these capacity variables has a positive effect on supermarket-channel participation (except for the share of non-productive adults in the household, which could have a positive or negative impact).
We also include education, farming experience, and age in the model. A high
level of education, measured in years of schooling, of the household head may
increase their access to inancial capital (Neven et al. 2009), while their farming
experience (the number of years of growing chillies) and age (years) may inluence
their market-channel choice (Woldie and Nuppenau 2009). Younger respondents
are more likely to participate in supermarket channels, because they may be more
enterprising, make decisions more quickly, and be more willing to try new technologies (Sharma, Kumar, and Singh 2009). The dependent variable is a dummy
variable that takes the value of one if the farmer participates in the supermarket
channel, and zero if the farmer participates in the traditional channel. In summary, the empirical model for channel selection in this study is as follows:
Channel choice = (household members, age of household head, education of household head, proportion of adults aged between 15 and 65, proportion of adults over 65,
land ownership in 2005, irrigated-land ownership in 2005, motorbike ownership in
2005, water-pump ownership in 2005, mist-blower ownership in 2005, power-tiller
ownership in 2005, storage-house ownership in 2005, chilli-farming experience,
mobile-phone ownership in 2005, distance from bitumen road)
In the impact model, the dependent variable is per capita net household income—
a function of a dummy variable for channel choice and the variables of incentives
and capacities (except for the distance from a bitumen road, which we treat as an
identiication variable).
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Determinants and Effects of Small Chilli Farmers’ Participation
453
We adapted the estimation procedures from Miyata, Minot, and Hu (2009)
and initially used a probit model to estimate the channel-choice equation. We
then used an OLS model to estimate net household income, and a treatmenteffects model to mitigate the possibility of selectivity bias. For the treatmenteffects model, we used maximum likelihood estimation, in which all parameters
in the channel-choice equation (the selection equation) and the net household
income equation (the outcome equation) are estimated simultaneously. In this
stage, we treated the variable of distance from the farmer’s house to a bitumen
road as an identiication variable because we do not believe it has an independent effect on income. This variable is therefore in the selection equation but not
the outcome equation. Besides, the correlation between the variables of ‘distance from bitumen road’ and ‘per capita net household income’ is low (0.003)
and not signiicant at 5%.
DATA COLLECTION
We collected data in one-on-one interviews with chilli farmers in West Java
during March–April 2010. Aside from being Indonesia’s largest chilli-farming
area, West Java has a large number of modern markets. Pandin (2009) reported,
for example, that in 2008 the province had 1,300 small convenience stores, 194
supermarkets, and 29 hypermarkets. Our 18-page survey questionnaire drew on
several months of interviews with key informants, including producers, traders, supermarket buyers, specialised wholesalers, food processors, and extension
agents.
Our survey sample included households that were selling chillies through traditional channels and those that were selling chillies through supermarket channels. The supermarket-channel sample came from a list of 96 chilli farmers in the
Ciamis district who were selling chillies to supermarkets. Supermarket buyers,
specialised suppliers, and dedicated wholesalers provided the 96 names. Our
interviews with key informants (specialised wholesalers) in Bandung revealed
that chillies sold in supermarkets in Bandung are produced mainly in the Ciamis
or Tasikmalaya districts. Our study team visited these districts to interview key
informants (including farmer groups, traders, and extension agents). In these districts, one farmer group and one trader supplied chillies through supermarket
channels. The former provided the names of 36 chilli farmers; the latter provided
the names of 60. All were interviewed during the survey.
To get a list of traditional-channel famers, we used a random sampling procedure, since there are no lists or censuses of chilli farmers in Indonesia. We selected
three districts in West Java: Garut, a major chilli production zone, and Ciamis
and Tasikmalaya, other production zones with substantial numbers of farmers
selling into the modern retail sector. We used a multistage sampling procedure to
select subdistricts, villages, and chilli farmers, and a systematic random sampling
method (Churchill and Iacobucci 2005) to select eight subdistricts in Garut and
three each in Ciamis and Tasikmalaya. The selection process was weighted by the
average annual chilli production in each subdistrict during 2004–8. We selected
three villages at random from each subdistrict, resulting in 42 villages. The survey
team visited the land-tax ofice in each village to compile a list of chilli-producing
households, selecting 12 households from each village list. This process yielded
504 chilli farmers.
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Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
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During data cleaning, we moved 17 households from the random sample who
sold to supermarkets (about 3% of the total random sample) to the supermarketchannel group. We eliminated 4 households from the supermarket sample and
17 households from the random sample owing to poor data quality. The inal
supermarket-channel group comprised 109 households and the inal traditionalchannel group comprised 470.
RESULTS AND DISCUSSION
Descriptive Statistics
Tables 2 and 3 present descriptive statistics for selected variables for farmers selling through the traditional and supermarket channels. Statistically signiicant
differences between these groups (at the 5% level) include the age and education of the household head and the household’s experience in chilli production.
The supermarket-channel farmers are younger, on average, but have more formal education; the traditional market farmers have more experience in producing
chillies, perhaps because they tend to be older.
The average farm size for both groups is less than one hectare, and is not statistically signiicantly different between the groups, although farmers in supermarket
channels have signiicantly larger areas under chillies. They also receive higher
prices for their chillies, although we found no signiicant differences between the
groups. Net household income and net chilli income, however, are signiicantly
greater for farmers in the supermarket channels than for farmers in traditional
channels. Farmers selling to supermarkets have more assets than farmers in the
traditional channel, particularly spraying equipment and storage space.
Similar to other studies (such as Reardon et al. 2009), the case presented here
inds that the supermarket channel pays higher prices, rewarding high quality.
Farmers selling to supermarkets are signiicantly more likely to sort their chillies
by size, colour, and quality, as well as being more likely to pack them in bags or
boxes and keep written records on the prices they received, the quantities they
sold, and the details of pesticide applications.
Producers in the traditional channel tend to sell to more buyers than those in
the supermarket channel. In the ive years before our survey, 66% of farmers in
the traditional channel sold chillies to more than one buyer. In the supermarket
channel, in contrast, 44% sold each chilli crop to the same buyer. Buyers in supermarket channels are signiicantly more likely to provide farmers with technical
assistance, including information about choosing varieties, inding quality seeds,
improving growing techniques, avoiding crop diseases, and increasing the overall quality of their product. The general aim of the buyers is to help the producers
meet the supermarket requirements.
Determinants of Market-Channel Choice
The coeficient of athrho (the arc-hyperbolic tangent of ρ) in the treatment-effects
model indicates that the correlation between the residuals in the selection and
outcome equations is statistically signiicant, suggesting the existence of selection
bias. We therefore use the results of this model instead of those of the probit and
OLS equations. Table 4 shows our estimates of the channel-choice equation and
the net-household-income equation.
TABLE 2 Household Characteristics, Farm Characteristics, and Income
of Chilli Farmers in Traditional and Supermarket Channels
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Variable
Household characteristics
Household members
Age of household head (years)
Education of household head (years)
Chilli-farming experience (years)
Proportion of adults aged between 15 & 65
(%)
Proportion of adults aged over 65 (%)
Owns mobile phone (%)
Distance to subdistrict market (km)
Farm characteristics
Land size (ha)
Irrigated land (ha)
Area planted with chilli (ha)
Production of the largest plot (tonnes)
Productivity of the largest plot (tonnes/ha)
Average chilli price in the last season
(Rp/kg)
Owned cattle/buffalo in the last 5 years
(% yes)
Owned a tractor in the last 5 years (% yes)
Owned a water pump in the last 5 years
(% yes)
Owned a storage house in the last 5 years
(% yes)
Bought/rented chilli land in the last 5
years (% yes)
Invested in water pump in the last 5
years (% yes)
Invested in spraying equipment in the
last 5 years (% yes)
Income (Rp million)
Gross household income
Net household income
Net income from chillies
Net income from other activities
Observations
** p < 0.05; *** p < 0.01.
SuperTraditional market
channel
channel
Total
sample
t-test
4.56
46.24
6.46
9.44
4.34
43.86
7.96
6.74
4.51
45.79
6.74
8.93
1.32
2.07**
–4.84***
3.85***
69.08
66.55
68.60
1.23
2.39
74
6.06
3.92
79
5.46
2.67
75
5.95
–1.49
–1.31
1.67
0.70
0.26
0.34
1.80
9.04
0.80
0.30
0.48
1.82
8.50
0.72
0.28
0.36
1.81
8.94
–1.14
–0.86
–2.72***
–0.05
0.68
6,233
8,323
6,628
–5.07***
5.95
6.25
6.01
–0.12
1.44
1.79
1.50
–0.27
18.89
24.11
19.87
–1.25
14.99
24.11
16.69
–2.34**
13.76
15.18
14.02
–0.39
5.54
8.04
6.01
–1.00
43.33
63.39
47.08
–3.88***
60.57
22.80
6.13
16.71
98.31
32.66
13.67
19.03
67.63
24.65
7.54
17.14
–2.09**
–3.47***
–4.82***
–0.90
470
109
579
456
Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
TABLE 3 Chilli Farmers’ Post-harvest Activities and Number of Buyers, in
Traditional and Supermarket Channels (% respondents answering ‘yes’)
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Variable
SuperTraditional market
channel channel
Total
sample
t-test
Activities prior to sale
Remove small or bad chilli
Sort into different groups by size
Sort into different groups by colour
Sort into different groups by quality
Put into bags or boxes
80.08
8.00
14.58
16.22
77.41
92.86
40.18
54.46
55.36
93.75
82.47
14.02
22.04
23.54
80.47
–3.22**
–9.47***
–9.89***
–9.42***
–3.98***
Record-keeping
On the amount of pesticides
On the dates of pesticide application
On chilli prices
On chilli quantities
11.70
5.95
21.97
21.15
45.54
14.29
81.25
80.36
18.03
7.51
33.06
32.22
–8.93***
–3.03***
–13.78***
–13.88***
Buyers
Had more than one buyer in the last 5 years
Had more than one buyer in the last year
Buyer provided technical assistance
66.32
33.26
6.98
56.25
30.36
58.93
64.44
32.72
16.69
2.01**
0.59
–15.80***
470
109
579
Observations
** p < 0.05; *** p < 0.01.
The number of years of formal education and chilli-farming experience, the
distance from the farmer’s house to a bitumen road, and storage-house ownership are all statistically signiicant in the channel-choice equation. Rao and Qaim
(2011) suggest that education levels inluence modern-market adoption because
farmers with more education are likely to be more conident in adjusting to new
market requirements and more innovative.
We found a negative relation between supermarket-channel participation and
the distance from the farmer’s house to a bitumen road. As travel time and transport costs increase, farmers are more likely to sell their chillies through a traditional channel. Specialised traders in modern channels tend to be especially
sensitive to distance-related transactions costs, and seek producers near paved
roads and with their own transport (Hernández, Reardon, and Berdegué 2007;
Reardon et al. 2009). The variable for chilli-farming experience also has a negative coeficient, perhaps because farmers often need to change their cultivation
practices to participate in supermarket channels. More experienced farmers may
be reluctant to do so.4
4. Kebede, Gunjal, and Cofin (1990) and Wozniak (1987) have also shown the negative
relation between the years of farming experience and the adoption of new technologies.
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TABLE 4 Determinants of Farmer Participation and the Impact on Household Income
Dependent variable: Channel
(1 = supermarket; 0 = traditional)
Coeficients
SE
Household members (persons)
Age of household head (years)
Education of household head (years)
Proportion of adults aged between 15 & 65 (%)
Proportion of adults aged over 65 (%)
Land ownership in 2005 (ha)
Irrigated-land ownership in 2005 (1= yes; 0 = no)
Mobile-phone ownership in 2005 (units)
Motorbike ownership in 2005 (units)
Water-pump ownership in 2005 (units)
Mist-blower ownership in 2005 (units)
Power-tiller ownership in 2005 (units)
Storage-house ownership in 2005 (units)
Chilli-farming experience (years)
Distance from house to bitumen road (km)
Channel (1 = supermarket; 0 = traditional)
–0.031
–0.005
0.066
–0.003
0.009
0.023
0.085
–0.076
0.164
–0.110
0.028
–0.143
0.376
–0.043
–0.544
0.048
0.008
0.023
0.004
0.008
0.091
0.141
0.080
0.108
0.096
0.072
0.424
0.165
0.012
0.222
Constant
–0.542
Variable
Athrho
Test of independent equation: Likelihood ratio chi2 (1)
Note: SE = standard error. Likelihood = –988.68. Wald-chi2 = 281.70.
* p < 0.10; ** p < 0.05; *** p < 0.01.
0.462
[|Z| > z]
0.522
0.486
0.004***
0.512
0.272
0.798
0.547
0.343
0.130
0.253
0.697
0.737
0.023**
0.000***
0.014**
0.241
Dependent variable:
Net income per capita (log)
Coeficients
SE
–0.193
0.000
0.036
–0.001
–0.007
0.205
0.023
0.170
0.171
0.120
0.136
0.378
0.405
0.019
0.027
0.004
0.014
0.002
0.005
0.059
0.081
0.046
0.066
0.058
0.041
0.264
0.108
0.006
0.000***
0.997
0.012**
0.764
0.177
0.001***
0.776
0.000***
0.010**
0.037**
0.001**
0.152
0.000***
0.004***
0.560
0.267
0.036**
1.206
0.269
0.000***
–0.314
0.173
0.070*
2.35
[|Z| > z]
458
Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
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Households with their own storage houses are more likely to participate in
supermarket channels. Chillies are a perishable commodity, so farmers can store
them for only a few days. By storing chillies in these dedicated buildings, farmers
can maintain the freshness and colour, for example, of their crop and are therefore
better able to provide supermarkets with a high-quality product.
Impacts of Supermarket-Channel Participation on Income
This study uses net per capita income, which is calculated by dividing net
household income by household size. As expected, farm size and all the asset
variables except power-tiller ownership have signiicant positive impacts on per
capita income. Likewise, mobile-phone ownership and years of education and
chilli-farming experience have signiicant positive effects on per capita income.
Household size has the expected negative impact, as additional household members reduce per capita income.
The coeficient of the channel-choice variable is statistically signiicant in the
income equation even after controlling for education, farm size, chilli-farming
experience, and various assets. Since the dependent variable is in logarithmic
form and the income coeficient is 0.56, the effect of supermarket participation
is e0.56 – 1 = 0.75, or 75%. In other words, the per capita income of supermarket
participants is 75% higher than that of traditional-channel participants, after controlling for other factors. These indings are in line with the results of previous
studies that associate participation in modern markets with higher household
income (Miyata, Minot, and Hu 2009; Rao and Qaim 2011).
CONCLUSIONS
This study contributes to the emerging literature on emerging modern channels.
We investigated the factors inluencing supermarket-channel participation and
household income by using a treatment effects model, which allowed us to test
and control for selectivity bias. We found that participating in supermarket channels is associated with an increase in per capita income, even after we controlled
for possible selectivity bias and resources reallocated from other activities. This
result is consistent with the previous literature (Miyata, Minot, and Hu 2009;
Neven et al. 2009; Rao and Qaim 2011).
Important determinants of supermarket participation include years of education and chilli-farming experience, the distance from a bitumen road, and storagehouse ownership. Facilitating participation in supermarket channels could
therefore be a useful strategy to help farmers increase their incomes. Our results
highlight the importance of education in giving farmers the capacity and willingness to enter supermarket channels, though training and advice from extension
agents may have similar effects. We also found that participants in the supermarket channel were more likely to sort and package their chillies and keep written records. This provides information on the skills that supermarkets and other
modern-sector buyers require, and thus the types of skills that farmers need in
order to adapt to changing markets. The fact that the distance from a bitumen
road is a signiicant determinant of participation in supermarket channels suggests the importance of infrastructure in reducing transaction costs in agricultural
marketing.
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Determinants and Effects of Small Chilli Farmers’ Participation
459
Farm size, irrigated land, and various assets (except storage-house ownership) were not signiicant determinants of participation in supermarket channels,
which suggests that small farmers and the resource-poor will not necessarily be
excluded from the growing supermarket channel. Low levels of education and
farming experience seem to be greater barriers to participation in supermarket
channels than farm size and farm assets.
The supermarket channel is still quite small, at least for chillies. In our random
sample of chilli farmers in West Java, only 3% reported that they sold their crop
through supermarket channels. Some of these farmers may be selling to traders
who sort and clean the product for resale to supermarkets, so the actual proportion may be greater. Nonetheless, this small share suggests that the supermarket
sector has a limited ability to absorb new suppliers. Preparing a large number of
farmers to sell into this channel could simply displace existing suppliers, reduce
the price premium for high-quality produce, or both. Thus, in addition to helping some farmers meet the growing demand from supermarkets, the government
should work to reduce the marketing and transactions costs in traditional markets, where the bulk of chillies are still sold. These efforts could include improving
the systems for collecting and disseminating market information; establishing a
clear framework of grades and standards, to motivate farmers to meet the quality
requirements of consumers; and providing new technology (such as cold-chain
systems between farmers and traditional markets, or better access to price information) through an effective research and extension system.
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Determinants and Effects of Small Chilli Farmers’
Participation in Supermarket Channels in
Indonesia
Sahara Sahara, Nicholas Minot, Randy Stringer & Wendy J. Umberger
To cite this article: Sahara Sahara, Nicholas Minot, Randy Stringer & Wendy J. Umberger
(2015) Determinants and Effects of Small Chilli Farmers’ Participation in Supermarket
Channels in Indonesia, Bulletin of Indonesian Economic Studies, 51:3, 445-460, DOI:
10.1080/00074918.2015.1110851
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Date: 17 January 2016, At: 23:18
Bulletin of Indonesian Economic Studies, Vol. 51, No. 3, 2015: 445–60
DETERMINANTS AND EFFECTS OF SMALL
CHILLI FARMERS’ PARTICIPATION IN
SUPERMARKET CHANNELS IN INDONESIA
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:18 17 January 2016
Sahara Sahara*
Department of Economics and InterCAFE, Bogor Agricultural University
Nicholas Minot*
International Food Policy Research Institute
Randy Stringer*
University of Adelaide
Wendy J. Umberger*
University of Adelaide
The number of supermarkets in Indonesia is increasing, but small-scale farmers
may be at risk of being excluded from these emerging marketing channels. Drawing
on data from our survey of 600 small-scale chilli farmers in West Java, we examine
the factors that inluence farmers’ decisions to participate in supermarket channels.
We use a treatment-effect model to examine the effect of supermarket participation on income, while controlling for the possibility of selectivity bias. Our results
indicate that participation in the supermarket channel signiicantly increases farm
income, even after we controlled for differences in education, chilli-farming experience, storage-house ownership, and the distance from the farmer’s house to a bitumen road.
Keywords: supermarkets, chilli
JEL classiication: Q12, Q13
INTRODUCTION
The dramatic growth of supermarket chains in developing countries over the past
10–15 years has been well documented (Reardon et al. 2003; Reardon, Timmer,
* InterCAFE = International Centre for Applied Finance and Economics. We wish to
thank David Shearer, from the Australian Centre for International Agricultural Research
(ACIAR), for his helpful comments and suggestions during our research. We acknowledge
and thank our research partner—the Indonesian Center for Agricultural Socio Economic
and Policy Studies (ICASEPS)—as well as Wahida, Ashari, and Nur Khoiriyah Agustin for
survey and research assistance. This article was made possible by inancial support from
ACIAR. All views, interpretations, and conclusions expressed are those of the authors and
not necessarily those of the supporting or cooperating institutions.
ISSN 0007-4918 print/ISSN 1472-7234 online/15/000445-16
http://dx.doi.org/10.1080/00074918.2015.1110851
© 2015 Indonesia Project ANU
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:18 17 January 2016
446
Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
and Minten 2012). In Indonesia, which has followed this trend, supermarkets rose
in number from 1 in 1970 to 284 in 2004 (Suryadarma et al. 2010), and, according
to the World Bank (2007), accounted for about 30% of retail food sales in 2006,
a three-fold increase in market share since 1999. It is expected that the share of
supermarkets in food retail will increase along with the rapid growth of per capita
income, urbanisation, and liberalisation of foreign investment.
The proliferation of supermarkets has created a new market opportunity for
farmers. As supermarket chains look to differentiate their products from those
sold in traditional markets and improve food safety, they develop and impose
private grades and standards, particularly for fresh fruits and vegetables. They
offer higher net prices than traditional markets to farmers who can meet these
requirements, which potentially increases farmers’ incomes (Reardon et al. 2009).
For small-scale farmers, however, who have limited access to information and
credit, meeting the grades and standards imposed by supermarkets can be dificult (Moustier et al. 2010). In addition, some supermarket chains try to guarantee
a certain quality of supply by using vertical coordination mechanisms such as
formal or informal contracts with farmers. Each contract has a transaction cost, so
it is cheaper for a supermarket to work with a small number of large farmers than
a large number of small farmers (Dries et al. 2009).
For these reasons, there is widespread concern among researchers and policymakers that small-scale farmers will be excluded from this emerging market. This
raises two important research questions: whether small farmers can participate
in supermarket channels, and whether participation in these channels increases
their net income.
In spite of the rapid growth in the number of supermarkets in developing countries, few empirical studies examine the determinants of small-farmer participation
in supermarket channels or the effects of this participation on economic aspects.
Rao and Qaim’s (2011) study examined factors associated with participating in
supermarket supply chains for fresh fruits and vegetables in Kenya. They found
that older farmers with larger farms who are members of farmer organisations are
more likely to participate in supermarket supply channels. Hernández, Reardon,
and Berdegué (2007), in contrast, found that participants in supermarket supply
channels for tomatoes in Guatemala are younger and less likely to be members of
farmer organisations than other farmers. Rao and Qaim concluded that larger farmers are more likely to participate in supermarket channels, as did Neven et al. (2009),
whereas Hernández, Reardon, and Berdegué did not ind farm size signiicant.
Furthermore, only a few studies—including Rao and Qaim’s—comprehensively assess the impact of supermarket participation on household income.
Other studies consider the income effects by comparing the gross margin of farm
crops between farmers selling to supermarket channels and farmers selling to
traditional channels, but, as Rao and Qaim note, this method is limited, because it
does not measure other variables that may inluence household income.
This article attempts to contribute to the emerging literature by focusing on
an important high-value agricultural product in Indonesia: chillies. More speciically, it examines the factors that inluence the participation of chilli growers in
the supermarket channel and investigates the effects of this participation on their
incomes. We focus on chillies for several reasons. It is a main ingredient in the
Indonesian daily diet, despite being native to Mexico and thus a non-traditional
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Determinants and Effects of Small Chilli Farmers’ Participation
447
food in Indonesia. Chillies are also produced by small farmers and, unlike other
fresh vegetable products that are harvested just once, at the end of the season,
they can be harvested weekly or fortnightly and can therefore provide a steady
cash low for small farmers. Although only small amounts of chillies end up in
supermarkets, they are a main item in the their fresh-produce sections. We surmise that their sales in supermarkets in Indonesia will increase along with the
rapid rise of supermarkets themselves, owing to an rising trend in the contribution of fresh-food products to supermarket food sales in Indonesia. The World
Bank (2007) reported that the share of fresh fruit and vegetables (FFVs) increased
from virtually zero to 8% of supermarket retail sales between 1998 and 2007.
Further, our interviews with key informants from the three leading supermarkets
in Indonesia (Carrefour, Giant, and Hypermart) revealed that the contribution of
FFVs to supermarket sales was about 15% in 2014, a two-fold increase in market
share since 2007. One informant stated that chillies are an important FFV product
in their chain’s stores.
INDONESIA’S CHILLI INDUSTRY
Chillies, both large and small varieties, are one of Indonesia’s most important
food crops, and most households consume fresh chillies daily. In 2012, the average consumption per capita per week of large chillies and small chillies was 0.317
grams and 0.269 grams, respectively (BPS 2012). Indonesia’s production of chillies
increased by about 8.7%, on average, between 2008 and 2013. Chilli production
increased from about 1.2 million tonnes in 2005 to more than 1.7 million tonnes
in 2013 (table 1), by which time the value of chillies in the Indonesian economy
was Rp 55 trillion—up from Rp 24 trillion in 2008—or 4.2% of the value of the
agricultural sector.1
An estimated 463,000 small producers grow chillies, planting and harvesting year-round (Mustafa, Ali, and Kuswanty 2006). Any increase in production
would potentially generate employment opportunities, especially in rural areas,
since chilli cultivation is labour-intensive. It requires more labour, for example,
than staple crops such as rice and maize; it is estimated that chilli cultivation
needs around 2.6 times more labour days than rice (Mustafa, Ali, and Kuswanty
2006). Chilli planting and harvesting require the most labour.
Java and Sumatra are the major producing areas of chillies in Indonesia, having
accounted for about 80% of national chilli production in 2008 and 87% in 2013.
West Java contributed around 20% of the country’s chilli production during 2008–
13, making it largest chilli-farming area in Indonesia, followed by East Java and
Central Java (table 1).
Around 95% of fresh chillies are sold through traditional wet markets, with the
balance going to supermarkets, small and large food processors, and exporters
(White et al. 2007). The traditional channel includes traders, collectors, and other
buyers who purchase chillies directly from farmers and sell to wholesale markets,
where chillies are sorted by size, variety, and colour. Compared with supermarket
channels, the traditional marketing channels of chillies involve many intermediaries (Chowdhury, Gulati, and Gumbira-Said. 2005). In most traditional channels,
1. In 2013, the total value of Indonesia’s agricultural sector was Rp 1,310 trillion.
448
Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
TABLE 1 Indonesia’s Chilli Production, Selected Provinces, 2008–13 (tonnes)
West Java
East Java
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Central Java
North Sumatra
Aceh
Bengkulu
West Nusa Tenggara
Indonesia
2008
2009
2010
2011
2012
2013
241,362
(20.9)
193,522
(16.8)
150,747
(13.1)
136,419
(11.8)
41,010
(3.6)
50,994
(4.4)
45,014
(3.9)
315,574
(22.9)
243,562
(17.7)
220,929
(16.0)
154,802
(11.2)
34,826
(2.5)
47,700
(3.5)
39,339
(2.9)
245,597
(18.5)
213,674
(16.1)
194,971
(14.7)
196,347
(14.8)
64,149
(4.8)
58,529
(4.4)
18,870
(1.4)
300,620
(20.3)
255,483
(17.2)
184,358
(12.4)
233,258
(15.7)
49,525
(3.3)
41,495
(2.8)
26,128
(1.8)
291,907
(17.6)
343,714
(20.7)
215,129
(13.0)
245,773
(14.8)
90,030
(5.4)
41,618
(2.5)
36,883
(2.2)
374,669
(21.7)
329,177
(19.1)
230,398
(13.3)
198,878
(11.5)
79,139
(4.6)
52,928
(3.1)
35,325
(2.0)
1,153,144 1,378,911 1,328,864 1,483,079 1,656,615 1,726,381
Source: Data from Badan Pusat Statistik (BPS), Indonesia’s central statistics agency.
Note: The values in parentheses refer to the share of national chilli production in each province.
no speciic farm-gate standards are imposed; farmers decide when to plant, which
varieties to plant, and how much to plant, without any input from buyers.
Most chilli farmers selling to supermarket channels, in contrast, are required
to meet speciic product standards, including of colour, variety, and length. The
reward for this effort is a premium from supermarkets that varies with the traditional market price (supermarkets tend to renegotiate prices fortnightly, using
prices in traditional wholesale markets as a reference).
The supermarket channel consists of farmers, traders and farmer groups, specialised wholesalers, and supermarkets (White et al. 2007). The transaction costs
associated with organising exchanges with thousands of small farmers create
opportunities for specialised wholesalers to act as intermediaries. Saung Mirwan
and Bimandiri are examples of specialised wholesalers in West Java (Chowdhury
et al. 2005; World Bank 2007). Similar to trends in other countries, in Indonesia
specialised wholesalers play a central role in organising emerging modern-market
channels, including for chillies. These wholesalers supply a range of supermarket
chains by organising hundreds of small farmers and giving them all the necessary product-speciic guidelines. They communicate intensively with individual
farmers via traders, ‘lead farmers’, or farmer associations, conveying supermarket requirements as well as helping to source quality seeds, providing technical
assistance, and offering related technical support. They are responsible for delivering clean, sorted, and packaged chillies to supermarkets. Farmers in supermarket channels know that they have to meet speciic standards. If they cannot do
so, traders and farmer groups may reject their products, forcing them to sell their
products through traditional channels.
Determinants and Effects of Small Chilli Farmers’ Participation
449
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The cost of entering the supermarket channel includes the necessary time to
learn and use different growing and handling techniques. Investment costs include
new equipment (especially for irrigation and pesticide spraying) and storage
space. Meeting these investment costs often proves challenging for chilli producers, since most are small-scale farmers with limited access to capital (Chowdhury
et al. 2005). Among the potential net beneits of selling through modern market
channels are higher proits and new relationships (with input suppliers and traders) that could increase productivity-enhancing knowledge and assets useful for
other horticultural crops.
ECONOMETRIC MODEL
Model Speciication
Our original research design aimed to gather information from individual households on the quantity of chillies sold to supermarkets and to traditional markets. Our
initial ieldwork suggested that many households sold through traditional markets
only, and that some households sold through the supermarket channel while also
selling a share of their production into the traditional market. We therefore used the
Tobit model to capture the range of chillies sold per household into the supermarket channel, from zero to 100%. Collecting speciic data on the share of chillies sold
through each channel was problematic, however, for a number of reasons.
Pretesting our household questionnaire made it clear that producers could not
give even a rough estimate of how much of their production was sold through
the supermarket and traditional channels, although they were quite conident in
their estimates of the yields per plant and the total quantity sold. Most producers harvest each chilli plant multiple times in one season, up to 12 times in some
cases. Thus, for our sample of farmers, we learned that the households sold chillies every two weeks, on average. Our survey conirmed that only a tiny fraction
of households keep records of any kind.
Chilli traders and wholesale market managers stated that even though they
pass on information to their client farmers on varieties, sizes, and other attributes
required by the supermarkets, they do much of the grading and sorting themselves. In other words, farmers often do not know what share of their production
goes to supermarkets.
To counter this problem, we used a probit model to test whether producers
have any participation in supermarket channels, and how those households differ from those with no participation in supermarket channels. Previous literature
demonstrates that the probit model can be used to examine the farm and household characteristics of farmers who sell any portion of their products through
modern channels, compared with those who do not (Reardon et al. 2009; Rao
and Qaim 2011; Neven et al. 2009; Hernández, Reardon, and Berdegué 2007). We
remain conident that testing the effects of modern-market participation on producers makes an important contribution to the literature, the policy debate, and
the design of development projects.2
2. At the same time, we acknowledge the reviewers’ point that it would have been preferable to be able to collect information on shares for a Tobit analysis.
450
Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
Reardon et al. (2009) equate a farmer’s decision to switch from the traditional to
the modern channel with a farmer’s decision to adopt new technologies. We can
thus use a probit model in which the dependent variable, z, takes the value of one
for farmers selling through a supermarket channel and a value of zero for farmers
selling through a traditional channel (see Rao and Qaim 2011; Neven et al. 2009;
Hernández, Reardon, and Berdegué 2007). It is assumed that z is a linear function
of explanatory variables, w, and an error tem, u:
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zj = wj + γ + uj
(1)
where subscript j indicates the farmer, and u has a zero mean and a variance of
σ2. As in farmer adoption decisions, here the explanatory variables, w, can be
divided into those representing farmers’ incentives and those representing farmers’ capacities.
Incentive variables include the net price of the product (controlling for product
quality), the cost of production and marketing (including transaction costs), and
risk factors. These are usually measured in relative terms, with the supermarket
value in the numerator and the traditional value in the denominator. Capacity
variables include assets that potentially inluence access to supermarket channels: farm assets, including land and non-land assets; collective capital, such as
public infrastructure; and access to credit, quality inputs, technical assistance, and
related information. Each capacity variable is expected to increase the probability
of a farmer’s ‘adopting’ a supermarket channel. For the incentive variables, relative net price is expected to have a positive effect on supermarket-channel adoption, while the relative costs and risks have negative effects.
Farmer participation in supermarket channels is expected to have important
economic impacts on net farm income, productivity, and total output. Hernández,
Reardon, and Berdegué (2007) found that tomato farmers selling through supermarket channels have higher input use (labour, land, pesticides, and fungicides)
and greater output than those selling through traditional channels. Likewise, kale
farmers selling through supermarket channels in Kenya use more inputs (land
and fertiliser), produce higher yields, and earn higher net incomes than those selling through traditional channels (Neven et al. 2009). Rao and Qaim (2011) found
that vegetable farmers participating in supermarket channels in Kenya are associated with higher per capita income, which helps to reduce poverty. This study
focuses on the impact of supermarket-channel adoption on net household income.
Following the literature (Rao and Qaim 2011; Miyata, Minot, and Hu 2009), we
express the income equation as follows:
yj = x jβ + δ z j + ε j
(2)
where y is per capita net household income, x is a vector of exogenous variables that may inluence household income, and z is a dummy variable indicating
supermarket participation. Ordinary least squares (OLS) does not provide a satisfactory solution for how to measure the effect of participation on income, unless
we can guarantee that there is no selectivity bias (Maddala 1983).
In practice, the decision to participate in a speciic market channel may depend
not only on observable variables but also on unobservable variables (such as
farmer self-initiative, entrepreneurial skills, and network relationships). Farmers
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Determinants and Effects of Small Chilli Farmers’ Participation
451
in supermarket channels may have higher individual abilities than farmers in traditional channels (Rao and Qaim 2011). In this case, farmers who chose to sell
through supermarket channels would have higher incomes regardless of whether
they participated in those channels. Hence, OLS would overestimate the impact
of participation (δ) because it contains both the effect of participating in the supermarket channel and the effect of unobservable variables (Greene 2008). For this
reason, previous studies use Heckman selection procedures, and one variation of
these procedures in the empirical literature is a treatment-effects model, which
consists of two equations: the outcome equation (the income equation, as in equation [2]) and the selection equation, containing the unobserved or latent variable
z* (whether the farmers participate in the supermarket channel). Speciically,
yj = x jβ + δ z j + ε j
z*j = w jγ + u j
(3)
where the observed decision in the selection equation is
⎧1, if z* > 0
⎪
j
zj = ⎨
⎪ 0, o herwise
⎩
The error term in the outcome equation, ε, and the selection equation, u, are
bivariate normal with a mean of zero.
Empirical Model
The design of this study involved multiple stages. We irst selected variables that
potentially inluence supermarket participation among small chilli farmers in
Indonesia. We then focused on the impact of channel selection on net household
income—chilli farmers may reallocate labour and land from other activities to
participate in supermarket channels.3 Focusing only on the income from chillies
may overstate the impact on household well-being (Miyata, Minot, and Hu 2009;
Rao and Qaim 2011). Additionally, as this study is interested in whether supermarket participants are better-off, (net) per capita income is a better measure of
household welfare.
We did not directly enter input and output prices into the empirical model,
because it was dificult to control for quality, location of sale, packaging, and other
endogenous farm-level decisions (Neven et al. [2009] excluded prices for similar
reasons). The incentives variables entered into the empirical model refer to the
opportunity to reduce transaction costs. The transaction-costs variables include
the distance, in kilometres, from a farmer’s house to a bitumen road (a proxy of
3. Net income is calculated from farming and off-farm activities. Household income from
farming includes the value of chilli income minus the cost of purchased inputs, income
from other agricultural production, livestock and animal-product sales, aquaculture, agricultural trading, rice-milling business and agricultural wage labour minus the cost of
purchased inputs. Off-farm household income includes wages and transfers. Among the
activities, income from chillies contributes about 34% of household income.
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452
Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
distance to output and input markets)) and a farmer’s access to communication
assets (dummy variable for mobile-phone ownership). Respondents who live farther from a bitumen road spend more time and money selling their produce or
buying inputs than those who live closer to a bitumen road. Hence, this distance
is expected to reduce supermarket-channel participation. Mobile-phone ownership may increase access to either input- or output-market information, and, in
contrast, is expected to increase supermarket-channel participation.
The capacity variables are the following farm assets: land size owned (hectares); irrigated land (a dummy variable that takes the value of one if the land
owned is irrigated, and zero otherwise); motorbike ownership (units); waterpump ownership (units); mist-blower ownership (units); power-tiller ownership
(units); and storage-house ownership (units). Our analysis uses the ownership of
assets in 2005, ive years before the survey was carried out. In this study, all the
supermarket-channel suppliers became entered this channel after 2005. By using
the 2005 assets, we avoid the endogeneity problem of supermarket participation
inluencing asset ownership rather than the reverse (Neven et al. 2009).
We also incorporate in our analysis the potential household labour supply for
chilli production by including, as capacity variables, the proportion of productive
adults (that is, the proportion of household members between 15 and 65 years) and
non-productive adults (the proportion of members over 65 years) in the household
and the number of household members. We do so because participation in modern
markets is more labour-intensive, given the higher quality standards that farmers
are expected to meet compared with those selling through traditional channels
(Miyata et al. 2009). We hypothesise that each of these capacity variables has a positive effect on supermarket-channel participation (except for the share of non-productive adults in the household, which could have a positive or negative impact).
We also include education, farming experience, and age in the model. A high
level of education, measured in years of schooling, of the household head may
increase their access to inancial capital (Neven et al. 2009), while their farming
experience (the number of years of growing chillies) and age (years) may inluence
their market-channel choice (Woldie and Nuppenau 2009). Younger respondents
are more likely to participate in supermarket channels, because they may be more
enterprising, make decisions more quickly, and be more willing to try new technologies (Sharma, Kumar, and Singh 2009). The dependent variable is a dummy
variable that takes the value of one if the farmer participates in the supermarket
channel, and zero if the farmer participates in the traditional channel. In summary, the empirical model for channel selection in this study is as follows:
Channel choice = (household members, age of household head, education of household head, proportion of adults aged between 15 and 65, proportion of adults over 65,
land ownership in 2005, irrigated-land ownership in 2005, motorbike ownership in
2005, water-pump ownership in 2005, mist-blower ownership in 2005, power-tiller
ownership in 2005, storage-house ownership in 2005, chilli-farming experience,
mobile-phone ownership in 2005, distance from bitumen road)
In the impact model, the dependent variable is per capita net household income—
a function of a dummy variable for channel choice and the variables of incentives
and capacities (except for the distance from a bitumen road, which we treat as an
identiication variable).
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Determinants and Effects of Small Chilli Farmers’ Participation
453
We adapted the estimation procedures from Miyata, Minot, and Hu (2009)
and initially used a probit model to estimate the channel-choice equation. We
then used an OLS model to estimate net household income, and a treatmenteffects model to mitigate the possibility of selectivity bias. For the treatmenteffects model, we used maximum likelihood estimation, in which all parameters
in the channel-choice equation (the selection equation) and the net household
income equation (the outcome equation) are estimated simultaneously. In this
stage, we treated the variable of distance from the farmer’s house to a bitumen
road as an identiication variable because we do not believe it has an independent effect on income. This variable is therefore in the selection equation but not
the outcome equation. Besides, the correlation between the variables of ‘distance from bitumen road’ and ‘per capita net household income’ is low (0.003)
and not signiicant at 5%.
DATA COLLECTION
We collected data in one-on-one interviews with chilli farmers in West Java
during March–April 2010. Aside from being Indonesia’s largest chilli-farming
area, West Java has a large number of modern markets. Pandin (2009) reported,
for example, that in 2008 the province had 1,300 small convenience stores, 194
supermarkets, and 29 hypermarkets. Our 18-page survey questionnaire drew on
several months of interviews with key informants, including producers, traders, supermarket buyers, specialised wholesalers, food processors, and extension
agents.
Our survey sample included households that were selling chillies through traditional channels and those that were selling chillies through supermarket channels. The supermarket-channel sample came from a list of 96 chilli farmers in the
Ciamis district who were selling chillies to supermarkets. Supermarket buyers,
specialised suppliers, and dedicated wholesalers provided the 96 names. Our
interviews with key informants (specialised wholesalers) in Bandung revealed
that chillies sold in supermarkets in Bandung are produced mainly in the Ciamis
or Tasikmalaya districts. Our study team visited these districts to interview key
informants (including farmer groups, traders, and extension agents). In these districts, one farmer group and one trader supplied chillies through supermarket
channels. The former provided the names of 36 chilli farmers; the latter provided
the names of 60. All were interviewed during the survey.
To get a list of traditional-channel famers, we used a random sampling procedure, since there are no lists or censuses of chilli farmers in Indonesia. We selected
three districts in West Java: Garut, a major chilli production zone, and Ciamis
and Tasikmalaya, other production zones with substantial numbers of farmers
selling into the modern retail sector. We used a multistage sampling procedure to
select subdistricts, villages, and chilli farmers, and a systematic random sampling
method (Churchill and Iacobucci 2005) to select eight subdistricts in Garut and
three each in Ciamis and Tasikmalaya. The selection process was weighted by the
average annual chilli production in each subdistrict during 2004–8. We selected
three villages at random from each subdistrict, resulting in 42 villages. The survey
team visited the land-tax ofice in each village to compile a list of chilli-producing
households, selecting 12 households from each village list. This process yielded
504 chilli farmers.
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Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
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During data cleaning, we moved 17 households from the random sample who
sold to supermarkets (about 3% of the total random sample) to the supermarketchannel group. We eliminated 4 households from the supermarket sample and
17 households from the random sample owing to poor data quality. The inal
supermarket-channel group comprised 109 households and the inal traditionalchannel group comprised 470.
RESULTS AND DISCUSSION
Descriptive Statistics
Tables 2 and 3 present descriptive statistics for selected variables for farmers selling through the traditional and supermarket channels. Statistically signiicant
differences between these groups (at the 5% level) include the age and education of the household head and the household’s experience in chilli production.
The supermarket-channel farmers are younger, on average, but have more formal education; the traditional market farmers have more experience in producing
chillies, perhaps because they tend to be older.
The average farm size for both groups is less than one hectare, and is not statistically signiicantly different between the groups, although farmers in supermarket
channels have signiicantly larger areas under chillies. They also receive higher
prices for their chillies, although we found no signiicant differences between the
groups. Net household income and net chilli income, however, are signiicantly
greater for farmers in the supermarket channels than for farmers in traditional
channels. Farmers selling to supermarkets have more assets than farmers in the
traditional channel, particularly spraying equipment and storage space.
Similar to other studies (such as Reardon et al. 2009), the case presented here
inds that the supermarket channel pays higher prices, rewarding high quality.
Farmers selling to supermarkets are signiicantly more likely to sort their chillies
by size, colour, and quality, as well as being more likely to pack them in bags or
boxes and keep written records on the prices they received, the quantities they
sold, and the details of pesticide applications.
Producers in the traditional channel tend to sell to more buyers than those in
the supermarket channel. In the ive years before our survey, 66% of farmers in
the traditional channel sold chillies to more than one buyer. In the supermarket
channel, in contrast, 44% sold each chilli crop to the same buyer. Buyers in supermarket channels are signiicantly more likely to provide farmers with technical
assistance, including information about choosing varieties, inding quality seeds,
improving growing techniques, avoiding crop diseases, and increasing the overall quality of their product. The general aim of the buyers is to help the producers
meet the supermarket requirements.
Determinants of Market-Channel Choice
The coeficient of athrho (the arc-hyperbolic tangent of ρ) in the treatment-effects
model indicates that the correlation between the residuals in the selection and
outcome equations is statistically signiicant, suggesting the existence of selection
bias. We therefore use the results of this model instead of those of the probit and
OLS equations. Table 4 shows our estimates of the channel-choice equation and
the net-household-income equation.
TABLE 2 Household Characteristics, Farm Characteristics, and Income
of Chilli Farmers in Traditional and Supermarket Channels
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Variable
Household characteristics
Household members
Age of household head (years)
Education of household head (years)
Chilli-farming experience (years)
Proportion of adults aged between 15 & 65
(%)
Proportion of adults aged over 65 (%)
Owns mobile phone (%)
Distance to subdistrict market (km)
Farm characteristics
Land size (ha)
Irrigated land (ha)
Area planted with chilli (ha)
Production of the largest plot (tonnes)
Productivity of the largest plot (tonnes/ha)
Average chilli price in the last season
(Rp/kg)
Owned cattle/buffalo in the last 5 years
(% yes)
Owned a tractor in the last 5 years (% yes)
Owned a water pump in the last 5 years
(% yes)
Owned a storage house in the last 5 years
(% yes)
Bought/rented chilli land in the last 5
years (% yes)
Invested in water pump in the last 5
years (% yes)
Invested in spraying equipment in the
last 5 years (% yes)
Income (Rp million)
Gross household income
Net household income
Net income from chillies
Net income from other activities
Observations
** p < 0.05; *** p < 0.01.
SuperTraditional market
channel
channel
Total
sample
t-test
4.56
46.24
6.46
9.44
4.34
43.86
7.96
6.74
4.51
45.79
6.74
8.93
1.32
2.07**
–4.84***
3.85***
69.08
66.55
68.60
1.23
2.39
74
6.06
3.92
79
5.46
2.67
75
5.95
–1.49
–1.31
1.67
0.70
0.26
0.34
1.80
9.04
0.80
0.30
0.48
1.82
8.50
0.72
0.28
0.36
1.81
8.94
–1.14
–0.86
–2.72***
–0.05
0.68
6,233
8,323
6,628
–5.07***
5.95
6.25
6.01
–0.12
1.44
1.79
1.50
–0.27
18.89
24.11
19.87
–1.25
14.99
24.11
16.69
–2.34**
13.76
15.18
14.02
–0.39
5.54
8.04
6.01
–1.00
43.33
63.39
47.08
–3.88***
60.57
22.80
6.13
16.71
98.31
32.66
13.67
19.03
67.63
24.65
7.54
17.14
–2.09**
–3.47***
–4.82***
–0.90
470
109
579
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Sahara Sahara, Nicholas Minot, Randy Stringer, and Wendy J. Umberger
TABLE 3 Chilli Farmers’ Post-harvest Activities and Number of Buyers, in
Traditional and Supermarket Channels (% respondents answering ‘yes’)
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Variable
SuperTraditional market
channel channel
Total
sample
t-test
Activities prior to sale
Remove small or bad chilli
Sort into different groups by size
Sort into different groups by colour
Sort into different groups by quality
Put into bags or boxes
80.08
8.00
14.58
16.22
77.41
92.86
40.18
54.46
55.36
93.75
82.47
14.02
22.04
23.54
80.47
–3.22**
–9.47***
–9.89***
–9.42***
–3.98***
Record-keeping
On the amount of pesticides
On the dates of pesticide application
On chilli prices
On chilli quantities
11.70
5.95
21.97
21.15
45.54
14.29
81.25
80.36
18.03
7.51
33.06
32.22
–8.93***
–3.03***
–13.78***
–13.88***
Buyers
Had more than one buyer in the last 5 years
Had more than one buyer in the last year
Buyer provided technical assistance
66.32
33.26
6.98
56.25
30.36
58.93
64.44
32.72
16.69
2.01**
0.59
–15.80***
470
109
579
Observations
** p < 0.05; *** p < 0.01.
The number of years of formal education and chilli-farming experience, the
distance from the farmer’s house to a bitumen road, and storage-house ownership are all statistically signiicant in the channel-choice equation. Rao and Qaim
(2011) suggest that education levels inluence modern-market adoption because
farmers with more education are likely to be more conident in adjusting to new
market requirements and more innovative.
We found a negative relation between supermarket-channel participation and
the distance from the farmer’s house to a bitumen road. As travel time and transport costs increase, farmers are more likely to sell their chillies through a traditional channel. Specialised traders in modern channels tend to be especially
sensitive to distance-related transactions costs, and seek producers near paved
roads and with their own transport (Hernández, Reardon, and Berdegué 2007;
Reardon et al. 2009). The variable for chilli-farming experience also has a negative coeficient, perhaps because farmers often need to change their cultivation
practices to participate in supermarket channels. More experienced farmers may
be reluctant to do so.4
4. Kebede, Gunjal, and Cofin (1990) and Wozniak (1987) have also shown the negative
relation between the years of farming experience and the adoption of new technologies.
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TABLE 4 Determinants of Farmer Participation and the Impact on Household Income
Dependent variable: Channel
(1 = supermarket; 0 = traditional)
Coeficients
SE
Household members (persons)
Age of household head (years)
Education of household head (years)
Proportion of adults aged between 15 & 65 (%)
Proportion of adults aged over 65 (%)
Land ownership in 2005 (ha)
Irrigated-land ownership in 2005 (1= yes; 0 = no)
Mobile-phone ownership in 2005 (units)
Motorbike ownership in 2005 (units)
Water-pump ownership in 2005 (units)
Mist-blower ownership in 2005 (units)
Power-tiller ownership in 2005 (units)
Storage-house ownership in 2005 (units)
Chilli-farming experience (years)
Distance from house to bitumen road (km)
Channel (1 = supermarket; 0 = traditional)
–0.031
–0.005
0.066
–0.003
0.009
0.023
0.085
–0.076
0.164
–0.110
0.028
–0.143
0.376
–0.043
–0.544
0.048
0.008
0.023
0.004
0.008
0.091
0.141
0.080
0.108
0.096
0.072
0.424
0.165
0.012
0.222
Constant
–0.542
Variable
Athrho
Test of independent equation: Likelihood ratio chi2 (1)
Note: SE = standard error. Likelihood = –988.68. Wald-chi2 = 281.70.
* p < 0.10; ** p < 0.05; *** p < 0.01.
0.462
[|Z| > z]
0.522
0.486
0.004***
0.512
0.272
0.798
0.547
0.343
0.130
0.253
0.697
0.737
0.023**
0.000***
0.014**
0.241
Dependent variable:
Net income per capita (log)
Coeficients
SE
–0.193
0.000
0.036
–0.001
–0.007
0.205
0.023
0.170
0.171
0.120
0.136
0.378
0.405
0.019
0.027
0.004
0.014
0.002
0.005
0.059
0.081
0.046
0.066
0.058
0.041
0.264
0.108
0.006
0.000***
0.997
0.012**
0.764
0.177
0.001***
0.776
0.000***
0.010**
0.037**
0.001**
0.152
0.000***
0.004***
0.560
0.267
0.036**
1.206
0.269
0.000***
–0.314
0.173
0.070*
2.35
[|Z| > z]
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Households with their own storage houses are more likely to participate in
supermarket channels. Chillies are a perishable commodity, so farmers can store
them for only a few days. By storing chillies in these dedicated buildings, farmers
can maintain the freshness and colour, for example, of their crop and are therefore
better able to provide supermarkets with a high-quality product.
Impacts of Supermarket-Channel Participation on Income
This study uses net per capita income, which is calculated by dividing net
household income by household size. As expected, farm size and all the asset
variables except power-tiller ownership have signiicant positive impacts on per
capita income. Likewise, mobile-phone ownership and years of education and
chilli-farming experience have signiicant positive effects on per capita income.
Household size has the expected negative impact, as additional household members reduce per capita income.
The coeficient of the channel-choice variable is statistically signiicant in the
income equation even after controlling for education, farm size, chilli-farming
experience, and various assets. Since the dependent variable is in logarithmic
form and the income coeficient is 0.56, the effect of supermarket participation
is e0.56 – 1 = 0.75, or 75%. In other words, the per capita income of supermarket
participants is 75% higher than that of traditional-channel participants, after controlling for other factors. These indings are in line with the results of previous
studies that associate participation in modern markets with higher household
income (Miyata, Minot, and Hu 2009; Rao and Qaim 2011).
CONCLUSIONS
This study contributes to the emerging literature on emerging modern channels.
We investigated the factors inluencing supermarket-channel participation and
household income by using a treatment effects model, which allowed us to test
and control for selectivity bias. We found that participating in supermarket channels is associated with an increase in per capita income, even after we controlled
for possible selectivity bias and resources reallocated from other activities. This
result is consistent with the previous literature (Miyata, Minot, and Hu 2009;
Neven et al. 2009; Rao and Qaim 2011).
Important determinants of supermarket participation include years of education and chilli-farming experience, the distance from a bitumen road, and storagehouse ownership. Facilitating participation in supermarket channels could
therefore be a useful strategy to help farmers increase their incomes. Our results
highlight the importance of education in giving farmers the capacity and willingness to enter supermarket channels, though training and advice from extension
agents may have similar effects. We also found that participants in the supermarket channel were more likely to sort and package their chillies and keep written records. This provides information on the skills that supermarkets and other
modern-sector buyers require, and thus the types of skills that farmers need in
order to adapt to changing markets. The fact that the distance from a bitumen
road is a signiicant determinant of participation in supermarket channels suggests the importance of infrastructure in reducing transaction costs in agricultural
marketing.
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Determinants and Effects of Small Chilli Farmers’ Participation
459
Farm size, irrigated land, and various assets (except storage-house ownership) were not signiicant determinants of participation in supermarket channels,
which suggests that small farmers and the resource-poor will not necessarily be
excluded from the growing supermarket channel. Low levels of education and
farming experience seem to be greater barriers to participation in supermarket
channels than farm size and farm assets.
The supermarket channel is still quite small, at least for chillies. In our random
sample of chilli farmers in West Java, only 3% reported that they sold their crop
through supermarket channels. Some of these farmers may be selling to traders
who sort and clean the product for resale to supermarkets, so the actual proportion may be greater. Nonetheless, this small share suggests that the supermarket
sector has a limited ability to absorb new suppliers. Preparing a large number of
farmers to sell into this channel could simply displace existing suppliers, reduce
the price premium for high-quality produce, or both. Thus, in addition to helping some farmers meet the growing demand from supermarkets, the government
should work to reduce the marketing and transactions costs in traditional markets, where the bulk of chillies are still sold. These efforts could include improving
the systems for collecting and disseminating market information; establishing a
clear framework of grades and standards, to motivate farmers to meet the quality
requirements of consumers; and providing new technology (such as cold-chain
systems between farmers and traditional markets, or better access to price information) through an effective research and extension system.
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