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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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ISSN 0007-4918 print/ISSN 1472-7234 online/15/000445-16 © 2015 Indonesia Project ANU http://dx.doi.org/10.1080/00074918.2015.1110851
* 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.
DETERMINANTS AND EFFECTS OF SMALL
CHILLI FARMERS’ PARTICIPATION IN
SUPERMARKET CHANNELS IN INDONESIA
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 participa-tion 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 experi-ence, storage-house ownership, and the distance from the farmer’s house to a bitu -men 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,
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 difi -cult (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 policy-makers 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 coun-tries, 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 farm-ers 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—comprehen -sively 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 specii
-cally, 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
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 sur-mise that their sales in supermarkets in Indonesia will increase along with the rapid rise of supermarkets themselves, owing to an rising trend in the contribu-tion 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 aver-age 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 harvest-ing 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 intermedi-aries (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.
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 tra-ditional 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, spe-cialised 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
neces-sary product-speciic guidelines. They communicate intensively with individual farmers via traders, ‘lead farmers’, or farmer associations, conveying supermar -ket requirements as well as helping to source quality seeds, providing technical assistance, and offering related technical support. They are responsible for deliv-ering clean, sorted, and packaged chillies to supermarkets. Farmers in
supermar-ket 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.
TABLE 1 Indonesia’s Chilli Production, Selected Provinces, 2008–13 (tonnes)
2008 2009 2010 2011 2012 2013
West Java 241,362 315,574 245,597 300,620 291,907 374,669 (20.9) (22.9) (18.5) (20.3) (17.6) (21.7)
East Java 193,522 243,562 213,674 255,483 343,714 329,177
(16.8) (17.7) (16.1) (17.2) (20.7) (19.1) Central Java 150,747 220,929 194,971 184,358 215,129 230,398 (13.1) (16.0) (14.7) (12.4) (13.0) (13.3) North Sumatra 136,419 154,802 196,347 233,258 245,773 198,878 (11.8) (11.2) (14.8) (15.7) (14.8) (11.5)
Aceh 41,010 34,826 64,149 49,525 90,030 79,139
(3.6) (2.5) (4.8) (3.3) (5.4) (4.6) Bengkulu 50,994 47,700 58,529 41,495 41,618 52,928 (4.4) (3.5) (4.4) (2.8) (2.5) (3.1) West Nusa Tenggara 45,014 39,339 18,870 26,128 36,883 35,325 (3.9) (2.9) (1.4) (1.8) (2.2) (2.0)
Indonesia 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.
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 produc-ers, 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 trad -ers) 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 house
-holds 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
supermar-ket 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 produc-ers 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
chil-lies 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 them-selves. 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 dif-fer from those with no participation in supermarket channels. Previous literature demonstrates that the probit model can be used to examine the farm and house-hold 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 pro -ducers 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 prefer -able to be -able to collect information on shares for a Tobit analysis.
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:
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 farm
-ers’ 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 chan -nels: 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, rela -tive net price is expected to have a posi-tive effect on supermarket-channel adop-tion, 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 super -market 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 sell-ing through traditional channels (Neven et al. 2009). Rao and Qaim (2011) found that vegetable farmers participating in supermarket channels in Kenya are asso-ciated 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=xjβ+δzj+εj (2)
where y is per capita net household income, x is a vector of exogenous
varia-bles that may inluence household income, and z is a dummy variable indicating
supermarket participation. Ordinary least squares (OLS) does not provide a satis -factory 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
in supermarket channels may have higher individual abilities than farmers in tra-ditional 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 super-market 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 equa-tion [2]) and the selecequa-tion equaequa-tion, containing the unobserved or latent variable z* (whether the farmers participate in the supermarket channel). Speciically,
yj=xjβ+δzj+εj
zj
*
=wjγ +uj (3)
where the observed decision in the selection equation is
zj=
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 super-market 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, ag-ricultural trading, rice-milling business and agag-ricultural 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.
distance to output and input markets)) and a farmer’s access to communication
assets (dummy variable for mobile-phone ownership). Respondents who live far-ther 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 owner-ship 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 (hec-tares); irrigated land (a dummy variable that takes the value of one if the land owned is irrigated, and zero otherwise); motorbike ownership (units); water-pump 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 pos-itive effect on supermarket-channel participation (except for the share of non-pro-ductive 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 tech-nologies (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 sum-mary, the empirical model for channel selection in this study is as follows:
Channel choice = (household members, age of household head, education of house-hold 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).
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
effects model to mitigate the possibility of selectivity bias. For the treatment-effects 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 independ -ent effect on income. This variable is therefore in the selection equation but not the outcome equation. Besides, the correlation between the variables of
‘dis-tance 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, trad-ers, supermarket buytrad-ers, specialised wholesaltrad-ers, food processors, and extension agents.
Our survey sample included households that were selling chillies through tra -ditional channels and those that were selling chillies through supermarket chan-nels. 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 dis-tricts, 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 proce-dure, 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.
During data cleaning, we moved 17 households from the random sample who sold to supermarkets (about 3% of the total random sample) to the supermarket-channel 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
traditional-channel group comprised 470.
RESULTS AND DISCUSSION
Descriptive Statistics
Tables 2 and 3 present descriptive statistics for selected variables for farmers
sell-ing through the traditional and supermarket channels. Statistically signiicant
differences between these groups (at the 5% level) include the age and
educa-tion of the household head and the household’s experience in chilli produceduca-tion.
The supermarket-channel farmers are younger, on average, but have more for-mal 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
statisti-cally 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
super-market 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 over-all 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.
of Chilli Farmers in Traditional and Supermarket Channels
Variable
Traditional channel
Super-market channel
Total
sample t-test
Household characteristics
Household members 4.56 4.34 4.51 1.32
Age of household head (years) 46.24 43.86 45.79 2.07** Education of household head (years) 6.46 7.96 6.74 –4.84*** Chilli-farming experience (years) 9.44 6.74 8.93 3.85*** Proportion of adults aged between 15 & 65
(%) 69.08 66.55 68.60 1.23
Proportion of adults aged over 65 (%) 2.39 3.92 2.67 –1.49
Owns mobile phone (%) 74 79 75 –1.31
Distance to subdistrict market (km) 6.06 5.46 5.95 1.67
Farm characteristics
Land size (ha) 0.70 0.80 0.72 –1.14
Irrigated land (ha) 0.26 0.30 0.28 –0.86
Area planted with chilli (ha) 0.34 0.48 0.36 –2.72*** Production of the largest plot (tonnes) 1.80 1.82 1.81 –0.05 Productivity of the largest plot (tonnes/ha) 9.04 8.50 8.94 0.68 Average chilli price in the last season
(Rp/kg) 6,233 8,323 6,628 –5.07***
Owned cattle/buffalo in the last 5 years
(% yes) 5.95 6.25 6.01 –0.12
Owned a tractor in the last 5 years (% yes) 1.44 1.79 1.50 –0.27 Owned a water pump in the last 5 years
(% yes) 18.89 24.11 19.87 –1.25
Owned a storage house in the last 5 years
(% yes) 14.99 24.11 16.69 –2.34**
Bought/rented chilli land in the last 5
years (% yes) 13.76 15.18 14.02 –0.39
Invested in water pump in the last 5
years (% yes) 5.54 8.04 6.01 –1.00
Invested in spraying equipment in the
last 5 years (% yes) 43.33 63.39 47.08 –3.88***
Income (Rp million)
Gross household income 60.57 98.31 67.63 –2.09**
Net household income 22.80 32.66 24.65 –3.47***
Net income from chillies 6.13 13.67 7.54 –4.82*** Net income from other activities 16.71 19.03 17.14 –0.90
Observations 470 109 579
** 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 owner
-ship 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 trans -port costs increase, farmers are more likely to sell their chillies through a tra-ditional 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
nega-tive 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.
TABLE 3 Chilli Farmers’ Post-harvest Activities and Number of Buyers, in Traditional and Supermarket Channels (% respondents answering ‘yes’)
Variable
Traditional channel
Super-market channel
Total
sample t-test
Activities prior to sale
Remove small or bad chilli 80.08 92.86 82.47 –3.22** Sort into different groups by size 8.00 40.18 14.02 –9.47*** Sort into different groups by colour 14.58 54.46 22.04 –9.89*** Sort into different groups by quality 16.22 55.36 23.54 –9.42*** Put into bags or boxes 77.41 93.75 80.47 –3.98***
Record-keeping
On the amount of pesticides 11.70 45.54 18.03 –8.93***
On the dates of pesticide application 5.95 14.29 7.51 –3.03***
On chilli prices 21.97 81.25 33.06 –13.78***
On chilli quantities 21.15 80.36 32.22 –13.88***
Buyers
Had more than one buyer in the last 5 years 66.32 56.25 64.44 2.01** Had more than one buyer in the last year 33.26 30.36 32.72 0.59 Buyer provided technical assistance 6.98 58.93 16.69 –15.80***
Observations 470 109 579
** p < 0.05; *** p < 0.01.
TABLE 4 Determinants of Farmer Participation and the Impact on Household Income
Variable
Dependent variable: Channel (1 = supermarket; 0 = traditional)
Dependent variable: Net income per capita (log)
Coeficients SE [|Z| > z] Coeficients SE [|Z| > z]
Household members (persons) –0.031 0.048 0.522 –0.193 0.027 0.000***
Age of household head (years) –0.005 0.008 0.486 0.000 0.004 0.997
Education of household head (years) 0.066 0.023 0.004*** 0.036 0.014 0.012**
Proportion of adults aged between 15 & 65 (%) –0.003 0.004 0.512 –0.001 0.002 0.764
Proportion of adults aged over 65 (%) 0.009 0.008 0.272 –0.007 0.005 0.177
Land ownership in 2005 (ha) 0.023 0.091 0.798 0.205 0.059 0.001***
Irrigated-land ownership in 2005 (1= yes; 0 = no) 0.085 0.141 0.547 0.023 0.081 0.776 Mobile-phone ownership in 2005 (units) –0.076 0.080 0.343 0.170 0.046 0.000***
Motorbike ownership in 2005 (units) 0.164 0.108 0.130 0.171 0.066 0.010**
Water-pump ownership in 2005 (units) –0.110 0.096 0.253 0.120 0.058 0.037**
Mist-blower ownership in 2005 (units) 0.028 0.072 0.697 0.136 0.041 0.001**
Power-tiller ownership in 2005 (units) –0.143 0.424 0.737 0.378 0.264 0.152
Storage-house ownership in 2005 (units) 0.376 0.165 0.023** 0.405 0.108 0.000***
Chilli-farming experience (years) –0.043 0.012 0.000*** 0.019 0.006 0.004***
Distance from house to bitumen road (km) –0.544 0.222 0.014**
Channel (1 = supermarket; 0 = traditional) 0.560 0.267 0.036**
Constant –0.542 0.462 0.241 1.206 0.269 0.000***
Athrho –0.314 0.173 0.070*
Test of independent equation: Likelihood ratio chi2 (1) 2.35
Note: SE = standard error. Likelihood = –988.68. Wald-chi2 = 281.70.
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 mem-bers 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
con-trolling 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 chan-nels 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 educa-tion and chilli-farming experience, the distance from a bitumen road, and storage-house 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 willing-ness to enter supermarket channels, though training and advice from extension agents may have similar effects. We also found that participants in the supermar-ket channel were more likely to sort and package their chillies and keep writ-ten 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 sug -gests the importance of infrastructure in reducing transaction costs in agricultural marketing.
Farm size, irrigated land, and various assets (except storage-house
owner-ship) 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 propor-tion 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 help-ing some farmers meet the growhelp-ing demand from supermarkets, the government should work to reduce the marketing and transactions costs in traditional mar-kets, 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 infor-mation) through an effective research and extension system.
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