00074918.2015.1104649
Bulletin of Indonesian Economic Studies
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Tomato Farmers and Modernising Value Chains in
Indonesia
Ricardo Hernández, Thomas Reardon, Ronnie Natawidjaja & Shobha Shetty
To cite this article: Ricardo Hernández, Thomas Reardon, Ronnie Natawidjaja & Shobha Shetty
(2015) Tomato Farmers and Modernising Value Chains in Indonesia, Bulletin of Indonesian
Economic Studies, 51:3, 425-444, DOI: 10.1080/00074918.2015.1104649
To link to this article: http://dx.doi.org/10.1080/00074918.2015.1104649
Published online: 29 Nov 2015.
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Date: 17 January 2016, At: 23:17
Bulletin of Indonesian Economic Studies, Vol. 51, No. 3, 2015: 425–44
TOMATO FARMERS AND MODERNISING
VALUE CHAINS IN INDONESIA
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Ricardo Hernández*
IFPRI
Thomas Reardon*
Michigan State University; University of Adelaide
Ronnie Natawidjaja*
Padjadjaran University
Shobha Shetty*
World Bank
The tomato value chain in Indonesia has transformed in the last two decades. We
assess this transformation here, focusing on small tomato farmers in West Java and
the determinants of their market-channel choices (as well as the technology correlates of those choices). These farmers sell to traditional village traders, urban and
modern wholesalers, and supermarkets, and they have all invested heavily in irrigation and rely on external inputs. We ind differences among farmers selling to different market channels. To wit, non-land assets—especially irrigation—are important to farmers participating in the supermarket, or modern, channel, but farm size
affects modern-channel participation only in high-level commercial zones (zones
dense in infrastructure and near highways). We also ind that modern-channel
farmers earn more proit than farmers in other channels but do not necessarily use
chemicals more intensively. Yet hardly any farmers sell graded tomatoes; the main
‘capture of rents’ goes to specialised and modernising wholesalers.
Keywords: tomatoes, modern markets, technology adoption
JEL classiication: D61, O3, Q12, Q13
INTRODUCTION
In the last two decades, food markets have globalised and agrifood value chains
in developing countries, including Indonesia, have transformed. The economic
development literature has begun to explore the implications of these changes
for farmers. Most attention has been on the emerging impacts on farmers of
linkages to export markets, supermarkets, large processing irms and their
contract-farming schemes, and the emerging category of so-called modernising
wholesalers. Yet the literature on the determinants and impacts of horticulture
farmers selling to supermarkets and large processing irms in Indonesia is still
relatively small. The few studies (such as Sahara and Gyau 2014) tend to focus
* This article builds on a report by the World Bank (2007). IFPRI = International Food Policy
Research Institute.
ISSN 0007-4918 print/ISSN 1472-7234 online/15/000425-20
http://dx.doi.org/10.1080/00074918.2015.1104649
© 2015 Indonesia Project ANU
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426
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
on the micro-determinants of the choice between the modern channel and other,
traditional channels, such as local ield brokers.1
Although these modern and traditional channels are important choices for horticulture farmers in Indonesia, this dichotomy neglects another important choice:
modernising wholesalers (Hernandez, Berdegué, and Reardon 2015). These nontraditional actors are large and usually based in the urban wholesale market, and
they often buy directly from farmers rather than rely on traditional village brokers. We include this third channel in this article’s econometric analysis of data
from our detailed survey in 2006 of tomato farmers in West Java. We do this in the
spirit of non-econometric pieces by Verhofstadt and Maertens (2013), for Rwanda,
and Reardon et al. (2012), for China, India, and Bangladesh, which emphasise the
importance of modernising wholesalers as an intermediate channel for farmers.
While micro-variables such as farm size and non-land assets help to determine market-channel choice, we posit that the type of production zone—whether
dense in infrastructure and commercial activity or not—inluences market-channel and technology choices on the basis of transaction costs and network and
cluster effects. This inluence is not measured in the literature on market-channel
choice in Indonesia but is particularly strong in West Java, where the Cipularang
Toll Road to Jakarta opened in 2005 (the year before our survey). The literature
usually evaluates the income pay-offs of participating in each channel, but it does
not test for technological intensiication-cum-modernisation as a correlate of market channel.
We test here for a correlation between market modernisation and technology
intensiication because the latter is an important policy goal of the Ministry of
Agriculture. We explore, in particular, the determinants of participation in certain
market channels and the choice of certain technologies, as well as the interaction
between market participation and the choice of technology. These market channels include traditional channels (such as local ield brokers); modernising channels (which comprise modernising wholesalers); and modern channels (such as
supermarkets and specialised and dedicated wholesalers for supermarkets).2 The
1. On export markets, see, for example, work by Minten, Randrianarison, and Swinnen (2009)
and Maertens and Swinnen (2009) on export irms and horticulture farms in Madagascar
and Senegal. On supermarkets, see articles by Blandon, Henson, and Cranield (2009), Rao,
Brümmer, and Qaim (2012), and Schipmann and Qaim (2011) on supermarkets and horticulture farmers in Honduras, Kenya, and Thailand, respectively, and, for Indonesia, by
Sahara and Gyau (2014) on chilli in West Java. On large processing irms and contract farming, see work by Barrett et al. (2012) and, on Indonesia, by Puspitawati et al. (2013), on
potatoes, and Simmons, Winters, and Patrick (2005) on seed corn, seed rice, and chickens.
On modernising wholesalers, see Hernandez, Berdegué, and Reardon’s (2015) article on the
determinants and impacts of small guava farmers in Mexico selling to these actors.
2. Specialised wholesalers sell one product or very few products, and sell to different actors
from different market channels. A wholesaler who specialises in selling tomatoes to supermarkets, other wholesalers, and traditional retailers would be a specialised wholesaler.
Dedicated wholesalers, in contrast, may sell a wide variety of products, but sell to actors
from a speciic market channel. They do not sell their graded products to other market
channels, such as to traditional retailers. A wholesaler who sells different types of graded
perishables to different supermarket chains would be a dedicated wholesaler. In some
cases, a given wholesaler can be both a specialised wholesaler and a dedicated wholesaler.
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Tomato Farmers and Modernising Value Chains in Indonesia
427
technologies include traditional technology, low-external-input-intensity technology, and ‘modernised’ external-input-intensive technology.
Tomatoes are an example of the recent, rapid emergence of non-traditional
food products in Indonesia, having been introduced into Asia only several hundred years ago by Portuguese and Spanish traders. By 1970, only 15,000 tonnes of
tomatoes were being produced in Indonesia—a tiny share of the country’s vegetable production and consumption. By 2010, Indonesia’s population had doubled
but annual tomato production had increased 60-fold, to nearly 900,000 tonnes.
This change was mirrored in production zones. By the 2000s, West Java, which
had traditionally focused on producing rice and tea, became Indonesia’s leading
tomato-producing province, growing half the country’s tomatoes.
Our interviews with key informants in Indonesia’s horticulture industry
revealed that farming in West Java has, in recent years, generally eschewed traditional techniques and adopted intensiication technologies that require more
fertiliser, pesticides, and irrigation. Actors in various segments of tomato supply chains told us that the predominant market channel 20 years ago was the
traditional system of village traders buying tomatoes from farmers and selling
them locally to rural assemblers, who then sold them to urban wholesale markets. In recent years, however, in a kind of disintermediation, large wholesalers
from rural and urban wholesale markets have increasingly been buying direct
from farmers. Since the 1990s, large, modernising wholesalers from Jakarta and
Bandung and other provincial capitals have increased their presence in production zones. In the 2000s, specialised and dedicated wholesalers bought, graded,
sorted, and packed tomatoes and other produce for delivery to supermarkets,
hotels, and restaurants.
CONTEXT AND DATA
Context
In the past few decades, West Java has evolved from Indonesia’s major rice bowl
to its major vegetable basket. The province’s infrastructure density and urban
share have increased sharply, largely because of the growth of Bandung and other
cities in West Java and the decrease in the time and cost to travel between rural
West Java and large urban markets in Jakarta. Of course, outside the main cities
of West Java there are differences between subzones—especially between valleys
and mountainous areas, and between rural hinterland areas and the small urban
areas of secondary and tertiary cities and towns (so-called rur-urban areas)—and
these differences have manifested themselves in the differing commercial characteristics of each subzone. As rural land and labour prices rose, along with incomedriven demand from local and island-wide urban markets, horticulture in West
Java converted (relatively low-paying) rice ields and forested land into (highpaying) farmland for fruit and vegetables. Low-value vegetables such as cabbage
were produced irst, followed by medium-value vegetables such as tomatoes and
potatoes.
Our ield research revealed that agricultural markets in West Java have evolved
through three stages. The province’s traditional market systems were dominated
by small village traders, who sold to villages and nascent urban markets. This
initial stage was replaced by an intermediate transformation stage, which saw the
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Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
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advent of medium- and large-scale traders. These traders were based in wholesale markets (in Jakarta and Bandung) and bought from farmers in the districts
around Bandung (and Bogor). The next stage, that of an emerging modern market
channel, added specialised and dedicated wholesalers, which sold to the incipient
supermarket and hotel and restaurant sectors. Food-product supplier Bimandiri,
for example, which had been a traditional wholesaler in the Bandung market in
the 1990s, began to set up warehouses and collection points off-market and contract with modern-market buyers of tomatoes and other produce.
Data
In February–July 2006, we conducted a district-level survey of farming households in West Java, using a structured questionnaire on farm and household
characteristics; farm production and input use; credit, technical assistance, and
participation in associations; and tomato marketing (including channels and
transaction terms). This survey had, overall, a one-year recall period and, for
assets, a ive-year recall period, the latter to create lagged asset variables exogenous to the respondents’ market and production choices at the time of the
questionnaire.
At the time of our survey, West Java had 25 kabupaten (districts).3 We identiied
a subset of the 12 districts that each produced at least 1,000 tonnes of tomatoes a
year, according to 2000–2004 data from Badan Pusat Statistik, Indonesia’s central
statistics agency. By identifying districts that produced high amounts of tomatoes,
we hoped to ind farmers in diverse market channels and who used diverse technologies and had diverse farm sizes and non-land assets. We also hoped to ind a
diversity of merchant types. The districts differed in production volumes, so we
used the sampling method of probability proportional to size (weighted). At this
sampling stage, we selected Bandung and Garut as the districts in which to conduct our household survey. We chose four kecamatan (subdistricts) from each. In
2006, Bandung had 30 subdistricts that produced more than 1,000 tonnes of tomatoes annually, while Garut had 28. We chose, at random, the Ciwidey, Lembang,
Pangalengan, and Pasirjambu subdistricts of Bandung, and the Cigedug, Cikajang,
Cisurupan, and Pasirwangi subdistricts of Garut.
For each selected subdistrict, we compiled a list of tomato farmers by drawing
on several sources, including the land-tax registration list, agricultural oficials,
farm leaders, and local traders. We then divided this list into two strata: farmers
who supplied the modern channel—that is, supermarkets—directly (or indirectly,
via specialised wholesalers), and farmers who supplied other channels (the intermediate, or ‘modernising wholesaler’ channel, and the traditional channel, comprising village traders). We selected 600 farmers across all subdistricts, assigning
300 farmers to Bandung and 300 to Garut. We then assigned 150 farmers to each
of the two strata within these districts. We weighted for the over-representation of
any subdistrict or market channel, thus controlling for sampling design and making the overall sample more representative of the population. After data cleaning,
we retained 596 usable household observations.
3. The district of West Bandung was created in 2007.
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Tomato Farmers and Modernising Value Chains in Indonesia
429
DESCRIPTIVE ANALYSIS OF TOMATO FARMERS
Tables 1–5 present selected descriptive statistics for our sample of farming
households. Each table has two sets of stratiications: the commercial zone of the
household’s subdistrict and the main marketing channel used by the household.
To stratify our sample by commercial zone, we used oficial statistics at the subdistrict level, and data from our own subdistrict survey from 2006, to create a score
for each subdistrict on the basis of two indicators: the share of households whose
primary activity is non-agricultural (which is thus a proxy for the rur-urban or
urban intensity of the subdistrict) and the share of land under horticultural crops
(as opposed to rice), which are nearly fully commercial while rice is semi-commercial or semi-subsistence. We therefore classiied two subdistricts in Bandung
(Lembang and Pangalengan) and two in Garut (Cisurupan and Cikajang) as being
in high-level commercial zones. We classiied the remaining subdistricts as being
in low-level commercial zones.
To stratify our sample by the main marketing channel used by households, we
deined each channel as follows: the traditional channel includes farmers that sell
mainly to village traders; the intermediate channel includes households that sell
directly to large wholesalers; and the modern channel includes households that sell
to supermarkets directly (or indirectly, via specialised and dedicated wholesalers).
Table 1 contains general household characteristics. All strata have similar
amounts of tomato-growing experience (around a decade), having started to produce tomatoes well before the recent changes in the market; specialised wholesalers for supermarkets began their operations in Bandung only four to ive years
before we conducted our survey, and in Garut only two to three years before. All
strata also have similar household sizes and similar ages and education levels of
household heads. Most households have concrete houses and with a regular supply of electricity and water. The overall proile is one of economically secure rural
households, regardless of the commercial intensity of their subdistrict or their
market-channel choice. Few households are members of cooperatives. The main
difference is that those in the modern channel—that is, those that sell to supermarkets—are more specialised in tomato production. This is as expected, given
the exigencies of the modern channel. Farmers in the intermediate channel—
those selling to modernising wholesalers—are highly specialised in tomato production only in low-level commercial zones, while around 70% of farmers in the
traditional channel declared tomato production as their main source of income.
Table 2 shows landholding and land-use characteristics by commercial zone and
market-channel choice. Tomato farms are small, averaging 0.7–0.8 hectares each,
which, in West Java, is slightly larger than the size of an average rice farm (0.6 hectares) (Brázdik 2006). Modern-channel farms are roughly 20%–30% larger than the
size of an average farm in the two other strata, in both commercial zones. Farms
in low-level commercial zones are slightly larger (15%) than those in high-level
commercial zones, largely because the former are in denser areas with higher land
prices. The share of rented land in total operated land is nearly 30% in high-level
commercial zones versus 15% in low-level commercial zones. Most rented land
is owned by rice farmers; we observed that many small rice farmers, when they
worked in town, rented out their land to horticulture farmers. Nearly all the strata
in both zone categories grew only horticultural products but very little to no rice.
Tomatoes composed, on average, roughly half the horticultural area of the farms.
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TABLE 1 Household Characteristics of Tomato Farmers, by Commercial Zone and Market Channel, 2004–5
Low-level commercial
Traditional Intermediate
Modern
High-level commercial
Overall
Traditional Intermediate
Modern
Overall
Households
175
57
47
279
152
122
43
317
Age of household head (years)
Education of household head
(years)
Tomato-farming experience of
household head (years)
Household size (members)
42.1
48.1
42.7
43.4
43.2
41.7
41.7
42.4
% of households with:
Concrete house
Electricity
Own water source
Part of household co-op (%)
Tomato farming as main
income (%)
6.8a
6.5a
8.6c
7.1
6.7a
7.2ab
7.2ab
7.0
11.3b
3.7a
8.9ab
4.0ab
11.1b
4.2b
10.8
3.8
10.5b
4.3b
9.4ab
4.1b
8.6a
4.3b
9.8
4.2
82.9
98.3
96.6
61.4
100.0
100.0
85.1
100.0
100.0
78.9
98.9
97.8
92.1
100.0
96.7
86.1
100.0
97.5
93.0
100.0
100.0
89.9
100.0
97.5
9.1c
1.8b
0.0a
6.1
12.5c
13.9c
0.0a
11.4
69.7b
91.2c
85.1c
76.7
71.1b
50.0a
93.0c
65.9
Note: The superscript a, b, and c show differences between groups, using a Tukey–Kramer test (p < 0.1). Co-op = cooperative.
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TABLE 2 Landholding and Land-Use Characteristics of Tomato Farmers, by Commercial Zone and Market Channel, 2004–5
Low-level commercial
Traditional Intermediate
Modern
High-level commercial
Overall
Traditional Intermediate
Modern
Overall
Operated land by status (ha)
Owned
Rented
Sharecropped-in
0.7ab
0.6ab
0.1a
0.0
0.7ab
0.6ab
0.1a
0.0
1.0b
0.8b
0.2ab
0.0
0.7
0.6
0.1
0.0
0.6a
0.5ab
0.2ab
0.0
0.7ab
0.4a
0.3b
0.0
0.9b
0.7b
0.2ab
0.0
0.7
0.5
0.2
0.0
Operated land by crop (ha)
Cultivated
Tomatoes
Other vegetables or fruit
Rice
Uncultivated
0.7
0.7
0.3
0.3
0.1
0.0
0.7
0.7
0.3
0.3
0.0
0.0
1.0
1.0
0.5
0.5
0.0
0.0
0.8
0.8
0.3
0.4
0.1
0.0
0.6
0.6
0.6
0.3
0.3
0.0
0.7
0.7
0.7
0.4
0.4
0.0
0.8
0.8
0.8
0.4
0.4
0.0
0.7
0.7
0.7
0.3
0.3
0.0
Note: The superscript a, b, and c show differences between groups, using a Tukey–Kramer test (p < 0.1).
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432
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
Table 3 shows tomato irrigation and seasonality, comparing circumstances in
1999–2000 with those in 2004–5. In 1999–2000, only 53.3% of farms in high-level
commercial zones irrigated their tomatoes; in 2004–5, 88.0% did. Small commercial farms invested heavily to shift from single-season cropping to multicropping,
presumably to match the continuous demand from the market for fresh tomatoes.
In 2004–5, 65.5% of the whole sample of tomato farmers multicropped: 65.5% of
traditional, 61.6% of modernising-channel farmers, and 77.7% of modern-channel
farmers. In ive years, farmers in the intermediate and modern channels doubled their land under tomatoes; farmers in the traditional channel, in contrast,
expanded their land by only 20%, on average, regardless of commercial zone.
Table 4 shows tomato production costs and proits per hectare, on average,
during 2004–5. Contrary to expectations, the total costs of modern-channel farmers are slightly lower than traditional-channel farmers. Modern-channel farmers
spend slightly less on external inputs, such as seed, fertiliser, and pesticides, than
intermediate- and traditional-channel farmers. This may be because modernchannel farmers are somewhat more informed and more allocatively eficient
than those in other channels, and manage their input use more carefully. But the
external-input intensity varies little across the strata.
In high-level commercial zones, the proit or revenue–cost ratio of modernchannel farmers is 12% higher than those of farmers in the two other channels
(which have similar ratios) when ignoring the imputed costs of family labour.
In low-level commercial zones, the advantage is 35%. The proit superiority of
modern-channel farmers could be due to their slightly lower input outlays (for
similar yields to those of farmers in the other channels) and to their receiving
price premiums of 2% in high-level commercial zones and 18% in low-level commercial zones.
Prices received by farmers in high-level commercial zones are similar across
market channels, while farmers in low-level commercial zones and the modern
market channel receive a signiicantly higher price than farmers in other channels.
This result mirrors those found among guava producers in Mexico (Hernandez,
Berdegué, and Reardon 2015), where modernising wholesalers have to pay a
premium in low-level commercial zones to guarantee quantity and quality. This
practice is often unnecessary in high-level commercial zones, because all market
channels have very competitive prices and because farmers in the modern channel beneit from lower risk (via implicit contracts) and a higher number of sales.
Table 5 gives details on tomato marketing. Households in all channels sell fresh
tomatoes every few weeks—a common pattern in Indonesia and other developing countries, especially in tropical and subtropical zones. In contrast, tomatoes
that go to processors are typically harvested in a single batch. Yields are similar
across all channels, but modern-channel farmers have greater areas under tomato
production and multicrop more often than other farmers. They therefore sell more
tomatoes than other farmers, in both zone categories: one-half and one-third more
than traditional-channel farmers in high- and low-level commercial zones, respectively. Farmers in the intermediate channel sell more tomatoes than those in the
traditional channel but fewer than those in the modern channel.
Grading is nearly absent for farm-level sales, across all market channels. The
intermediaries, regardless of channel, buy all the tomatoes, of all grades, from
the farmers. Our interviews with intermediaries revealed that modernising
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TABLE 3 Irrigation and Cropping by Tomato Farmers, by Commercial Zone and Market Channel, 1999–2000 and 2004–5
Low-level commercial
Traditional Intermediate
Modern
High-level commercial
Overall
Traditional Intermediate
Modern
Overall
% of land irrigated
Crop-land (2005)
Tomatoes (2005)
Tomatoes (2000)
65.3ab
88.0
56.2
61.0a
87.6
46.3
68.8ab
85.9
66.0
65.0
87.6
55.8
83.3c
85.2
55.9
76.4b
89.7
49.2
76.5b
93.1
55.8
79.7
88.0
53.3
Tomato seasons (2004–5)
(% of farmers)
1
2
3
41.4
27.6
31.0
35.1
24.6
40.4
19.1
44.7
36.2
36.3
29.9
33.8
27.6
40.1
32.2
41.8
36.9
21.3
25.6
34.9
39.5
32.8
38.2
29.0
Tomato seasons (1999–2000)
(% of farmers)
0
1
2
3
26.9
36.0
14.3
22.9
14.0
12.3
33.3
40.4
8.5
87.2
2.1
2.1
21.1
39.8
16.1
22.9
23.0
15.8
30.3
30.9
32.0
22.1
22.1
23.8
14.0
7.0
41.9
37.2
25.2
17.0
28.7
29.0
Tomatoes (ha) (2004–5)
Tomatoes (ha) (1999–2000)
0.6a
0.6b
0.7ab
0.5ab
0.9b
0.7ba
0.6
0.6
Note: The superscript a, b, and c show differences between groups, using a Tukey–Kramer test (p < 0.1).
0.7ab
0.5ab
0.7ab
0.3a
0.7ab
0.4ab
0.7
0.5
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TABLE 4 Costs and Proits of Tomato Farmers, by Commercial Zone and Market Channel, 2004–5
Low-level commercial
Traditional Intermediate
Production costs (Rp million)
Agro-inputs
(% of total cost)
Seed
Fertilisers
Pesticides
Labour
(% of total cost)
Family
Hired
Land rent
(% of total cost)
Plastic, stakes, fuel
(% of total cost)
High-level commercial
Modern
Overall
Traditional Intermediate
Modern
Overall
13.0b
(41.7)
0.9
6.1
6.1
14.0b
(44.9)
2.8
11.3
1.9
(6.1)
2.2
(7.1)
12.9ab
(41.3)
0.8
6.0
6.1
13.7b
(43.9)
3.0
10.7
2.5
(8.0)
2.1
(6.7)
12.6a
(48.8)
1.0
5.4
6.2
8.7a
(33.7)
1.5
7.2
2.2
(8.5)
2.3
(8.9)
12.9
(42.6)
0.9
6.0
6.1
13.1
(43.2)
2.6
10.5
2.1
(6.9)
2.2
(7.3)
13.6b
(48.1)
0.9
6.3
6.4
10.0ab
(35.3)
1.6
8.4
2.4
(8.5)
2.3
(8.1)
13.6b
(47.2)
0.9
6.2
6.4
10.6ab
(36.8)
1.8
8.8
2.2
(7.6)
2.4
(8.3)
12.7a
(49.6)
0.8
5.5
6.4
8.1a
(31.6)
1.5
6.6
2.4
(9.4)
2.4
(9.4)
13.5
(48.0)
0.9
6.2
6.4
10.0
(35.6)
1.7
8.3
2.3
(8.2)
2.3
(8.2)
31.2c
31.2c
25.8a
30.3
28.3b
28.8b
25.6a
28.1
Proit (Rp million / ha, unless otherwise stated)
39.3a
Revenue
39.9a
Yield (tonnes/ha)
51.8
52.7
758.0a
Price (Rp/kg)
793.0ab
31.2c
Total cost
31.2c
45.8b
50.6
936.0c
25.8a
40.7
51.8
810.0
30.3
42.1ab
52.7
823.0b
28.3b
42.1ab
52.1
830.0b
28.8b
44.3b
54.3
838.0b
25.6a
42.4
52.7
828.0
28.1
20.1c
1.8
21.5c
1.9
10.5
1.4
13.1
1.5
13.8b
1.5
15.4b
1.6
13.2b
1.5
15.0b
1.6
18.7bc
1.7
20.2c
1.8
14.2
1.5
15.9
1.6
Total cost
Proit, including family labour
Revenue/cost
Proit, excluding family labour
Revenue/cost
8.7a
1.3
11.5a
1.4
8.1a
1.3
11.1a
1.4
Note: The superscript a, b, and c show differences between groups, using a Tukey–Kramer test (p < 0.1). Discrepancies are due to rounding.
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TABLE 5 Marketing by Tomato Farmers by Commercial Zone and Market Channel, 2004–5
Low-level commercial
Traditional Intermediate
High-level commercial
Modern
Overall
Traditional Intermediate
Modern
Overall
Times sold during season
7.9a
7.6a
8.3b
7.9
8.4b
7.7a
10.5bc
8.4
Tonnes sold during season
Graded
Ungraded
14.4a
0.0a
14.4a
18.4b
0.0a
18.4b
22.8c
1.4b
21.4ab
16.6
0.2
16.4
15.3ab
0.0a
15.3ab
18.1ab
0.0a
18.1b
23.2c
0.3ab
22.9c
17.4
0.0
17.4
Trader payment system (%)
In advance
On harvest
Shortly after harvest
On consignment
Other
ab
0.0
0.6
9.1
90.3
0.0
b
0.0
7.0
21.1
71.9
0.0
ab
0.0
0.0
0.0
95.7
4.3
0.0
1.8
10.0
87.5
0.7
ab
0.0
0.0
2.6
97.4
0.0
b
0.0
0.0
14.8
85.2
0.0
a
0.0
0.0
0.0
90.7
9.3
0.0
0.0
6.9
91.8
1.3
Note: The superscript a, b, and c show differences between groups, by using a Tukey–Kramer test (p < 0.1).
436
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
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wholesalers and the specialised and dedicated wholesalers for supermarkets
grade and sell into different markets, while traditional wholesalers sell ungraded
tomatoes. This means that the intermediaries, rather than the farmers, capture
the proits from achieving quality in tomato production. Table 5 also shows that
no farmer, in any channel, received an advance from a trader, contradicting the
conventional wisdom that traders ‘tie’ advances to credit or output market transactions. Farmers tend to have regular relationships with traders and sell mainly
on consignment, even in the modern channel.
ECONOMETRIC MODEL
We model market-channel choice as a variation on the output supply decision,
and we model input demands to relect technology choice. We do this using two
sets of equations. The irst set models these choices as a function of the economic
determinants—risk, relative prices of outputs and inputs, and a vector of quasiixed capital assets, as well as various context-speciic shifters. These outputsupply and input-demand equations derive theoretically from a proit function,
without requiring the assumption of proit maximisation We do not restate the
initial theoretical derivation of Sadoulet and De Janvry (1995) and further contributions from Holloway, Barrett, and Ehui 2005) here, but their system of output
supply and factor demand functions in decision prices, p*, is
(
q = q p* , z q
)
(1)
The output supply functions are speciied here to distinguish the different market channels. The basic model is broad enough to link market-channel decisions
to transaction costs (Barrett et al. 2012), a subset of input costs; it is also broad
enough to link channel and technology decisions to thresholds of land and nonland assets (Carter and Barrett 2006).
The second set of equations models production as a technical function of
land, labour, and capital, without economic variables; we add the production
function in order to derive marginal physical products (MPPs). We then value
the MPPs at output prices to derive marginal value products (MVPs), which
we then compare with factor prices to measure allocative eficiency (Lau and
Yotopoulos 1971).
To estimate the above equations, in general, we (a) estimate the function of
market-channel choice and (b) estimate the technology economic choices (inputdemand functions) and the technical relations (the production functions), after
having controlled for the conditional probability of market-channel participation.
In the irst of these steps, we estimated a multinomial logit; we used Bourguignon,
Fournier, and Gurgand’s (2007) extension of Dubin and McFadden’s (1984)
approach to correct for selection bias. The regression models the participation of a
farming household in a given market channel, with three channels as the choices
(traditional, intermediate, and modern).
The implementation model for market-channel and technology choices is as
follows:
Y1 = Xβ1 + u1
Y j* = zγ j + η j
(2)
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Tomato Farmers and Modernising Value Chains in Indonesia
437
where j = 1, 2, 3. The subscript j represents the categorical variable of the market
channel among the three alternatives (modern = 1, intermediate= 2, and traditional
= 3), based on utilities, Y j*. The vector z represents the explanatory variables for all
market-channel alternatives, a set of farmer characteristics that together relect
the incentive and capacity variables inluencing the producer’s indirect utility.
The vector X represents the explanatory variables in the production function.
Consistent coeficients of the market-channel determinants (Y j ) can be obtained
by maximum likelihood estimation methods. The problem is how to estimate the
vector of coeficients of the production function β1, considering that the disturbance
term (u1), might not be independent of all (η j ) .
Dubin and McFadden’s model uses a linearity assumption. Bourguignon,
Fournier, and Gurgand suggest a variation, making u1 linear on a set of normal
distributions and allowing u1 to be normal as well. The production function equation in (1) is then conditional on choosing the modern channel (j = 1):
⎡
P ⎤
Y1 = X1β1 + σ ⎢ r1*m ( P1 ) + ∑ rj*m ( Pj ) j ⎥ + w1
Pj − 1 ⎦
j=2,3
⎣
(3)
The determinants are as follows:
• Socio-demographic variables: household head’s education (years); household
head’s tomato-growing experience (years); household head’s age (years); and
household size (members)
• Micro-asset variables: ive-year lagged farm size interacted with location
(Bandung or Garut); distance to a paved highway (kilometres); lagged
percentage of farmland under irrigation; and a lagged dummy variable for
membership in a farmers’ association or cooperative
• Meso-asset variables: lagged number of packing houses in the subdistrict
(units) and a dummy variable for the subdistrict’s commercial zone (low or
high level)
We used lagged assets to avoid causality problems in the market-channel or
input choice. Moreover, we separated the land effect in the two districts because
of the different farming systems and scarcity of land between the two, with Garut
less dense than Bandung.
In the second stage, the input-demand functions have the following determinant variables:
• Prices: tomato price (rupiah per kilogram); price of fertiliser (rupiah per
kilogram); labour wage (rupiah per day); and price of pesticides (rupiah per
kilogram)
• Socio-demographic variables: household head’s education (years); household
head’s age (years); and household size (members)
• Micro-asset variables: owned land (hectares); owned land × Bandung dummy
variable; distance to a paved highway (kilometres); and the percentage of
farmland under irrigation
• Meso-asset variables: dummy for high-level commercial zone
• Controls for selection bias: inverse Mills ratio 1 and inverse Mills ratio 2
• Dummy variable for dry season
438
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
TABLE 6 Determinants of Market-Channel Choice
Market channel
Age of household head (years)
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Education of household head (years)
Tomato-farming experience of household head (years)
Household size (members)
Distance to paved highway (km)
Dummy for high-level commercial zone
Lagged land owned (ha)
Lagged land owned × Bandung dummy
Lagged number of packing houses in district (units)
Lagged participation in cooperative (yes = 1; no = 0)
Lagged share of operated land under irrigation
Observations
Pseudo R2
Wald Chi2 (32)
Probability > Chi2
Intermediate
Modern
0.006
(0.015)
0.024
(0.063)
–0.070**
(0.024)
–0.035
(0.116)
–0.210
(0.354)
1.007**
(0.331)
–0.048
(0.135)
–0.665
(0.452)
0.008**
(0.003)
0.029
(0.337)
1.166*
(0.620)
0.021
(0.028)
0.152
(0.122)
–0.046
(0.062)
–0.197
(0.203)
0.104
(0.175)
4.069**
(0.954)
–1.895**
(0.794)
2.112**
(1.025)
–0.121**
(0.025)
–0.945
(0.816)
2.514**
(1.159)
596
0.748
858.96
0.000
* p < 0.1; ** p < 0.05; *** p < 0.01.
REGRESSION RESULTS
Determinants of Market-Channel Choice
To avoid causation problems, we used lagged assets in our regression instead
of endogenous, current-period asset variables. We tested all lagged-asset variables for endogeneity, after Rivers and Vuong (1988), and found no evidence of
it. The salient results of the determinants of market-channel choice (table 6) are
as follows.
Lagged farm size has ambiguous effects on market-channel participation. It
had a positive effect on modern-channel participation in Bandung, where land is
scarce, but a negative effect in Garut. It had no effect on farm size in the intermediate channel in either category of commercial zone. In an attempt to determine
why smaller farmers in the more rural district of Garut are in the modern channel, we draw on the results of our rapid, rural, pre-survey appraisal. In Bandung,
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Tomato Farmers and Modernising Value Chains in Indonesia
439
near the city, larger farmers with more capital tend to make frequent shipments
to specialised wholesalers and proit from nearby commercial opportunities. In
Garut, a more rural subdistrict, with bigger parcels of land, the larger farmers
tend to sell large volumes to inter-island traders who come to that area for large
volumes, leaving the modern channel—a smaller and more specialised channel
than in Bandung—to smaller farmers, who nevertheless are richer (in terms of
non-land assets) than a typical small farmer in Garut.
Access to irrigation is another important determinant of modern-channel participation. The lagged irrigation share has an important positive effect on participation in the modern and intermediate channels. Such consistency of production
is an important attribute that dedicated wholesalers look for in farmers. This asset
effect is similar to that found, for example, among tomato farmers in Guatemala
(Hernandez, Reardon, and Berdegué 2007). Moreover, large wholesalers in urban
markets require a year-round supply of produce for their varied clients, such as
supermarkets, restaurants, and hotels.
Meso-level characteristics play an important role in modern- and intermediatechannel participation. Being in a high-level commercial zone favours participation in the modern and intermediate channels. Moreover, the (lagged) number
of packing houses in the subdistrict has a positive effect on participation in the
intermediate channel by lowering transaction costs; traders in this channel tend
to own those packing houses, as our pre-survey appraisal revealed. These are
key results, because buyers in the food industry tend to source their products
from sellers in high-level commercial zones, where transaction costs are lower
and where there is more chance of inding an abundance of good-quality products
to meet the market requirements. For this point in relation to contract farming, see
Barrett et al.’s (2012) article.
Input-Demand Functions
Our input-demand analysis (table 7) reveals that the use of labour by farmers is
more responsive to factor input prices in the modern channel than in the two other
market channels. The labour demand of modern-channel farmers responds positively to the price of pesticides in the labour equation, indicating that pesticides
substitute for weeding labour in this channel.4 Farmers in the intermediate channel, in contrast, are not sensitive to the price of pesticides. The own-price coeficient
(the wage) is negative, as expected. Owning land in Bandung has a strong positive effect on labour use among modern-channel farmers, which implies that the
quality demands of supermarkets require high levels of crop care and that farmers
often have to irrigate their tomato plants manually during the dry season.
Our analysis also reveals that there is a strong complementarity of fertiliser
use and irrigation, and that transaction costs (proxied by the distance to a paved
highway) have a negative effect on fertiliser use. The demand for fertiliser and
pesticides from farmers in the intermediate channel increases with rises in output
4. In this article, the term ‘pesticides’ includes herbicides. We measure pesticide use in
cash expenditure on pesticides, given that pesticides are very heterogeneous and we need
to aggregate by value. We measure fertiliser use in the same way for the same reason. We
measure labour use in days.
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TABLE 7 Input-Demand Regressions
Labour
Traditional Intermediate
Output price
(Rp/kg)
Labour wage
(Rp/day)
Price of pesticides
(Rp/kg)
Price of fertiliser
(Rp/kg)
Age of HH head
(years)
Education of HH
head (years)
HH size (members)
Owned land (ha)
Owned land ×
Bandung dummy
Distance to paved
highway (km)
% of tomato land
irrigated
Inverse Mills ratio 1
0.44
(0.27)
–0.01
(0.01)
–0.001**
(0.001)
0.00
(0.001)
1.36
(1.64)
5.60
(7.97)
2.02
(12.20)
14.54
(17.30)
88.41**
(37.76)
43.72
(31.29)
–587.40**
(64.90)
2.61*
(1.53)
–1.91
(4.46)
0.02
(0.02)
0.004
(0.005)
0.01
(0.01)
3.32
(2.59)
7.59
(11.95)
36.62**
(17.68)
111.82*
(66.01)
–12.16
(114.32)
63.24
(50.62)
–960.50**
(127.50)
–1.04
(2.88)
Pesticides
Modern
–15.89**
(5.65)
–0.20**
(0.07)
0.003**
(0.001)
0.17**
(0.06)
12.09
(3.87)
6.381
(23.62)
78.09
(28.56)
71.66
(85.78)
230.08**
(170.30)
63.47
(14.53)
–1,669.30
(168.90)
24.98
(14.61)
Traditional Intermediate
–4.28**
(1.15)
–0.19**
(0.05)
0.90**
(0.14)
–21.77**
(5.15)
–7.51
(6.96)
6.09
(33.92)
–76.99
(51.93)
–165.84**
(73.65)
–17.42
(160.79)
110.87
(133.21)
280.60
(276.30)
–24.28**
(6.53)
Fertiliser
Modern
33.65**
–69.50**
(16.01)
(11.67)
–0.311**
–0.70**
(0.07)
(0.15)
–1.01
2.10**
(1.76)
(0.18)
–105.99**
696.42**
(44.17)
(118.25)
–6.60
–5.56
(9.29)
(8.00)
–7.80
17.87
(42.91)
(48.82)
19.06
40.14
(63.51)
(59.02)
–288.00
–130.96
(237.07)
(177.26)
385.21
–1,406.05**
(410.60)
(558.58)
–298.81*
14.69
(181.81)
(30.03)
–15.70
308.80
(458.10)
(349.10)
–20.70**
–25.46
(10.34)
(30.19)
Traditional Intermediate
–1.34
(0.95)
–0.02
(0.04)
0.18
(0.12)
–2.90
(4.27)
–0.97
(5.77)
4.00
(28.12)
–30.55
(43.05)
31.93
(61.06)
315.60**
(133.31)
151.86
(110.44)
165.70
(229.10)
19.23**
(5.42)
49.17**
(12.25)
–0.02
(0.05)
–3.49**
(1.35)
–136.60**
(33.79)
–6.61
(7.11)
–3.48
(32.83)
–25.42
(48.59)
166.31
(181.35)
51.50
(314.10)
220.94
(139.08)
732.30**
(350.40)
–1.03
(7.91)
Modern
–32.48**
(11.79)
–0.13**
(0.15)
0.18
(0.18)
334.91**
(119.48)
6.55
(8.08)
39.75
(49.33)
139.63**
(59.64)
342.93*
(179.11)
350.55
(564.42)
–114.41**
(30.35)
361.00
(352.80)
50.82*
(30.50)
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Inverse Mills ratio 2
73.67**
(15.11)
8.11
(32.09)
130.42**
(55.29)
67.41**
(25.72)
60.77
(55.51)
173.79
(519.23)
108.05
(20.93)
155.08**
(79.59)
204.75
(230.30)
R2
Prob > F
0.32
0.00
0.37
0.00
0.76
0.00
0.69
0.00
0.70
0.00
0.88
0.00
0.14
0.00
0.35
0.00
0.55
0.00
Observations
327
179
84
327
179
84
327
179
84
Dummy for dry
season
Dummy for high-level
commercial zone
Note: HH = household.
* p < 0.1; ** p < 0.05; *** p < 0.01.
–142.23**
(64.33)
–3,142.10**
(136.65)
920.91**
(235.42)
–318.51**
(92.39)
–2,796.27**
(199.36)
–3,015.98
(1864.85)
11.26
(43.25)
–2,017.15**
(164.47)
58.41
(475.91)
192.70**
(53.33)
–248.59**
(113.29)
–673.43**
(195.18)
202.41**
(70.68)
6.31
(152.51)
–6,632.90**
(1,426.59)
–195.46**
(43.71)
–227.92
(166.19)
–685.60
(480.89)
442
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
TABLE 8 Marginal Value Products of Factors versus
Factor Prices, by Market Channel (Rp)
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Traditional
Land (ha)
Fertiliser (kg)
Labour (day)
Intermediate
Modern
MVP
Factor cost
MVP
Factor cost
MVP
Factor cost
41.0 m
443
2,839
2.2 m
2,024
8,710
36.4 m
97
1,968
2.2 m
2,024
8,710
4,146
8,710
Note: MVP = marginal value product.
prices and decreases with rises in input prices. This suggests that farmers in this
channel are sensitive to variations in input and output prices, implying a commercial orientation. Farmers in the traditional channel do not exhibit this sensitivity.
Estimates of Allocative Eficiencies: Comparison among Groups
The allocative eficiencies of farmers in different market channels can be derived
by estimating farm production functions, after controlling for endogenous stratiication in each market channel. We can analyse these eficiencies by comparing
the MVPs of factors such as land, fertiliser, and labour with the prices of these
factors. Table 8 shows the statistically signiicant results for factors with results in
the estimation of the production function.
The MVP of land is well above the factor cost of rent for farmers in the traditional and intermediate channels, indicating a severe land constraint. The MVP
of labour, in contrast, is well below the factor cost of wages in all channels, but
the overuse of labour (from the viewpoint of allocative eficiency) is most severe
among farmers in the intermediate channel. This suggests that there are not
enough off-farm labour opportunities to allow these farmers to reduce their use
of farm labour to eficient levels. The MVP of fertiliser, like that of labour, is well
below the factor cost of an additional kilogram of fertiliser. As with labour, the
overuse of fertiliser is most severe among farmers in the intermediate channel.
Overall, our analysis of MVPs shows that tomato farmers in all channels would
beneit from expanding their production. Although there is evidence of the overuse of purchased variable inputs, especially by farmers in the traditional and
intermediate channels, we ind that farmers in the modern channel tend to be
more allocatively eficient.
CONCLUSIONS
Tomato farms in the modern channel in Indonesia may be 25% bigger than the
country’s average tomato farm, but they are still small. In this channel, farm size
matters only in high-level commercial zones; in the intermediate and traditional
channels, farm size does not have a signiicant effect. This implies that a relative
‘exclusion effect’ exists in the modern channel. The different effects of different
commercial zones on farm size in market-channel choice have not been tested
in the regional literature. We also ind that commercial zones matter to farmers
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Tomato Farmers and Modernising Value Chains in Indonesia
443
participating in modern and intermediate markets (that is, those selling to supermarkets and modernising wholesalers, respectively), with dense, rur-urbanised
zones near highways being a key determinant. This inding is consistent with
Barrett et al.’s (2012) prediction for contract farming. Infrastructure matters to
market-channel modernisation.
Non-land assets—such as irrigation, in particular—are important in determining the choice of the modern and intermediate channels. In our 2006 survey, while
most tomato farmers adopted irrigation during the ive-year recall period for
assets, the farmers in the modern channel did so irst and tended to multicrop the
most. This concords with indings in the literature, such as Hernandez, Reardon,
and Berdegué’s (2007) assertion that this ‘continuity’ of supply (and the quality
that water control confers) is associated with the modern channel. Yet we also
ind that the intermediate channel requires consistency of supply, as large urban
wholesalers in this channel have among their clientele those that require a yearround tomato supply consistent in volume and quality.
Contrary to conventional wisdom, modern-channel farmers do not use pesticides more intensively than other farmers. In contrast, farmers in the intermediate and traditional channels use more labour and fertiliser than is allocatively
eficient and yet do not have higher yields than farmers in the modern channel. In
fact, farmers in the modern channel earned more proit than those in the intermediate and traditional channels (owing to a combination of slightly lower costs and
modest price premiums). Our descriptive results show, however, that hardly any
farmers in any channel sold graded tomatoes; in this early stage of market modernisation in Indonesia, the main ‘capture of rents’ goes to specialised wholesalers
for supermarkets and to modernising wholesalers. These wholesalers sell tomatoes of different qualities of and at different prices, per our pre-survey appraisal.
In Indonesia, the intermediate market channel has become a second-tier modern channel. Farmers in this channel, who sell to modernising wholesalers, share
several traits with farmers in the modern channel, who sell directly or indirectly
to supermarkets: both groups use high levels of irrigation; increase their areas of
land under tomatoes over time; and are sensitive to input prices, implying a commercial orientation. Yet farmers in the intermediate channel also share many traits
with those in the traditional channel, such as incurring high production costs and
being allocatively ineficient.
REFERENCES
Barrett, Christopher B., Maren E. Bachke, Marc F. Bellemare, Hope C. Michelson, Sudha
Narayanan, and Thomas F. Walker. 2012. ‘Smallholder Participation in Contract Farming: Comparative Evidence from Five Countries’. World Development 40 (4): 715–30.
Blandon, Jose, Spencer Henson, and John Cranield. 2009. ‘Small-Scale Farmer Participation in New Agri-Food Supply Chains: Case of the Supermarket Supply Chain for Fruit
and Vegetables in Honduras’. Journal of International Development 21 (7): 971–84.
Bourguignon, François, Martin Fournier, and Marc Gurgand. 2007. ‘Selection Bias Corrections Based on the Multinomial Logit Model: Monte Carlo Comparisons’. Journal of
Economic Surveys 21 (1): 174–205.
Brázdik, František. 2006. ‘Non-parametric Analysis of Technical Eficiency: Factors Affecting Eficiency of West Java Rice Farms’. CERGE-EI Working Paper 286, Center for Economic Research and Graduate Education, Economic Institute, Prague.
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444
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
Carter, Michael R., and Christopher B. Barrett. 2006. ‘The Economics of Poverty Traps and
Persistent Poverty: An Asset-Based A
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Tomato Farmers and Modernising Value Chains in
Indonesia
Ricardo Hernández, Thomas Reardon, Ronnie Natawidjaja & Shobha Shetty
To cite this article: Ricardo Hernández, Thomas Reardon, Ronnie Natawidjaja & Shobha Shetty
(2015) Tomato Farmers and Modernising Value Chains in Indonesia, Bulletin of Indonesian
Economic Studies, 51:3, 425-444, DOI: 10.1080/00074918.2015.1104649
To link to this article: http://dx.doi.org/10.1080/00074918.2015.1104649
Published online: 29 Nov 2015.
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Date: 17 January 2016, At: 23:17
Bulletin of Indonesian Economic Studies, Vol. 51, No. 3, 2015: 425–44
TOMATO FARMERS AND MODERNISING
VALUE CHAINS IN INDONESIA
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Ricardo Hernández*
IFPRI
Thomas Reardon*
Michigan State University; University of Adelaide
Ronnie Natawidjaja*
Padjadjaran University
Shobha Shetty*
World Bank
The tomato value chain in Indonesia has transformed in the last two decades. We
assess this transformation here, focusing on small tomato farmers in West Java and
the determinants of their market-channel choices (as well as the technology correlates of those choices). These farmers sell to traditional village traders, urban and
modern wholesalers, and supermarkets, and they have all invested heavily in irrigation and rely on external inputs. We ind differences among farmers selling to different market channels. To wit, non-land assets—especially irrigation—are important to farmers participating in the supermarket, or modern, channel, but farm size
affects modern-channel participation only in high-level commercial zones (zones
dense in infrastructure and near highways). We also ind that modern-channel
farmers earn more proit than farmers in other channels but do not necessarily use
chemicals more intensively. Yet hardly any farmers sell graded tomatoes; the main
‘capture of rents’ goes to specialised and modernising wholesalers.
Keywords: tomatoes, modern markets, technology adoption
JEL classiication: D61, O3, Q12, Q13
INTRODUCTION
In the last two decades, food markets have globalised and agrifood value chains
in developing countries, including Indonesia, have transformed. The economic
development literature has begun to explore the implications of these changes
for farmers. Most attention has been on the emerging impacts on farmers of
linkages to export markets, supermarkets, large processing irms and their
contract-farming schemes, and the emerging category of so-called modernising
wholesalers. Yet the literature on the determinants and impacts of horticulture
farmers selling to supermarkets and large processing irms in Indonesia is still
relatively small. The few studies (such as Sahara and Gyau 2014) tend to focus
* This article builds on a report by the World Bank (2007). IFPRI = International Food Policy
Research Institute.
ISSN 0007-4918 print/ISSN 1472-7234 online/15/000425-20
http://dx.doi.org/10.1080/00074918.2015.1104649
© 2015 Indonesia Project ANU
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426
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
on the micro-determinants of the choice between the modern channel and other,
traditional channels, such as local ield brokers.1
Although these modern and traditional channels are important choices for horticulture farmers in Indonesia, this dichotomy neglects another important choice:
modernising wholesalers (Hernandez, Berdegué, and Reardon 2015). These nontraditional actors are large and usually based in the urban wholesale market, and
they often buy directly from farmers rather than rely on traditional village brokers. We include this third channel in this article’s econometric analysis of data
from our detailed survey in 2006 of tomato farmers in West Java. We do this in the
spirit of non-econometric pieces by Verhofstadt and Maertens (2013), for Rwanda,
and Reardon et al. (2012), for China, India, and Bangladesh, which emphasise the
importance of modernising wholesalers as an intermediate channel for farmers.
While micro-variables such as farm size and non-land assets help to determine market-channel choice, we posit that the type of production zone—whether
dense in infrastructure and commercial activity or not—inluences market-channel and technology choices on the basis of transaction costs and network and
cluster effects. This inluence is not measured in the literature on market-channel
choice in Indonesia but is particularly strong in West Java, where the Cipularang
Toll Road to Jakarta opened in 2005 (the year before our survey). The literature
usually evaluates the income pay-offs of participating in each channel, but it does
not test for technological intensiication-cum-modernisation as a correlate of market channel.
We test here for a correlation between market modernisation and technology
intensiication because the latter is an important policy goal of the Ministry of
Agriculture. We explore, in particular, the determinants of participation in certain
market channels and the choice of certain technologies, as well as the interaction
between market participation and the choice of technology. These market channels include traditional channels (such as local ield brokers); modernising channels (which comprise modernising wholesalers); and modern channels (such as
supermarkets and specialised and dedicated wholesalers for supermarkets).2 The
1. On export markets, see, for example, work by Minten, Randrianarison, and Swinnen (2009)
and Maertens and Swinnen (2009) on export irms and horticulture farms in Madagascar
and Senegal. On supermarkets, see articles by Blandon, Henson, and Cranield (2009), Rao,
Brümmer, and Qaim (2012), and Schipmann and Qaim (2011) on supermarkets and horticulture farmers in Honduras, Kenya, and Thailand, respectively, and, for Indonesia, by
Sahara and Gyau (2014) on chilli in West Java. On large processing irms and contract farming, see work by Barrett et al. (2012) and, on Indonesia, by Puspitawati et al. (2013), on
potatoes, and Simmons, Winters, and Patrick (2005) on seed corn, seed rice, and chickens.
On modernising wholesalers, see Hernandez, Berdegué, and Reardon’s (2015) article on the
determinants and impacts of small guava farmers in Mexico selling to these actors.
2. Specialised wholesalers sell one product or very few products, and sell to different actors
from different market channels. A wholesaler who specialises in selling tomatoes to supermarkets, other wholesalers, and traditional retailers would be a specialised wholesaler.
Dedicated wholesalers, in contrast, may sell a wide variety of products, but sell to actors
from a speciic market channel. They do not sell their graded products to other market
channels, such as to traditional retailers. A wholesaler who sells different types of graded
perishables to different supermarket chains would be a dedicated wholesaler. In some
cases, a given wholesaler can be both a specialised wholesaler and a dedicated wholesaler.
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Tomato Farmers and Modernising Value Chains in Indonesia
427
technologies include traditional technology, low-external-input-intensity technology, and ‘modernised’ external-input-intensive technology.
Tomatoes are an example of the recent, rapid emergence of non-traditional
food products in Indonesia, having been introduced into Asia only several hundred years ago by Portuguese and Spanish traders. By 1970, only 15,000 tonnes of
tomatoes were being produced in Indonesia—a tiny share of the country’s vegetable production and consumption. By 2010, Indonesia’s population had doubled
but annual tomato production had increased 60-fold, to nearly 900,000 tonnes.
This change was mirrored in production zones. By the 2000s, West Java, which
had traditionally focused on producing rice and tea, became Indonesia’s leading
tomato-producing province, growing half the country’s tomatoes.
Our interviews with key informants in Indonesia’s horticulture industry
revealed that farming in West Java has, in recent years, generally eschewed traditional techniques and adopted intensiication technologies that require more
fertiliser, pesticides, and irrigation. Actors in various segments of tomato supply chains told us that the predominant market channel 20 years ago was the
traditional system of village traders buying tomatoes from farmers and selling
them locally to rural assemblers, who then sold them to urban wholesale markets. In recent years, however, in a kind of disintermediation, large wholesalers
from rural and urban wholesale markets have increasingly been buying direct
from farmers. Since the 1990s, large, modernising wholesalers from Jakarta and
Bandung and other provincial capitals have increased their presence in production zones. In the 2000s, specialised and dedicated wholesalers bought, graded,
sorted, and packed tomatoes and other produce for delivery to supermarkets,
hotels, and restaurants.
CONTEXT AND DATA
Context
In the past few decades, West Java has evolved from Indonesia’s major rice bowl
to its major vegetable basket. The province’s infrastructure density and urban
share have increased sharply, largely because of the growth of Bandung and other
cities in West Java and the decrease in the time and cost to travel between rural
West Java and large urban markets in Jakarta. Of course, outside the main cities
of West Java there are differences between subzones—especially between valleys
and mountainous areas, and between rural hinterland areas and the small urban
areas of secondary and tertiary cities and towns (so-called rur-urban areas)—and
these differences have manifested themselves in the differing commercial characteristics of each subzone. As rural land and labour prices rose, along with incomedriven demand from local and island-wide urban markets, horticulture in West
Java converted (relatively low-paying) rice ields and forested land into (highpaying) farmland for fruit and vegetables. Low-value vegetables such as cabbage
were produced irst, followed by medium-value vegetables such as tomatoes and
potatoes.
Our ield research revealed that agricultural markets in West Java have evolved
through three stages. The province’s traditional market systems were dominated
by small village traders, who sold to villages and nascent urban markets. This
initial stage was replaced by an intermediate transformation stage, which saw the
428
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
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advent of medium- and large-scale traders. These traders were based in wholesale markets (in Jakarta and Bandung) and bought from farmers in the districts
around Bandung (and Bogor). The next stage, that of an emerging modern market
channel, added specialised and dedicated wholesalers, which sold to the incipient
supermarket and hotel and restaurant sectors. Food-product supplier Bimandiri,
for example, which had been a traditional wholesaler in the Bandung market in
the 1990s, began to set up warehouses and collection points off-market and contract with modern-market buyers of tomatoes and other produce.
Data
In February–July 2006, we conducted a district-level survey of farming households in West Java, using a structured questionnaire on farm and household
characteristics; farm production and input use; credit, technical assistance, and
participation in associations; and tomato marketing (including channels and
transaction terms). This survey had, overall, a one-year recall period and, for
assets, a ive-year recall period, the latter to create lagged asset variables exogenous to the respondents’ market and production choices at the time of the
questionnaire.
At the time of our survey, West Java had 25 kabupaten (districts).3 We identiied
a subset of the 12 districts that each produced at least 1,000 tonnes of tomatoes a
year, according to 2000–2004 data from Badan Pusat Statistik, Indonesia’s central
statistics agency. By identifying districts that produced high amounts of tomatoes,
we hoped to ind farmers in diverse market channels and who used diverse technologies and had diverse farm sizes and non-land assets. We also hoped to ind a
diversity of merchant types. The districts differed in production volumes, so we
used the sampling method of probability proportional to size (weighted). At this
sampling stage, we selected Bandung and Garut as the districts in which to conduct our household survey. We chose four kecamatan (subdistricts) from each. In
2006, Bandung had 30 subdistricts that produced more than 1,000 tonnes of tomatoes annually, while Garut had 28. We chose, at random, the Ciwidey, Lembang,
Pangalengan, and Pasirjambu subdistricts of Bandung, and the Cigedug, Cikajang,
Cisurupan, and Pasirwangi subdistricts of Garut.
For each selected subdistrict, we compiled a list of tomato farmers by drawing
on several sources, including the land-tax registration list, agricultural oficials,
farm leaders, and local traders. We then divided this list into two strata: farmers
who supplied the modern channel—that is, supermarkets—directly (or indirectly,
via specialised wholesalers), and farmers who supplied other channels (the intermediate, or ‘modernising wholesaler’ channel, and the traditional channel, comprising village traders). We selected 600 farmers across all subdistricts, assigning
300 farmers to Bandung and 300 to Garut. We then assigned 150 farmers to each
of the two strata within these districts. We weighted for the over-representation of
any subdistrict or market channel, thus controlling for sampling design and making the overall sample more representative of the population. After data cleaning,
we retained 596 usable household observations.
3. The district of West Bandung was created in 2007.
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Tomato Farmers and Modernising Value Chains in Indonesia
429
DESCRIPTIVE ANALYSIS OF TOMATO FARMERS
Tables 1–5 present selected descriptive statistics for our sample of farming
households. Each table has two sets of stratiications: the commercial zone of the
household’s subdistrict and the main marketing channel used by the household.
To stratify our sample by commercial zone, we used oficial statistics at the subdistrict level, and data from our own subdistrict survey from 2006, to create a score
for each subdistrict on the basis of two indicators: the share of households whose
primary activity is non-agricultural (which is thus a proxy for the rur-urban or
urban intensity of the subdistrict) and the share of land under horticultural crops
(as opposed to rice), which are nearly fully commercial while rice is semi-commercial or semi-subsistence. We therefore classiied two subdistricts in Bandung
(Lembang and Pangalengan) and two in Garut (Cisurupan and Cikajang) as being
in high-level commercial zones. We classiied the remaining subdistricts as being
in low-level commercial zones.
To stratify our sample by the main marketing channel used by households, we
deined each channel as follows: the traditional channel includes farmers that sell
mainly to village traders; the intermediate channel includes households that sell
directly to large wholesalers; and the modern channel includes households that sell
to supermarkets directly (or indirectly, via specialised and dedicated wholesalers).
Table 1 contains general household characteristics. All strata have similar
amounts of tomato-growing experience (around a decade), having started to produce tomatoes well before the recent changes in the market; specialised wholesalers for supermarkets began their operations in Bandung only four to ive years
before we conducted our survey, and in Garut only two to three years before. All
strata also have similar household sizes and similar ages and education levels of
household heads. Most households have concrete houses and with a regular supply of electricity and water. The overall proile is one of economically secure rural
households, regardless of the commercial intensity of their subdistrict or their
market-channel choice. Few households are members of cooperatives. The main
difference is that those in the modern channel—that is, those that sell to supermarkets—are more specialised in tomato production. This is as expected, given
the exigencies of the modern channel. Farmers in the intermediate channel—
those selling to modernising wholesalers—are highly specialised in tomato production only in low-level commercial zones, while around 70% of farmers in the
traditional channel declared tomato production as their main source of income.
Table 2 shows landholding and land-use characteristics by commercial zone and
market-channel choice. Tomato farms are small, averaging 0.7–0.8 hectares each,
which, in West Java, is slightly larger than the size of an average rice farm (0.6 hectares) (Brázdik 2006). Modern-channel farms are roughly 20%–30% larger than the
size of an average farm in the two other strata, in both commercial zones. Farms
in low-level commercial zones are slightly larger (15%) than those in high-level
commercial zones, largely because the former are in denser areas with higher land
prices. The share of rented land in total operated land is nearly 30% in high-level
commercial zones versus 15% in low-level commercial zones. Most rented land
is owned by rice farmers; we observed that many small rice farmers, when they
worked in town, rented out their land to horticulture farmers. Nearly all the strata
in both zone categories grew only horticultural products but very little to no rice.
Tomatoes composed, on average, roughly half the horticultural area of the farms.
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TABLE 1 Household Characteristics of Tomato Farmers, by Commercial Zone and Market Channel, 2004–5
Low-level commercial
Traditional Intermediate
Modern
High-level commercial
Overall
Traditional Intermediate
Modern
Overall
Households
175
57
47
279
152
122
43
317
Age of household head (years)
Education of household head
(years)
Tomato-farming experience of
household head (years)
Household size (members)
42.1
48.1
42.7
43.4
43.2
41.7
41.7
42.4
% of households with:
Concrete house
Electricity
Own water source
Part of household co-op (%)
Tomato farming as main
income (%)
6.8a
6.5a
8.6c
7.1
6.7a
7.2ab
7.2ab
7.0
11.3b
3.7a
8.9ab
4.0ab
11.1b
4.2b
10.8
3.8
10.5b
4.3b
9.4ab
4.1b
8.6a
4.3b
9.8
4.2
82.9
98.3
96.6
61.4
100.0
100.0
85.1
100.0
100.0
78.9
98.9
97.8
92.1
100.0
96.7
86.1
100.0
97.5
93.0
100.0
100.0
89.9
100.0
97.5
9.1c
1.8b
0.0a
6.1
12.5c
13.9c
0.0a
11.4
69.7b
91.2c
85.1c
76.7
71.1b
50.0a
93.0c
65.9
Note: The superscript a, b, and c show differences between groups, using a Tukey–Kramer test (p < 0.1). Co-op = cooperative.
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TABLE 2 Landholding and Land-Use Characteristics of Tomato Farmers, by Commercial Zone and Market Channel, 2004–5
Low-level commercial
Traditional Intermediate
Modern
High-level commercial
Overall
Traditional Intermediate
Modern
Overall
Operated land by status (ha)
Owned
Rented
Sharecropped-in
0.7ab
0.6ab
0.1a
0.0
0.7ab
0.6ab
0.1a
0.0
1.0b
0.8b
0.2ab
0.0
0.7
0.6
0.1
0.0
0.6a
0.5ab
0.2ab
0.0
0.7ab
0.4a
0.3b
0.0
0.9b
0.7b
0.2ab
0.0
0.7
0.5
0.2
0.0
Operated land by crop (ha)
Cultivated
Tomatoes
Other vegetables or fruit
Rice
Uncultivated
0.7
0.7
0.3
0.3
0.1
0.0
0.7
0.7
0.3
0.3
0.0
0.0
1.0
1.0
0.5
0.5
0.0
0.0
0.8
0.8
0.3
0.4
0.1
0.0
0.6
0.6
0.6
0.3
0.3
0.0
0.7
0.7
0.7
0.4
0.4
0.0
0.8
0.8
0.8
0.4
0.4
0.0
0.7
0.7
0.7
0.3
0.3
0.0
Note: The superscript a, b, and c show differences between groups, using a Tukey–Kramer test (p < 0.1).
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432
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
Table 3 shows tomato irrigation and seasonality, comparing circumstances in
1999–2000 with those in 2004–5. In 1999–2000, only 53.3% of farms in high-level
commercial zones irrigated their tomatoes; in 2004–5, 88.0% did. Small commercial farms invested heavily to shift from single-season cropping to multicropping,
presumably to match the continuous demand from the market for fresh tomatoes.
In 2004–5, 65.5% of the whole sample of tomato farmers multicropped: 65.5% of
traditional, 61.6% of modernising-channel farmers, and 77.7% of modern-channel
farmers. In ive years, farmers in the intermediate and modern channels doubled their land under tomatoes; farmers in the traditional channel, in contrast,
expanded their land by only 20%, on average, regardless of commercial zone.
Table 4 shows tomato production costs and proits per hectare, on average,
during 2004–5. Contrary to expectations, the total costs of modern-channel farmers are slightly lower than traditional-channel farmers. Modern-channel farmers
spend slightly less on external inputs, such as seed, fertiliser, and pesticides, than
intermediate- and traditional-channel farmers. This may be because modernchannel farmers are somewhat more informed and more allocatively eficient
than those in other channels, and manage their input use more carefully. But the
external-input intensity varies little across the strata.
In high-level commercial zones, the proit or revenue–cost ratio of modernchannel farmers is 12% higher than those of farmers in the two other channels
(which have similar ratios) when ignoring the imputed costs of family labour.
In low-level commercial zones, the advantage is 35%. The proit superiority of
modern-channel farmers could be due to their slightly lower input outlays (for
similar yields to those of farmers in the other channels) and to their receiving
price premiums of 2% in high-level commercial zones and 18% in low-level commercial zones.
Prices received by farmers in high-level commercial zones are similar across
market channels, while farmers in low-level commercial zones and the modern
market channel receive a signiicantly higher price than farmers in other channels.
This result mirrors those found among guava producers in Mexico (Hernandez,
Berdegué, and Reardon 2015), where modernising wholesalers have to pay a
premium in low-level commercial zones to guarantee quantity and quality. This
practice is often unnecessary in high-level commercial zones, because all market
channels have very competitive prices and because farmers in the modern channel beneit from lower risk (via implicit contracts) and a higher number of sales.
Table 5 gives details on tomato marketing. Households in all channels sell fresh
tomatoes every few weeks—a common pattern in Indonesia and other developing countries, especially in tropical and subtropical zones. In contrast, tomatoes
that go to processors are typically harvested in a single batch. Yields are similar
across all channels, but modern-channel farmers have greater areas under tomato
production and multicrop more often than other farmers. They therefore sell more
tomatoes than other farmers, in both zone categories: one-half and one-third more
than traditional-channel farmers in high- and low-level commercial zones, respectively. Farmers in the intermediate channel sell more tomatoes than those in the
traditional channel but fewer than those in the modern channel.
Grading is nearly absent for farm-level sales, across all market channels. The
intermediaries, regardless of channel, buy all the tomatoes, of all grades, from
the farmers. Our interviews with intermediaries revealed that modernising
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TABLE 3 Irrigation and Cropping by Tomato Farmers, by Commercial Zone and Market Channel, 1999–2000 and 2004–5
Low-level commercial
Traditional Intermediate
Modern
High-level commercial
Overall
Traditional Intermediate
Modern
Overall
% of land irrigated
Crop-land (2005)
Tomatoes (2005)
Tomatoes (2000)
65.3ab
88.0
56.2
61.0a
87.6
46.3
68.8ab
85.9
66.0
65.0
87.6
55.8
83.3c
85.2
55.9
76.4b
89.7
49.2
76.5b
93.1
55.8
79.7
88.0
53.3
Tomato seasons (2004–5)
(% of farmers)
1
2
3
41.4
27.6
31.0
35.1
24.6
40.4
19.1
44.7
36.2
36.3
29.9
33.8
27.6
40.1
32.2
41.8
36.9
21.3
25.6
34.9
39.5
32.8
38.2
29.0
Tomato seasons (1999–2000)
(% of farmers)
0
1
2
3
26.9
36.0
14.3
22.9
14.0
12.3
33.3
40.4
8.5
87.2
2.1
2.1
21.1
39.8
16.1
22.9
23.0
15.8
30.3
30.9
32.0
22.1
22.1
23.8
14.0
7.0
41.9
37.2
25.2
17.0
28.7
29.0
Tomatoes (ha) (2004–5)
Tomatoes (ha) (1999–2000)
0.6a
0.6b
0.7ab
0.5ab
0.9b
0.7ba
0.6
0.6
Note: The superscript a, b, and c show differences between groups, using a Tukey–Kramer test (p < 0.1).
0.7ab
0.5ab
0.7ab
0.3a
0.7ab
0.4ab
0.7
0.5
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TABLE 4 Costs and Proits of Tomato Farmers, by Commercial Zone and Market Channel, 2004–5
Low-level commercial
Traditional Intermediate
Production costs (Rp million)
Agro-inputs
(% of total cost)
Seed
Fertilisers
Pesticides
Labour
(% of total cost)
Family
Hired
Land rent
(% of total cost)
Plastic, stakes, fuel
(% of total cost)
High-level commercial
Modern
Overall
Traditional Intermediate
Modern
Overall
13.0b
(41.7)
0.9
6.1
6.1
14.0b
(44.9)
2.8
11.3
1.9
(6.1)
2.2
(7.1)
12.9ab
(41.3)
0.8
6.0
6.1
13.7b
(43.9)
3.0
10.7
2.5
(8.0)
2.1
(6.7)
12.6a
(48.8)
1.0
5.4
6.2
8.7a
(33.7)
1.5
7.2
2.2
(8.5)
2.3
(8.9)
12.9
(42.6)
0.9
6.0
6.1
13.1
(43.2)
2.6
10.5
2.1
(6.9)
2.2
(7.3)
13.6b
(48.1)
0.9
6.3
6.4
10.0ab
(35.3)
1.6
8.4
2.4
(8.5)
2.3
(8.1)
13.6b
(47.2)
0.9
6.2
6.4
10.6ab
(36.8)
1.8
8.8
2.2
(7.6)
2.4
(8.3)
12.7a
(49.6)
0.8
5.5
6.4
8.1a
(31.6)
1.5
6.6
2.4
(9.4)
2.4
(9.4)
13.5
(48.0)
0.9
6.2
6.4
10.0
(35.6)
1.7
8.3
2.3
(8.2)
2.3
(8.2)
31.2c
31.2c
25.8a
30.3
28.3b
28.8b
25.6a
28.1
Proit (Rp million / ha, unless otherwise stated)
39.3a
Revenue
39.9a
Yield (tonnes/ha)
51.8
52.7
758.0a
Price (Rp/kg)
793.0ab
31.2c
Total cost
31.2c
45.8b
50.6
936.0c
25.8a
40.7
51.8
810.0
30.3
42.1ab
52.7
823.0b
28.3b
42.1ab
52.1
830.0b
28.8b
44.3b
54.3
838.0b
25.6a
42.4
52.7
828.0
28.1
20.1c
1.8
21.5c
1.9
10.5
1.4
13.1
1.5
13.8b
1.5
15.4b
1.6
13.2b
1.5
15.0b
1.6
18.7bc
1.7
20.2c
1.8
14.2
1.5
15.9
1.6
Total cost
Proit, including family labour
Revenue/cost
Proit, excluding family labour
Revenue/cost
8.7a
1.3
11.5a
1.4
8.1a
1.3
11.1a
1.4
Note: The superscript a, b, and c show differences between groups, using a Tukey–Kramer test (p < 0.1). Discrepancies are due to rounding.
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TABLE 5 Marketing by Tomato Farmers by Commercial Zone and Market Channel, 2004–5
Low-level commercial
Traditional Intermediate
High-level commercial
Modern
Overall
Traditional Intermediate
Modern
Overall
Times sold during season
7.9a
7.6a
8.3b
7.9
8.4b
7.7a
10.5bc
8.4
Tonnes sold during season
Graded
Ungraded
14.4a
0.0a
14.4a
18.4b
0.0a
18.4b
22.8c
1.4b
21.4ab
16.6
0.2
16.4
15.3ab
0.0a
15.3ab
18.1ab
0.0a
18.1b
23.2c
0.3ab
22.9c
17.4
0.0
17.4
Trader payment system (%)
In advance
On harvest
Shortly after harvest
On consignment
Other
ab
0.0
0.6
9.1
90.3
0.0
b
0.0
7.0
21.1
71.9
0.0
ab
0.0
0.0
0.0
95.7
4.3
0.0
1.8
10.0
87.5
0.7
ab
0.0
0.0
2.6
97.4
0.0
b
0.0
0.0
14.8
85.2
0.0
a
0.0
0.0
0.0
90.7
9.3
0.0
0.0
6.9
91.8
1.3
Note: The superscript a, b, and c show differences between groups, by using a Tukey–Kramer test (p < 0.1).
436
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
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wholesalers and the specialised and dedicated wholesalers for supermarkets
grade and sell into different markets, while traditional wholesalers sell ungraded
tomatoes. This means that the intermediaries, rather than the farmers, capture
the proits from achieving quality in tomato production. Table 5 also shows that
no farmer, in any channel, received an advance from a trader, contradicting the
conventional wisdom that traders ‘tie’ advances to credit or output market transactions. Farmers tend to have regular relationships with traders and sell mainly
on consignment, even in the modern channel.
ECONOMETRIC MODEL
We model market-channel choice as a variation on the output supply decision,
and we model input demands to relect technology choice. We do this using two
sets of equations. The irst set models these choices as a function of the economic
determinants—risk, relative prices of outputs and inputs, and a vector of quasiixed capital assets, as well as various context-speciic shifters. These outputsupply and input-demand equations derive theoretically from a proit function,
without requiring the assumption of proit maximisation We do not restate the
initial theoretical derivation of Sadoulet and De Janvry (1995) and further contributions from Holloway, Barrett, and Ehui 2005) here, but their system of output
supply and factor demand functions in decision prices, p*, is
(
q = q p* , z q
)
(1)
The output supply functions are speciied here to distinguish the different market channels. The basic model is broad enough to link market-channel decisions
to transaction costs (Barrett et al. 2012), a subset of input costs; it is also broad
enough to link channel and technology decisions to thresholds of land and nonland assets (Carter and Barrett 2006).
The second set of equations models production as a technical function of
land, labour, and capital, without economic variables; we add the production
function in order to derive marginal physical products (MPPs). We then value
the MPPs at output prices to derive marginal value products (MVPs), which
we then compare with factor prices to measure allocative eficiency (Lau and
Yotopoulos 1971).
To estimate the above equations, in general, we (a) estimate the function of
market-channel choice and (b) estimate the technology economic choices (inputdemand functions) and the technical relations (the production functions), after
having controlled for the conditional probability of market-channel participation.
In the irst of these steps, we estimated a multinomial logit; we used Bourguignon,
Fournier, and Gurgand’s (2007) extension of Dubin and McFadden’s (1984)
approach to correct for selection bias. The regression models the participation of a
farming household in a given market channel, with three channels as the choices
(traditional, intermediate, and modern).
The implementation model for market-channel and technology choices is as
follows:
Y1 = Xβ1 + u1
Y j* = zγ j + η j
(2)
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Tomato Farmers and Modernising Value Chains in Indonesia
437
where j = 1, 2, 3. The subscript j represents the categorical variable of the market
channel among the three alternatives (modern = 1, intermediate= 2, and traditional
= 3), based on utilities, Y j*. The vector z represents the explanatory variables for all
market-channel alternatives, a set of farmer characteristics that together relect
the incentive and capacity variables inluencing the producer’s indirect utility.
The vector X represents the explanatory variables in the production function.
Consistent coeficients of the market-channel determinants (Y j ) can be obtained
by maximum likelihood estimation methods. The problem is how to estimate the
vector of coeficients of the production function β1, considering that the disturbance
term (u1), might not be independent of all (η j ) .
Dubin and McFadden’s model uses a linearity assumption. Bourguignon,
Fournier, and Gurgand suggest a variation, making u1 linear on a set of normal
distributions and allowing u1 to be normal as well. The production function equation in (1) is then conditional on choosing the modern channel (j = 1):
⎡
P ⎤
Y1 = X1β1 + σ ⎢ r1*m ( P1 ) + ∑ rj*m ( Pj ) j ⎥ + w1
Pj − 1 ⎦
j=2,3
⎣
(3)
The determinants are as follows:
• Socio-demographic variables: household head’s education (years); household
head’s tomato-growing experience (years); household head’s age (years); and
household size (members)
• Micro-asset variables: ive-year lagged farm size interacted with location
(Bandung or Garut); distance to a paved highway (kilometres); lagged
percentage of farmland under irrigation; and a lagged dummy variable for
membership in a farmers’ association or cooperative
• Meso-asset variables: lagged number of packing houses in the subdistrict
(units) and a dummy variable for the subdistrict’s commercial zone (low or
high level)
We used lagged assets to avoid causality problems in the market-channel or
input choice. Moreover, we separated the land effect in the two districts because
of the different farming systems and scarcity of land between the two, with Garut
less dense than Bandung.
In the second stage, the input-demand functions have the following determinant variables:
• Prices: tomato price (rupiah per kilogram); price of fertiliser (rupiah per
kilogram); labour wage (rupiah per day); and price of pesticides (rupiah per
kilogram)
• Socio-demographic variables: household head’s education (years); household
head’s age (years); and household size (members)
• Micro-asset variables: owned land (hectares); owned land × Bandung dummy
variable; distance to a paved highway (kilometres); and the percentage of
farmland under irrigation
• Meso-asset variables: dummy for high-level commercial zone
• Controls for selection bias: inverse Mills ratio 1 and inverse Mills ratio 2
• Dummy variable for dry season
438
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
TABLE 6 Determinants of Market-Channel Choice
Market channel
Age of household head (years)
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Education of household head (years)
Tomato-farming experience of household head (years)
Household size (members)
Distance to paved highway (km)
Dummy for high-level commercial zone
Lagged land owned (ha)
Lagged land owned × Bandung dummy
Lagged number of packing houses in district (units)
Lagged participation in cooperative (yes = 1; no = 0)
Lagged share of operated land under irrigation
Observations
Pseudo R2
Wald Chi2 (32)
Probability > Chi2
Intermediate
Modern
0.006
(0.015)
0.024
(0.063)
–0.070**
(0.024)
–0.035
(0.116)
–0.210
(0.354)
1.007**
(0.331)
–0.048
(0.135)
–0.665
(0.452)
0.008**
(0.003)
0.029
(0.337)
1.166*
(0.620)
0.021
(0.028)
0.152
(0.122)
–0.046
(0.062)
–0.197
(0.203)
0.104
(0.175)
4.069**
(0.954)
–1.895**
(0.794)
2.112**
(1.025)
–0.121**
(0.025)
–0.945
(0.816)
2.514**
(1.159)
596
0.748
858.96
0.000
* p < 0.1; ** p < 0.05; *** p < 0.01.
REGRESSION RESULTS
Determinants of Market-Channel Choice
To avoid causation problems, we used lagged assets in our regression instead
of endogenous, current-period asset variables. We tested all lagged-asset variables for endogeneity, after Rivers and Vuong (1988), and found no evidence of
it. The salient results of the determinants of market-channel choice (table 6) are
as follows.
Lagged farm size has ambiguous effects on market-channel participation. It
had a positive effect on modern-channel participation in Bandung, where land is
scarce, but a negative effect in Garut. It had no effect on farm size in the intermediate channel in either category of commercial zone. In an attempt to determine
why smaller farmers in the more rural district of Garut are in the modern channel, we draw on the results of our rapid, rural, pre-survey appraisal. In Bandung,
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Tomato Farmers and Modernising Value Chains in Indonesia
439
near the city, larger farmers with more capital tend to make frequent shipments
to specialised wholesalers and proit from nearby commercial opportunities. In
Garut, a more rural subdistrict, with bigger parcels of land, the larger farmers
tend to sell large volumes to inter-island traders who come to that area for large
volumes, leaving the modern channel—a smaller and more specialised channel
than in Bandung—to smaller farmers, who nevertheless are richer (in terms of
non-land assets) than a typical small farmer in Garut.
Access to irrigation is another important determinant of modern-channel participation. The lagged irrigation share has an important positive effect on participation in the modern and intermediate channels. Such consistency of production
is an important attribute that dedicated wholesalers look for in farmers. This asset
effect is similar to that found, for example, among tomato farmers in Guatemala
(Hernandez, Reardon, and Berdegué 2007). Moreover, large wholesalers in urban
markets require a year-round supply of produce for their varied clients, such as
supermarkets, restaurants, and hotels.
Meso-level characteristics play an important role in modern- and intermediatechannel participation. Being in a high-level commercial zone favours participation in the modern and intermediate channels. Moreover, the (lagged) number
of packing houses in the subdistrict has a positive effect on participation in the
intermediate channel by lowering transaction costs; traders in this channel tend
to own those packing houses, as our pre-survey appraisal revealed. These are
key results, because buyers in the food industry tend to source their products
from sellers in high-level commercial zones, where transaction costs are lower
and where there is more chance of inding an abundance of good-quality products
to meet the market requirements. For this point in relation to contract farming, see
Barrett et al.’s (2012) article.
Input-Demand Functions
Our input-demand analysis (table 7) reveals that the use of labour by farmers is
more responsive to factor input prices in the modern channel than in the two other
market channels. The labour demand of modern-channel farmers responds positively to the price of pesticides in the labour equation, indicating that pesticides
substitute for weeding labour in this channel.4 Farmers in the intermediate channel, in contrast, are not sensitive to the price of pesticides. The own-price coeficient
(the wage) is negative, as expected. Owning land in Bandung has a strong positive effect on labour use among modern-channel farmers, which implies that the
quality demands of supermarkets require high levels of crop care and that farmers
often have to irrigate their tomato plants manually during the dry season.
Our analysis also reveals that there is a strong complementarity of fertiliser
use and irrigation, and that transaction costs (proxied by the distance to a paved
highway) have a negative effect on fertiliser use. The demand for fertiliser and
pesticides from farmers in the intermediate channel increases with rises in output
4. In this article, the term ‘pesticides’ includes herbicides. We measure pesticide use in
cash expenditure on pesticides, given that pesticides are very heterogeneous and we need
to aggregate by value. We measure fertiliser use in the same way for the same reason. We
measure labour use in days.
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TABLE 7 Input-Demand Regressions
Labour
Traditional Intermediate
Output price
(Rp/kg)
Labour wage
(Rp/day)
Price of pesticides
(Rp/kg)
Price of fertiliser
(Rp/kg)
Age of HH head
(years)
Education of HH
head (years)
HH size (members)
Owned land (ha)
Owned land ×
Bandung dummy
Distance to paved
highway (km)
% of tomato land
irrigated
Inverse Mills ratio 1
0.44
(0.27)
–0.01
(0.01)
–0.001**
(0.001)
0.00
(0.001)
1.36
(1.64)
5.60
(7.97)
2.02
(12.20)
14.54
(17.30)
88.41**
(37.76)
43.72
(31.29)
–587.40**
(64.90)
2.61*
(1.53)
–1.91
(4.46)
0.02
(0.02)
0.004
(0.005)
0.01
(0.01)
3.32
(2.59)
7.59
(11.95)
36.62**
(17.68)
111.82*
(66.01)
–12.16
(114.32)
63.24
(50.62)
–960.50**
(127.50)
–1.04
(2.88)
Pesticides
Modern
–15.89**
(5.65)
–0.20**
(0.07)
0.003**
(0.001)
0.17**
(0.06)
12.09
(3.87)
6.381
(23.62)
78.09
(28.56)
71.66
(85.78)
230.08**
(170.30)
63.47
(14.53)
–1,669.30
(168.90)
24.98
(14.61)
Traditional Intermediate
–4.28**
(1.15)
–0.19**
(0.05)
0.90**
(0.14)
–21.77**
(5.15)
–7.51
(6.96)
6.09
(33.92)
–76.99
(51.93)
–165.84**
(73.65)
–17.42
(160.79)
110.87
(133.21)
280.60
(276.30)
–24.28**
(6.53)
Fertiliser
Modern
33.65**
–69.50**
(16.01)
(11.67)
–0.311**
–0.70**
(0.07)
(0.15)
–1.01
2.10**
(1.76)
(0.18)
–105.99**
696.42**
(44.17)
(118.25)
–6.60
–5.56
(9.29)
(8.00)
–7.80
17.87
(42.91)
(48.82)
19.06
40.14
(63.51)
(59.02)
–288.00
–130.96
(237.07)
(177.26)
385.21
–1,406.05**
(410.60)
(558.58)
–298.81*
14.69
(181.81)
(30.03)
–15.70
308.80
(458.10)
(349.10)
–20.70**
–25.46
(10.34)
(30.19)
Traditional Intermediate
–1.34
(0.95)
–0.02
(0.04)
0.18
(0.12)
–2.90
(4.27)
–0.97
(5.77)
4.00
(28.12)
–30.55
(43.05)
31.93
(61.06)
315.60**
(133.31)
151.86
(110.44)
165.70
(229.10)
19.23**
(5.42)
49.17**
(12.25)
–0.02
(0.05)
–3.49**
(1.35)
–136.60**
(33.79)
–6.61
(7.11)
–3.48
(32.83)
–25.42
(48.59)
166.31
(181.35)
51.50
(314.10)
220.94
(139.08)
732.30**
(350.40)
–1.03
(7.91)
Modern
–32.48**
(11.79)
–0.13**
(0.15)
0.18
(0.18)
334.91**
(119.48)
6.55
(8.08)
39.75
(49.33)
139.63**
(59.64)
342.93*
(179.11)
350.55
(564.42)
–114.41**
(30.35)
361.00
(352.80)
50.82*
(30.50)
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Inverse Mills ratio 2
73.67**
(15.11)
8.11
(32.09)
130.42**
(55.29)
67.41**
(25.72)
60.77
(55.51)
173.79
(519.23)
108.05
(20.93)
155.08**
(79.59)
204.75
(230.30)
R2
Prob > F
0.32
0.00
0.37
0.00
0.76
0.00
0.69
0.00
0.70
0.00
0.88
0.00
0.14
0.00
0.35
0.00
0.55
0.00
Observations
327
179
84
327
179
84
327
179
84
Dummy for dry
season
Dummy for high-level
commercial zone
Note: HH = household.
* p < 0.1; ** p < 0.05; *** p < 0.01.
–142.23**
(64.33)
–3,142.10**
(136.65)
920.91**
(235.42)
–318.51**
(92.39)
–2,796.27**
(199.36)
–3,015.98
(1864.85)
11.26
(43.25)
–2,017.15**
(164.47)
58.41
(475.91)
192.70**
(53.33)
–248.59**
(113.29)
–673.43**
(195.18)
202.41**
(70.68)
6.31
(152.51)
–6,632.90**
(1,426.59)
–195.46**
(43.71)
–227.92
(166.19)
–685.60
(480.89)
442
Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
TABLE 8 Marginal Value Products of Factors versus
Factor Prices, by Market Channel (Rp)
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Traditional
Land (ha)
Fertiliser (kg)
Labour (day)
Intermediate
Modern
MVP
Factor cost
MVP
Factor cost
MVP
Factor cost
41.0 m
443
2,839
2.2 m
2,024
8,710
36.4 m
97
1,968
2.2 m
2,024
8,710
4,146
8,710
Note: MVP = marginal value product.
prices and decreases with rises in input prices. This suggests that farmers in this
channel are sensitive to variations in input and output prices, implying a commercial orientation. Farmers in the traditional channel do not exhibit this sensitivity.
Estimates of Allocative Eficiencies: Comparison among Groups
The allocative eficiencies of farmers in different market channels can be derived
by estimating farm production functions, after controlling for endogenous stratiication in each market channel. We can analyse these eficiencies by comparing
the MVPs of factors such as land, fertiliser, and labour with the prices of these
factors. Table 8 shows the statistically signiicant results for factors with results in
the estimation of the production function.
The MVP of land is well above the factor cost of rent for farmers in the traditional and intermediate channels, indicating a severe land constraint. The MVP
of labour, in contrast, is well below the factor cost of wages in all channels, but
the overuse of labour (from the viewpoint of allocative eficiency) is most severe
among farmers in the intermediate channel. This suggests that there are not
enough off-farm labour opportunities to allow these farmers to reduce their use
of farm labour to eficient levels. The MVP of fertiliser, like that of labour, is well
below the factor cost of an additional kilogram of fertiliser. As with labour, the
overuse of fertiliser is most severe among farmers in the intermediate channel.
Overall, our analysis of MVPs shows that tomato farmers in all channels would
beneit from expanding their production. Although there is evidence of the overuse of purchased variable inputs, especially by farmers in the traditional and
intermediate channels, we ind that farmers in the modern channel tend to be
more allocatively eficient.
CONCLUSIONS
Tomato farms in the modern channel in Indonesia may be 25% bigger than the
country’s average tomato farm, but they are still small. In this channel, farm size
matters only in high-level commercial zones; in the intermediate and traditional
channels, farm size does not have a signiicant effect. This implies that a relative
‘exclusion effect’ exists in the modern channel. The different effects of different
commercial zones on farm size in market-channel choice have not been tested
in the regional literature. We also ind that commercial zones matter to farmers
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Tomato Farmers and Modernising Value Chains in Indonesia
443
participating in modern and intermediate markets (that is, those selling to supermarkets and modernising wholesalers, respectively), with dense, rur-urbanised
zones near highways being a key determinant. This inding is consistent with
Barrett et al.’s (2012) prediction for contract farming. Infrastructure matters to
market-channel modernisation.
Non-land assets—such as irrigation, in particular—are important in determining the choice of the modern and intermediate channels. In our 2006 survey, while
most tomato farmers adopted irrigation during the ive-year recall period for
assets, the farmers in the modern channel did so irst and tended to multicrop the
most. This concords with indings in the literature, such as Hernandez, Reardon,
and Berdegué’s (2007) assertion that this ‘continuity’ of supply (and the quality
that water control confers) is associated with the modern channel. Yet we also
ind that the intermediate channel requires consistency of supply, as large urban
wholesalers in this channel have among their clientele those that require a yearround tomato supply consistent in volume and quality.
Contrary to conventional wisdom, modern-channel farmers do not use pesticides more intensively than other farmers. In contrast, farmers in the intermediate and traditional channels use more labour and fertiliser than is allocatively
eficient and yet do not have higher yields than farmers in the modern channel. In
fact, farmers in the modern channel earned more proit than those in the intermediate and traditional channels (owing to a combination of slightly lower costs and
modest price premiums). Our descriptive results show, however, that hardly any
farmers in any channel sold graded tomatoes; in this early stage of market modernisation in Indonesia, the main ‘capture of rents’ goes to specialised wholesalers
for supermarkets and to modernising wholesalers. These wholesalers sell tomatoes of different qualities of and at different prices, per our pre-survey appraisal.
In Indonesia, the intermediate market channel has become a second-tier modern channel. Farmers in this channel, who sell to modernising wholesalers, share
several traits with farmers in the modern channel, who sell directly or indirectly
to supermarkets: both groups use high levels of irrigation; increase their areas of
land under tomatoes over time; and are sensitive to input prices, implying a commercial orientation. Yet farmers in the intermediate channel also share many traits
with those in the traditional channel, such as incurring high production costs and
being allocatively ineficient.
REFERENCES
Barrett, Christopher B., Maren E. Bachke, Marc F. Bellemare, Hope C. Michelson, Sudha
Narayanan, and Thomas F. Walker. 2012. ‘Smallholder Participation in Contract Farming: Comparative Evidence from Five Countries’. World Development 40 (4): 715–30.
Blandon, Jose, Spencer Henson, and John Cranield. 2009. ‘Small-Scale Farmer Participation in New Agri-Food Supply Chains: Case of the Supermarket Supply Chain for Fruit
and Vegetables in Honduras’. Journal of International Development 21 (7): 971–84.
Bourguignon, François, Martin Fournier, and Marc Gurgand. 2007. ‘Selection Bias Corrections Based on the Multinomial Logit Model: Monte Carlo Comparisons’. Journal of
Economic Surveys 21 (1): 174–205.
Brázdik, František. 2006. ‘Non-parametric Analysis of Technical Eficiency: Factors Affecting Eficiency of West Java Rice Farms’. CERGE-EI Working Paper 286, Center for Economic Research and Graduate Education, Economic Institute, Prague.
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Ricardo Hernandez, Thomas Reardon, Ronnie Natawidjaja, and Shobha Shetty
Carter, Michael R., and Christopher B. Barrett. 2006. ‘The Economics of Poverty Traps and
Persistent Poverty: An Asset-Based A