market structure and price variability of agricultural...
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Market Structure and Price Variability of Agricultural Commodities
in Central Sulawesi Province, Indonesia
Triana Anggraenie
Accomplished at the Tropical and International Agriculture
Faculty of Agricultural Sciences Georg-August University of Goettingen
Anggraenie, Triana
Market Structure and Price Variability of Agricultural Commodities in Central Sulawesi Province, Indonesia
Masterarbeit im wissenschaftlichen Studiengang Agrarwissenschaften an der Georg-August Universität Göttingen, Fakultät für Agrarwissenschaften
Studienrichtung: 1. Prüfer: Prof. Dr. Manfred Zeller 2. Prüfer: Dr. Bernhard Bruemmer Abgabetermin: 07 September 2005 angefertigt im:
STATUTORY DECLARATION
I herewith declare that I composed my thesis submitted independently without having
used any other sources or means than stated therein.
date: 07 September 2005 signature:
ABSTRACT
In the vicinity of Lore Lindu National Park, Central Sulawesi as a focus of research
area, integration of small farmers in the agricultural markets plays for rural
livelihoods. Agricultural markets in developing countries are often characterized by
inadequate physical and marketing infrastructure, high transport cost, and entry
barriers, therefore rural markets can be thin and isolated. Consequently, farmers deal
with prices that are volatile.
A survey was conducted to analyse structure, conduct and performance (SCP) of the
agricultural markets and to measure variability in agricultural input and output prices
across time and space. SCP analysis of the markets is mostly based on descriptive
statistics and analysis of variability in agricultural prices are conducted using
descriptive and econometric ARCH model.
In the research area, the typical agricultural marketing system is characterised by
oligopsonist market with regard to farmer producers, where market is dominated by
few large traders. For some commodities, there is a sort of vertical integration.
Degree of barrier to market entry in term of market license requirement is fairly low.
Involving in agricultural trading is relatively easy as indicated without any market
license required for small-scale business.
Each trader determines purchasing price independently. Price in Palu central market
is a primary reference for price setting. Payment method to farmers can be done by
cash, credit and in kind (barter). Due to written contract is not provided in credit
transaction, close relation and trust between the two participants are needed.
Road and market place are used as proxy of degree to market access. With better
market access, price of raw commodities such as cocoa increases and price variability
decreases. Price of consumer goods such as sugar increases and price variability
decreases with rising distance to central market. ARCH model applied for cocoa
indicates that prices are serially correlated and residuals of current and previous
periods are correlated.
i
Table of Contents
Abstract iTable of Contents iiList of Tables ivList of Figures viList of Abbreviations vii 1. INTRODUCTION 1 1.1 Background 1 1.2 Research questions and objectives 4
1.3 Outline of the study 4 2. LITERATURE REVIEW 7 2.1 Agricultural marketing 7 2.2 Structure-Conduct-Performance paradigm 14 2.3 Alternative approaches of market analysis 20 2.4 Agricultural price variability 21 2.4.1 Sources and implications of seasonal and spatial price variability 21 2.4.2 Analysis of commodity price variability and risk 23 2.5 Summary 26 3. METHODOLOGY 28 3.1 Description of the study area 28 3.2 Sampling procedure, data collection, entry and cleaning 29 3.3 Methodology used in descriptive analysis 31 3.4 Methodology used in econometric analysis 35 3.4.1 Diagnostic and testing 35 3.4.2 ARCH model for price variability 37 3.5 Conceptual framework 39 3.6 Summary 41 4. AGRICULTURAL MARKET STRUCTURE 42 4.1 Cocoa market 42 4.2 Coffee market 48 4.3 Rice market 49 4.4 Maize market 54 4.5 Fertilizer market 56 4.6 Barriers to market entry 59 4.7 Summary 60 5. AGRICULTURAL MARKET CONDUCT 63
ii
5.1 Profile of agricultural traders 63 5.2 Business practices 66 5.3 Trade associations 70 5.4 Summary 71 6. PERFORMANCE OF AGRICULTURAL MARKET 73 6.1 Access to market and infrastructure 73 6.2 Farmer’s share and gross marketing margin 74 6.3 Uncertainties, break even price and sensitivity analysis 78 6.4 Seasonal and spatial price variability 80 6.4.1 Seasonal price variability 81 6.4.1.1 Seasonal price variability of fertilizer 81 6.4.1.2 Seasonal price variability of cocoa 83 6.4.1.3 Seasonal price variability of coffee 86 6.4.1.4 Seasonal price variability of rice 87 6.4.1.5 Seasonal price variability of sugar 92 6.4.1.6 Seasonal price variability of cooking oil 94 6.4.2 Spatial price variability 97 6.5 Econometric results on price variability 103 6.6 Summary 108 7. CONCLUSIONS AND POLICY IMPLICATIONS 113 7.1 Major results 113 7.1.1 Agricultural market structure 113 7.1.2 Agricultural market conduct 115 7.1.3 Agricultural market performance 117 7.2 Policy implications 120 REFERENCES 122 APPENDICES 127
iii
List of Tables
Table 3.1 Survey villages of the study 28Table 4.1 General characteristics of cocoa traders 45Table 4.2 General characteristics of rice traders 51Table 5.1 Characteristics of agricultural traders in research area 64Table 5.2 Specialization of trader activity in marketing chain 68Table 5.3 Relation between business duration and having regular and
searching new suppliers 69
Table 6.1 Characteristics of market access 73Table 6.2 Prices for cocoa in the marketing channel in October 2003 74Table 6.3 Prices for rice in the marketing channel in October 2003 75Table 6.4 Marketing margin for each participant in the marketing
channel 75
Table 6.5 Gross marketing margin of cocoa during 2003 76Table 6.6 Gross marketing margin of IR 66 super rice during 2003 77Table 6.7 Gross marketing margin of cimandi rice during 2003 77Table 6.8 Break even price and sensitivity analysis of cocoa 79Table 6.9 Break even price and sensitivity analysis of coffee 79Table 6.10 Break even Price and sensitivity analysis of rice 80Table 6.11 Lowest and highest cocoa price and seasonal gap in January-
December 2003 84
Table 6.12 Lowest and highest coffee price and seasonal gap in January-December 2003
87
Table 6.13 Lowest and highest cimandi rice price and seasonal gap in January-December 2003
90
Table 6.14 Lowest and highest IR 66 super rice price and seasonal gap in January-December 2003
92
iv
Table 6.15 Lowest and highest sugar price and seasonal gap in January-December 2003
93
Table 6.16 Lowest and highest cooking oil (super quality) price and seasonal gap in January-December 2003
95
Table 6.17 Lowest and highest cooking oil (medium quality) price and seasonal gap in January-December 2003
96
Table 6.18 Producer price distribution 99Table 6.19 Consumer price distribution 102Table 6.20 Unit root test using Dickey-Fuller test for natural logarithm
of weekly cocoa prices 103
Table 6.21 Unit root test using Dickey-Fuller test for natural logarithm of weekly rice prices
104
Table 6.22 Unit root test using Dickey-Fuller test for natural logarithm of weekly sugar prices
105
Table 6.23 Diagnostic test on homoscedastic model for cocoa 106Table 6.24 Diagnostic test on homoscedastic model for sugar 107Table 6.25 Diagnostic test on homoscedastic model for two varieties of
rice 107Table 6.26 Estimation of ARCH model for cocoa 108Table 6.27 Estimation of ARCH model for sugar 108 Table A1 Producer prices (Rp/kg), standard deviation and variability
for different commodities in January–December 2003 127
Table A2 Consumer prices (Rp/kg), standard deviation and variability for different commodities in January–December 2003
129
Table A3 Correlation between cimandi rice and fertilizer in Bolapapu 130
v
List of Figures
Figure 2.1 Stages in a marketing system 8Figure 2.2 CDF for the probability distribution of gross margin 11Figure 2.3 Schematic categorization of issues in agricultural marketing 13Figure 2.4 Relationship between structure, conduct and performance 14Figure 3.1 Lorenz curve 34Figure 3.2 Framework for market analysis 40Figure 4.1 Marketing channel of cocoa 44Figure 4.2 Volume distribution of cocoa traded among traders 47Figure 4.3 Marketing channel of coffee 49Figure 4.4 Marketing channel of rice 52Figure 4.5 Volume distribution of rice traded among traders 54Figure 4.6 Marketing channel of dried kernel maize 55Figure 6.1 Weekly price of urea (Rp/kg) in 2003 81Figure 6.2 Weekly prices of NPK (Rp/kg) in 2003 83Figure 6.3 Weekly price of cocoa (Rp/kg) in January- December 2003 85Figure 6.4 Weekly FOB (Rp/kg) from Palu shipping port in 2003 85Figure 6.5 Weekly producer price of coffee (Rp/kg) in January -
December 2003 in 86
Figure 6.6 Weekly producer price of cimandi rice (Rp/kg) in January -December 2003
89
Figure 6.7 Weekly consumer price of cimandi rice (Rp/kg) in January-December 2003
89
Figure 6.8 Weekly producer price of IR 66 super rice (Rp/kg) in January -December 2003
91
Figure 6.9 Weekly consumer price of IR 66 super rice (Rp/kg) in January -December 2003
91
Figure 6.10 Weekly consumer price of sugar (Rp/kg) in January-December 2003
94
Figure 6.11 Weekly consumer price of super cooking oil (Rp/kg) in January-December 2003
95
Figure 6.12 Weekly consumer price of medium cooking oil (Rp/kg) in January-December 2003
97
vi
List of Abbreviations
ANOVA = Analysis of Variance AR = Autoregressive ARCH = Autoregressive Conditional Heteroscedasticity CV = Coefficient of Variation FGLS = Feasible Generalized Least Squares kg = Kilogram LLNP = Lore Lindu National Park OLS = Ordinary Least Squares Rp = Rupiah STORMA = Stability of Rain Forest Margin TGMM = Total Gross Marketing Margin
vii
1. INTRODUCTION
1.1 Background
Agriculture is one of the most important sectors of the Indonesian economy. In
Central Sulawesi Province, the agricultural sector makes an important contribution to
the regional economic development. In 2001, it generated 48% of the gross regional
domestic product. Food crops and cash crops were 15% and 22%, respectively
(Central Sulawesi Bureau for Statistics, 2003). Rice is the most important crop in the
food crops category and cocoa in the cash crops category.
Most of people who live in rural villages depend on agricultural activities as essential
sources for generating livelihoods. In the vicinity of Lore Lindu National Park,
Central Sulawesi as a focus of research area, integration of small farmers in the
agricultural markets, including export market (in this case for cocoa) plays for rural
livelihoods. Research of STORMA (Stability of Rainforest Margins) sub project A4
in 2001-2002 found that in the research area the most important source of income for
rural household is mainly based on agricultural activities: crop income, livestock
income, and income from employment offered in the agricultural sector. On average,
the three agricultural income sources account for 62% of the total household income.
From the three activities, income from cropping activities accounts for 45% of the
total household income and 92 % of all households has income from crops. The most
common crops grew by the households in the research area are wetland rice, maize,
cocoa and coffee. Rice and maize are used for home consumption as well as for sale,
while cocoa and coffee are mainly sold (Schwarze, 2004).
To improve the standard of living particularly for people who engage in agricultural
activities, agricultural and rural development should be taken place regarding to some
factors. According to Zeller and Minten (2000), in order to generate income, the rural
households combine two different factors, internal and external. Internal factor is
household resources that consist of physical capital, human capital and social capital.
External factors are access to financial market, agricultural input and output markets,
and land market, as well as the price and transaction cost in those markets.
1
There are specific challenges of marketing in the agricultural sector due to the
characteristics of agricultural products such as raw material, bulky and perishable,
quality variation, seasonal variability in production, geographic concentration of
production, and varying cost of production. All those characteristics give rise to
market failures in the economy. Market failure refers to the situation in which market
fails to attain economic efficiency.
Moreover, agricultural markets in developing countries are often characterized by
inadequate physical and marketing infrastructure, information asymmetry among
producers and traders, and entry barriers. These factors contribute to high transaction
costs which can cause arbitrage failure and lead to inefficient allocation of resources
(McNew, 1996). Arbitrage failure refers to the situation where spatial price
differences exceed the transaction cost and other transfer cost involved in moving
good between the two markets (Park, et al., 2002). Summarized from many
literatures, Sexton, et al. (1991) found that the failure may be occurred due to
impediments to efficient arbitrage, such as trade barriers, imperfect information, or
risk aversion.
High transport cost and low agricultural productivity are also found in agricultural
market in developing countries, therefore rural food markets can be thin and isolated.
Consequently, farmers are confronted with food prices that are volatile and highly
correlated with their own agricultural output (Fafchamps, 1992).
Performance of agricultural input and output markets are as important as access for
the farm households to those markets. The access for the farm households to markets
can be proxied by the presence of infrastructure and market institutions. Following
Wanmali (1992) rural infrastructure can be divided into three different categories,
soft, institutional and hard infrastructures. Soft infrastructure consists of
transportation vehicles, communication and information, input distribution, marketing
and financial services. Government agencies, cooperation and trader organisations
are included as institutional infrastructure. Hard infrastructure is the presence of
physical facility such as roads, electricity and irrigation system.
2
Price has important role. For the farm producers, price contains information on
expected income as well as investment planning. The same situation as expected
income and investment planning is what price guided to the traders. What crops to
buy, where to buy and to sell, when to buy and to sell are some considerations to
make purchase, sale and investment decisions. Consequently, high variability of
prices can have unfavourable impact on both producer and consumer, threaten farm
incomes, prevent producers from making investment in agriculture, and eventually
drive resources away from agriculture. Thus, price variability can be a serious
concern for producer, traders and consumers alike.
Overcoming these problems for market development in a way that avoids
disadvantages to the farm households is a major challenge, therefore it is important to
study agricultural input and output markets, producer prices and its variability in
terms of space and time and as well as the factors influencing them. Besides, as
producer for most agricultural commodities who are greatly influenced by the price of
the agricultural output being produced and sold, farm households are consumers of
basic food items such as rice, sugar, vegetable oil and as well as fertilizer for
agricultural input. Obviously, the welfare situation of the households and the ability
to purchase consumer goods are affected by the consumer prices and its variability.
Therefore, it is also necessary to observe consumer price level and its variability
across the space and time.
With respect to the importance of agricultural development in accelerating overall
regional economic development, market, marketing process and infrastructures are
some aspects that should be developed in the agricultural sector. Therefore, an
improved knowledge of the agricultural input and output markets and the patterns of
price variability and some factors behind those would give additional information for
policy makers in providing a favourable policy environment for the whole society,
farm producers, intermediate agents and consumers.
3
1.2 Research questions and objective
The objective of this study is to analyse structure, conduct and performance of the
agricultural input and output markets and to measure variability in agricultural input
and output prices across time and space. The study will analyse agricultural inputs,
outputs but also some basic food items.
The study is expected to give answers to the following questions:
• How are the agricultural commodities markets being organized? Is the
agricultural commodities trade composed of many small traders, who compete
with each other or is it dominated by few large participants?
• Are there any barriers to market entry? If so, what are the major barriers?
Which can be altered by policy, e.g. with respect to legal framework?
• What approaches do the traders use in selling, buying and pricing activities?
• What is the role of producer associations (cooperatives) or trade associations for
marketing of agricultural input and output?
• To what extent do the communities have access to agricultural input and output
markets?
• What is the marketing margin i.e. differences between producer price and the
retail price paid by consumers and within the agents in the marketing chain?
• What are the patterns of price variability of agricultural commodities in term of
time and space? Do villages with lower access to market have lower producer or
higher consumer prices? Do villages with lower access to market have more
seasonal fluctuation?What are the policy implications of the findings to above
questions?
4
1.3 Outline of the study
This study consists of seven chapters, divided into the following description. Chapter
1 presents background information on market and price of agricultural commodities.
Research questions and objective are formulated in this chapter.
Chapter 2 reviews some theoretical and empirical literature on market and price
analysis. The theories that are described in this chapter are agricultural marketing,
structure conduct performance paradigm, agricultural price variability and alternative
approaches to conduct market and price analysis. Some advantages and limitations of
the theories and are described as well.
Chapter 3 focuses on the detailed methodology used for analysis. The chapter
presents a sampling procedure, sources and different type of the data. The process of
data collection and weakness occurred during the data collection is described. Then, it
is continued by the explanation of entry and cleaning process to get reliable data for
analysis. The chapter ends with the focus on the descriptive and econometric analysis.
Descriptive analysis such as mean, standard deviation, coefficient of variation,
methods to compare means, gini coefficient and Lorenz curve, seasonal and spatial
price spreads are detail explained. Econometric analysis such as diagnostic and
testing procedure for time series data (unit root test) and ARCH (Autoregressive
conditional heteroskedastic) model are detail described. Then, based on the theories
presented in the previous chapter, conceptual framework is formulated at the end of
this chapter to guide further analysis.
Chapter 4 presents detailed analyses of the structure of agricultural input and output
markets. The structure of cocoa, coffee, rice, maize and fertilizer markets in the
research area is described in detail. The findings from survey of traders are used to
describe the marketing channels by which the agricultural outputs move from the
farm to local or urban consumers. Another issue focused on market structure is
barriers to market entry. Relating to the research question in chapter 1, this chapter
ends with a review of the structure of the agricultural markets.
5
Chapter 5 provides information about agricultural market conduct in the research
area. This chapter discusses general information about the profile of agricultural
traders and their business practises. In relation to the research question presented in
the chapter 1, this chapter ends with a summary of the conduct of the agricultural
markets.
Chapter 6 concentrates on the analyses of the performance of the agricultural market.
First section examines degree of access to market and infrastructures. Second section
analyses marketing margin and farmer’s share. Then it is followed by uncertainty,
break-even price and sensitivity analysis. Fourth section analyses seasonal and
spatial variability of prices. This section presents figures and tables on the price
movement and it allows us to compare between villages. Then, in the next section
the results of the econometric analysis on price variability are described. Regarding to
the research question in the chapter 1, this chapter ends with a summary of the
performance of the agricultural markets and the price variability.
With regard to the research question presented in the first chapter, chapter 7
concludes the results of this study and considers some policy implications from the
findings of the study.
6
2. LITERATURE REVIEW
This chapter presents some theoretical and empirical literature on market and
price analysis. The theory of agricultural marketing, structure conduct
performance paradigm, alternative approaches of market and price analysis are
described.
2.1 Agricultural marketing
Agricultural marketing refers to the performance of all business activities
involved in the flow of products and services from the point of initial agricultural
production until they are in the hands of consumer (Kohls and Uhl, 1990).
Marketing can be seen as a transformation process of the commodity in time,
space and form (Kotler, 1994). Those three dimensions refer to the condition
where consumers are able to buy commodity at different time from its harvest and
consumption, different place from farmers field and in the different form which is
preferred to be consumed. Another function of agricultural marketing is
transmission of price signals between farmer producers and consumers (Timmer,
1986; Ellis, 1992).
All institutions involved in moving goods and transforming from producer to end
consumers depicts a marketing channel. Figure 2.1 presents marketing channel for
a typical agricultural commodity. Assemblers (rural collectors), processors,
wholesalers, and retailers collaborate with farmers in the forward flow of the
agricultural commodity from farm producers to consumers. The hourglass
configuration describes the facts that the commodity is concentrated into larger
quantities and fewer firms as it moves to processor and then is broken down into
smaller quantities as it moves to many retailer and even more consumers (Rhodes
and Dauve, 1998).
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Flow of payments
Production Assembly Processing Wholesaling Retailing Consumption
Flow of product
Figure 2.1 Stages in a marketing system (Rhodes and Dauve, 1998).
Marketing functions in agricultural commodities begin on farm and in villages.
Farmer producers grow and harvest the crops either to be consumed by their
family or supplied to the next marketing agents.
Rural assembler is the first link between the farm producer and other middlemen.
The assembler has activities of purchasing commodity from the scattered rural
production and assembled at local village, or sub-district level, or processing firm.
Therefore, in addition to assembly, transport is another key function in the
marketing process provided by this type of trader.
Processing is a process of transforming commodity from prior to onward
distribution. The processing enterprises use the agricultural commodities as raw
material to be processed into different form of products which are preferred by
consumers.
Wholesaling is the changing hands of commodity in bulk. Wholesalers generally
establish their shops around towns and large cities that are connected by
infrastructure facilities. Then, wholesaler sell the commodity either to retailer, to
exporters or export directly to foreign markets (Ellis, 1992; Mendoza, 1995).
Retailer has basic function in distributing commodities to the final consumers,
particularly in petty trading.
The marketing channels vary considerably in complexity and length. It could be
very simple and directly from farmers to consumers in the local market. The
longest pattern occurs when the products move from the farmers to the final
consumers through all the marketing institutions. Each step within the channel has
an activity that enhances the usefulness of the product or in other words, each
agent performs marketing functions and adds value to the product. The nature of
8
the product and the form in which preferred by consumers determine the variety
of the marketing channels.
There are features of agricultural products such as raw material, undifferentiated
products (which are usually referred to commodity), bulky, perishable, variation
in quality, seasonal variability in production, geographic concentration of
production, and varying cost of production, which determine marketing activities.
The characteristics of product and the functions that have been performed by the
agents to meet all the three dimensions (time, space, and form) influence price
formation. Moreover, the number of middlemen within the channel and the costs
of marketing services provided in assembling, processing, transporting and
retailing product and profit taken by each level would obviously affect the price
formation.
The difference price between any two participants in the marketing channel is
referred as marketing margin. A marketing margin may be defined alternatively
as (1) a difference between the price paid by consumers and that obtained by the
producers, or as (2) the price of a collection of marketing services that is the
outcomes of the demand for and the supply of such marketing services (Tomek
and Robinson, 1991). When there are some participants in the marketing channel,
the margin can be calculated at different levels.
In term of marketing activities, marketing margin contains a variety of costs with
respect to the marketing process. Marketing costs generally consist of traders
profit, wages, interest, rents and storage costs, transportation costs, processing
costs, other costs for assembling, processing, packaging and retailing activities
and as well as transaction costs. The marketing margins among commodities may
vary due to characteristic and perishability of the products, number of participants
in the marketing channel, costs occurred in the marketing process and
differentiation of marketing functions performed.
Farm producers are price takers, who have very limited control over prices
received for their products. Moreover, the price often exhibits variability, that is,
changes over the time. The variability could means losses or profit, hence it leads
to a great uncertainty. Uncertainty is important concept since it is found to reduce
9
production, investment and consumption and thereby trade (Sandmo, 1971).
Therefore it is important to appraise the prices in the context of risk
circumstances.
The great bulk of analytical investigation of risk has been concerned with “pure
risk” that is, variation about some measures of “representative” performance such
as the mean. This measurement, however, can be applied when farmers are risk
neutral or indifference. It means that the farmers as decision makers can make
decision solely and comfortably on the basis of the mean or expected value of
pertinent uncertain quantities (Anderson and Dillon, 1992).
However, risk neutral is encountered very rarely to be found in decision makers,
most people are risk averse and generally farmers are risk averse. Evidence of
farmers risk aversion was found in many of their action, include work by Brink
and Mc Carl, Dillon and Scandizzo, Biswanger as had been summarized by
Robison and Fleisher (1984). Any form of risk aversion implies a preference for
low variability in income and thus in prices (Barret, 1996).
Because of risk aversion of most farmers, it may be argued that a concept of
downside risk is more relevant in analysis of risk in agriculture. Downside risk
can be defined as a shorthand description for situation in which any significant
deviation from the norm lead to worse outcomes (Anderson, et al., 1977;
Hardaker, et al., 1997). Downside risk is concerned with the “placement” of risk
in a distribution. One distribution is said to have more downside risk than another
if it has more dispersion below a specific target or if it more skewed to the left
(Menezes, et al., 1980).
Another approach to measure downside risk in farmer side is using graphical
representation of probability distribution (Anderson, et al., 1977). The S-shaped
cumulative distribution function (CDF) can be applied in the measurement of
down side risk. CDF may be defined as P (x ≤ X*), it means that the probability
that x is less than or equal to a particular value of X*.
Then, the CDF can be combined with sensitivity and break-even analysis. Since
farmers face risk that prices may lower than expected, calculation of the
probability that price is below break-even point and the acceptable declining of
10
prices to cover costs of production is important to avoid losses. If price decrease
below the variable costs, it will hurt the farmer producers even in the short run.
Figure 2.2 shows a CDF with a hypothetical value of gross margin. The figure
illustrates the downside risk, which can be defined as probability of cases where
break-even point occurred or the gross margin equal to zero. As commodity price
takers, the farmers should develop marketing plans to obtain higher prices in order
to cover per unit total cost of production.
0.000
0.200
0.400
0.600
0.800
1.000
lessthan 0
0 1-100 101-200 201-300 301-400 401-500 501-600
Range in Gross Margin
Cum
ulat
ive
prob
abili
ty
Figure 2.2 CDF for the probability distribution of gross margin
Marketing margin can also be high because of high real marketing costs.
Frequently farm-retail margin are high because the transport system to major
urban retail markets is inefficient and costly. A study carried out by FAO in
assessing retail-farm gate margins for rice in Africa in 1985 reported that the large
differences between the marketing margins are primarily due to genuine
differences in the cost of delivering rice to retail markets rather than to innate
inefficiency and excess profits by the agents involved in the distribution chain.
The study recorded that road networks are not as intensive, transport services are
less frequent and more costly, and average haulage distances are greater (Colman
and Young, 1997).
11
Marketing margins also show some extent of the market structure. Imperfect
competition will generate high marketing margin because of abnormal profit taken
by traders. Degree of competitiveness in the markets generally affects the extent
of trader’s profit, transaction costs, and price transmission between markets
(Minten, 1999).
Another approach to observe marketing margin is price spread. The price spread
can be classified into spatial and temporal dimensions (Ahmed and Rustagi,
1987). First category of spatial spread is price spreads between producer and final
consumers of a product market, also known as farmer’s share. It represents a
category in which the marketing margin is equivalent to the spread in prices at the
two ends. The other category of spatial spread reflects the differences in prices at
various regional markets at a particular time. The marketing margin and spatial
price spread are the same where (a) the two price points are integrated by a
functioning market or trade link and (b) the law of one price holds between the
two regions (Sexton, et al., 1991; Baulch 1997).
With regard to the temporal dimension, there are two common types of the
spreads in agricultural prices. These are the annual variation (price fluctuation
between years) and the seasonal variation (within a year).
Ritson (1997) illustrates schematic categorization of issues in agricultural
marketing with respect to its problem, analysis and policy (see figure 2.3). Figure
2.3 shows that there are three kinds of problems in agricultural marketing,
different type of analysis and potential marketing policy to overcome those
problems. Arrows in the figure represent the main relationship among elements.
Three kinds of problems in agricultural marketing are market power, excessive
margin and price signals. A growing concentration of food manufacturing and
distribution allows the marketing sector to exploit market power to detriment of
the farm sector and perhaps also consumers. This power might be expressed in
the form of excess profit or efficiency losses due to the lack of competitive
pressure, but in either case would be viewed in agricultural marketing as
“excessive margin”. But in addition to the impact of market power, margins
might be excessive, so it is often believed, because of inefficiency in the structure
12
and organization of the marketing sector. Third, much attention has been devoted
to the efficiency of the agricultural market price mechanism in communicating
information between farmers and consumers - in particular, the problems of price
instability obscuring useful price messages between consumers and producers,
and price cycles delivering false message to expand and contract production. In
addition there is the question of whether price formation reflects efficient relations
between markets over time and space, and the contribution of futures markets to
pricing efficiency (Ritson, 1997: 13).
Problems in agricultural marketing
Structure Efficiency
Problem Market Power Excessive margins Price signals
Analysis S-C-P Marketing margin ** Market price
Policy Marketing board, Market intelligence Trade and price Cooperatives and and grading controls Competition policy Figure 2. 3 Schematic categorization of issues in agricultural marketing **= ARCH Adapted from Ritson (1997) with additional information added by the author
Agricultural market analysis is divided into three categories, application of
structure-conduct-performance analysis, analysis of marketing margin and price
movement over time and space. In addition to those analyses, this study use
econometric ARCH model to observe price variability.
Potential marketing policies based on previous problems are legal measures such
as competition policy, price controls, formation of producer marketing group or
cooperatives to counterbalance the power and various activities to improve
marketing efficiency such as quality standard and grading.
13
2.2 Structure – Conduct – Performance paradigm
Industrial organization is a subject of economic science. It concerns with the
functioning of markets, and in particular, the ways in which firms interact and
compete with each other. The S-C-P postulates that the market performance or
social welfare features of the equilibrium in the economy is determined by the
conduct of the firms, which in turn is determined by the structural characteristics
of the markets (Figure 2.4). This paradigm was first formalised by Mason (1939).
Afterwards, Bain (1956) modified S-C-P paradigm based on the neoclassical
theory of the firm.
Performance Conduct Structure
Figure 2.4 Relationship between structure, conduct and performance (Fergusson, 1994).
Market structure refers to the characteristics and composition of the organization
of a market, which influence strategically the nature of competition and pricing
within a market. The structure can be identified by considering (either jointly or
separately) the number and size distribution of seller-buyer (degree of market
concentration), competition, entry condition (degree of difficulty for new entrants
to enter the market), products differentiation, and the extent of firms are
diversified or integrated (Fergusson, 1994).
According to degree of competition, markets can be classified into three different
forms 1) a high degree of competition, also called perfect competition; 2)
monopoly; and 3) imperfect competition (Stiglitz, 1997). Monopoly is the most
extreme situation when only one firm supplies the entire market, therefore no
competition. The forms of imperfect market competition are oligopoly and
monopolistic competition. Oligopoly is a situation when there are few firms
supply the market and each worries about how rivals will respond to any action it
undertakes. Monopolistic competition is defined as many firms (more firms than
in oligopoly, but not enough for perfect competition) exist in the market and each
firm can ignore the reactions of any rivals.
14
The market has a major role as an instrument in achieving balance between
demand and supply, price formation function and as well as resource allocation.
From neo-classical point of view, ideally, perfect competition market supposes to
generate efficient outcomes and lead to the maximum total contribution to
producers and consumers welfare, since both parties cannot influence the price
and profit or utility of each party is at pareto optimal condition (Janssen and van
Tilburg, 1997).
Economists use perfect competition as a standard benchmark for the assessment
of market efficiency. In the perfect competition, market is supplied by a large
number of firms for large number of buyers therefore no individual can influence
price. The firms provide relatively homogenous products so one firm is
essentially a perfect substitute for the other firms. There are no artificial
restrictions on demand, supply or prices, such as government intervention or
collusion among firms. Mobility of resources and products exists in the economy
i.e. free of market entry barriers for a new firm (Tomek and Robinson, 1991). All
suppliers and consumers have perfect information, therefore perfect foresight and
market is certainty. However, actual market performance and its welfare impact
depend critically on how efficiently markets generate and transmit price signals
and how efficiently marketing activities are carried out (Timmer, 1986).
The basic principle behind the SCP paradigm is the perfect competition and
monopoly which are viewed as opposite ends of a spectrum of market structures
along which all market lie (essentially the models of perfect competition,
monopoly and monopolistic condition together with the various models of
oligopoly).
Mellor (1969) observed that in low-income countries substantial market
imperfections were found. The imperfect competition may be resulting from high
degree of barriers to market entry due to underlying technical and demand factors
such as unequal access to capital and information, inadequate size of market for
an economically viable competition, product differentiation, and natural factors.
In most developing countries, the typical agricultural marketing system is
characterised by a highly atomistic production side, in which there are numerous
15
farmers growing perishable crops on small farms dispersed all over the country
side; and by an oligopoly market, where there are few traders (Kahlon and
George, 1985; Mendoza and Rosegrant, 1995). In addition, for some agricultural
products monopoly seems to be important features due to creation of state trading
organisations often called marketing boards (Colman and Young, 1997). Another
type of market imperfection in agricultural commodities arises from the
inadequate development of marketing infrastructure leading to costly and
uncertain costs involved in marketing process (Kahlon and George, 1985).
The most frequently used approach to measure market structure is market
concentration. Market concentration refers to number and size market distribution
of sellers and buyers in the market. Differences in the number and size
distribution of firms are key factors distinguishing the theoretical models of
competition in the market. Moreover, concentration can illustrate the degree of
market power. Concentration contributes to the firm behaviour within the market
due to its impact on interdependence action among firms.
The concentration shows the extent to which production or marketing of a
particular good or service is confined to a few large firms. The fewer the number
of firms and/or the more disparate their sizes, the more concentrated (and – the
implication is – the less competitive) the market (Fergusson, 1994:38-40).
Measures of market concentration seek to transform the information on the
number and size distribution of firms into a single value. Some are absolute
measures which combine the number of firms present and their size disparities.
With one exception (the concentration ratio) these consider all the firms in a
market, that is, they are summary measures. Relative concentration measures, in
contrast, focus on the effectively ignored differences in the number of firms
present. It is generally argued that the higher market concentration (the market is
more concentrated) and the more unequal the size distribution of firms and imply
less competitive behaviour and thus inefficiency. However, it is also notified the
critical interpretation of such relationship in isolation from other determinant
factors like barrier to market entry and economies of scale (Scott, 1995).
The four main absolute measures are:
16
1. Concentration ratio (CRx)
This measures the cumulative market share of the largest X firms (ranked in
descending order size) in the market. The typical values of X are 4, 8, and 20.
The most frequent choice is the CR4 (the four-firm concentration ratio), that is the
sum of the market share of the largest four firms in the market. Kohls and Uhl
(1990) suggest that the CR4 has less or equal to 33% indicating a competitive
market structure, the concentration ratio of 33% to 50% and above 50% may
indicate a weak and strong oligopsonist market structure, respectively.
This measure is widely used in empirical studies, however it reveals some
limitations (Waldman and Jensen, 1998). First, since CRx describes the
percentage of market share held by a specific number of firms, changes in market
share outside the largest firms will not affect the CRx. Second, CRx provides no
information about the distribution of market shares among the top firms.
2. Herfindahl-Hirschman Index (HHI)
This is defined as the sum of the squares of the market shares (output of the firm
divided by total output) of all firms in the market. This method shows an
advantage compared to the CRx, whereas data on all firms in the market are used
in the calculation. This point leads economists to prefer the HHI to simple
concentration ratio such as CR4 (Pepall, et al., 2002). However, the HHI is very
sensitive to the market share of the largest firms, due to the squaring of market
shares.
3. Hannah and Kay Index (HK)
The formula is almost similar to the HHI, instead of squaring, the market shares
are raised to the power α.
4. Entropy index
Market shares are weighted by the logarithm of the market shares.
The examples of relative concentration measures are:
1. Variance of the logarithms of firm size.
2. Gini coefficient which is derived from the Lorenz curve.
The Gini coefficient measures degree of concentration of a variable in a
distribution. It compares the Lorenz curve of an empirical distribution with
17
perfect equality line (the 45o line). The value of the Gini coefficient ranges
between 0, where there is no concentration (perfect equality) and 1, where the
concentration is full (perfect inequality). With regard to its limitation, Gini
coefficient does not have unique value. It means that one or the same Gini
coefficient might arise from two different Lorenz curve1.
Considering the advantages and weakness of the approaches, this study applies
concentration ratio (CR4) and Gini coefficient which is derived from the Lorenz
curve to measure market concentration. The reasons are: (1) this measure combine
graph and values; (2) not only consider the largest four but also all firms in the
market; and (3) distribution of market share or sales can be seen from the graph.
According to Bain (1956) there are some elements of market structure that act as
barrier to market entry. The concept of barrier to entry can be defined as any
factors or market conditions that place potential new firms at a competitive
disadvantage with incumbent firms, thus it prevents the new firms to long run
entry into a market. According to this definition, a barrier to entry exists if a new
firm cannot achieve the same level of profits after entry that an established firms
earned before entry occurred (Waldman and Jensen, 1998). Three main types of
barrier to entry are: economies of scale, absolute cost advantages, and product
differentiation. Economies of scale or increasing return to scale can be defined as
a condition when a proportionate increase in all inputs results in a more than
proportionate increase in output (Stiglitz, 1997). In this case, with constant per-
unit input prices, long run average costs decrease as the quantity of output
increases. It acts as a barrier to entry in situation where there is a potential effect
of entry (due to increasing output in the market) on the market price of the
product. Control over crucial inputs, products protected by patents, access to
superior resources or production technologies or lower cost finance are sources of
absolute cost advantage. Product differentiation can act as barrier because it gives
individual producers some market power. Existing producers with differentiated
products have built up consumer goodwill and can raise its price without loosing
all consumers.
1 Refer to lecture scripts of Dr. Bernhard Bruemmer
18
Conduct is the patterns of behaviour, which enterprises follow in adapting or
adjusting to the markets in which they sell (or buy). It focuses methods employed
to set prices, whether independently or in collusion with others in the market,
sales promotion (advertising), and coordination policies and the extent of
predatory or exclusionary tactics directed against established rivals or potential
entrants.
In the case of perfect competition market when the concentration is relatively low,
there will be no personal contact in setting prices. Different from the perfect
market, oligopoly market has medium to high degree of concentration, collusive
behaviour within the firms in the market is likely be taken place.
Performance represents the economic results of structure and conduct, which
concerns to the question whether or not the firms operation enhance economic
welfare to the economy. It emphasizes the performance of the marketing system
as a whole. The performance is commonly measured in terms of productive and
allocative (economic) efficiency. In a broad sense innovation (progressiveness),
equity and employment creation are also considered as performance assessment
Several indicators can be utilized to assess market performance are stability of
price and marketing margin or spread prices, costs and volume of output, net
returns, farmer’s share of retail price and proportion of consumer’s income which
has been spent. Price efficiency also utilized to analyse market in its dimensions
of space and time (Pomeroy and Trinidad cited from Scott, 1995). Other
measurements can be utilized to assess market performance by applying degree of
market integration, relationship between transfer costs and inter-market price
differences, and relationship between seasonal price and storage costs to indicate
market competitiveness through time (Harris cited from Abbot, 1993).
Nevertheless, essential purpose of evaluating performance of a marketing system
might be thought as how well the system performs what society and the market
participants expect of it (Crawford, 1997). Consumers are likely to evaluate a
marketing system in terms of its performance in avoiding high and instability
prices. Farmers concern on accessibility of marketing infrastructure at reasonable
cost and factors influencing prices as well. Society is likely to give consideration
19
to the marketing system’s contribution to employment. Government will take into
account contribution of marketing system on employment, investment, and
economic growth.
The most important hypothesis generated by the structure-conduct-performance
school of thought is that as market structure moves away from perfect
competition, market efficiency will decrease (Jabbar, et al., 1997; Dessalegn, et
al., 1998).
2.3 Alternative approaches of market analysis
Several authors focus on industrial organization theory to analyse the efficient
markets. Industrial organization framework suggests a relationship among
structure, conduct, and performance of the market namely S-C-P paradigm.
However, according to some literatures here are some major problems faced in
empirical application of S-C-P paradigm (Jabbar et al, 1997). The problems are:
1) under some circumstances, a given structure may not lead to theoritically
anticipated conduct and performance; 2) industrial organization studies focussed
maily on structure and performance, mush less attention has been given on
conduct due to data and measurement problems and underdeveloped nature o the
theory on conduct; 3) market performance depends not only on relationships
among similar firms but also on different categories firms in the marketing
environment.
Based on typology developed by Knudsen (cited from Fergusson, 1994), The S-C-
P focus its analysis on the decision makers with the main interest is in how these
groups interact and respond to changes in market circumstances. Another
assumption is the price system is the only explicitly modelled device that is
identified as a means for co-ordinating different activities. Implicitly, it stated
that there are no costs associated with the use of the price-mechanism or
transaction costs are zero.
The industrial organization theory seems to be incomplete since it basically limits
the study on price mechanism (based on assumptions of neo-classical economics)
and there was found also some empirical deficiencies. Therefore, there are also
20
some alternative approaches to market analysis, such as transaction cost
economics (Williamson, 1975), institutional economics, or commodity system
analysis.
In addition, the collection of primary data on market structure, conduct and
performance has often been substituted by a heavy reliance on the analysis of
secondary price series data. The analysis of market performance has often been
limited to market efficiency analysis based on price integration parameters, such
as market integration or low of one price (Jansen and van Tilburg, 1997).
The SCP analysis according to Jansen and van Tilburg (1997), can be a good
starting point for the analysis of marketing system. However, it will be better if
the analysis is complemented by analytical approaches which take into account
the development of the marketing system in the economy.
Considering advantages and limitations of the presented theories and empirical
literatures in previous sections, this study applies the SCP analysis and is
complemented by analysis of price series data.
2.4 Agricultural price variability
This section describes some sources and implications of price variability.
Seasonal and spatial price variability are separately explained. Several methods to
measure price variability are reviewed in the next sub section. This sub section
ends with the selection of appropriate model used in this study.
2.4.1 Sources and implications of seasonal and spatial price variability
Agriculture sector in Indonesia contributed 19.6% and 17.2% to total gross
domestic product in 1999 and 2000, respectively (World Bank, 2005). Although
the agricultural sector is declining component of Indonesian economy, agricultural
product prices remain important economically and politically. Agricultural
commodity prices, their levels, variability and its determinants are of central
importance in the agricultural sector. Price is important determinants for farm
income, cost of food for consumers, important determinants of consumer welfare,
21
export earnings for countries engaged in commodity trade, and profit for
agricultural marketing traders.
Market condition and all the market forces through supply and demand conditions
of agricultural commodities potentially affect the price formation and its
variability. Comparing to other non-farm goods and services, agricultural
commodity prices are more volatile. Increasing agricultural price variability can
have detrimental impacts on both producer of agricultural commodities and
consumers (Binswanger and Rosenzweig, 1986; Sahn and Delgado, 1989). The
price variability also undermines traders and processors incomes and the income
transfer among market participants.
In general, price variability can be classified into two different dimensions.
Different regions (spatial dimension) and different point of time (seasonal
dimension) are considered.
Seasonal price variability
The nature of agricultural commodities is that the production patterns have
seasonal character, while consumption is more or less stable throughout the year
leads the prices to be seasonally volatile. Seasonal price differences refers to all
aspects of storage i.e. storage costs due to seasonal variation in production
(supply) and demand. Other factors that are considered influence seasonal
variability are trader’s profit and transaction costs, including risk premium
(Ahmed, 1988).
Minten (1999) reported that two reasons could be invoked for seasonal price
movement in Madagascar, the cost of capital (Badiane, et al., 1997; Zeller, 1993)
and non-competitive market practices (Alderman and Shively 1996; Sahn and
Delgado 1989).
The patterns of production and storage tend to stimulate prices are lowest at
harvest and post-harvest time in rural areas and the commodities may flow from
these areas to urban areas. The prices will be higher in lean season when harvest
period approaches. The prices in urban area have the same patterns, they are also
seasonal but fluctuations are dampened. These are distressingly common
22
characteristics of low-income economies phenomenon of seasonal flow reverse
direction as the harvest approaches again and rural farm household exhaust stocks
and become food buyer (Barret, 1996). In this season, prices in rural areas
become higher compared to prices in harvest and post-harvest periods. This
phenomenon results from urban concentration of storage, including of imported
transactional stocks, and from seasonal net demand in producing area (Timmer
1986; Barrett 1996; Minten 1999).
Spatial price variability
Again, the nature of many agricultural commodities are geographic concentration
in production, even remoteness of the farm producers to final consumers will
affect flow of the commodities and costs involved in moving the commodities
from the surplus to the consumer area. This feature means that presence of
infrastructure facilities such as roads and market site will influence price levels
and its variability. Taking the case of Madagascar, it is found that spatial
variability between communities is linked to the distance to a paved road, quality
of the road, access to soft infrastructure (measured by access to credit,
information, security and agricultural inputs) and degree of competition between
traders (Minten, 1999).
In most cases, price levels and its variability in spatial dimension can be explained
by transportation cost, including normal traders profit, transaction costs and other
transfer costs involved in moving goods from location of sale to final purchase.
Study in Madagascar found that high transportation and transaction costs lead to
significant spatial price variation for inputs and levels of outputs (Zeller and
Minten, 2000).
2.4.2 Analysis of commodity price variability
This sub chapter reviews several methods to measure price variability. This sub
chapter ends with the selection of appropriate model to be used in the analysis.
First measure of price variability is monthly price indexes. It calculates difference
between the lowest and highest monthly indexes. The average spread in seasonal
prices can be measured by the lowest price as a percentage of the highest prices.
23
Second approach, which is more comprehensive than price indexes, is descriptive
statistics. It measures the characteristics of distribution of price in levels and
period-to-period changes. Mean, and standard deviation are basic statistics as
measurement of central tendency and dispersion of the data. Although the
standard deviation can be used as a measure of variability, comparing directly two
or more standard deviations can lead to falsely conclude the results. Besides
depend on the value of mean, the standard deviation of the different unit
measurements cannot be compared. To solve this fault, coefficient of variation, as
a relative measurement can be applied.
Since the coefficient of variation is unit free, it facilitates comparisons of price
changes in different directions across different periods of time and for different
commodities. Although this measurement provides some information on the
nature of price variability, it ignores the dynamic properties of prices.
In time series data, there are some characteristic movements or components of
information that should be addressed before attempting to use the data for
prediction. Often the time series data can be broken down into four different basic
components: (1) a long-term movement (sometimes called trend, T). Many
economic time series have a common tendency of growing overtime or at least
over certain periods have downward trends; (2) a cyclical component (C); (3) a
seasonal movement (S). If a time series is observed at weekly or monthly intervals
it may exhibit seasonality, there is a recurring pattern or seasonality within each
year; (4) a random, error or irregular component: this movement is basically just
noise.
The difference between a cyclical and a seasonal component is that the latter
occurs at regular (seasonal) intervals, while cyclical factors usually have a longer
duration that varies from cycle to cycle. The random component is a small scale
variations that are not accounted for by long-term, cyclic, or seasonal movements
(Makridakis, et al., 1998; Wooldridge, 2003).
As it is known that the time series data may consist of the four movement
components, those behaviors should be accounted for before using the data for the
further prediction. Those characteristics should be recognized because ignoring
24
the components i.e. the series are trending can lead us to falsely conclude the
analysis. Decomposition and adjustment is purposed to isolate those components,
that is, to de-compose the series into the trend effect, seasonal effects, and
remaining variability. Adding a time trend in the regression analysis will eliminate
the trending problem. If the series are seasonally not adjusted yet, a set of seasonal
dummy variables is included to account for seasonality either in dependent or
independent variables.
The weekly-collected price series in this research covered one-year period or the
number of observations are around 52, the underlying components in the data
cannot be clearly observed. Therefore, the series are not decomposed and it
straightforwardly used into the analysis.
Another method to calculate price variability is using an econometric model to
overcome possible weaknesses from previous approaches. Before estimating the
data using econometric time series model, diagnostic checking should be
conducted in order to satisfy the requirements of assumptions in the time series
analysis. The time series data should be stationary, that means its mean, variance
and covariances remain constant over time. The term non-stationary refers to the
condition when one of the characteristics of stationary cannot be satisfied. Non-
stationary time series data indicates the variances are infinite and implies that least
square estimation will not be valid and inferences derived from the results are
highly suspect. Unit roots test is the main techniques to test the stationarity of the
data. One of the several possible methods to conduct unit root test is Dickey
Fuller (D-F) test.
Time series data are that often exhibit the phenomenon of volatility clustering,
that is, periods in which their prices show wide swing for an extended time period
followed by periods in which there is relative calm (Gujarati, 2003). Systematic
behaviour of prices are usually in form of the “today” prices level may be
correlated with “yesterday” price or even “prices two days ago”. It implies that
“today’s variability” may be correlated with “previous days variability”. ARCH
(Autoregressive Conditional Heteroskedasticity) model that was introduced by
Engle (1982) can be applied to circumvent the tendency of price changes, which is
25
frequently associated with the clustering of price changes. In this model,
conditional variance of error term at time t depends on the squared error term in
the previous time period. The variation from the ARCH model is GARCH model
(Generalized autoregressive conditional heteroskedasticity), proposed by
Bollerslev (1986). The ARCH model can be generalized to the GARCH model
by adding conditional variance in the previous time period in the right hand side.
Shively (1996) measured some descriptive statistics such as coefficient of
variation, skewness and kurtosis as a picture of maize price variability. The results
confirmed that the wholesale maize price in Ghana during 1978-1993 were
volatile. However, different results were obtained from the formal analysis of
maize price levels and its variances using econometric ARCH model. The
previous calculations using descriptive statistics were incorrect and could not be
verified.
Moreover, the use of the ARCH model circumvents many of limitations of
previous studies that employed the industrial organization paradigm of market
structure, conduct and performance, and the methods of price spread analysis and
bivariate price correlation that have been utilized in evaluating market
performance (Mendoza and Rosegrant, 1995).
Recent studies by Aradhyula and Holt (1988, 1989, 1990), Han, Jansen and
Penson (1990), Yang, Koo and Wilson (1992), Barret (1995, 1997), Mendoza and
Rosegrant (1995), Shively (1996) adopt the ARCH/GARCH to estimate
variability in agricultural series data.
In this research seasonal price spread, coefficient of variation and ARCH model
are used in the analysis of price variability.
2.5 Summary
This chapter reviewed the theoretical and empirical literature on market and price
analysis of agricultural commodities. This review guides the formulation of
hypotheses and the conceptual framework. The conceptual framework is the
foundation to guide the further analysis.
26
Starting with the concept of agricultural marketing, this sub chapter review some
aspects related with the concept of marketing in general term and particularly in
agricultural commodity. Marketing channel for typical agricultural commodity
and intermediary agents involved in the marketing process are explained.
Due to their position as price takers, farmer producers face risk that the price
received is lower than expected. The measurement of downside risk to calculate
the probability that price is below break-even point and the acceptable declining
of prices to cover costs of production is important.
In order to analyse how well a market performs its functions, structure conduct
performance (SCP) paradigm which is highly depend on the collection primary
data can be applied. The SCP postulates that the structural characteristic of a
market determines the conduct of the firms in that market and in turn will
determine the market performance. The basic principle of the SCP is the perfect
competitive and monopoly which are viewed as opposite ends of a market
structure. Imperfect competition such as monopolistic competition and oligopoly
lie in between the two ends. Although there are some limitations in the SCP
analysis, this approach is good starting point for the analysis of marketing system.
To carry out market analysis, this study use structure conduct performance
paradigm, and it is combined with the analysis of time series data to observe the
seasonal and spatial variability in agricultural prices. The next chapter deals with
the methodology used for the analysis throughout the study.
27
3. METHODOLOGY
This chapter focuses on the detailed methodology used to answer the research
questions. After presenting a sampling procedure, sources and different type of
the data, process of data collection, weakness occurred during the data collection,
entry and cleaning are described. Then, it is continued by the explanation of the
descriptive and econometric analysis. Descriptive analysis which contains mean,
standard deviation, coefficient of variation, methods to compare mean (t-test and
ANOVA), Gini coefficient and Lorenz curve, seasonal and spatial price spreads
are described. Econometric analysis which is started from diagnostic and testing
procedure for time series data such as unit root test and then it is followed by the
explanation of ARCH model are detail described. This chapter ends with
conceptual framework which is formulated based on the theoretical and empirical
literature to guide the analysis of the whole study.
3.1 Description of the study area
The research areas are villages located in the forest margin of the Lore Lindu
National Park (LLNP). The LLNP is situated in Central Sulawesi Province,
Indonesia. This research covers 8 villages in 3 subdistricts and provincial capital
city (Palu) as a central market of agricultural commodities (Table 3.1).
Table 3.1 Survey villages of the study
Research area Subdistricts Villages
Sigibiromaru Maranata Pandere Sidondo II Palolo Sintuwu Berdikari Rahmat Kulawi Bolapapu Tomado Municipality of Palu (central market)
28
3.2 Sampling procedure, data collection, entry and cleaning For the analysis purposes, village and market survey was conducted through
STORMA survey. Besides Tomado, all survey villages are sub-sample in
STORMA project A4. These villages were randomly selected on the basis of the
STORMA sampling frame (Zeller, et al., 2002). Tomado is sub sample of
IMPENSO.
Primary data were collected through two types of questionnaires that had been
developed for different sources of information (village level survey and trader
survey). The village level survey was conducted through a continuous weekly
price survey of key agricultural inputs (Urea and NPK fertilizers) and major cash
and food crops as well as basic food items (see appendices). The data were
collected during January until December 2003.
The collected data consist of producer and consumer (retail) prices. Producer
prices represent prices at a primary market or farm-gate prices for cocoa, coffee
and different varieties of rice. Consumer or retail prices are prices at the final
consumers in each survey area. The retail prices that were gathered during time of
surveys were prices for fertilizers (urea and NPK), different varieties of rice,
sugar, cooking oil for two different qualities; super and medium.
The price data were collected in 8 villages and in addition the prices of the same
commodities were asked in Palu market. In each village and Palu, each
respondent was selected and asked to fulfill the price questionnaire at the same
day, for example on Wednesday every week. In order to facilitate the process of
data collection, these selected respondents were the persons who permanently live
in those areas. Once in every month, the researcher or enumerator collected the
price questionnaires and brought them to STORMA office to be processed.
The process of data collection was designed to be effective and efficient, however,
in the real situation there were some weaknesses occurred. The respondents did
not write the questionnaires at the same day. It has some implications on the
analysis. If the respondents wrote the questionnaire at the same day during that
week, the price levels and its movements can be accurately compared. Chapter 6
shows this limitation. Graphs of prices show that the price movement for
29
different commodities started from 1st until 52nd weeks in 2003. It represents the
price levels in the same week for all research areas but the day could be different
from one place to another.
The other drawback is weakness of monitoring during the fill in questionnaires
because of the lag time between writing the questionnaire up and the data
collection.
A case study of the structure and behaviour of agricultural traders was conducted
during September 2003 until January 2004 through trader survey. For this study,
trader samples were selected using snowball sampling procedure so as to capture
traders engaged in marketing of agricultural commodities in the research area.
The snowball sampling is one of the approaches that do not imply randomisation
and is considered to be non-probability sampling. Although it may suffer lack of
representativeness, that means there is no way of knowing whether the samples
are representative of the population, this method is used in studies of difficult-to-
find populations. When it is impossible to do probability sampling under real
research condition, non-probability sampling is used (Black, 1999; Bernard,
2000).
This research applies snowball sampling because the sampling frame or lists of
population of traders is not available in the research area. To construct a sampling
frame is impossible because of time consuming and too costly. Following
Bernard (2000), the procedure of snowball sampling was: (1) asking the village
headmen for the list of persons with desired characteristics i.e. involved in
agricultural trading and marketing; (2) once the preliminary list had been
available, it was showed to several traders who were on the list and asked them to
give names of others who they thought as appropriate subjects to be contacted and
should be on the list; (3) the process continued until the list became “saturated”,
that was, no new names were offered; (4) the trader samples were selected based
on different stage in the marketing chain. Since the snowball sample may suffer
lack of representativeness, all the results on the structure and behaviour of the
agricultural traders presented in this study cannot be as inferences about the
traders population in the research area.
30
The questionnaire was developed to cover information on: general information
about trader such as age, sex, level of education, household size and composition;
various aspects of their marketing business such as product handled, type of
supplier, type of clients, geographical scope of trading operation, marketing
functions performed, and cost of marketing activities; their perception on the level
of competition and problems they deal with in undertaking their business.
Secondary data were collected to enrich knowledge of agricultural commodities
market in central Sulawesi. The main sources of secondary data were different
organizations at the regional level such as Agricultural offices, Trade and Industry
office, Customs office, and other government and private organizations.
The price and trader data were entered in SPSS files. To reduce the typing errors,
the entry is compared with the information in the questionnaires. After entering,
the data were cleaned to check the missing values, wild codes, inconsistencies and
extreme values.
3.3 Methodology used in descriptive analysis
The data are analysed using different software packages, SPSS, STATA 8.0 and
Limdep. Descriptive statistics are mostly calculated using SPSS software
package.
For the single data set (single time series) the most common descriptive statistics
are mean, standard deviation, and variance. The mean is a measure the center of
the data set. The standard deviation and variance are calculated to measure the
spread of the data. Coefficient of variation (CV) is defined as the standard
deviation divided by the mean. The coefficient of variation expresses the
dispersion of observed data values as a percent of the mean. All is used in this
study to describe access to market and infrastructures, and its relation with price
level and its variability.
Consumer goods flow from central market to villages. Relationship between price
of consumer goods in central market and price in villages is treated as additive
model. It can be found in the equations below:
31
Pc = Price in central market
Pr = Price in remote village
Tcr = Transportation costs
crcr TPP +=
crcr TPP += cr PP >
)(),()()( crcrrcr TVarTPCovPVarPVar ++=
if = constant crT )()( cr PVarPVar =
Coefficient of variation (CV) = µ
SD
cccc
cc PCVPVar
PPVar
CV === )()(
ccrc
cc
crc
c
r
rr CV
TPPCV
TPPVar
PPVar
CV ÷+
=+
==)()(
11 >+=+
=c
cr
c
crc
r
c
PT
PTP
CVCV
The equations show that the price of consumer goods will be higher and the
coefficient of variation will be lower in the remote village compared to those in
the central market.
In contrast to consumer goods, the flow of agricultural commodities (raw
materials) is started from villages to central market. The equations below show
that the producer price will be lower and the coefficient of variation of agricultural
commodity in the remote village will be higher compared to those in the central
market.
)(),()()( crcrrc
crrc
crrc
TVarTPcCovPVarPVarTPP
TPP
++=+=
+=
rc PP >
if = constant crT )()( cr PVarPVar =
32
11
)()(
)()(
>+=+
=
÷+
=+
==
===
r
cr
r
crr
c
r
rcrr
rr
crr
r
c
cc
rrrr
rr
PT
PTP
CVCV
CVTPPCV
TPPVar
PPVar
CV
PCVPVarP
PVarCV
To observe differences in the mean between different groups, independent
samples t tests and Analysis of Variance (ANOVA) are applied. To compare
means of 2 groups, independent samples t is used and for more than 2 groups one-
way ANOVA is applied. Before proceeding the t test and ANOVA the SPSS
produce automatically Levene test statistic to calculate the equality (homogeneity)
of variances in the different groups. The distribution of the dependent variable for
one of the groups being compared must have the same variance as the distribution
for the other group being compared. The SPSS results on Levene test shows the
value that can be verified whether the value is significant or not. If the value is
not significant, it means that the assumption of homogeinity variance is not
violated. From this point, when the assumption of homogeneity can be assumed,
in independent t samples test we follow along the top line and the bottom line if
the assumption is violated.
Levene's statistic is also calculated for the variances in the ANOVA. If this value
is significant, it shows the evidence that the homogeneity assumption has been
violated. In this case, the analysis should be re-run by selecting option for "Equal
Variances Not Assumed".
To measure market concentration, concentration ratio (CR4) and Gini coefficient
are used. The concentration ratio (CR4) measures cumulative share of the largest
four firm (ranked in descending order of size) and is calculated as :
∑=
=x
iix SCR
1 where:
CRx = the X firm concentration ratio
Si = the percentage market share of the ith firm
33
Figure 3.1 shows a Lorenz curve for a hypothetic market. Here all the firms
(traders) are ranked by size and cumulated in ascending order. Then, it is plotted
against the cumulative percentage of output. The greater deviation of the curve
from the perfect equality line, the greater inequality in firm sizes. The Gini
coefficient summarizes this information into a single value.
Referring to Figure 3.1 Gini coefficient is estimated by comparing the area
between diagonal and Lorenz curve to the maximum possible triangle area
between diagonal and the curve.
Cumulated percentage of traders
100806040200
Cum
ulat
ed p
erce
ntag
e of
vol
ume
outp
ut tr
aded
100
80
60
40
20
0
Perfect equality
Lorenz curve
Figure 3.1 Lorenz curve (own illustration)
The Gini coefficient is calculated as2:
1
221 1
−
−+=
∑=
NC
N
ii
G
ν where:
N = number of observations (traders)
vi = cumulated percentage of market share (volume output traded)
2 lecture script of Dr. Bernhard Bruemmer
34
The marketing margin is defined as a difference between the price paid by
consumers and that obtained by the producers. Therefore it can be used as an
approach to measure spatial price spread. Another parameter related to marketing
margin is the farmer’s share. It can be defined as the ratio of producer price to
consumer (retail) price or the portion of the price paid by final consumer that
belongs to the farmer producers.
Mathematically, the total gross marketing margin (TGMM) and farmer’s share
can be calculated through these formulas:
iceConsumerpriceFarmgatepriceConsumerprTGMM −
= ;
100×=iceConsumerpriceFarmgatepreFarmershar or
1001 ×−=iceConsumerpr
TGMMeFarmershar
Seasonal price spread can be seen from the peaks and troughs in plot of price
series and measured by the lowest price as a percentage of the highest price.
3.4 Methodology used in econometric analysis
STATA and Limdep software packages are used for measurement time series
model. Before conducting econometric analysis, some techniques are applied to
adjust and test some assumptions in the time series data.
The price series in this study are transformed into natural logarithm. It is useful to
minimize the increasing variation in the price series and it is more interpretable
compared to other transformations (Makridakis, et al., 1998). Changes in a log
value are relative (percent) changes on the original scale.
3.4.1 Diagnostic and Testing
Since time series analysis needs a stationary data, before using the data into the
analysis, the first step should be done is unit root test. Finding unit roots in the
35
data set means that the price series are non-stationary. The technique used in this
study to find unit roots is Dickey Fuller test.
The Dicky-Fuller (DF) test is used to test whether the time series is a stationary
series, under the formula:
ttt uYY ++=∆ −11 lnln δβ (1) or
ttt uYtY +++=∆ −121 lnln δββ (2)
Where:
= Natural logarithm of the first differences in weekly prices tYln∆
1ln −tY = Lagged of natural logarithm of the weekly prices
t = time or trend variable
The null hypothesis (H0) is that γ = 0; that is, there is a unit root- the time series is
not stationary. The alternative hypothesis is that γ is less than zero; H1: γ < 0. It
means the time series is stationary. If the null hypothesis is rejected, the
dependent variable is found to be stationary. Taking first differences of a non-
stationary variable is often can remove the non-stationary problem. Then, the
stationary data will be used in the estimating of ARCH process.
The ARCH (Autoregressive conditional heteroscedaticity) model allows for the
presence of heteroskedastic variance. In order to determine the presence of
heteroskedasticity, a formal test for the present of ARCH should be applied. A
Langrange multiplier test statistic is used to test under null hypothesis of no
ARCH errors or the conditional variances are homoscedastic. The alternative
hypothesis is that the model follows an ARCH form, which means that the
conditional error variance is given by an ARCH(p) process or conditional
variances are heterokedastic.
In an autoregressive (AR) model, the realization of present´s outcome is a
function of past outcomes. Formally, autoregressive model of order p or AR(p) is
witten as:
tptpttt YYYY εαααα +++++= −−− ...21110
36
The AR(p) model show the outcomes in the past p periods have a direct impact on
the present outcome. As shown in the equation, the outcome in period t-1, t-2,
until t-p directly affect the present outcome. Akaike Informaton Criterion (AIC)
is used to determine the lag length in the AR(p) model.
The test procedure to determine the presence of heteroskedasticity as proposed by
Engle [1982] is (1) run the original model for lnprice in the equation 5 using OLS;
(2) save the residuals from the regression; (3) regress the squared residuals on a
constant and p lagged values of the squared residuals as given in the equation (3).
( ) 2110
2 ˆˆ −+= tt aaE εε (3)
The null hypothesis is rejected if the test statistic exceeds the critical value from a
chi-square distribution with p degrees of freedom. A test statistic is calculated as:
T·R2 (4)
where R2 is obtained from the auxiliary regression of squared error (ε2 t ) and p
degree of freedom is equal to the number of autoregressive term in the auxiliary
regression.
3.4.2 ARCH model for price variability
The ARCH (Autoregressive Conditional Heteroskedasticity) model explicitly
models time varying conditional variances by relating them to squared error term
in the previous periods. Formally the ARCH (p) model estimated for agricultural
prices is given by equations (5) and (6),
(5) tpt
p
iit YY εββ ++= −
=∑ lnln
10
where :
tYln = Natural logarithm of the commodity weekly prices
1ln −tY = Lagged of natural logaritm of the weekly price
p = identification of autoregressive process (AR) using Akaike Information
Criteria (AIC)
tjt
p
jjt νεααε ++= −
=∑ 2
10
2 (6)
37
The random schock, tε , conditional on all historical information contained in
information set ψt-1 is normally distributed with zero mean and follows an ARCH
process with conditional variances h2t:
tε ⏐ ( )tt hN ,0~1−Ψ
Procedure for estimating the ARCH(p) model is based on method of scoring
(Engle, 1982). In line with the scoring method, Greene (2000:798) proposed four-
steps procedure for estimating the ARCH (p). Let the sample consist of yt and xt
for t=1,…,T. The procedure involves the following steps3:
1. Compute b = (X’X)-1X´y and e = y – Xb using all T observations. This result is
the initial estimator of β. It is consistent and asymptotically normally distributed,
but inefficient.
2. Regress e2t on a constant and e2
t-1 to obtain a = (a0, a1)´ using observation 2, ….,
T. This method is the usual approach for consistent estimation of the variance
parameters in an FGLS (feasible generalized least squares) procedure.
Under null hypothesis of conditional homoscedaticity, (T–1) times the R2 in this
second regression is a Lagrange multiplier statistic whose limiting distribution is
chi-squared with one degree of freedom. This result can be used to test the
hypothesis of homoscedaticity against the alternative of the ARCH model.
3. Using (a0, a1) computed at step 2, compute ht = a0 + a1 e2t-1, gt = (e2
t/ht – 1), zt1
= 1/ht, and zt2 = (e2t-1/ht – 1) for observations 2, … , T. Collect T – 1 observations
in g = (gt)t=2,…T and Z = (zt1, zt2)t=2,…,T. Compute update dα = (Z´Z)-1Z´g. The
asymptotically efficient estimator of α = (α0, α1)´is α´ = a + dα. This estimator is
asymptotically normally distributed.
4. Re-compute ht for observations 2,…, T using α´ from step 3. Then, for
observation 2, …., T – 1, compute rt and st.
3 The procedure for estimating the ARCH model in this study is closely following Green (2000:798). Steps 1 and 2 are procedure to test homoscedaticity of error variance as discussed on the previous sub section.
38
⎟⎟⎠
⎞⎜⎜⎝
⎛−⎟⎟
⎠
⎞⎜⎜⎝
⎛−=
⎟⎟⎠
⎞⎜⎜⎝
⎛+=
+
+
+
+
1ˆ1
ˆ21
1
21
1
1
2
1
1
t
t
ttt
t
t
tt
he
hhs
he
hr
α
α
let v = (etst/rt)t=2,…,T-1 and W = (rtx´t)t=1,…,T-1. Compute update dβ = (W´W)-1W´v.
Then, the asymptotically efficient estimator of β is β´= b+ dβ. This estimator is
asymptotically normally distributed.
3.5 Conceptual framework
The framework of industrial organization is used for market analysis. The
framework shows three main components that are structural characteristics of a
market (market structure), competitive behaviour of market participants (conduct)
and in turn structure and conduct influence performance of the market (Figure
3.2).
The first component of the framework is structure, which can be divided into three
types market whether highly competitive, monopoly or imperfect competition. In
order to define the structure, there are indicators will be applied such as marketing
channel, barriers to market entry, degree of competition and concentration.
Marketing channel is depicted by looking at different levels, started from farm-
level marketing to traders (market) level and final consumers.
Market conduct refers to buying and selling activities and pricing behaviour of
each participant in the market. Buying and selling activities comprises such as
sources of commodities, buying and selling practices. Pricing behaviour
comprises factors in price setting.
Structure and conduct will influence performance of the market, which can be
measured by indicators such as marketing margins of each levels in marketing
channel; variability in farm producer and consumer prices in term of time and
space, and down-side risk regarding to the variability of agricultural price
received. The dynamic of price is captured by ARCH model that is preceded by
some diagnostic and test for price series data. Dickey fuller test, ARCH LM tests
for the price series are applied.
39
Degree of competition: - Highly competitive - Monopoly - Imperfect competition (oligopoly or monopolistic competition)
Marketing channel
Barriers to market entry
Degree of concentration and
competition
STRUCTURE
CONDUCT
PERFORMANCE
Buying and selling activities
- Sources of commodities - Buying & selling
practices
Pricing behaviour
- Who sets price? - Factors in price
setting
Marketing margin
Farm gate and consumer price variability in term of space and time
Farm level marketing
Trader level marketing
Figure 3.2. Framework for market analysis (own depicted, 2003)
40
3.6 Summary
This chapter presented methodology used to answer the research questions. For the
survey, 7 villages were selected from 12 villages (sub sample of STORMA sub
project A4), and one additional village was selected from sub-sample of IMPENSO.
For weekly price survey, the questionnaire was asked in those villages and to
compare the prices with the price in urban area, the same questionnaire was asked in
Palu (central market). For trader survey the sample was selected using snowball
sampling. This method is used because the sampling frame was not available. To
construct a sampling frame is time consuming and too costly.
There are various methods in descriptive and econometric analysis used to measure
structure and performance of agricultural markets in this study. Starting with the
calculation of mean, standard deviation, coefficient of variation, the analysis is
completed by the t test and Analysis of Variance (ANOVA) to compare the mean
between two or more different groups. Lorenz curve and Gini coefficient is used to
measure degree of concentration, which in turn shows some extent of competition and
structure of a market. Marketing margin, price spread and variability of prices in
term of seasonal and spatial are calculated to measure performance of agricultural
markets. This study use econometric approach to complement the measurement of
market performance by measuring variability of prices.
41
4. AGRICULTURAL MARKET STRUCTURE
This chapter attempts to answer the research question as previously presented in the
chapter 1. The questions are (1) How are the agricultural commodities markets being
organized? Is the agricultural commodities trade composed of many small traders,
who compete with each other or is it dominated by few large participants? (2) Are
there any barriers to market entry? If so, what are the major barriers? Which can be
altered by policy, e.g. with respect to legal framework?
In relation to those research questions, this chapter deals with a discussion of the
structure of the agricultural markets. The market structure of each commodity is
separately discussed. In the next section the barriers to market entry is described.
This chapter ends with the summary of the structure of the agricultural markets.
4.1 Cocoa Market In contrast to many other agricultural commodities such as rice and sugar, which are
highly regulated by the government, cocoa market in Indonesia is relatively free from
government intervention. The market is open to private traders, without any
involvement of marketing boards or the National Logistics Agency. Price controls,
export quota, and exclusive trade licensing are not imposed in the cocoa industry.
Moreover, since the government promotes non-oil exports by removing export taxes,
cocoa market tends to grow (Akiyama and Nishio, 1996).
The cocoa market in Central Sulawesi works similarly to the previously described
conditions; it performs with limited intervention from local government. The local
government levies a “retribution” charge when cocoa is passes through certain
checkpoints during transport from producer areas to urban markets or shipping ports.
This is principally implemented with the aim of increasing regional income in
decentralization era.
Marketing of cocoa is basically started from villages, where village traders have a
direct relationship with farmer producers. In this research, 20 traders were
interviewed. These traders can be classified into village and sub district assemblers.
42
The 18 village assemblers tend to operate in one village where they live. The 2 sub
district assemblers do their business in various villages within a sub-district.
Before it is ready to be marketed, cocoa beans must be prepared through post harvest
activities. To obtain good quality cocoa beans, the beans should be fermented and
dried after harvest. However, according to explanation from traders and some
farmers, it is common in the research area that cocoa beans are not fermented or are
partially fermented. Of course, this reduces quality and may, in turn, affect price
received.
Farmers cut the cocoa fruits and without the fermentation process the beans are then
spread out in a layer and dried under the sun to get dried beans. Sometimes cocoa is
partially fermented by chance particularly during the rainy season. This occurs when
there is a lot of rain and no sunshine. During this season, cocoa should be kept inside
the farmers` house, therefore accidental partial fermentation occurs.
Further explanations from traders and agricultural officers, attribute unwillingness to
ferment their cocoa due to market limitations for fermented cocoa. Even after
complete fermentation, there is no price differentiation paid by traders; hence there is
no incentive for farmers to ferment their cocoa beans.
Starting from the farmer, there are various marketing channels to sell dried cocoa
beans as can be seen in Figure 4.1 and characteristics of the traders involved in this
marketing system are presented in Table 4.1.
The term municipal assembler is introduced here since the wholesaler term is not
completely accurate. Technically speaking, a wholesaler handles huge amounts of
cocoa but in the research area when farmers bring small amount to this trader the
beans are received as well. Hence, the term municipal assembler is a substitute for the
wholesaler. From this point of sale, cocoa flows to exporters who will ship it to
importing countries. As shown in Figure 4.1 farmers can sell their cocoa either to
village assembler, sub district assembler or his agents. Farmers prefer selling the
beans in their village if amount cocoa beans sold are not big enough. When farmers
have at least one sack of cocoa beans (50 kg), they prefer sell directly to the
43
municipal assembler in Palu. In marketing their cocoa, farmers from remote villages
will choose sub-district assemblers who operate near their villages. Without any
contractual obligations, farmers can decide to whom they sell their cocoa, and it
depends on the price offered by traders. However, prior to harvesting period when
lack of capital is faced by most farmers, some village or sub-district traders usually
provide loans to the farmers. As a consequence of the loan provided by the traders, it
is obligatory to sell the cocoa beans to these traders.
Farmer
Agents of sub- district assembler Village Assembler
Sub district assembler
Figure 4.1 Marketing channel o
Trading is the main occupation
of the village traders reported
occupation is self-employment
are cocoa farmers who grow c
own farm, most village asse
Municipal Assembler/
Wholesaler
Exporter
f cocoa
of all sub district and most village traders. Only 17%
that trading is a part-time business and their main
in agriculture. Furthermore, 78% of the village traders
ocoa on their own farm. Besides collecting from their
mblers collect cocoa from other farmers within the
44
village, in order to resell it to sub district or municipal assembler. The village
assemblers conduct their business on a small-scale basis with an average volume
traded of 501 kg per month. The sub district assemblers handle large amount of cocoa
with an average volume traded of 10.875 kg per month.
Table 4.1 General characteristics of cocoa trader
Characteristics Village assembler
(N=18)
Sub district assembler
(N=2) Age (years) 40 52
Male 78 100 Gender (%) Female 22 0 Primary school 33 0 Secondary school 39 0
Highest level of school attendance (%)
High school 28 100 Trading as main occupation (%)
83 100
Average number of years in business
7 25
Family background in trading (%)
27 100
Agricultural crops traded (average no)
2 3
Cocoa producer (%) 78 50
Besides being older, the sub-district traders are also well educated and more
experienced, having spent much more time engaged in cocoa trade than the village
traders. On average, the sub district traders have been in the business for 25 years.
Furthermore, all of them come from families that have experience in cocoa trade.
Comparing to the level of education of total household sample in subproject A4, the
education level of cocoa traders is relatively higher. On average, 30.7% of the
household members completed secondary school and 12.4% completed high school
(Schwarze, 2004). Number of village traders completed secondary and high schools
are 39% and 28%, respectively. All sub-district traders completed high school.
45
The village and sub district traders do not specialize on cocoa trading. The average
number of crops traded is at least two commodities and they usually manage a small
retail shop.
In order to obtain a supply of cocoa and to anticipate competition between traders,
particularly for big traders, there are important strategies that have been implemented
in conducting their business. For the procurement, these traders cannot depend solely
on farmers produce; growing cocoa on their own farms is necessary. Hiring itinerant
agents who work on a fee basis is another strategy. These agents have other
occupation as “ojek” that is, using a motorcycle for public transport. Hence, because
of their mobility, it is easy for them to collect and buy cocoa beans from farmers
within the village and in neighbour villages.
Market Concentration
Market concentration refers to the number and relative size distribution of traders in a
market. In this study, CR4, Gini coefficient and Lorenz Curve were used as measures
of market concentration. For the cocoa traders in the sample, the CR4 is 82 %. It is
indicating a strong oligopsonist market since the largest four traders accumulated
market share of 82% from total market share of cocoa traders in the sample. The Gini
coefficient is 0,78. When the value moves far from zero it indicates a high degree of
inequality of volume cocoa traded, the distribution of cocoa volume traded is un-
equal and market is more highly concentrated. As can be seen from the Lorenz curve
below, the largest 20% of the traders account for about 80% of the volume of cocoa
traded in the research area. The bottom 60% have an insignificant share of less than
10%. The rest of the traders account for the remaining cocoa traded.
46
Cumulated percentage of cocoa traders
100806040200
Cum
ulat
ed p
erce
ntag
e of
vol
ume
coco
a tra
ded
100
80
60
40
20
0
Perfect equality
Lorenz curve
Figure 4.2 Volume distribution of cocoa traded among traders
As indicated in the previous literatures, when the Gini coefficient shows unequal
distribution of the firms, the market is more concentrated and it implies of less
competitive pressure. Thus, it may lead to inefficiency. However, it is notified as
well to be careful in the interpretation of this relationship. The other factors such as
barrier to market entry and economies of scale should be considered before making
any judgment about the market condition. From individual firm point of view,
inequality also shows some extent of the economies of scale where the big traders is
more efficient in term of costs occurred in trading activities compared to the small
ones. Transaction costs occurred in the trading activities such as searching
information and negotiation process which have fixed cost character give more
advantages towards the big traders. It should be less expensive on a per-unit basis to
operate at a large volume.
47
4.2 Coffee Market There are only a limited number of traders and processor who actively engaged in the
coffee business in the research area. Only in two villages, Bolapapu and Berdikari,
could the coffee traders and hullers be interviewed during the survey.
The four coffee traders can be classified into two different levels. Two traders operate
as village assemblers and two run their business on sub-district levels. The village
traders tend to gather coffee only from the villages where they reside. They manage a
small scale business. Unfortunately respondents were not able to recall how many kg
of coffee had been traded during the year because coffee was temporarily obtainable
and the volume was not large. On the other hand, sub-district assemblers operate in
various villages within a sub-district with average volume traded of more than 9000
kg per month.
For the procurement system, big traders depend on farmers produce, growing coffee
on their own farms and hire itinerant collectors. As in cocoa trade itinerant collectors
are hired who act as agents on a fee basis. These collectors have the primary
occupation, of using motorcycles for passenger service. Due to their mobility, it is
easy for them to collect and buy coffee beans from farmers within the village and
neighbour villages.
Not all traders specialize their operation in coffee. Most trade more than two crops.
The combination of crops traded varies from coffee, cocoa, rice, and maize. Majority
of traders deal with green coffee beans (kopi beras) that are hulled and ready to be
sold. Only one trader who owns a hulling machine handles dried coffee beans (kopi
glondong).
At the farm level, after picking up ripe berries, farmers spread them out to dry it in
the sun. The process takes some days. The whole dried berries are then mechanically
or manually hulled to get “green coffee beans” which are ready to be sold. The huller
who owns the machine to hull dried coffee beans receives a fee for this service.
Farmers then can sell their coffee beans through a few different channels, as seen in
Figure 4.3.
48
Producer
r
Villag
Sub-dis
Co
Asse
Figure 4.3 Marketing channel of coffee
The flow of coffee marketing is as follow:
1. Although it is rare, farmers can directly se
2. Direct sale to the village assembler
3. Direct sale to the sub-district assemblers o
4. In a huge amount, farmers can directly s
municipal assembler in Palu.
4.3 Rice Market
Rice is by far, one of the most important crops gro
its role as a dominant staple food. Accord
consumption of people in Central Sulawesi is 19
mostly fulfilled by grain, which accounts for 59%
average people consume 140.9 kg of rice per capit
Rice is grown for home consumption as well as
coffee, which are mainly sold, the role of rice in fa
Hulle
e Assemblers
trict Assemblers
ffee Factory
Municipal mbler/Wholesaler
ll the coffee beans to the huller
r through agents of these traders
ell the coffee beans to wholesaler/
wn in Central Sulawesi, because of
ing to SUSENAS 1999, energy
22 gram per capita per day and is
of the total energy consumed. On
a per year.
for sale. Compared to cocoa and
rmer income is not as significant as
49
those cash crops. However, rice is still of central interest, particularly in term of food
security.
Due to its importance for food security, state intervention is frequently found in
marketing of rice. Through its marketing parastatal, the government provides one
particular channel for farmers to sell their produce. This non-monopoly parastatal,
namely BULOG (Badan Urusan Logistik = National Logistics Agency) is not an
exclusive channel in marketing of rice. Farmer can also choose some alternative
market outlets through private traders. Thus, in general rice is marketed either
through marketing parastatal of state or is freely traded in the private system.
This section will describe the channel by which rice is marketed in the research area
and the characteristics of main participants who actively engaged in this process. It
covers two different levels of market, in village or local and urban markets and
marketing channel of rice in the research area is carried out by private traders as can
be seen in Figure 4.4 and general characteristics of the traders are reported in Table
4.2.
Prior to marketing of rice, farmers process the harvested paddy to remove its husks,
either mechanically or manually. Milling mechanically is preferred, since it enables
farmers to reduce working time. The paddy is processed by a miller on a fee basis.
This fee is paid in kind of rice and varies from one village to another.
In the research area there are 23 middlemen who do intermediary rice trading
between farmer producers and consumers, and 7 of which run a rice milling business.
These millers tend not to specialize on rice milling, since they buy and sell rice as
well. Four of these millers procure their supply from their own farms, other farmers
or even from accumulation of fees. However, their business covers only the villages
they live. The rest operate their business in various villages within a sub-district.
50
Table 4.2 General characteristics of rice traders
Characteristics Village assembler
(N=8)
Sub district assembler
(N=4)
Local retailer (N=11)
Age (years) 44 43 47 Male (%) 100 75 82 Gender Female (%) 0 25 18 Primary school 0 0 18 Secondary school 50 0 36
School Attendance (%)
High school 50 100 46 Trading as main occupation (%)
50 100 55
Rice Producer (%) 50 25 55 Rice Miller (%) 50 75 0 Local Retailer (%) 63 50 100 Average number of years in business
10 19 7
Family background in trading (%)
38 75 27
Agricultural crops traded (average no)
1 3 2
The other channel to sell processed rice is directly to a village assembler. These
assemblers obtain their supply of rice only from the village they dwell in. Afterwards
it is sold to retailers in local markets, local consumers, or sub-district assemblers. Of
8 traders, 4 manage less than 450 kg of rice per month, while the other traders handle
larger volumes. Only half the traders reported that trading is their main occupation.
A sub-district assembler buys milled paddy from several villages within a sub-district
and from this point rice flows to a wholesaler at the regency or municipal level, to
retailers or to local consumers. These traders run larger businesses with an average
volume of rice traded more than 5000 kg per month and tend to be more specialized
in trading than village assemblers and retailers.
A wholesaler deals with huge amounts of rice and is usually located in urban markets.
In this stage retailers perform their important role in urban area by purchasing the rice
and then sell it in small amounts to urban consumers.
51
Paddy
Village or Local Market
Urban Markets
s
Regenc
Figure 4.4 Marketing channe
Note : Farmers brin processed in
After payinbe consume
This is different from the common p
a wholesaler and sells it to consume
farmers to a retailer who operates in
where a market is not available). T
business mainly by retailing rice. So
Farmer
Ricer
Miller Retailer
VillageAssemble
Retailer
y/Municipal Wholesal
Sub-district Assembler
l of rice
g Un husked paddy t order to get rice g some fee farmers tad or sold
rocess where a retaile
rs. In the villages ric
the local market or sh
here are 11 traders in
urce of supply could
Consumers in local
market
er
Consumers in urban markets or
other regions
o rice miller to be further
ke their processed rice to
r buys processed rice from
e can be sold directly from
ops (particularly in villages
the villages that run their
be obtained from his or her
52
own farm, other farmers or a miller. Here trade is on a small basis with an average
volume traded of 200 kg per month.
Without any contractual obligations, farmers are free to choose to which traders they
want to sell their rice. However, most farmers receive a consumption loan, working
capital or some agricultural inputs such as fertilizer from traders or millers and have
an obligation to sell their rice to these traders or millers.
Market Concentration
Market concentration refers to the number and relative size distribution of traders in a
market. CR4, Gini coefficient and Lorenz Curve were used as measures of market
concentration. For the rice traders in the sample (village and sub-district assemblers,
and local village retailer), the CR4 is 86 %. It is indicating an oligopsonist market
since the largest four rice traders accumulated 86% from total market share of rice
traders in the sample. The Gini coefficient is 0,80. It is an indication of an un-equal
distribution and highly concentrated rice market. As can be seen from the Lorenz
curve below, the largest 20% of the traders account for about 90% of the volume of
rice traded in the research area. The bottom 74% have an insignificant share of less
than 10%. The rest of the traders account for the remaining rice traded.
Although the Gini coefficient shows unequal distribution of the traders which
implying the rice market is concentrated and less competitive. Thus it may lead to
inefficiency. However, the other factors such as barrier to entry and economies of
scale should be considered before making any judgment about the market condition.
From individual firm point of view, inequality also shows some extents of the
economies of scale where the big traders is more efficient in term of costs occurred in
trading activities compared to the small ones. The transaction costs occurred in the
trading activities give more advantages towards the big traders.
53
Cumulated percentage of rice traders
9687787061524335261790
Cum
ulat
ed p
erce
ntag
e of
vol
ume
rice
trade
d
100
80
60
40
20
0
Perfect equality
Lorenz curve
Figure 4.5 Volume distribution of rice traded among traders
4.4 Maize Market Nine maize traders were selected and interviewed. Nearly all traders in the research
area who engaged in the dried kernel maize business are village assemblers who live
within the farming village and tend to limit their procurement operations to their own
village. Only two traders buy their maize from other villages, but they handle a small
volume of maize, and are therefore they are still categorized as village assemblers.
According to the volume of maize handled, the traders can be grouped into two
groups. Four of the traders handle dried kernel maize with an average volume less
than 1000 kg per month. The rest buys and sells dried kernel maize in larger volume
with average amount of more than 1000 kg per month.
54
Only one trader specializes in maize trading. The other traders buy and sell on
average more than two other commodities, which include of some combination of
cocoa, coffee or rice. Two traders provided fertilizers for farmers with a credit
system. In order to procure their supply, only two traders depend on farmers produce.
The traders who have other activities in agricultural farming, such as growing maize
on their own farm have an additional source of supply to be sold.
There are various channels of marketing maize. These channels start on the farm and
flows to consumers in urban market. This can be seen in Figure 4.6.
P
Animal Husbandry
Perform as Processor
Farmer
Figure 4.6 Marketing channel of drie
The flow of maize through the mark
there are some post harvest activitie
the common post harvest activities f
Maize is typically dried under the s
processes are carried out completely
traders.
Maize flows from farmers to villag
buyers, that are shown in the figure,
further processing, the village assem
Village Assembler
erform as Retailer in local market
Municipal Assembler/ Wholesaler
Retailer in central market Local Consumers
d kernel maize
et begins at farm level. Prior to marketing maize
s that farmers must do. Drying and shelling are
ound in the villages before it is ready to be sold.
un and then shelled manually. After these two
, maize is ready to be transported and then sold to
e assemblers and from this point of sale various
can be identified as market outlets. Without any
blers have some optional channels through which
55
to market their dried kernel maize, for instance, directly to animal husbandry,
wholesalers, municipal assembler or to retailer in central market (Palu). It is also
found that the traders sell their maize to local markets in order to meet the needs of
local consumers. The dried kernel can be processed into other forms, such as starch.
This adds value, which in turn influences price. Some traders who own machines to
mill the kernel benefit from this increasing value. They can sell this new form, maize
starch to animal husbandry firms and receive a higher price.
4.5 Fertilizer Market Starting in December 1998 distribution of fertilizer in Indonesia was liberalized and
followed a free market mechanism. Nevertheless, in order to facilitate farmers
obtaining fertilizer at on affordable price, the central government of Indonesia
through the ministry of trade and industry and together with the ministry of
agriculture, arrange a procurement and distribution system of subsidized fertilizers in
February 2003. The subsidized fertilizers are urea, SP-36, ZA and NPK. The
subsidized fertilizers are allocated for food crops farmers, animal husbandry farmers,
and small-scale estates. Farmers in the research areas apply mostly urea to their rice
paddy fields; therefore this part will focus in describing the marketing channel of
urea.
According to the ministerial decree of trade and industry no 70/MPP/Kep/2/2003,
producers of subsidized fertilizers are responsible for procurement and distribution of
those fertilizers from the first line (line I) until the fourth line (line IV) in the
provincial area where they are assigned.
As indicated in the decree, the marketing channel of subsidized fertilizers can be
describes as follows:
Line I, refers to is a warehouse of a fertilizer factory. Line II, is a warehouse of
fertilizers located in provincial capital cities. Line III, is a warehouse of a producer or
distributor located at the municipal or regency level. The distributors are companies
assigned by the producer to purchase, store, and sell the subsidized fertilizers in huge
56
amounts to be sold to consumers through retailers as the fourth line (line IV). These
retailers are private or firms located in sub-districts and their main activity is selling
directly to final consumers in retail. They can be can be engaged in distribution of
subsidized fertilizer after receiving an assignment from the distributor.
As stated in the regulation, the warehouse line III is responsible for selling fertilizers
to distributors in the same line. However, line III does not operate in the distribution
of fertilizers in Central Sulawesi, and in this case line II simultaneously performs as
line III.
Previously PT Pupuk Sriwijaya (PUSRI) covered circulation of fertilizers in central
Sulawesi. However, since 2003 PT Pupuk Bontang Kalimantan Timur has been
responsible for distributing fertilizers in this province.
The Indonesian ministerial of agriculture, through ministerial decree no
427/Kpts/SR.130/8/2003 decided the highest retail price (in fourth line) by
consumers. The highest retail price (HET = Harga Eceran Tertinggi) for urea in
period (1 August – 31 December 2003) is Rp 1050/kg and Rp 1750/kg for NPK.
These prices should be unchanged in all areas in, both urban and rural in Indonesia
including remote villages in central Sulawesi.
In the research area, the common fertilizer used is urea and it is mainly applied to
fertilize rice paddy fields. Because demand for other kinds of fertilizer and other
agricultural inputs are lower, fertilizer traders in the research area focus their
activities on retailing urea. However, these traders do not specialize only on buying
and selling fertilizers. They also engage in other agricultural commodities such as
rice, and retail food items and other consumer goods. Hence, fertilizers are often
found in shops which sell food and consumer goods. These traders frequently fail to
pay attention in proper storage of fertilizer. They put fertilizers together with other
consumers good. Besides being seriously harmful to consumers health, this is not
permitted by regulation.
57
Although the study area covers 8 villages, fertilizer traders were only found in 4
villages; Sintuwu, Berdikari, Maranata and Bolapapu. Five retailers were selected
and interviewed during the survey time.
Generally, fertilizers trade is highly interrelated with food crops activities particularly
rice farming. To accelerate their business and to keep their supply rice traders or
millers offer farmers who frequently sell their rice or hulled their paddy to get urea or
other input required for rice farming and pay it with some charges soon after
harvesting. This is a common phenomenon found in marketing of fertilizers in the
research area.
For the traders or millers this system can be described as a sort of vertical integration
since it provides a controllable flow between input and output received. However, for
the farmer, it is an uneasy situation because some traders demand high prices. The
farmers do not have any bargaining power to negotiate how much to charge or how
prices should be paid. The payment varies from one trader to another.
In Bolapapu, fertilizers are openly sold in rice miller to all consumers. There are two
different payment systems: cash or barter. Even though lower prices are offered if
consumers pay cash, most people choose the barter system. Actually, barter is
another term of credit that occured in all villages. One kg of urea can be obtained by
paying one kg of rice. Of course, the price of fertilizer is relatively high in this
system as compared to the common trading system. The prices are Rp 1050/kg and
Rp 2600/kg of subsidized urea and rice respectively. In other words, the price that
should be paid during harvest season increases more than 100% off the subsidized
price of urea. Nevertheless, the farmers choose this barter system because of the time
available before payment.
Since there are only two planting seasons for a year in this village, it means that from
planting to harvest season takes 6 months. As a consequence there will be 6 months
gestation period before payment. According to the trader, this high price is a rational
consequence of barter the system because of gestation period and calculation of
interest rate.
58
The other villages have very similar trading systems. In Sidondo II, the rice miller
offers the barter system as well. Similar to the situation in Bolapapu, the
compensation is one bag of rice (50 kg) for one sack of urea (50 kg). This cost can be
transferred into cash in the amount of Rp 70000. In other words, farmers can
purchase urea from the retailer with cash and it costs Rp 1400/kg. Since the normal
price of rice is Rp 2000/kg, if the farmers choose this barter system they will lose Rp
600/kg.
In comparison to traders in Bolapapu, traders in Berdikari and Sintuwu concentrate
their selling to what are called “member of trader”. A farmer who frequently sells
their rice or maize or hulled their paddy is referred to as a “member of trader”.
Although the urea can be bought with cash, the majority of consumers choose the
credit system. In Berdikari the credit system is called as “ijon”. Similar to the barter
system in Bolapapu, the price of urea and other inputs is higher since the traders
calculate gestation period as well as interest rate. In Berdikari the price of urea is
20% higher than the cash price.
Sintuwu has a different system than the other villages. According to some farmers´
explanation, itinerant traders who operate and collect maize in this village usually
provide fertilizers during the planting season. During the harvest period, farmers are
obliged to sell their maize to these traders. Income from this transaction will be
received after calculating the amount of input used.
Almost in all survey villages farmers receive credit in form of fertilizers and other
inputs from rice miller. Some farmers occasionally purchase urea and other
agricultural inputs in Palu when they sell their produce in this central market. Due to
those factors the fertilizer market in the research area do not rapidly grow
4.6 Barriers to market entry
Most of traders reported that it is relatively easy to be involved in agricultural trading
as indicated without any market license required for small-scale business. Only big
traders who deal with high volume traded need this license.
59
It can be verified since ministerial decree of trade and industry no
289/MPP/Kep/10/2001 states that a firm is required to have a market license (SIUP =
Surat Ijin Usaha Perdagangan) if its financial and equity capital is at least Rp 200
million not including land and building. If a trader has capital less than the lowest
criterion, such as itinerant trader, no license is needed to trade. Although the license is
issued by local (regency or municipal) government at location where trading is
conducted, it allows traders to trade everywhere within Indonesian territory.
As stated in the decree, procedure of application, and rules of the license such as fee,
expired period should be the same from one region to another. Nevertheless, since
decentralization policy is implemented and as a reason for improving regional
development and income, application of the decree are various from one region to
another. According to explanation of trade officer, this situation leads to confusion
and inefficiency in trading business.
4.7 Summary
This chapter describes structure of agricultural markets. The structure is explained by
flow of marketing channel, market concentration and barrier to market entry. Due to
some limitations of data collection, structures of agricultural markets are mostly
described in term of marketing channel. Compared to coffee, maize and fertilizer
markets, cocoa and rice are described more detail.
The flow of cocoa beans is started from the farmer producer to village or sub-district
assemblers. The Sub-district assemblers handle higher volume of cocoa traded
compared to the village assemblers because they operate in larger area. The sub-
district assembler collect cocoa beans from some villages within a sub-district and
village assembler limit their procurement only in one village. From this point of sale,
the cocoa beans are transported to wholesaler/municipal assemblers in Palu before
being exported.
Cocoa market exhibits an oligopsonist market with regard to the farmer producers as
indicated by Gini coefficient of 0,78. The Lorenz curve shows that the largest 20%
60
of the traders account for about 80% of the volume of cocoa traded in the research
area. The concentration ratio of CR4 is 82 %. It means that that the largest four of the
traders in the sample accumulated market share of 82% compared to all market
participants in the cocoa market.
Different to cocoa which can be sold directly after drying without any further process,
rice should be milled to remove its husks before it is sold. Since most farmers do not
mill their paddy manually, the paddy is transported to the miller to be processed.
Then the flow of rice is starting from this milling process. After paying some fee, the
farmer can sell their rice to local retailer or assembler. Due to its function as
dominant staple food, it implies of unique marketing system. It can be divided into
two different marketing systems for local and urban consumers. The local retailers
manage small basis business and retail the rice to the local consumers in their
villages. The village and sub-district assemblers sell their commodity to
wholesaler/municipal assembler in Palu or other urban areas.
For the rice traders in the sample (village and sub-district assemblers and local village
retailer), the CR4 is 86 % and the Gini coefficient is 0,80. It is indicating an
oligopsonist market with regard to the farmer producers. The largest four rice traders
accumulated 86% from total market share of rice traders in the sample. The Lorenz
curve shows that the largest 20% of the traders account for about 90% of the volume
of rice traded in the research area. The rest of the traders account for the remaining
rice traded.
The cocoa and rice markets show an indication of an un-equal distribution and
concentrated market. This condition implies of less competitive market due to
oligopsonist nature. Technically speaking, it may lead to inefficiency. However, one
should be careful in interpreting this relationship. The other factors such as barrier to
market entry and economies of scale should be considered before making any
judgment about market condition.
61
Inequality also shows some extents of the economies of scale where the big traders is
more efficient in term of costs occurred in trading activities compared to the small
ones. Transaction costs occurred in the trading activities such as searching
information and negotiation process which have fixed cost character give more
advantages towards the big traders. It should be less expensive on a per-unit basis to
operate at a large volume.
By looking at the characteristic of cocoa and rice traders, it can be seen that level of
education of these traders is relatively higher compared to other households in the
research area.
Markets for coffee and maize are not well developed as cocoa and rice markets.
Market destination of these commodities is limited either to the local consumers or to
local industry such as food processing industry or poultry.
The same case for fertilizer, this market is not well developed yet. Retailer of
fertilizers operate only in 4 out of 8 villages. These retailers perform as rice miller
too. The marketing system mainly by giving loan to the farmers in kind of fertilizers,
mainly urea and the payment will be made during the harvest, directly after the
milling process. For the traders or millers this system can be described as a sort of
vertical integration since it provides a controllable flow between input and output
received.
The barrier to market entry can be defined as a potential factor that prevents the new
entrants to enter the market. Technically speaking, market licensing requirement is
one potential factor that prevents new entrants to enter the market. In the research
area there is limited barrier to entry market in term of this license. The market license
should be held only by big traders with asset more than Rp 200 million.
62
5. AGRICULTURAL MARKET CONDUCT Based on the research questions presented in the chapter 1 on the subject of
approaches used by the traders in selling, buying and pricing activities, and the role of
producer associations (cooperatives) or trade associations for marketing of
agricultural input and output, this chapter discusses the profile of the traders and the
conduct of agricultural market. The explanation is not discussed separately between
commodities. Behaviour of agricultural traders is explained in general.
5.1 Profile of agricultural traders
In the research area 36 traders who involved in trading agricultural commodities
either cash or food crops mainly cocoa, coffee, rice and maize and agricultural input
were interviewed during the survey. The selected traders are those who operate in the
whole time within a year. There are also some seasonal traders do businesses in the
villages. Because their seasonal activity and often live outside the villages they are
not covered by this study. Profile of traders can be divided into four different aspects
according to their resource endowment. Human, financial, physical and social capital
will be described in this section.
Human capital Generally the traders are relatively young with average age of 40 years. The majority
of agricultural traders are male, nevertheless one quarter of the sample are female.
On average, 41.7 %, 30.5% and 27.8% of traders educated at primary, secondary, and
high school, respectively. The percentage of traders who completed high school are
relatively higher compared to the household sample in STORMA household survey.
On average, 12.4% of the household sample completed high school.
Two third of traders do not come from family which have experience in trading. The
rest who come from family with trading background receive assistance from their
parents such as equipments, working capital and the most important knowledge of
entrepreneurships. Experience of traders being active in trade varies from 1 to 25
years, with average years in trading is 9 years.
63
More than half traders have employees who come from their relatives. The average
numbers employees from relatives are 1.6 and 1.1 for male and female worker
respectively. The workers from relatives usually do not receive wage except living
cost and other private needs are fulfilled. If these employees receive wage, the
payment also lower compared to the standard wage. Only 14% of the traders hire
permanent workers. For big traders, temporary employees are hired during harvest
season when huge amount of produce should be handled. The wage for permanent
and temporary employee differs from one to the other villages and it ranges from Rp
5000 to Rp 15.000 per day.
Some of big traders also hire some agents who work on fee basis. These agents
collect produce from farmers within and neighbour villages. This system is found
mainly in procurement supply of cocoa.
Table 5.1 Characteristics of agricultural traders in research area Characteristics of traders based on their human capital Average age (years) 40 Gender (%) Male 75 Female 25 School attendance (%) Primary school 41,7 Secondary school 30,5 High school 27,8 Family background in trade (%) 33 Average number of years in business (years) 9
64
Financial capital More than 90% of traders reported that their own capital is a main source of working
capital. Although it is very limited, some traders finance their business by external
sources. Loans from non-governmental organization or bank are found to start up or
expand the business.
Physical capital In order to bring products from farmer producers to consumers, equipments are
necessary to help and make those marketing task easier. Physical capital refers to the
equipments such as transportation, storage, and communication facilities used in daily
trading activities.
Only 25% from all traders have storage facility. A warehouse usually has multiple
functions, to store stocks and put milling machine. All commodities regardless to its
purpose, to be further processed, to sell or home consumed and milling machine are
placed together in this warehouse. Majority of traders store their stock in their house.
Telephone as important tool of communication particularly to search out information
on prices or to offer product and in some cases to negotiate with clients is extremely
limited used by the traders. Only 8% of total traders have telephone. Conventional
method of communication such as face to face is the most important source of market
information.
Motorcycle is transportation facility owned and commonly used by almost all traders
to collect and sell their stocks. For small traders, motorcycle is necessary to transport
one or two sack of their commodities, mainly cocoa. Transportation cost that should
be paid by traders vary depend on the vehicles used, motorcycle or car. Motorcycle
whose own by almost all traders, gasoline is only costs counted for transportation.
The average cost paid for gasoline is Rp 4500 per a return trip. For one trip, traders
can load up to 2 sacks (each sack contains at least 50 kg).
Car and truck are owned by big traders to transport all commodities to next buyer in
central market or in other regency. For traders who do not have own car to transport
65
big volume, public transportation is a choice. Calculation of transportation cost using
own vehicle varies between traders, some traders reported only gasoline as
transportation cost and the other adds some additional cost for driver and his
assistants. Therefore the costs range from Rp 25 to Rp 100 per kg per trip and the
average cost is Rp 80.50 per kg per trip. However some traders reported that they do
not spend transportation cost since big traders from Palu collect their products or they
sell their product in retail.
Social capital No traders are member and involved in formal organization such as cooperative or
trade association. None of traders involved in the informal organizations such as
informal saving group. However, since most of traders (69%) are local people,
relation with farmers as their supplier is easier to be managed. All traders notify that
personal reputation and good relationship with suppliers, buyers and people
surrounding their environment is one of key success in conducting their business.
5.2 Business practices
Quantity and quality inspection
All traders do inspection in order to obtain product that is suitable with the need and
demand of next buyer. For quantity purposes, weighing is done in the first process of
receiving process. After weighing, quality inspection is done with the purpose of
reducing losses because price will be deducted if quality is not fit with the
requirements. In term of quality inspection, moist contents, cleanliness, and texture
are activities conducted by the traders. Although these activities are important, all
traders depend only on visual inspection. None of them have tools to do such
inspections because as they said the tools are expensive. Process of inspection does
not take too much time and it varies from 1 until 5 minutes with average time is 1.63
minute. Long Experience as trader helps them to avoid losses from receiving low
quality of products and losses of expected profit.
66
Price setting and payment methods Purchasing price to be paid by each trader is determined independently. Without any
contractual obligation farmers can choose trader who offers higher price. Price in
Palu as central market is a primary source for price setting. Negotiation of price can
be applied in some conditions for example when farmers bring a lot of product and
differentiation of opinion concerning on quality and measures.
Cash is well known as payment method to farmers. Credit and in kind (barter) can be
done particularly when long relationships between trader and farmers are closely tied.
For all these contractual agreements, no written contract is provided thus close
relation and trust between the two participants are needed.
Farmers preferably choose in-kind payment when they have only small amount of
commodity. The exchange of cocoa and rice with basic food items and other
consumer goods are frequently made.
The universal transaction method applied with next buyer is cash. Big traders from
Palu such as municipal assembler, wholesaler or exporters provide credit for traders
who operate in villages so they can pay cash to the farmers particularly in harvest
season. Usually these payment systems are made when they have close relationship as
a result of regular supplies products.
Specialization in agricultural marketing and marketing functions performed
Traders tend not to specialize on one crop of five commodities surveyed, cocoa,
coffee, rice, maize and chemical agricultural input. On average, 53% of traders
specialize on one commodity and the rest deal with more than one commodity.
Apart of trading, most of traders have other activities either in agricultural activity or
non-farm enterprise. On average, 64% and 33% of total traders are farmers and have
non-farm enterprise, respectively. All these activities are conducted to facilitate and
make their business easier, such as for procurement and in-kind payment (barter).
67
Traders are not fully specialized in marketing function performs in the marketing
chain. As it can be seen in Table 5.2, for example one trader can be perform as sub-
district assembler, miller and retail at the same time for different commodities.
Table 5.2 Specialization of trader activity in marketing chain
% Traders Perform as
Rural assembler 97
Sub-district assembler 11
Processor (Miller) 28
Retailer 61
Relationship and network with suppliers and buyers
More than two third of traders have regular suppliers and next buyers. Majority of
traders admit that good price and short distance are influencing factors for choice of
supplier. Searching for new supplier is relatively easy according to 69% traders. 25
% reported that to find new supplier is very easy and in contrast only 6 % feel
difficult. In relation with business duration, frequencies of having regular
suppliers/buyers and searching for new supplier/ buyers can be seen in Table 5.3.
New entrants which experiences in trading less than 10 year (these traders started up
their business 1 year ago) have difficulties of searching new supplier. According to
explanation of cash crop traders, competition on searching of new supplier sometimes
fairly high, particularly when big traders from Palu travel directly to their village and
offer farmers relatively higher price than rural traders.
Reasons for choice of buyer are good price and regular transaction. As indicated by
some traders, the old-standing players in central market will make a fool new entrants
without enough experience in trading. Therefore they preferably choose buyer who
already had regular transaction. The regular transactions, which in turn develop trust
between each other, have additional benefit on financial support such as credit.
During harvest season, when cash should be prepared to pay bulk of produce, big
trader from Palu will provide credit to rural/local traders.
68
Table 5.3 Relation between business duration and having regular and searching new suppliers
Business Duration % Traders
Less than 10 years
(N=20)
At least 10 years
(N=16)
Having regular supplier 80 69
Searching for new supplier
Very easy 20 31
Easy 70 69
Difficult 10 0
Having regular buyer 80 81
Searching for new buyer
Very easy 10 19
Easy 70 56
Difficult 20 19
Very difficult - 6
Searching for new buyer is reported very easy and easy according to 64% and 14%of
total traders. 19% and 3% traders consider that finding new buyer is difficult and
very difficult respectively. In accordance with their business duration, it can be seen
in Table 5.3 some traders in both groups experience difficulties in searching new
buyer. The problem is more pronounced in food crops compared to cash crop traders.
For example, maize traders have limited outlets to sell their stocks, poultry industry
and retail market are the main market available. Thus, finding new buyer is a
problematical issue.
Conflict between traders and his/her suppliers and buyers sometimes occurred during
the trading process. Disagreement over result on quality inspection and measures are
69
the most cases. Direct negotiation within a family atmosphere is a main solution for
this situation.
Market coverage Most of traders collect their supplies within area with radius less than 10 km. It
means that those traders procure their stocks mainly from village where they reside.
Only 14% travel more than 10 km to purchase their supplies and it is conducted by
big traders who operate in various villages.
Besides selling in his/her village and neighbour village, the most important market
destination of traders is Palu, where central market for agricultural commodities
located. Majority of traders travel more than 15 km to sell their stock and only 14%
of traders limit their selling area only in his/her village.
5.3 Trade associations Of the four outputs and agricultural input surveyed, trade association in Central
Sulawesi mainly works in cash crop such as cocoa (ASKINDO = Asosiasi Kakao
Indonesia, The Indonesian Cocoa Association). None of traders in the villages,
however, involves in this organization.
According to some exporters, ASKINDO as a room for persons or firms who active
engaged in cocoa trading only cover cocoa exporters as their member. As far as their
explanation, direct benefit of being a member of this organization, however, not as
much as they wish for.
Contrary to the explanation, as indicated by Akiyama and Nishio, 1996, the
association has a great contribution to policy applied in cocoa sector. The cocoa
market can be performed without or with very limited intervention of government.
The organization still being active to implement some important agendas to improve
the cocoa industry in Indonesia so their role to give benefit to all participants in cocoa
industry including farmer producers will be achieved.
70
5.4 Summary
This chapter describes the conduct of agricultural market by description the behaviour
of agricultural traders in the sample. It explains profile of the traders, approaches
used by the traders in conducting their business such as selling, buying and pricing
activities, and the role of producer associations (cooperatives) or trade associations
for marketing of agricultural input and output.
There are 36 selected traders in the research area involved in trading agricultural
commodities mainly cocoa, coffee, rice, maize and fertilizer. Profile of traders can be
divided into four different resource endowments, human, financial, physical and
social capital.
Generally, the traders are relatively young with average age of 40 years and educated.
One third of the traders come from family with trading background and this gives an
additional advantage as they receive assistance from their parents such as equipments,
working capital and knowledge of entrepreneurships. Relatives is important source of
employee since more than half traders have employees who come from their relatives.
During harvest season when huge amount of produce should be handled, large traders
hire temporary employees.
To finance their business own capital is a main source of working capital. Only 25%
from all traders have storage facility which has multiple functions. Although it is
important tool of communication particularly to search market information, only 8%
of total traders have telephone. Due to that fact, face-to-face communication is the
most important source of market information. Motorcycle is transportation facility
owned and commonly used by almost all traders to collect and sell their stocks
particularly to transport one or two sack of their commodities.
No traders are member and involved in formal and informal organizations. Most
traders (69%) are local people, therefore relation with people surrounding their
environment is easier to be managed which in turn influence their success in
conducting their business.
71
For quantity inspection, weighing is done in the first process of receiving process,
and then it is followed by simple quality inspection trough visual inspection.
Purchasing price paid by each trader is determined independently. Cash is well
known as payment method to farmers. The other methods, credit and in kind (barter)
can be done when long relationships between trader and farmers are closely tied. For
all these contractual agreements no written contract is provided. Thus close relation
and trust between the two participants are needed. Traders tend not to specialize on
one crop of five commodities surveyed, not fully specialized in marketing function
performs in the marketing chain and have other activities either in agricultural
activity or non-farm enterprise.
More than two third of traders have regular suppliers and next buyers. New entrants
whose experience in trading is less than 10 year have difficulties of searching new
supplier. Searching for new buyer is reported very easy and easy according to 64%
and 14%of total traders. 19% and 3% traders consider that finding new buyer is
difficult and very difficult respectively. The problem of difficulties in searching new
buyers is more pronounced in food crops compared to cash crop traders.
Most of traders collect their supplies within village where they reside. Only 14%
travel more than 10 km to purchase their supplies and it is conducted by large traders
who operate in various villages. Majority of traders travel more than 15 km to sell
their stock. None of traders in the villages are being a member of trade association
such as ASKINDO = Asosiasi Kakao Indonesia, The Indonesian Cocoa Association).
The members of this association are mainly exporters.
72
6. PERFORMANCE OF AGRICULTURAL MARKET In relation to the research questions presented in the first chapter on the subject of
performance of agricultural market, this chapter attempts to answer the following
questions: (1) to what extent do the communities have access to agricultural input and
output markets? (2) what is the marketing margin i.e. differences between producer
price and the retail price paid by consumers and within the agents in the marketing
chain? (3) what are patterns of price variability of agricultural commodities in term of
time and space? Do villages with lower access to market have lower producer or
higher consumer prices? Do villages with lower access to market have more seasonal
fluctuation?
6.1 Access to market and infrastructure Table 6.1 shows that in the research area most of villages are accessible by car, which
connected those villages to the provincial capital city, Palu. Only one village,
Tomado in sub-district Kulawi is not accessible by car because road available in the
village is walking track. The nearest road accessed by car can be reached after
walking by 4 hours.
Table 6.1 Characteristics of market access
Village Accessible by car
Type of road Condition during rainy season
Presence of market site
The nearest market
Tomado No Walking track - (4 hours walking) NO Bolapapu Sintuwu Yes A/CR Difficult NO Rahmat Berdikari Yes A/CR No Problem NO Bahagia Sidondo II Yes A/CR No Problem NO Bobo Maranata Yes A/CR No Problem YES - Pandere Yes A/CR No Problem YES - Rahmat Yes G/DR Difficult YES - Bolapapu Yes A/CR No Problem YES -
Four villages of the village sub-samples are reported have regular market and the rest
do not have market. Road and market place are infrastructures available in the
villages which are utilized as a proxy of degree to market access. In relation to the
73
two infrastructures, all the villages can be arranged into 3 different groups starting
from the lowest until the highest degree of market access: 1) not accessible by car and
no market-low access; 2) accessible by car and no market-medium access; and 3)
accessible by car and have market-good acess. The fourth group to be compared to
other groups is Palu as central market. Tomado has characteristics of the first group.
The second group consists 3 villages, Sintuwu, Berdikari and Sidondo II. Maranata,
Pandere, Rahmat and Bolapapu belong to the third group.
6.2 Farmer’s share and gross marketing margin
Based on selling and buying prices, marketing margin at different levels can be
calculated using the gross margin formula. Using data in Table 6.2 and 6.3 the results
for two commodities are presented in Table 6.4.
Net marketing margin can be calculated if data on marketing costs are available.
Because marketing costs are not well documented, marketing costs are difficult to be
collected, thus the marketing margin presented in the table only the gross marketing
margin.
Table 6.2 Prices for cocoa in the marketing channel in October 2003
Marketing participant Selling price (Rp/kg)
Farmer producers 8655
Village Assembler (VA) 10480
Municipal Assembler (MA) 11016
Consumer (Proxied by fob Palu)* 11016 * = not a seller, only a buyer
74
Table 6.3 Prices for rice in the marketing channel in October 2003
Local market
Marketing participant Selling price (Rp/kg)
Farmer producers 1810
Village Assembler 2269
Retailer in the village (RV) 2348
Consumer in the village* 2348
Urban market
Farmer producers 1810
Village Assembler 2269
Retailer in Palu (RU) 2781
Consumer in Palu* 2781 * = not a seller, only a buyer
Table 6.4 Marketing margin for each participant in the marketing channel
Cocoa (%) Rice (%) Gross Marketing Margin (GMM) in the local market in the urban market
Total GMM 21.43 22.91 34.92
GMMRA 16.56 19.55 16.51
GMMMA 4.87 - -
GMMRV - 3.36 -
GMMRU - - 18.41
Farmer’s share 78.57 77.09 65.08
Variability of the marketing margin
Some studies have attempted to show that variability in prices provoke changes in
magnitude of the marketing margins. Therefore it is important to observe whether the
margin relatively stable or fluctuate if commodities prices change. The variability of
the marketing margins are presented in Tables 6.5, 6.6, and 6.7.
75
Gross marketing margin of cocoa in all villages are reported in the Table 6.5. It
exhibits fluctuations during 2003. The average gross marketing margin; differential
prices between producer price and consumer price (approached by FOB Palu) is
16.90%.
Table 6.5 Gross marketing margin of cocoa during 2003 Months Gross marketing margin of cocoa Tomado Sintuwu Sidondo 2 Pandere Bolapapu Rahmat January n.a 2.61 14.51 n.a 3.72 4.55 February n.a 5.36 18.56 20.77 7.56 5.36 March 15.22 18.86 22.49 16.58 10.68 18.86 April 3.82 12.94 17.09 9.46 5.81 12.94 May 0.69 11.62 15.27 16.63 18.00 11.62 June 13.96 31.16 32.02 25.79 31.16 31.16 July 28.07 23.04 28.96 8.09 10.09 22.30 August 26.62 n.a 24.79 4.84 19.28 20.20 September 29.32 n.a 26.01 10.55 7.24 18.28 October 29.20 14.67 22.84 8.77 20.12 21.71 November 21.12 10.68 28.55 6.43 24.37 20.19 December 31.21 18.31 20.46 5.41 20.46 16.16 AVERAGE 19.92 14.93 22.63 12.12 14.87 16.94 16.90
Table 6.6 shows that gross marketing margin in Pandere, Sintuwu and Rahmat have
negative values. The GMM formula (differential prices between producer price and
consumer price in central market) shows that consumer prices in those villages are
relatively higher compared to those in Palu. During the first six months, Rahmat and
Sintuwu have negative marketing margin and around 2 months in Pandere. The
negative margin might be occurred due to several reasons. First, there is no trade link
between the two areas. Second, it is an indication that reversal flow of IR 66 Super
rice occurred in those villages. After harvest, rice flows to urban market and during
the lean season rice is bought from Palu and transported to these villages.
In contrast to IR 66 rice, cimandi rice shows different phenomena. The margins
always have positive values and relatively stable throughout the year. Although the
margin in Rahmat is calculated based on consumer price in that village, the margins
76
remain positive and stable. According to Thomsen and Foote as cited by Mendoza
(1995), except during periods of definite changes in prices of goods or in the rates of
costs, the margin rate percentage remains stable throughout the years. This situation
is caused by some of the cost components change less rapidly than prices. Prices may
change everyday, but marketing costs do not change for weeks or even months.
Table 6.6 Gross marketing margin of IR 66 super rice in 2003
Months Gross marketing margin of IR 66 super Tomado Pandere* Sintuwu* Rahmat*
January n.a n.a n.a n.a February n.a 0.00 0.00 0.00 March 29.30 2.33 -4.19 -4.19 April 27.27 0.00 -7.39 -7.39 May 33.33 7.14 -3.57 -3.57 June 35.00 -3.75 -7.50 -8.13 July 17.95 -17.95 -12.82 -7.69 August n.a n.a n.a n.a September 35.71 24.11 n.a 9.60 October 49.30 25.39 24.72 8.99 November 36.79 25.93 16.05 12.59 December 31.03 8.05 2.30 -2.01 AVERAGE 32.86 7.12 0.84 -0.18
* = Based on consumer price in the village Table 6.7 Gross marketing margin of cimandi rice in 2003
Months Gross marketing margin of cimandi rice Sidondo 2 Maranata Pandere Bolapapu Rahmat*
January n.a n.a n.a n.a n.a February 18.37 19.39 12.24 24.49 12.76 March 11.92 17.17 7.07 24.85 5.05 April 13.60 14.00 4.00 21.28 1.00 May 17.00 23.20 16.00 17.60 9.50 June 23.27 26.53 11.22 13.47 7.76 July 23.96 25.00 0.00 11.67 9.90 August n.a n.a n.a n.a n.a September 29.00 32.00 28.00 17.60 14.00 October 32.00 33.50 29.60 15.52 13.00 November 25.16 30.32 31.18 20.86 19.14 December 15.48 23.21 19.05 11.43 10.71 AVERAGE 20.98 24.43 15.84 17.88 10.28 17.88
77
6.3 Uncertainties, Break Even Price and sensitivity analysis Depending solely on expected (average) prices as risk calculation are not applicable
for risk averse agents. Since farmers are risk averse, calculation of downside risk,
which can be defined as a shorthand description for situation in which any significant
deviations from the norm or expected situation lead to worse outcomes is important
to be measured. If prices decrease below the variable costs, it will hurt the farmer
producers even in the short-run. As commodity price takers, the farmers cannot
determine market price and since there is a risk that prices may be lower than
expected, it is important to assess the acceptable declining of prices to cover cost of
production.
In order to get the possible decline of prices to cover per unit variable cost of
production, the collected producer prices of cocoa, coffee, and rice during the
research time in 2003 are compared with the break-even price of those commodities.
The break even price is a situation where the gross margin is zero. The Break-even
prices are calculated based on gross margin analysis from household survey
conducted by STORMA sub project A4 in 2001. The gross margin is the value
obtained from the difference between the expected gross income earned and the
expected variable costs incurred in the farm activity.
Comparing the two variables directly will not be meaningful because time periods are
not the same. To overcome the problem of time differentiation, prices are adjusted
for inflation using Consumer Price Index (CPI) Central Sulawesi. The CPIs are IDR
299.39 and 342.10 in 2001 and 2003, respectively.
The possible declines of some commodities prices to the break-even prices both in
nominal and percentage are presented in Tables 6.8, 6.9 and 6.10. The variable costs
of production included in the calculation are land preparation, planting or seed,
fertilizer, irrigation, pesticide and transport or processing. All tables show that the
prices can drop quite a bit to cover variable cost. In all villages cocoa and coffee can
drop up to more than 90% of average prices and rice up to more 60%.
78
Therefore it can be concluded that there is no short-run downside risk faced by the
farmers in the research area. The short run down side risk for tree crops is low
basically because most costs incurred are fixed such as planting of trees and
opportunity cost of land.
The advantage of gross margin analysis is can be used for a short-term farm analysis.
One of the shortcomings of the gross margin analysis is leaving the remuneration of
fixed factors such as family labour and land. In the long run, however, all costs
incurred in the production (variable and fixed) must be covered.
Table 6.8. Break Even Price and sensitivity analysis of cocoa Sensitivity analysis
Possible decline of prices to BEP Village Average price of cocoa
in 2003 (Rp/kg) Nominal (%)
Tomado 10115 9862 97.50Sintuwu 11321 11068 97.77Berdikari 10938 10685 97.69Sidondo 2 10058 9805 97.48Maranata 12885 12632 98.04Pandere 11330 11077 97.77Bolapapu 11204 10951 97.74Rahmat 10808 10555 97.66CPI Central Sulawesi in 2001= 299.39 and in 2003=342.10
Table 6.9 .Break Even Price and sensitivity analysis of coffee Sensitivity analysis
Possible decline of prices to BEP Village Average price of coffee
in 2003 (Rp/kg) Nominal (%)
Tomado 4167 4148 99.53 Sintuwu 5173 5154 99.62 Berdikari 4166 4147 99.53 Maranata 5170 5151 99.62 Pandere 4584 4565 99.58 Bolapapu 5040 5021 99.61 Rahmat 4902 4883 99.60 CPI Central Sulawesi in 2001= 299.39 and in 2003=342.10
79
Table 6.10 Break Even Price and sensitivity analysis of rice Sensitivity analysis
Possible decline of prices to BEP Village Average price of rice
in 2003 (Rp/kg) Nominal (%)
Tomado 1740 1090 62.63 Sintuwu 2000 1350 67.49 Berdikari 1809 1159 64.06 Sidondo 2 2109 1459 69.17 Maranata 2208 1558 70.55 Pandere 2358 1708 72.43 Bolapapu* 2460 1810 73.57 Rahmat 2250 1600 71.10 CPI Central Sulawesi in 2001= 299.39 and in 2003=342.10
* = Price of Cimandi Rice
6.4 Seasonal and spatial price variability
From viewpoint of physical transmission from production side to final consumer,
marketing has three dimensions, time, space and form. Concerning on the spatial and
temporal dimensions this section describes results on the analysis of price and
marketing margin. The form dimension represents the final product has different
form compared to its sale by farmers or in other words the final product has value
added. The form dimension is relatively complex since it involves several participants
in the market, which are not included in the survey such as processing firms,
therefore it is not covered in this analysis.
Degree of access to market influences price level, marketing margin (price spread)
and its variability. Villages with higher access to market are expected to receive
higher prices for producer prices and pay lower prices for consumer prices. With
regard to the price variability, it can be expected that remote villages will have higher
variability in producer price and lower variability in consumer prices.
80
6.4.1 Seasonal price variability Seasonality is one of characteristic of agricultural activities. Different growing
season and harvesting time, consumption patterns, timely marketing period and as
well as prices are some features of this characteristic.
6.4.1.1 Seasonal price variability of fertilizer
Figures 6.1 and 6.2 show that prices of urea and NPK in year 2003 relatively stable.
According to explanation of agricultural officer in Palu, on average application of
fertilizer by the farmers in Central Sulawesi relatively low compared to other
provinces. There are not many differences in demand of fertilizers between planting
time and other seasons. No demand shock contributes to the stability of fertilizer
prices.
In the research area, prices for different fertilizer can be gathered only in two villages,
Bolapapu and Berdikari. Bolapapu is village with good access to market meanwhile
Berdikari has low access to market, since market place is not available in this village.
Weeks in 2003
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4643
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41
Pric
e of
ure
a (R
p/kg
)
3000
2000
1000
0
Villages
berdikari
bolapapu
palu
Figure 6.1 Weekly price of urea (Rp/kg) in January-December 2003
81
The lowest price in Berdikari was Rp 1050 but in Palu was Rp 1130. These two
prices cannot be compared because the lowest price in Berdikari occurred during
week 32-36, meanwhile there was no data available in Palu market. The highest price
in Berdikari is Rp 1300 and Rp 1250 in Palu. Similar to the case of urea, the lowest
prices of NPK in Berdikari and Palu cannot be comparable since data were not
available during weeks 30-36 in Palu market. The lowest and highest prices of NPK
are Rp 1520 and 2500 in Berdikari respectively. In Palu the lowest and highest prices
are Rp 1800 and Rp 1950.
The prices in Palu can be expected not excessively deviate from Berdikari, because
the prices in Berdikari could be the price in Palu without any calculation of
transportation costs. There are two possibilities in explaining this condition. First,
There is a mistake occurred in data collection process. Although respondent had been
asked to write the purchase price of urea in the village, he wrote the price regardless
the place where it is bought. Second, during those weeks, Indonesian government
through ministry of industry and trade together with ministry of agriculture launched
a fertilizer subsidy project for agricultural sector. Through ministerial decree of
industry and trade number 70/MPP/Kep/2/2003 and ministerial decree of agriculture
number 427/Kpts/TP.130/8/2003 the highest retail price of urea was set up Rp
52.500,00/zak or Rp 1.050,00/kg wherever it is bought.
Figures 6.1 and 6.2 show that prices of urea and NPK in Bolapapu were highest
during weeks 28-30. One possibility to explain those circumstances is those weeks
were harvesting time and it implied that the repayment of borrowed fertilizer should
be accomplished.
In Berdikari, seasonal spread of urea is relatively lower compared to NPK, where
during the peaks, price of urea rises up to 24% and 60% for NPK. In Bolapapu,
differential prices between the peak and the trough for urea are relatively similar to
Berdikari. The difference are 24% for Urea and 40% for NPK. Meanwhile, in Palu
both fertilizers seem to show relatively stabile almost the year, whereas the seasonal
spread between the two seasons around 10%.
82
Weeks in 2003
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4643
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3431
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107
41
Pric
e of
NP
K (R
p/kg
)
4000
3000
2000
1000
Villages
berdikari
bolapapu
palu
Figure 6.2 Weekly prices of NPK (Rp/kg) in January–December 2003
6.4.1.2 Seasonal price variability of cocoa
Cocoa is one of main cash crop in Central Sulawesi. It provides high contribution to
regional income and particularly to the household income since cocoa beans is one of
major exported commodity from this region.
In Central Sulawesi harvesting period of cocoa can be attained twice a year. The first
period is between September until December, and it produces highest production,
therefore it is called main crop. The second harvest is between March – July, it is
called intermediary or mid crop.
With regard to harvesting period, it allows to predict that the prices will decrease
during these two harvest times. In relation with supply side, in weeks 36 – 52 it is
expected that the prices might fall and during week 10 – 31 (in the mid crop) the price
will not decrease as deep as the main crop season.
83
It can be seen from Figure 6.3 that prices of cocoa in the research area are
characterized by almost similar in timing of peaks and troughs. Figure 6.4 shows the
FOB (free on board) price from shipping port. This is used as proxy of world market
of cocoa. The figure shows that the FOB decreased in weeks 21 – 25. It shows that
on average the world cocoa price has similar trend to the local prices. It seems that
the local price follows the movement of the FOB price and do not follow the pattern
of harvesting time.
Since cocoa beans are exported commodity, one possibility to explain unusual
circumstance is looking at world market of cocoa. The world prices of cocoa were
influenced by supply and demand of world cocoa production, therefore the local
prices can not be estimated separately from prices, supply and demand of cocoa
world production.
Table 6.11 Lowest and highest cocoa price and seasonal gap (January-December 2003)
Cocoa producer price (Rp/kg) Villages Lowest Month Highest Month
Seasonal Spread
Mean 7800 14500 Tomado SD 447.21
October 500.00
April 6700
Mean 8000 16125 Sintuwu SD 353.55
June 853.91
February 8125
Mean 7700 13875 Sidondo II SD 447.21
November 853.91
February 6175
Mean 9687.50 16062.50 Maranata SD 375.00
September 1419.73
April 6375
Mean 8625 13775 Pandere SD 2625.99
June 263.00
March 5150
Mean 8000 16125 Rahmat SD 353.55
June 853.91
February 8125
Mean 8000 15750 Bolapapu SD 816.50
June
February 7750
Mean 10776 17038 Palu (FOB) SD 996.05
June 981.07
February 6261.85
84
Weeks in 2003
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Coc
oa p
rodu
cer p
rice
(Rp/
kg)
18000
16000
14000
12000
10000
8000
6000
Villages
sintuwu
berdikari
maranata
pandere
sidondo II
bolapapu
rahmat
tomado
Figure 6.3 Weekly price of cocoa (Rp/kg) in January- December 2003
Weeks in 2003
504542373330272421171410741
FOB
cac
ao (R
p/kg
) in
Cen
tral S
ulaw
esi
20000
18000
16000
14000
12000
10000
8000
Figure 6.4 Weekly FOB (Rp/kg) from Central Sulawesi shipping port in January-December 2003
85
Although peaks and troughs are relatively similar in all villages, different villages
have differences in seasonal spread. The biggest amplitudes during this year were
found in Rahmat and Sintuwu. The price during the peak was double than price in
the trough. Market site is not available in Sintuwu, however people from this village
can easily for selling their cocoa to Rahmat, the nearest village with a market. Since
Rahmat is accessible by car, it performs as a destination market for commodities from
Sintuwu, and as source of information for traders who operates in Sintuwu.
6.4.1.3 Seasonal price variability of coffee
Coffee is not an export commodity because it is mainly produced to support regional
demand. Prices of coffee received by farmers are not so high compared to cocoa
prices. Most of coffee trees still can be found in Bolapapu but in other villages, most
of trees have been replaced by cocoa.
Weeks in 2003
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Cof
fee
prod
ucer
pric
e (R
p/kg
)
9000
8000
7000
6000
5000
4000
3000
2000
1000
Villages
sintuwu
berdikari
maranata
pandere
bolapapu
rahmat
tomado
Figure 6.5 Weekly producer price of coffee (Rp/kg) in January-December 2003
There are variations of coffee prices in villages during January to December 2003.
During week 10 – 20 (early of March-middle of May), prices in Bolapapu, Maranata
86
and Sintuwu were higher compared to the other weeks. The highest price was in
Bolapapu Rp 8000/kg. The biggest amplitude in the seasonal spread within a year
could be found in this village. The prices during the lack season were more than
double compared to the prices after the harvest season. The smallest amplitude could
be found in Tomado, a remote village. The prices during the lack season increased
33% than the prices after the harvest season.
Table 6.12 Lowest and highest coffee producer price and seasonal gap in January- December 2003
Coffee producer price (Rp/kg) Villages Highest Month
Seasonal Spread Lowest Month
Mean 3750 5000 Tomado SD 288.68
November 0
May 1250
Mean 3750 6625 Sintuwu SD 0
January 342.33
March 2875
Mean 3750 4750 Berdikari SD 238.05
October -
June 1000
Mean 4225 6375 Maranata SD 95.75
October 250
April 2150
Mean 3625 6031 Pandere SD 171.16
December 148.78
June 2406
Mean 3750 6625 Rahmat SD 0
January 342.33
March 2875
Mean 3675 8000 Bolapapu SD 537.74
September 0
March 4250
6.4.1.4 Seasonal price variability of rice
In the research area, the variety of rice varies from one village to another. There are
villages with many varieties available in it shops or markets and on the other hand
there are villages with limited varieties. In Bolapapu, respondent had reported only
prices of cimandi and IR 66 super was the only variety reported by respondent in
Tomado. Therefore, in order to make comparison, only two varieties from local and
high yielding variety (cimandi and IR 66 super, respectively) have been selected and
will be discussed. Price of rice could be gathered in two different marketing
participants, producer and consumer. Producer price is prices received by farmer
producers and consumer price is price paid by final consumers.
87
Producer price of cimandi could be collected in Sidondo II, Maranata, Pandere and
Bolapapu. The biggest amplitude in the seasonal spread within a year could be found
in Pandere with the price during the lean season was 50 % higher than after harvest
season. The smallest amplitude could be found in Bolapapu with differentiation price
during the two seasons was 17 %. Consumer prices of cimandi were gathered in all
villages and Palu. The similar condition for consumer prices were found, whereas the
biggest and smallest amplitudes occured in Pandere and Bolapapu where during the
lean season the price was 50 % higher than after harvest season.
The consumer price in Palu as central market and mostly as destination market of rice
from all villages is expected will be higher compare to all villages. From figure 6.7 it
can be seen that prices in Palu were higher compare to other villages and relatively
stabile with CV 7,43% during the year. The stability of prices in Palu is caused by
sufficiency stock of rice available in Palu. Rice from all villages in research area and
other regions flow to Palu.
Figure 6.7 shows that the consumer prices in the villages tended to increase from
early of the year except in Palu whereas at that time the prices were relatively stabile.
The highest price occurred in Pandere during week 9-17, one reason for this situation
was rural farm household exhausted stocks and became food buyer. In this season the
reversal flow from Palu to the village might occur and prices in the village became
higher compared to prices in the post-harvest periods.
88
Weeks in 2003
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Pro
duce
r pric
e of
cim
andi
rice
(Rp/
kg)
4000
3000
2000
1000
Villages
sintuwu
berdikari
maranata
pandere
sidondo II
bolapapu
Figure 6.6 Weekly producer price of cimandi rice (Rp/kg) in January-December 2003
Weeks in 2003
5249
4643
4037
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41
Con
sum
er p
rice
of c
iman
di ri
ce (R
p/kg
)
4000
3000
2000
1000
Villages
sintuwu
berdikari
maranata
pandere
sidondo II
bolapapu
rahmat
palu
Figure 6.7 Weekly consumer price of cimandi rice (Rp/kg) in January-December 2003
89
Table 6.13 Lowest and highest price and seasonal spread of cimandi rice in January – December 2003
Price of cimandi rice (Rp/kg) Producer price Consumer (retail) price
Villages
Lowest Highest Seasonal Spread
Lowest Highest Seasonal spread
Mean 2281 3094 813 SD 62.50 62.50
Sintuwu
Month November April Mean 2125 2725 600 2375 3075 700 SD 0 205.40 125 68.47
Sidondo II
Month October March November April Mean 2016 2688 672 2172 2813 641 SD 31.25 125.00 31.25 125.00
Maranata
Month December April December April Mean 2000 3000 1000 2167 3250 1083 SD 0 0 72.17 153.09
Pandere
Month November July November April Mean 2250 2650 400 2520 2770 250 SD 0 57.74 83.67 44.72
Bolapapu
Month January June January October Mean 2344 3094 750 SD 62.50 62.50
Rahmat
Month December April Mean 2625 3125 500 SD 306.19 0
Palu
Month December April 386.47 IR 66 super is one of high yielding variety rice grown in Pandere, Maranata,
Berdikari and Tomado. Comparing to the local rice variety, cimandi, average prices
of IR 66 super were relatively lower. The biggest amplitude of producer price
occurred in Pandere with differential price between lean and harvest season was 50%.
In other village, the price during the lean season was 30 % higher than after harvest
season.
90
Weeks in 2003
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Pro
duce
r pric
e of
IR 6
6 (R
p/kg
)
3000
2800
2600
2400
2200
2000
1800
1600
1400
Villages
berdikari
maranata
pandere
sidondo II
Figure 6.8 Weekly producer price of IR 66 super rice (Rp/kg) in January-December 2003
Weeks in 2003
5249
4643
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41
Con
sum
er p
rice
of IR
66
(Rp/
kg)
4000
3000
2000
1000
Villages
sintuwu
berdikari
maranata
pandere
sidondo II
rahmat
palu
Figure 6.9 Weekly consumer price of IR 66 super rice (Rp/kg) in January-December 2003
91
Table 6.14 Lowest and highest consumer prices and seasonal spread of IR 66 super rice in January – December 2003
Price of IR 66 super rice (Rp/kg) Villages Producer price Consumer (retail) price
Lowest Highest Seasonal Spread
Lowest Highest Seasonal spread
Sintuwu Mean 2094 2953 859 SD 187.50 138.58 Month October April Tomado Mean 1410 2000 590 1800 2500 700 SD 201.25 0 273.86 - Month October July October July Pandere Mean 1875 2875 1000 2042 3000 958 SD 0 0 57.74 0 Month November July November July Rahmat Mean 2213 2953 740 SD 104.58 138.58 Month November April Palu Mean 2175 2800 625 SD 273.86 68.47 Month December September Prices in villages are commonly lower compared to those in urban market such as
Palu, however consumer price of IR 66 super tends to rise exceedingly in early of the
year particularly in Pandere and Sintuwu. The similar condition occurred in week 45-
50 whereas prices in the villages were higher compared to prices in Palu. The biggest
amplitude of consumer price in the seasonal spread within the year could be found in
Pandere with the price during the lean season was 50 % higher than after harvest
season. In these two periods, reverse direction of rice from Palu to the villages could
be one reason for the high prices.
6.4.1.5 Seasonal price variability of sugar
As it is expected, price of sugar in the villages were higher compared to the price in
Palu since the flow of sugar move from Palu to those villages. The prices in
Berdikari were not included in the figure and in the analysis because of unreliability
of the data.
92
The biggest amplitude in the seasonal spread during the year occurred in Sidondo II
with the differential price between the lowest and the highest reached up to 40%. In
Sidondo II, the lowest price was Rp 4125/kg and the highest was Rp 5625/kg.
However the highest price was Rp 6000/kg occurred in Tomado. Since the lowest
price in Tomado Rp 5167/kg, the amplitude between the peak and the trough was 1,2
times or the increasing price between the two level of prices was 20%.
Table 6.15 Lowest and highest consumer prices and seasonal spread of sugar in January – December 2003
Sugar price (Rp/kg) Seasonal Villages Lowest Month Highest Month spread
Tomado Mean 5167 March 6000 September 833 SD 288.68 0 Sintuwu Mean 4333 January 5375 April 1042 SD 288.68 250 Sidondo II Mean 4125 January 5625 May 1500 SD 250 250 Maranata Mean 4013 January 4938 April 925 SD 62.92 125 Pandere Mean 4750 September 5940 March 1190 SD 0 134.16 Bolapapu Mean 4500 January 5875 July 1375 SD 0 250 Rahmat Mean 4038 January 5375 April 1337 SD 149.30 250 Palu Mean 4200 February 5125 May 925
SD 0 250
93
Weeks in 2003
5249
4643
4037
3431
2825
2219
1613
107
41
Sug
ar p
rice
(Rp/
kg)
7000
6000
5000
4000
3000
sintuwu
berdikari
maranata
pandere
sidondo II
bolapapu
rahmat
palu
tomado
Figure 6.10 Weekly consumer price of sugar (Rp/kg) in January-December 2003 6.4.1.6 Seasonal price variability of cooking oil Two types of cooking oil are gathered to capture various quality of product available
and consumed by the villagers. Super and medium quality of cooking oil prices were
collected during research time. Although classification has been created,
standardization of quality are various from village to village. For example in Sidondo
II, consumers can buy palm oil in special package (bottled) with a trademark or brand
and it is called as super quality of cooking oil. In other villages, super quality is oil
sold without any brand and un-bottled. Traders pour the oil directly to a plastic or
used bottle and it can be measured in kind of liter or kg. In order to get consistency
of measurement data, Sidondo II and Berdikari are excluded from the analysis.
94
Table 6.16 Lowest and highest consumer prices and seasonal spread of cooking oil super quality in January – December 2003
Price of cooking oil – super quality (Rp/kg) Villages Lowest Month Highest Month
Seasonal Spread
Mean 4167 6000 Tomado SD 288.68
March 0
December 1833
Mean 4217 7500 Sintuwu SD 635.09
January 0
October 3283
Mean 4238 6000 Maranata SD 25
October 0
February 1762
Mean 5060 6776 Pandere SD 155.56
February 162.23
July 1716
Mean 3905 4950 Rahmat SD 122.98
June 0
March 1045
Mean 3000 6000 Bolapapu SD 0
January 0
October 3000
Mean 4750 5500 Palu SD 288.68
March 273.86
September 750
Weeks in 2003
5249
4643
4037
3431
2825
2219
1613
107
41
Pric
e of
coo
king
oil-
supe
r qua
lity
(Rp/
kg)
8000
7000
6000
5000
4000
3000
2000
Villages
sintuwu
maranata
pandere
bolapapu
rahmat
palu
tomado
Figure 6.11 Weekly consumer price of cooking oil-super quality (Rp/kg) in January -December 2003
95
Figure 6.11 shows the timing of peaks and troughs of super quality of cooking oil in
all villages and in Palu. The biggest amplitude in the seasonal spread within the year
could be found in Bolapapu. The prices during the peak reached up to double or was
100 % higher than the trough. The smallest amplitude occurred in Palu with the
differential price between the two conditions was around 10%. For medium quality of
cooking oil the biggest amplitude during the year took place also in Bolapapu. The
differential price between the peak and the though was 80%.
Table 6.17 Lowest and highest consumer (retail) prices and seasonal spread of cooking oil - medium quality in January – December 2003
Price of cooking oil – medium quality (Rp/kg) Villages Lowest Month Highest Month
Seasonal spread
Mean 3667 4250 Tomado SD 0
March 288.68
May 583
Mean 3850 5000 Sintuwu SD 0
May 0
October 1150
Mean 4875 6000 Sidondo II SD 750
September 0
May 1125
Mean 4000 6000 Maranata SD 0
October -
February 2000
Mean 4125 6215 Pandere SD 0
February 150.62
July 2090
Mean 3850 4455 Rahmat SD 0
September 122.98
June 605
Mean 3300 5913 Bolapapu SD 0
January 275
November 2613
Mean 4480 5500 Palu SD 204.94
April 500.00
September 1020
96
Weeks in 2003
5249
4643
4037
3431
2825
2219
1613
107
41
Pric
e of
coo
king
oil-
med
ium
qua
lity
(Rp/
kg)
8000
7000
6000
5000
4000
3000
Villages
sintuwu
maranata
pandere
sidondo II
bolapapu
rahmat
palu
tomado
Figure 6.12 Weekly consumer price of medium cooking oil (Rp/kg) in January -December 2003
6.4.2 Spatial price variability
Table 6.18 shows distribution of producer price for different commodities. The
distribution is classified into three groups based on degree to market access, low,
medium or high. Central market is not included since producer price in this study is
defined as price received by producer in farm gate or in local market.
Spatial or location of producer will influence price received as well as its variability.
The lower degree of market access, the producer prices will be lower and the
variability will be higher.
Among three groups, cocoa prices are high in villages with good access to market.
With rising distance from Palu central market, the cocoa prices are getting lower.
The lowest prices received by farmers in remote village. Cocoa in exported prices
97
(free on board) is collected to compare with the prices in village prices. Starting from
the lowest access to market, average prices of cocoa are (in Rp/kg) 10.063, 10.757,
11610 and 13.200, respectively.
One-way ANOVA is used to observe significances of the differences in average
prices for each group. Before proceeds the ANOVA the SPSS produce automatically
Levene´s statistic to calculate the equality (homogeneity) of variances in the different
groups. Under this test, the significant value (0,138) is not significant which means
that equal variances are assumed. Under the assumption of homogeneity of variances,
the ANOVA results that the average prices between groups are significantly different
at one percent significance level. Nevertheless, this value does not exactly provide
information which average are significantly different to which other average prices.
Coefficient of Variation (CV) is calculated to observe the variability in the prices.
Contrary to the average price levels, variability of cocoa shows expected
phenomenon that remote villages will have higher variability of prices. The values of
CV arise from 18,73% in central market, 21,73% in villages with good access to
market, 22,54% in villages with low access to market and the highest is 28,45% in the
remote area.
Producer price of coffee shows similar phenomenon as cocoa. The price levels are
lowest in the remote area and in villages with good access to market the price levels
are higher. Since the Levene´s statistics give significant value at one percent level, the
assumption of homogeneity variances are violated. One–way ANOVA under this
condition (equal variances not assumed) shows that the differences in price levels
between groups are highly significant at one percent level.
However, the variability of coffee prices shows an opposite as it is expected. The
variation in prices in rural area is the lowest compared to the other groups. This
remote area cannot be reached by car and does not have market. The remoteness
arrives at low competition between traders compared to what occurred in adjacent or
urban area.
98
Table 6.18 Producer Price Distribution
Degree of Market Access Price (Rp/kg) Low
(Group 1) Medium
(Group 2) High
(Group 3) Mean 10062.5 (*) 10757.14 (*) 11610.43 (*) SD 2862.64 2425.13 2522.826 CV 28.45 % 22.54 % 21.73 %
Cocoa
N 40 140 211
Mean 4162.5 (*) 4641.32 (*) 4929.80 (*) SD 523.64 909.24 1146.043 CV 12.58 % 19.59 % 23.25 %
Coffee
N 40 72 211
Mean - 2275.85 (*) 2482.40 (*) SD - 250.39 294.249
Membramo rice
CV - 11.00 % 11.85 %
Mean - 2272.03 (*) 2543.48 (*) SD - 241.18 357.67
Kepala rice
CV - 10.62 % 14.06 %
Mean - 1809.09 (*) 2274.38 (*) SD - 199.78 283.914
IR 46 rice
CV - 11.04 % 12.48 %
Mean - 2074.6 (*) 2275.97 (*) SD - 245.61 284.138
IR 66 rice
CV - 11.84 % 12.48 %
Mean 1733.75 (*) 1934.38 (*) 2280.83 (*) SD 279.74 208.02 280.83 CV 16.13 % 10.75 % 12.31 %
IR 66 super rice
N 40 44 103 CV is calculated as (standard deviation/mean)*100 (*) between groups difference statistically significant at the one per cent level
In the research area, the variety of rice varies from one village to another. There are
villages with many varieties available in it shops or markets; on the other hand there
are villages that the traders sell only one or two varieties. Therefore only one variety
(IR 66 super) can be comparable in all groups. The lowest producer price level and
the highest variability of IR 66 super are found in remote area. In villages with good
access to market (group 3) farmers receive higher prices. The average price level
99
(Rp/kg) is 1733 in remote area, 1934 and 2280 in villages with medium and high
access to market, respectively. The Levene´s statistics results on assumption of equal
variances are violated therefore equal variances not assumed is selected in the
subsequently step of analyses. One-way ANOVA shows that the price levels are
significantly different between groups at one percent level of significance.
Table 6.19 shows distribution of consumer prices for different basic food items and
fertilizers. Flow of food items and fertilizers, which are not produced in the villages
such as sugar, generally moves from central market to rural area. Therefore, it can be
expected that the prices in villages will be higher compared to central market.
However, there are also basic food items which villages can sufficiently produced and
enough to be consumed by the villagers, such as rice. Referring to the equation
presented in the methodology, variability of consumer price is expected will be higher
in central market and decrease according to distance to this market.
Price of sugar in remote area is the highest and the lowest is in central market. Under
condition of equal variance not assumed, the differences of average price between
groups are highly significant at one percent significance level. Variability of sugar
prices in remote village is the lowest. The consumer price in the remote village are
relatively higher compared to other villages, therefore it contributes to produce the
price variability which is relatively low.
Two types of cooking oil are gathered to capture various quality of product available
and consumed by the villagers. Super and medium quality of cooking oil prices are
collected during research time. Although classification has been created,
standardization of quality are various from village to village. In one village, Sidondo
II, consumers can buy palm oil in special package (bottled) with a trademark or brand
and it is called as super cooking oil. In other villages, super quality is oil sold without
any brand and un-bottled. Traders pour the oil directly to a plastic or used bottle and
it can be measured in liter or kg. In order to get consistency of measurement, data
from Sidondo II is excluded from the table.
100
The average prices for cooking oil seem to be inconsistent. It might be occurred due
to different opinion on the quality. The prices in remote village are lower compared
to the central market. Therefore, calculation of variability for super and medium
quality seems to be inconsistent.
In the research area, prices of fertilizer (Urea and NPK) can be gathered only in
Berdikari and Bolapapu. These two villages can be used as representative of villages
that have low and good access to market. Although Berdikari is a village with low
access to market (group 2) because market site is not available, this village has good
access to road therefore people from this village can be easily purchase different
fertilizer from nearest market or even from Palu. It can be seen from the table that
prices are lower compared to Bolapapu and almost similar to the Palu prices. A
reason for lower fertilizer prices in Berdikari is that reported prices are simply prices
without any additional costs such as transportation cost.
Differential prices between Palu and Bolapapu are caused by transport cost and other
cost involved in transporting fertilizer in these two regions and unique system applied
in fertilizer trade. In Bolapapu there are 2 alternatives available for purchasing
fertilizers, cash or credit. Most of the farmers in this village choose the second
alternative to acquire fertilizers. In other words credit is the most favoured system in
the village. According to explanation of fertilizer trader and respondent, the common
fertilizer applied by most of people in the village is urea. In the planting time,
farmers receive a loan in kind of fertilizers from the shop and the repayment will be
accomplished in harvesting time and it charges 1 kg rice for 1 kg urea.
101
Table 6.19 Consumer Price Distribution Degree of Market Access Price
(Rp/kg) Low (Group 1)
Medium (Group 2)
High (Group 3)
Central Market (Group 4)
Mean 5837.50 (*) 4743.57 (*) 4764.45 (*) 4588.10 (*) SD 327.92 550.63 515.791 352.138
Sugar
CV 5.62 % 11.61 % 10.83 % 7.68 % Mean 4727.27 (*) 6709.06 (*) 5237.52 (*) 5062.79 (*) SD 516.76 1683.61 994.358 412.324 CV 10.93 % 25.09 % 18.96 % 8.14 %
Super cooking oil
N 33 80 206 43 Mean 3975 (*) 5754.14 (*) 4837.09 (*) 4779.07 (*) SD 251.92 1442.66 825.225 433.457
Medium cooking oil
CV 6.34 % 25.07 % 17.06 % 9.07 % Mean - 2682.68 (*) 2713.77 (*) 2919.19 (*) SD - 294.5 285.371 197.799
Membramo rice
CV - 10.98 % 10.52 % 6.78 % Mean - 2685.16 (*) 2780 (*) 2919.19 (*) SD - 289 291.06 197.799
Kepala rice
CV - 10.76 % 10.47 % 6.78 % Mean - 2434.9 (*) 2499.68 (*) 2720.35 (*) SD - 403.53 286.397 228.234
IR 46 rice
CV - 16.57 % 11.46% 8.39 % Mean - 2477.07 ** 2480.33 ** 2584.88 (**) SD - 294.9 290.33 249.114
IR 66 rice
CV - 11.91 % 11.71 % 9.64 % Mean 2203.75 2384.23 (*) 2480.33 (*) 2578.49 (*) SD 310.58 357.23 304.125 250.035
IR 66 super rice
CV 14.09 % 14.98 % 12.26 % 9.70 % Mean - 2624.12 (*) 2625.36 (*) 3020.35 (*) SD - 286.63 261.844 224.404
Cimandi
CV - 10.92 % 9.97 % 7.43 % Mean - 2618.02 (*) 2662.04 (*) 2884.3 (*) SD - 287.1 231.48 195.09
Buri-buri rice
CV - 10.97 % 8.70 % 6.76 % Mean - 1206.84 (*) 2628.18 (*) 1201.16 (*) SD - 82.252 117.364 32.893
Urea
CV - 6.82 % 4.47 % 2.74 % Mean - 2000.13 (*) 2978.18 (*) 1855.81 (*) SD - 272.852 190.215 52.064
NPK
CV - 13.64 % 6.39 % 2.81 % CV is calculated as (standard deviation/mean)*100 (*) between groups difference statistically significant at the one per cent level (**) between groups difference statistically significant at the ten per cent level Number of observations for Group 1, 2, 3 and 4 are 40, 140, 211 and 43, respectively and otherwise is directly pointed out in the table.
102
6.5 Econometrics results on price variability Due to some missing data, not all commodities prices in all villages can be analysed.
Cocoa and two varieties of rice are selected to represent the important commodities in
the research area particularly for perennial and annual crops. To observe the
variability in prices of food items, sugar is selected.
The underlying assumption of stationary time series data should be fulfilled before
attempting to use the data in the analysis. Before estimating the variables using
ARCH model, a diagnostic testing is applied. Dickey Fuller is used to test whether
the natural logarithm of prices of cocoa, rice and sugar are stationary.
Tables 6.20, 6.21 and 6.22 shows the results from DF test. It should be noted that
the critical values are different for different models, without trend (only constant
variable) or with a trend variable in the right hand side. The tables below show that
the estimated t values in absolute value are higher than the critical value. This means
that in all villages the natural logarithm of price data reveals stationary series.
Table 6.20 Unit root test using Dickey-Fuller test for natural logarithm of weekly cocoa prices (number of observations = 51) Natural logaritm of cocoa prices
Trend variable
Test statistic (c = without
trend)
Test statistic (t = with trend)
Coefficient Std error LnFOB -1.610 -3.819 -.0044 .0013 LnTomado* -1.432 -1.562 -.0017 .0019 LnSintuwu -1.636 -1.853 -.0014 .0013 Ln Sidondo 2 -1.422 -2.155 -.0021 .0013 LnBolapapu -1.371 -1.674 -.0015 .0013 LnRahmat -1.515 -1.757 -.0014 .0013 Critical values for c at 1% = -2.405, 5% = -1.677 and 10% = -1.299 (N = 51) for t at 1% = -4.148, 5% = -3.499 and 10% = -3.179 (N = 51) for c at 1% = -2.426, 5% = -1.685 and 10% = -1.304 (N = 41) for t at 1% = -4.233, 5% = -3.536 and 10% = -3.202 (N = 41) * Number of observations of Tomado are 41
103
Table 6.20 shows that the natural logarithm of cocoa prices in all villages are
stationary. The test statistic for FOB prices shows that the time variable is
statistically significant. Therefore it can be concluded that the natural logarithm of
FOB prices is stationary around deterministic trend.
Almost all natural logarithm of rice prices is stationary except in Maranata. First
differences of the natural logarithm of prices series often remove the non-stationary
problem. Table 6.21 shows that the first differencing of the natural logarithm of rice
prices in Maranata, the test statistics for are less negative than negative critical value.
In other words, the first differences of natural logarithms of rice prices in Maranata
are stationary. The natural logarithm of rice prices in Sidondo2 and Tomado are
stationary around deterministic trend. Table 6.22 shows that natural logarithm of
sugar prices is stationary.
Table 6.21 Unit root test using Dickey-Fuller test for natural logarithm of weekly rice prices Natural logaritm
of rice prices Number of
observations Test statistic (c = without
trend)
Test statistic (t = with
trend)
Coefficient of trend variable*
Cimandi rice LnPalu 41 -2.691 -3.012 -.0010 (.0008) LnSidondo2 51 -2.083 -3.579 -.0018 (.0006) LnBolapapu 51 -2.375 -2.242 4.61e-06 (.0004)LnMaranata 51 -0.952 -2.706 -.0015 (.0006) First Diff of LnMaranata
50 -8.825 -8.792 -.0002 (.0003)
IR 66 Super rice LnPalu 41 -2.680 -2.871 -.0010 (.0009) Ln Tomado 41 -2.621 -3.512 -.0039 (.0017) Critical values for c at 1% = -2.405, 5% = -1.677 and 10% = -1.299 (N = 51) for t at 1% = -4.148, 5% = -3.499 and 10% = -3.179 (N = 51) for c at 1% = -2.426, 5% = -1.685 and 10% = -1.304 (N = 41) for t at 1% = -4.233, 5% = -3.536 and 10% = -3.202 (N = 41) * Standard error in paratheses
104
Table 6.22 Unit root test using Dickey-Fuller test for natural logarithm of weekly sugar prices (number of observations = 51) Natural logaritm of sugar prices
Trend variable
Test statistic (c = without
trend)
Test statistic (t = with trend)
Coefficient Std error LnPalu* -1.941 -2.268 -.0007 .0004 LnTomado* -3.486 -3.543 .0006 .0006 Ln Sidondo 2 -2.769 -2.824 .0005 .0007 LnMaranata -2.183 -2.303 -.0003 .0002 LnBolapapu -2.099 -2.382 .0007 .0006 LnRahmat -3.028 -3.039 -.0005 .0005 Critical values for c at 1% = -2.405, 5% = -1.677 and 10% = -1.299 (N = 51) for t at 1% = -4.148, 5% = -3.499 and 10% = -3.179 (N = 51) for c at 1% = -2.426, 5% = -1.685 and 10% = -1.304 (N = 41) for t at 1% = -4.233, 5% = -3.536 and 10% = -3.202 (N = 41) * Number of observations are 41
Before conducting diagnostic test for the presence of ARCH effect, Akaike
Information Criterion (AIC) is calculated to determine the lag length in the AR(p)
model. Afterwards, the analysis is followed by regression the equations which
contains the lag length as indicated by AIC using OLS. Most of the results show that
only the first or the second degree of autoregressive is statistically significant.
As seen in the figure of cocoa price movement in the previous sub chapter, it seems
that the cocoa price has a structural break. To cover this phenomenon, dummy
variable is created and included in the regression analysis. However, the coefficients
of dummy are not statistically significant. Therefore it is not included in the further
analysis.
According to the AIC, the lag length for cocoa in Bolapapu is AR(3), however only
AR(1) and AR(2) are statistically significant. AIC suggests the lag length for cocoa
in Rahmat is AR (3) and the OLS for this model is statistically significant. For sugar
in Sidondo2, the lag length is statistically significant until the optimal AR(2) as
suggested by AIC. Based on the DF test, trend variable is included in the diagnostic
test for rice in Sidondo2 and Tomado and for cocoa in Palu.
105
Diagnostic test for ARCH effects are conducted based on the procedure proposed by
Engle (1982) that is (1) run the original model AR(1) or AR(2) for lnprice in using
OLS; (2) save the residuals from the regression; (3) regress the squared residuals on a
constant and 1 lagged values of the squared residuals.
Diagnostic test for ARCH effects in the autoregressive AR(1), AR(2), with or without
trend variable are reported in Table 6.23, 6.24 and 6.25. Under the hypothesis of H0
= no ARCH effects and H1=ARCH(p) disturbance, the tables suggest that not all
series are subject to ARCH. The two varieties of rice in all areas show the
conditional variances are homoscedastic. The same results are found for sugar,
except in Sidondo2. The cocoa price series in Palu, Bolapapu and Rahmat shows that
the model follows an ARCH form, which means that the conditional error variance is
given by an ARCH(1) process or conditional variances are heterokedastic.
In the presence of ARCH effect, the analysis is continued to estimate the ARCH
model. All results is reported in Table 6.26 and 6.27.
Table 6.23 Diagnostic test on homoscedastic model for cocoa
Research area AR(p) LM test for Cocoa (df=1) (OLS) TR2 P > χ2
Palu 1 5.531 0.0187 Palu (trend) 1 6.861 0.0088 Bolapapu 1 8.341 0.0039 Bolapapu 2 10.125 0.0063* Rahmat 1 4.177 0.0410 Rahmat 3 0.693 0.4051 Sintuwu 1 1.219 0.2695 Sidondo2 1 0.024 0.8671 Tomado 1 0.031 0.8605 * df =2
106
Table 6.24 Diagnostic test on homoscedastic model for sugar Research area AR (p) LM test for sugar (df=1)
(OLS) TR2 P > χ2
Palu 1 0.023 0.8786 Bolapapu 1 0.310 0.5775 Rahmat 1 0.027 0.8689 Maranata 1 0.170 0.6801 Sidondo2 1 8.229 0.0041 Sidondo2 2 5.214 0.0224 Tomado 1 0.122 0.7271
Table 6.25 Diagnostic test on homoscedastic model for two varieties of rice*
Research area LM test for cimandi (df=1) LM test for IR 66 Super (df=1)
TR2 P > χ2 TR2 P > χ2
Palu 0.015 0.9028 0.003 0.9575 Bolapapu 0.541 0.4620 - - Maranata * 0.431 0.5115 - - Sidondo2 0.495 0.4817 - - Sidondo2 (trend) 0.023 0.8792 - - Tomado - - 0.011 0.9159 Tomado (trend) - - 0.065 0.7988 * The OLS analysis is based on AR(1) The point estimates in the mean and variance regression for AR(1) and AR(2) are
statistically significant for cocoa price in Bolapapu and sugar price in Sidondo2. The
slope in the mean equation estimates for the first and the second order autoregressive
indicates serially correlated prices. The coefficients on the lag variance are positive
and significant, indicating that residuals of current and previous periods are strongly
correlated. It shows the presence of conditional heteroscedasticity in error terms of
the mean equation.
107
Table 6.26 Estimation of ARCH model for cocoa
Mean equation: Dependent variable is cocoa weekly prices
Independent variable
Palu Palu1 Bolapapu Bolapapu Rahmat
Constant 1.026 (0.521)
4.033 (1.145)
0.612 (0.326)
0.782 (0.221)
0.640 (0.419)
Lag (t-1) price
0.892 (0.551)
0.587 (0.117)
0.934 (0.035)***
1.218 (0.131)***
0.931 (0.045)
Lag (t-2) price
- - - -0.302 (0.122)**
-
Time trend - -0.004 (0.148)
- - -
Variance equation: Dependent variable is conditional variance in cocoa price Constant 0.005
(0.001) 0.003
(0.001) 0.003
(0.001) 0.0006
(0.0009) 0.005
(0.001) Lag (t-1) variance
0.076 (0.206)
0.312 (0.258)
0.561 (0.247)**
1.149 (0.124)***
0.279 (0.233)
Lag (t-2) variance
- - - 0.210 (0.125)**
-
1 Time trend is included in the equation * statistically significant at 90% level; ** statistically significant at 95% level *** statistically significant at 99% level
Table 6.27 Estimation of ARCH model for sugar
Mean equation: Dependent variable is sugar weekly prices Independent variable Sidondo2 Sidondo2
Constant 2.1743 (0.6442)*** 1.875 (0.614)** Lag (t-1) price 0.7455 (0.07511)*** 0.467 (0.154)** Lag (t-2) price - 0.313 (0.149)**
Variance equation: Dependent variable is conditional variance in cocoa price Constant 0.002 (0.0006)*** 0.0018 (0.006)*** Lag (t-1) variance 0.516 (0.2657)** 0.6787 (0.2449)* * statistically significant at 90% level; ** statistically significant at 95% level *** statistically significant at 99% level
6.6 Summary This chapter describes the following aspects of market performance: (1) communities
access to agricultural markets; (2) gross marketing margin and risk analysis; and (3)
108
patterns of seasonal and spatial price variability of agricultural commodities in
relation to the degree of market access.
Road and market place are infrastructures in the villages which are used as proxy of
degree to market access. With regard to the two infrastructures, all villages can be
arranged into 3 groups, 1) not accessible by car and do not have market-low access;
2) accessible by car and do not have market-medium access; and 3) accessible by car
and the have market-good access. The fourth group is central market.
Gross marketing margin is measured by the difference between retail price paid by
final consumers and price received by farmer producers. Based on average price in
October 2003, gross margin of cocoa, rice in rural area and rice in central market are
21.43%, 22.91%, and 34.92%, respectively. The gross marketing margin shows
another concept of farmer share, that is the portion of the price paid by the consumer
that belongs to the farmer producers. Farmer share of cocoa, rice in rural and central
markets are 78.57%, 77.09% and 65.08%. The average gross marketing margin in
2003 for cocoa and cimandi rice are 16.90% and 17.88%.
Farmers are price takers who have limited control over price. If prices decrease
below the variable costs, it will hurt the farmer producers. The possible declines of
some commodities prices to cover per unit cost of production are more than 90% for
cocoa and coffee and up to more 60% for rice. Therefore it can be concluded that
there is no short-run downside risk faced by the farmers in the research area. The
short run down side risk for tree crops is low basically because most costs incurred
are fixed such as planting of trees and opportunity cost of land.
One of the shortcomings of the gross margin analysis is leaving the remuneration of
fixed factors such as family labour and land. In the long run, however, all costs
incurred in the production (variable and fixed) must be covered.
Seasonal variability of urea seems to be stabile almost the year with the seasonal
spread between the peak and the trough in the research area is less than 25%. The
price movement in cocoa shows that the time of peaks and troughs in all villages are
109
relatively similar. However, seasonal spread is different between villages. The
biggest amplitude in 2003 was found in Rahmat and Sintuwu, whereas the price
during the peak was double than price in the trough. Price of coffee shows a seasonal
variability as indicated by the higher price in the lack season. Bolapapu has the
biggest amplitude with the price in the lack season reaches more than double
compared to the price after harvest. The biggest seasonal spread for two varieties of
rice, cimandi and IR 66 super is found in Pandere with the price during the lean
season was 50 % higher than the price after harvest season. The prices in Pandere in
some weeks were relatively higher compared to the price in Palu. In this season the
reversal flow from Palu to the village might occur and prices in the village became
higher compared to prices in Palu. The biggest seasonal spread of sugar occurred in
Sidondo II with the differential price between the lowest and the highest reached up
to 40%. However the highest price was Rp 6000/kg occurred in Tomado, a remote
village. Since the lowest price in Tomado Rp 5167/kg, the seasonal spread between
the peak and the trough was 20%. The lower value of seasonal spread in Tomado
occurred because the lowest price in this village is already higher compared to the
lowest price in other villages. The biggest seasonal spread of cooking oil is found in
Bolapapu. The price during the peak was 100 % higher than the trough. The smallest
amplitude occurred in Palu with the differential price between the peak and the trough
was around 10%.
In relation to the degree of access to market, cocoa prices are high in villages with
good access to market. With rising distance from Palu central market, the cocoa
prices are getting lower. The lowest prices received by farmers in remote village.
Starting from the lowest access to market, average prices of cocoa are (in Rp/kg)
10.063, 10.757, 11610 and 13.200, respectively. Under the assumption of
homogeneity of variances, the ANOVA results that the average prices between
groups are significantly different at one percent significance level.
Coefficient of Variation (CV) is calculated to observe the variability in the prices.
Variability of cocoa shows that remote villages have higher variability of prices. The
110
values of CV arise from 18,73% in central market, 21,73% in villages with good
access to market, 22,54% in villages with low access to market and the highest is
28,45% in the remote area. The lowest producer price level and the highest
variability of IR 66 super are found in remote area.
Flow of food items which are not produced in the villages such as sugar, generally
moves from central market to rural area. Therefore, it can be expected that the prices
in villages will be higher compared to central market. Referring to the equation
presented in the methodology, variability of consumer price is expected will be higher
in central market and decrease according to distance to this market.
The average price of sugar in remote area is the highest and the lowest is in central
market. Under condition of equal variance not assumed, the differences of average
price between groups are highly significant at one percent significance level. The
variability of sugar prices in remote village is the lowest.
Another approach to observe variability is using time series analysis. Before
estimating the variables using econometric ARCH model, a diagnostic testing
(Dickey Fuller - DF test) is used to test whether the natural logarithm of prices of
cocoa, rice and sugar are stationary.
The DF test shows that the natural logarithm of cocoa prices in all villages are
stationary. When time variable is included in the analysis, only in the natural
logarithm of FOB the test is statistically significant. Therefore it can be concluded
that the natural logarithm of FOB prices is stationary around deterministic trend.
Almost all natural logarithm of rice prices is stationary except in Maranata. First
differences of the natural logarithm of thre Maranata prices series remove the non-
stationary problem. Natural logarithm of sugar prices are stationary.
Diagnostic test for ARCH (autoregressive conditional heteroscedasticity) effects in
the autoregressive AR(1), AR(2), with or without trend variable are conducted
Under the hypothesis of H0 = no ARCH effects and H1=ARCH(p) disturbance, the
tests suggest that not all series are subject to ARCH. The two varieties of rice in all
111
areas show the conditional variances are homoscedastic. The same results are found
for sugar, except in Sidondo2. The cocoa price series in Palu, Bolapapu and Rahmat
shows that the model follows an ARCH form, which means that the conditional error
variance is given by an ARCH(1) process or conditional variances are
heterokedastic.
In the presence of ARCH effect, the analysis is continued to estimate the ARCH
model. The point estimates in the mean and variance regression for AR(1) and AR(2)
are statistically significant for cocoa price in Bolapapu and sugar price in Sidondo2.
The slope in the mean equation estimates for the first and the second order
autoregressive indicates serially correlated prices. The coefficients on the lag
variance are positive and significant, indicating that residuals of current and previous
periods are strongly correlated. It shows the presence of conditional
heteroscedasticity in error terms of the mean equation.
112
7. CONCLUSIONS AND POLICY IMPLICATIONS This chapter summarizes the findings in the relation with the research questions as
presented in the previous chapters and attempts to answer question on the policy
implications of the findings.
7.1 Major results
7.1.1 Agricultural market structure The agricultural market structure is explained either by flow of marketing channel,
market concentration or barrier to market entry. Due to some limitations of data,
structures of coffee, maize and fertilizer markets are described in term of flow of
marketing channel. Agricultural commodities markets are organized differently
depend on the characteristics of the commodity.
Cocoa beans are sold from the farmer producer to village or sub-district assemblers.
The Sub-district assemblers handle higher volume of cocoa than that by village
assemblers because they operate in some villages within a sub-district. The village
assemblers limit their procurement only in villages where they live. Market
destination (final consumers) of the beans is export market in Palu. Therefore, the
cocoa beans are then transported from the assemblers to wholesaler/municipal
assemblers in Palu before being exported.
Cocoa market is dominated by few large traders (sub-district assemblers) and exhibits
an oligopsonist market with regard to the farmer producers as indicated by Gini
coefficient and the concentration ratio (CR4). The Gini coefficient is of 0,78. The
Lorenz curve shows that the largest 20% of the traders account for about 80% of the
volume of cocoa traded in the research area. The concentration ratio of CR4 is 82 %.
It means that that the largest four of the traders in the sample accumulated market
share of 82% compared to all market participants in the cocoa market.
Different from cocoa which can be sold directly after drying without any further
process, rice should be milled, either manually or mechanically to remove its husks.
To meet the demand of local and urban consumers for this dominant staple food,
113
there are two different markets for rice, local and urban markets. Therefore, rice is
traded not only in the local market but also transported to the urban market. After
milling, the farmer can sell their rice to local retailer or assembler.
Wholesaler/municipal assembler in Palu or other urban areas are the market
destination of the village and sub-district assemblers.
Rice market in the research area is composed of many traders and few of which are
large traders. Small retailers are operated in local market. For the rice traders in the
sample (village and sub-district assemblers and local village retailer), the CR4 is 86 %
and the Gini coefficient is 0,80. It is indicating an oligopsonist market with regard to
the farmer producers. The largest four rice traders accumulated 86% from total
market share of rice traders in the sample. The Lorenz curve shows that the largest
20% of the traders account for about 90% of the volume of rice traded in the research
area. The rest of the traders account for the remaining rice traded.
The cocoa and rice market shows an indication of an un-equal distribution and
concentrated market. This condition implies of less competitiion due to oligopsonist
nature. Technically speaking, it may lead to inefficiency. However, one should be
careful in interpreting this relationship. Other factors such as barrier to market entry
should be considered before making any judgment about the market condition.
Inequality also shows some extent economies of scale (as part of barrier to market
entry) where big traders are more efficient in term of costs occurred in trading
activities compared to the small ones. Transaction costs occurred in the trading
activities such as searching information and negotiation process which have fixed
cost character, give more advantages towards the big traders. Per-unit basis operation
at a large volume will be less expensive.
Compared to cocoa market which grow rapidly in the research area, markets for
coffee and maize are relatively thin. Market destination of coffee and maize are
limited either to the local consumers or to particular industry in Palu such as food
processing industry (coffee powder) or poultry. There are limited number of traders
engaged in the coffee and maize business in the research area. All maize traders in the
114
research area are village assemblers who tend to limit their procurement operations of
the dried kernel maize to their own village.
Market for fertilizer is not well developed yet. Fertilizer retailers sell only in 2 out of
8 survey villages. Farmers occasionally purchase urea and other agricultural inputs by
cash in Palu when they sell their produce in this central market. For the fertilizer
traders marketing system of fertilizer can be described as a sort of vertical integration.
Retailers who operate in villages perform as rice miller too. The marketing system is
by giving loan to the farmers in kind of fertilizers, mainly urea and the payment will
be made during the harvest, directly after the milling process. Therefore, it provides a
controllable flow between input and output received.
The barrier to market entry can be defined as a potential factor that prevents the new
entrants from entering the market. Technically speaking, market licensing
requirement is one potential factor to market entry. Most traders reported that it is
relatively easy to be involved in agricultural trading as indicated by limited barrier to
market entry. The market license should be held only by big traders whose asset is
more than IDR 200 million.
7.1.2 Agricultural market conduct There are 36 selected traders in the research area involved in trading agricultural
commodities. In general the traders are relatively young with average age of 40 years
and educated. On average, 41.7 %, 30.5% and 27.8% of traders educated at primary,
secondary, and high school, respectively. One third of the traders come from family
with trading background and this gives an additional advantage as they receive
assistance from their parents such as equipments, working capital and knowledge of
entrepreneurships. More than half traders have employees who come from their
relatives. Big traders hire temporary employees during harvest season when huge
amount of produce should be handled.
Own capital is a main source of working capital. Only 25% from all traders have
storage facility which has multiple functions. Although telephone is important tool of
115
communication particularly to search market information, only 8% of total traders use
it. Due to that fact, face-to-face communication is the most important source of
market information. Motorcycle is a transportation facility owned and commonly
used by almost all traders to collect and sell their stocks particularly to transport one
or two sack of their commodities.
No traders are member and involved in formal and informal organizations. Most
traders (69%) are local people, therefore relation with people surrounding their
environment is easier to be managed which in turn influence their success in
conducting their business.
For quantity inspection, weighing is done in the first process, and then it is followed
by simple quality inspection trough visual observation. Price paid by each trader is
determined independently. Cash is well known as payment method to farmers. Theo
other methods, credit and in kind (barter) can be done when long relationships
between trader and farmers are closely tied because for all these contractual
agreements, no written contract is provided. Thus close relation and trust between the
two participants are required precondition.
Traders tend not to specialize on one crop of five commodities surveyed, and not
fully specialized in marketing function performs in the marketing chain. For example,
one trader can perform as village assembler, owns milling machine, and at the same
time he/she retail rice in local market. Apart of trading, most traders have other
activities either in agricultural activity or non-farm enterprise.
More than two third of traders have regular suppliers and next buyers. New entrants
have difficulties of searching new supplier. The problem in searching new buyers is
more pronounced in food crops compared to cash crop.
Most traders collect their supplies within village where they reside. Only 14% travel
more than 10 km to purchase their supplies and this is conducted by large traders who
operate in various villages. Majority of traders travel more than 15 km to sell their
stock. None of traders in the villages are being a member of trade association such as
116
ASKINDO = Asosiasi Kakao Indonesia, The Indonesian Cocoa Association). The
association consists only cocoa exporters as their member.
7.1.3 Agricultural market performance
Generally, farmers and traders in the research area differ in their access to market.
Degree of access to market is distinguished based on two criteria, 1) accessibility by
car, which connected those villages to the provincial capital city, Palu and 2) market
site operated in the villages. With regard to the two infrastructures, all villages can be
arranged into 3 groups, 1) low access - not accessible by car and do not have market;
2) medium - accessible by car and do not have market; and 3) high - accessible by car
and the have market. Central market is the fourth group.
Gross marketing margin is measured by the difference between retail price paid by
final consumers and price received by farmer producers. Based on average price in
October 2003, gross margin of cocoa, rice in rural area and rice in central market are
21.43%, 22.91%, and 34.92%, respectively. The gross marketing margin shows
another concept of farmer share, that is the portion of the price paid by the consumer
that belongs to the farmer producers. Farmer share of cocoa, rice in rural and central
markets are 78.57%, 77.09% and 65.08%. The average gross marketing margin in
2003 for cocoa and cimandi rice are 16.90% and 17.88%.
Farmers are price takers who have limited control over price. If prices decrease
below the variable costs, it will hurt the farmer producers. Since farmers face risk
that prices may lower than expected, calculation of the probability that price is below
break event point is important. The possible declines of some commodities prices to
cover per unit variable cost of production are more than 90% for cocoa and coffee
and up to more 60% for rice. Therefore it can be concluded that there is no short-run
faced by the farmers in the research area. The short run down side risk for tree crops
is low basically because most costs incurred are fixed such as planting of trees and
opportunity cost of land.
117
One of the shortcomings of the gross margin analysis is leaving the remuneration of
fixed factors such as family labour and land. In the long run, however, all costs
incurred in the production (variable and fixed) must be covered.
Agricultural price commodities are seasonally volatile due to due to seasonal
characteristic of production and consumption patterns. Seasonal variability of urea
seems to be stabile almost the year with the seasonal spread between the peak and the
trough in the research area is less than 25%. The price movement in cocoa shows that
the time of peaks and troughs in all villages are relatively similar. However, seasonal
spread is different between villages. The biggest amplitude in 2003 was found in
Rahmat and Sintuwu, whereas the price during the peak was double than price in the
trough. Price of coffee shows a seasonal variability as indicated by the higher price
in the lack season. Bolapapu has the biggest amplitude with the price in the lack
season reaches more than double compared to the price after harvest. The biggest
seasonal spread for two varieties of rice, cimandi and IR 66 super is found in Pandere
with the price during the lean season was 50 % higher than the price after harvest
season. The prices in Pandere in some weeks were relatively higher compared to the
price in Palu. In this season the reversal flow from Palu to the village might occur and
prices in the village became higher compared to prices in Palu. The biggest seasonal
spread of sugar occurred in Sidondo II with the differential price between the lowest
and the highest reached up to 40%. However the highest price was Rp 6000/kg
occurred in Tomado, a remote village. Since the lowest price in Tomado Rp 5167/kg,
the seasonal spread between the peak and the trough was 20%. The lower value of
seasonal spread in Tomado occurred because the lowest price in this village is already
higher compared to the lowest price in other villages. The biggest seasonal spread of
cooking oil is found in Bolapapu. The price during the peak was 100 % higher than
the trough. The smallest amplitude occurred in Palu with the differential price
between the peak and the trough was around 10%.
In relation to the degree of access to market, cocoa prices are high in villages with
good access to market. With rising distance from Palu central market, the cocoa
118
prices are getting lower. The lowest prices received by farmers in remote village.
Starting from the lowest access to market, average prices of cocoa are (in Rp/kg)
10.063, 10.757, 11610 and 13.200, respectively. Under the assumption of
homogeneity of variances, the ANOVA results that the average prices between
groups are significantly different at one percent significance level.
Coefficient of Variation (CV) is calculated to observe the variability in the prices.
Variability of cocoa shows that remote villages have higher variability of prices. The
values of CV arise from 18,73% in central market, 21,73% in villages with good
access to market, 22,54% in villages with low access to market and the highest is
28,45% in the remote area.
The lowest producer price of IR 66 super is found in the remote area. Villages with
good access to market receive higher producer prices. The highest variability is found
in the remote village.
In contrast to commodity prices, prices for basic food items in villages are relatively
higher. Food items that are not produced in the villages are transported from central
market to these areas, therefore prices for consumer goods are higher here. Referring
to the equation presented in the methodology, variability of consumer price is
expected will be higher in central market and decrease according to distance to this
market.
The average price of sugar in remote area is the highest and the lowest is in central
market. Under condition of equal variance not assumed, the differences of average
price between groups are highly significant at one percent significance level. The
variability of sugar prices in remote village is the lowest.
Another approach to observe variability is using time series analysis. Before
estimating the variables using econometric ARCH model, a diagnostic testing
(Dickey Fuller - DF test) is used to test whether the natural logarithm of prices of
cocoa, rice and sugar are stationary.
119
The DF test shows that the natural logarithm of cocoa prices in all villages are
stationary. When time variable is included in the analysis, only in the natural
logarithm of FOB the test is statistically significant. Therefore it can be concluded
that the natural logarithm of FOB prices is stationary around deterministic trend.
Almost all natural logarithm of rice prices is stationary except in Maranata. First
differences of the natural logarithm of thre Maranata prices series remove the non-
stationary problem. Natural logarithm of sugar prices is stationary.
Diagnostic test for ARCH (autoregressive conditional heteroscedasticity) effects in
the autoregressive process AR(1), AR(2), with or without trend variable are
conducted. Under the hypothesis of H0 = no ARCH effects and H1=ARCH(p)
disturbance, the tests suggest that not all series are subject to ARCH. The two
varieties of rice in all areas show that the conditional variances are homoscedastic.
The same results are found for sugar, except in Sidondo2. The cocoa price series in
Palu, Bolapapu and Rahmat shows that the model follows an ARCH form, which
means that the conditional error variance is given by an ARCH(1) process or
conditional variances are heterokedastic.
In the presence of ARCH effect, the analysis is continued to estimate the ARCH
model. The point estimates in the mean and variance regression for AR(1) and AR(2)
are statistically significant for cocoa price in Bolapapu and sugar price in Sidondo2.
The slope in the mean equation estimates for the first and the second order
autoregressive indicates serially correlated prices. The coefficients on the lag
variance are positive and significant, indicating that residuals of current and previous
periods are strongly correlated. It shows the presence of conditional
heteroscedasticity in error terms of the mean equation.
7.2 Policy Implications
Analysis of agricultural markets and the way prices behave provide valuable insight
into the observation whether markets work efficiently and give benefit in particular to
all participants in the markets and in general for the whole society. According to the
120
findings and the results of some statistical and econometrical measurements, there are
some policy implications might be applicable to improve market performance and its
welfare impacts.
1) Farmers in the remote area receive low agricultural prices and pay higher basic
food prices compared to other villages that have better access to markets. The
variability in the producer prices exhibits a similar phenomenon, where the remote
area has higher variability. Based on those phenomena, intervention of government is
required to reduce the price levels and the variability. Development of infrastructure
such as road is necessary to support the flow of either commodities from producer
area to consumers in the urban market or basic food product from urban market to the
villages.
2) The downside risk analysis shows that there is no downside risk in the short run
faced by the farmer producers. The possible decline of prices to cover per unit
variable cost of production are quite high. On average, cocoa, coffee and rice prices
can drop by more than 60% from the average prices. Since only variable cost is
included in the calculation of downside risk, the possible declining of prices are quite
high. However, one should be careful to interpret this result. There are also fixed
costs occurred in the production process, but they are not covered by the analysis.
In the long run production process, the two costs should be covered. Therefore, for
further research it is necessary to cover not only variable but also fixed costs in the
analysis.
3. Due to some limitations in applying structure-conduct-performance (SCP)
paradigm to analyse market condition, the further research on alternative approaches
of market analysis such as transaction costs theory can be considered.
121
REFERENCES
Abott, John. 1993. “Marketing, the rural poor and sustainability” In: Abott, J.C. (ed) Agricultural and Food Marketing in Developing Countries: Selected readings. CTA. The Netherlands.
Ahmed, Raisuddin. 1988. “Pricing Principles and Public Intervention in Domestic Markets” In: Mellor, J.W. and R. Ahmed. 1988. Agricultural Price Policy for Developing Countries. Johns Hopkins University Press. Baltimore.
Ahmed, Raisuddin and Narendra Rustagi. 1987. Marketing and Price Incentives in African and Asia Countries: A Comparison. In: Elz, Dieter (Ed). In Agricultural Marketing Strategy and Pricing Policy. International Bank for Reconstruction and Development. Washington DC.
Akiyama, Takamasa., and Akihiko Nishio. 1996. “Indonesia´s Cocoa Boom: Hands-Off Policy Encourages Smallholder Dynamism”. Policy Research Working Paper 1580. The World Bank.
Alderman , H., and G. Shively. 1996. “ Economic Reform and Food Prices: Evidence from Market in Ghana”. World Development 24.
Anderson, J.R., J. Dillon, J.B. Hardaker. 1977. Agricultural Decision Analysis. The Iowa State University Press. Ames. Iowa.
Anderson, J.R., and J. Dillon. 1992. Risk Analysis in Dry Land Farming System.FAO. Rome.
Aradhyula, S.V, and M.T. Holt. “GARCH Time Series Model: An Application to Retail Livestock Prices”. Western Journal of Agricultural Economics 13.
Bain, Joseph S, 1956. Industrial organization. John Wiley and Son, New York.
Badiane, O., F. Goletti, M. Kherallah, 1997. Agricultural Input and Output: Marketing Reform in African Countries. Final Donor Report. IFPRI: Washington D.C.
Barret, C. 1996. “Urban Bias in Price Risk: The Geography of Food Price Distributions in Low-Income Economies”. The Journal of Development Studies 32.
Baulch, Bob. 1997. “Transfer Costs, Spatial Arbitrage, and Testing for Food Market Integration”. American Journal of Agricultural Economics 79.
Bernard, Russel H. 2000. Social Research Methods. Qualitative and Quantitative Approaches. Sage Publications. London.
Binswanger, H.P., and M. Rosenzweig. 1986. “Behavioral and Material Determinants of Production Relations in Agriculture”. The Journal of Development Studies 22.
122
Black, Thomas R. 1999. Doing Quantitative Reseach in the Social Sciences. An Integrated Approach to Research Design, Measurement and Statistics. Sage Publications. London.
Central Sulawesi Bureau for Statistics. 2003. Gross Regional Domestic Product Statistics. http://sulteng.bps.go.id/pdrb.htm
Colman, D., and T. Young. 1997. Principles of Agricultural Economics Markets and Prices in Less Developed Countries. Cambridge University Press. UK.
Crawford, I.M. 1997. Agricultural and Food Marketing Management. FAO. Rome
Dessalegn, G., T.S. Jayne, and J.D. Shaffer, 1998, Market Structure, Conduct and Performance: Constraints on Reform of Ethiopian Grain Market. Working Paper 8. Grain Market Research Project. Addis Abeba.
Ellis, F. 1992. Agricultural Policies in Developing Countries. Cambridge University Press. Cambridge.
Engle, R. 1982. “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflations. Econometrica 50 No 4 (July 1982): 987-1008.
Fafchamps, M. 1992. Cash Crop Production, Food Price Volatility, and Rural Market Integration in the Third World. American Journal of Agricultural Economics. 74 (1): 90-99.
Fergusson, R. and Glenys J. Fergusson. 1994. Industrial Economics: Issues and Perspectives . The Macmillan Press. Hampsire.
Gujarati, D. 2003. Basic Econometrics 4th Edition. Mc Graw-Hill. New York.
Green, William H. 2000. Econometric Analysis. 4th Edition. Prentice Hall. New Jersey.
Hardaker, J.B., R.B.M. Huirne, and J.R. Anderson. 1997. Coping with Risk in Agriculture. CAB International. Oxon.
Harris-White, B. 1993. “There is Method in My Madness: or is it Vice Versa? Measuring Agricultural Market Performance” In: Abott, J.C. (ed) Agricultural and Food Marketing in Developing Countries: Selected readings. CTA. The Netherlands.
Holt, M. T. , and S.V. Aradhyula. 1990. “Price Risk in Supply Equation: Model: An Application of GARCH Time Series Models to the US Broiler Market”. Southern Economic Journal 57.
Jabbar, M.A, Tambi and Mullins. 1997. A Methodology for characterizing dairy marketing systems. ILRI. Nairobi.
Janssen, W.G and Aad van Tilburg. 1997. “Marketing Analysis for Agricultural Development: Suggestion for a New Research Agenda” In: Wierenga, Berend ,
123
et al. Agricultural Marketing and Consumer Behaviour in a Changing World. Kluwer Academic Publishers. London.
Kahlon, A.S., and George M.V. 1985. Agricultural Marketing and Price Policies. Alllied Publ. Private. New Delhi.
Kohls, R.L. and N.U. Uhl. 1990. Marketing of Agricultural Product, 7th edn. Macmillan, New ork.
Kotler, P. 1994. Marketing Management: Analysis, Planning and Control, 8 edn. Prentice-Hall.nglewood Cliffs, NJ.
Makridakis, S., S.C. Wheelwright, R.J. Hyndman. 1998. Forecasting: Method and Applications. John Wiley and Sons. New York.
McNew, K. 1996. Spatial Market Integration: Definition, Theory and Evidence. Agricultural and Resource Economics Review 25 (April 1996): 1-11.
Mellor, John. 1969. “Agricultural Price Policy in the Context of Economic Development”. American Journal of Agricultural Economics, 5.
Mendoza, M.S and M.W. Rosegrant. 1995. “ Pricing Conduct of Spatially Differentiated Markets”, In: Scott, Gregory J.(Ed). Prices, products and people: Analyzing Agricultural Market in Developing Countries. Boulder. London.
Mendoza, M.S and M.W. Rosegrant. 1995. “ Pricing behaviour in Philippine corn markets : Implications for Market Efficiency . Research report 102. International Food Policy Research Institute. Washington D.C.
Mendoza, M., and C. Randrianarisoa. 1998. Structure and Behavior of Traders and Market Performance. In Structure and Conduct of Major Agricultural Input and Output Markets and Respons to Reforms by Rural Households in Madagascar. Final Donor Report to USAID. IFPRI-FOFIFA. Washington DC.
Menezes, C., C. Geiss, and J. Tressler. 1980. “ Increasing Downside Risk”. American Economic Review 70.
Minten, Bart. 1999. Infrastructure, Market Access, and Agricultural Prices. MSSD Discussion Paper No 26. IFPRI. Washington DC.
Park, A., H. Jin, S. Rozelle, and J. Huang. 2002. Market Emergence and Transition: Arbitrage, Transaction costs, and Autarky in China’s Grain Market. American Journal of Agricultural Economics, 84 (Februry 2002): 67-82.
Pepall, Lynne, D.J. Richards, and G. Norman. 2002. Industrial Organization: Contemporary Theory and Practice. South Western. Ohio.
Pomeroy, R.S., and A.C. Trinidad, 1995. “ Industrial organization and Market Analysis”, In: Scott, Gregory J. (Ed). 1995. Prices, products and people: Analyzing Agricultural Market in Developing Countries. Boulder. London.
Ritson, C. 1997. Agro-food marketing. CAB International. Oxon.
124
Rhodes, V.J and J.L Dauve. 1998. 5th Edition. The Agricultural Marketing System. Holcomb Hathaway. Arizona.
Robison, Lindon J., and Beverly Fleisher. 1984. Decision Analysis in Agricultural Setting: An Introduction. Agricultural Economocs Report. Michigan State University.
Sahn, D., and C. Delgado. 1989. “The Nature and Implication for Market Interventions of Seasonal Food Price Variability.” Seasonal Variability in Third World Agriculture: The Consequences for Food Security. D. Sahn, ed. The Johns Hopkins University Press. Baltimore
Scott, Gregory J.(Ed). 1995. Prices, products and people: Analyzing Agricultural Market in Developing Countries. Boulder. London
Schwarze, Stefan. 2004. Determinants of Income Generating Activities of Rural Households - A Quantitative Study in the Vicinity of the Lore Lindu National Park in Central Sulawesi/Indonesia. Ph.D thesis, Institute of Rural Development, University of Goettingen, Germany.
Sexton, R. J., C. King and H. F. Carman. 1991. Market Integration, Efficiency of Arbitrage, and Imperfect Competition: Methodology and Application to US Celery. American Journal of Agricultural Economics, 73 (August): 568-580.
Shively, G. E. 1996. “Food Price Variability and Economic Reform: An ARCH Approach for Ghana”. American Journal of Agricultural Economics, 78 (February): 126-136.
Stiglitz, J.E. 1997. Principles of Microeconomics. Norton. New York
Wanwali, Sudhir. 1992. Rural Infrastructure, the Settlemet System and Development of the Regional Economy in Southern India. Research Report 91. IFPRI. Washington DC.
Timmer, P. 1986. Getting Prices Right: The Scope and Limits of Agricultural Price Policy. Cornell University Press. New York.
Tomek, W.G., and K.L. Robinson. 2003. Agricultural Product Prices. Cornell University Press. Itacha.
Waldman, Don E., and E. J. Jensen. 1998. Industrial Organization: Theory and Practice. Addison-Wesley. Massachusetts.
Williamson, O.E. 1975. Market and Hierarchies: Analysis and Antitrust Implications. New York. Free Press.
Wooldridge, Jeffrey M. 2003. Introductory Econometrics-A Modern Approach. Thomson South Western. Ohio. USA.
World Bank. 2005. Data and Statistics on Indonesia. http://siteresources.worldbank.org/INTINDONESIA/Resources/Country-Data/National_Inc_Accts.pdf
125
Zeller, Manfred and B. Minten (Ed). 2000. Beyond Market Liberalization. Ashgate Publishing. England.
Zeller, Manfred, S. Schwarze, T. van Rheenen. 2002. Statistical Sampling Frame and Methods Used for the Selection of Villages and Households in the Scope of the Research Programme on Stability of Rain Forest Margins in Indonesia. STORMA Discussion Paper Series No. 1. Bogor, Indonesia.
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APPENDICES Table A1 Producer prices (Rp/kg), standard deviation and variability for different commodities in January–December 2003
Rice Village Cocoa CoffeeMembramo Cimandi IR 46 IR 66 IR 66 S Kepala
Mean 10115.38 4166.67 1739.74 SD 2880.2
526.81 280.78 Tomado
CV 28.47 12.64 16.14 Mean 11321.43 5172.79 2169.64 2000.00 2000.00SD 2747.11 1036.17 151.97 117.85 0.00
Sintuwu
CV 24.26 20.03 7.00 5.89 0.00 Mean 4158.33 2060 2048 1809.09 1900.00 1809.09 2130.56 SD 376.17 187.08 180.56 199.78 212.13 199.78 203.99
Berdikari
CV 9.05 9.08 8.82 11.04 11.16 11.04 9.57 Mean 10057.69 2379.81 2379.81 2179.09 2159.72 2379.81SD 2383.88 232.16 232.16 216.16 32.94 232.16
Sidondo II
CV 23.70 9.76 9.76 9.92 1.53 9.76Mean 12884.69 5322.45 2434.95 2294.64 2195.05 2198.98 2204.08 SD 2189.18 734.43 235.92 219.86 212.74 219.37 215.67
Maranata
CV 16.99 13.80 9.69 9.58 9.69 9.98 9.79 Mean 11330.00 4619.44 2541.67 2541.67 2377.78 2377.78 2377.78 2541.67SD 1927.95 947.66 361.5 361.5 334.44 334.44 334.44 361.5
Pandere
CV 17.02 20.51 14.22
14.22 14.07
14.07
14.07
14.22 Mean 11203.85 5080.77 2473.08
SD 2845.70 1593.62 175.86Bolapapu
CV 25.40 31.37 7.11Mean 10807.69 4921.87 SD 2740.68 978.23
Rahmat
CV 25.36 19.88 Mean 13200.58
SD 2474.30Palu
CV 18.74
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Table A2 Consumer prices (Rp/kg), standard deviation and variability for different commodities in January–December 2003
Rice Village Sugar Superveg oil
Medium veg oil Buri-buri Cimandi IR 46 IR 66 IR 66 S Kepala Mem-
bramo Mean 5833.33 4687.5 3974.36 2208.97 SD 331.13
470.93 255.18 312.85 Tomado
CV 5.68 10.05 6.42 14.16 Mean
4852.38 5557.74 4390.48 2631.71 2657.81 2716.35 2694.44 2552.42 2870.69 2937.50
SD 366.42 1417.70 487.03 296.46 294.51 195.44 222.89 335.45 256.57 161.05Sintuwu
CV 7.55 25.51 11.09 11.27 11.08 7.19 8.27 13.14 8.94 5.48Mean 4286.11 8025.00 7582.50 2378 2378 2102.27 2260.61 2112.50 2502.08 2373SD 433.69 811.14 1283.72 291.93 291.93 324.03 335.06 314.03 314.61 286.31
Berdikari
CV 10.12 10.11 16.93 12.28 12.28 15.41
14.82 14.87 12.57 12.07Mean 5067.31 5725.96 2702.87 2725.96 2519.23 2500 2725.96 2725.96SD 514.77 493.238 226.11 230.94 220.551 88.39 230.94 230.94
Sidondo II
CV 10.16 8.61 8.37
8.47 8.75 3.54 8.47 8.47Mean 4556.12 5380.61 4796.94 2443.88 2385.42 2327.81 2133.52 2595.66 SD 290.59 674.68 531.35 215.47 264.64 243.25 116.2 248.73
Maranata
CV 6.38 12.54 11.08 8.82 11.09 10.45 5.45 9.58 Mean 5041.00 6472.89 5687.50 2736.11 2561.11 2561.11 2566.67 2736.11 2736.11SD 538.87 404.31 647.80 373.31 346.06 346.06 347.07 373.31 373.31
Pandere
CV 10.69 6.25 11.39 13.64 13.51
13.51 13.52 13.64 13.64Mean 4975.00 4980.77 4825.48 2658.65SD
476.04 878.45 804.48
95.86Bolapapu
CV 9.57 17.64 16.67 3.61Mean 4630.77 4400 4172.6 2668.27 2696.08 2569.71 2572.12 2563.73 2824.52 2824.52SD 430.82 385.08 325.48 233.70 227.77 236.35 238.84 236.34 197.27 197.27
Rahmat
CV 9.30 8.75 7.80 8.76 8.45 9.20 9.29 9.22 6.98 6.98Mean
4588.10 5062.79 4779.07 2884.30 3020.35 2720.35 2584.88 2578.49 2919.19 2919.19
SD 352.14 412.32 433.46 195.09 224.40 228.23 249.11 250.03 197.80 197.80Palu
CV 7.68 8.14 9.07 6.76 7.43 8.39 9.64 9.70 6.78 6.78
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Table A3 Correlation between Cimandi rice and fertilizer in Bolapapu mean of
producer price of
cimandi rice
mean of consumer price of
cimandi rice
mean of consumer price of
urea
mean of consumer price of
NPK mean of consumer price of cimandi rice in bolapapu 2003
Pearson Correlation ,868(**) 1 ,932(**) ,242
Sig. (2-tailed) ,000 , ,000 ,448 N 12 12 12 12
** Correlation is significant at the 0.01 level (2-tailed).
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QUESTIONNAIRE ON PRICE SURVEY Village (ID) : _____________________ ( ) Enumerator : __________________________________________ Date of Survey (dd/mm/yy) : ___/___/___(please try to do price survey every Wednesday). Note : Buyer is trader who buy commodities and fertilizer´s seller is trader who sell fertilizers in this village. If no trader performs as previously mentioned, (it means that farmers have to sell their commodities and buy fertilizer outside their village), please note distance (km), time, and transportation used. 1. Producer price
Commodities Price (Rp/kg) Price (Rp/liter) Number of buyer Cocoa Coffee
Membrano Kepala Cimandi Buri-buri IR 46 IR 66
Rice
IR 66 Super 2. Consumer price for agricultural inputs
Agricultural inputs Price (Rp/kg) Price (Rp/sack) Number of seller Urea NPK KCL
3. Consumer price for basic food items
Food items Price (Rp/kg) Price (Rp/liter) Number of seller Membrano Kepala Cimandi Buri-buri IR 46 IR 66
Rice
IR 66 Super Sugar Cooking oil (super quality)
Cooking oil (medium quality)
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