Testing Market Spatial Differentials: An Application to Indonesia‘s Intranational Rice Price
TESTING MARKET SPATIAL DIFFERENTIALS:
AN APPLICATION TO INDONESIA’S
INTRANATIONAL RICE PRICE
NUGROHO ARI SUBEKTI
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015
STATUTORY DECLARATION
I, Nugroho Ari Subekti, hereby declare that the master thesis entitled
―Testing Spatial Market Differentials: An Application to Indonesia‘s Intranational
Rice Price‖ is my original work under the supervision of Advisory Committe and
has not been submitted in any form and to another higher education institution.
This thesis is submitted independently without having used any other source or
means stated therein. Any source of information originated from published and
unpublished work already stated in the part of references of this thesis.
Herewith I passed the thesis copyright to Bogor Agricultural University.
Bogor, August 2015
Nugroho Ari Subekti
H151120381
RINGKASAN
NUGROHO ARI SUBEKTI. Pengujian Perbedaan Pasar Secara Spasial:
Penerapan Pada Harga Beras Domestik di Indonesia. Dibimbing oleh IMAN
SUGEMA and FLORIAN PLOECKL.
Karya tulis ini mempelajari faktor-faktor dari perbedaan harga beras
selama musim panen dan selama Bulan Ramadhan dan Hari Raya Idul Fitri.
Tulisan ini juga mempelajari jumlah dari perubahan harga beras berdasarkan data
yang diambil pada pasar beras eceran selama periode tahun 2008 hingga tahun
2013. Dengan menggunakan analisis regresi Pooled Least Squares (PLS),
ditemukan bahwa selama periode masa panen, perbedaan harga beras antar
propinsi terkait dengan perbedaan karakteristik antar provinsi seperti population,
total produksi beras, pendapatan per kapita, jumlah dari perubahan harga beras
dan jarak. Namun demikian, jarak tidak mempengaruhi secara signifikan terhadap
jumlah dari perubahan harga beras. Disamping itu, migrasi risen tidak memiliki
dampak pada harga beras selama musim panen dan selama Ramadhan dan Idul
Fitri.
Daerah di Pulau Sumatra dan grup dari pulau-pulau di area Indonesia
tengah memiliki harga beras yang cenderung lebih tinggi dari harga beras di
daerah Pulau Jawa dan cenderung memiliki kondisi harga beras yang lebih stabil
dibandingkan di Pulau Jawa. Selanjutnya, frekuensi perubahan harga beras
cenderung turun sejak tahun 2008. Di satu sisi, variabel interaksi (variabel jarak
dan variabel dummy pulau mempengaruhi perbedaan harga beras selama periode
musim panen khususnya di Pulau Sumatra dan Pulau-pulau di kawasan Indonesia
tengah. Di sisi lain, variabel interaksi antara pendapatan perkapita dan dummy
tahun mempengaruhi secara signifikan semua variabel bebas.
Kata kunci: Perbedaan harga beras, musim panen, periode Ramadhan dan Idul
Fitri, frekuensi perubahan harga beras, Pooled Least Square (PLS)
SUMMARY
NUGROHO ARI SUBEKTI. Testing Spatial Market Differentials: An
Application to Indonesia‘s Intranational Rice Price. Under supervision of IMAN
SUGEMA and FLORIAN PLOECKL
This paper examines the determinants of rice price differences during
harvest seasons and the Ramadhan and Eid period. It also studies the frequency of
rice price changes in Indonesian cities using data from retail rice markets during
the period of 2008–2013. Using Pooled Least Squares (PLS) regression analysis,
we found that during harvest season, rice price differences between provinces
respond to variations in provincial characteristics, such as population; total rice
production, per capita income; frequency of rice price changes (price volatility)
and distance. However, distance was not found to be statistically significant
influence frequency of the rice price changes. In addition, recent migration does
not significantly affect rice prices during harvest season and Ramadhan and Eid
period.
Regions in Sumatra Island and Middle Island group have consistently
higher rice prices and lower number rice price changes than regions in Java Island
group. Furthermore, rice price changes have fallen since 2008. On the one hand,
the interaction variables (distance and island dummies) influence rice price
disparity during harvest season particularly in Sumatra Island and Middle Island
group. On the other hand, the interaction variable between percapita income and
year dummies are significant in any regression.
Key words: Rice price differences, harvest season, Ramadhan and Eid period,
Frequency of rice price changes, Pooled Least Squares (PLS)
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TESTING MARKET SPATIAL DIFFERENTIALS:
AN APPLICATION TO INDONESIA’S
INTRANATIONAL RICE PRICE
NUGROHO ARI SUBEKTI
Master Thesis
as a requirement to obtain a degree
Master of Science in
Economics Program
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015
Externally Advisory Committee Examiner: Prof. Dr. Hermanto Siregar, MSc
Thesis Title : Testing Market Spatial Differentials:
An Application to Indonesia‘s Intranational Rice Price
Name
: Nugroho Ari Subekti
NIM
: H151120381
Approved
Advisory Committee,
Dr. Iman Sugema
Dr. Florian Ploeckl
Agreed
Coordinator of Major Economics
Dean of Postgraduate School
Dr. Lukytawati Anggraeni, SP, M.Si
Dr. Ir. Dahrul Syah, MScAgr
Examination Date: 9 July 2015
20156 June 2015
Submission Date:19 Agustus
ACKNOWLEDGEMENTS
I would like to thank my supervisors, Dr. Florian Ploeckl and Dr. Iman
Sugema, for their aspiring guidance, invaluably constructive criticism and friendly
advise during the thesis writing. I am sincerely grateful to them for sharing their
truthful and illuminating views on a number of issues related to the thesis.
I am indebted to Niranjala Seimon, Augustine Bhaskarraj, and everyone at
the International Student Centre for their continuous support and motivation
during my study at The University of Adelaide.
I owe my deepest gratitude to the Ministry of Trade Republic of Indonesia,
Bogor Agricultural University, The University of Adelaide, and the Australian
Award Scholarship for giving me the opportunity to study my master‘s degree.
I would like to acknowledge Elite Editing for giving me editorial assistance
and providing remarkably valuable feedback to improve the quality of my thesis.
The editorial intervention was restricted to Standards D and E of the Australian
Standards for Editing Practice
I would also like to thank ‗Mbak Ida‘ for helpful discussions; the honours
cohort for endless good times; Kahfi ‗sahabatku‘ for making complicated data
easy to analyse; and the rest of my family, especially my wife who cares for my
lovely daughter, for their support and encouragement throughout the year.
Bogor, August 2015
Nugroho Ari Subekti
TABLE OF CONTENTS
TABLE OF CONTENTS
xii
LIST OF TABLES
xiii
LIST OF FIGURES
xiii
1 INTRODUCTION
1
2 BACKGROUND
2
Food Security Policies
Area, Production and Yield
Exports and Imports
Rice Price
Domestic Rice Price
Domestic and World Rice Price Temporal Variations
Domestic Spatial Rice Price Variation
Domestic Rice Price Intervention
2
2
6
7
7
7
9
11
3 LITERATURE REVIEW
Determinants of Price Differences
Model Specification
Description of the Dataset and Descriptive Statistics
11
11
14
17
4 ESTIMATION AND ANALYSIS
19
5 CONCLUSION
26
REFERENCES
27
BIOGRAPHY
30
LIST OF TABLES
Table 2.1 Indonesia, Annual Rice Exports and Imports (1000 tonnes)
Table 2.2 Staple Food Price Variation
Table 2.3 Rice Price Coefficient Variation in 2014
Table 3.1 Description of the Variables
Table 3.2 Descriptive Statistics
Table 3.3 Descriptive Statistics, Fixed Variables
Table 4.1 Rice Price During Harvest Season Estimated Results
Table 4.2 Rice Price During Ramadhan and Eid Period Estimated Results
Table 4.3 Frequency of Rice Price Changes Estimated Results
6
10
11
16
18
18
21
23
25
LIST OF FIGURES
Figure 2.1 Indonesia Paddy Production (Tonnes)
Figure 2.2 Indonesian Paddy Harvested Area (1000 Ha)
Figure 2.3 Indonesian Paddy Productivity (Kg/Ha)
Figure 2.4 Rice Production Areas in Indonesia in 2013 (1000 tonnes)
Figure 2.5 Medium Quality Rice Price, 1969–2002
Figure 2.6 Domestic and World Rice Prices, 1969–2002
Figure 2.7 Domestic and World Rice Prices, 2001–2011
Figure 2.8 Spatial Rice Price Variability 2008–2013
Figure 3.1 Theoretical Framework
3
3
4
5
7
8
9
10
14
1
1 INTRODUCTION
The notion of price convergence refers to the Law of One Price (LOP), according
to which in all locations the value of a given good will be the same when quoted in the
same currency (Dornbush 1985). An international multi-good version of the LOP is the
simplest and strongest form of Purchasing Power Parity (PPP) used in determining the
relative value of currencies and the exchange rate. The exchange rate, according to
absolute PPP, is equal to the ratio of the foreign price to the domestic of a given
aggregate bundle of products. The international PPP literature is the main motivation
behind the literature on city price convergence.
Numerous studies have been conducted to investigate the empirical validity of the
LOP. However, in cross-country studies price dispersions lean to dwell persistently over
time, fading away only relative slowly. In Europe, there is proof of convergence for
traded goods, but not for non-traded goods (Rogers 2001). In North America, the
volatility of prices among Canadian city pairs is slightly lower than for US city pairs.
Rogers (2001) also found that the limit effect on US-Mexican relative prices is larger
than the equivalent effect on US-Canadian prices. The general conclusion of these
studies is that absolute PPP does not hold for non-traded goods and services, or in the
presence of transaction costs and other non-tariff trade barriers (Lafrance & Lawrence
2002).
Previous evidence about the failure of the LOP has led to investigations on the
validity of PPP across cities or regions. The benefits of conducting intranational studies
of price convergence rather than international studies is the lack of trade barriers and
nominal exchange rate fluctuations. Parsely and Wei (1996) estimated 51 commodity
prices from 48 cities in the US and found that distance between cities affected
differential rates of convergence. They showed that neighbouring cities had higher rates
of convergence of relative prices than cities farther apart. Other research has found that
transportation costs limit price convergence between US cities (Cecchetti, Mark &
Sonora 2002).
In recent years, due to rapid advances in regional cooperation, the examination of
market and regional integration has been a primary issue. Intermarket price differentials
are one way to measure the degree of market integration. The market is poorly
integrated if price differentials are large (in relative terms) and well-integrated if they
are small. Price differentials are a primary concern for the Indonesian government,
which has an explicit commitment to promoting food price stability.
This study supplements to the literature by providing new evidence on two
separate related issues: the determinants of market integration across provinces in
Indonesia, and the determinants of price differentials between provinces. It does this by
examining rice commodity markets. The thesis attempts to discover the determinants of
rice price differences between cities in Indonesia. What are the factors that explain rice
price dispersion and the number of rice price changes in Indonesia?
We assess spatial rice price variations using yearly time series of rice price data
from 2008–2013, taken from each region in Indonesia during harvest season and the
Ramadhan and Eid period. It also analyses factors affecting the frequency of rice price
changes in regions in Indonesia. The Pooled Least Square (PLS) test will be used to
evaluate the differences in rice prices within Indonesia and the factors associated with
rice price variability. The goal is to evaluate factors which have influenced rice market
2
price dispersion during the periods of harvest and Eid also the factors which have affect
the frequency of rice price alterations.
The thesis is organised as follows: chapter 2 explains the background of rice as a
commodity in Indonesia. Chapter 3 contains the literature review and also the model
exposition. It describes relevant studies, the dataset and a model specification for testing
rice price differences and number of rice price changes in Indonesian cities. Chapter 4
gives the empirical results and chapter 5 presents conclusions.
2 BACKGROUND
Food Security Policies
Price stability and rice self-sufficiency are the essence of Indonesia‘s food
security programme. This was based on food availability with twin strategies: price
stability and rice self-sufficiency. In order to maintain price stability, in 1967 the
government of Indonesia established the Bureau of Logistics (BULOG). The task of
BULOG was to maintain stable supplies of rice and other major foodstuffs. It did this
first by setting a floor rice price so that the farm gate received a rice price above
production costs. Second, during harvest season BULOG bought rice that was not
absorbed by the market. Third, to assist in achieving self-sufficiency the government
provided significant subsidies through cheap fertiliser, pesticide and financial support
during planting season.
Area, Production and Yield
Although it has fluctuated somewhat, the long-term growth of rice production in
Indonesia has been very impressive, proceeding at an average rate of 2.5% per annum
over 1969–1998. The growth in the average rice yield at 5% per year was very
impressive during 1978–1984, as a result of a special rice intensification programme
(INSUS). This enabled Indonesia to achieve rice self-sufficiency for the first time in
1984. McCulloch and Timmer (2008) examined the achievement of Indonesian selfsufficiency in rice and considered that this was because the government put effort into
subsidising irrigation infrastructure, inputs, and research and extension frameworks,
which helped to sustain a rapid annual growth of rice output. Since then, rice yield has
been declining.
3
Ye
Figure 2.1 Indonesia Paddy Production (Tonnes)
Source: (Indonesia Ministry of Agriculture 2012)
Y
Figure 2.2 Indonesian Paddy Harvested Area (1000 Ha)
Source: (Indonesia Ministry of Agriculture 2012)
4
Ye
Figure 2.3 Indonesian Paddy Productivity (Kg/Ha)
Source: (Indonesia Ministry of Agriculture 2012)
Figures 2.1, 2.2 and 2.3 present respectively the production of rice, the area of rice
harvested and the rice yield in Indonesia from 1970–2012. In Figure 2.3, yield growth
was 3.3% per year over the 1980–1990 period, slowing down to an average rate of 0.7%
during 1991–1997. In 1998, due to adverse weather conditions related to La Nina and El
Nino, the rice yield dropped by 5.2%. However, since then the rice yield has been
gradually increasing.
As shown in Figure 2.2, expansion of the area harvested has been relatively slow,
reflecting increasing competition for limited land for both agricultural and nonagricultural uses in Java, and the high costs of opening new land in the outer islands.
The growth rate in total harvested area was about 1.2% over the 1969–1998 period. Due
to the combined effect of lack of land conversion in Java, bad weather and the economic
crisis, after 1998 the total harvested area of rice was relatively constant. However, over
the last decade (2000 – 2010), the harvested area of rice has been increasing.
Up to 2013, Java still dominated rice production in Indonesia. Throughout the
period 1969–1998, Java accounted for over 50% of the area harvested and around 58%
of rice production. Yields in Java are 30%–40% higher than in other regions. The
dominant role of Java in rice production is attributable to the fact that most of the
irrigated area is located in Java, and as a consequence rice intensification programmes
took place in this region.
5
Aceh
1.956 tonnes
West Kalimantan
1.367 tonnes
North Sumatra
3.628 tonnes
Central Sulawesi
1.020 tonnes
West Sumatra
2.519 tonnes
South Sumatra
3.669 tonnes
Lampung
3.320 tonnes
South Kalimantan
2.093 tonnes
Mollucas
South Sulawesi
5.438 tonnes
Banten
2.045 tonnes
West Java
11.664 tonnes
Central Java
9.648 tonnes
Papua
East Java
12.398 tonnes
West Nusa Tenggara
2.116 tonnes
Figure 2.4 Rice Production Areas in Indonesia in 2013 (1000 tonnes)
Source: (Indonesia Ministry of Agriculture 2012)
6
Exports and Imports
Today, Indonesia has become one of the biggest nett rice importers in the
world. Rice imports have risen steadily since Indonesia declared rice selfsufficiency in 1984. Imports of rice were 24,000 tonnes in 1985, but in the
following year it climbed to 131,000 tonnes (see Table 2.1). The subsequent
increase in imports was a combined result of increased domestic demand and
various domestic supply shocks. In 1997–1998, total rice imports rose
dramatically because of the economic crisis and severe drought. During the last
decade (2000 – 2013), the rice imports fluctuate ranging from the lowest at about
250 thousand tonnes in 2008 and the highes at around 3.5 million tonnes in 2001.
Table 2.1 Indonesia, Annual Rice Exports and Imports (1000 tonnes)
Year
Exports
Imports
Year
1960
0
1064
1987
0
50
1961
0
1025
1988
104
384
1962
0
1043
1989
0
77
1963
0
1010
1990
0
192
1964
0
203
1991
0
539
1965
0
308
1992
472
22
1966
0
354
1993
222
1120
1967
0
628
1994
0
3081
1968
0
604
1995
0
1081
1969
0
956
1996
0
839
1970
0
516
1997
0
5765
1971
0
762
1998
0
3729
1972
0
1638
1999
0
1500
1973
0
1056
2000
0
1500
1974
0
671
2001
0
3500
1975
0
1309
2002
0
2750
1976
0
1989
2003
0
650
1977
0
1824
2004
50
500
1978
0
1934
2005
0
539
1979
14
2040
2006
0
2000
1980
64
543
2007
0
350
1981
0
364
2008
10
250
1982
0
1068
2009
0
1150
1983
0
419
2010
0
3098
1984
392
53
2011
0
1960
1985
212
24
2012
0
650
1986
150
131
2013
0
1225
Source: (United States Departement of Agriculture 2015)
Exports
Imports
7
Rice Price
Domestic Rice Price
The rice price in Indonesia is basically determined on the open market.
However, some seasonal conditions influence the rice price, causing the
government to intervene in the market. BULOG has used its floor and ceiling
price mechanisms to help promote seasonal price stabilisation. Beginning in the
early 1970s, BULOG succeeded in stabilising domestic rice prices by maintaining
a reserve stock (Sidik 2004), even though major events like the food crisis in 1974
and the economic crisis of 1998. For more than thirty years, the price stabilisation
strategy was considered as successful and contributed significantly to socioeconomic stability, providing a solid foundation for economic development.
Figure 2.5 Medium Quality Rice Price, 1969–2002
Source: (Timmer 2004)
Domestic and World Rice Price Temporal Variations
Indonesian rice price data between 1969 and 1997, shown in Figures 2.5 and
2.6, suggests that stabilisation was carried out successfully during this period.
Domestic rice prices were considerably more stable than the price on the world
market (Figure 2.6). Price stability appears to have fostered economic growth,
adding on average half a percentage point to the annual growth rate during 1969–
1991 (Timmer 1997).
8
World
Domestic
Figure 2.6 Domestic and World Rice Prices, 1969–2002
Source: (Dodge and Gemessa 2012)
In 1973–1974, Indonesia was shocked by the world food crisis. Various
government programmes were implemented to boost rice production. The
government supplied improved crop varieties, provided extension services and
subsidised fertilisers and pesticides. BULOG, at the same time, supplied
producers with a profitable environment by stabilising rice prices.
However, following the economic crisis that affected Indonesia in 1997, the
government of Indonesia deregulated trade of food crops and liberalised trade of
commodities including rice. Monopoly rights that had been enjoyed by BULOG
were abolished by mid-1998. BULOG‘s tasks including trade policy, stockholding
and domestic market purchases to set and enforce ceiling and floor prices could no
longer be undertaken (Dawe 2008). This situation affected domestic rice prices,
which rose above import parity, fuelled by panic buying and speculative hoarding.
After the country‘s recovery, rice prices stayed about 30% above the world
price and slightly above the real level of the period of rice price stability from the
mid-1970s to the mid-1990s (see Figure 2.7).
9
Domestic
World
Pr
Figure 2.7 Domestic and World Rice Prices, 2001 - 2011
Source: (Dodge and Gemessa 2012)
The global community was shocked by soaring food prices in 2007, after
three decades of relatively stable price levels. The global crisis also affected
domestic rice prices. By January 2008, domestic prices stood at 94% above their
January 2004 level, and 27% above the world price.
Domestic Spatial Rice Price Variation
Indonesia is geographically dispersed and its many regional economies are
poorly integrated. Economic diversity and geographical conditions affect price
differences between regions. Inconsistent market behaviour can indicate
inefficient distribution systems, since logistics and transportation problems have
an enormous effect on the price and availability of rice.
Figure 2.8 presents rice price series in six different cities representing the
six different islands in Indonesia. Price differences between regions are quite high.
However, regional prices tend to move together in the long term, and price
stability is maintained over time.
10
13,000
12,000
11,000
10,000
9,000
8,000
7,000
6,000
5,000
1/01/08
4/22/08
8/12/08
12/02/08
3/19/09
7/09/09
10/29/09
2/12/10
6/04/10
9/24/10
1/08/11
4/30/11
8/20/11
12/10/11
4/01/12
7/22/12
11/11/12
2/26/13
6/18/13
10/08/13
4,000
Banda Aceh
JAYAPURA
JAKARTA
MANADO
PONTIANAK
KUPANG
Figure 2.8 Spatial Rice Price Variability 2008–2013
Source: (Indonesia Ministry of Trade 2013)
A simple tool for studying differentials between markets is Coeficient
Variation (CV). This coefficient gauges the difference of price across markets and
is procured by dividing the standard deviation of prices in different markets by the
mean of price in the markets (Mark 2010). Table 2.2 shows price variations
between markets and the national average CV for eight staple foods, as calculated
by Indonesian‘s Ministry of Trade. Rice price variation between markets was low
and decreased in the period 2008–2011, but increased slightly at about 1.8 per
cent in 2008 to around 3.0 percent in 2014.
Table 2.2 Staple Food Coefficient Variation
No.
COMMODITY
PRICE VARIATION RATIO (PROVINCE TO NATIONAL)
2008
2009
2010
2011
2012
2013
2014
2.5
1.5
1.4
1.8
2.0
3.0
MEAN
1
RICE
4.5
2
SUGAR
2.7
1.0
1.2
1.3
1.1
2.1
1.4
1.5
3
CORN
1.3
3.3
1.8
2.0
1.9
2.4
2.3
2.1
4
FLOUR
1.1
5.4
2.4
4.4
2.6
1.8
3.0
2.9
5
COOKING OIL
1.1
1.2
1.3
1.7
1.3
1.7
2.2
1.5
6
CHICKEN
1.4
2.7
1.3
1.8
1.7
1.5
2.2
1.8
7
BEEF
1.1
1.5
1.6
1.4
1.1
1.3
1.9
1.4
8
EGG
1.2
2.2
1.3
1.2
1.7
1.4
1.3
1.4
Source: (Indonesia Ministry of Trade 2014)
2.3
11
Based on the average value, Table 2.2 shows that the government has
succeeded in maintaining stability in staple prices at the national level. It can be
seen that the average value of the rice price is 2.3 per cent below the government
target value at 2.5 per cent. By contrast, Table 2.3 portrays price variations within
regions in 2014, revealing that rice prices are quite different across regions and
cities. This suggests that staple food prices do not spatially converge.
Table 2.3 Rice Price Coefficient Variation in 2014
Source: (Indonesia Ministry of Trade 2014)
Domestic Rice Price Intervention
Prior to the 2007 crisis, economists and food experts argued that the private
sector and international trade will promote a mechanism to stabilise supply and
the rice price (Dorosh 2008). When this failed, the government began making
attempts to stabilise prices in 2007 through ad hoc imports by BULOG, and
eventually by giving it greater autonomy to conduct stabilisation efforts
(McCulloch & Timmer 2008). Since 2009, the private sector has been allowed to
export certain rice varieties where the rate of shattering (breaking of rice grain
into pieces) is below 5%. It may also import other types of rice where the rate of
shattering varies from 5% to 25%. At the same time the RASKIN programme, a
rice subsidy for the poor, was expanded to help ameliorate the effects of the rice
price hikes on the poor.
3 LITERATURE REVIEW
Determinants of Price Differences
This section examines the literature on spatial price differences. The
literature has more typically focused on measuring spatial market integration, but
12
here we also consider past research into determinants of spatial price disparities,
with emphasis on empirical studies.
Goodwin and Schroeder (1991) examined market cattle in the US, and
investigated four factors affecting market integration: distance between markets;
the amount of market information reflected in prices in a particular market; the
market volume; and the degree of concentration in the packing market. They
found that distance is a significant deterrent to market integration. In addition, the
meat packing market increased the level of market integration.
Escobal and Cordano (2008) evaluated the effect of investment in
infrastructure on market integration for potatoes. They formally addressed the
question of the determinants of market integration. They found that distance is
important, as well as other factors that are susceptible to policy intervention such
as the availability of information and transport and communications infrastructure.
Goletti, Ahmed and Farid (1995) studied rice market integration and its
determinants in Bangladesh in 64 districts, for the period 1989–1992. Three broad
structural elements of market integration were taken into account: the density of
paved roads, railway infrastructure, road distance between markets, the number of
labour strikes in the area, telephones per capita, density of bank branches;
volatility of policy; and differences in production per capita. The authors revealed
that the distance between markets, telephone density and labour strikes affected
integration negatively. Instead, integration was positively affected market
integration.
Marks (2010) provided a historical account of rice market integration in
Indonesia over the period 1920–2006 among different cities in the archipelago by
using cointegration techniques. Nine cities were put in the study: Jakarta,
Semarang and Surabaya in the island of Java, Medan and Palembang in Sumatra,
Banjarmasin and Pontianak in Kalimantan and Manado and Makassar in
Sulawesi. For many city pairs, they rejected the hypothesis of cointegration:
Mears (1961) stated that the only factor that influence the price dispersion among
regions in Indonesia is the cost of transport (cited in Marks 2010). In the period
1969–1986, there was a quite rapid adjustment to equilibrium when price
differences appeared in different areas. In the subsequent period (1987–2006)
alterations were limited, and the efficient functioning of markets was significantly
less in this period. Although most markets still reached a long-term equilibrium,
the speed of adjustment was slower.
Varela, Aldaz-Carroll and Iacovone (2013) captured provincial
heterogeneity in several dimensions: for example, production conditions,
geography, infrastructure and income per capita (PCI). They found remoteness
and the interaction of remoteness and infrastructure had significant effects on the
rice price. In particular, they found price differences were relatively inelastic to
the degree of remoteness. The effect of remoteness was attenuated by good
transport infrastructure, although the level of infrastructure was statistically
insignificant on its own. Output per capita of rice significantly affected price
differences. Provinces that produced more rice relative to their population faced a
lower price for the product. Productivity differences did not seem to affect price,
nor did spatial contiguity. The effect of differences in the quality of rice consumed
associated with PCI seemed to dominate, as the effect of PCI was positive and
significant.
13
Ismet, Barkley and Llewelyn (1998) examined rice market integration over
time. They found that government intervention in terms of rice procurement
significantly influenced market integration during the post self-sufficiency period
(1985–1993) and the entire period 1982–1993. The results indicated that the larger
the rice procurement, the higher the degree of market integration, suggesting that
the procurement programme significantly affected dynamic price adjustments.
The period following self-sufficiency was a time when national income
grew rapidly, generally between 5–8% annually. In this period PCI was found to
be positively and significantly associated with market integration. This indicated
that economic development encouraged market integration, perhaps reducing the
need for government intervention during times of economic expansion, thereby
reducing programme costs. The role of BULOG in the rice market may therefore
be more important during drought periods or economic downturns. The outcomes
for the entire period suggest that the purchases of rice by BULOG had a
significant effect on market integration. For the post-self-sufficiency period, sales
of rice by BULOG also had a significant positive effect, along with PCI.
However, most other variables were not significant.
Based on the previous elaboration, therefore conceptual framework in this
thesis is created as Figure 3.1
14
Rice Import
Procurement
Government Rice
Price
Government Rice
Stock
Retail Rice
Price
Volatility
Rice
BULOG
Domestic Retail
Rice Price
International
Rice Price
Spatial Retail Rice
Price Disparity
The determinants influence spatial
retail rice price disparity in
indonesia:
1. Population
2. Per capita income
3. Total rice production
4. Frequency of rice price changes
5. Distance
6. Migration
7. Location
Policy Implications
Figure 3.1 Theoretical Framework
Note:
: Research focus
Model Specification
Following the studies conducted by Varela, Aldaz-Carroll and Iacovone
(2013) and Ismet, Barkley and Llewelyn (1998), variables such as total production
in each province, PCI and distance may influence the rice price differential
15
between regions in Indonesia. However, in our model specification we include
other variables; namely population in each province, recent migration, and
number of rice price changes. In addition, island group dummy variables and year
dummy variables are included. We also include variables for interaction between
distance and the island group dummies and between PCI and year dummies.
A Pooled Ordinary Least Square (OLS) regression model has been applied
to evaluate the determinants of spatial price differences. This is also known as a
constant coefficient model, where both intercepts and slopes are constant; where
time series data and cross-sectional data are pooled together, thus assuming that
there is no significant cross-section or temporal effect. For rice prices during
harvest season, our model is as follows:
PHARVESTit = 0 + 1 POP_2010it + 2 PCIit + 3 PROD_TOTit + 4
NUM_VOLit + 5 LOG_DISit + 6 MIG_RECENTit + 7
SUMATRAit + 8 MIDDLEit + 9 EASTit + 10 LGDISMTRAiT +
11 LGDISMIDDLEit + 12 LGDISEASTit + 13 Y08it + 14 Y09it
+ 15 Y10it + 16 Y11it + 17 Y12it + 18 PCI_Y08it + 19
PCI_Y09it + 20 PCI_Y10it + 21 PCI_Y11it + 22 PCI_Y12 + eij
(1)
We use the same model (1) to examine the dependent variable P_EID of
prices during Ramadhan and Eid, and also in (2) to analyse the factors that
influence rice price changes NUM_VOLit in the regions:
NUM_VOLit = 0 + 1 POP_2010it + 2 PCIit + 3 PROD_TOTit + + 4
LOG_DISit + 5 MIG_RECENTit + 6 SUMATRAit + 7
MIDDLEit + 8 EASTit + 9 LGDISMTRAiT + 10
LGDISMIDDLEit + 11 LGDISEASTit + 12 Y08it + 13 Y09it + 14
Y10it + 15 Y11it + 16 Y12it + 17 PCI_Y08it + 18 PCI_Y09it +
19 PCI_Y10it + 20 PCI_Y11it + 21 PCI_Y12 + eij
(2)
The independent and dependent variables are described in table 3.1
Population, migration and PCI capture demand-push effects. Supply conditions
are captured by total production, distance, the frequency of rice price changes and
island dummies.
Island groupings are created to capture the different levels of island
development in Indonesia. We have aggregated Indonesian regions into four
different island groups represented by dummy variables JAVA, SUMATRA,
MIDDLE and EAST. In general, Java Island is more advanced in development
than the other island groupings.
Year dummies are included to capture the different rice price levels each
year between 2008–2012 when compared to 2013. Distance and island dummy
interaction terms aim to capture the effect of infrastructure on price differences
and rice price volatility. Interaction terms between PCI and year dummies are
included to capture the effects of PCI changes in specific years on rice prices and
number of rice price changes.
16
Table 3.1 Description of the Variables
Dependent variable
Symbol
Description/Measurement
Rice price during harvest season
PHARVEST
Rice price during first harvest period in
March (Rupiah per kilogram)
Rice price during Eid period
P_EID
Rice price during Eid period (Rupiah per
kilogram)
Number of rice price changes
NUM_VOL
Frequency of rice price changes during a
year in the particular province
Symbol
POP_2010
Description
Number of inhabitants by province based
on census 2010 in millions
PCI
Real per capita income expressed in
Indonesian rupiah at constant prices for
base year 2000 in each province
PROD_TOT
Rice production is the total annual output
of rice (in kilograms) in each province
MIG_RECENT
Numbers of those whose province of
residence at the time of enumeration was
different from his/her residence 5 years
ago (thousand)
Number of rice price changes
NUM_VOL
Frequency of rice price changes during a
year in the particular province
Distance
LOG_DIS
Natural logarithm of the distance from
the five closest markets which are as a
benchmark namely Medan, Batam,
Jakarta, Surabaya, Makasar.
Group of cities in Sumatra
Island
SUMATRA
1 if located in Sumatra Island; and 0
otherwise
Group of cities in Kalimantan,
Sulawesi, Bali and West Nusa
Tenggara Islands
MIDDLE
1 if located in those islands: and 0
otherwise
Group of cities in Moluccas,
East Nusa Tenggara and Papua
EAST
1 if located in those islands; and 0
otherwise
Independent variable
Province Population
PCI
Total Rice Production
Recent Migration
Year 2008
Y08
1 if year 2008; and 0 otherwise
Year 2009
Y09
1 if year 2009; and 0 otherwise
Year 2010
Y10
1 if year 2010; and 0 otherwise
Year 2011
Y11
1 if year 2011; and 0 otherwise
Y12
1 if year 2012; and 0 otherwise
Year 2012
Interaction variable between
LOG_DIS and SUMATRA
LGDISMTRA
Multiplication regions where are located
in Sumatra island grouping and the
distance variable
Interaction variable between
LOG_DIS and MIDDLE
LGDISMIDDLE
Multiplication regions where are located
in Middle island grouping and the
distance variable
Interaction variable between
LOG_DIS and EAST
LGDISEAST
Multiplication regions where are located
in East island grouping and the distance
variable
Interaction between PCI and
Y08
PCI_Y08
Multiplication between PCI variable and
year dummy 2008
Interaction between PCI and
PCI_Y09
Multiplication between PCI variable and
17
Y09
year dummy 2009
Interaction between PCI and
Y10
PCI_Y10
Multiplication between PCI variable and
year dummy 2010
Interaction between PCI and
Y11
PCI_Y11
Multiplication between PCI variable and
year dummy 2011
Interaction between PCI and
Y12
PCI_Y12
Multiplication between PCI variable and
year dummy 2012
Error term
eij
Error term capturing all other factors
affecting price differences
Description of the Dataset and Descriptive Statistics
Table 3.2 presents the mean, standard deviation, minimum and maximum
values across provinces for the principal variables used in this analysis. We use
retail rice prices during the period 2008–2013 for 30 capital cities in Indonesia
except three provinces including Bangka Belitung, Riau Island and West Papua
due to lack of data. There are a great many rice varieties in Indonesia. The chosen
variety of rice used in this study is medium quality rice, even though its market
name may be different. By choosing a similar quality of rice across regions, it is
assumed that any price variability is due to spatial and seasonal effects and not to
the presence of physical quality and variety differences in the product.
These data have been collected since 2008 by the Indonesian Ministry of
Trade following the food crisis of 2007, in order to monitor commodity price
fluctuations. The other data come from the National Bureau of Statistics of
Indonesia. We use the rice price during harvest time, which is usually in
February–May, and during Eid Mubarak when rice demand increases. In addition,
it also examines the factors that trigger the number of rice price changes in each
region.
18
Table 3.2 Descriptive Statistics
PHARVEST
P_EID
PROD_TOT
PCI
NUM_VOL
SAMPLE: 30 EACH YEAR
MEAN
2008
5505
5665.67
1684.76
7165.43
14.17
2009
5575
5632.54
1795.81
7466.50
14.93
2010
6339
6647.87
2211.88
7807.03
23.90
2011
7431
7264.77
2193.71
8192.75
25.23
2012
7910
7858.73
2300.07
8625.48
19.37
2013
8318
8373.82
2059.50
9048.07
15.63
2008
710
672.94
2391.13
6364.40
13.14
2009
750
652.85
2604.29
6583.89
13.97
2010
697
1238.11
3240.60
6940.36
25.31
2011
1532
769.94
3076.33
7371.67
31.41
2012
990
738.45
3269.23
7808.13
27.79
2013
988
908.44
2990.28
8163.17
17.50
2008
4500
4800
0.069
2518.91
1
2009
4500
4750
0.069
2582.90
1
2010
5000
5000
0.069
2666.02
3
2011
5000
5600
9.516
2767.46
1
2012
5500
6400
11.044
2867.82
1
2013
6500
6500
10.268
2976.62
1
2008
8125
7813
10111.069
37599.56
51
2009
8125
7813
11322.681
38951.56
53
2010
8625
11843.2
11737.07
40939.43
113
2011
12000
8875
11633.891
43195.94
127
2012
10000
9325
12198.707
45509.95
131
2013
11000
11000
12083.162
47774.70
89
SD
MINIMUM
MAXIMUM
Table 3.3 Descriptive Statistics, Fixed Variables
POP_2010
DISTANCE
RECENT MIGRATION
MEAN
7801.49
506.83
6.99
SD
10619.80
36.63
16.60
MINIMUM
1038.10
0.00
-24.92
MAXIMUM
43053.70
2381.00
39.99
It can be seen from Table 3.2 that considerable provincial heterogeneity in
prices occurs in Indonesia. This is clear when one looks at the difference between
19
the maximum and the minimum values of each variable, showing large rice price
differences from province to province. During harvest time, for example, in 2008
the lowest rice price per kilogram was Rp. 4500 and the highest was Rp. 8125, an
increase of almost 100%. The highest harvest rice price occurred in 2012 at Rp.
12,000. During the Eid period, the lowest price was in 2010 at Rp. 5000 and the
highest rice price reached Rp. 11,843 in 2010. Again, during Eid Mubarak 2010
the rice price differed between regions by more than 100%.
Rice production varies among regions in Indonesia. In general, the average
rice yield has increased since 2008 and reached a peak in 2012 of about 2.3
million tonnes. The largest rice crop was approximately 12 million tonnes. The
size of the crop may affect the number of rice price changes. In some regions the
price tends to fluctuate, while in others prices are stable. For instance in 2011, the
minimum number of rice price changes is one but in another place the maximum
number of rice price changes is more than one hundred.
As shown in Table 3.2, PCI has been growing gradually in Indonesia since
2008. However, an immense PCI disparity exists between provinces. For instance
in 2013, the largest PCI was Rp. 47.7 million. In contrast, the lowest PCI was only
Rp. 2.7 million.
This PCI gap creates a migration phenomenon. Some regions may be a
labour force destination and others will be abandoned by their residents. The data
show (Table 3.3) that the maximum number of residents leaving a region is
24,000 and the maximum number arriving in a region is about 40,000 new
persons. The greatest distance is between Jayapura and Surabaya at about 2381
kilometre. A distance of 0 refers to the five main cities as a benchmark.
4 ESTIMATION AND ANALYSIS
This chapter presents the result of the PLS regression analysis. The analysis
will be divided into two parts. First, regressions are conducted with three different
dependent variables, namely PHARVEST, P_EID and NUM_VOL. Second, we
test the result of the PLS by making use of the Robust Standard Errors check. The
usual OLS standard errors are inaccurate with clustered panel data when there are
cluster effects, and Robust Standard Errors are more accurate when cluster
correlations and heteroscedasticity are present (Wooldridge 2012 cited in Vuko
and Cular 2014).
Our model is estimated by pooled OLS regression analysis. With panel data,
usual OLS standard errors are incorrect unless there is no cluster effect and so
robust standard errors that allow cluster correlation (and heteroskedasticity)
should be used. Standard errors clustered by a region are unbiased and produce
correctly sized confidence intervals regardless of the city effect being permanent
or temporary. Consequently, we use White standard errors which are robust to
within cluster correlation. Also, since many panel data sets have more cities than
years, a common approach is to include dummy variables for each time period (to
absorb the time effect). If the time effect is fixed the time dummies completely
remove the correlation between observations in the same time period.
20
In order to simplify the identification of independent variables, they will be
divided into three different classifications. The first group consists of core
independent variables POP_2010, PROD_TOT, PCI, MIG_RECENT, NUM_VOL
and LOG_DIS. The second group includes island dummy variables such as
SUMATRA, MIDDLE and EAST and year dummy variables Y08, Y09, Y10, Y11
and Y12. The last group of variables consists of interaction variables between
LOG_DIS and the island dummy variables to give LGDISMTRA, LGDISMIDDLE
and LGDISEAST, and interactions between PCI and year dummy variables
PCI_Y08, PCI_Y09, PCI_Y09, PCI_Y10, PCI_Y11 and PCI_Y12.
Tables 4.1, 4.2 and 4.3 show combination between result of the PLS
regression and Robust Standard Errors for the regressions on PHARVEST, P_EID
and NUM_VOL respectively. Re-estimation of the three PLS regressions shows
the new standard error estimates, t-statistic values and probabilities, which reflect
the robust calculation of the coefficient covariances.
21
Table 4.1 Rice Price During Harvest Season Estimated Results
Independent
variable
Coefficient
Std. Error
t-statistic
p-value
(1)
(2)
(3)
(4)
(5)
C (constant)
5602.804***
542.1496
10.33442
0.0000
POP_2010
0.036536***
0.012597
2.900370
0.0043
PROD_TOT
-0.047572***
0.013982
-3.402347
0.0008
PCI
0.053008***
0.006474
8.187771
0.0000
NUM_VOL
8.479534***
2.782518
3.047432
0.0027
LOG_DIS
245.1244***
44.11058
5.557043
0.0000
MIG_RECENT
-0.561838
3.927162
-0.143065
0.8864
SUMATRA
2516.635***
592.9720
4.244104
0.0000
MIDDLE
964.1613
379.8524
2.538252
0.0121
EAST
1526.262
4476.482
0.340951
0.7336
LGDISMTRA
-200.9904
48.41895
-4.151070
0.0001
LGDISMIDDLE
-91.20796
43.51625
-2.095952
0.0377
LGDISEAST
-184.2378
660.4519
-0.278957
0.7806
Y08
-2620.510***
5.526762
-474.1493
0.0000
Y09
-2565.732***
5.761473
-445.3258
0.0000
Y10
-1941.223***
12.35299
-157.1460
0.0000
Y11
-1006.074***
18.56306
-54.19761
0.0000
Y12
-463.8062***
6.393216
-72.54663
0.0000
PCI_08
-0.013619***
0.001067
-12.75806
0.0000
PCI_09
-0.013421***
0.000862
-15.56572
0.0000
PCI_10
-0.004497***
0.000384
-11.70217
0.0000
PCI_11
0.010960***
0.000358
30.64635
0.0000
PCI_12
0.006708***
0.000267
25.08640
0.0000
Observations
R-squared
Adjusted R-squared
F-test
Prob > F
180
***,
**,
*
means
significant at
0.637641 1%, 5% and
10%
level
15.31752
respectively
0.000000
0.682177
Source: (Author‘s calculation from PLS Regression and Robust Standard Error)
22
Table 4.1 presents the result of the regression analysis for the rice price
during harvest time. As the table shows, the F-statistic of the model is
significantly different from zero indicating that a subset of the explanatory
variables explains the variation in the rice price during harvest season. The value
of the coefficient of determination (R2) indicates that 68% of the variation in rice
price during the harvest period is explained by the model. In addition, the adjusted
R-square shows that after adjusting for the degrees of freedom the model still
explains about 63% of the total variation of the model. In other words, about 37%
of the systematic variation of PHARVEST is left unaccounted for by the model
which has been captured by the stochastic disturbance term.
The t-ratios for each variable show that the core independent variables
POP_2010, PCI, NUM_VOL and LOG_DIS have positive sign and are
statistically significant at the 1% level, since their calculated t-values were greater
than the critical t-value. This means that higher values of these variables in
regions increase the rice price during the harvest period. For instance, a 1%
increase in distance increases the regional rice price per kilogram by about Rp.
254 ceteris paribus. However, variable PROD_TOT has a different sign. It is
marked negative and statistically significant at the 1% level. It means that total
rice production in each region will decrease the rice price disparity. It is inline to
micro economic theory that addition in supply side will create new equilibrium. In
this context, total rice production will push rice price to reduce. However,
MIG_RECENT variable does not significantly affect the rice price.
Island dummy variables are significant for SUMATRA and MIDDLE. From
the regression, Sumatra Island has rice prices about Rp. 2516 per kilogram higher
than Java Island. Midde Island has rice price about Rp. 964 per kilogram higher
than Java. These results convince that in fact rice prices in Java Island tend to
lower than other islands. All the year dummy variables have negative signs and
are significant at the 1% level. This means that rice prices were significantly
lower from about Rp. 2620 in 2008 to around Rp. 463 in 2012 than in 2013. In
other words, we can say that the rice price rose substantially from 2008–2012 in
the aftermath of the economic crisis.
The interaction variables, LGDISMTRA and LGDISMIDDLE, are significant
at level 1% and 5% respectively, indicating that they have a role influencing the
rice price during the harvest period. In Sumatra regions, distance will grow the
rice price at about Rp. 200 during harvest time. In Middle regions, distance
influence the rice price to increase at about Rp. 91 during yield period. Other
variables interactions, PCI and year dummies, are significant at level 1% and have
positive sign. It means that per capita income influence the rice price disparities
from 2008 to 2012. In other words, we can say that PCI differentiations during
research period triggered different level of rice demand. In the end, this
circumstances created the spatial rice price disparities during harvest period.
23
Table 4.2 Rice Price During Ramadhan and Eid Period Estimated Results
Independent
variable
(1)
Coefficient
(2)
Std. Error
t-statistic
p-value
(3)
(4)
(5)
C (constant)
6140.999***
441.1916
13.91912
0.0000
POP_2010
0.040046***
0.006104
6.561144
0.0000
PROD_TOT
-0.054047***
0.012733
-4.244798
0.0000
PCI
0.046473***
0.004236
10.97059
0.0000
NUM_VOL
10.42811*
5.559893
1.875596
0.0626
LOG_DIS
105.3166***
16.14582
6.522839
0.0000
MIG_RECENT
0.768166
1.764200
0.435419
0.6639
SUMATRA
1848.117***
153.7274
12.02204
0.0000
MIDDLE
522.8583**
236.3624
2.212105
0.0284
EAST
-1010.285
3389.814
-0.298035
0.7661
LGDISMTRA
-75.07856
45.99691
-1.632252
0.1046
LGDISMIDDLE
72.74048
47.98977
1.515749
0.1316
LGDISEAST
250.2861
494.2685
0.506377
0.6133
Y08
-2527.537***
13.11072
-192.7840
0.0000
Y09
-2563.668***
8.451713
-303.3312
0.0000
Y10
-1730.070***
30.60091
-56.53654
0.0000
Y11
-1030.431***
42.34942
-24.33165
0.0000
Y12
-560.5464***
19.18971
-29.21078
0.0000
PCI_08
-0.013688***
0.000901
-15.19277
0.0000
PCI_09
-0.014875***
0.000837
-17.77722
0.0000
PCI_10
-0.002072
0.001510
-1.371440
0.1722
PCI_11
-0.016078***
0.001078
-14.91027
0.0000
PCI_12
0.004541***
0.000335
13.56315
0.0000
Observations
R-squared
Adjusted R-squared
F-test
Prob > F
180
0.688239
0.644553
15.75413
0.000000
***, **, *
means
significant at
1%, 5% and
10%
level
respectively
Source: (Author‘s calculation from PLS Regression and Robust Standard Error)
24
Table 4.2 shows the results of regression on rice prices during the
Ramadhan and Eid period. Similar result has been emerged for F-statistic,
coefficient of determination (R2) and the adjusted R-square wi
AN APPLICATION TO INDONESIA’S
INTRANATIONAL RICE PRICE
NUGROHO ARI SUBEKTI
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015
STATUTORY DECLARATION
I, Nugroho Ari Subekti, hereby declare that the master thesis entitled
―Testing Spatial Market Differentials: An Application to Indonesia‘s Intranational
Rice Price‖ is my original work under the supervision of Advisory Committe and
has not been submitted in any form and to another higher education institution.
This thesis is submitted independently without having used any other source or
means stated therein. Any source of information originated from published and
unpublished work already stated in the part of references of this thesis.
Herewith I passed the thesis copyright to Bogor Agricultural University.
Bogor, August 2015
Nugroho Ari Subekti
H151120381
RINGKASAN
NUGROHO ARI SUBEKTI. Pengujian Perbedaan Pasar Secara Spasial:
Penerapan Pada Harga Beras Domestik di Indonesia. Dibimbing oleh IMAN
SUGEMA and FLORIAN PLOECKL.
Karya tulis ini mempelajari faktor-faktor dari perbedaan harga beras
selama musim panen dan selama Bulan Ramadhan dan Hari Raya Idul Fitri.
Tulisan ini juga mempelajari jumlah dari perubahan harga beras berdasarkan data
yang diambil pada pasar beras eceran selama periode tahun 2008 hingga tahun
2013. Dengan menggunakan analisis regresi Pooled Least Squares (PLS),
ditemukan bahwa selama periode masa panen, perbedaan harga beras antar
propinsi terkait dengan perbedaan karakteristik antar provinsi seperti population,
total produksi beras, pendapatan per kapita, jumlah dari perubahan harga beras
dan jarak. Namun demikian, jarak tidak mempengaruhi secara signifikan terhadap
jumlah dari perubahan harga beras. Disamping itu, migrasi risen tidak memiliki
dampak pada harga beras selama musim panen dan selama Ramadhan dan Idul
Fitri.
Daerah di Pulau Sumatra dan grup dari pulau-pulau di area Indonesia
tengah memiliki harga beras yang cenderung lebih tinggi dari harga beras di
daerah Pulau Jawa dan cenderung memiliki kondisi harga beras yang lebih stabil
dibandingkan di Pulau Jawa. Selanjutnya, frekuensi perubahan harga beras
cenderung turun sejak tahun 2008. Di satu sisi, variabel interaksi (variabel jarak
dan variabel dummy pulau mempengaruhi perbedaan harga beras selama periode
musim panen khususnya di Pulau Sumatra dan Pulau-pulau di kawasan Indonesia
tengah. Di sisi lain, variabel interaksi antara pendapatan perkapita dan dummy
tahun mempengaruhi secara signifikan semua variabel bebas.
Kata kunci: Perbedaan harga beras, musim panen, periode Ramadhan dan Idul
Fitri, frekuensi perubahan harga beras, Pooled Least Square (PLS)
SUMMARY
NUGROHO ARI SUBEKTI. Testing Spatial Market Differentials: An
Application to Indonesia‘s Intranational Rice Price. Under supervision of IMAN
SUGEMA and FLORIAN PLOECKL
This paper examines the determinants of rice price differences during
harvest seasons and the Ramadhan and Eid period. It also studies the frequency of
rice price changes in Indonesian cities using data from retail rice markets during
the period of 2008–2013. Using Pooled Least Squares (PLS) regression analysis,
we found that during harvest season, rice price differences between provinces
respond to variations in provincial characteristics, such as population; total rice
production, per capita income; frequency of rice price changes (price volatility)
and distance. However, distance was not found to be statistically significant
influence frequency of the rice price changes. In addition, recent migration does
not significantly affect rice prices during harvest season and Ramadhan and Eid
period.
Regions in Sumatra Island and Middle Island group have consistently
higher rice prices and lower number rice price changes than regions in Java Island
group. Furthermore, rice price changes have fallen since 2008. On the one hand,
the interaction variables (distance and island dummies) influence rice price
disparity during harvest season particularly in Sumatra Island and Middle Island
group. On the other hand, the interaction variable between percapita income and
year dummies are significant in any regression.
Key words: Rice price differences, harvest season, Ramadhan and Eid period,
Frequency of rice price changes, Pooled Least Squares (PLS)
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TESTING MARKET SPATIAL DIFFERENTIALS:
AN APPLICATION TO INDONESIA’S
INTRANATIONAL RICE PRICE
NUGROHO ARI SUBEKTI
Master Thesis
as a requirement to obtain a degree
Master of Science in
Economics Program
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015
Externally Advisory Committee Examiner: Prof. Dr. Hermanto Siregar, MSc
Thesis Title : Testing Market Spatial Differentials:
An Application to Indonesia‘s Intranational Rice Price
Name
: Nugroho Ari Subekti
NIM
: H151120381
Approved
Advisory Committee,
Dr. Iman Sugema
Dr. Florian Ploeckl
Agreed
Coordinator of Major Economics
Dean of Postgraduate School
Dr. Lukytawati Anggraeni, SP, M.Si
Dr. Ir. Dahrul Syah, MScAgr
Examination Date: 9 July 2015
20156 June 2015
Submission Date:19 Agustus
ACKNOWLEDGEMENTS
I would like to thank my supervisors, Dr. Florian Ploeckl and Dr. Iman
Sugema, for their aspiring guidance, invaluably constructive criticism and friendly
advise during the thesis writing. I am sincerely grateful to them for sharing their
truthful and illuminating views on a number of issues related to the thesis.
I am indebted to Niranjala Seimon, Augustine Bhaskarraj, and everyone at
the International Student Centre for their continuous support and motivation
during my study at The University of Adelaide.
I owe my deepest gratitude to the Ministry of Trade Republic of Indonesia,
Bogor Agricultural University, The University of Adelaide, and the Australian
Award Scholarship for giving me the opportunity to study my master‘s degree.
I would like to acknowledge Elite Editing for giving me editorial assistance
and providing remarkably valuable feedback to improve the quality of my thesis.
The editorial intervention was restricted to Standards D and E of the Australian
Standards for Editing Practice
I would also like to thank ‗Mbak Ida‘ for helpful discussions; the honours
cohort for endless good times; Kahfi ‗sahabatku‘ for making complicated data
easy to analyse; and the rest of my family, especially my wife who cares for my
lovely daughter, for their support and encouragement throughout the year.
Bogor, August 2015
Nugroho Ari Subekti
TABLE OF CONTENTS
TABLE OF CONTENTS
xii
LIST OF TABLES
xiii
LIST OF FIGURES
xiii
1 INTRODUCTION
1
2 BACKGROUND
2
Food Security Policies
Area, Production and Yield
Exports and Imports
Rice Price
Domestic Rice Price
Domestic and World Rice Price Temporal Variations
Domestic Spatial Rice Price Variation
Domestic Rice Price Intervention
2
2
6
7
7
7
9
11
3 LITERATURE REVIEW
Determinants of Price Differences
Model Specification
Description of the Dataset and Descriptive Statistics
11
11
14
17
4 ESTIMATION AND ANALYSIS
19
5 CONCLUSION
26
REFERENCES
27
BIOGRAPHY
30
LIST OF TABLES
Table 2.1 Indonesia, Annual Rice Exports and Imports (1000 tonnes)
Table 2.2 Staple Food Price Variation
Table 2.3 Rice Price Coefficient Variation in 2014
Table 3.1 Description of the Variables
Table 3.2 Descriptive Statistics
Table 3.3 Descriptive Statistics, Fixed Variables
Table 4.1 Rice Price During Harvest Season Estimated Results
Table 4.2 Rice Price During Ramadhan and Eid Period Estimated Results
Table 4.3 Frequency of Rice Price Changes Estimated Results
6
10
11
16
18
18
21
23
25
LIST OF FIGURES
Figure 2.1 Indonesia Paddy Production (Tonnes)
Figure 2.2 Indonesian Paddy Harvested Area (1000 Ha)
Figure 2.3 Indonesian Paddy Productivity (Kg/Ha)
Figure 2.4 Rice Production Areas in Indonesia in 2013 (1000 tonnes)
Figure 2.5 Medium Quality Rice Price, 1969–2002
Figure 2.6 Domestic and World Rice Prices, 1969–2002
Figure 2.7 Domestic and World Rice Prices, 2001–2011
Figure 2.8 Spatial Rice Price Variability 2008–2013
Figure 3.1 Theoretical Framework
3
3
4
5
7
8
9
10
14
1
1 INTRODUCTION
The notion of price convergence refers to the Law of One Price (LOP), according
to which in all locations the value of a given good will be the same when quoted in the
same currency (Dornbush 1985). An international multi-good version of the LOP is the
simplest and strongest form of Purchasing Power Parity (PPP) used in determining the
relative value of currencies and the exchange rate. The exchange rate, according to
absolute PPP, is equal to the ratio of the foreign price to the domestic of a given
aggregate bundle of products. The international PPP literature is the main motivation
behind the literature on city price convergence.
Numerous studies have been conducted to investigate the empirical validity of the
LOP. However, in cross-country studies price dispersions lean to dwell persistently over
time, fading away only relative slowly. In Europe, there is proof of convergence for
traded goods, but not for non-traded goods (Rogers 2001). In North America, the
volatility of prices among Canadian city pairs is slightly lower than for US city pairs.
Rogers (2001) also found that the limit effect on US-Mexican relative prices is larger
than the equivalent effect on US-Canadian prices. The general conclusion of these
studies is that absolute PPP does not hold for non-traded goods and services, or in the
presence of transaction costs and other non-tariff trade barriers (Lafrance & Lawrence
2002).
Previous evidence about the failure of the LOP has led to investigations on the
validity of PPP across cities or regions. The benefits of conducting intranational studies
of price convergence rather than international studies is the lack of trade barriers and
nominal exchange rate fluctuations. Parsely and Wei (1996) estimated 51 commodity
prices from 48 cities in the US and found that distance between cities affected
differential rates of convergence. They showed that neighbouring cities had higher rates
of convergence of relative prices than cities farther apart. Other research has found that
transportation costs limit price convergence between US cities (Cecchetti, Mark &
Sonora 2002).
In recent years, due to rapid advances in regional cooperation, the examination of
market and regional integration has been a primary issue. Intermarket price differentials
are one way to measure the degree of market integration. The market is poorly
integrated if price differentials are large (in relative terms) and well-integrated if they
are small. Price differentials are a primary concern for the Indonesian government,
which has an explicit commitment to promoting food price stability.
This study supplements to the literature by providing new evidence on two
separate related issues: the determinants of market integration across provinces in
Indonesia, and the determinants of price differentials between provinces. It does this by
examining rice commodity markets. The thesis attempts to discover the determinants of
rice price differences between cities in Indonesia. What are the factors that explain rice
price dispersion and the number of rice price changes in Indonesia?
We assess spatial rice price variations using yearly time series of rice price data
from 2008–2013, taken from each region in Indonesia during harvest season and the
Ramadhan and Eid period. It also analyses factors affecting the frequency of rice price
changes in regions in Indonesia. The Pooled Least Square (PLS) test will be used to
evaluate the differences in rice prices within Indonesia and the factors associated with
rice price variability. The goal is to evaluate factors which have influenced rice market
2
price dispersion during the periods of harvest and Eid also the factors which have affect
the frequency of rice price alterations.
The thesis is organised as follows: chapter 2 explains the background of rice as a
commodity in Indonesia. Chapter 3 contains the literature review and also the model
exposition. It describes relevant studies, the dataset and a model specification for testing
rice price differences and number of rice price changes in Indonesian cities. Chapter 4
gives the empirical results and chapter 5 presents conclusions.
2 BACKGROUND
Food Security Policies
Price stability and rice self-sufficiency are the essence of Indonesia‘s food
security programme. This was based on food availability with twin strategies: price
stability and rice self-sufficiency. In order to maintain price stability, in 1967 the
government of Indonesia established the Bureau of Logistics (BULOG). The task of
BULOG was to maintain stable supplies of rice and other major foodstuffs. It did this
first by setting a floor rice price so that the farm gate received a rice price above
production costs. Second, during harvest season BULOG bought rice that was not
absorbed by the market. Third, to assist in achieving self-sufficiency the government
provided significant subsidies through cheap fertiliser, pesticide and financial support
during planting season.
Area, Production and Yield
Although it has fluctuated somewhat, the long-term growth of rice production in
Indonesia has been very impressive, proceeding at an average rate of 2.5% per annum
over 1969–1998. The growth in the average rice yield at 5% per year was very
impressive during 1978–1984, as a result of a special rice intensification programme
(INSUS). This enabled Indonesia to achieve rice self-sufficiency for the first time in
1984. McCulloch and Timmer (2008) examined the achievement of Indonesian selfsufficiency in rice and considered that this was because the government put effort into
subsidising irrigation infrastructure, inputs, and research and extension frameworks,
which helped to sustain a rapid annual growth of rice output. Since then, rice yield has
been declining.
3
Ye
Figure 2.1 Indonesia Paddy Production (Tonnes)
Source: (Indonesia Ministry of Agriculture 2012)
Y
Figure 2.2 Indonesian Paddy Harvested Area (1000 Ha)
Source: (Indonesia Ministry of Agriculture 2012)
4
Ye
Figure 2.3 Indonesian Paddy Productivity (Kg/Ha)
Source: (Indonesia Ministry of Agriculture 2012)
Figures 2.1, 2.2 and 2.3 present respectively the production of rice, the area of rice
harvested and the rice yield in Indonesia from 1970–2012. In Figure 2.3, yield growth
was 3.3% per year over the 1980–1990 period, slowing down to an average rate of 0.7%
during 1991–1997. In 1998, due to adverse weather conditions related to La Nina and El
Nino, the rice yield dropped by 5.2%. However, since then the rice yield has been
gradually increasing.
As shown in Figure 2.2, expansion of the area harvested has been relatively slow,
reflecting increasing competition for limited land for both agricultural and nonagricultural uses in Java, and the high costs of opening new land in the outer islands.
The growth rate in total harvested area was about 1.2% over the 1969–1998 period. Due
to the combined effect of lack of land conversion in Java, bad weather and the economic
crisis, after 1998 the total harvested area of rice was relatively constant. However, over
the last decade (2000 – 2010), the harvested area of rice has been increasing.
Up to 2013, Java still dominated rice production in Indonesia. Throughout the
period 1969–1998, Java accounted for over 50% of the area harvested and around 58%
of rice production. Yields in Java are 30%–40% higher than in other regions. The
dominant role of Java in rice production is attributable to the fact that most of the
irrigated area is located in Java, and as a consequence rice intensification programmes
took place in this region.
5
Aceh
1.956 tonnes
West Kalimantan
1.367 tonnes
North Sumatra
3.628 tonnes
Central Sulawesi
1.020 tonnes
West Sumatra
2.519 tonnes
South Sumatra
3.669 tonnes
Lampung
3.320 tonnes
South Kalimantan
2.093 tonnes
Mollucas
South Sulawesi
5.438 tonnes
Banten
2.045 tonnes
West Java
11.664 tonnes
Central Java
9.648 tonnes
Papua
East Java
12.398 tonnes
West Nusa Tenggara
2.116 tonnes
Figure 2.4 Rice Production Areas in Indonesia in 2013 (1000 tonnes)
Source: (Indonesia Ministry of Agriculture 2012)
6
Exports and Imports
Today, Indonesia has become one of the biggest nett rice importers in the
world. Rice imports have risen steadily since Indonesia declared rice selfsufficiency in 1984. Imports of rice were 24,000 tonnes in 1985, but in the
following year it climbed to 131,000 tonnes (see Table 2.1). The subsequent
increase in imports was a combined result of increased domestic demand and
various domestic supply shocks. In 1997–1998, total rice imports rose
dramatically because of the economic crisis and severe drought. During the last
decade (2000 – 2013), the rice imports fluctuate ranging from the lowest at about
250 thousand tonnes in 2008 and the highes at around 3.5 million tonnes in 2001.
Table 2.1 Indonesia, Annual Rice Exports and Imports (1000 tonnes)
Year
Exports
Imports
Year
1960
0
1064
1987
0
50
1961
0
1025
1988
104
384
1962
0
1043
1989
0
77
1963
0
1010
1990
0
192
1964
0
203
1991
0
539
1965
0
308
1992
472
22
1966
0
354
1993
222
1120
1967
0
628
1994
0
3081
1968
0
604
1995
0
1081
1969
0
956
1996
0
839
1970
0
516
1997
0
5765
1971
0
762
1998
0
3729
1972
0
1638
1999
0
1500
1973
0
1056
2000
0
1500
1974
0
671
2001
0
3500
1975
0
1309
2002
0
2750
1976
0
1989
2003
0
650
1977
0
1824
2004
50
500
1978
0
1934
2005
0
539
1979
14
2040
2006
0
2000
1980
64
543
2007
0
350
1981
0
364
2008
10
250
1982
0
1068
2009
0
1150
1983
0
419
2010
0
3098
1984
392
53
2011
0
1960
1985
212
24
2012
0
650
1986
150
131
2013
0
1225
Source: (United States Departement of Agriculture 2015)
Exports
Imports
7
Rice Price
Domestic Rice Price
The rice price in Indonesia is basically determined on the open market.
However, some seasonal conditions influence the rice price, causing the
government to intervene in the market. BULOG has used its floor and ceiling
price mechanisms to help promote seasonal price stabilisation. Beginning in the
early 1970s, BULOG succeeded in stabilising domestic rice prices by maintaining
a reserve stock (Sidik 2004), even though major events like the food crisis in 1974
and the economic crisis of 1998. For more than thirty years, the price stabilisation
strategy was considered as successful and contributed significantly to socioeconomic stability, providing a solid foundation for economic development.
Figure 2.5 Medium Quality Rice Price, 1969–2002
Source: (Timmer 2004)
Domestic and World Rice Price Temporal Variations
Indonesian rice price data between 1969 and 1997, shown in Figures 2.5 and
2.6, suggests that stabilisation was carried out successfully during this period.
Domestic rice prices were considerably more stable than the price on the world
market (Figure 2.6). Price stability appears to have fostered economic growth,
adding on average half a percentage point to the annual growth rate during 1969–
1991 (Timmer 1997).
8
World
Domestic
Figure 2.6 Domestic and World Rice Prices, 1969–2002
Source: (Dodge and Gemessa 2012)
In 1973–1974, Indonesia was shocked by the world food crisis. Various
government programmes were implemented to boost rice production. The
government supplied improved crop varieties, provided extension services and
subsidised fertilisers and pesticides. BULOG, at the same time, supplied
producers with a profitable environment by stabilising rice prices.
However, following the economic crisis that affected Indonesia in 1997, the
government of Indonesia deregulated trade of food crops and liberalised trade of
commodities including rice. Monopoly rights that had been enjoyed by BULOG
were abolished by mid-1998. BULOG‘s tasks including trade policy, stockholding
and domestic market purchases to set and enforce ceiling and floor prices could no
longer be undertaken (Dawe 2008). This situation affected domestic rice prices,
which rose above import parity, fuelled by panic buying and speculative hoarding.
After the country‘s recovery, rice prices stayed about 30% above the world
price and slightly above the real level of the period of rice price stability from the
mid-1970s to the mid-1990s (see Figure 2.7).
9
Domestic
World
Pr
Figure 2.7 Domestic and World Rice Prices, 2001 - 2011
Source: (Dodge and Gemessa 2012)
The global community was shocked by soaring food prices in 2007, after
three decades of relatively stable price levels. The global crisis also affected
domestic rice prices. By January 2008, domestic prices stood at 94% above their
January 2004 level, and 27% above the world price.
Domestic Spatial Rice Price Variation
Indonesia is geographically dispersed and its many regional economies are
poorly integrated. Economic diversity and geographical conditions affect price
differences between regions. Inconsistent market behaviour can indicate
inefficient distribution systems, since logistics and transportation problems have
an enormous effect on the price and availability of rice.
Figure 2.8 presents rice price series in six different cities representing the
six different islands in Indonesia. Price differences between regions are quite high.
However, regional prices tend to move together in the long term, and price
stability is maintained over time.
10
13,000
12,000
11,000
10,000
9,000
8,000
7,000
6,000
5,000
1/01/08
4/22/08
8/12/08
12/02/08
3/19/09
7/09/09
10/29/09
2/12/10
6/04/10
9/24/10
1/08/11
4/30/11
8/20/11
12/10/11
4/01/12
7/22/12
11/11/12
2/26/13
6/18/13
10/08/13
4,000
Banda Aceh
JAYAPURA
JAKARTA
MANADO
PONTIANAK
KUPANG
Figure 2.8 Spatial Rice Price Variability 2008–2013
Source: (Indonesia Ministry of Trade 2013)
A simple tool for studying differentials between markets is Coeficient
Variation (CV). This coefficient gauges the difference of price across markets and
is procured by dividing the standard deviation of prices in different markets by the
mean of price in the markets (Mark 2010). Table 2.2 shows price variations
between markets and the national average CV for eight staple foods, as calculated
by Indonesian‘s Ministry of Trade. Rice price variation between markets was low
and decreased in the period 2008–2011, but increased slightly at about 1.8 per
cent in 2008 to around 3.0 percent in 2014.
Table 2.2 Staple Food Coefficient Variation
No.
COMMODITY
PRICE VARIATION RATIO (PROVINCE TO NATIONAL)
2008
2009
2010
2011
2012
2013
2014
2.5
1.5
1.4
1.8
2.0
3.0
MEAN
1
RICE
4.5
2
SUGAR
2.7
1.0
1.2
1.3
1.1
2.1
1.4
1.5
3
CORN
1.3
3.3
1.8
2.0
1.9
2.4
2.3
2.1
4
FLOUR
1.1
5.4
2.4
4.4
2.6
1.8
3.0
2.9
5
COOKING OIL
1.1
1.2
1.3
1.7
1.3
1.7
2.2
1.5
6
CHICKEN
1.4
2.7
1.3
1.8
1.7
1.5
2.2
1.8
7
BEEF
1.1
1.5
1.6
1.4
1.1
1.3
1.9
1.4
8
EGG
1.2
2.2
1.3
1.2
1.7
1.4
1.3
1.4
Source: (Indonesia Ministry of Trade 2014)
2.3
11
Based on the average value, Table 2.2 shows that the government has
succeeded in maintaining stability in staple prices at the national level. It can be
seen that the average value of the rice price is 2.3 per cent below the government
target value at 2.5 per cent. By contrast, Table 2.3 portrays price variations within
regions in 2014, revealing that rice prices are quite different across regions and
cities. This suggests that staple food prices do not spatially converge.
Table 2.3 Rice Price Coefficient Variation in 2014
Source: (Indonesia Ministry of Trade 2014)
Domestic Rice Price Intervention
Prior to the 2007 crisis, economists and food experts argued that the private
sector and international trade will promote a mechanism to stabilise supply and
the rice price (Dorosh 2008). When this failed, the government began making
attempts to stabilise prices in 2007 through ad hoc imports by BULOG, and
eventually by giving it greater autonomy to conduct stabilisation efforts
(McCulloch & Timmer 2008). Since 2009, the private sector has been allowed to
export certain rice varieties where the rate of shattering (breaking of rice grain
into pieces) is below 5%. It may also import other types of rice where the rate of
shattering varies from 5% to 25%. At the same time the RASKIN programme, a
rice subsidy for the poor, was expanded to help ameliorate the effects of the rice
price hikes on the poor.
3 LITERATURE REVIEW
Determinants of Price Differences
This section examines the literature on spatial price differences. The
literature has more typically focused on measuring spatial market integration, but
12
here we also consider past research into determinants of spatial price disparities,
with emphasis on empirical studies.
Goodwin and Schroeder (1991) examined market cattle in the US, and
investigated four factors affecting market integration: distance between markets;
the amount of market information reflected in prices in a particular market; the
market volume; and the degree of concentration in the packing market. They
found that distance is a significant deterrent to market integration. In addition, the
meat packing market increased the level of market integration.
Escobal and Cordano (2008) evaluated the effect of investment in
infrastructure on market integration for potatoes. They formally addressed the
question of the determinants of market integration. They found that distance is
important, as well as other factors that are susceptible to policy intervention such
as the availability of information and transport and communications infrastructure.
Goletti, Ahmed and Farid (1995) studied rice market integration and its
determinants in Bangladesh in 64 districts, for the period 1989–1992. Three broad
structural elements of market integration were taken into account: the density of
paved roads, railway infrastructure, road distance between markets, the number of
labour strikes in the area, telephones per capita, density of bank branches;
volatility of policy; and differences in production per capita. The authors revealed
that the distance between markets, telephone density and labour strikes affected
integration negatively. Instead, integration was positively affected market
integration.
Marks (2010) provided a historical account of rice market integration in
Indonesia over the period 1920–2006 among different cities in the archipelago by
using cointegration techniques. Nine cities were put in the study: Jakarta,
Semarang and Surabaya in the island of Java, Medan and Palembang in Sumatra,
Banjarmasin and Pontianak in Kalimantan and Manado and Makassar in
Sulawesi. For many city pairs, they rejected the hypothesis of cointegration:
Mears (1961) stated that the only factor that influence the price dispersion among
regions in Indonesia is the cost of transport (cited in Marks 2010). In the period
1969–1986, there was a quite rapid adjustment to equilibrium when price
differences appeared in different areas. In the subsequent period (1987–2006)
alterations were limited, and the efficient functioning of markets was significantly
less in this period. Although most markets still reached a long-term equilibrium,
the speed of adjustment was slower.
Varela, Aldaz-Carroll and Iacovone (2013) captured provincial
heterogeneity in several dimensions: for example, production conditions,
geography, infrastructure and income per capita (PCI). They found remoteness
and the interaction of remoteness and infrastructure had significant effects on the
rice price. In particular, they found price differences were relatively inelastic to
the degree of remoteness. The effect of remoteness was attenuated by good
transport infrastructure, although the level of infrastructure was statistically
insignificant on its own. Output per capita of rice significantly affected price
differences. Provinces that produced more rice relative to their population faced a
lower price for the product. Productivity differences did not seem to affect price,
nor did spatial contiguity. The effect of differences in the quality of rice consumed
associated with PCI seemed to dominate, as the effect of PCI was positive and
significant.
13
Ismet, Barkley and Llewelyn (1998) examined rice market integration over
time. They found that government intervention in terms of rice procurement
significantly influenced market integration during the post self-sufficiency period
(1985–1993) and the entire period 1982–1993. The results indicated that the larger
the rice procurement, the higher the degree of market integration, suggesting that
the procurement programme significantly affected dynamic price adjustments.
The period following self-sufficiency was a time when national income
grew rapidly, generally between 5–8% annually. In this period PCI was found to
be positively and significantly associated with market integration. This indicated
that economic development encouraged market integration, perhaps reducing the
need for government intervention during times of economic expansion, thereby
reducing programme costs. The role of BULOG in the rice market may therefore
be more important during drought periods or economic downturns. The outcomes
for the entire period suggest that the purchases of rice by BULOG had a
significant effect on market integration. For the post-self-sufficiency period, sales
of rice by BULOG also had a significant positive effect, along with PCI.
However, most other variables were not significant.
Based on the previous elaboration, therefore conceptual framework in this
thesis is created as Figure 3.1
14
Rice Import
Procurement
Government Rice
Price
Government Rice
Stock
Retail Rice
Price
Volatility
Rice
BULOG
Domestic Retail
Rice Price
International
Rice Price
Spatial Retail Rice
Price Disparity
The determinants influence spatial
retail rice price disparity in
indonesia:
1. Population
2. Per capita income
3. Total rice production
4. Frequency of rice price changes
5. Distance
6. Migration
7. Location
Policy Implications
Figure 3.1 Theoretical Framework
Note:
: Research focus
Model Specification
Following the studies conducted by Varela, Aldaz-Carroll and Iacovone
(2013) and Ismet, Barkley and Llewelyn (1998), variables such as total production
in each province, PCI and distance may influence the rice price differential
15
between regions in Indonesia. However, in our model specification we include
other variables; namely population in each province, recent migration, and
number of rice price changes. In addition, island group dummy variables and year
dummy variables are included. We also include variables for interaction between
distance and the island group dummies and between PCI and year dummies.
A Pooled Ordinary Least Square (OLS) regression model has been applied
to evaluate the determinants of spatial price differences. This is also known as a
constant coefficient model, where both intercepts and slopes are constant; where
time series data and cross-sectional data are pooled together, thus assuming that
there is no significant cross-section or temporal effect. For rice prices during
harvest season, our model is as follows:
PHARVESTit = 0 + 1 POP_2010it + 2 PCIit + 3 PROD_TOTit + 4
NUM_VOLit + 5 LOG_DISit + 6 MIG_RECENTit + 7
SUMATRAit + 8 MIDDLEit + 9 EASTit + 10 LGDISMTRAiT +
11 LGDISMIDDLEit + 12 LGDISEASTit + 13 Y08it + 14 Y09it
+ 15 Y10it + 16 Y11it + 17 Y12it + 18 PCI_Y08it + 19
PCI_Y09it + 20 PCI_Y10it + 21 PCI_Y11it + 22 PCI_Y12 + eij
(1)
We use the same model (1) to examine the dependent variable P_EID of
prices during Ramadhan and Eid, and also in (2) to analyse the factors that
influence rice price changes NUM_VOLit in the regions:
NUM_VOLit = 0 + 1 POP_2010it + 2 PCIit + 3 PROD_TOTit + + 4
LOG_DISit + 5 MIG_RECENTit + 6 SUMATRAit + 7
MIDDLEit + 8 EASTit + 9 LGDISMTRAiT + 10
LGDISMIDDLEit + 11 LGDISEASTit + 12 Y08it + 13 Y09it + 14
Y10it + 15 Y11it + 16 Y12it + 17 PCI_Y08it + 18 PCI_Y09it +
19 PCI_Y10it + 20 PCI_Y11it + 21 PCI_Y12 + eij
(2)
The independent and dependent variables are described in table 3.1
Population, migration and PCI capture demand-push effects. Supply conditions
are captured by total production, distance, the frequency of rice price changes and
island dummies.
Island groupings are created to capture the different levels of island
development in Indonesia. We have aggregated Indonesian regions into four
different island groups represented by dummy variables JAVA, SUMATRA,
MIDDLE and EAST. In general, Java Island is more advanced in development
than the other island groupings.
Year dummies are included to capture the different rice price levels each
year between 2008–2012 when compared to 2013. Distance and island dummy
interaction terms aim to capture the effect of infrastructure on price differences
and rice price volatility. Interaction terms between PCI and year dummies are
included to capture the effects of PCI changes in specific years on rice prices and
number of rice price changes.
16
Table 3.1 Description of the Variables
Dependent variable
Symbol
Description/Measurement
Rice price during harvest season
PHARVEST
Rice price during first harvest period in
March (Rupiah per kilogram)
Rice price during Eid period
P_EID
Rice price during Eid period (Rupiah per
kilogram)
Number of rice price changes
NUM_VOL
Frequency of rice price changes during a
year in the particular province
Symbol
POP_2010
Description
Number of inhabitants by province based
on census 2010 in millions
PCI
Real per capita income expressed in
Indonesian rupiah at constant prices for
base year 2000 in each province
PROD_TOT
Rice production is the total annual output
of rice (in kilograms) in each province
MIG_RECENT
Numbers of those whose province of
residence at the time of enumeration was
different from his/her residence 5 years
ago (thousand)
Number of rice price changes
NUM_VOL
Frequency of rice price changes during a
year in the particular province
Distance
LOG_DIS
Natural logarithm of the distance from
the five closest markets which are as a
benchmark namely Medan, Batam,
Jakarta, Surabaya, Makasar.
Group of cities in Sumatra
Island
SUMATRA
1 if located in Sumatra Island; and 0
otherwise
Group of cities in Kalimantan,
Sulawesi, Bali and West Nusa
Tenggara Islands
MIDDLE
1 if located in those islands: and 0
otherwise
Group of cities in Moluccas,
East Nusa Tenggara and Papua
EAST
1 if located in those islands; and 0
otherwise
Independent variable
Province Population
PCI
Total Rice Production
Recent Migration
Year 2008
Y08
1 if year 2008; and 0 otherwise
Year 2009
Y09
1 if year 2009; and 0 otherwise
Year 2010
Y10
1 if year 2010; and 0 otherwise
Year 2011
Y11
1 if year 2011; and 0 otherwise
Y12
1 if year 2012; and 0 otherwise
Year 2012
Interaction variable between
LOG_DIS and SUMATRA
LGDISMTRA
Multiplication regions where are located
in Sumatra island grouping and the
distance variable
Interaction variable between
LOG_DIS and MIDDLE
LGDISMIDDLE
Multiplication regions where are located
in Middle island grouping and the
distance variable
Interaction variable between
LOG_DIS and EAST
LGDISEAST
Multiplication regions where are located
in East island grouping and the distance
variable
Interaction between PCI and
Y08
PCI_Y08
Multiplication between PCI variable and
year dummy 2008
Interaction between PCI and
PCI_Y09
Multiplication between PCI variable and
17
Y09
year dummy 2009
Interaction between PCI and
Y10
PCI_Y10
Multiplication between PCI variable and
year dummy 2010
Interaction between PCI and
Y11
PCI_Y11
Multiplication between PCI variable and
year dummy 2011
Interaction between PCI and
Y12
PCI_Y12
Multiplication between PCI variable and
year dummy 2012
Error term
eij
Error term capturing all other factors
affecting price differences
Description of the Dataset and Descriptive Statistics
Table 3.2 presents the mean, standard deviation, minimum and maximum
values across provinces for the principal variables used in this analysis. We use
retail rice prices during the period 2008–2013 for 30 capital cities in Indonesia
except three provinces including Bangka Belitung, Riau Island and West Papua
due to lack of data. There are a great many rice varieties in Indonesia. The chosen
variety of rice used in this study is medium quality rice, even though its market
name may be different. By choosing a similar quality of rice across regions, it is
assumed that any price variability is due to spatial and seasonal effects and not to
the presence of physical quality and variety differences in the product.
These data have been collected since 2008 by the Indonesian Ministry of
Trade following the food crisis of 2007, in order to monitor commodity price
fluctuations. The other data come from the National Bureau of Statistics of
Indonesia. We use the rice price during harvest time, which is usually in
February–May, and during Eid Mubarak when rice demand increases. In addition,
it also examines the factors that trigger the number of rice price changes in each
region.
18
Table 3.2 Descriptive Statistics
PHARVEST
P_EID
PROD_TOT
PCI
NUM_VOL
SAMPLE: 30 EACH YEAR
MEAN
2008
5505
5665.67
1684.76
7165.43
14.17
2009
5575
5632.54
1795.81
7466.50
14.93
2010
6339
6647.87
2211.88
7807.03
23.90
2011
7431
7264.77
2193.71
8192.75
25.23
2012
7910
7858.73
2300.07
8625.48
19.37
2013
8318
8373.82
2059.50
9048.07
15.63
2008
710
672.94
2391.13
6364.40
13.14
2009
750
652.85
2604.29
6583.89
13.97
2010
697
1238.11
3240.60
6940.36
25.31
2011
1532
769.94
3076.33
7371.67
31.41
2012
990
738.45
3269.23
7808.13
27.79
2013
988
908.44
2990.28
8163.17
17.50
2008
4500
4800
0.069
2518.91
1
2009
4500
4750
0.069
2582.90
1
2010
5000
5000
0.069
2666.02
3
2011
5000
5600
9.516
2767.46
1
2012
5500
6400
11.044
2867.82
1
2013
6500
6500
10.268
2976.62
1
2008
8125
7813
10111.069
37599.56
51
2009
8125
7813
11322.681
38951.56
53
2010
8625
11843.2
11737.07
40939.43
113
2011
12000
8875
11633.891
43195.94
127
2012
10000
9325
12198.707
45509.95
131
2013
11000
11000
12083.162
47774.70
89
SD
MINIMUM
MAXIMUM
Table 3.3 Descriptive Statistics, Fixed Variables
POP_2010
DISTANCE
RECENT MIGRATION
MEAN
7801.49
506.83
6.99
SD
10619.80
36.63
16.60
MINIMUM
1038.10
0.00
-24.92
MAXIMUM
43053.70
2381.00
39.99
It can be seen from Table 3.2 that considerable provincial heterogeneity in
prices occurs in Indonesia. This is clear when one looks at the difference between
19
the maximum and the minimum values of each variable, showing large rice price
differences from province to province. During harvest time, for example, in 2008
the lowest rice price per kilogram was Rp. 4500 and the highest was Rp. 8125, an
increase of almost 100%. The highest harvest rice price occurred in 2012 at Rp.
12,000. During the Eid period, the lowest price was in 2010 at Rp. 5000 and the
highest rice price reached Rp. 11,843 in 2010. Again, during Eid Mubarak 2010
the rice price differed between regions by more than 100%.
Rice production varies among regions in Indonesia. In general, the average
rice yield has increased since 2008 and reached a peak in 2012 of about 2.3
million tonnes. The largest rice crop was approximately 12 million tonnes. The
size of the crop may affect the number of rice price changes. In some regions the
price tends to fluctuate, while in others prices are stable. For instance in 2011, the
minimum number of rice price changes is one but in another place the maximum
number of rice price changes is more than one hundred.
As shown in Table 3.2, PCI has been growing gradually in Indonesia since
2008. However, an immense PCI disparity exists between provinces. For instance
in 2013, the largest PCI was Rp. 47.7 million. In contrast, the lowest PCI was only
Rp. 2.7 million.
This PCI gap creates a migration phenomenon. Some regions may be a
labour force destination and others will be abandoned by their residents. The data
show (Table 3.3) that the maximum number of residents leaving a region is
24,000 and the maximum number arriving in a region is about 40,000 new
persons. The greatest distance is between Jayapura and Surabaya at about 2381
kilometre. A distance of 0 refers to the five main cities as a benchmark.
4 ESTIMATION AND ANALYSIS
This chapter presents the result of the PLS regression analysis. The analysis
will be divided into two parts. First, regressions are conducted with three different
dependent variables, namely PHARVEST, P_EID and NUM_VOL. Second, we
test the result of the PLS by making use of the Robust Standard Errors check. The
usual OLS standard errors are inaccurate with clustered panel data when there are
cluster effects, and Robust Standard Errors are more accurate when cluster
correlations and heteroscedasticity are present (Wooldridge 2012 cited in Vuko
and Cular 2014).
Our model is estimated by pooled OLS regression analysis. With panel data,
usual OLS standard errors are incorrect unless there is no cluster effect and so
robust standard errors that allow cluster correlation (and heteroskedasticity)
should be used. Standard errors clustered by a region are unbiased and produce
correctly sized confidence intervals regardless of the city effect being permanent
or temporary. Consequently, we use White standard errors which are robust to
within cluster correlation. Also, since many panel data sets have more cities than
years, a common approach is to include dummy variables for each time period (to
absorb the time effect). If the time effect is fixed the time dummies completely
remove the correlation between observations in the same time period.
20
In order to simplify the identification of independent variables, they will be
divided into three different classifications. The first group consists of core
independent variables POP_2010, PROD_TOT, PCI, MIG_RECENT, NUM_VOL
and LOG_DIS. The second group includes island dummy variables such as
SUMATRA, MIDDLE and EAST and year dummy variables Y08, Y09, Y10, Y11
and Y12. The last group of variables consists of interaction variables between
LOG_DIS and the island dummy variables to give LGDISMTRA, LGDISMIDDLE
and LGDISEAST, and interactions between PCI and year dummy variables
PCI_Y08, PCI_Y09, PCI_Y09, PCI_Y10, PCI_Y11 and PCI_Y12.
Tables 4.1, 4.2 and 4.3 show combination between result of the PLS
regression and Robust Standard Errors for the regressions on PHARVEST, P_EID
and NUM_VOL respectively. Re-estimation of the three PLS regressions shows
the new standard error estimates, t-statistic values and probabilities, which reflect
the robust calculation of the coefficient covariances.
21
Table 4.1 Rice Price During Harvest Season Estimated Results
Independent
variable
Coefficient
Std. Error
t-statistic
p-value
(1)
(2)
(3)
(4)
(5)
C (constant)
5602.804***
542.1496
10.33442
0.0000
POP_2010
0.036536***
0.012597
2.900370
0.0043
PROD_TOT
-0.047572***
0.013982
-3.402347
0.0008
PCI
0.053008***
0.006474
8.187771
0.0000
NUM_VOL
8.479534***
2.782518
3.047432
0.0027
LOG_DIS
245.1244***
44.11058
5.557043
0.0000
MIG_RECENT
-0.561838
3.927162
-0.143065
0.8864
SUMATRA
2516.635***
592.9720
4.244104
0.0000
MIDDLE
964.1613
379.8524
2.538252
0.0121
EAST
1526.262
4476.482
0.340951
0.7336
LGDISMTRA
-200.9904
48.41895
-4.151070
0.0001
LGDISMIDDLE
-91.20796
43.51625
-2.095952
0.0377
LGDISEAST
-184.2378
660.4519
-0.278957
0.7806
Y08
-2620.510***
5.526762
-474.1493
0.0000
Y09
-2565.732***
5.761473
-445.3258
0.0000
Y10
-1941.223***
12.35299
-157.1460
0.0000
Y11
-1006.074***
18.56306
-54.19761
0.0000
Y12
-463.8062***
6.393216
-72.54663
0.0000
PCI_08
-0.013619***
0.001067
-12.75806
0.0000
PCI_09
-0.013421***
0.000862
-15.56572
0.0000
PCI_10
-0.004497***
0.000384
-11.70217
0.0000
PCI_11
0.010960***
0.000358
30.64635
0.0000
PCI_12
0.006708***
0.000267
25.08640
0.0000
Observations
R-squared
Adjusted R-squared
F-test
Prob > F
180
***,
**,
*
means
significant at
0.637641 1%, 5% and
10%
level
15.31752
respectively
0.000000
0.682177
Source: (Author‘s calculation from PLS Regression and Robust Standard Error)
22
Table 4.1 presents the result of the regression analysis for the rice price
during harvest time. As the table shows, the F-statistic of the model is
significantly different from zero indicating that a subset of the explanatory
variables explains the variation in the rice price during harvest season. The value
of the coefficient of determination (R2) indicates that 68% of the variation in rice
price during the harvest period is explained by the model. In addition, the adjusted
R-square shows that after adjusting for the degrees of freedom the model still
explains about 63% of the total variation of the model. In other words, about 37%
of the systematic variation of PHARVEST is left unaccounted for by the model
which has been captured by the stochastic disturbance term.
The t-ratios for each variable show that the core independent variables
POP_2010, PCI, NUM_VOL and LOG_DIS have positive sign and are
statistically significant at the 1% level, since their calculated t-values were greater
than the critical t-value. This means that higher values of these variables in
regions increase the rice price during the harvest period. For instance, a 1%
increase in distance increases the regional rice price per kilogram by about Rp.
254 ceteris paribus. However, variable PROD_TOT has a different sign. It is
marked negative and statistically significant at the 1% level. It means that total
rice production in each region will decrease the rice price disparity. It is inline to
micro economic theory that addition in supply side will create new equilibrium. In
this context, total rice production will push rice price to reduce. However,
MIG_RECENT variable does not significantly affect the rice price.
Island dummy variables are significant for SUMATRA and MIDDLE. From
the regression, Sumatra Island has rice prices about Rp. 2516 per kilogram higher
than Java Island. Midde Island has rice price about Rp. 964 per kilogram higher
than Java. These results convince that in fact rice prices in Java Island tend to
lower than other islands. All the year dummy variables have negative signs and
are significant at the 1% level. This means that rice prices were significantly
lower from about Rp. 2620 in 2008 to around Rp. 463 in 2012 than in 2013. In
other words, we can say that the rice price rose substantially from 2008–2012 in
the aftermath of the economic crisis.
The interaction variables, LGDISMTRA and LGDISMIDDLE, are significant
at level 1% and 5% respectively, indicating that they have a role influencing the
rice price during the harvest period. In Sumatra regions, distance will grow the
rice price at about Rp. 200 during harvest time. In Middle regions, distance
influence the rice price to increase at about Rp. 91 during yield period. Other
variables interactions, PCI and year dummies, are significant at level 1% and have
positive sign. It means that per capita income influence the rice price disparities
from 2008 to 2012. In other words, we can say that PCI differentiations during
research period triggered different level of rice demand. In the end, this
circumstances created the spatial rice price disparities during harvest period.
23
Table 4.2 Rice Price During Ramadhan and Eid Period Estimated Results
Independent
variable
(1)
Coefficient
(2)
Std. Error
t-statistic
p-value
(3)
(4)
(5)
C (constant)
6140.999***
441.1916
13.91912
0.0000
POP_2010
0.040046***
0.006104
6.561144
0.0000
PROD_TOT
-0.054047***
0.012733
-4.244798
0.0000
PCI
0.046473***
0.004236
10.97059
0.0000
NUM_VOL
10.42811*
5.559893
1.875596
0.0626
LOG_DIS
105.3166***
16.14582
6.522839
0.0000
MIG_RECENT
0.768166
1.764200
0.435419
0.6639
SUMATRA
1848.117***
153.7274
12.02204
0.0000
MIDDLE
522.8583**
236.3624
2.212105
0.0284
EAST
-1010.285
3389.814
-0.298035
0.7661
LGDISMTRA
-75.07856
45.99691
-1.632252
0.1046
LGDISMIDDLE
72.74048
47.98977
1.515749
0.1316
LGDISEAST
250.2861
494.2685
0.506377
0.6133
Y08
-2527.537***
13.11072
-192.7840
0.0000
Y09
-2563.668***
8.451713
-303.3312
0.0000
Y10
-1730.070***
30.60091
-56.53654
0.0000
Y11
-1030.431***
42.34942
-24.33165
0.0000
Y12
-560.5464***
19.18971
-29.21078
0.0000
PCI_08
-0.013688***
0.000901
-15.19277
0.0000
PCI_09
-0.014875***
0.000837
-17.77722
0.0000
PCI_10
-0.002072
0.001510
-1.371440
0.1722
PCI_11
-0.016078***
0.001078
-14.91027
0.0000
PCI_12
0.004541***
0.000335
13.56315
0.0000
Observations
R-squared
Adjusted R-squared
F-test
Prob > F
180
0.688239
0.644553
15.75413
0.000000
***, **, *
means
significant at
1%, 5% and
10%
level
respectively
Source: (Author‘s calculation from PLS Regression and Robust Standard Error)
24
Table 4.2 shows the results of regression on rice prices during the
Ramadhan and Eid period. Similar result has been emerged for F-statistic,
coefficient of determination (R2) and the adjusted R-square wi