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PRICE TRANSMISSION MECHANISM IN
THE PHILIPPINE RICE INDUSTRY
by
Mary Joanne R. Matriz
A thesis submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Master of Science in Agricultural and Resource Economics.
Spring 2008
Copyright 2008 Mary Joanne R. Matriz All Rights Reserved
1457128
1457128 2008
PRICE TRANSMISSION MECHANISM IN
THE PHILIPPINE RICE INDUSTRY
by
Mary Joanne R. Matriz
Approved: __________________________________________________________ Thomas W. Ilvento, Ph.D. Professor in charge of thesis on behalf of the Advisory Committee Approved: __________________________________________________________ Thomas W. Ilvento, Ph.D. Chair of the Department of Food and Resource Economics Approved: __________________________________________________________ Robin W. Morgan, Ph.D. Dean of the College of Agriculture and Natural Resources Approved: __________________________________________________________ Carolyn A. Thoroughgood, Ph.D.
Vice Provost for Research and Graduate Studies
iii
ACKNOWLEDGMENTS
I want to thank my advisor, Dr. Thomas W. Ilvento, for his guidance and
assistance in the structure, format and content of this thesis. I also want to express my
deepest gratitude to Dr. Titus O. Awokuse, member of my thesis committee, for his
technical inputs in the methodology and analysis of this thesis as well as his continued
advice and encouragement in shaping my career as an economist. I am also grateful to
the other member of my thesis committee, Dr. Conrado M. Gempesaw, for his help in
improving my thesis specifically in the choice of my thesis topic. I would also like to
extend my appreciation to Dr. Siyan Wang, my time series econometrics professor, for
her suggestions in doing the model estimations.
I also want to thank Ronald, my fiancé, for the inspiration, love and all-
out support that he provided throughout my research work and stay in the graduate
school. Finally, I would like to express my love and gratitude to my beloved family in
the Philippines (Mama, Papa, Don and Doll) who always serves as my inspiration to
be the best in everything that I do.
iv
TABLE OF CONTENTS
LIST OF TABLES ........................................................................................................ vi LIST OF FIGURES.....................................................................................................viii ABSTRACT .................................................................................................................. ix Chapter 1 INTRODUCTION.............................................................................................. 1
1.1 Importance of Asymmetric Price Transmission ........................................ 1 1.2 Overview of the Philippine Rice Industry ................................................. 2
1.2.1 Rice Production and Consumption................................................ 3 1.2.2 Rice Prices and Government Intervention .................................... 9 1.2.3 Philippine Rice Prices versus other Asian countries................... 13
1.3 Thesis Motivation and Objectives of the Study ...................................... 19 1.4 Thesis Organization................................................................................. 21
2 REVIEW OF LITERATURE........................................................................... 22
2.1 Review of Asymmetric Price Transmission ............................................ 22 2.1.1 Definition of Asymmetric Price Transmission............................ 22 2.1.2 Types of Asymmetric Price Transmission .................................. 23 2.1.3 Possible Reasons for Vertical Asymmetric Price
Transmission ............................................................................... 24 2.2 Previous Empirical Studies on Vertical Asymmetric Price
Transmission............................................................................................ 25 2.2.1 Wolffram-Houck Model.............................................................. 25 2.2.2 VAR and Other Models............................................................... 30
2.3 Chapter Summary.................................................................................... 33 3 EMPIRICAL METHODOLOGY..................................................................... 35
3.1 Data.......................................................................................................... 35 3.2 Time Series Analyses .............................................................................. 35
3.2.1 Analysis of Unit Root (Non-stationarity).................................... 37 3.2.2 Cointegration Analysis ................................................................ 38
3.3 Wolffram-Houck Model .......................................................................... 39 3.4 VAR Model ............................................................................................. 41 3.5 Chapter Summary.................................................................................... 43
v
4 EMPIRICAL RESULTS .................................................................................. 44 4.1 Results of the Cusum of Squares Test ..................................................... 44 4.2 Results of the Chow Breakpoint Test...................................................... 50 4.3 Results of the Unit Root Test .................................................................. 51 4.4 Results of the Cointegration Test ............................................................ 52 4.5 Results of the Granger-Causality Test..................................................... 53 4.6 Wolffram-Houck Model Estimation........................................................ 74
4.6.1 Price Changes in Pre-Liberalization and Liberalization Regime......................................................................................... 74
4.6.2 Results of Wolffram-Houck Model Estimation .......................... 75 4.7 Results of the VAR Model Estimation.................................................... 83 4.8 Chapter Summary.................................................................................... 89
5 CONCLUSIONS AND POLICY IMPLICATIONS........................................ 90 REFERENCES ............................................................................................................. 93 APPENDIX .................................................................................................................. 99
vi
LIST OF TABLES
Table 1.1 Average Annual Growth Rates of Rough Rice Production, Area Harvested and Yield in the Philippines, 1970 to 2006 (in percentage) ................................................................................................ 4
Table 1.2 Rice Supply and Utilization Accounts, Philippines, 1978 to 2006 (in thousand metric tons)........................................................................... 5
Table 1.3 Philippine Rice Production and Consumption, 1978 to 2005 (in thousand metric tons) ................................................................................ 8
Table 1.4 Philippine Rice Imports by Country, 1984 to 2006 (in percentage) ......... 9
Table 1.5 Annual Average Rice Prices, 1973 to 2006 (in pesos per kilogram) ...... 12
Table 1.6 Farm Prices of Rough Rice in Selected Asian Countries (in US dollar per metric ton)............................................................................... 14
Table 1.7 Wholesale Prices of Milled Rice in Selected Asian Countries (in US dollar per metric ton)......................................................................... 16
Table 1.8 Retail Prices of Milled Rice in Selected Asian Countries (US dollar per metric ton)............................................................................... 18
Table 2.1 List of Studies on Wolffram-Houck Model ............................................ 29
Table 4.1 Results of the Chow Breakpoint Test...................................................... 50
Table 4.2 Results of Unit Root Test ........................................................................ 52
Table 4.3 Results of Cointegration Test .................................................................. 53
Table 4.4 VAR Estimation for the Whole Period (1973 to 2005)........................... 55
Table 4.5 VAR Estimation for the First Subperiod (1973 to 1984) ........................ 59
vii
Table 4.6 VAR Estimation for the Second Subperiod (1985 to 2005).................... 63
Table 4.7 Lag Order Selection for VAR, Whole Period (1973 to 2005)................. 67
Table 4.8 Lag Order Selection for VAR, First Subperiod (1973 to 1984).............. 68
Table 4.9 Lag Order Selection for VAR, Second Subperiod (1985 to 2005) ......... 69
Table 4.10 Results of the OLS-based Granger Causality Test.................................. 71
Table 4.11 Results of the VAR-based (Pairwise) Granger Causality Test ............... 72
Table 4.12 Summary of Monthly Rice Price Changes in the Philippines, 1973 to 2005..................................................................................................... 75
Table 4.13 Results of Wolffram-Houck Model Estimation (Whole Period)............. 77
Table 4.14 Results of the Wolffram-Houck Model Estimation (Subperiods)........... 78
Table 4.15 Comparison of the Results of this Thesis and Reeder’s Study (2000) ...................................................................................................... 82
Table 4.16 Variance Decompositions on the Philippine Rice Prices, All Period (1973 to 2005).............................................................................. 86
Table 4.17 Variance Decompositions on the Philippine Rice Prices, First Subperiod (1973 to 1984)........................................................................ 87
Table 4.18 Variance Decompositions on the Philippine Rice Prices, Second Subperiod (1985 to 2005)........................................................................ 88
viii
LIST OF FIGURES
Figure 1.1 Rice Production and Consumption, 1978 to 2005.................................... 6
Figure 1.2 Rice Surplus and Deficit, 1978 to 2005 ................................................... 7
Figure 1.3 Philippine Palay/Rice Prices, 1973 to 2005 ........................................... 10
Figure 1.4 Farm Prices of Philippine and Thailand Rough Rice, 1980 to 1998...... 15
Figure 1.5 Wholesale Prices of the Philippine and Thailand Milled Rice, 1980 to 1997 .......................................................................................... 17
Figure 1.6 Retail Prices of the Philippine and Indonesia Milled Rice, 1980 to 2000........................................................................................... 19
Figure 4.1 Time Plot of the Philippine Rice Farm Prices, 1973 to 2005................. 45
Figure 4.2 Time Plot of the Philippine Rice Wholesale Prices, 1973 to 2005 ........ 46
Figure 4.3 Time Plot of the Philippine Rice Retail Prices, 1973 to 2005 ............... 46
Figure 4.4 Plot of Recursive Residuals for the Cusum of Squares Test: Farm to Retail Prices, 1973 to 2005....................................................... 47
Figure 4.5 Plot of Recursive Residuals for the Cusum of Squares Test: Farm to Wholesale Prices, 1973 to 2005................................................ 47
Figure 4.6 Plot of Recursive Residuals for the Cusum of Squares Test: Retail to Farm Prices, 1973 to 2005....................................................... 48
Figure 4.7 Plot of Recursive Residuals for the Cusum of Squares Test: Retail to Wholesale Prices, 1973 to 2005 .............................................. 48
Figure 4.8 Plot of Recursive Residuals for the Cusum of Squares Test: Wholesale to Farm Prices, 1973 to 2005................................................ 49
Figure 4.9 Plot of Recursive Residuals for the Cusum of Squares Test: Wholesale to Retail Prices, 1973 to 2005 .............................................. 49
ix
ABSTRACT
Vertical price transmission analysis measures the speed and the magnitude in
which price changes in certain market level are being transmitted to another. As a
whole it provides insights on the efficiency of a commodity’s market structure,
welfare distribution in an industry, as well as the existence of market power among the
key players. Despite its importance, there is only one study in the Philippines that
focuses on price transmission.
This thesis, therefore, examines the existence of asymmetric price transmission
across market levels (farm, wholesale and retail) in the Philippine rice industry. Two
econometric models known as the Wolffram-Houck and the VAR models are used in
the analysis of recently available price data from 1973 to 2005. To account for the
level of government intervention in the market, the data are divided into 2 subperiods
representing the period of heavy government control and rice liberalization regime.
In general, the results of this study suggest that price symmetry (in terms of
speed and magnitude) exists at all levels of the rice market with or without heavy
government intervention. Symmetric price transmission in rice industry can be
explained by the longer storage rate of rice and less level of processing involved in
rice production. Hence there is no incentive for traders to exercise market power. In
x
addition, farm price accounts for most of the variability in wholesale and retail prices
in both of the subperiods. Further, wholesale price explains more of the retail price’
variations in the rice liberalization regime than in the period of heavy government
control in the industry.
1 1
Chapter 1
INTRODUCTION
Rice has been the topic of various studies conducted in many developing
countries since it is considered a staple food of the majority of the population. These
topics ranged from the production and marketing aspects of rice. Developing
countries tend to depend on agriculture where most of the resources (e.g., land) are
allocated to a single commodity. In the Philippines that commodity is rice. While
numerous papers on rice marketing (e.g., logistics system and market structure
analysis) have been published in the Philippines, there is only one study available
which evaluated the interactions of rice prices using an econometric model on price
transmission (Reeder, 2000). Most of these rice marketing-related studies used the
traditional mark-up price determination approach by interviewing farmers,
wholesalers and retailers. This thesis, therefore, examines the existence of asymmetric
price transmission across market levels (farm, wholesale and retail markets) in the
Philippine rice industry using two econometric models known as the Wolffram-Houck
and the vector autoregressive (VAR) models with the use of recently available data.
1.1 Importance of Asymmetric Price Transmission
The interaction of prices along the supply chain (vertical price
transmission) as a whole provides insights on the efficiency of a commodity’s market
structure, welfare distribution in an industry, as well as the existence of market power
among the key players. Price transmission analysis determines how the changes
2 2
(increase or decrease) in the farm prices are being transmitted to wholesale and retail
prices. It measures the amount of time as well as the level of magnitude in which these
changes are transmitted. Symmetric price transmission exists if wholesale price
(output price) responds similarly and instantaneously to both an increase and decrease
in farm price (input price). On the other hand, asymmetric price transmission exists
when output price responds at different speed and magnitude given the increase or
decrease in input price. This is very common since the input’s price adjustment differs
when the output price increases or decreases. Peltzman (2000) even concluded that
asymmetric price transmission is very dominant in most of the producer and consumer
markets. In the agriculture sector alone, asymmetric price transmission is found to be
evident in vegetables, fish, meat, and dairy products’ markets worldwide according to
studies by Ward (1982), Kinnucan and Forker (1987), Boyd and Brorsen (1988), Hahn
(1990), Griffith and Piggott (1994), Miller and Hayenga (2001) and Bakucs and Ferto
(2005), among others.
Price asymmetry in agricultural markets is explained by the retailers’
reluctance to raise prices when farm prices increase given the risk that they would be
left with unsold rotten perishables (Ward, 1982). Other reasons for asymmetry, as
identified in other papers, include inventory management strategies, government
policies and non-competitive behavior in the market, among others.
1.2 Overview of the Philippine Rice Industry
Rice plays a crucial role in the Philippine economy. It is considered as the
staple food of 80 percent of the population and is a major source of income for
Filipino farmers. It has a 20.1 percent share on the food component of the consumer
price index (CPI) as indicated in the Philippine’s Family Income and Expenditure
3 3
Survey in 2003. Palay (e.g., unhulled or rough rice) is consistently the major crop of
the country and is grown on 44.6 percent of the total farms (2002 Philippine Census of
Agriculture). Being an important political commodity, the government has heavily
intervened in the production and consumption of rice.
1.2.1 Rice Production and Consumption
Rice is being produced in irrigated and rainfed areas in the Philippines. Its
production has been generally increasing for the last 36 years. From a harvest of about
5.3 million metric tons (mmt) in 1970, it reached 15.3 mmt in 2006. As shown in
Table 1.1, this increase is mainly attributed to productivity gains (yield) which rose
from 1.71 mt per hectare (ha) in 1970 to 3.68 mt per ha. in 2006. Area harvested,
however, has been decreasing through time.
The country’s rice production is seasonal in nature (characterized with
peak and lean months all year round), which can be seen in the quarterly distribution
of the annual production. It is during the last quarter of the year (October to
December) that almost half of the annual rough rice output is harvested. Thus, this
quarter is called as the peak season. The lean months, however, start from July to
September where rough rice production is very low. To ensure an adequate supply of
rice over the year, a 90-day supply of rice is maintained by the National Food
Authority (NFA) every July 1 of the year. The accumulated carry-over stock by the
end of the last quarter also helps for a continuous supply of rice from the first to the
third quarter of the year. The NFA is a government corporation which is mandated to
stabilize rice supplies and maintain prices at levels profitable to producers and
affordable to consumers.
4 4
Table 1.1 Average Annual Growth Rates of Rough Rice Production, Area Harvested and Yield in the Philippines, 1970 to 2006 (in percentage)
1970-1979 1980-1989 1990-1999 2000-2006
Total Rough Rice Production 4.31 2.35 3.24 3.85 Area Harvested 1.51 (0.02) 1.93 0.57 Yield 2.83 2.29 0.95 3.25 Irrigated Rough Rice Production 5.14 4.00 3.86 3.83 Area Harvested 0.83 3.05 2.95 0.86 Yield 4.21 0.87 0.58 2.95 Rainfed Rough Rice Production 3.36 (0.39) 1.99 3.97 Area Harvested 2.21 (3.06) 0.46 0.01 Yield 1.34 2.58 0.85 3.89
Source: Bureau of Agricultural Statistics (BAS), Philippines *Figures in parenthesis are negative growth rates
As indicated in Table 1.2, during the period 1978 to 2005, an average of 2
percent of the total supply of rice is used as seeds, 5 percent as feeds, barely 1 percent
as exports, and 3 percent as processed food such as rice flour and noodles. The
remaining 70 percent is used for local food consumption which is mainly affected by
population growth. In 2005, the country’s total population was already 85.2 million
with an average growth rate of 2 percent annually.
5 5
Table 1.2 Rice Supply and Utilization Accounts, Philippines, 1978 to 2006 (in thousand metric tons)
Supply Utilization Net Food Disposable
Per Capita Year Prod-
uction Im-
ports Gross Supply
Ex-ports Seeds
Feeds &
WasteProc'd Total
Kg./Yr G/Day 1978 4615 0.1 5923 48 170 300 - 3811 83.22 228.001979 4957 0.03 6551 165 171 298 - 4032 85.72 234.841980 4970 0.25 6855 263 169 321 - 4456 92.23 252.691981 5142 0.25 6788 95 168 321 - 4593 92.73 254.041982 5417 - 7028 0.5 163 352 - 4647 91.50 250.681983 4756 - 6622 40 149 303 - 4639 89.12 244.161984 5120 189 6800 2 155 332 - 5167 96.85 265.341985 5759 538 7441 0.1 162 374 - 5150 94.20 258.091986 6047 2 7804 - 170 393 - 5224 93.28 255.561987 5585 - 7602 111 160 363 - 5393 94.03 257.621988 5867 181 7623 - 166 381 - 5558 94.64 259.301989 6186 196 7900 - 172 402 - 5637 93.80 256.981990 6095 606 8391 - 163 396 244 5689 92.53 253.5 1991 6326 - 8225 10 168 411 253 5263 83.71 229.3 1992 5970 1 8091 35 157 388 239 5599 87.13 238.7 1993 6170 202 8045 a/ 161 401 247 5792 88.52 242.5 1994 6892 - 8336 - 179 448 276 5935 86.49 237 1995 6894 264 8656 - 184 448 276 6326 92.55 253.6 1996 7379 867 9668 - 194 480 295 6906 98.73 270.5 1997 7370 722 9885 - 188 479 295 6944 97.05 265.9 1998 5595 2171 9745 a/ 155 364 224 6723 91.91 251.8 1999 7708 834 10821 a/ 196 501 308 7451 99.68 273.102000 8103 639 11107 a/ 198 527 324 7892 103.2 282.6 2001 8472 808 11446 a/ 199 551 339 8086 103.8 284.302002 8679 1196 12146 a/ 198 564 347 8589 108 296 2003 8829 886 12163 a/ 197 574 353 8677 107 293.2 2004 9481 1001 12844 a/ 202 616 379 9596 116.1 318.1 2005 9550 1822 13423 a/ 200 621 382 10126 118.80 325.5 Ave. 6688.2 618 9026 70 176.5 433.1 305 6353 96.39 264.07
% share to total supply
0.79 1.98 4.80 3 70
a/- less than 1 thousand metric tons Source: BAS, Philippines
6 6
While rice production was increasing at an average growth rate of 3
percent annually, the demand for rice was rising at a faster rate of 4 percent.
Specifically, the average per capita consumption of rice is 96 kilogram (kg) per year
and is increasing at 1.4 percent on a yearly basis. Hence, production is not sufficient to
meet the increasing demand of the population. The gap between the production and
demand for rice is depicted in Figure 1.1, 1.2 and Table 1.3 below. This gap has been
increasing since 1996.
-4000
-2000
0
2000
4000
6000
8000
10000
12000
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Year
Qua
ntity
Production Consumption Surplus/Deficit
Figure 1.1 Rice Production and Consumption, 1978 to 2005 (in thousand metric tons)
7 7
-2000
-1500
-1000
-500
0
500
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Year
Qua
ntity
Figure 1.2 Rice Surplus and Deficit, 1978 to 2005
To compensate for this excess demand, the Philippines has been
constantly importing rice from other Asian countries. Table 1.4 indicates that for the
last 2 decades, more than half of the country’s imports were sourced from Vietnam, 20
percent came from Thailand and 14 percent came from China. The Philippines is also
an importer of US long grain rice through the Public Law 480 Title 1 program. Rice
imports have tremendously raised from 100 mt in 1978 to 1.8 million mt in 2005.
8 8
Table 1.3 Philippine Rice Production and Consumption, 1978 to 2005 (in thousand metric tons)
Year Production Consumption Surplus/Deficit Imports 1978 4615 4329 286 0.1 1979 4957 4666 291 0.03 1980 4970 5209 (239) 0.25 1981 5142 5177 (35) 0.25 1982 5417 5162 255 - 1983 4756 5131 (375) - 1984 5120 5656 (536) 189 1985 5759 5686 73 538 1986 6047 5787 260 2 1987 5585 6027 (442) - 1988 5867 6105 (238) 181 1989 6186 6211 (25) 196 1990 6095 6492 (397) 606 1991 6326 6105 221 - 1992 5970 6418 (448) 1 1993 6170 6601 (431) 202 1994 6892 6838 54 - 1995 6894 7234 (340) 264 1996 7379 7875 (496) 867 1997 7370 7906 (536) 722 1998 5595 7466 (1871) 2171 1999 7708 8456 (748) 834 2000 8103 8941 (838) 639 2001 8472 9175 (703) 808 2002 8679 9698 (1019) 1196 2003 8829 9801 (972) 886 2004 9481 10793 (1312) 1001 2005 9550 11329 (1779) 1822
Average 6569 7010 (441) 571 Source: BAS, Philippines *Figures in parenthesis are negative numbers (deficit)
9 9
Table 1.4 Philippine Rice Imports by Country, 1984 to 2006 (in percentage)
Country Share to Total Imports Vietnam 51.43 Thailand 20.51 China 13.72 India 6.03 Indonesia 1.48 USA 4.58 Pakistan 0.94 Myanmar 0.83 Australia 0.21 Netherlands 0.19 Spain 0.01 Taiwan 0.08 Total 100 Source: National Food Authority, Philippines
1.2.2 Rice Prices and Government Intervention
The seasonality in rice production affects the trend in (average) rice prices
(farm, wholesale and retail prices). As shown in Figure 1.3, during the lean season, the
price of rice is at the highest and gradually decreases as harvest season comes. In
particular, farm price is at its peak in July (the beginning of lean season) and starts to
decrease in September, right before the start of the harvest season. Wholesale and
retail prices, however, are very high in August and start to fall in October.
10 10
0.00
2.00
4.00
6.00
8.00
10.00
12.00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pric
e
Farmgate Wholesale Retail
Figure 1.3 Philippine Palay/Rice Prices, 1973 to 2005 (in pesos per kilogram)
A big disparity in prices at all market levels is also evident. Wholesale and
retail prices, specifically, are more than twice the farm price (Table 1.5). On the
average, farm price amounts to P4.54 per kg while wholesale and retail prices are
around P8.57 and P9.28 per kg., respectively. It is also interesting to note that all of
these prices increased at an average of 9 percent per year. The price relationship above
suggests that if the farm price increases, wholesale and retail prices will also increase.
Therefore, to protect the welfare of both the farmers and consumers from
the changes of rice prices due to seasonality in production, the government directly
intervenes through the NFA. This agency is tasked to procure rough rice from the
farmers as well as distribute/inject rice into the market. The former protects farmers
from receiving low market prices of rough rice during peak season while the latter
protects the consumers from paying high market prices of rice during the lean season.
In order to perform these mandates, the NFA uses 2 pricing policies namely: (i) farm
support price to farmers and the (ii) subsidized retail price to consumers. While the
11 11
level of support price varies depending on the government’s budget for rough rice
procurement, the subsidized retail price is always lower than the market prices.
A previous study (Cororaton, 2006) indicates that the NFA rough rice
procurement has declined dramatically from 7.2 percent of total production in 1980 to
0.5 percent in 2002. This is attributed to the decline in the agency’s annual budget.
The NFA’s rice distribution, however, has been stable since 1999 and is at 13 percent
in 2002. Given the relatively insignificant intervention of the NFA in terms of rough
rice procurement, several studies analyzing the affectivity and efficiency of the agency
recommended the need for its urgent reform. But given the political nature of rice
itself, and that the largest commodity that the agency handles is rice, the debate to
restructure the NFA is difficult and ongoing.
12 12
Table 1.5 Annual Average Rice Prices, 1973 to 2006 (in pesos per kilogram)
Year Farm Growth
Rate WholesaleGrowth
Rate Retail Growth
Rate 1973 0.77 1.31 1.39 1974 0.94 22.08 1.73 32.06 1.86 33.81 1975 0.98 4.26 1.71 (1.16) 1.86 0.00 1976 0.98 0.00 1.91 11.70 1.97 5.91 1977 1.00 2.04 1.96 2.62 2.05 4.06 1978 0.98 (2.00) 1.91 (2.55) 2.03 (0.98) 1979 1.04 6.12 2.10 9.95 2.24 10.34 1980 1.15 10.58 2.20 4.76 2.35 4.91 1981 1.30 13.04 2.48 12.73 2.61 11.06 1982 1.36 4.62 2.64 6.45 2.72 4.21 1983 1.52 11.76 2.85 7.95 3.04 11.76 1984 2.47 62.50 4.47 56.84 4.63 52.30 1985 3.24 31.17 6.05 35.35 6.40 38.23 1986 2.82 (12.96) 5.40 (10.74) 5.92 (7.50) 1987 2.99 6.03 5.50 1.85 5.99 1.18 1988 3.16 5.69 6.08 10.55 6.61 10.35 1989 4.01 26.90 7.42 22.04 7.86 18.91 1990 4.74 18.20 8.38 12.94 8.92 13.49 1991 4.77 0.63 8.50 1.43 9.24 3.59 1992 4.82 1.05 8.91 4.82 9.65 4.44 1993 5.40 12.03 10.02 12.46 10.84 12.33 1994 5.90 9.26 11.27 12.48 12.21 12.64 1995 7.24 22.71 14.06 24.76 15.18 24.32 1996 8.13 12.29 15.84 12.66 17.13 12.85 1997 7.92 (2.58) 15.22 (3.91) 16.53 (3.50) 1998 8.30 4.80 15.78 3.68 17.10 3.45 1999 7.87 (5.18) 15.75 (0.19) 17.26 0.94 2000 8.42 6.99 15.91 1.02 17.59 1.91 2001 8.17 (2.97) 15.99 0.50 17.54 (0.28) 2002 8.82 7.96 16.52 3.31 18.00 2.62 2003 8.84 0.23 16.51 (0.06) 17.95 (0.28) 2004 9.45 6.90 17.30 4.78 18.71 4.23 2005 10.43 10.37 19.14 10.64 20.73 10.80
Average 4.54 9.20 8.57 9.43 9.28 9.44 Source: BAS, Philippines *Figures in parenthesis are negative growth rates
13 13
1.2.3 Philippine Rice Prices versus other Asian countries
The following tables and figures on rice prices (Tables 1.6 to 1.8 and
Figures 1.4 to 1.6) show that the domestic prices of rice in all market levels (farm,
wholesale and retail) are generally higher than in the rest of the Asian countries. On
the average, Thailand has the cheapest price for both farm and wholesale markets
while Indonesia has the lowest retail price.
The huge difference in the country’s production cost as compared to the
other neighboring countries could be a reason for the disparity in rice prices. In 1999,
for instance, the total production cost per hectare in the Philippines amounted to $888
(in U.S. dollars) while Thailand and Vietnam only incurred $636 and $683,
respectively (NEDA-UNDP study, 2005). In all countries except Thailand, more than
50 percent of the production expenditure was paid for labor. Thailand, however, had
the highest cost incurred for machine rental and fuel, which implies the country’s
adoption of mechanized farming.
14 14
Table 1.6 Farm Prices of Rough Rice in Selected Asian Countries (in US dollar per metric ton)
Ratio of Philippine Price with Year Philippines Bangladesh Indonesia Thailand
Bang. Indo. Thai. 1980 139.81 181.94 186.61 154.54 0.77 0.75 0.90 1981 159.49 176.32 205.77 131.81 0.90 0.78 1.21 1982 154.57 162.30 213.18 127.70 0.95 0.73 1.21 1983 121.51 167.02 159.47 121.09 0.73 0.76 1.00 1984 117.37 186.31 160.83 98.35 0.63 0.73 1.19 1985 173.56 156.91 157.58 84.72 1.11 1.10 2.05 1986 145.53 177.90 130.21 113.84 0.82 1.12 1.28 1987 148.66 180.94 112.54 149.53 0.82 1.32 0.99 1988 161.78 181.82 148.31 157.37 0.89 1.09 1.03 1989 191.40 172.42 145.76 141.21 1.11 1.31 1.36 1990 194.16 177.00 153.57 140.99 1.10 1.26 1.38 1991 164.48 174.45 162.02 149.22 0.94 1.02 1.10 1992 184.63 129.99 162.57 141.18 1.42 1.14 1.31 1993 196.17 123.00 147.09 147.20 1.59 1.33 1.33 1994 212.30 167.47 180.96 153.36 1.27 1.17 1.38 1995 286.66 166.41 202.35 191.17 1.72 1.42 1.50 1996 310.07 134.36 - 227.15 2.31 - 1.37 1997 268.75 159.65 - 149.20 1.68 - 1.80 1998 198.34 - - 111.73 - - 1.78 1999 201.33 - - - - - - Ave. 186.53 165.34 164.30 141.65 1.15 1.06 1.32 Source: IRRI Atlas of Rice and World Rice Statistics (http://www.irri.org/science/ricestat/)
15 15
0
50
100
150
200
250
300
350
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
Year
Pric
e
Philippines Thailand
Figure 1.4 Farm Prices of Philippine and Thailand Rough Rice, 1980 to 1998 (in US dollar per metric ton)
16 16
Table 1.7 Wholesale Prices of Milled Rice in Selected Asian Countries (in US dollar per metric ton)
Ratio of Philippine Price with Year Philippines Bangladesh Indonesia Thailand
Bang. Indo. Thai. 1980 286.28 360.71 354.07 276.90 0.79 0.81 1.03 1981 307.59 396.22 384.64 297.89 0.78 0.80 1.03 1982 314.99 342.81 414.26 237.04 0.92 0.76 1.33 1983 244.82 340.66 360.73 238.61 0.72 0.68 1.03 1984 223.95 363.63 338.23 205.54 0.62 0.66 1.09 1985 333.69 318.79 325.06 169.73 1.05 1.03 1.97 1986 299.90 355.97 336.05 163.08 0.84 0.89 1.84 1987 272.24 374.86 293.21 197.20 0.73 0.93 1.38 1988 318.16 375.67 299.58 254.69 0.85 1.06 1.25 1989 313.25 349.55 284.74 278.37 0.90 1.10 1.13 1990 356.23 345.96 295.20 250.84 1.03 1.21 1.42 1991 330.42 339.62 - 271.79 0.97 - 1.22 1992 371.62 274.45 327.11 245.35 1.35 1.14 1.51 1993 397.49 275.46 310.00 206.12 1.44 1.28 1.93 1994 459.12 338.97 377.18 256.66 1.35 1.22 1.79 1995 584.99 348.31 445.61 304.78 1.68 1.31 1.92 1996 663.23 298.64 432.91 325.77 2.22 1.53 2.04 1997 572.79 339.03 382.21 281.63 1.69 1.50 2.03 1998 425.53 359.20 245.77 - 1.18 1.73 - 1999 446.66 354.50 344.24 - 1.26 1.30 - 2000 - - 231.40 - - - - Ave. 376.15 342.65 339.11 247.89 1.12 1.10 1.50
Source: IRRI Atlas of Rice and World Rice Statistics (http://www.irri.org/science/ricestat/)
17 17
0
100
200
300
400
500
600
700
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Year
Pric
e
Philippines Thailand
Figure 1.5 Wholesale Prices of the Philippine and Thailand Milled Rice, 1980 to 1997 (in US dollar per metric ton)
18 18
Table 1.8 Retail Prices of Milled Rice in Selected Asian Countries (US dollar per metric ton)
Ratio of Philippine Price with Year Philippines Bangladesh Indonesia Thailand
Bang. Indo. Thai. 1980 312.92 399.61 317.39 380.76 0.78 0.99 0.82 1981 297.47 421.12 362.48 362.05 0.71 0.82 0.82 1982 333.72 379.75 385.53 334.35 0.88 0.87 1.00 1983 270.03 358.98 334.33 318.30 0.75 0.81 0.85 1984 233.53 389.59 321.66 308.08 0.60 0.73 0.76 1985 360.56 344.80 290.84 261.38 1.05 1.24 1.38 1986 334.81 377.67 269.77 270.08 0.89 1.24 1.24 1987 304.81 404.20 236.03 274.07 0.75 1.29 1.11 1988 339.50 405.29 278.22 339.22 0.84 1.22 1.00 1989 335.79 374.03 282.48 366.23 0.90 1.19 0.92 1990 378.03 371.71 281.64 382.10 1.02 1.34 0.99 1991 367.54 371.31 285.59 382.99 0.99 1.29 0.96 1992 378.28 316.05 297.55 397.91 1.20 1.27 0.95 1993 436.58 317.92 345.93 413.23 1.37 1.26 1.06 1994 462.15 380.50 416.52 440.24 1.21 1.11 1.05 1995 542.98 376.61 483.41 517.82 1.44 1.12 1.05 1996 724.64 311.56 505.91 593.73 2.33 1.43 1.22 1997 629.45 336.75 441.67 - 1.87 1.43 - 1998 465.39 377.53 254.85 - 1.23 1.83 - 1999 490.15 380.13 394.02 - 1.29 1.24 - 2000 440.14 - 277.80 - - 1.58 - Ave 401.83 369.76 336.36 373.09 1.10 1.20 1.01
Source: IRRI Atlas of Rice and World Rice Statistics (http://www.irri.org/science/ricestat/)
19 19
0
200
400
600
800
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Year
Pric
e
Philippines Indonesia
Figure 1.6 Retail Prices of the Philippine and Indonesia Milled Rice, 1980 to 2000 (in US dollar per metric ton)
1.3 Thesis Motivation and Objectives of the Study
Given that rice prices are generally increasing more than decreasing, as
discussed in Chapter 1.2.2 above, it is worthy to investigate the reasons why this
happens as well as how the changes in the farm prices are being transmitted to the
wholesale and retail prices. Results of the previous rice market structure studies in the
Philippines attributed this rising trend in prices to the existence of traders’ market
power which enables them to manipulate rice prices. It is believed that this market
power is being exercised by the traders due to the following factors. First,
transportation facilities and alternative market outlets for small-scale farmers are not
adequate. Since the majority of the rice farmers are small-scale farmers and are
located in far-flung areas, they cannot afford to bring and sell their produce to the
market because of high transportation cost. Thus, most of the farmers opt to sell their
20 20
produce on a picked-up basis to traders who usually offer a lower price than the
market price (National Economic and Development Authority-United Nations
Development Program’s study, 2005). Second, credit-marketing tie-up with traders
exists. Due to the minimal income being earned from small-scale farming, farmers
rely on informal money lenders (usually traders) for emergency loan requirements.
These traders lend money right away to the farmers with high interest rates. Traders
also provide for production inputs to farmers on a charge-to-crop basis such that
farmers will not have a choice but to sell their produce to them at whatever price they
would offer. Often times, traders dictate rice prices lower than the market price.
Because of the above set-up for rice marketing, it is also believed that traders hoard
enough supply of rice to create an artificial rice shortage which will again enable them
to distort (increase) the market price for rice.
The assumption that the increasing trend in rice prices due to the traders’
market power is actually a result of the lack of empirical studies which use price
transmission models (e.g., Wolffram-Houck and VAR models) for the country’s rice
industry. The Wolffram-Houck model may validate the above assumption by
determining the existence of asymmetric price transmission in the industry. The VAR
model also helps to identify the size or magnitude of the price transmission from one
market level to another.
Furthermore, this study seeks to confirm the results of the study by Reeder
(2000) entitled “Asymmetric Prices: Implications on Trader’s Market Power in
Philippine Rice.” Reeder’s work is the first study in the Philippines which used price
transmission model to find evidence that supports the claim that Filipino rice traders
exert their market power to deliberately increase the price of rice during a market
crisis. Results of the study, however, disproved the allegation against the traders. Rice
21 21
traders adjust their prices upwards when they experience an increase in cost while
similarly passing on savings to consumers as price discounts when prices are falling.
The Wolffram-Houck and the VAR models will be employed in this study
to validate the above results by Reeder. These models use the recent available data on
rice prices hence extending the timeframe of Reeder’s work.
The use of the above models can also be replicated to other studies on
market structure especially those that deal with other agricultural commodities. These
models can provide empirical explanations, using another approach, on the current
problems that the agriculture sector is facing, enabling the policymakers to provide
sound policies for the country’s agricultural development.
1.4 Thesis Organization
This paper is organized as follows: Chapter 2 provides a review of
literature on asymmetric price transmission related to agricultural markets. This
includes the detailed discussion on the causes of asymmetry in price transmission, the
different models used and the results of the study. Chapter 3 discusses the empirical
methodology which includes the description of the data, time series analyses, as well
as the empirical models. Results of estimating the 2 models as well as the other tests
used in the study are presented in Chapter 4. Chapter 5 provides conclusions generated
from the empirical results. This also discusses policy implications of the results of the
analysis as well as some limitation of the study. Results of relevant data analysis are
also indicated in the Appendix.
22 22
Chapter 2
REVIEW OF LITERATURE
This Chapter briefly discusses the types of asymmetric price transmission
as well as the possible reasons for its occurrence. The findings of previous empirical
studies on agricultural economics using the Wolffram-Houck Model and other models
for asymmetric price transmission such as the VAR model are also provided in this
Chapter.
2.1 Review of Asymmetric Price Transmission
2.1.1 Definition of Asymmetric Price Transmission
The economic literature on the analysis of price linkages can be classified
as follows: (i) those that deal with horizontal price linkages and, (ii) those that deal
with vertical price linkages. The former deals with spatial price relationships (e.g.,
links between prices at different locations) which are typically concerned with market
integration. The latter is about the price linkages across different market levels (e.g.,
links between farm, wholesale and retail prices). This type of price interaction is the
focus of this study.
Vertical price transmission is characterized by the magnitude, speed and
nature of adjustments through the supply chain to market shocks that are generated at
different levels of the marketing process (Vavra and Goodwin, 2005). It specifically
determines the following aspects of adjustment: (i) magnitude of the response at each
market level due to a shock of a given size at another market level, (ii) speed of
23 23
adjustment which includes lag effect, (iii) direction of adjustment which differ when a
shock is transmitted upwards or downwards the supply chain, and (iv) the nature of
the adjustment in relation to positive or negative price shocks.
The nature of adjustment determines whether there is symmetric or
asymmetric price transmission. For instance, if the change in the farm price (input
price) is divided into 2 types, positive change (increase) and negative change
(decrease), it is possible to know the similarity of price transmission. Symmetric price
transmission exists when the wholesale or retail price (output price) responds at the
same magnitude and speed to the positive and negative change in the farm price.
Asymmetric price transmission, however, occurs when this response differs (in terms
of magnitude and speed) when farm price increases or decreases. Vavra and Goodwin
(2005) further explained that asymmetries can occur within any aspect of the
adjustment process since price transmission might be asymmetric in its speed and
magnitude and could differ depending on whether the price shock is positive or
negative and is being transmitted upwards or downwards along the supply chain.
2.1.2 Types of Asymmetric Price Transmission
Frey and Manera (2005) pointed out that the widely used classification of
price asymmetries is between short-run and long-run asymmetries. A short-run
analysis compares the intensity of output price variations to positive or negative
changes in input prices, while a long-run perspective is needed if the empirical
investigation concentrates on the identification of the reaction time, length of
fluctuations as well as speed of adjustment towards an equilibrium level. For example,
as elaborated by Romain, et al (2002), short-run asymmetry occurs when the
immediate effect of a variation in the farm price on retail price is not the same when
24 24
price is increasing as when it is decreasing. In the long-run, the effects can be the
same. However, long-run asymmetry occurs when a change to input price is not fully
transmitted to the output price after a complete adjustment period. In the short-run, the
impacts could be similar.
2.1.3 Possible Reasons for Vertical Asymmetric Price Transmission
A number of reasons for vertical asymmetric price transmission have been
identified in the literature. Vavra and Goodwin (2005) provide a comprehensive
summary of the causes of vertical asymmetric price transmission as cited in the
previous studies.
Research has attributed the sluggish adjustment of retail prices to the
changes in menu costs such as advertising and labeling and the risk to the retailer’s
reputation if its price changes are frequent. When inflation occurs, the use of these
menu costs may lead to more resistance in decreasing prices than otherwise.
Price asymmetry is also caused by the asymmetries in the underlying cost
of adjustments. For example, retailers selling perishable products may hesitate to
increase their prices when farm price increases given the risk that they might be left
with spoiled products which will no longer be sold.
Inventory management practices also result in price asymmetry. To avoid
early depletion of stocks, retailers may reduce their price more slowly than price at the
farm level. It can be argued that the accumulation of stocks by retailers and the
accounting method known as first in first out (FIFO) also lead to price asymmetry.
The FIFO method causes the firm to not adjust its output immediately when cost
changes occur, but wait until the stocks of inputs bought at the old price are depleted.
25 25
Government intervention on supporting producer prices may also lead to
price asymmetry. It is far more common that government will intervene when farm
price falls than when it rises. In addition, the differences on market structure as well
transmission of information among countries also cause the asymmetry.
The majority of the studies, however, cited the presence of non-
competitive markets as the culprit for asymmetry. The presence of market powers
among traders enables them to manipulate prices to capture a significant share of
profits from the market. It is argued that retailers try to maintain their normal profit
margin when price rises, but they try to capture the larger margins that result, at least
temporarily, when wholesale prices fall.
2.2 Previous Empirical Studies on Vertical Asymmetric Price Transmission
2.2.1 Wolffram-Houck Model
The estimation of asymmetric price transmission was initially done by
Tweeten and Quance (1969) who used a dummy variable technique to estimate
irreversible supply functions wherein dummy variables split the input price into
increasing and decreasing input prices. This enables the estimation of price
adjustments for the 2 input prices. Wolffram (1971) came up with a related technique
called a variable-splitting technique which includes the first differences of prices in
the equation to include the effect of cumulative variations in each variable. Houck
(1977) further refined this to exclude the initial observations since the level of the first
observation has no explanatory power when it comes to measuring the differential
effects of each observation. This Wolffram-Houck asymmetry model is based on the
26 26
assumption that output price adjusts more fully and rapidly to increases in input prices
than to decreases.
This model has been applied to most of the studies on agricultural
economics with varying techniques and results. Some of the studies, as outlined by
von Cramon-Taubadel (1997) and Frey and Manera (2005), found the existence and
non-existence of asymmetry while others have had mixed results. The following
studies by Ward (1982), and Kinnucan and Forker (1987) support price asymmetry.
Specifically, Ward (1982) adopted Houck’s (1977) specification but
included the lags of exogenous variables to be able to measure the differences in the
length of lags when the input price was increasing or decreasing. He estimated the
impact of wholesale prices on retail prices and shipping point prices (FOB) using
monthly data of different types of vegetables in the US market. He found that long-run
and short-run asymmetries are evident in the distributed lag effect of the cumulative
wholesale prices variations on both FOB and retail prices.
Kinnucan and Forker (1987) examined the price transmission from farm
to retail prices of the US major dairy products (fluid milk, cheese, butter and ice
cream). They used monthly data from January 1971 to December 1981 where
marketing costs are used in the estimation process. Results of the study suggested that
there is asymmetry in the farm-retail price transmission in the US dairy sector. The
cumulative effect of the increase in farm price on retail price surpasses the cumulative
effect of a decrease in farm price. The average lags corresponding to the rise in farm
prices were smaller than to a fall in farm prices.
The studies below, however, suggest that price symmetry is also present
in some of the agricultural commodities’ markets. Boyd and Brorsen (1988) used the
weekly farm, wholesale and retail prices of the US pork from 1974 and 1981. They
27 27
were the first to differentiate the degree (magnitude) and speed of transmission by
using lags. They showed that the effect of a rise and fall in the pork’s farm price to its
wholesale price were the same. This result is also true for the wholesale and retail
price transmission.
In the Philippines, this approach was initially done by Reeder (2000) on
the rice market by using monthly price data from April 1973 to September 1996 to
observe the transmission mechanism between farm to wholesale prices and wholesale
to retail prices. The results of the study showed that there is no price asymmetry
transmission among these market levels, which nullified the conventional local
wisdom on the existence of trader’s market power to manipulate prices. The results
specifically indicated that market shocks coming from the farm level are transmitted
as price changes in the wholesale market before being reflected as price changes in the
retail market. This is attributed to the cost plus pricing strategy by the traders wherein
they minimize their profit by increasing prices in response to a rise in cost, and
decreasing prices when cost are falling. In other words, Filipino traders utilize a
constant margin in valuing rice.
Parrott et al. (2001) employed the Kinnucan and Forker model in
analyzing the transmission mechanism between the US fresh tomatoes retail and FOB
prices weighted by the volume of shipments. They used weekly data from June 1988
to December 1993 and showed that weekly prices respond similarly to both rising and
falling FOB prices.
The following empirical studies by Griffith and Piggot (1994), Zhang et
al. (1995), Worth (2000) and Girapunthong et al (2004) revealed both the presence
and the absence of price asymmetry. Griffith and Piggot (1994) used monthly prices
for the Australian beef, lamb and pork at all market levels from January 1971 to
28 28
December 1988 to analyze the price interaction between farm and wholesale prices,
farm and retail prices and retail to wholesale prices. Using Kinnucan and Forker’s
specification, the study showed that there is symmetric price transmission in the pork
market. In the beef market, however, asymmetric transmission between the farm and
retail prices as well as retail and wholesale prices occured. In addition, the lamb
market exhibit asymmetric transmission between the farm and wholesale prices and
retail and wholesale prices.
Zhang et al. (1995) tested the wholesale price of peanuts and the price of
peanut butter starting January 1984 to July 1992 and found out that there is price
asymmetry transmission in the short-run and price symmetry transmission in the long-
run.
The relationship between shipping point prices and retail prices of some
of the fresh vegetables available in the market is also examined by Worth (2000). He
used Kinnucan and Forker’s model and price monthly data from January 1980 to May
1999 and uncovered that only carrots and tomatoes have symmetric price
transmission.
Just like Parrott et al., Girapunthong et al (2004) also studied the US fresh
tomato market by using Ward’s specification and monthly farm, wholesale and retail
prices from May 1975 to February 1998. The results of the study indicate that there is
price symmetry transmission between the farm and retail market while price
asymmetry is evident between the farm and wholesale market. The foregoing studies
on the Wolffram-Houck model are summarized in Table 2.1.
29 29
Table 2.1 List of Studies on Wolffram-Houck Model
Author, Year
Commodity Methodological Approach
Result
Ward, 1982
US vegetables Houck’s specification
Asymmetry in retail-wholesale and shipping-wholesale markets
Kinnucan and Forker, 1987
US dairy products (fluid milk, cheese, butter and ice cream)
Prices in levels, with marketing cost in the equation
Asymmetry in farm-retail market
Boyd and Brorsen, 1988
US pork Same as Kinnucan and Forker’s
Symmetry in farm-wholesale and wholesale-retail markets
Reeder, 2000
Philippine rice Same as Kinnucan and Forker’s except that prices are in first difference, equation in log form, with seasonal dummy
Symmetry in farm-wholesale and wholesale-retail markets
Parrot et al., 2001
US fresh tomato Kinnucan and Forker’s specification
Symmetry in shipping-retail market
Griffith and Piggot, 1994
Australian beef, lamb and pork
Kinnucan and Forker’s specification
Symmetry in farm-wholesale and farm-retail in pork market Asymmetry in farm-wholesale and wholesale-retail in beef market Asymmetry in farm-wholesale and wholesale-retail in lamb market
Zhang et al., 1995
US peanut and peanut butter
Kinnucan and Forker’s specification
Asymmetry in short-run Symmetry in long-run
Worth, 2000
US vegetables Kinnucan and Forker’s specification
Symmetry on shipping-retail markets of carrots and tomatoes
30 30
Table 2.1 continued
Author, Year
Commodity Methodological Approach
Result
Girapunthong et al., 2004
US fresh tomato Ward’s specification Symmetry in farm-retail market Asymmetry in farm and wholesale market
2.2.2 VAR and Other Models
As the use of Wolffram-Houck model became prominent in the
agricultural researches, Cramon-Taubadel (1997) criticized this approach by proving
its inconsistency with the properties of time series variables. He pointed out that since
most of the time series data (e.g., prices at different market levels) are non-stationary
(with mean and variance changing over time), the use of the Wolffram-Houck model
is irrelevant. He explained that the presence of first-order autocorrelation in the
previous specifications using the model is an indication that there is a spurious
regression (Granger and Newbold, 1974). This result may lead the researchers to
conclude that there is a significant relationship among the variables involved but in
real sense it has no economic meaning. On the other hand, if the prices are
cointegrated (the linear combination of integrated and non-stationary variables is
stationary) then spurious regression is avoided. As described by Tian (2006), Mohanty
et al (1995) tried to address this issue of spurious regression by estimating the
Wolffram-Houck model using the first differences of the variables. However, von
Cramon-Taubadel (1998) nullified this approach and stated that this model is
incompatible with a cointegrated system since estimating the model in its first
differences results to a loss in information on the long run relationship. This
31 31
exposition resulted in the development of new models (e.g., error correction model or
ECM) on price transmission. Since then the test for cointegration and non-stationarity
of time series variables has became a requirement before using any models on
asymmetric price transmission.
Von Cramon-Taubadel (1998) was the first to introduce the asymmetric
error correction model where the concept of cointegration is already incorporated.
This model allows for asymmetric adjustments by distinguishing between positive and
negative shocks to error correction terms. It is also believed that this model works well
for determining the asymmetric price transmission between cointegrated non-
stationary time series data.
Previous studies that adopted this approach are also listed by Frey and
Manera (2005). The list includes the following: (i) von Cramon-Taubadel and Loy
(1996) for spatial asymmetric transmission on the world wheat markets, (ii) Scholnick
(1996) for asymmetric adjustment on interest rates, (iii) von Cramon-Taubadel (1998)
for the German pork market, among others.
Multivariate extensions of Wolffram-Houck Model (an example of
autoregressive distributive lag model) have been used in recent empirical studies and
these include vector autoregressive (VAR), vector error correction (VEC) and vector
regime switching (VRS) models among others. VAR models, as discussed by Frey and
Manera in 2005, are used by various authors to examine the magnitude of price
transmission. Capps in 1993 utilized the VAR specification and showed that there is
asymmetry in the contemporaneous impact of cumulative wholesale price changes in
15 meat products of Houston market. In 1997, Willet et. al. used a VAR model with
the dependent variable expressed in first difference level and the independent
variables divided into positive or negative changes of input prices to illustrate that
32 32
shipping point prices of red delicious apples respond symmetrically to wholesale and
retail prices in US. Shepherd (2004) also examined the differences in the transmission
mechanism of coffee prices prior and after the liberalization process in Brazil,
Colombia, Guatemala, India, Mexico and Uganda. The results of the VAR model
estimation showed that after liberalization, asymmetries disappeared in India and
Mexico. Awokuse (2007) also used the VAR to show whether China’s food market
liberalization policies in the 1990’s resulted in interregional rice market integration.
The VEC model is a generalization of the ECM model discussed above.
Samples of studies, as outlined by Fey and Manera (2005), that use this model include
the paper of Kirchgassner and Kubbler (1992) which analyzes the price transmission
between German wholesale and retail prices of gasoline and light heating oil and their
corresponding spot prices before and after 1980. The study concluded there is
symmetric price transmission after 1980.
The VRS model is specially developed for those VAR or VEC models
with multiple regimes. Aguero in 2003 examines the transmission between the
wholesale and retail prices of rice, tomatoes and potatoes in Peru. Symmetic
transmission is found out for tomatoes but not for rice and potatoes.
This thesis uses the Wolffram-Houck model given its main objective to
validate the results presented in the previous study by Reeder (2000) on price
transmission in the Philippine rice industry. Though this thesis and Reeder’s paper
used the same model, the W-H model employed on this study has a different
specification from Reeder’s model. Instead of using an intercept, a time trend is
employed as one of the explanatory variables. In addition, recently available data is
utilized, thereby, extending the timeframe of the study as well as capturing the recent
development in the Philippine rice market liberalization regime. Further, the VAR
33 33
model is also used to further elaborate the magnitude and speed of price transmission
across rice market levels.
The Reeder paper divided the data into sub-periods (1973 to 1985 and
1986 to 1996) to account for the degree of government’s control in the rice market. It
should be noted that between 1973 and 1985, the government heavily intervened in
the rice market by not allowing the private sector to participate in rice trading. Beyond
1985, however, the government implemented rice deregulation by slowly allowing the
private sector to be involved in rice marketing.
2.3 Chapter Summary
Most of the earlier studies on asymmetric price transmission used the
Wolffram-Houck model which assumes that the output price (response variable) is
caused by the increasing and decreasing phases of the input price (explanatory
variable). This model was used with different specifications. Earlier studies such as
those by Ward (1982) and Kinnucan and Forker (1987) used price level as the
dependent variables. Studies [e.g., those by Bailey and Brorsen (1996) and Bernard
and Willet (1996)] which tested for the time series properties (non-stationary,
cointegrated) of the data used price in first difference form. In 1998, the asymmetric
error correction model was introduced by Cramon-Taubadel to address the
cointegration property of the time series data. After this, various models have been
developed which are known as the multivariate extension of the Wolffram-Houck
model. These models include the vector autoregressive (VAR), vector error correction
(VEC) and vector regime switching (VRS) models among others.
34 34
This thesis adopts the Wolffram-Houck model with prices expressed in
first difference form to solve for the non-stationarity problem of the data. The VAR
model is likewise used to address this non-stationarity problem.
35 35
Chapter 3
EMPIRICAL METHODOLOGY
This Chapter includes the description of the data to be used in the
estimation of the Wolffram-Houck and the VAR models, methods on analyzing time-
series data. In addition, the step by step procedure in doing the Wolffram-Houck and
VAR estimation is also discussed here. The Wolffram-Houck model in this study
follows the Kinnucan and Forker’s specification (1987) except that marketing cost is
not included due to the unavailability of data.
3.1 Data
The monthly data on the Philippine rice prices at all market levels as
published by the Philippine Department of Agriculture- Bureau on Agricultural
Statistics is used in the study. The data on farm, wholesale and retail prices gathered
from 1973 to 2005 is expressed in pesos per kilogram. These data are used to
determine the transmission mechanism of all prices at different market levels during
the said period. Given the long time span of the price time series data, the Cusum of
Squares and Chow Breakpoint tests are employed to determine the presence of
structural break in the dataset.
3.2 Time Series Analyses
Before proceeding with any time series estimation, the unit root test is
needed in order to determine the stationarity of the data. This is because most of the
time series data are non-stationary and models of asymmetric price transmission are
36 36
only applicable for stationary data. The Wolffram-Houck model, in particular, is
proven to be incompatible with non-stationary and cointegrated time series data. The
Error Correction model and the VAR estimated in levels, however, are suited for non-
stationary and cointegrated data.
Time series, as defined by Vavra and Goodwin (2005), is a sequence of
data points measured at successive times at a given time intervals (weekly, monthly,
quarterly, yearly, etc.). It exhibits a trend component, among others. The trend, can be
positive or negative, is the long term pattern in the time series. If a time series does not
show a rising or falling pattern, it is said to be stationary in the mean. A non-stationary
time series can become stationary after differencing d times. It is said to be integrated
at I(d) where d is the order of integration. The order of integration is the number of
unit roots contained in the series.
On the other hand, cointegration deals with the long run relationship
among non-stationary, integrated variables. This is said to exist when the linear
combination of integrated variables are stationary and the variables have similar unit
root. This also implies that while variables move apart from each other in the short
run, they will move together in the long run. Cointegrated variables are best used for
an error correction model. VAR model using data in levels can also be used for
cointegrated variables.
There are several techniques available to analyze time series data. The
most popular one is the time plot where a given variable is plotted against time. Others
include the unit root test (e.g., Augmented Dickey-Fuller and Phillips-Perron tests)
and cointegration test (e.g., Johansen cointegration test).
37 37
3.2.1 Analysis of Unit Root (Non-stationarity)
In testing for the unit root, the following model with autoregressive
process of order 1 [AR (1)] is used:
Pt = β0 + β1Pt-1 + εt (3.1)
If β1= 1, then series Pt has a unit root (series is non-stationary). For a
series to be stationary, β1 should be less than unity. Equation 3.1 is only applicable to
a series without serial correlation or with AR (1) process. For series which are
correlated at higher order lags, the Augmented Dickey Fuller test is applicable. It
corrects for higher order correlation by assuming that a given series follows an AR(i)
process and adding i lagged difference terms of the dependent variable to the right
hand side of the regression. It is specified as:
ΔPt = β0 + β1Pt-1 + ΣαiΔPt-i + εt (3.2)
with the same null and alternative hypothesis as with the Dickey-Fuller test:
H0: β1 = 1
H1: β1 < 1
The number of lag lengths is determined through the use of model
selection criteria such as the Akaike information criterion (AIC), Schwarz-Bayesian
information criterion (SIC), Hannan-Quinn information criterion (HQ) and others. In
Eviews, lag lengths are selected from the model that gives the lowest value of model
selection criterion. The lag lengths should be large enough to erase serial correlation
in the residual. In choosing exogenous variables to be included in the model, the
following are taken into consideration. First, the intercept and time trend is included if
38 38
the series suggests time trend but does not have zero mean. Second, only the intercept
is used when the series does not reflect time trend. Third, the intercept and time trend
are not included if the series has no time trend but has zero mean.
3.2.2 Cointegration Analysis
The Johansen cointegration test is viewed as multivariate generalization of
the Dickey-Fuller test (Enders, 2004). As explained in Eview’s 4.1 User’s Guide, the
cointegration test determines whether a group of non-stationary series is cointegrated
or not. The VAR model:
yt = A1yt-1 + …. + Apyt-p + Bxt + εt (3.3)
where yt is a k-vector of non-stationary I(1) variables, xt is a d-vector of deterministic
variables, and εt is a vector of innovations, can be expressed as:
P-1
Δyt = πyt-1 + Σ Γi Δyt-i + Bxt + εt (3.4) i=1
p p
where π = Σ Ai-I and Γi = - Σ Aj (3.5) i=1 j=i+1
If the coefficient matrix π has reduced rank r < k , then there exist k x r
matrices α and β each with rank r such that π =αβ’ and β’yt is I(0). The rank r is the
number of cointegrating relations and each column of β is the cointegrating vector.
To determine whether cointegrating vectors exist, the trace test is used.
This method is based on the log-likelihood ratio ln [Lmax(r)/Lmax(k)] and is
conducted sequentially for r = k-1,….,1,0. This tests the null hypothesis that the
cointegration rank is less than or equal to r against the alternative hypothesis that the
39 39
cointegration rank is equal to k. The alternative hypothesis implies that a series is
trend stationary.
3.3 Wolffram-Houck Model
The Wolffram-Houck model is expressed as:
i j Pout,t = β0t + Σ βt
+ΔP+in, t + Σ βt
-ΔP-in,t + εt (3.6)
t=0 t=0
where Pout,t = t period output price deviations from initial values
Pin = input price
t = time trend
i ΣΔP+
in,t = accumulated increasing input prices t=0
j ΣΔP-
in,t = accumulated decreasing input prices t=0
εt = error term (assumed to be εt ~N(0, σ2)
Pout,t = Pout,t – Pout,0 (3.7)
ΔPin,t = Pin,t+1 – Pin,t (3.8)
ΔP+in,t = max (ΔPin,t , 0) (3.9)
ΔP-in,t = min (ΔPin,t , 0) (3.10)
40 40
Equation 3.6 assumes that output price is caused by the input price.
i j ΣΔP+
in, t is always positive and ΣΔP-in,t is always negative. i and j are the lag orders of
t=0 t=0
accumulated increasing and decreasing prices, respectively. As cited by Wang (2006),
Meyer and von Cramon-Taubadel (2004) explained that the number of lags of
accumulated increasing and decreasing prices can be different from each other since
there is no a priori reason to assume that they are equal. But in most empirical studies,
i and j are always equal given the set-up for speed asymmetry hypothesis test below.
In practice, the higher order of lags is used in the estimation.
Equation 3.6 is used to measure both the magnitude (degree) and the
speed of price transmission. In the long run, to determine whether asymmetry in
magnitude exists, the coefficients of the accumulated increasing and decreasing input
prices are compared. Hence, to test if there is a significant difference in the magnitude
of responses of output prices when input prices move up or down, the following
hypotheses are used.
i j
H0: Σ βt+ = Σ βt
- t=0 t=0
i j H1: Σ βt
+ ≠ Σ βt- (3.11)
t=0 t=0
To test for asymmetry in the speed of price transmission on a specific
period, however, the individual coefficients are compared using the hypotheses below.
This specifically tests whether the output prices adjust at the same speed when input
prices go up or down.
41 41
H0: βi
+ = βj-
H1: βi+ ≠ βj
- (3.12)
Each of these hypotheses is separately tested using the Wald Coefficient
test in E-views. Failure to reject the null hypotheses in Equation 3.11 and 3.12
indicates price symmetry.
The significance and the magnitude of each coefficient in the model can
also be used to further elaborate the results from the hypotheses tests above. For
instance, when the coefficient for the lagged input price (βt-1+) is significantly different
from zero, then the output price continues to respond to input price changes in the
second month. If the coefficients for the current months’ price changes (βt+ and βt
-) are
larger than those for the lagged price changes (βt-1+ and βt-1
-), this means that most of
the adjustment is completed in the month following the shocks. Output price
overshoots in reacting to price changes in the input price when the coefficients for the
current price changes (βt- and βt
+) are larger than 1. Moreover, if the coefficient for
the positive price changes for the current month (βt+) is larger than the coefficient for
the negative price changes for the current month (βt-) price increases in the input price
have more immediate impact on output price than price decreases.
3.4 VAR Model
As defined in Eviews 4.1 User’s Guide, the VAR is commonly used for
forecasting systems of interrelated time series thus this avoid the need for structural
modeling by treating every endogenous variable in the system as a function of the
lagged values of all of the endogenous variables in the system. Hence, simultaneity is
not an issue and OLS yields consistent estimates. The VAR model is represented as:
42 42
yt = A1yt-1 + …. + Apyt-p + Bxt + εt (3.13)
where yt is a k vector of endogenous variables (farm, wholesale and retail prices); xt is
a d vector of endogenous variables (farm, wholesale and retail prices); A1,….,Ap and
B are matrices of coefficients to be estimated; and εt is a vector of innovations
(uncertainty) that may be contemporaneously correlated but are uncorrelated with
their own lagged values and with all of the right-hand side variables.
For example, the VAR model for the farm and wholesale price with 2
lagged values and a constant as the only exogenous variable can be written as follows:
Wt = a11Wt-1 + a12Ft-1 + b11Wt-2 + b12Ft-2 + c1 + ε1t
Ft = a21Wt-1 + a22Ft-1 + b21Wt-2 + b22Ft-2 + c2 + ε2t (3.14)
where aij, bij, and ci are the parameters to be estimated. In order to determine the price
transmission mechanism using this VAR model, the analysis of the forecast error
variance decompositions (FEVDs) is employed. This FEVD analysis uses the
Choleski decomposition as the causal ordering of the variables where the variable that
is believed to have the greatest impact in the VAR model is ranked first. Variance
decomposition is obtained by shocking the VAR equation for each endogenous
variable by 1 standard deviation of the innovation term. The FEVD, therefore, is the
contribution of each source of innovations to the variance of the n-period-ahead
forecast error for each endogenous variable for horizons specified in the model. The
analysis of the FEVD also allows for the determination of the exogenous or
endogenous variables in the VAR model at various forecast horizons.
43 43
3.5 Chapter Summary
This thesis employed the unit root test (ADF and PP test) to determine
whether the price data is stationarity or non-stationary. As mentioned in the literature,
this test is primarily required in time series modeling. Based from the results of this
unit root test, the Wolffram Houck (Equation 3.6) and the VAR models are estimated.
Compared to Reeder’s paper, the Wolffram-Houck in this study uses time trend
instead of an intercept and seasonal dummy variable is omitted. In addition, prices are
estimated using their first difference form. Equation 3.6 allows for testing whether
asymmetry in price transmission at all market levels exists in terms of magnitude
(long run) and speed (short run). The VAR model is estimated to further elaborate the
price transmission for all market levels through the use of the FEVD. Moreover, given
the long time period of the dataset (1973 to 2005), the Cusum of Squares and Chow
Breakpoint tests are applied to determine the presence of structural break.
44 44
Chapter 4
EMPIRICAL RESULTS
The following results of the various tests presented in Chapter 3 are
discussed in this Chapter. First, the results from the Cusum of Squares and Chow
Breakpoint tests are presented, given the very long span (396 months) of the price
time-series data. Second, the stationarity of the time-series price data is presented as
obtained from the Augmented Dickey-Fuller and Phillips-Perron tests. Third, the
results of the cointegration test are presented. Fourth, the direction of price causality
from the Granger Causality (GC) tests is presented. This section includes the
comparison of results from the OLS-based GC test and from the GC test conducted
under the context of the VAR model. Fifth, the existence of price asymmetry or
symmetry (in terms of magnitude and speed) on the various marketing levels of the
Philippine rice industry is discussed from the estimation of the Wolffram-Houck
model. Lastly, the results of the FEVD analysis from the VAR model estimation are
presented.
As used in the previous studies, all of the time series data on this thesis are
expressed in logarithms since this helps the volatility of the data to be more constant
as well as solve for the heteroscedasticity (unequal variance) problem of the data.
4.1 Results of the Cusum of Squares Test
Figure 4.1 to 4.3 show the time plot of the farm, wholesale and retail
prices. All of the graphs clearly suggest that there might be structural breaks in the
45 45
data for the period 1985 as the trend seems to increase after 1984. To empirically test
for this, the Cusum of Squares test is conducted. From the examination of the
recursive residuals, the Cusum of Squares test results suggest a possible break in 1985
as the cumulative sum of residuals move out of the 5 percent critical lines (Figures 4.4
to 4.9)
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1975 1980 1985 1990 1995 2000 2005
FARM
Figure 4.1 Time Plot of the Philippine Rice Farm Prices, 1973 to 2005
46 46
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1975 1980 1985 1990 1995 2000 2005
WHOLESALE
Figure 4.2 Time Plot of the Philippine Rice Wholesale Prices, 1973 to 2005
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1975 1980 1985 1990 1995 2000 2005
RETAIL
Figure 4.3 Time Plot of the Philippine Rice Retail Prices, 1973 to 2005
47 47
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1975 1980 1985 1990 1995 2000 2005
CUSUM of Squares 5% Significance
Figure 4.4 Plot of Recursive Residuals for the Cusum of Squares Test: Farm to Retail Prices, 1973 to 2005
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1975 1980 1985 1990 1995 2000 2005
CUSUM of Squares 5% Significance
Figure 4.5 Plot of Recursive Residuals for the Cusum of Squares Test: Farm to Wholesale Prices, 1973 to 2005
48 48
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1975 1980 1985 1990 1995 2000 2005
CUSUM of Squares 5% Significance
Figure 4.6 Plot of Recursive Residuals for the Cusum of Squares Test: Retail to Farm Prices, 1973 to 2005
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1975 1980 1985 1990 1995 2000 2005
CUSUM of Squares 5% Significance
Figure 4.7 Plot of Recursive Residuals for the Cusum of Squares Test: Retail to Wholesale Prices, 1973 to 2005
49 49
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1980 1985 1990 1995 2000 2005
CUSUM of Squares 5% Significance
Figure 4.8 Plot of Recursive Residuals for the Cusum of Squares Test: Wholesale to Farm Prices, 1973 to 2005
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1980 1985 1990 1995 2000 2005
CUSUM of Squares 5% Significance
Figure 4.9 Plot of Recursive Residuals for the Cusum of Squares Test: Wholesale to Retail Prices, 1973 to 2005
50 50
4.2 Results of the Chow Breakpoint Test
The results of the Chow tests in Table 4.1 below further validate the
structural break for the period 1985. Hence, the dataset are divided into 2 subperiods:
(i) 1973 to 1984 and (ii) 1985 to 2005.
Table 4.1 Results of the Chow Breakpoint Test
All Period (1973 to 2005) F-statistic P-value farm to wholesale 2.04* 0.02 wholesale to retail 2.46** 0.00 farm to retail 3.58** 0.00 wholesale to farm 1.68* 0.02 retail to wholesale 2.53** 0.00 retail to farm 3.29** 0.00 rejection of null hypothesis at: ** 1%, *2%
Based from the history of the Philippine rice industry, it is in 1985 when
the government started to deregulate the rice industry. Unlike the earlier years when
the government, through the NFA, exclusively import rice, it is in 1985 when the NFA
slowly started to allocate import quotas to the public (transition stage to rice
liberalization regime). It is already in 1996 when food security in staple cereals (e.g.
rice and corn) in times and places of natural or man-made calamities or emergencies
was included in the mandate of the NFA. This food security mandate includes the
stabilization of supply and prices of rice at farm and consumer levels. Rice industry
deregulation is further strengthened by the passage of the Agriculture and Fisheries
Modernization Act (AFMA) in 1997 which defined food security as a way to make
food available and affordable to all Filipinos through domestic production or
importation.
51 51
The empirical analyses conducted on this study are, therefore, divided into
2 subperiods: (i) 1973-1984 to represent pre-rice liberalization period, (ii) 1985-2005
for the period of rice liberalization which is further strengthened with the NFA’s
mandate on food security. A separate analysis for the whole time span of the data
(from 1973 to 2005) is also included for each test. The significance level of 5 percent
or lower is used for all of the tests.
4.3 Results of the Unit Root Test
To test for non-stationarity of all the price variables, the unit root tests
known as the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests are
conducted. This is to compare the results of one test to another when inconclusive
results arise. These tests have null hypothesis of non-stationarity. Both the intercept
and time trend are included in testing for unit root of all prices in levels since all of the
price series shows apparent time trend but does not appear to have a zero mean
(Figure 4.1 to 4.3). However, in testing for unit root of prices in first difference, only
the intercept is included since time trend is already removed in this process making it
not significant.
As indicated in Table 4.2, while both the ADF and PP tests yield similar
results for all prices (in levels and in first difference), the maximum lag length or
bandwidth chosen by the SIC (for ADF test) and Newey-West (for PP test) differs. In
summary, both the ADF and PP tests results suggest that price variables estimated in
levels are non-stationary while those estimated in first differences are already
stationary I(1). Therefore, the Wolffram-Houck model is estimated using prices at first
difference form. The cointegration test is also needed to verify whether these data are
cointegrated or not.
52 52
Table 4.2 Results of Unit Root Test
Augmented Dickey-Fuller Phillips-Perron Market Level Level 1st Difference Level 1st Difference
All years (1973 to 2005) Farm Price -1.98 -5.15 (1, 14) -2.30 -13.72 (1, 24) Wholesale Price -2.58 -14.04 (1, 1) -2.14 -13.36 (1, 10) Retail Price -1.95 -17.31 (1, 0) -2.07 -17.17 (1, 6)
First Subperiod (1973 to 1984) Farm Price -1.04 -9.84 (1, 0) -1.20 -9.68 (1, 12) Wholesale Price -1.22 -8.57 (1, 0) -1.64 -8.38 (1, 14) Retail Price -2.02 -11.46 (1, 0) -1.94 -11.45 (1, 10)
Second Subperiod (1985 to 2005) Farm Price -2.49 -3.53 (1, 11) -2.27 -9.74 (1, 22) Wholesale Price -1.96 -11.25 (1, 1) -1.71 -10.40 (1, 25) Retail Price -2.02 -11.55 (1, 0) -1.55 -11.08 (1, 13) First number inside the parenthesis - rejection of null hypothesis at 1% significance level: Second number inside the parenthesis - lag length chosen by SIC for ADF, bandwidth chosen by Newey-West for PP
4.4 Results of the Cointegration Test
To determine whether there are cointegrating relationships among all of
the prices, the Johansen Cointegration test is used. The optimum lag length as
specified by the SIC is utilized for all of the subperiods as well as for the whole
period. For the whole period and second subperiod the lag length chosen by the SIC is
2 months while for the first subperiod the lag length is 1 month.
The trace test in Table 4.3 indicates that for the whole period, there are 3
cointegrating equations at 5 percent significance level and for the first and second
subperiods there are 2 cointegrating equations. These results imply that a VAR model
estimated in level should be used to analyze the transmission of prices from one
market level to another.
53 53
Table 4.3 Results of Cointegration Test
All Period (1973 to 2005)
First Subperiod (1973 to 1985)
Second Subperiod (1986 to 1995)
Number of Cointegrating Vectors (r ) Trace Stat C(5%) Trace Stat C(5%) Trace Stat C(5%)
None 137.78** 29.68 75.60** 29.68 101.92** 29.68 At most 1 63.35** 15.41 30.44** 15.41 43.3** 15.41 At most 2 3.88* 3.76 0.39 3.76 1.29 3.76 *(**) - rejection of null hypothesis of cointegration rank r at 5% (1%) significance level
4.5 Results of the Granger-Causality Test
To determine the causation of prices, the Granger-Causality test is
employed. The GC test is done in two ways: (i) the GC test in the context of OLS and,
(ii) the GC test in the context of vector autoregression (VAR). The GC test answers
the question of whether the past values of y (output price) and x (input price) causes
the current value of y using an F-test. The input price, x, is said to Granger-causes the
output price, y, when the past values of x helps in the prediction of y or when the past
values of x is statistically significant. This test has a null hypothesis of x does not
Granger cause y.
The GC test in the context of VAR, also known as the Pairwise GC test,
allows endogenous variable to be treated as exogenous. For each equation in the VAR,
the output displays Wald statistics for the joint significance of each of the other lagged
endogenous variables in that equation. The statistic in the last row (All) is the statistic
for joint significance of all other lagged endogenous variables in the equation (Eviews
4.1 User’s Guide).
Lag length determination plays a very critical role in the conduct of GC
test. Model selection criteria such as the AIC, SBC, HQIC and others are usually used
as basis in determining the optimum lag length. In Eviews estimation, the model that
54 54
gives the lowest information criterion is used for the selection of the optimum lag
length. Lag length is the number of lags of x (not of y) in the OLS-based GC test
equation. In doing the GC test, it is best to start with a larger lag length (at least 12 for
a monthly data and 4 for a quarterly data) although the literature says that there is
really no maximum lag length for time series data.
Lag length determination in the OLS-based GC test might take time since
there is a need to re-estimate the OLS over and over again by decreasing the number
of lag lengths until a regression results in the lowest information criterion. But unlike
this OLS-based GC test, the Pairwise GC test is easier to do as the estimated VAR
equation automatically computes for the optimum lag length through the VAR lag
order selection criteria option in Eviews.
For this thesis, the lag length determined through the VAR equations
(Tables 4.4 to 4.6) for the whole period and the 2 subperiods with the minimum SIC is
used for both the OLS-based and Pairwise GC test. The corresponding lag lengths (2
months for both the whole period and second subperiod and 1 month for the first
subperiod) selected by the SIC for the whole period and 2 subperiods are shown in
Tables 4.7 to 4.9.
55 55
Table 4.4 VAR Estimation for the Whole Period (1973 to 2005)
Vector Autoregression Estimates Date: 04/21/08 Time: 13:02 Sample(adjusted): 1974:01 2005:12 Included observations: 384 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL FARM(-1) 1.163409 0.428143 0.327468
(0.06376) (0.04661) (0.04757) [ 18.2471] [ 9.18563] [ 6.88423]
FARM(-2) -0.259300 -0.216789 -0.200154 (0.08763) (0.06406) (0.06537) [-2.95914] [-3.38422] [-3.06162]
FARM(-3) -0.153680 -0.086510 -0.053949 (0.08843) (0.06465) (0.06598) [-1.73782] [-1.33817] [-0.81770]
FARM(-4) 0.002742 0.052251 0.048362 (0.08617) (0.06299) (0.06428) [ 0.03182] [ 0.82952] [ 0.75231]
FARM(-5) 0.092648 0.018392 0.079360 (0.08317) (0.06080) (0.06205) [ 1.11396] [ 0.30249] [ 1.27897]
FARM(-6) -0.171729 -0.126757 -0.140316 (0.08303) (0.06070) (0.06194) [-2.06835] [-2.08838] [-2.26524]
FARM(-7) 0.171374 0.095561 0.050437 (0.08334) (0.06092) (0.06217) [ 2.05638] [ 1.56855] [ 0.81120]
FARM(-8) -0.207417 -0.044225 -0.021023 (0.08364) (0.06114) (0.06240) [-2.47991] [-0.72330] [-0.33691]
FARM(-9) 0.076174 -0.048284 -0.085758 (0.08169) (0.05972) (0.06095) [ 0.93245] [-0.80851] [-1.40709]
FARM(-10) -0.049218 0.009796 0.073711 (0.08179) (0.05979) (0.06102) [-0.60176] [ 0.16383] [ 1.20796]
FARM(-11) 0.287589 0.054321 0.023963
(0.08110) (0.05929) (0.06051) [ 3.54596] [ 0.91619] [ 0.39602]
56 56
Table 4.4 continued Vector Autoregression Estimates Date: 04/21/08 Time: 13:02 Sample(adjusted): 1974:01 2005:12 Included observations: 384 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL FARM(-12) 0.015377 -0.031337 -0.037600
(0.06543) (0.04783) (0.04881) [ 0.23502] [-0.65514] [-0.77026]
WHOLESALE(-1) -0.081618 0.808162 0.442090 (0.10464) (0.07650) (0.07807) [-0.77996] [ 10.5644] [ 5.66270]
WHOLESALE(-2) -0.131342 -0.075585 -0.284483 (0.12818) (0.09370) (0.09563) [-1.02469] [-0.80665] [-2.97488]
WHOLESALE(-3) 0.173100 -0.046681 -0.017612 (0.12965) (0.09478) (0.09672) [ 1.33518] [-0.49254] [-0.18208]
WHOLESALE(-4) 0.010772 0.063826 0.046992 (0.12978) (0.09487) (0.09682) [ 0.08300] [ 0.67277] [ 0.48535]
WHOLESALE(-5) -0.058915 0.089663 0.094744 (0.12788) (0.09349) (0.09541) [-0.46069] [ 0.95908] [ 0.99303]
WHOLESALE(-6) -0.097641 -0.133092 -0.145179 (0.12696) (0.09281) (0.09472) [-0.76909] [-1.43402] [-1.53276]
WHOLESALE(-7) 0.133511 0.039862 0.102132 (0.12715) (0.09295) (0.09486) [ 1.05000] [ 0.42883] [ 1.07662]
WHOLESALE(-8) -0.063561 -0.128887 -0.057063 (0.12620) (0.09226) (0.09415) [-0.50366] [-1.39705] [-0.60607]
WHOLESALE(-9) -0.028542 0.025574 -0.027376 (0.12649) (0.09247) (0.09437) [-0.22564] [ 0.27656] [-0.29009]
WHOLESALE(-10) 0.139371 0.027856 -0.087215 (0.12514) (0.09148) (0.09336) [ 1.11372] [ 0.30450] [-0.93416]
57 57
Table 4.4 continued Vector Autoregression Estimates Date: 04/21/08 Time: 13:02 Sample(adjusted): 1974:01 2005:12 Included observations: 384 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL WHOLESALE(-11) -0.197567 0.045441 -0.015201
(0.12127) (0.08865) (0.09047) [-1.62917] [ 0.51258] [-0.16802]
WHOLESALE(-12) -0.120463 -0.019382 0.037734 (0.10540) (0.07705) (0.07864) [-1.14288] [-0.25153] [ 0.47985]
RETAIL(-1) 0.143076 0.054724 0.423743 (0.09444) (0.06904) (0.07046) [ 1.51497] [ 0.79264] [ 6.01401]
RETAIL(-2) 0.089972 -0.085151 0.183288 (0.10098) (0.07382) (0.07534) [ 0.89097] [-1.15346] [ 2.43284]
RETAIL(-3) 0.073161 0.215183 0.128694 (0.10321) (0.07545) (0.07700) [ 0.70882] [ 2.85185] [ 1.67126]
RETAIL(-4) 0.022821 -0.085836 -0.115362 (0.10090) (0.07376) (0.07528) [ 0.22618] [-1.16369] [-1.53249]
RETAIL(-5) 0.105660 0.032986 0.055750 (0.10068) (0.07360) (0.07512) [ 1.04944] [ 0.44816] [ 0.74219]
RETAIL(-6) 0.057697 0.110381 0.139538 (0.10070) (0.07362) (0.07513) [ 0.57294] [ 1.49936] [ 1.85725]
RETAIL(-7) -0.067839 -0.029279 -0.051432 (0.10100) (0.07383) (0.07535) [-0.67169] [-0.39655] [-0.68256]
RETAIL(-8) 0.056559 0.069130 -0.014515 (0.10036) (0.07337) (0.07487) [ 0.56356] [ 0.94225] [-0.19386]
58 58
Table 4.4 continued Vector Autoregression Estimates Date: 04/21/08 Time: 13:02 Sample(adjusted): 1974:01 2005:12 Included observations: 384 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL RETAIL(-9) -0.076263 -0.031042 0.089963
(0.10051) (0.07348) (0.07499) [-0.75877] [-0.42248] [ 1.19974]
RETAIL(-10) -0.063165 -0.046686 -0.000846 (0.10072) (0.07363) (0.07514) [-0.62714] [-0.63406] [-0.01126]
RETAIL(-11) 0.060609 0.002820 0.068957 (0.09767) (0.07140) (0.07287) [ 0.62052] [ 0.03950] [ 0.94629]
RETAIL(-12) -0.052348 -0.012255 -0.061540 (0.08499) (0.06213) (0.06341) [-0.61594] [-0.19725] [-0.97055]
C -0.015235 0.026975 0.024369 (0.01906) (0.01393) (0.01422) [-0.79943] [ 1.93621] [ 1.71392]
R-squared 0.998489 0.999190 0.999175 Adj. R-squared 0.998332 0.999106 0.999090 Sum sq. resids 0.079404 0.042435 0.044197 S.E. equation 0.015127 0.011059 0.011286 F-statistic 6367.573 11892.64 11678.69 Log likelihood 1084.027 1204.330 1196.519 Akaike AIC -5.453265 -6.079842 -6.039161 Schwarz SC -5.072604 -5.699181 -5.658500 Mean dependent 0.534465 0.810430 0.841707 S.D. dependent 0.370359 0.369882 0.374075 Determinant Residual Covariance 1.42E-12 Log Likelihood (d.f. adjusted) 3602.866 Akaike Information Criteria -18.18680 Schwarz Criteria -17.04482
59 59
Table 4.5 VAR Estimation for the First Subperiod (1973 to 1984)
Vector Autoregression Estimates Date: 04/21/08 Time: 12:57 Sample(adjusted): 1974:01 1984:12 Included observations: 132 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL FARM(-1) 0.855297 0.216204 0.326356
(0.11520) (0.09644) (0.11797) [ 7.42437] [ 2.24193] [ 2.76637]
FARM(-2) -0.023788 0.031561 -0.158847 (0.14152) (0.11847) (0.14493) [-0.16809] [ 0.26640] [-1.09606]
FARM(-3) -0.100365 -0.109273 -0.103280 (0.13522) (0.11319) (0.13847) [-0.74224] [-0.96537] [-0.74586]
FARM(-4) 0.015657 0.107891 0.035583 (0.12675) (0.10610) (0.12980) [ 0.12353] [ 1.01685] [ 0.27414]
FARM(-5) 0.289564 -0.003163 0.173154 (0.12202) (0.10214) (0.12495) [ 2.37315] [-0.03097] [ 1.38577]
FARM(-6) -0.269356 -0.157893 -0.179551 (0.12551) (0.10507) (0.12853) [-2.14605] [-1.50278] [-1.39694]
FARM(-7) 0.190222 0.158093 0.087487 (0.12745) (0.10669) (0.13051) [ 1.49254] [ 1.48181] [ 0.67032]
FARM(-8) -0.132269 -0.097788 -0.124508 (0.12630) (0.10573) (0.12934) [-1.04728] [-0.92493] [-0.96268]
FARM(-9) 0.027470 -0.124984 -0.183126 (0.11574) (0.09689) (0.11852) [ 0.23735] [-1.29000] [-1.54506]
FARM(-10) -0.136873 0.091896 0.158076 (0.11565) (0.09681) (0.11843) [-1.18350] [ 0.94921] [ 1.33473]
FARM(-11) 0.280111 0.036838 0.119731 (0.11276) (0.09439) (0.11548) [ 2.48408] [ 0.39025] [ 1.03686]
60 60
Table 4.5 continued Vector Autoregression Estimates Date: 04/21/08 Time: 12:57 Sample(adjusted): 1974:01 1984:12 Included observations: 132 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL FARM(-12) 0.121938 0.055769 -0.062749
(0.09887) (0.08277) (0.10125) [ 1.23329] [ 0.67381] [-0.61973]
WHOLESALE(-1) -0.104272 0.822800 0.449553 (0.14701) (0.12306) (0.15055) [-0.70929] [ 6.68599] [ 2.98616]
WHOLESALE(-2) -0.228723 -0.143470 -0.278223 (0.19139) (0.16022) (0.19600) [-1.19506] [-0.89548] [-1.41954]
WHOLESALE(-3) 0.034234 -0.160278 -0.191460 (0.19160) (0.16039) (0.19621) [ 0.17867] [-0.99928] [-0.97578]
WHOLESALE(-4) 0.034357 0.199158 0.131377 (0.19086) (0.15977) (0.19545) [ 0.18001] [ 1.24650] [ 0.67216]
WHOLESALE(-5) -0.070730 0.063318 0.128410 (0.18713) (0.15665) (0.19163) [-0.37798] [ 0.40421] [ 0.67010]
WHOLESALE(-6) -0.061421 0.045984 0.006495 (0.18208) (0.15242) (0.18646) [-0.33733] [ 0.30169] [ 0.03483]
WHOLESALE(-7) 0.166365 -0.059225 0.120788 (0.18214) (0.15247) (0.18652) [ 0.91341] [-0.38844] [ 0.64760]
WHOLESALE(-8) -0.023862 -0.190518 -0.122093 (0.18743) (0.15690) (0.19194) [-0.12731] [-1.21426] [-0.63610]
WHOLESALE(-9) 0.116481 0.159728 0.001731 (0.18616) (0.15584) (0.19064) [ 0.62570] [ 1.02497] [ 0.00908]
WHOLESALE(-10) 0.171443 0.020651 -0.136867 (0.18250) (0.15277) (0.18689) [ 0.93943] [ 0.13517] [-0.73235]
61 61
Table 4.5 continued Vector Autoregression Estimates Date: 04/21/08 Time: 12:57 Sample(adjusted): 1974:01 1984:12 Included observations: 132 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL WHOLESALE(-11) -0.292754 -0.064427 -0.135108
(0.16793) (0.14057) (0.17197) [-1.74334] [-0.45832] [-0.78566]
WHOLESALE(-12) -0.197312 -0.014878 0.185389 (0.15046) (0.12595) (0.15408) [-1.31138] [-0.11812] [ 1.20319]
RETAIL(-1) 0.263327 0.034240 0.251532 (0.11377) (0.09524) (0.11651) [ 2.31446] [ 0.35950] [ 2.15886]
RETAIL(-2) 0.140762 -0.072742 0.233214 (0.11848) (0.09918) (0.12133) [ 1.18810] [-0.73344] [ 1.92219]
RETAIL(-3) 0.069286 0.343664 0.337651 (0.12371) (0.10356) (0.12669) [ 0.56007] [ 3.31854] [ 2.66526]
RETAIL(-4) -0.053372 -0.202926 -0.127726 (0.12332) (0.10324) (0.12629) [-0.43278] [-1.96566] [-1.01137]
RETAIL(-5) 0.082424 0.006433 0.009429 (0.12568) (0.10521) (0.12870) [ 0.65582] [ 0.06115] [ 0.07326]
RETAIL(-6) 0.074647 0.092971 0.044056 (0.12485) (0.10451) (0.12785) [ 0.59792] [ 0.88960] [ 0.34459]
RETAIL(-7) -0.024444 0.037182 -0.010348 (0.12468) (0.10438) (0.12768) [-0.19604] [ 0.35624] [-0.08104]
RETAIL(-8) 0.004321 0.075460 0.033909 (0.12329) (0.10321) (0.12626) [ 0.03504] [ 0.73113] [ 0.26856]
62 62
Table 4.5 continued Vector Autoregression Estimates Date: 04/21/08 Time: 12:57 Sample(adjusted): 1974:01 1984:12 Included observations: 132 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL RETAIL(-9) -0.119342 -0.044470 0.179793
(0.12251) (0.10255) (0.12546) [-0.97415] [-0.43362] [ 1.43311]
RETAIL(-10) -0.057369 -0.081798 -0.003206 (0.12681) (0.10615) (0.12986) [-0.45240] [-0.77056] [-0.02469]
RETAIL(-11) 0.109419 0.033163 0.027986 (0.12201) (0.10213) (0.12494) [ 0.89683] [ 0.32471] [ 0.22399]
RETAIL(-12) -0.089336 -0.077417 -0.201861 (0.11375) (0.09522) (0.11649) [-0.78536] [-0.81300] [-1.73288]
C 0.005129 0.046546 0.024989 (0.03332) (0.02789) (0.03412) [ 0.15392] [ 1.66875] [ 0.73235]
R-squared 0.989961 0.992479 0.987961 Adj. R-squared 0.986156 0.989630 0.983399 Sum sq. Resids 0.019904 0.013948 0.020873 S.E. equation 0.014475 0.012117 0.014823 F-statistic 260.2183 348.2515 216.5615 Log likelihood 393.4761 416.9451 390.3381 Akaike AIC -5.401152 -5.756744 -5.353607 Schwarz SC -4.593094 -4.948686 -4.545549 Mean dependent 0.076484 0.353683 0.378316 S.D. dependent 0.123022 0.118986 0.115045 Determinant Residual Covariance 3.91E-12 Log Likelihood (d.f. adjusted) 1171.747 Akaike Information Criteria -16.07192 Schwarz Criteria -13.64774
63 63
Table 4.6 VAR Estimation for the Second Subperiod (1985 to 2005)
Vector Autoregression Estimates Date: 04/21/08 Time: 13:00 Sample: 1985:01 2005:12 Included observations: 252 Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL FARM(-1) 1.273360 0.519530 0.346196
(0.08465) (0.05676) (0.04789) [ 15.0418] [ 9.15301] [ 7.22835]
FARM(-2) -0.295992 -0.356776 -0.266533 (0.12020) (0.08059) (0.06800) [-2.46250] [-4.42687] [-3.91936]
FARM(-3) -0.150168 -0.022519 -0.025708 (0.12288) (0.08239) (0.06952) [-1.22211] [-0.27332] [-0.36980]
FARM(-4) 0.029894 -0.005528 0.027953 (0.12658) (0.08487) (0.07162) [ 0.23616] [-0.06513] [ 0.39031]
FARM(-5) -0.110845 0.060378 0.057639 (0.12801) (0.08583) (0.07242) [-0.86589] [ 0.70345] [ 0.79585]
FARM(-6) 0.099130 -0.004501 -0.003109 (0.12901) (0.08650) (0.07299) [ 0.76839] [-0.05204] [-0.04259]
FARM(-7) 0.038597 -0.014573 -0.060007 (0.13017) (0.08728) (0.07364) [ 0.29652] [-0.16697] [-0.81484]
FARM(-8) -0.214054 -0.075910 -0.031930 (0.12878) (0.08635) (0.07286) [-1.66216] [-0.87913] [-0.43825]
FARM(-9) 0.082262 0.036441 0.023938 (0.12791) (0.08576) (0.07236) [ 0.64315] [ 0.42492] [ 0.33080]
FARM(-10) 0.112494 0.014606 0.050270 (0.12760) (0.08556) (0.07219) [ 0.88158] [ 0.17071] [ 0.69632]
FARM(-11) 0.188466 0.025263 -0.024288 (0.12742) (0.08544) (0.07209) [ 1.47904] [ 0.29569] [-0.33690]
64 64
Table 4.6 continued Vector Autoregression Estimates Date: 04/21/08 Time: 13:00 Sample: 1985:01 2005:12 Included observations: 252 Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL FARM(-12) -0.026543 -0.064545 -0.027412
(0.09960) (0.06678) (0.05635) [-0.26651] [-0.96654] [-0.48647]
WHOLESALE(-1) -0.208497 0.641211 0.244699 (0.18072) (0.12117) (0.10225) [-1.15368] [ 5.29164] [ 2.39323]
WHOLESALE(-2) -0.063193 -0.128269 -0.244195 (0.20528) (0.13764) (0.11614) [-0.30784] [-0.93192] [-2.10259]
WHOLESALE(-3) 0.091719 0.222241 0.349966 (0.20825) (0.13963) (0.11782) [ 0.44042] [ 1.59161] [ 2.97031]
WHOLESALE(-4) 0.062022 -0.243418 -0.247884 (0.21156) (0.14185) (0.11969) [ 0.29316] [-1.71600] [-2.07098]
WHOLESALE(-5) -0.026298 0.173483 0.223633 (0.21480) (0.14402) (0.12153) [-0.12243] [ 1.20456] [ 1.84022]
WHOLESALE(-6) -0.248792 -0.398741 -0.448693 (0.21723) (0.14565) (0.12290) [-1.14528] [-2.73761] [-3.65085]
WHOLESALE(-7) 0.193851 0.326687 0.434470 (0.22265) (0.14928) (0.12596) [ 0.87067] [ 2.18836] [ 3.44914]
WHOLESALE(-8) -0.141799 -0.263477 -0.241939 (0.22619) (0.15166) (0.12797) [-0.62689] [-1.73726] [-1.89057]
WHOLESALE(-9) -0.260905 -0.045946 -0.005006 (0.22520) (0.15099) (0.12741) [-1.15856] [-0.30429] [-0.03929]
WHOLESALE(-10) 0.148636 -0.031508 -0.150123 (0.22298) (0.14951) (0.12616) [ 0.66658] [-0.21074] [-1.18998]
65 65
Table 4.6 continued Vector Autoregression Estimates Date: 04/21/08 Time: 13:00 Sample: 1985:01 2005:12 Included observations: 252 Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL WHOLESALE(-11) -0.083746 0.092693 0.108629
(0.22195) (0.14882) (0.12557) [-0.37732] [ 0.62287] [ 0.86508]
WHOLESALE(-12) -0.242102 0.003469 -0.032584 (0.18250) (0.12237) (0.10325) [-1.32658] [ 0.02835] [-0.31558]
RETAIL(-1) 0.129682 0.299843 0.815915 (0.19963) (0.13385) (0.11294) [ 0.64962] [ 2.24014] [ 7.22421]
RETAIL(-2) 0.046561 -0.103612 -0.082313 (0.23583) (0.15812) (0.13342) [ 0.19744] [-0.65527] [-0.61694]
RETAIL(-3) 0.192805 -0.018494 -0.101423 (0.23342) (0.15651) (0.13206) [ 0.82599] [-0.11817] [-0.76800]
RETAIL(-4) 0.068008 0.168134 0.082962 (0.23409) (0.15696) (0.13244) [ 0.29052] [ 1.07120] [ 0.62641]
RETAIL(-5) -0.076791 -0.161112 -0.202014 (0.23472) (0.15738) (0.13279) [-0.32716] [-1.02373] [-1.52125]
RETAIL(-6) 0.249409 0.428980 0.543234 (0.23167) (0.15533) (0.13107) [ 1.07657] [ 2.76165] [ 4.14459]
RETAIL(-7) -0.122192 -0.295352 -0.395251 (0.23108) (0.15494) (0.13074) [-0.52878] [-1.90623] [-3.02323]
RETAIL(-8) 0.192709 0.326962 0.198846 (0.23624) (0.15840) (0.13366) [ 0.81573] [ 2.06415] [ 1.48773]
66 66
Table 4.6 continued Vector Autoregression Estimates Date: 04/21/08 Time: 13:00 Sample: 1985:01 2005:12 Included observations: 252 Standard errors in ( ) & t-statistics in [ ]
FARM WHOLESALE RETAIL RETAIL(-9) 0.079615 -0.122770 -0.047436
(0.23822) (0.15972) (0.13477) [ 0.33421] [-0.76864] [-0.35196]
RETAIL(-10) -0.214236 0.034713 0.087182 (0.23824) (0.15974) (0.13479) [-0.89925] [ 0.21731] [ 0.64681]
RETAIL(-11) 0.238423 0.078921 0.067433 (0.23805) (0.15961) (0.13468) [ 1.00155] [ 0.49445] [ 0.50068]
RETAIL(-12) -0.037844 -0.100676 -0.028096 (0.17839) (0.11961) (0.10093) [-0.21213] [-0.84168] [-0.27837]
C -0.011344 0.017126 0.025595 (0.02381) (0.01596) (0.01347) [-0.47643] [ 1.07270] [ 1.90000]
R-squared 0.994277 0.997493 0.998233 Adj. R-squared 0.993318 0.997073 0.997937 Sum sq. resids 0.047864 0.021518 0.015321 S.E. equation 0.014921 0.010004 0.008441 F-statistic 1037.517 2376.148 3373.086 Log likelihood 722.0987 822.8325 865.6340 Akaike AIC -5.437291 -6.236766 -6.576460 Schwarz SC -4.919082 -5.718556 -6.058250 Mean dependent 0.774360 1.049678 1.084437 S.D. dependent 0.182534 0.184918 0.185836 Determinant Residual Covariance 3.62E-13 Log Likelihood (d.f. adjusted) 2536.920 Akaike Information Criteria -19.25333 Schwarz Criteria -17.69870
67 67
Table 4.7 Lag Order Selection for VAR, Whole Period (1973 to 2005)
VAR Lag Order Selection Criteria Endogenous variables: FARM WHOLESALE RETAIL Exogenous variables: C Date: 04/21/08 Time: 13:02 Sample: 1973:01 2005:12 Included observations: 384 Lag LogL LR FPE AIC SC HQ
0 1901.414 NA 1.02E-08 -9.887572 -9.856707 -9.875329 1 3500.128 3164.121 2.59E-12 -18.16733 -18.04387 -18.11836 2 3557.715 113.0750 2.01E-12 -18.42039 -18.20434* -18.33469* 3 3571.849 27.53194 1.95E-12 -18.44713 -18.13849 -18.32471 4 3574.757 5.619296 2.02E-12 -18.41540 -18.01416 -18.25625 5 3577.454 5.169577 2.09E-12 -18.38257 -17.88874 -18.18670 6 3589.433 22.77283 2.05E-12 -18.39809 -17.81167 -18.16549 7 3595.527 11.48939 2.09E-12 -18.38295 -17.70394 -18.11363 8 3602.412 12.87231 2.11E-12 -18.37193 -17.60033 -18.06588 9 3623.128 38.41175 1.98E-12 -18.43296 -17.56876 -18.09018
10 3634.222 20.39597 1.96E-12 -18.44386 -17.48707 -18.06435 11 3655.967 39.63958* 1.84E-12* -18.51024* -17.46085 -18.09401 12 3661.225 9.503132 1.87E-12 -18.49075 -17.34877 -18.03779
* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion
68 68
Table 4.8 Lag Order Selection for VAR, First Subperiod (1973 to 1984)
VAR Lag Order Selection Criteria Endogenous variables: FARM WHOLESALE RETAIL Exogenous variables: C Date: 04/21/08 Time: 12:59 Sample: 1973:01 1984:12 Included observations: 132 Lag LogL LR FPE AIC SC HQ
0 777.2334 NA 1.61E-09 -11.73081 -11.66529 -11.70418 1 1149.522 722.0142 6.57E-12 -17.23518 -16.97311* -17.12869* 2 1159.979 19.80452 6.43E-12 -17.25725 -16.79863 -17.07089 3 1171.893 22.02363 6.15E-12* -17.30141* -16.64623 -17.03517 4 1178.233 11.43121 6.41E-12 -17.26111 -16.40937 -16.91500 5 1181.147 5.120953 7.04E-12 -17.16889 -16.12060 -16.74291 6 1187.336 10.59642 7.37E-12 -17.12630 -15.88145 -16.62045 7 1195.708 13.95392 7.46E-12 -17.11679 -15.67539 -16.53107 8 1200.702 8.096653 7.97E-12 -17.05610 -15.41814 -16.39051 9 1209.141 13.29775 8.08E-12 -17.04760 -15.21309 -16.30213
10 1214.447 8.119108 8.61E-12 -16.99162 -14.96055 -16.16629 11 1228.675 21.12607* 8.02E-12 -17.07083 -14.84321 -16.16563 12 1236.874 11.80162 8.21E-12 -17.05869 -14.63452 -16.07362
* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion
69 69
Table 4.9 Lag Order Selection for VAR, Second Subperiod (1985 to 2005)
VAR Lag Order Selection Criteria Endogenous variables: FARM WHOLESALE RETAIL Exogenous variables: C Date: 04/21/08 Time: 13:01 Sample: 1985:01 2005:12 Included observations: 252 Lag LogL LR FPE AIC SC HQ
0 1521.467 NA 1.17E-09 -12.05133 -12.00931 -12.03442 1 2447.421 1822.513 8.09E-13 -19.32874 -19.16067 -19.26111 2 2504.541 111.0655 5.53E-13 -19.71064 -19.41652* -19.59229*3 2513.724 17.63835 5.52E-13 -19.71210 -19.29193 -19.54303 4 2519.614 11.17276 5.66E-13 -19.68742 -19.14120 -19.46763 5 2522.667 5.717316 5.93E-13 -19.64021 -18.96794 -19.36971 6 2532.769 18.68007 5.88E-13 -19.64896 -18.85063 -19.32773 7 2539.687 12.62883 5.98E-13 -19.63244 -18.70806 -19.26049 8 2548.665 16.17499 5.99E-13 -19.63226 -18.58184 -19.20959 9 2564.890 28.84342 5.66E-13 -19.68960 -18.51312 -19.21621
10 2580.117 26.70769* 5.39E-13 -19.73902 -18.43649 -19.21491 11 2589.541 16.30558 5.38E-13* -19.74239* -18.31381 -19.16756 12 2596.943 12.62977 5.46E-13 -19.72970 -18.17507 -19.10415
* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion
The results of the two GC tests, divided into subperiods identified by the
Cusum of Squares and Chow Breakpoint tests, are indicated in Table 4.10 and Table
4.11 below. Table 4.10 illustrates the results of the OLS-based GC test while Table
4.11 shows the results of the VAR-based GC test. Given that both the OLS-based and
Pairwise GC tests used the lag length determined by the VAR equation, the two tests
generated results with slight difference. In particular, both of the tests have the same
price causality direction for the first subperiod (farm to retail, farm to wholesale,
wholesale to retail). For the whole period, both of the tests came up with the results
that farm prices affect both wholesale and retail prices (F->W and F->R), retail prices
70 70
affect farm prices (R->F), and wholesale prices affect retail prices (W->R). The OLS-
based GC test however, suggests another causality direction from wholesale price to
farm price (W->F). In the case of the second subperiod, both of the tests suggest the
same price same causality directions as in the whole period except for the retail to
wholesale (R->W) and wholesale to farm (W->F) causality that are suggested by the
OLS-based GC tests.
In other words, for the whole period and second sub-period, both the OLS
and VAR-based GC tests demonstrate bidirectional causality among farm and retail
prices as well as farm and wholesale prices. The first subperiod, however, only shows
a distinct unidirectional causality from F->W, F->R and W->R.
71 71
Table 4.10 Results of the OLS-based Granger Causality Test
Prices Null Hypothesis F-statistic P-value All Period (1973 to 2005)
wholesale doesn't Granger cause farm 3.50 * 0.03 farm and wholesale farm doesn't Granger cause wholesale 53.02 ** 0.00
retail doesn't Granger cause farm 6.62 ** 0.00 farm and retail farm doesn't Granger cause retail 54.68 ** 0.00 retail doesn't Granger cause wholesale 2.34 0.10 retail and
wholesale wholesale doesn't Granger cause retail 54.15 ** 0.00 First Subperiod (1973 to 1985)
wholesale doesn't Granger cause farm 0.34 0.56 farm and wholesale farm doesn't Granger cause wholesale 34.97** 0.00
retail doesn't Granger cause farm 0.02 0.90 farm and retail
farm doesn't Granger cause retail 31.31** 0.00 retail doesn't Granger cause wholesale 0.00 0.99 retail and
wholesale wholesale doesn't Granger cause retail 24.51** 0.00 Second Subperiod (1986 to 1995)
wholesale doesn't Granger cause farm 17.03** 0.00 farm and wholesale farm doesn't Granger cause wholesale 76.04** 0.00
retail doesn't Granger cause farm 20.20** 0.00 farm and retail
farm doesn't Granger cause retail 62.94** 0.00 retail doesn't Granger cause wholesale 5.73** 0.00 retail and
wholesale wholesale doesn't Granger cause retail 21.77** 0.00 rejection of null hypothesis at significance level: ** 1% and * 5%
72 72
Table 4.11 Results of the VAR-based (Pairwise) Granger Causality Test
Dependent Variable Null Hypothesis F-statistic P-value
All Period (1973 to 2005) wholesale doesn't Granger cause farm 2.00 0.37 farm retail doesn't Granger cause farm 8.13* 0.02 farm doesn't Granger cause wholesale 100.06** 0.00 wholesale retail doesn't Granger cause wholesale 0.34 0.84 farm doesn't Granger cause retail 42.26** 0.00
retail wholesale doesn't Granger cause retail 41.34** 0.00
First Subperiod (1973 to 1984) wholesale doesn't Granger cause farm 0.67 0.41
farm retail doesn't Granger cause farm 0.35 0.55 farm doesn't Granger cause wholesale 36.87** 0.00
wholesale retail doesn't Granger cause wholesale 1.72 0.19 farm doesn't Granger cause retail 16.87** 0.00
retail wholesale doesn't Granger cause retail 10.68** 0.00
Second Subperiod (1985 to 2005) wholesale doesn't Granger cause farm 2.79 0.25
farm retail doesn't Granger cause farm 6.05* 0.05 farm doesn't Granger cause wholesale 144.96** 0.00
wholesale retail doesn't Granger cause wholesale 4.38 0.11 farm doesn't Granger cause retail 79.55** 0.00
retail wholesale doesn't Granger cause retail 6.1* 0.05
rejection of null hypothesis at significance level: ** 1% and * 5%
Given the above, the subsequent analyses on Wolffram-Houck and VAR
model estimation follows the price causality directions common to both the OLS- and
VAR-based GC tests (results of the VAR-based GC test).
For the first subperiod (1973 to 1984), the result that farm price causes
both wholesale and retail prices dominates since this is when the government heavily
intervened in the rice market. The NFA has the sole authority to import rice such that
when farm prices go up, the government will distribute rice in the market through the
NFA’s rice imports and when farm price go down, the government will purchase as
much rice as possible to prevent the further decline in farm prices. Further, price
73 73
information dissemination is still not fully developed during this time since only the
government is the source of price information. During this time, the public are not yet
allowed to do rice importation. There is no incentive for the traders to participate in
the rice trading. Hence, the level of farm prices precedes the level of consumer prices.
The second subperiod (1985 to 2005) however, illustrates bidirectional
causality between farm and retail prices. As discussed in the Cusum of Squares and
Chow Breakpoint tests’ results, this period represents the liberalization period of the
Philippine rice industry as further strengthened by the passage of the AFMA which
includes the food security mandate of the NFA. This mandate aims to make food such
as rice available and affordable through domestic production or importation. This
activity encourages private sector participation in the rice market. The government’s
aim to meet the demands of rice globalization necessitates strong information
dissemination among the industry key players. Starting 1996, the government has set-
up ways to easily transmit price information among the farmers, middlemen and
consumers of rice. These includes the public’s access of rice prices on (i) the websites
of the NFA, Department of Agriculture and other related agencies, (ii) cellular phones
through text messaging, (iii) farmers’ radio programs and others. Through these, all
the key players are updated on the current rice prices thus they can easily adjust their
prices for the following months given the current price at different marketing levels.
The increase in competition in the market brought by the increase in import volume
also prompted the stakeholders to be aware of the current price at all marketing levels.
These results contradict the results of various studies such as the 2005 NEDA-UNDP
study entitled “From Seed to Shelf: a Logistical Evaluation of the Philippine
Agriculture” that government programs on price information dissemination are not
effective.
74 74
It is also interesting to note that the directions of causality from the VAR-
based GC test are completely different from the upward unidirectional causality (e.g.
farm to wholesale, wholesale to retail) of the previous studies on price transmission.
4.6 Wolffram-Houck Model Estimation
4.6.1 Price Changes in Pre-Liberalization and Liberalization Regime
The time series data on rice prices is classified into 2 subperiods. First is
the pre-liberalization period of the Philippine rice industry (1973 to 1984). Second is
the rice liberalization regime (1985 to 2005). While this thesis divided the dataset into
subperiods to compare the price transmission mechanism during the pre-liberalization
period and liberalization period in the rice industry, it should be noted that this study
does not directly analyze government intervention in the rice industry.
Table 4.12 below summarizes the monthly price increases and decreases
for each of the subperiods specified above. The size and number of price increases and
decreases for all the prices at different marketing level are generally not the same.
Farm prices decreased more than they increased while consumer prices (wholesale and
retail prices) decreased more than they increase. The trend in farm prices is typically
not supportive of the government’s aim to make rice affordable to farmers. Ideally,
farm price should increase for the farmers to have enough profits from their produce.
The trend in the consumer prices, however, supports the government’s aim to make
rice affordable to consumers. This can also be explained by the effective rice
distribution program of the NFA.
75 75
Table 4.12 Summary of Monthly Rice Price Changes in the Philippines, 1973 to 2005
Number of months with Period
Increase Decrease
Monthly average
price increase
Monthly average
price decrease
Ratio of price increase to decrease
Farm Price 1973 to 1984 62 82 0.07 -0.02 3.50 1985 to 2005 121 131 0.25 -0.18 1.39 1973 to 2005 183 213 0.19 -0.12 1.58
Wholesale Price 1973 to 1984 64 80 0.11 -0.03 3.67 1985 to 2005 94 158 0.41 -0.16 2.56 1973 to 2005 158 238 0.29 -0.12 2.42
Retail Price 1973 to 1984 50 94 0.14 -0.03 4.67 1985 to 2005 76 176 0.47 -0.11 4.27 1973 to 2005 126 270 0.34 -0.08 4.25
Rice liberalization, in general, is serving its purpose in terms of
affordability of prices to both the farmers and consumers since farm prices increased
more during the second subperiod than during the earlier period. Consumer prices,
however, had a higher average price decrease during the rice liberalization regime
than the time when the government heavily intervened in the market.
4.6.2 Results of Wolffram-Houck Model Estimation
Given the unit root test results that all prices are non-stationary in levels
and are stationary in first-difference form, the W-H model is estimated with prices in
first- difference form. The response variable, as presented in Equation 3.6, is the
output price deviation from initial value while the explanatory variables include the
time trend and the sums of both the input price increases and decreases. The time plots
of these variables are demonstrated in Appendix 1. Lag lengths of input price
increases and decreases are selected based on the minimum SIC.
76 76
When the W-H model was initially estimated, serial autocorrelation was
observed as reflected by the fairly small Durbin-Watson (DW) statistics. This DW stat
should be close to 2 for the serial correlation to be removed. In order to do this, the W-
H model is re-estimated with the first-order autoregressive adjustment or AR(1) as
suggested by Cochrane and Orcutt (1949). This procedure, as used by Mohanty, et. al.
in 1995 and Aguiar and Santana in 2002, produces a reasonable DW stat as well as
adjusted R-squared. Initially, a seasonal dummy variable is included in the estimation
but turned out to be insignificant hence the W-H model estimation results in Table
4.13 and Table 4.14 do not include seasonal dummy but already corrected the serial
correlation problem from the initial estimation.
The results are based on the price causality results of the VAR-based GC
test and are classified into 2 subperiods (as illustrated by the Cusum of Squares and
Chow Breakpoint tests results) including the whole period of the price data.
Magnitude (long run) and speed (short run) asymmetry are tested using
Equation 3.11 and 3.12, respectively. In testing for the asymmetry in magnitude, the
coefficients of accumulated increasing and decreasing input prices are tested using the
Wald coefficient test wherein an F-test statistic is used to test the null hypothesis of
price symmetry. If the former is significantly different from the later, then there is
asymmetric price transmission. In the case of speed asymmetry, both the individual
coefficients of the increasing and decreasing input prices are compared. If these are
tested to be statistically different from each other then the null hypothesis of
symmetric speed price transmission is rejected. Both null hypotheses of magnitude
and speed symmetry are rejected at 5 percent or lower significance level.
77 77
Table 4.13 Results of Wolffram-Houck Model Estimation (Whole Period)
All Period (1973 to 2005) Coefficient F -> R F->W R -> F W -> R βt
+ 0.32** 0.30** 0.44** 0.67** βt-1
+ 0.40** 0.40** 0.47** 0.69** βt-2
+ 0.29** 0.26** 0.15 0.60** βt-3
+ 0.28** 0.31** 0.12 0.56** βt-4
+ 0.24** 0.16** 0.07 0.52** βt-5
+ 0.50** βt-6
+ 0.41** βt-7
+ 0.35** βt-8
+ 0.34** βt-9
+ 0.35** βt-10
+ 0.34** βt-11
+ 0.33** βt-12
+ 0.31** βt-13
+ 0.24** βt-14
+ 0.18** βt
- 0.05 0.10 0.54** 0.24** βt-1
- 0.14 0.27** 0.49** 0.59** βt-2
- 0.17* 0.37** 0.41** 0.48** βt-3
- 0.12 0.15* 0.14 0.53** βt-4
- -0.05 0.08 0.02 0.46** βt-5
- 0.44** βt-6
- 0.40** βt-7
- 0.48** βt-8
- 0.32* βt-9
- 0.33* βt-10
- 0.05 βt-11
- 0.08 βt-12
- 0.20 βt-13
- -0.25* βt-14
- 0.07 Adjusted R2 0.998 0.998 0.998 0.999 D-W Statistics 1.83 1.57 1.64 2.40 Σβ+ 1.53 1.42 1.25 6.39 Σβ- 0.43 0.97 1.60 4.40 β+ = β- (F-Stat) 2.73* 2.98* 0.86 3.11** Σβ+= Σβ- (FStat) 4.84* 0.93 0.46 1.21
significance level: **1%, *5%
78 78
Table 4.14 Results of the Wolffram-Houck Model Estimation (Subperiods)
First Sub-period (1973 to 1985) Coefficient F -> R F -> W W -> R βt
+ 0.34** 0.29** 0.51** βt-1
+ 0.30** 0.26** 0.80** βt-2
+ 0.47** βt-3
+ 0.42* βt-4
+ 0.33* βt-5
+ 0.33* βt-6
+ 0.27* βt
- 0.19 0.04 0.28 βt-1
- 0.02 -0.04 0.42* βt-2
- 0.32 βt-3
- 0.12 βt-4
- 0.01 βt-5
- -0.13 βt-6
- -0.15 Adjusted R2 0.972 0.978 0.980 D-W Statistics 1.85 1.42 2.64 Σβ+ 0.64 0.55 3.13 Σβ- 0.20 0.00 0.86 β+ = β- (F-Stat) 0.86 1.40 0.70 Σβ+= Σβ- (F-Stat) 1.37 2.74 2.59 Second Sub-period (1986 to 1995) F -> R F -> W R -> F W -> R βt
+ 0.25** 0.39** 0.76** 0.64** βt-1
+ 0.51** 0.65** 0.49** 0.69** βt-2
+ 0.50** 0.58** 0.28 0.52** βt-3
+ 0.40** 0.56** 0.42* 0.53** βt-4
+ 0.44** 0.53** 0.54** 0.48** βt-5
+ 0.39** 0.50** 0.59** 0.39** βt-6
+ 0.33* 0.42** 0.31 0.27** βt-7
+ 0.28 0.37** 0.23 0.37** βt-8
+ 0.25 0.38** 0.12 0.28** βt-9
+ 0.10 0.23 0.12 0.29** βt-10
+ 0.13 0.10 0.16 0.24** βt-11
+ 0.09 0.14 0.20*
79 79
Table 4.14 continued
Second Sub-period (1986 to 1995) Coefficient F -> R F -> W R -> F W -> R βt-12
+ 0.04 0.11 0.02 βt-13
+ 0.05 0.10 βt-14
+ 0.12 βt
- 0.10 0.26** 0.67** 0.39** βt-1
- 0.32** 0.47** 0.43 0.65** βt-2
- 0.33** 0.52** 0.17 0.49** βt-3
- 0.37** 0.47** -0.42 0.59** βt-4
- 0.36** 0.39** -0.78** 0.43** βt-5
- 0.41** 0.41** -1.14** 0.52** βt-6
- 0.43** 0.45** -0.94** 0.36* βt-7
- 0.33* 0.30* -1.10** 0.46** βt-8
- 0.42** 0.35** -1.21** 0.26 βt-9
- 0.34* 0.31** -1.48** 0.25 βt-10
- 0.25 0.16 -0.81** -0.06 βt-11
- 0.02 0.02 0.03 βt-12
- -0.01 -0.13 -0.02 βt-13
- 0.00 -0.04 βt-14
- -0.03 Adjusted R2 0.997 0.997 0.994 0.998 D-W Statistics 1.90 1.91 1.77 1.89 Σβ+ 3.88 4.97 4.02 5.03 Σβ- 3.65 3.98 -6.61 4.30 β+ = β- (F-Stat) 0.87 1.17 4.84** 1.34 Σβ+= Σβ- (F-Stat) 0.01 0.25 21.19** 0.17
significance level: **1%, *5%
In general, for the whole period (Table 4.13), speed and magnitude
symmetry simultaneously exist for W->F price causality direction only as given by the
insignificant F-stat. For the rest of the causality directions, asymmetry in price
transmission exists.
80 80
In the short run, for the first subperiod (Table 4.14), symmetric price
transmission exists for all of the price causality directions specified. In particular,
wholesale and retail prices adjust at the same speed given the rising and falling farm
prices (speed symmetry). Similarly, in the long run, the cumulative effect on
wholesale and retail prices of the increase in the farm price is the same as the
cumulative effect of the decrease in the farm price (magnitude symmetry). For
instance, wholesale price will increase by P0.55 for a P1.00 increase in the farm price
that occurred over a period of 1 month. Wholesale price, however, will not decrease
when farm price decreases by P1.00. The reaction of wholesale price to the increase in
farm price is not considered as statistically different to its reaction when farm price
decreases. This is because both the increase and decrease in the farm price are passed
at the same period to the wholesale price. In the case of farm to retail price
relationship, retail price will increase by P0.64 (decrease by P0.20) to a P1.00 increase
(decrease) in the farm price. These responses of the retail price to the changes in the
farm price are not significantly different from each other.
For all of the price relationships above, the coefficient of the positive
lagged input price (βt-1+) is significantly different from zero which means that
wholesale and retail prices continue to respond to the increase in the farm price in the
second month. Also the coefficients for the current price changes (βt+ and βt
-) are
larger than those for the lagged price changes (βt-1+ and βt-1
-) which means that most of
the adjustment in both the wholesale and retail prices are completed in the month
following the shocks in the farm price. In addition, the coefficient for the positive
price changes for the current month (βt+) is larger than the coefficient for the negative
price changes for the current month (βt-) which means that increases in the farm price
81 81
have more immediate impact on both the wholesale and retail prices than decreases in
farm price.
The above symmetric price transmission (in terms of speed and
magnitude) are also observed from the F->W, F->R and W->R price relationships in
the second subperiod. In the case of the R->F price relationship, however, asymmetry
in speed and magnitude is observed. This means that farm price adjusts at different
speed and magnitude to an increase and decrease in the retail price. Specifically, when
retail price increases by P1.00, farm price will increase by P4.02 while if retail price
decreases by P1.00, farm price will decrease by P6.61. Given the magnitude of the
changes in the farm price, this result should be carefully interpreted. A test using
another model is needed to further verify this result.
The symmetric price (speed and magnitude) transmission for all
subperiods (except for the R->F price relationship in the second subperiod) as
depicted by the results of the W-H model estimation above indirectly shows that trade
liberalization policies adopted by the government do not negatively affect the price
transmission mechanism in the Philippine rice industry. Even in the period of strong
government control on rice market (first subperiod), prices respond at the same speed
and magnitude just like when the rice market is already liberalized (second subperiod).
Furthermore, the notion that Filipino rice traders have strong market
power to manipulate rice prices is believed to be false given that wholesale and retail
prices reacts similarly at the same speed and magnitude to an increase or decrease in
farm (as explained in the whole period and 2 subperiods). Also retail prices respond at
the same speed and magnitude to both the increase and decrease in wholesale prices
(as shown in both the subperiods). For both the first and second subperiods as well as
the whole period, market shocks originate from the farm, which are then passed at the
82 82
wholesale and retail levels. So there is really no way for the traders to deliberately
manipulate rice prices in the market. These results are consistent with the results of the
previous study by Reeder (2000). The comparison of these results to Reeder’s work is
presented in the following Table 4.15.
Table 4.15 Comparison of the Results of this Thesis and Reeder’s Study (2000)
Thesis Reeder’s Work Price causality directions
(Whole period) Unidirectional causality from farm to wholesale, wholesale to retail and bidirectional causality between retail and farm (First Subperiod) Unidirectional causality from farm to wholesale, farm to retail, and wholesale to retail (Second Subperiod) Unidirectional causality from farm to wholesale, wholesale to retail and bidirectional causality between retail and farm
(Whole period) Unidirectional causality from farm to wholesale and wholesale to retail (First Subperiod) Unidirectional causality from farm to wholesale and wholesale to retail (Second Subperiod) Unidirectional causality from farm to wholesale and wholesale to retail
Speed (short-run) price transmission
(Whole period) Asymmetric for F-W, F-R, W-R; symmetric for R-F (First Subperiod) Symmetric for F-W, F-R, W-R (Second Subperiod) Symmetric for F-W, F-R, W-R; asymmetric for R-F
(Whole period) Symmetric for W-R; asymmetric for F-W (First Subperiod) Symmetric for W-R; asymmetric for F-W (Second Subperiod) Asymmetric for W-R and F-W
83 83
Table 4.15 continued
Thesis Reeder’s Work Magnitude (long-run) price transmission
(Whole period) Symmetric for F-W, W-R, R-F; asymmetric for F-R (First Subperiod) Symmetric for F-W, F-R, W-R (Second Subperiod) Symmetric for F-W, F-R, W-R; asymmetric for R-F
(Whole period) Symmetric for W-R and F-W (First Subperiod) Symmetric for W-R and F-W (Second Subperiod) Symmetric for W-R and F-W
Time frame (Whole period) 1973 to 2005 (First Subperiod) 1973 to 1984 – pre rice liberalization stage (Second Subperiod) 1985 to 2005 – rice liberalization period
(Whole period) 1973 to 1996 (First Subperiod) 1973 to 1985 – pre rice liberalization stage (Second Subperiod) 1986 to 2005 – rice liberalization period
Specification With time trend With intercept and seasonal dummy
4.7 Results of the VAR Model Estimation
Unlike the Wolffram-Houck model, the forecast error variance
decompositions (FEVDs) do not test whether asymmetry in speed and magnitude of
price transmission exists for the price relationships. The use of the FEVDs in the VAR
model, however, may provide additional insights on the level of magnitude and speed
of price transmission as well as how the adoption of liberalization policies affects the
price transmission in the Philippine rice industry. The FEVDs also allow for the
determination of which of the rice market prices (farm, wholesale and retail levels) are
84 84
exogenous or endogenous relative to each other at various forecast horizons (1 to 12
months). The more exogenous the price is, the less impact it has on the other prices.
The standard error presented in Table 4.16 captures the level of uncertainty and this
increases with the forecast horizons. The sum of the variance decompositions for all of
the prices should add up to 100 after adjusting for rounding errors.
For all of the subperiods as well as the whole period, farm price appears to
be exogenous in the first month since 100 percent of its variability is explained by its
own price. This means that farm price has less impact for both the consumer prices for
the first month. But in the succeeding periods, the farm price becomes less exogenous
as its own share of variability decreased to 92 percent (for both the whole period and
second subperiod) and 99 percent (for the first subperiod) in the 12th month.
For the whole period, farm price appears to be the most influential to both
the wholesale and retail prices. Specifically, farm price accounts for around 37 and 86
percent of the variations in the wholesale price for the short (first month) and long run
(12th month), respectively. These finding are consistent with the results of the VAR-
based GC test above. While wholesale price explains a higher amount (30 percent) of
variations in retail prices than the farm price for the short run (first month), farm price
accounts for around 83 percent in the long run (1 year). In terms of the speed of
transmission, both the wholesale and retail prices adjust at almost the same speed to
the variations in farm prices as given by the changes in the FEVDs of the farm price as
the forecast horizon becomes larger. The magnitude of the variations in the wholesale
(retail) price as accounted by the farm price increased from 37 (27) in the short run to
86 (83) percent in the long run.
For the first subperiod (Table 4.17), farm price is the most influential to
consumer prices explaining 41 and 38 percent (in the first month) of the variability in
85 85
wholesale and retail prices, respectively. Wholesale price also explains a significant
portion (19 percent) of the variability in retail prices in the first month. Again, these
results are similar with the price causality directions generated from the VAR-based
GC test. The speed of transmission from farm to consumer prices is faster than in the
case of wholesale to retail prices since during the first month farm price explains 41
(38) percent of the variability in wholesale (retail) price and this increased to 94 (92)
percent in the 12th month. Wholesale price, however, accounts for 19 percent of the
variability in retail prices in the first month which further decreased to 4 percent in the
12th month.
While farm price still explains a sizeable portion of the variability in both
wholesale (30 percent) and retail (19 percent) prices in the second subperiod (Table
4.18), wholesale price accounts for a greater portion (46 percent) of the changes in the
retail price than the farm price. The speed of price transmission from farm to
consumer prices is still almost the same with the first subperiod. On the other hand,
the speed of transmission from wholesale to retail prices becomes faster as compared
to the first subperiod. These results indirectly show that the liberalization of the
Philippine rice industry helps consumer prices to become more integrated in the sense
that the changes on retail price are now being affecting by not only the farm but also
by the wholesale price.
86 86
Table 4.16 Variance Decompositions on the Philippine Rice Prices, All Period (1973 to 2005)
Variance Decomposition Horizon (Month) SE
Farm Wholesale Retail Farm
1 0.02 100.00 0.00 0.00 2 0.03 99.98 0.00 0.02 3 0.04 99.56 0.01 0.42 4 0.05 98.81 0.11 1.09 5 0.05 97.82 0.31 1.87 6 0.05 96.76 0.62 2.62 7 0.06 95.74 1.00 3.26 8 0.06 94.82 1.39 3.79 9 0.06 94.02 1.78 4.19
10 0.07 93.34 2.15 4.51 11 0.07 92.77 2.48 4.75 12 0.07 92.29 2.77 4.93
Wholesale 1 0.01 36.51 63.49 0.00 2 0.02 59.24 40.75 0.01 3 0.03 71.52 28.44 0.04 4 0.04 78.12 21.66 0.22 5 0.04 81.75 17.69 0.55 6 0.05 83.79 15.22 0.99 7 0.05 84.94 13.59 1.47 8 0.05 85.58 12.47 1.95 9 0.06 85.93 11.67 2.40
10 0.06 86.12 11.08 2.80 11 0.06 86.22 10.63 3.15 12 0.07 86.27 10.28 3.45
Retail 1 0.01 26.79 29.96 43.26 2 0.02 48.10 30.37 21.54 3 0.03 61.61 24.02 14.37 4 0.03 69.54 19.65 10.81 5 0.04 74.31 16.66 9.04 6 0.04 77.29 14.64 8.07 7 0.05 79.22 13.22 7.55 8 0.05 80.52 12.21 7.27 9 0.06 81.42 11.46 7.11
10 0.06 82.07 10.90 7.03 11 0.06 82.56 10.46 6.99 12 0.06 82.93 10.11 6.96
87 87
Table 4.17 Variance Decompositions on the Philippine Rice Prices, First Subperiod (1973 to 1984)
Variance Decomposition Horizon (Month) SE
Farm Wholesale Retail Farm
1 0.02 100.00 0.00 0.00 2 0.03 99.85 0.06 0.09 3 0.04 99.67 0.13 0.21 4 0.04 99.50 0.19 0.30 5 0.05 99.38 0.24 0.39 6 0.06 99.28 0.28 0.45 7 0.06 99.20 0.30 0.50 8 0.06 99.14 0.33 0.54 9 0.07 99.09 0.34 0.57
10 0.07 99.05 0.36 0.59 11 0.08 99.02 0.37 0.61 12 0.08 98.99 0.38 0.63
Wholesale 1 0.02 40.58 59.42 0.00 2 0.02 56.44 43.11 0.45 3 0.03 68.65 30.83 0.51 4 0.04 77.07 22.53 0.40 5 0.04 82.61 17.08 0.30 6 0.05 86.26 13.48 0.26 7 0.05 88.70 11.04 0.26 8 0.06 90.40 9.31 0.29 9 0.06 91.62 8.05 0.32
10 0.06 92.54 7.10 0.37 11 0.07 93.24 6.36 0.40 12 0.07 93.80 5.76 0.44
Retail 1 0.02 37.57 18.71 43.72 2 0.03 52.00 19.89 28.12 3 0.03 63.56 17.06 19.37 4 0.04 72.18 13.59 14.22 5 0.04 78.33 10.72 10.95 6 0.05 82.63 8.60 8.77 7 0.05 85.66 7.08 7.26 8 0.06 87.82 5.99 6.19 9 0.06 89.40 5.19 5.41
10 0.06 90.59 4.59 4.82 11 0.07 91.52 4.12 4.36 12 0.07 92.25 3.75 4.00
88 88
Table 4.18 Variance Decompositions on the Philippine Rice Prices, Second Subperiod (1985 to 2005)
Variance Decomposition Horizon (Month) SE
Farm Wholesale Retail Farm
1 0.02 100.00 0.00 0.00 2 0.03 98.92 1.06 0.01 3 0.04 98.25 1.70 0.06 4 0.04 97.99 1.66 0.35 5 0.04 97.65 1.45 0.89 6 0.04 96.99 1.47 1.54 7 0.05 96.07 1.77 2.16 8 0.05 95.09 2.23 2.68 9 0.05 94.17 2.73 3.10
10 0.05 93.34 3.21 3.45 11 0.05 92.60 3.67 3.73 12 0.05 91.94 4.08 3.98
Wholesale 1 0.01 30.37 69.63 0.00 2 0.02 63.93 35.89 0.17 3 0.02 78.06 21.65 0.28 4 0.03 83.54 15.86 0.60 5 0.03 85.50 13.34 1.16 6 0.04 85.88 12.27 1.85 7 0.04 85.55 11.90 2.55 8 0.04 85.00 11.84 3.17 9 0.04 84.44 11.89 3.67
10 0.04 83.97 11.95 4.08 11 0.05 83.58 12.00 4.41 12 0.05 83.26 12.05 4.69
Retail 1 0.01 18.86 45.92 35.22 2 0.02 48.77 33.06 18.17 3 0.02 66.33 22.03 11.64 4 0.03 74.45 16.47 9.08 5 0.03 78.04 13.81 8.15 6 0.03 79.50 12.60 7.90 7 0.04 79.98 12.11 7.91 8 0.04 80.04 11.96 8.00 9 0.04 79.97 11.96 8.07
10 0.04 79.88 12.00 8.12 11 0.04 79.81 12.04 8.15 12 0.05 79.75 12.08 8.17
89 89
4.8 Chapter Summary
The results of the Cusum of Squares and Chow Breakpoint tests suggest
that there is a structural break for the period 1985. The dataset are then divided into 2
subperiods: (i) 1973 to 1984 and (ii) 1985 to 2005 to represent the period of heavy
government control and liberalization regime in the rice industry, respectively. Also,
both the ADF and PP tests’ results indicate that price variables estimated in levels are
non-stationary while those estimated in first differences are already stationary I(1).
Therefore, the Wolffram-Houck model is estimated using prices at first difference
form. The cointegration test results imply that there are cointegrating relationships for
both the subperiods and the whole period. Hence, a VAR model is estimated in levels
to analyze the transmission of prices from one market level to another. Prior to
estimating the 2 models, the OLS- and VAR-based GC tests are conducted to
determine the directions of price causality. The results of the VAR-based GC test are
used in estimating both of the models. Farm price is found to cause both wholesale
and retail prices for the first subperiod while bidirectional causality between farm and
retail prices is found for the second subperiod.
The Wolffram-Houck model estimation results suggest that price
symmetry (in terms of speed and magnitude) exists at all levels of rice market with or
without heavy government intervention. Symmetric price transmission in rice can be
explained by the longer storage rate of rice and less level of processing involved in
rice production. Hence there is no incentive for traders to exercise market power. In
addition, the VAR model estimation indicates that farm price accounts for most of the
variability in wholesale and retail prices in both of the subperiods. Further, wholesale
price explains more of the retail price’ variations in the rice liberalization regime than
in the period of heavy government control in the industry.
90 90
Chapter 5
CONCLUSIONS AND POLICY IMPLICATIONS
This thesis examines the existence of asymmetric price transmission
across market levels (farm, wholesale and retail) in the Philippine rice industry using
two econometric models known as the Wolffram-Houck and the VAR model with the
use of recently available price data (1973 to 2005). Price transmission analysis
measures the speed and the magnitude in which price changes in certain market level
are being transmitted to another. As a whole it provides insights on the efficiency of a
commodity’s market structure, welfare distribution in an industry, as well as the
existence of market power among the key players. This study extends Reeder’s
(2000) price transmission study on the Philippine rice by using a longer time period as
well alternative tests and models. Just like Reeder’s study, subperiods are used to
account for the stages of rice liberalization in the country. This study, however, differs
since it includes longer time period for the rice liberalization stage. The use of the
Wolffram-Houck model aims to verify the results of Reeder’s study specifically the
claim that Filipino traders exercise market power thereby manipulating rice prices at
the disadvantage of the Filipino consumers. The VAR model further elaborates the
speed and magnitude of price transmission in the industry.
In general, the results of Wolffram-Houck model suggest that price
symmetry (in terms of speed and magnitude) exists at all levels of the rice market with
or without heavy government intervention. This conclusion is similar to the results of
Reeder’s study. While the direction of price causality differs from Reeder’s work, it is
91 91
noted that output price adjust at the same time to the increase or decrease in input
price. Also, the cumulative increase and decrease in the input price have the same
effect to the output price. As presented in the first and second subperiods, for instance,
both the wholesale price increases and decreases are passed at the same speed to the
retail price. The effects of these price changes to the retail price are not significantly
different from each other. Hence, there is no evidence on traders’ market power in the
industry. Symmetric price transmission in rice can be explained by the longer storage
rate of rice. Unlike other perishable agricultural commodities, rice can be stored for
more than a year as long as it meets the proper moisture content (approximately 14%)
for storage. The level of processing involved in rice production is also very simple as
compared to other agricultural products. Rice only needs to be milled before it can be
sold to the ultimate consumers hence there is no incentive for traders to exercise
market power.
As regards the subperiods on rice liberalization, it is noted that during the
time of government control (first subperiod) in the rice market, there is an upward
unilateral causality among prices (e.g., farm to wholesale, farm to retail and wholesale
to retail). This result is exactly the same with the results of Reeder (2000). However,
during the second subperiod when rice trading is liberalized, the causality of prices
become bilateral which is explained by the government’s effort to meet the demand of
rice globalization such as strong information dissemination campaign and more
intense local market competition. It should be noted that the impact of government
intervention in the rice industry is not directly analyzed in this study. But the effects of
such intervention can be indirectly derived from the results of the study given the use
of the first and second subperiods which represent the period of heavy government
control and rice liberalization in the industry, respectively.
92 92
The results from the VAR model analysis also indicate that farm price
causes most of the changes in consumer prices prior and before rice liberalization.
While wholesale price also accounts for the variability in the retail price for both of
the subperiods, it is during the liberalization stage when wholesale price affects most
of the changes in the retail price. This means that consumer prices become more
integrated during the rice liberalization period. Again, this can also be explained by
the strong price information dissemination campaign done by the government to meet
the demand of globalization.
The results of this study can be used as a basis in designing a much
needed intervention in the Philippine rice industry. Instead of implementing programs
and projects that decrease traders’ participation in rice marketing (as suggested by the
various studies on rice such as the 2005 NEDA-UNDP study entitled “From Seed to
Shelf: a Logistical Evaluation of the Philippine Agriculture, the Medium Term
Philippine Development Plan 2004-2010 and others), the government should focus on
projects that facilitate the liberalization of rice industry.
This study, however, can still be improved if recent advances in statistical
modeling are used. Given that the dataset suggests cointegrating relationship, a more
advanced asymmetric price transmission models such as the error correction model
(ECM) or the threshold ECM could be used to further refine as well as verify the
results of this thesis.
93 93
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99 99
APPENDIX
Time Plot of the Farm Price Deviations from Initial Value, 1973 to 2005
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1975 1980 1985 1990 1995 2000 2005
FINI
Time Plot of the Accumulated Decreasing Farm Prices, 1973 to 2005
-2.4
-2.0
-1.6
-1.2
-0.8
-0.4
0.0
1975 1980 1985 1990 1995 2000 2005
FNSUM
100 100
Time Plot of the Accumulated Increasing Farm Prices, 1973 to 2005
0.0
0.4
0.8
1.2
1.6
2.0
2.4
2.8
3.2
3.6
1975 1980 1985 1990 1995 2000 2005
FPSUM
Time Plot of the Wholesale Price Deviations from Initial Value, 1973 to 2005
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1975 1980 1985 1990 1995 2000 2005
WINI
101 101
Time Plot of the Accumulated Decreasing Wholesale Prices, 1973 to 2005
-1.6
-1.4
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
1975 1980 1985 1990 1995 2000 2005
WNSUM
Time Plot of the Accumulated Increasing Wholesale Prices, 1973 to 2005
0.0
0.4
0.8
1.2
1.6
2.0
2.4
2.8
1975 1980 1985 1990 1995 2000 2005
WPSUM
102 102
Time Plot of the Retail Price Deviations from Initial Value, 1973 to 2005
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1975 1980 1985 1990 1995 2000 2005
RINI
Time Plot of the Accumulated Decreasing Retail Prices, 1973 to 2005
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
1975 1980 1985 1990 1995 2000 2005
RNSUM
103 103
Time Plot of the Accumulated Increasing Retail Prices, 1973 to 2005
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1975 1980 1985 1990 1995 2000 2005
RPSUM
