124 Due to the lack of subsidy data, market price data are not the net price data and could be
treated as the subsidised price, hence the average price p was used as an approximation
of
s t
p
see Figure 1. Plug
128120.97 p
=
either into Equation 5.10 or Equation 5.11, to get the competitive price
128121
c t
p =
.
Following the same steps as in scenario 1, gives the same results for the competitive price in scenarios two and three. Therefore, it could
be concluded that the competitive price
c t
p
is likely to be higher than p .
5.6 Concluding comments
This chapter has presented the estimation techniques and results of the model, which was specified in the previous chapter. The Bayesian technique was used as it has the ability to
impose the three properties of the model, namely stability, convexity and interpretable market power index. While the stability and convexity properties hold in most of the
replications, market power property appears to be strongly rejected. The public estates appear to act as the leader and have some degree of market power, while the private
companies act as the followers and tend to behave competitively. This might be explained by the changes in both the demand and supply sides. On the demand side, the elasticity of
demand changed from very elastic to inelastic, providing all sellers in the market with an ability to exert market power. On the supply side, the increase their market share and
vertical integration provided the private companies with an ability to respond to the public estates’ current actions, making the public estates behave more cooperatively.
Given the fact that the public estates exert some degree of market power, indirect and direct policies that assume perfect competition appear no longer applicable.
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Appendix 5 Appendix 5.1
Research data
CPO CPO
Crude Palm
Government Private
nominal demand
coconut oil cooking oil
groups groups
Year price
nominal price nominal price
production production
Rpkg tonnes
Rpkg Rpkg
tonnes tonnes
1968 NA
NA NA
NA 122,369
59,075 1969
64.0 22.4
NA 87.2
128,561 60,240
1970 66.0
36.0 104.20
90.9 147,003
69,824 1971
74.0 30.4
110.05 169.9
170,304 79,653
1972 72.9
27.6 101.39
184.1 189,261
80,203 1973
90.7 25.6
182.43 200.0
207,448 82,229
1974 128.5
38.4 128.00
221.7 243,641
104,035 1975
148.4 38.4
161.56 239.4
271,171 126,082
1976 144.8
22.6 207.73
316.3 286,096
144,910 1977
144.1 60.8
303.66 455.8
336,891 120,716
1978 168.6
95.2 407.03
483.8 336,224
165,060 1979
206.7 160.0
396.62 498.1
438,756 201,724
1980 222.5
185.6 388.85
500.2 498,858
221,544 1981
245.3 359.2
426.85 476.9
533,399 265,616
1982 248.8
407.2 360.82
476.3 598,653
285,212 1983
276.7 478.4
550.68 648.0
710,431 269,102
1984 405.5
612.8 743.29
779.4 814,015
329,144 1985
421.5 642.2
691.50 569.7
861,173 339,241
1986 426.4
524.8 538.51
746.5 912,306
384,919 1987
427.0 702.6
625.63 762.4
988,480 352,413
1988 502.0
774.0 673.15
874.5 1,102,692
454,495 1989
547.5 948.0
774.91 905.8
1,184,226 597,039
1990 525.3
981.6 597.37
708.3 1,247,156
788,506 1991
572.5 990.4
967.56 822.6
1,360,363 883,918
1992 722.5
1211.2 996.12
961.4 1,489,745
1,076,900 1993
700.7 1427.2
985.11 987.7
1,469,156 1,190,272
1994 911.3
1588.0 950.85
545.5 1,571,501
1,597,227 1995
1,093.6 1692.3
1098.06 1362.6
1,613,848 1,864,379
1996 996.5
2022.3 NA
NA 1,706,852
2,058,259 1997
1,138.5 2208.4
2410.10 1959.2
1,586,879 2,578,806
1998 2,408.5
3,288.1 NA
NA 1,501,747
3,084,099 1999
2,435.4 3,625.3
1756.93 3398.0
1,468,949 3,438,830
2000 2,204.8
3,909.4 2248.86
3909.3 1,460,954
3,633,901 2001
2,220.8 4,082.8
2750.00 2402.4
1,519,289 4,079,151
2002 3,109.1
3,901.8 3542.00
2562.4 1,607,734
4,587,871 2003
3,704.4 3,910.7
3953.00 NA
1,543,528 4,627,744
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Appendix 5.2 Estimation result for the demand equation
Dependent Variable: Q Method: Least Squares
Sample adjusted: 1970 2002 Included observations: 31 after adjustments
Variable Coefficient
Std. Error t-Statistic
Prob. LOGP
-1166.553 512.7563 -2.275064
0.0317 LOGP1
-2280.002 315.3408 -7.230280
0.0000 LOGP2
354.0936 287.5915
1.231238 0.2297
LOGPLOGP1 493.7347
62.44299 7.906968
0.0000 D3
414.5342 203.5406
2.036617 0.0524
C 4835.280
799.4283 6.048422
0.0000 R-squared
0.989727 Mean dependent var 1025.427
Adjusted R-squared 0.987673 S.D. dependent var
1252.898 S.E. of regression
139.1060 Akaike info criterion 12.88033
Sum squared resid 483761.8 Schwarz criterion
13.15788 Log likelihood
-193.6452 F-statistic 481.7333
Durbin-Watson stat 1.727595 ProbF-statistic
0.000000
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Appendix 5.3 Estimation result for the adjustment system
System: MOTION4 Estimation Method: Seemingly Unrelated Regression
Sample: 1968 2003 Coefficient
Std. Error t-Statistic
Prob. C1
0.446186 0.134205
3.324656 0.0016
C2 0.903125
0.055598 16.24371
0.0000 C3
0.045293 0.050994
0.888214 0.3782
C4 -0.040686
0.019012 -2.139990
0.0367 C5
-0.103981 0.028255
-3.680136 0.0005
C7 0.306430
0.227938 1.344357
0.1843 C8
0.246309 0.094430
2.608376 0.0116
C9 0.703124
0.086609 8.118380
0.0000 C10
0.171891 0.032291
5.323267 0.0000
C11 0.060158
0.047989 1.253592
0.2152 Determinant residual covariance
1.72E-06 Equation: LOGGT=C1+C2LOGGT3+C3LOGPT3+C4D01+C5
D02 Observations: 33
R-squared 0.992531 Mean dependent var
5.886660 Adjusted R-squared
0.991464 S.D. dependent var 0.327913
S.E. of regression 0.030295 Sum squared resid
0.025699 Durbin-Watson stat
1.265200 Equation: LOGPT=C7+C8LOGGT3+C9LOGPT3+C10D01
+C11D02 Observations: 33
R-squared 0.993080 Mean dependent var
5.743803 Adjusted R-squared
0.992091 S.D. dependent var 0.578594
S.E. of regression 0.051455 Sum squared resid
0.074132 Durbin-Watson stat
1.787956
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Chapter 6 Policy Implications and Conclusions
