Introduction
In the previous chapter, the theoretical framework for the behavioural equations was discussed. In the first section of this chapter, the results for the behavioural equations for six regions will be discussed in detail. These equations will be estimated using two-stage least squares and the first-order autocorrelation correction will be used when an autocorrelation problem occurs. The supply equation is an identity consisting of the area harvested, yield and the conversion of paddy to rice37. The exogenous variables used in the consumption demand, area harvested, yield and stocks demand equations will be collapsed into the intercept, thus creating a new set of demand and supply equations to be used in the spatial equilibrium model. The following section describes the inclusion of the estimated parameters into the simulation model and the dynamic simulation of the model for the period from 1982 to 2009. The model validation, using graphical and statistical methods will be discussed in the final section.
Estimation Results
Behavioural equations for this study were estimated using annual data from 1980 to 2009. Definitions and sources of data are given in Appendix A. As time series data are subject to trends over time, all the data were tested for stationarity using the Augmented Dickey-Fuller test (Engle and Granger 1987). To avoid spurious regressions and biased t-statistics, a deterministic time trend was included in some of the equations to capture the trends.
The behavioural equations for the spatial equilibrium model consist of four stochastic equations and one identity for each country: Malaysia, Thailand, Vietnam, Pakistan, Indonesia and the rest of the world. Altogether, there were 24 stochastic equations
37
Paddy is rice in the husk, which is still in the field (also known as un-milled rice) and rice is the final product for consumption after removing of the husks and polishing. Usually paddy is harvested with 25 percent moisture content and then sent to the mills for drying and producing rice. So, the conversion rate is the rate at which the paddy is converted into rice. This rate differs across the countries in this study with a range of 0.65 to 0.67.
95 included in the model. The equations were consumption demands, stocks demands, area harvested and yield equations, and six identities for the supply functions. The Time Series Processor (TSP) software was used to estimate the simultaneous behavioural equations.
All the equations were diagnosed for misspecification problems using the Ramsey Regression Specification Error Test (RESET) and found that the linear functional forms were appropriate for all the countries. The ordinary least squares (OLS) method was used to estimate the area harvested equations. However, the two-stage least squares (2SLS) method was used to estimate the consumption demand, stocks demand and yield equations since the price was an endogenous variable. A set of instrumental variables was used in the two-stage least squares procedures.
A Durbin-Watson test was used to detect any autocorrelation problems, and if found, then the model was corrected using the Cochrane-Orcutt procedure. With autocorrelation the mean of the error term generally remains constant at zero. If the serial correlation is ignored, all inferences are invalid and the problems worsen if lagged dependent variables exist in the model (Pindyck and Rubinfeld 1998). A correlation will exist between the error term and one of the explanatory variables when there is a lagged dependent variable in which case the OLS estimates become biased. Therefore, it was essential to test for autocorrelation using the Durbin-Watson test. When the lagged dependent variables were used, as in the area harvested equations, the Durbin-Watson test is not valid and thus, Durbin‟s h (Dh) test was used to diagnose autocorrelation problems.
Several regression measures, including the R-squared (R2), the t-statistic, standard errors, F-ratio and Durbin-Watson statistic (or Durbin‟s h-statistic in autocorrelation cases) were used to evaluate the estimated relationships. The R2 is the coefficient of determination which measures the goodness of fit between the estimated regressions38, whereas the t-statistic is used to test the significance of individual
38
Unlike in the case of a single regression, the R2 is used as an informal measure of the goodness of fit in the multiple regression systems and to validate the regression analysis under different alternatives (Pindyck and Rubenfield 1998).
96 parameters. Whilst the t-statistic is used for the individual significance, the F-ratio is a test for the overall significance of the regression model.
The prior expectations for the signs for each of the equations were discussed in the previous chapter. Most of the behavioural equations conformed to the expected signs but in some countries, there were a few exceptions. However, the price variable in the stocks demand equation was removed as it was not significant in all the countries. It was assumed, that in all the countries, the stocks demand largely represents the transactions motive only and with little in the way of speculative demand. In the next section, the results for each country‟s behavioural equations are provided in detail.
Malaysia sub-model
The estimates of the behavioural equations for Malaysia are given in Table 6.1. The sub-model estimated coefficients appear to conform to the theoretical expectations and to have significant results.
In the consumption demand equation, the income (GDP1) and population (POP1) variables were significant at one and five per cent levels respectively. The estimated income coefficient had a positive sign which indicates that rice is a normal good in Malaysia and this result was consistent with the study by Tey et al. (2008). Yet, other studies have shown that rice is an inferior good (Ito et al. 1989; Baharumshah 1991). As shown in Table 6.1, the goodness of fit, R2, has a high value reflecting the fact that the regression equation explains 97 per cent of variation in the dependent variable.
The stocks demand equation was estimated as a function of the price of rice (P1), production (S1) and lagged closing stocks (D7(-1)) but the result was not satisfactory as the sign on the price variable was not consistent with the theory and for solutions to the spatial equilibrium model to be obtained a non-positive coefficient is required. Thus, the price variable was removed from the equation and it was assumed that the transactions motive for the stock demand was more significant in Malaysia. The equation was further improved by incorporating an intercept (DM196) dummy
97 variable to reflect the influences of BERNAS39 after its privatization in 1996. However, the dummy variable was not statistically significant.
The area harvested equation was estimated several times using various substitutes for paddy planting area but the results were not as expected. Despite the inclusion of the price of palm oil (PPO1) in the equation, the R2 was still low at 0.304. However, after incorporating the per unit subsidy into the area harvested model, the R2 was generally improved to 0.55 and provided some significant results. The per unit subsidy40 (PPS1) had a positive coefficient that was significant at the one per cent level. This indicates that the area harvested in Malaysia is likely to be strongly dependent on the subsidies given by the government.
Finally, the yield equation was estimated as a function of the rainfall (R1) and fertilizer consumption per hectare (FC1). The rice yield was found to be responsive to the fertilizer consumption per hectare as the parameter was highly significant at the one per cent level. The rainfall was not statistically significant and one possible reason is that approximately half of the paddy land depends on irrigation and the drainage system and is not rainfed. The R2 was 0.75 which was considered a good fit and the signs of the coefficients were as expected. The reported yield function in Table 6.1 was re-estimated with R&D expenditures included and the results will be discussed in the next chapter.
39
The role of BERNAS has been discussed in detail in the earlier chapters.
40
The per unit subsidy was obtained by dividing the total subsidies by the total production of paddy. Subsidies consisted of the paddy price subsidy, fertilizer subsidy and other incentives. Details on these subsidies were explained in Chapter 3.
98 Table 6.1 The behavioural estimations for Malaysia
Consumption Demand
D1 = -2.183 – 0.146E-03 P1 + 0.189E-03 PW1+ 0.130E-05 GDP1 + 0.273POP1 - 0.128T (1.849) (0.364E-03) (0.155E-03) (0.316E-06)*** (0.147)** (0.073)*
R2 = 0.971 Adjusted R2 = 0.964 DW = 1.676 Stocks Demand D7 = -0.114 + 0.162 S1(-1) + 0.688D7(-1)+ 0.059DM196 (0.276) (0.243) (0.167)*** (0.065)* R2 = 0.735 Adjusted R2 = 0.704 D.W. = 1.797 Area Harvested
A1 = 0.468 + 0.212 A1(-1) + 0.544E-05 P1(-1) - 0.209E-05PPO1 + 0.205E-03PPS1 (0.095)*** (0.153) (0.128E-04) (0.342E-05) (0.448E-04)***
R2 = 0.554 Adjusted R2 = 0.453 D.W h. = 2.137
Yield
Y1 = 2.124 + 0.613E-03R1 + 0.475 FC1 (0.242)*** (0.139E-02) (0.055) ***
R2 = 0.767 Adjusted R2 = 0.749 D.W. = 1.699
Note : Figures in parentheses denote the standard errors and *** , ** and * indicate significant at 1, 5 and 10 per cent significant level respectively. DM196 is the intercept dummy variable used in the stocks demand equation to reflect BERNAS‟s involvement in the rice industry after 1996.
Thailand sub-model
The estimated coefficients for the behavioural equations in Thailand conform to prior expectations. The results are presented in Table 6.2. It is apparent that rice is a normal good as the coefficient for the income variable (GDP2) was positive and statistically significant at the five per cent level. This result is inconsistent with previous studies on domestic demand in Thailand which showed a negative income coefficient (Ito et al. 1989 and Isvilanonda 2002). However, from a recent study by Isvilanonda and Kongrith (2008) it was found that the estimated expenditure (income) elasticity for the whole country was 0.082, thus in the more recent work rice can be considered as a normal good. The time trend was removed from the consumption demand equation as all the variables were integrated of order 1, I(1) .
For the stocks demand equation, the lagged production and lagged stocks demand coefficient estimates were highly significant at the one per cent level which indicated that the transactions motive is likely to play an important role in Thailand‟s rice
99 industry. Dummy variables were used to capture the structural changes in Thailand but the results were not satisfactory. Thus, those dummy variables were removed from the equation.
In the area harvested equation, the price of cassava was included as a substitute crop and the coefficient had the expected sign as in theory but was not significant. Other crops that could be substitutes for rice, such as the price of palm oil, rubber and maize were also included in the equation but the results did not have the negative sign as expected and thus were removed from the equation. The lagged area harvested was highly significant at the one per cent level and this result was consistent with the study by Sachchamarga and Williams (2004). Based on the compilation from previous studies on Thailand‟s rice industry, the price elasticity of supply ranged from 0.02 to 0.65 with an average of 0.25 (Chouen et al. 2006; Siamwalla and Setboonsarng 1989; Vanichjakvong 2002). The result from this study also found a similar elasticity of an average of 0.23.
The yield equation was regressed as a function of the fertilizer consumption per hectare and annual rainfall. Both the coefficients were found to be significant at one and five per cent levels respectively. From the results, we found that the farmers in Thailand were very responsive towards the rainfall and the usage of fertilizers.
100 Table 6.2 The behavioural estimations for Thailand
Consumption Demand
D2 = 7.769 – 0.285E-04P2 + 0.689E-02 POP2+ 0.204E-06GDP2 (0.180)*** (0.282E-04) (0.0359) (0.990E-07)** R2 = 0.827 Adjusted R2 = 0.806 D.W. = 1.774 Stocks Demand D8 = -2.148 + 0.194 S2(-1) + 0.676 D8(-1) (0.870)** (0.069)*** (0.171) *** R2 = 0.715 Adjusted R2 = 0.693 D.W. = 1.858 Area Harvested
A2 = 2.896 + 0.665 A2(-1) + 0.315E-04 P2(-1) – 0.8494E-05PC2 + 0.0164 T
(1.797)* (0.205)*** (0.672E-04) (0.393E-03) (0.017) R2 = 0.676 Adjusted R2 = 0.603 D.W. h = 2.048 Yield Y2 = 1.174 + 0.379E-03 R2 + 4.180 FC2 (0.283)*** (0.182E-03)** (0.380)*** R2 = 0.845 Adjusted R2 = 0.833 D.W. = 1.622
Note : Figures in parentheses denote the standard errors and *** , ** and * indicate significant at 1, 5 and 10 per cent significant levels.
Vietnamese sub-model
The estimated coefficient signs for all behavioural equations for Vietnam were found to be consistent with the theory and the results are as presented in Table 6.3. All the variables were found to be statistically significant at one per cent except for the income coefficient at the five per cent significance level. Since the coefficient on the income variable was positive, rice in Vietnam can be regarded as a normal good and this result was consistent with the conclusions from previous studies, including Vu Hoang (2009) and Quang Le (2008) and Minot and Goletti (2000).
Unlike Malaysia and Thailand, Vietnam‟s stocks demand equation showed a higher R2 of 0.92. Stocks demand was strongly dependent on the lagged stocks and lagged production. The price variable was tested in the model but the estimated coefficient had a positive sign which was contradictory to the theory. Assuming that the transactions motive is vital to the Vietnamese rice industry, the price variable was removed from the model.
101 All of the behavioural equations had an R2 greater than 0.92 which revealed that the regressions were well behaved. The area harvested equation was regressed on the lagged area harvested, lagged price and a time trend. Dummy variables were used in the model to capture the changes in the economic conditions in Vietnam during the study period, but the estimates were not significant and the results were not adequate for a conclusion. Therefore, the dummies were removed from the model.
Similar to the other countries, farmers in Vietnam were found to respond to the fertilizer consumption per hectare and irrigation levels. These variables were found to be statistically significantly at the one and five per cent level respectively. A one per cent increase in the fertilizer consumption was found to lead to a 6.93 per cent increase in the yield. It is likely the yield equation could be improved further if weather variables were available to include in the model.
Table 6.3 The behavioural estimations for Vietnam
Consumption Demand
D3 = -8.059 – 0.382E-06 P3 + 0.305 POP3+ 0.243E-08 GDP3 (1.657)*** (0.146E-06)*** (0.252)*** (0.957E-09)** R2 = 0.967 Adjusted R2 = 0.964 D.W. = 1.355 Stocks Demand D9 = -0.294 + 0.032 S3(-1) + 0.704D9(-1) (0.159)* (0.014)** (0.127)*** R2 = 0.920 Adjusted R2 = 0.914 D.W. = 1.808 Area Harvested
A3 = 8.684 + 0.089 A3(-1) + 0.262E-07 P3(-1) + 0.034E-03 T (25.15) (0.211)*** (0.358E-07) (0.135) R2 = 0.964 Adjusted R2 = 0.959 D.W. = 1.872 Yield Y3 = 1.305 + 0.394IRRI3 + 6.933FC3 (0.357)*** (0.472)** (0.147)*** R2 = 0.955 Adjusted R2 = 0.951 D.W. = 1.669
Note : Figures in parentheses denote the standard errors and *** , ** and * indicate significant at 1, 5 and 10 per cent significant levels.
102
Pakistan sub-model
The two-stage least squares (2SLS) results for the consumption demand, stocks demand and yield and the ordinary least squares result for the area harvested in Pakistan are presented in Table 6.4. The results of behavioural equation estimations were found to be consistent with the theory and as expected. It is apparent that the price of wheat, when used as a substitute for rice, was highly significant at the one per cent level. In Pakistan, rice is the second staple food after wheat. This situation differs in other countries, including Malaysia, Thailand, Indonesia and Vietnam, where rice is the main staple food. Though, results from previous studies found that rice is a normal good in Pakistan (Mukhtar 2009; Muhamad 2008), for this study it was not possible to identify whether rice was a normal or inferior good in Pakistan since there was no significant income parameter for the model.
The stocks demand was regressed on the lagged stocks demand and lagged supply. Inclusion of the rice price in the stocks demand model did not provide any meaningful results, thus it was removed from the model. The results could not be improved further, even though various variables, including dummies, rice price, and production were used. Only the lagged stocks demand was found to be statistically significant at the one per cent level.
The estimated coefficients for the area harvested equation were consistent with a
priori expectations. Unlike the demand estimates, the area harvested results appeared
to agree with the study by Muhamad (2008). The price of wheat was used to represent the substitute crop for paddy area, which was tested separately in the model, but the results were not satisfactory, thus the variable was removed from the model. The results maybe better if the price of other substitute crops were to be included in the model but data limitations restricted the possible variables.
The yield equation was estimated as a function of fertilizer consumption and irrigation. The irrigation variable was found to be positive and statistically significant at the one per cent level. This result suggests that in Pakistan, the rice yield is largely dependent on the irrigation system.
103 Table 6.4 The behavioural estimations for Pakistan
Consumption Demand D4 = 5.858 – 0.178E--04 P4 + 0.671E-04 PW4 + 0.047 GDP4 + 0.144 T (3.984) (0.105E-04)*(0.177E-04)*** (0.049) (0.156) R2 = 0.652 Adjusted R2 = 0.593 D.W. = 1.085 Stocks Demand D10 = 0.210 + 0.037 S4(-1) + 0.637 D10(-1) -0.556E-02T (0.319) (0.112) (0.153)*** (0.013) R2 = 0.416 Adjusted R2 = 0.346 D.W. = 1.958 Area Harvested A4 = 1.523 + 0.152 A4(-1) + 0.798E-05 P4(-1) + 0.0208 T (0.684)** (0.382) (0.669E-05) (0.0105)** R2 = 0.846 Adjusted R2 = 0. 819 D.W.h = 1.985 Yield Y4 = -0.335 + 1.358IRRI4 + 0.097FC4 (0.369) (0.228)*** (0.195) R2 = 0.808 Adjusted R2 = 0.793 D.W. = 1.034
Note : Figures in parentheses denote the standard errors and *** , ** and * indicate significant at 1, 5 and 10 per cent significant levels.
Indonesian sub-model
The behavioural estimations for Indonesia were found to conform to the prior expectations for all the coefficient signs. The consumption demand equation was regressed on the price of rice, price of wheat, population and a time trend. The gross domestic product (GDP) was included as an income proxy but was found not to have any significant results and was replaced with a time trend. Since there was no income parameter, it was not possible to identify whether rice was a normal or inferior good in Indonesia. The population variable parameter was found to be positive and highly significant at the one per cent level which reflected the influence on the rice demand in Indonesia of population.
In the stocks demand estimation, the lagged stocks demand parameter appeared to be significant at the one per cent level. Despite numerous tests conducted to improve the stocks demand estimates, the R2 was still low at 0.57. Similar to Malaysia, the Indonesian rice industry is controlled by a state trading agency called Bulog. If the
104 influence of Bulog could be captured into the model, the results would likely be better.
In the area harvested equation, the price of cassava was included in the model and the coefficient negatively influenced the paddy planted area. In the earlier models, the price of corn, maize and sugar were included as substitute crops to the rice planted area, but the coefficients were found not to be statistically significant and with unexpected signs. Thus, these variables were removed from the model. Farmers seem to respond to the previous year‟s area harvested, as the lagged area harvested parameter had a positive sign and a significant coefficient.
The rice yield in Indonesia was estimated as a function of irrigation, fertilizer consumption and a time trend. The estimate for the fertilizer consumption variable in the yield equation was found to be highly significant at the one per cent level. This finding was similar to Haryati and Aji (2005) where paddy productivity tended to decline if the fertilizer price rises since the farmers tended to reduce fertilizer usage.
Table 6.5 The behavioural estimations for Indonesia
Consumption Demand
D5 = -196.9 – 0.858E-06 P5 + 0.218E-06 PW5 + 1.497 POP5 - 4.117 T (68.35)*** (0.803E-06) (0.714E-06) (0.465)*** (1.482)*** R2 = 0.968 Adjusted R2 = 0.963 D.W. = 1.458 Stocks Demand D11 = -0.493 + 0.061 S5(-1) + 0.690 D11(-1) (1.414) (0.052) (0.152)*** R2 = 0.571 Adjusted R2 = 0.538 D.W. = 1.718 Area Harvested
A5 = 1.239 + 0.892 A5(-1) + 0.243E-07 P5(-1) – 0.167-07 PCA5(-1) (1.199) (0.120)*** (0.216E-06) (0.536E-06) R2 = 0.881 Adjusted R2 = 0. 866 D.W. h = 1.828 Yield Y5 = 3.143 + 0.077 IRRI5 + 3.135 FC5 + 0.877E-02 T (1.068)*** (0.255) (0.829)*** (0.581E-02) R2 = 0.824 Adjusted R2 = 0.803 D.W. = 1.335
Note : Figures in parentheses denote the standard errors and *** , ** and * indicate significant at 1, 5 and 10 per cent significant levels.
105
Rest of the world sub-model
The estimated results for all the behavioural equations in the rest of the world: the consumption demand, stocks demand, area harvested and yield for the rest of the world are given in Table 6.6. The consumption demand was regressed only on the price of rice and income since the coefficient for the price of wheat (assumed as a substitute good for rice), was found not to be significant and thus, removed from the equation. The coefficients were found to be statistically significant at the one per cent level and consistent with a priori expectations. The demand equation fitted the data well as the R2 was 0.92.
Similar to the other sub-models in this study, the stocks demand for the rest of the world was regressed on the lagged supply, lagged stock demand and a time trend. All the variables were found to be statistically significant at the one per cent level and the high R2 of 0.93 indicated that the variables were a good fit for the model.