CHAPTER VIII MODELING. regression models. The U.S. Forest Service conducted a study to project trends in

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1 CHAPTER VIII MODELING Various studies related to timber and timber products have been conducted using regression models. The U.S. Forest Service conducted a study to project trends in demand for paper and board using several independent variables. The various possibilities of variables are; population, households, gross national product (GNP), disposable personal income, and industrial production (Hair, 1967). The effect of recent federal regulations restricting the export of softwood logs from state-owned timber was tested using data on individual sales from the state of Washington and the hedonic regression model (Johnson, 1995). It was found that a partial ban on Washington log exports in effect in 1991 and 1992 did not reduce the state's timber revenues, which suggests that the state of Washington has substantial power in the export market for high quality timber. In Indonesia, Ariffin (1994) studied supply and demand relationships for hardwood logs. He found that log price, as measured by weighted average index price of log, and inventory significantly influenced the supply and demand of hardwood logs. Other variables that significantly influenced log demand were the price of plywood and the Indonesian-Japanese exchange rate. In this study, a model was developed to quantify the relationship of raw material supply variables and economic variables with the export of furniture from Malaysia. This regression equation will be used with the purpose not only to know the relationship between two or more variables but also to predict the unknown values of export furniture 54

2 based on the known values of the other variables. In developing this model, the export of furniture is selected as the dependent variable, while the independent variables that were selected are: X 1 = production of rubberwood sawn timber X 2 = total import of logs and sawn timber into Peninsular Malaysia X 3 = Export of rubberwood sawn timber X 4 = import price indexes (IPI) of furniture into the United States and X 5 = exchange rate of the Malaysian Ringgit to the U.S. Dollar. The selection of the above independent variables was based on the preceding discussion of the development of the furniture industry and market that have associated the above factors with the export performance of furniture. Raw material has always been identified as the important factor that influences the export performance of the furniture and its competitiveness. The exchange rate has been identified as one of the factors that influences the export of a product in the international market. With the recent currency crisis that hit most Asian countries, this factor becomes very critical. As the price of furniture per unit volume is difficult to obtain, the import price indexes have been selected to study the long term price trends of the product, which can also relate to the exchange rates factor. Due to the difficulty in obtaining older data, and the fact that export of rubberwood furniture started in the 1980s, the study covers the period from 1982 to The definition of furniture follows the description of products under the United Nations nomenclature system, the Harmonized Commodity Description and Coding 55

3 System, as illustrated in Table 19 of the Appendix. The data was obtained from the Statistics Department of Malaysia based on the export declaration made by the exporters. Export value data of Malaysian wooden furniture, as shown in Table 12, is used in this study as the dependent variable. Because almost all exports of Malaysian furniture came from Peninsular Malaysia, these figures can be taken to represent total exports from Malaysia. Furthermore, the rubberwood processing industry in two states of eastern Malaysia, Sabah and Sarawak, has not been established as yet. The study uses data of rubberwood log production of Peninsular Malaysia because 80% of wooden furniture is made of rubberwood. These annual figures of log production were compiled by the Forestry Department Headquarters based on data provided by twelve state departments in Peninsular Malaysia. However, since 1995, the state Forest Department has started to abolish the collection of harvest royalties on rubberwood logs. This has affected the collection of log production data by the Forest Department, whereby the figures reported by the department were found to be very low. In view of this problem, for production figures from 1995 to 1997, this study uses data obtained from a study of rubberwood supply and demand conducted by the Malaysian Timber Industry Board (MTIB) and Malaysian Timber Council. For projection figures for year 1997 onwards, the MTIB and MTC study take into account input data from the Rubberwood Smallholders Authority (RISDA, 1997), which estimated the area that will be subject to replantation. Using this log production data, the sawn timber production, which is an independent variable X 1, can be estimated based on the assumptions that 80 percent of logs were consumed by the sawmilling sector and of a 36 percent recovery rate for conversion to sawn timber. The independent variable, X 2, is the total volume of logs and sawn timber imported into 56

4 Peninsular Malaysia. For this purpose, this study uses import data compiled by the Statistics Department. To reflect the realistic supply of raw material, the study takes into account the export of rubberwood sawn timber, which is the independent variable X 3. These exports are subject to export levy and quota rules implemented since Table 20 contains data for production of sawn timber, total import of logs and sawn timber, export of sawn timber, and supplies that are available for domestic downstream processing. The Bureau of Labor Statistics (BLS) of the United States Department of Labor provided the Import Price Index (IPI) for furniture. This price data was collected by the Department on about 12,000 import items by mail questionnaires, after interviewing with reporting companies (BLS, 1998). The price change by an index number is normally expressed as a percent change and most of them are quoted in FOB (free on board) of foreign port (BLS, 1998). Data from the year 1980 to 1993 were compiled quarterly under the furniture and parts thereof, using the SIC-based classification system; while data for after 1993 were compiled on a monthly basis under the SITC Rev. No. 82 (U.S. Department of Labor, 1998). For this study, the average figures, either monthly or quarterly were used to reflect the annual Indexes, which are the variable X 4, as shown in Table 21. The currency exchange rates of the Malaysian Ringgit to the U.S. Dollar from 1982 to 1997 were obtained from the central bank, Bank Negara Malaysia (1998). The exchange rates from year 1982 to 1997, which are the variable X 5, are shown in Table

5 Data Analysis Regression analysis is a statistical technique that can be used to relate two or more variables (Aaker et al, 1997). For example, the multiple regression equation that contains two regression coefficients and independent variables can be expressed as: Y= ß 0 + ß 1 X 1 + ß 2 X 2 + e In this equation, ß 0 is the intercept and ß 1 and ß 2 are regression coefficients associated with the independent variables X 1, and X 2. An error term, e, describes the effects on Y of all factors other than the value of all independent variables. The prediction equation of the multiple regression model that contains two independent variables is: Y= a + b 1 X 1 + b 2 X 2 ; In this case, Y is the predicted variable and b 1 and b 2 are called the partial regression coefficients (Aaker et al, 1997). The partial regression coefficient b 1 reflects the changes in Y when X 1 changes by one unit, keeping the other variables constant. The ability of the regression model to predict is measured by the coefficient of determination or R-square and the significance of the regression of coefficient. The R- square is the square of the correlation between X and Y, and therefore lies between 0 and 1. The adjusted R-square is the population of Y variation, which can be predicted from X in the population; while R-square is the proportion of variation in Y which can be predicted from X in the sample. The significance of R-square can be conducted by using an F-test, where the F value from the table is compared to the F value from calculation. Using the F-test, we will reject the null hypothesis, when F calc >F table. 58

6 Inclusion of the independent variables in the final model depends on their significance. In order to evaluate the significance of ß 1 and ß 2, taking into account its variation, we will use a statistical hypothesis test. The hypothesis test can be written as: Null hypothesis H 0 : ß 1 =0 Alternative hypothesis H a : ß 1 0 Using the t-test, we will reject the null hypothesis, when t calc >t table. This means that their coefficients are not zero and help to estimate the dependent variable. Another way of testing the significance of the independent variable against the above hypothesis is by using the p-values. The rejection rule is that we reject the null hypothesis if alpha is greater than p-value (Aaker et al, 1997). Hence, the smaller the p- value, the stronger the evidence to reject H 0 (Aaker et al, 1997). In this case, alpha is the level of confidence that is applied for the testing of significance. In developing the model, we will start with all independent variables and later reduce the number of variables to produce the best model based on the values of the coefficient of determination (R-square) and the significance of the variables. In view of the limitation on the number of observations, it is felt appropriate to reduce the variability within the groups so that the model will be more meaningful. The independent variables under study can be broadly categorized under two factors; supply of raw material and economic. The supply of raw material variable comes from domestic sawn timber production and the import of raw material. This total supply has to be subtracted from export volume to reflect the actual domestic supply that is available for furniture manufacturing, thus forming the independent variable X 6. The economic variable, on the other hand, is the multiplication of Import Price Indexes (IPI) by the exchange rate, 59

7 which reflects the conversion of IPI to its equivalent in Malaysian Ringgit. Both of these variables have been regressed individually where the signs of the regression coefficient show that they both have positive relationships to independent variable, export of furniture. Thus, the regression model to predict the export of wooden furniture, can be expressed in two new variables, as below: Y= a + b 6 X 6 + b 7 X 7 ; where X 6 = Supply = X 1 + X 2 -X 3 and, X 7 = Economic = X 4 * X 5. Data for total supply from these two sources of raw materials X 6, is taken as the total supply of rubberwood sawn timber and total import of logs and sawn timber less the export volume which is shown in the last column of Table 20, while data for X 7 is the index prices which have been converted to corresponding Malaysian Ringgits. The conversion of IPI to corresponding exchange rates is shown in Table 22. These variables form the time-series regression analysis to predict changes in a dependent variable over time. The above data input of the variables were regressed using data analysis statistical tools within Microsoft's Excel program for the estimates of regression coefficients b 6, b 7, the intercept and the measures of prediction of the model. Elasticity for a non-linear relationship indicates percentage changes in X given percentage changes in Y. In this case, elasticity changes as we move along the demand curve, while for linear it remains constant. Elasticity greater than 1 indicates that there is an elastic relationship between independent variable X and dependant variable Y, while elasticity less than one indicates that the relationship is inelastic. Elasticity equal to one 60

8 indicated that there is unielasticity relationship between the independent variable of X and Y (Tomek and Robinson, 1995). To understand the relationship between any two variables, the coefficient correlation, r, will also be determined. The correlation coefficient reflects the relationships or measures of association between two variables (X and Y) but does not imply any causal relationship between the variables (Aaker et al, 1997). Sample correlation, r, always lies between 1 and 1, where r = 1 indicates a perfect positive linear association, r = -1 indicates a perfect negative linear association, and r = 0 indicates no association or an absence of linear association. 61