An alternative approach to identify key industries: issues to selection criteria.
Wassily Leontief introduced the input-output analysis in the mid-1930s which became a standard tool in development planning (Miller, Blair 2009). The analysis has the advantage of showing the impacts of a specific action, and the ripple-effects that the action or event has on other sectors of the economy. Within this analytical framework, Rasmussen (1956) and Hirschman (1958) respectively developed measures for intersectoral linkages and application of intersectoral linkages in the identification of key economic sectors to maximizing impacts of investment. They contended that developing economies should aim investment in key sectors that are known to have a high degree of forward and backward intersectoral linkages. Since Rasmussen (1956), researchers have suggested many measures to observe the strength of backward and forward linkages for identifying key sectors. Several measures are widely accepted as standard actions and influential than others for investment selection (Dietzenbacher et al. 1993; Hoen 2002). The linkage measures mostly designed to identify key sectors in terms of employment, value added or input consumption to the policy goal of expanding output. Interestingly, each of these measures has found slightly different in interpretations and applications but typically surrounding by dispute and controversy (Hewings 1982; Cella 1984; Sonis et al. 1995; Miller, Lahr 2001; Sanchez-Choliz, Duarte 2003).
Therefore, even though there is a good number of studies and applications of intersectoral linkage measures related to weak forward and backward linkages are existed in the subject but more could be done as the gap in key sectors' identification (classification) in the literature remain outstanding. As our knowledge goes, to minimize the gap no study has been considered in the area by comparing the use of different linkages submethods to identify key sectors for expansion of foreign direct investment (FDI) or no study has been measured probable policy goals by alternative selection criteria. In addition, no study has been deliberated to select key sectors by the frequency of their occurrences in the different linkage measures to justify potential outcomes. We understand from the literature that mostly developing and transitional economics suffered to identify the policy goal of expanding FDI and struggled to obtain potential output with limited investment. The evidence of planning challenge even can be found in large developing countries like in India, Argentina and Brazil where they struggled to implement the industrial strategies (Ma 1997; Mann 1997; UNCTAD 1996; Gulati 1997; Jiaqin, Huei 2002). As public sector investment considered a large share in those developing countries, and hence the potential efficiency gains supposed to guide public investment from an economic viewpoint.
Hence, the proper knowledge and appropriate selection of key sectors could help to guide correct investment in the key industries and benefit of investment could be maximized (McMann, Randolph 2011; OECD 2011; Fornahl et al. 2010; Hitt et al. 2009; Kuratko 2009; Audretsch 2009; Hsu 2007). Literature shows that to identify the effective policy goal of expanding FDI India, Brazil and Argentina have encountered lots of difficulties (Gulati 1997; Jiaqin, Huei 2002). Therefore, the arguments place again in the application of intersectoral linkages related to particular weak forward and backward linkage measure as many of those linkage measures differ only slightly but results in outcome that are fairly different (Ma 1997; Mann 1997; Gulati 1997; Jiaqin, Huei 2002). We understand that using the same data set different forward and backward linkage measures yield different selection of key sectors and thus resulting in different policy outcome, which may place complexities in policy design. Therefore, academia should come up with a precise linkage measure by alternative selection criteria in the application of intersectoral linkages. We identify that Malaysia is not an exception in the realm even though it has fundamentally a liberal foreign direct investment policy as the issue is concern.
Malaysia is basically considered a small open economy with a liberal foreign direct investment incentives' friendly provision to attract investment. Following FDI intensives in the 80s, its inflows have increased more than ten folds over the last three decades (Fig. 1). However, the average FDI inflow has been hanging in MYR15.0 billion per year without signs of increasing between the year 1990 and 2005 (Table 1). Eventually, FDI has decreased from MYR20.3 billion to 17.9 billion while domestic direct investment (DDI) increased from MYR8.1 billion to MYR13.2 billion between 1990 and 2005 (Table 2) (1). It reveals from the Table 2 that some key sectors (i.e. identified by the Malaysian Second and Third Industrial Master Plan) experienced dropping tendency in FDI and DDI especially on textile and textile products; paper, printing and publishing; petroleum and petrochemical; non-metal manufactures; rubber products and beverage and tobacco. It should be noted that the Third Industrial Master Plan (20062020) targeted on key industries, which are (a) non-resource-based industries: electrical and electronics, medical devices, textiles and apparel, machinery and equipment, metals, transport equipment; (b) resource-based industries: petrochemicals, pharmaceuticals, wood-based, rubber-based, oil palm-based, and food processing (MMP 2009) (2).
Following on the evidence of the last two decades our question is very straight forward: why the key sectors that are identified by the Malaysian Third Industrial Master Plan experienced negative tendency? Has the Malaysian Third Industrial Master Plan some lacking to identify key industries? If China able to attract international companies to place global competitiveness that driven many multinational companies to look for other alternative manufacturing avenues, then why Malaysia could not? Therefore, can we state that Malaysian did not identify the key sector's investment in right time? We understand that the identification of key sectors is fundamental; so that the limited foreign and domestic investment can correctly direct to key industries to achieve maximum development impacts. With this background, our goal is to show how the Malaysian economy has lost out from correct identification of key sectors in the Malaysian Third Industrial Master Plan. Apart from that, our aim is also to show the difference of key sectors' classification between this study and the Malaysian Third Industrial Master Plan (2006-2020).
As correct identification of key sectors remained a planning challenge in Malaysia to maximize economic outcomes; hence, to overcome of these problems, we proposed an alternate approach that allows for better selection of key sectors. We placed a one step forward strategy in the linkage measures by augmenting information from different key sector measures. The set of key sectors with the largest impacts are selected to identify key industries by our study. Our approach is based on three steps: (i) use of different methods to identify key sectors; (ii) the intended policy goals as a criterion of selection; (iii) the selection of the key sectors chosen according to the frequency of their occurrences in the different methods. The method employed in this paper is rigorous, which made its findings quite robust for the key sectors' selection criteria. In addition, the identification of key sectors is especially important for government to make corresponding policies to attract FDI and DDI to the sectors which may play vital roles in driving economic growth. Therefore, we believe that experiences from this study national policy maker would be able to implement a right industrial strategy in future.
1. Methodology and study approach
The analytical approach is based on the Leontief's input-output framework (Miller, Blair 1985, 2009). Given an n-sector economy with intersectoral transaction matrix Z and sectoral total output vector X, the direct input requirement matrix, A, is given by:
A = Z[([??]).sup.-1], (1)
where, [??] is the diagonalized matrix of X. Elements in the direct input requirement matrix indicate the value of input from sector i used by sector j to produce one dollar's worth of output.
We understand that input-output model describes the relationships among economic sectors through the use of a system of linear equations that represent each sector's identity between the total output produced, and the output purchased and consumed by all the other sectors of the system. In matrix notation this system of linear equations is:
X = AX + Y, (2)
where, Y is final demand vector. Equation (2) is the fundamental equation of the open Leontief system, which states that the gross output (X) is the sum of all intermediate input demand (AX) and final demand (Y). Solving equation (2) for total output yields equation (3) where I is an n by n identity matrix and B is the Leontief inverse or total requirement matrix.
X = [(I - A).sup.-1] Y = BY. (3)
To measure the intersectoral linkage of a particular sector means we must compute and evaluate its forward linkage (FL) and backward linkage (BL) with the rest of the economy. (3) Note however, since the forward linkage essentially deals with downstream output supply, despite some reservation by some authors (Oosterhaven 1988; Oosterhaven 1996; Dietzenbacher 1997), researchers generally use the Ghosh supply-side model in the computation of FL (Miller, Blair 1985). The supply-side direct output coefficients are given by:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (4)
where, r is the scalar diagonalized matrix of A (i.e. direct output coefficients). It follows that the Ghosh direct and indirect output coefficients are given by (Miller, Blair 1985):
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)
where, r is the scalar diagonalized matrix of B (i.e. direct and indirect output coefficients). Based on the Leontief framework, Rasmussen (1956) and Hirschman (1958) suggested (6) and (7) below as indicators of strength of BL and FL.
BL = 1/n [n.summation over (i=1)] [b.sub.ij] = 1/n [B.sub.*j] (6)
FL = 1/n [n.summation over (j=1)] [b.sub.ij] = 1/n [B.sub.i*], (7)
where, B is the Leontief inverse matrix (i.e. total requirement matrix of the equation (3)) and parameters Bj and Bi indicate the value of inputs from sector i used by sector j to produce one dollar's worth of output in the economy. In addition, [b.sub.ij] are the coefficients of matrix B, where Hazari (1970) suggested modification to the measures by dividing the terms in (6) and (7) by a global average as in (8) below:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (8)
where, n indicate the numbers of industry in the economy. This would allow inter-industry comparison. With normalizations procedure, the linkage indicators become:
BL = [U.sub.j] = 1/n [B.sub.*j]/1/[n.sup.2] [n.summation over (j=1)] [B.sub.*j] (9)
FL = [U.sub.i] = 1/n [B.sub.i*]/1/[n.sup.2] [n.summation over (i=1)] [B.sub.i*] (10)
where, BL is the backward linkage and FL is the forward linkage. In addition, Uj and Ui indicate the value of inputs from sector i used by sector j. Under this method, a sector is said to have strong backward linkage if [U.sub.j] > 1 and strong forward linkage if [U.sub.i] > 1. A key sector is defined as those sectors having [U.sub.j] and [U.sub.i] greater than 1.
To eliminate selection error due to extreme values in the calculation of average, Hazari (1970) also suggested using the coefficient of variation to complement (9) and (10) in identifying key sectors (Bharadwaj 1966). Following Hazari (1970), Lenzen (2003), and Cai, Leung (2004), the coefficients of variations associated with BL and FL are defined as in (11) and (12) respectively:
[V.sub.j] = [square root of [1/[n - 1]] [n.summation over (i=1)] [([b.sub.ij] - 1/n [B.sub.*j]).sup.2]/1/n [B.sub.*j]] (11)
[V.sub.i]= [square root of [1/[n - 1]] [n.summation over (j=1)] [([b.sub.ij] - 1/n [B.sub.*j]).sup.2]/1/n [B.sub.i*]], (12)
where, [V.sub.j] indicates the coefficients of variations associated with BL and Vj indicate the coefficients of variations associated with FL. Under this method, a sector is said to have strong backward linkage if [U.sub.j] > 1 and small [V.sub.j]. Similarly, a sector has strong forward linkage if [U.sub.i] > 1 and small [V.sub.i].
All indices mentioned above are pure measure of sectoral interdependence that do not account for the level of economic activities and/or the policy context of key sectors computation (Lenzen 2003; Soofi 1992; Cuello, Mansouri 1992). To remedy this deficiency, researchers recommended incorporating weighting scheme into BL and FL measures (Rasmussen 1956; Hirschman 1958, Hazari 1970; Laumas 1976; Soofi 1992; Cuello, Mansouri 1992). Following Soofi (1992), Claus, Li (2003), the weighted BL and FL measures are calculated as follows.
Let the final demand weighted Leontief inverse elements be [b.sup.w.sub.ij] where:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (13)
Similar to (7) and (8), the corresponding weighted BL and FL are:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (14)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (15)
where, w (i.e. sectoral final demand over total final demand) indicates the weights of BL and FL, and [b.sub.ij] are the coefficients of Leontief inverse matrix. The same calculations could be made using other schemes of weights. For an open economy, export weighted BL and FL are also appropriate. Under this method, a sector is considered to have strong backward linkage if [U.sub.j] > 1 and a strong forward linkage if [U.sub.i] > 1.
Another measure of sector potential that account for relative size of a sector is based on output-to-final demand elasticity (Mattas, Chandra 1994; Ciobanu et al. 2004; Miller, Blaire 2009). Simply put, this measure of sector potential quantifies the impact of one percent change in final demand to the percentage change in total output. Following Mattas, Chandra (1994), the output-to-final demand elasticity of sector j, which can be indicated by O[E.sub.xj], is calculated as follows:
BL = O[E.sub.xyj] = [n.summation over (i=1)] [b.sub.ij] [y.sub.j]/x = [B.sub.*j] [y.sub.j]/x. (16)
Essentially, O[E.sub.xyj] (backward linkage in the output-to-final demand elasticity of sector j) is similar to equation (6) or (13) but is weighted by the ratio of final demand to total output. As such, O[E.sub.xyj] is a measure of BL and taken together with a similarly weighted forward measure, could be used for key sectors' identification. The forward linkage output-to-final demand elasticity which can similar be indicated by O[E.sub.xyi] can is calculated as follows:
FL = [OE.sub.xyi], = [n.summation over (j=1)] [b.sub.ij] [y.sub.j]/x = [B.sub.i*] [y.sub.j]/x, (17)
where, O[E.sub.xyi] (forward linkage in the output-to-final demand elasticity of sector i). Under this method, strong backward linkage is associated with larger value of O[E.sub.xyj]. Similarly, strong forward linkage is associated with larger value of O[E.sub.xyi] (4).
Each of the measure presented in this section will result in different key sector selection albeit many overlapping. To discriminate among the selections, we propose simulating the planned investment impact on the targeted economy-wide variables such as on output, value added and employment. In the next section, we demonstrated the application of this proposed methodology.
2. Method application and results
To reveal the proposed alternate approach, we studied the frequency of occurrence by our five different scenarios on Malaysian economy. The government of Malaysia seeks to expand output of key manufacturing sectors by encouraging direct investment into these sectors through provisions of appropriate incentives. Assume also that the targeted level of investment is 10 percent increase in direct investment in key manufacturing sectors and 5 percent for manufacturing sectors that are strong in either BL or FL.
We used Malaysian Input-output Table (2000) for BL and FL computations and 2005 Malaysian Investment Statistics for simulating investment impacts (DOS 2006). The original input-output table comprised 94 sectors but was aggregated into 30 sectors exactly following 9th Development Plan (MDP 2006). However, Malaysian 9th Development Plan considered 19 sectors for national planning (i.e. Table 2) but we used 11 more industries to identify better outcomes to support the 10th Malaysian Development Plan (RMK-10). The value-added row in the input-output table was proportionate into 30 sectors to fulfill our objectives. We also collected additional value-added data from Malaysian capital composition matrix. And finally, employment data are collected from DOS (2006). Appendix A provides the detail sector classification and the corresponding line number of the Input-Output Table. Our estimation comprised of 19 manufacturing industries (line 3 to line 21 of the new table) with the rest being in the agricultural and mining (line 1 and line 2) and service sectors (line 22 to line 30).
For the purpose of identifying key manufacturing sectors, our alternate approaches were computed based on Input-Output relative methods on linkage measures. They are:
1. Method I: Based on (9) and (10) with standard I-O linkage measures.
2. Method II: Based on combination of coefficient of variation both for BL and FL. That is BL and FL as in Method I but is complemented by (11) and (12).
3. Method III: Based on (13), (14) and (15) with final demand weights on output; value-added and employment.
4. Method IV: Based on (14) and (15) with export weights.
5. Method V: Based on BL and FL output-to-final demand elasticity in (16) and (17) respectively.
For each of the method, we also estimated the economy-wide impact of additional investments in identified key manufacturing sectors under the assumption that government will encourage direct investment increased provide incentives to stimulate 10 percent increase in total direct investment in key industries but only 5 percent increase in total direct investment for industries that are strong in either BL or FL.
Results in Table 3 were obtained by applying equations (9) for backward linkage and (10) for forward linkage. Details of the result are presented in Table B1 in Appendix B under column heading Method I. For the 19 manufacturing industries, key sectors' results using this method are as follows.
a) Weak BL and weak FL: 21.1% (4 of 19).
b) Weak BL and strong FL: 10.5%; (2 of 19).
c) Strong BL and weak FL: 42.1%; (8 of 19).
d) Strong BL and strong fl: 26.3%. (5 of 19).
Based on the outcome and the assumed investment scenario mentioned above, the total additional output generated would amount to MYR1.70 billion (US$ 1 = 3.3RM).
Results in Table 4 were obtained by applying equations in (9) and (10), but each is complemented by coefficients of variations in (11) and (12) respectively. Details of the result are presented Table B1 in Appendix B under column heading Method II. Key sectors' results using this method are as follows.
a) Weak BL and weak FL: 21.1% (4 of 19).
b) Weak BL and strong FL: 10.5%; (2 of 19).
c) Strong BL and weak FL: 42.1%; (8 of 19).
d) Strong BL and strong fl: 26.3%. (5 of 19).
As expected, relative to results of Method I, Method II resulted in fewer key sectors where food processing industry and paper, printing and publishing industry were filtered out by coefficient of variations. Under the same investment scenario, the total additional output generated is MYR1.45 billion.
Results in Table 5 were obtained by applying final demand weights on output, value-added and employment in the calculation of (13) and (14-15). Details of the result are presented in Table B2 in Appendix B under column heading Method III. Summary of the outcome on out put using this method are as follows.
a) Weak BL and weak FL: 78.9% (15 of 19).
b) Weak BL and strong FL: 0%; (0 of 19).
c) Strong BL and weak FL: 0%; (0 of 19).
d) Strong BL and strong fl: 21.1%. (4 of 19).
Outcome of this method is radically different from those obtained using Method I and Method II. All of the manufacturing sub-sector fell either in the "Weak BL and weak FL" or the "Strong BL and strong FL" category with petroleum and petrochemical industry being common to all methods thus far. Additional output using this method is also the highest, i.e., MYR2.40 billion. This figure is considerably much higher than the additional output obtained in method I and method II.
Summary of the outcome on value-added using method III (Table 5 and Table C1 in Appendix C) are as follows.
a) Weak BL and weak FL: 78.9% (15 of 19).
b) Weak BL and strong FL: 0%; (0 of 19).
c) Strong BL and weak FL: 0%; (0 of 19).
d) Strong BL and strong FL: 21.1%. (4 of 19).
The outcome of this method is very similar from those obtained on output. The weights on Table C1 indicate that like final demand impacts, the value-added also placed similar impacts in the manufacturing sub-sectors and those are different from those obtained using Method I and Method II.
Results in Table 6 were obtained by applying export weights (i.e. as an alternative of foreign exchange earnings) in the calculation of (14) and (15). Details of the outcomes are presented in Table B2 (Appendix B). Under this scheme, the 19 manufacturing industries are distributed to fulfill our goal as follows.
a) Weak BL and weak FL: 74.9% (14 of 19).
b) Weak BL and strong FL: 0.0%; (0 of 19).
c) Strong BL and weak FL: 5.2%; (1 of 19).
d) Strong BL and strong FL: 21.0%. (4 of 19).
Result of this scheme based on weights is presented in Appendix C (Table C1) which is overall similar to outcome obtained from Method III, except petroleum and petrochemicals. This is expected since petroleum and petrochemical industry has a final demand weight than export weight. On the other hand, chemicals and chemical product industry has a higher export weight than final demand weight (Appendix C). Under the same investment scenario, total additional output generated is MYR2.7 billion.
Results in Table 7 were calculated by applying (16) and (17). Details of the result are presented in Table B2 in Appendix B under column heading Method V. For the 19 manufacturing industries, BL ranges from a maximum of 0.2046 (electrical and electronic industry) to a minimum of 0.0010 (leather and leather product industry). FL ranges from a maximum of 0.1774 (electrical and electronic industry) to a minimum of 0.0008 (leather and leather product industry). BF and FL averaged 0.0312 and 0.0278 respectively. Under this scheme, we defined key industries as those industries with BF and FL greater than 0.0278 (5). Under this scheme we found that the 19 manufacturing industries are distributed s follows.
a) Weak bl and weak FL: 73.7% (14 of 19).
b) Weak bl and strong FL: 0%; (0 of 19).
c) Strong bl and weak FL: 5.3%; (0 of 19).
d) Strong bl and strong FL: 21.1%. (5 of 19).
Outcome of this scheme is exactly the same as those obtained using Method III. Under the same investment scenario, total additional output generated is MYR2.70 billion.
Finally, we estimated the outcomes on employment using equation no (13) and the weights are presented on Table C1 (column 5) in Appendix C. Here we used Malaysian 2005 labor force data (DOS 2006: 232) and based on our findings and rankings the top key weighted sectors are as follows:
1. Electric and electrical products (row 18 in Table C1).
2. Machinery manufacture (row 17 in Table C1).
3. Food processing (row 3 in Table C1).
4. Petrol and coal industries (row 11 in Table C1).
5. Chemicals and chemical products (row 10 in Table C1).
The key sector identification was prepared with a high degree of the frequency of their occurrences in five sub-methods to determine the key sectors. As our knowledge goes, so far this is the first time that key sectors' identification was done based on frequency of occurrences by simultaneously using five different methods in any economy, particularly in Malaysia. Summary of findings based on frequency of occurrence are as follows:
1. Food processing: Methods I, II, III, IV, and V.
2. Chemical and chemical product manufacture: Methods I, II, IV, and V.
3. Petroleum and petrochemical industries: Methods I, II, III, and V.
4. Machinery manufacture: Methods III, IV, and V.
5. Electrical and electronic products: Methods III, IV, and V.
6. Paper, printing and publishing: Method I and II.
7. Non-metal manufacture: Method I and II.
By our alternative approach, we identified key industries in Malaysia taking those sectors that had appeared in at least three methods, which are food processing, machinery manufacture, electrical and electronic products, chemical and chemical product manufacture, and petroleum and petrochemical industries. In this way, while not all method results in the same key manufacturing industries, it at least allows us to identify the important sectors that are key industry to the economy. On the other hand, by taking key weighted scheme on employment, we found that the key manufacturing industries in Malaysia are electrical and electrical products; machinery manufacture; food processing; petrol and coal industries; and chemicals and chemical products which are similar as in our proposed alternate approach.
Since all methods are the legitimate methods for key sectors' computation, hence we select key sectors based on the potential impact on output, value-added and finally on employment by using frequency of occurrence. In this study, we thus conclude that key manufacturing industries following on the impacts on output, value-added, employment and with exports weights or alternatively foreign exchange earners in Malaysia, as identified and classified are (i) food processing, (ii) machinery manufacture, (iii) electrical and electronic products, (iv) chemical and chemical product manufacture, and (v) petroleum and petrochemical industries. These results differ from the Malaysian Third Industrial Master Plan (MMP 2009).
Since the 1950's, many key sectors' identification measures have been developed. While these measures are very similar, their outcomes on key sectors are quite different as an application for policy choice. Therefore, we proposed an alternative approach that resolves this issue. Our approach provided a potential outcome to take account of further initiatives and justified why one method is chosen over others for a right investment decision directed to key industries. We further applied this alternative approach to select key sectors in Malaysia as the public sector investment still remains a large share in the national economy. We utilized the magnitude of impacts on output, value-added, employment, export earning for the identification process and based on the outcomes the key industries identified are (i) food processing, (ii) machinery manufacture, (iii) electrical and electronic products, (iv) chemical and chemical product manufacture, and (v) petroleum and petrochemical industries. Hence, the classification of key sectors in this study is quite straight forward to find out a future guideline to minimize Malaysian previous policy gap and to set a possible way forward for future investment decision.
The major contribution of this study (a) the formation of an alternate approach to identify key sectors, (b) the explanation why Malaysia is distress to identify the correct key sectors in the concurrent policy goal. It is very reasonable that unless selecting the right industrial sectors for investment decision, sustain economic growth may turn down in the future. We notice from the Malaysian Second and Third Industrial Master Plan that some key sectors such as textile and textile products; paper, printing and publishing; petroleum and petrochemical; non-metal manufactures; rubber products and beverage and tobacco are experienced negative impacts on FDI and DDI. Our purpose for this study is to help finalizing correct key industries, especially for the Malaysian forthcoming Development Plan. We understand that Malaysian government may have different economic and political agendas to uphold economic growth by other ways, but the correct identification of key sectors is crucial so that both limited foreign and domestic investment are directed to key industry's to achieve maximum growth. We suggest that this study would offer a specific direction for the concern policy maker to implement a right future industrial strategy in Malaysia.
Caption: Fig. 1. Real foreign direct investment inflows and domestic direct investment, 1985-2005 Sources: MDP (2006); DOS (2006).
Table A1. Sector classification Line Line number in original table Sectors/industries no. (1) (1), (2), (3), (4), (5), (6), Agriculture, forestry & (7), (8) fisheries (2) (9), (10), (11) Mining & quarry (3) (12), (13), (14), (15), (16), Food processing (17), (18), (19), (20), (21), (22) (4) (23), (24), (25) Bevearage and tobacco (5) (26), (27), (28), (29) Textile & textile products (6) (30), (31) Leather & leather products (7) (32), (33) Wood & wood products (8) (34) Furniture and fixtures (9) (35), (36) Paper, printing & publishing (10) (37), (38), (39), (40), (41) Chemical & chemical products (11) (42) Petroleum and petrochemical (12) (43), (44) Rubber products (13) (45) Plastic products (14) (46), (47), (48), (49) Non-metal manufactures (15) (50), (51) Basic metal products (16) (52), (53), (54) Fabricated metal manufactures (17) (55), (56) Machine manufactures (18) (57), (58), (59) Electrical & electronic products (19) (60), (61), (62), (63) Transportation equipment (20) (64) Measuring & scientific instruments (21) (65) Other manufactures (22) (66), (67) Utilities (23) (68) Construction (24) (69) Wholesale & retail trade (25) (70) Hotel & rest. (26) (71), (72) Transportation & communication (27) (73), (74), (75) Finance & insurance (28) (76), (77) Real estate & house ownership (29) (78), (79), (81), (83), (84), Business & individuals service (85), (86), (87), (88), (89), (90) (30) (80), (82), (91), (92), (93), Government services (94)
Table B1. Key sectors: Method I and Method II Method I Line Sector [U.sub.j] [U.sub.i] [U.sub.j] 1 Agriculture, forestry & 0.935 1.304 0.935 fisheries 2 Mining & quarry 0.719 1.052 0.719 3 Food processing 1.550 1.313 1.550 4 Beverage and tobacco 1.042 0.823 1.042 5 Textile & textile products 1.020 0.855 1.020 6 Leather & leather products 1.151 0.849 1.151 7 Wood & wood products 1.280 0.885 1.280 8 Furniture and fixtures 1.133 0.754 1.133 9 Paper, printing & 1.039 1.274 1.039 publishing 10 Chemical & chemical 1.144 1.110 1.144 products 11 Petroleum and petro- 1.049 1.180 1.049 chemical 12 Rubber products 1.140 0.920 1.140 13 Plastic products 0.929 0.853 0.929 14 Non-metal manufactures 1.097 1.308 1.097 15 Basic metal products 0.978 1.202 0.978 16 Fabricated metal 0.944 1.237 0.944 manufactures 17 Machine manufactures 0.799 0.723 0.799 18 Electrical & electronic 0.849 0.750 0.849 products 19 Transportation equipment 1.017 0.994 1.017 20 Measuring & scientific 0.896 0.737 0.896 instruments 21 Other manufactures 1.028 0.990 1.028 22 Utilities 0.916 1.465 0.916 23 Construction 1.073 0.743 1.073 24 Wholesale & retail trade 0.801 1.308 0.801 25 Hotel & rest. 1.156 0.998 1.156 26 Transportation & 0.966 1.006 0.966 communication 27 Finance & insurance 0.811 0.761 0.811 28 Real estate & house 0.775 0.936 0.775 ownership 29 Business & individuals 0.902 0.983 0.902 services 30 Government services 0.863 0.687 0.863 Method II Line Sector [U.sub.i] [V.sub.j] [V.sub.i] 1 Agriculture, forestry & 1.304 4.130 3.161 fisheries 2 Mining & quarry 1.052 5.004 3.551 3 Food processing 1.313 3.687 4.361 4 Beverage and tobacco 0.823 3.615 4.690 5 Textile & textile products 0.855 4.095 4.999 6 Leather & leather products 0.849 3.132 4.373 7 Wood & wood products 0.885 3.190 4.312 8 Furniture and fixtures 0.754 3.336 5.143 9 Paper, printing & 1.274 4.089 3.373 publishing 10 Chemical & chemical 1.110 3.619 3.786 products 11 Petroleum and petro- 1.180 3.827 3.273 chemical 12 Rubber products 0.920 3.737 4.701 13 Plastic products 0.853 3.971 4.402 14 Non-metal manufactures 1.308 3.807 3.517 15 Basic metal products 1.202 4.297 3.602 16 Fabricated metal 1.237 3.975 3.187 manufactures 17 Machine manufactures 0.723 4.522 5.089 18 Electrical & electronic 0.750 4.468 5.168 products 19 Transportation equipment 0.994 4.317 4.504 20 Measuring & scientific 0.737 4.118 5.123 instruments 21 Other manufactures 0.990 3.585 3.769 22 Utilities 1.465 4.089 2.567 23 Construction 0.743 3.312 4.892 24 Wholesale & retail trade 1.308 4.456 2.806 25 Hotel & rest. 0.998 3.188 3.677 26 Transportation & 1.006 4.064 3.969 communication 27 Finance & insurance 0.761 4.565 4.967 28 Real estate & house 0.936 4.920 4.133 ownership 29 Business & individuals 0.983 4.137 3.859 services 30 Government services 0.687 4.134 5.330 Table B2. Key sectors: Method III, Method IV and Method V Method III Line Sector [U.sub.j] [U.sub.i] 1 Agriculture, forestry & fisheries 0.916 1.406 2 Mining & quarry 1.079 1.777 3 Food processing 1.595 1.589 4 Beverage and tobacco 0.449 0.148 5 Textile & textile products 0.709 0.614 6 Leather & leather products 0.432 0.025 7 Wood & wood products 0.905 0.451 8 Furniture and fixtures 0.587 0.253 9 Paper, printing & publishing 0.435 0.334 10 Chemical & chemical products 0.941 0.911 11 Petroleum and petrochemical 1.129 1.174 12 Rubber products 0.643 0.392 13 Plastic products 0.547 0.444 14 Non-metal manufactures 0.470 0.222 15 Basic metal products 0.525 0.485 16 Fabricated metal manufactures 0.326 0.222 17 Machine manufactures 2.880 3.274 18 Electrical & electronic products 4.905 5.814 19 Transportation equipment 0.711 0.654 20 Measuring & scientific instruments 0.458 0.188 21 Other manufactures 0.616 0.144 22 Utilities 0.452 0.341 23 Construction 1.703 1.685 24 Wholesale & retail trade 0.607 1.035 25 Hotel & rest. 0.972 0.799 26 Transportation & communication 1.574 1.908 27 Finance & insurance 0.497 0.440 28 Real estate & house ownership 0.639 0.838 29 Business & individuals services 0.932 1.081 30 Government services 1.364 1.349 Method IV Line Sector [U.sub.j] [U.sub.i] 1 Agriculture, forestry & fisheries 0.716 1.007 2 Mining & quarry 1.404 2.418 3 Food processing 1.464 1.558 4 Beverage and tobacco 0.405 0.091 5 Textile & textile products 0.702 0.615 6 Leather & leather products 0.431 0.030 7 Wood & wood products 0.942 0.673 8 Furniture and fixtures 0.672 0.379 9 Paper, printing & publishing 0.332 0.184 10 Chemical & chemical products 1.185 1.401 11 Petroleum and petrochemical 1.116 0.898 12 Rubber products 0.693 0.537 13 Plastic products 0.568 0.497 14 Non-metal manufactures 0.514 0.301 15 Basic metal products 0.687 0.729 16 Fabricated metal manufactures 0.363 0.279 17 Machine manufactures 4.193 4.963 18 Electrical & electronic products 7.208 8.855 19 Transportation equipment 0.440 0.234 20 Measuring & scientific instruments 0.588 0.273 21 Other manufactures 0.786 0.175 22 Utilities 0.331 0.028 23 Construction 0.553 0.128 24 Wholesale & retail trade 0.544 1.045 25 Hotel & rest. 0.457 0.010 26 Transportation & communication 1.489 1.770 27 Finance & insurance 0.264 0.190 28 Real estate & house ownership 0.062 0.001 29 Business & individuals services 0.672 0.702 30 Government services 0.218 0.029 Method V Line Sector [U.sub.j] [U.sub.i] 1 Agriculture, forestry & fisheries 0.03136 0.04291 2 Mining & quarry 0.03776 0.05422 3 Food processing 0.05831 0.04847 4 Beverage and tobacco 0.00581 0.00450 5 Textile & textile products 0.02277 0.01875 6 Leather & leather products 0.00104 0.00075 7 Wood & wood products 0.02030 0.01378 8 Furniture and fixtures 0.01182 0.00772 9 Paper, printing & publishing 0.00846 0.01019 10 Chemical & chemical products 0.02917 0.02780 11 Petroleum and petrochemical 0.03245 0.03584 12 Rubber products 0.01511 0.01197 13 Plastic products 0.01503 0.01356 14 Non-metal manufactures 0.00580 0.00679 15 Basic metal products 0.01227 0.01481 16 Fabricated metal manufactures 0.00526 0.00677 17 Machine manufactures 0.11244 0.09992 18 Electrical & electronic products 0.20460 0.17743 19 Transportation equipment 0.02079 0.01997 20 Measuring & scientific instruments 0.00709 0.00573 21 Other manufactures 0.00463 0.00438 22 Utilities 0.00662 0.01039 23 Construction 0.07567 0.05143 24 Wholesale & retail trade 0.01970 0.03159 25 Hotel & rest. 0.02875 0.02437 26 Transportation & communication 0.05700 0.05823 27 Finance & insurance 0.01459 0.01343 28 Real estate & house ownership 0.02157 0.02558 29 Business & individuals services 0.03085 0.03300 30 Government services 0.05266 0.04117
Table C1. Key sector weights Line Sector Final Export demand Weights Weights 1 Agriculture, Forestry and 0.0323 0.0227 Fisheries 2 Mining and quarrying 0.0506 0.0676 3 Food processing 0.0362 0.0349 4 Beverage and tobacco 0.0054 0.0033 5 Textiles, Fabrics and Apparel 0.0215 0.0211 6 Leather and footwear 0.0009 0.0010 7 Sawmill and wood products 0.0153 0.0224 8 Manufacture of furniture 0.0100 0.0148 9 Paper, board, and printed products 0.0078 0.0042 10 Chemicals and chemical products 0.0246 0.0371 11 Petrol & coal industries 0.0298 0.0224 12 Rubber industries and products 0.0128 0.0171 13 Manufacture plastic products 0.0156 0.0171 14 Non-metal ore manufactures 0.0051 0.0068 15 Iron & steel industries, and 0.0121 0.0178 non-ferrous manufacture 16 Metal and metal fabrication 0.0054 0.0066 industries 17 Machinery manufacture 0.1356 0.2018 18 Electric and electrical products 0.2322 0.3474 19 Transportation equipments 0.0197 0.0069 manufacture 20 Measurement and scientific 0.0076 0.0109 equipments manufacture 21 Other manufacturing 0.0043 0.0052 22 Electricity & gas, and waterworks 0.0070 0.0006 23 Building and construction 0.0679 0.0051 24 Wholesale & retail trade 0.0237 0.0235 25 Hotels & restaurants 0.0240 0.0003 26 Transport & communication 0.0568 0.0518 27 Finance and insurance 0.0173 0.0073 28 Real estate & ownership dwellings 0.0268 0.0000 29 Business and private services 0.0329 0.0210 30 Government services 0.0588 0.0012 Line Sector Value-added Employment weights weights 1 Agriculture, Forestry and 0.0226 0.4377 Fisheries 2 Mining and quarrying 0.0354 0.0183 3 Food processing 0.0253 0.7201 4 Beverage and tobacco 0.0038 0.1074 5 Textiles, Fabrics and Apparel 0.0151 0.4277 6 Leather and footwear 0.0006 0.0179 7 Sawmill and wood products 0.0107 0.3044 8 Manufacture of furniture 0.0070 0.1989 9 Paper, board, and printed products 0.0055 0.1552 10 Chemicals and chemical products 0.0172 0.4894 11 Petrol & coal industries 0.0209 0.5928 12 Rubber industries and products 0.0090 0.2546 13 Manufacture plastic products 0.0109 0.3103 14 Non-metal ore manufactures 0.0036 0.1015 15 Iron & steel industries, and 0.0085 0.2407 non-ferrous manufacture 16 Metal and metal fabrication 0.0038 0.1074 industries 17 Machinery manufacture 0.0949 2.6975 18 Electric and electrical products 0.1625 4.6192 19 Transportation equipments 0.0138 0.3919 manufacture 20 Measurement and scientific 0.0053 0.1512 equipments manufacture 21 Other manufacturing 0.0030 0.0855 22 Electricity & gas, and waterworks 0.0049 0.0040 23 Building and construction 0.0475 0.3117 24 Wholesale & retail trade 0.0166 0.3840 25 Hotels & restaurants 0.0168 0.1612 26 Transport & communication 0.0398 0.3094 27 Finance and insurance 0.0121 0.0427 28 Real estate & ownership dwellings 0.0188 0.1313 29 Business and private services 0.0230 0.1612 30 Government services 0.0412 0.4284
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Received 01 September 2012; accepted 24 September 2012
Abul Quasem Al-Amin (1), Abdul Hamid Jaafar (2)
(1) International Business School (IBS), Universiti Teknologi Malaysia (UTM), Menara Razak Jalan Semarak 54100 Kuala Lumpur, Malaysia
(2) Department of Economics, Faculty of Business and Economics, National University of Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia
E-mails: (1) firstname.lastname@example.org (corresponding author); (1) email@example.com; (2) firstname.lastname@example.org
(1) Since independence in 1957, Malaysia experienced relatively high economic growth that averaged about 6% in the 1960s and 1970s. During those decades, the major engines of growth were the agricultural plantation sector and the mining and quarry sector. The agricultural plantation sector largely comprised of rubber, cocoa, and oil palm while mining and quarry comprised mainly of tin ore mining. In spite of the large agriculture and mining dominance, Malaysia transformed itself into a manufacturing based export-oriented economy through a series of investment policies which started with the Pioneer Industries Ordinance of 1958. This ordinance promoted import-substitution of various manufacturing outputs. Later, the ordinance was replaced by the Investment Incentives Act of 1968 which stresses on exports.
(2) Yeoh and Zhao (2005) asserted that FDI were practically non-existent before the introduction of the 1968 Investment Incentives Act. However, following the act, FDI considered an important source of capital and technology. To further boost FDI inflow, the Promotion of Investment Act was introduced in 1986. In the same year Malaysia launched its First Industrial Master Plan and 12 key industrial sectors were identified to be developed. These industrial sectors were rubber, palm oil, food, wood-based industries, chemical and petrochemical, non-ferrous metals, non-metallic minerals, electrical and electronics, transport equipment, machinery and engineering, iron and steel, and textiles and apparel. In 1996, the second Industrial Master Plan was launched. Among other targets and concerns, this plan stressed on cluster-based approach to developing the industrial sector and on deepening industrial linkages.
(3) When a sector increases its production, there will be additional demand for inputs from other sectors of the economy that are related to it. This kind of direct and indirect upstream input-relationships is termed backward linkage. From another perspective, increased output in one sector means more is available to be used as input in other sectors of the economy. This kind of direct and indirect downstream output-relationships is termed forward linkage.
(4) Weights in (14), (15), (16) and (17) could easily accommodate other policy goals such as employment, value added, or pollution by changing the weights to those associated with the particular policy goals. These weights could also be appropriately changed to accommodate combined policy goals.
(5) We took the lower of the two means as demarcation for key sector so as to exclude the possibility inadvertent exclusion of potential sector. This demarcation is still arbitrary because other cut-off measure would work as well and would result in larger or smaller list of key sector.
Abul Quasem Al-AMIN is currently an Associate Professor at International Business School, Universiti Teknologi Malaysia, Malaysia. His research interest includes, environmental modelling, economic sustainability, modelling on optimal pollution taxation for environmental aspects, ecological economics and economics of climate change. He is currently associated with Green Growth Modelling Forum (C2GMF), Green Growth Inventory and Research Centre, South Korea, Asia Pacific Network on Global Climate Change (APN), Institute for Global Environmental Strategies (IGES) Japan, and has a particular interest on the connections between environmental management, sustainability, climate and human behaviour.
Abdul Hamid JAAFAR is currently a Professor at the Faculty of Economics and Business, National University of Malaysia. His research interest includes on welfare economics, external shocks and impact on food price increase, economic behavior modeling, investment policy challenge, estimation of backward and forward linkages and industrial policy modeling. He has more than 20 years of research experience on welfare issue and policy related economic modeling. He is attached with fundamental research with the Ministry of higher education in Malaysia.
Table 1. Sources of foreign direct investment for year 2000 and 2005 Country 2000 2005 Australia 0.7% 0.9% Hong Kong 1.7% 0.6% India 0.0% 3.1% Japan 14.5% 20.5% Rep Korea 3.6% 3.8% Singapore 9.0% 16.3% Taiwan 4.6% 2.4% Thailand 0.1% 0.8% U. K. 3.9% 0.6% U.S.A. 37.7% 28.8% GDR 8.3% 2.2% Other countries 15.8% 20.0% Total 100.0% 100.0% Sources: MDP (2006); DOS (2006). Table 2. Distribution of FDI and DDI by industries, 2000 and 2005 (million MYR; US$1 = MYR3.30) 2000 Industries FDI DDI Food processing 539.6 518.6 Beverage & tobacco 107.7 5.9 Textile & textile products 731.9 454.5 Leather & leather products 2.8 2.9 Wood & wood products 172.5 288.7 Furniture & fixtures 106.8 238.2 Paper, printing & publishing 2,118.9 1,312.2 Chemical & chemical products 585.6 377.1 Petroleum, & petrochemical 1,763.8 583.2 Rubber products 668.4 274.6 Plastic products 289.9 326.4 Non-metal manufactures 1,527.6 238.6 Basic metal products 428.0 358.6 Fabricated. metal manufactures 163.0 247.1 Machine manufactures 418.0 400.8 Electrical & electronic products 10,209.7 1,972.8 Transportation equipments 273.1 399.8 Measuring & scientific instruments 166.5 17.4 Other manufactures 50.7 49.6 2005 Industries FDI DDI Food processing 531.9 925.6 Beverage & tobacco 77.6 16.8 Textile & textile products 146.2 227.8 Leather & leather products 3.6 5.4 Wood & wood products 77.2 283.3 Furniture & fixtures 68.5 448.2 Paper, printing & publishing 123.8 829.7 Chemical & chemical products 869.5 851.6 Petroleum, & petrochemical 133.0 601.7 Rubber products 215.2 557.8 Plastic products 594.8 585.3 Non-metal manufactures 596.1 325.4 Basic metal products 430.5 2,774.5 Fabricated. metal manufactures 250.6 508.2 Machine manufactures 570.0 457.4 Electrical & electronic products 11,318.9 2,474.8 Transportation equipments 503.8 912.3 Measuring & scientific instruments 1,364.5 62.5 Other manufactures 12.4 325.5 Sources: MDP (2006); DOS (2006). Table 3. Summary of results using Method I [U.sub.j] > 1 but [U.sub.i] <1 [U.sub.j] > 1 and [U.sub.i] > 1 Beverage and tobacco Food processing Textile & textile products Paper, printing & publishing Leather & leather products Chemical & chemical products Wood & wood products Petroleum and petrochemical Furniture and fixtures Non-metal manufactures Rubber products Transportation equipments Other manufactures [U.sub.j] < 1 and [U.sub.i] < 1 [U.sub.j] < 1 and [U.sub.i] > 1 Plastic products Basic metal products Machine manufactures Fabricated metal manufactures Electric & electronic products Measuring & scientific instruments Table 4. Key sectors, Method II [U.sub.j] > 1 & [V.sub.j] < 4 but [U.sub.j] > 1 & [V.sub.j] < 4 and [U.sub.i] > 1 & [V.sub.i] < 4 [U.sub.i] < 1 & [V.sub.i] < 4 or Chemical & chemical products [U.sub.i] > 1 & [V.sub.i] > 4 Beverages and tobacco Petroleum and petrochemical Textile & textile products Non-metal manufactures Leather & leather products Wood & wood products Furniture and fixtures Rubber products Transportation equipments Other manufactures Food processing [U.sub.j] < 1 & [V.sub.j] > 4 and [U.sub.j] < 1 and [U.sub.i] > 1 [U.sub.i] < 1 & [V.sub.i] > 4 Plastic products Basic metal products Machine manufactures Febricated metal manufactures Electric & electronic products Paper, printing & publishing Measuring & scientific instruments Table 5. Key sectors, Method III [U.sub.j] > 1 and [U.sub.i] <1 [U.sub.j] > 1 and [U.sub.i] > 1 Machine manufactures Petroleum & petrochemical Food processing Electric & electronic products [U.sub.j] < 1 and [U.sub.i] < 1 [U.sub.j] < 1 and [U.sub.i] > 1 Rubber products Plastic products Non-metal manufactures Basic metal products Fabricated metal manufactures Transportation equipment Measuring & scientific instruments Other manufactures Leather & leather products Wood & wood products Furniture and fixtures Chemical & chemical products Paper, printing & publishing Beverage and tobacco Table 6. Key sectors, Method IV [U.sub.j] > 1 and [U.sub.i] <1 [U.sub.j] > 1 and [U.sub.i] > 1 Petroleum and petrochemical Food processing Chemical & chemical products Machine manufactures Electric & electronic products [U.sub.j] < 1 and [U.sub.i] < 1 [U.sub.j] < 1 and [U.sub.i] > 1 Beverage and tobacco Textile & textile products Leather & leather products Wood & wood products Furniture and fixtures Paper, printing & publishing Rubber products Plastic products Non-metal manufactures Basic metal products Fabricated metal manufactures Transportation equipment Measuring & scientific instruments Other manufactures Table 7. Key sectors, Method V [OE.sub.xyj] > 1 and [OE.sub.xyj] > 1 and [OE.sub.xyj] < 1 [OE.sub.xyj] > 1 Food processing Petrol and coal industries Machinery manufacture Electrical & electronic products Chemical & chem product manufacture [OE.sub.xyj] < 1 and [OE.sub.xyj] < 1 and [OE.sub.xyj] < 1 [OE.sub.xyj] < 1 Beverage and tobacco Textiles, Fabrics and apparel Leather and foot wear Sawmill and wood products Manufacture of furniture Paper, board, and printed products Chemicals and chemical products Rubber industries and products Manufacture plastic products Non-metal ore manufactures Iron and steel industries, and non-ferrous manufacture Metal and metal fabrication industries Transportation equipments manufacture Measurement and scientific equipments manufacture Other manufacturing
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|Author:||Amin, Abul Quasem Al-; Jaafar, Abdul Hamid|
|Publication:||Journal of Business Economics and Management|
|Date:||Jun 1, 2014|
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