Selection Indicators of Core Industries

According to the above principles, the detail indictors are described as follows:

1. Value-added Indicator:

Since industries in FTZs are involved with international trade, this study uses the rate of value-added transshipment based on the principle (1). Furuichi (2004) measures the rate of import trading cost rate as the indicator of selecting competitive port. The rate of import trading cost is defined as follows:

The rate of import trading cost =

(

CIFij FOBij ⁻¹

)

(1) where

CIF refers to the transport cost of export goods from country i to country j.

ij

FOB represents the value of goods after value-added activity from country i to

ij

country j.

In this equation, the higher rate is, the higher transport cost in import goods.

International Monetary Fund (IMF) defines FOB (free on board) values to include the transaction value of goods and the value of services performed to deliver goods to the border of the exporting country. CIF (cost, insurance, and freight) values include the transaction value of goods, the value of services performed to deliver goods to the border of the exporting country and the value of the services performed to deliver goods from the border of the exporting country to the border of the importing country. And export FOB is thus in practice the same as ′export at the frontier of the economic territory′.

The United Nations (1998) recommends that the value of imported goods be CIF and the value of exported goods be FOB. Chasomeris (2003) pointed out that the factor CIF/FOB has several drawbacks. One of which is measurement error; the CIF/FOB factor is calculated for those countries that report the total value of imports at CIF and FOB values, both of which involve some measurement error. The second concern is that the measure aggregates over all commodities imported, so it is biased if high transport cost countries systematically import lower transport cost goods. Finally, the measure aggregates over the different sources of supply, so for each importer there is a single CIF/FOB measure, not a full set of CIF/FOB measures for imports from each supplying country (Chasomeris, 2003).

Following the above literatures, CIF/FOB may not be a perfect indicator. However, it’s an

easy way to understand and measure added value of transshipment. And most important is the data of FOB and CIF can be available and obtained in national trading data. To achieve the competitiveness objectives of FTZs, this study propose to choose core industries in FTZs through a criterion of the rate of value-added transshipment within the FTZs (as seen in Figure3-3). In order to analyze whole industries of value-added transshipment, we defined the indicator of added value for identifying core industries in FTZs as follows:

The rate of value-added transshipment=

(

FOBj −CIFj

)

/FOBj (2) where

CIF refers to import costs for industry j

_j

FOB represents the value of re-export goods after value-added activity for

j

industry j.

The added value of each industry category of transit goods are computed based on Taiwan trading standard in which the import portion adopts CIF, whereas the export and re-export price is FOB obtained from the statistics data of Taiwan international trade. The higher the rate of value-added transshipment for industry j, the higher potential to be selected as the core industry.

Figure 3-3 Concept of Value-added Transshipment

arrival port

Import value export value

Value-added activities

Departure port

Added value

Ac tivi ty Val u e

Raw material price

Raw material price

Added value Added value

Market A Market B

2. Market Share Indicator

Based on the principle (2), the candidates for the core industries into FTZs should have the potential to create a higher market share among competition ports in the study area.

Equation 3 expresses the indicator of market share:

% 100

*

∑1

=

= _n

i ij ij ij

F

S F (3)

where

S and

_ij

F denotes the market share and the freight volume of industry

_ij

j

in port i , respectively. n denotes the total number of competing ports. Traditionally, the freight volumes are employed as one of indicator to evaluate the competitiveness among ports. In this study, the competition ports are selected primarily based on the freight volume of high potential developing ports in East Asia region, particularly in China.

These competition parts include Ningbo, Shanghai, Guangzhou, Shenzhen and Xiamen with the growth rates higher than 15% (Table 3-1). The higher the market share, the higher potential to be selected as the core industry.

Table 3-1 The Cargo Growth Rate of Competition (Cargo) Ports

unit：10,000TEU Cargo

Rank of

2005 Port County

2005 2004

Growth Rate(%)

1 Singapore Singapore 2319 2133 2.8

2 Hong Kong China 2260 2198 8.7

3 Shanghai China 1808 1456 24.2

4 Shenzhen China 1620 1365 18.7

6 Kaohsiung Taiwan 947 971 -2.5

15 Ningbo China 519 401 29.6

18 Guangzhou China 468 331 41.6

23 Xiamen China 334 287 16.4

Source: Containersation International, 2006

3. Forward and Backward Linkage Indicator

The input-output impact coefficients table

(

^{I− A}

)

⁻¹, also known as the Leontief (1936) inverted matrix, is utilized to analyze the industries interrelatedness. In the input-output analysis, there are two relationships among industries. The backward linkage denotes the relationship between each individual industrial and industries providing input materials.

Oppositely, forward linkage refers to the cross-industry relationship between each individual industry and those product consumers. Generally, the contribution of each industry to the total industries is assessed by the degree of interrelationship. Decision makers select the industries with both relatively high backward and forward linkage as the leading industry placed on the high priority list for investment and development.

The elements of the Leontief inverse incorporate both direct and indirect connections between sectors. Therefore, a measure of backward linkage of sector j would be given by the sum of elements of the direct and indirect coefficients matrix,

( I

− A

)

⁻¹, where A is the direct-input coefficient matrix. Similarly, a measure of the direct and indirect forward linkage of sector i is given by the sum of the ith row. Those elements in

( I

− A

)

⁻¹ are denoted as

b , and

_ij bij is the inter-industry interdependent coefficient in the matrix.

The sum of column elements in the Leontief inverted matrix is used to measure the backward linkage effect, whereas the sum of row elements is used to calculate the forward linkage effect:

where BLj and

FL refer to the coefficients for backward linkage and forward

i

linkage respectively, The normalization is generally denoted as：

∑

where n represents the total number of industry categories. RB_jandRF_i are the normalized coefficients for backward linkage and forward linkage respectively. RB_j is also known as the index power of dispersion, and

RB

_j >1 indicates that j industry’s backward linkage value is greater than the mean standard for all industries. Similarly,RF_i is also known as the index power of response, and

RF

_i >1 indicates that i industry’s forward linkage value is greater than the mean standard for all industries.

Hirschman (1958) defines key industries as those for which both indices are greater than the average linkage for the whole economy since these industries dominate through their forward and backward linkages (Hirschman, 1958). Industries are divided into four categories based on the performances of forward and backward linkages and depicted in Table 3-2. The meaning of each category is described as follows:

CategoryⅠ：The index values of sensitivity of dispersion and the index of power of dispersion are greater than the mean value for all industries. This signifies that not only such industries can drive other industries, but also would accommodate the development of other industries, and can be said to be indispensable industries. Hence, such key industries can drive the overall economic development.

CategoryⅡ：The index value of sensitivity of dispersion is high, but the index value of power of dispersion is low. This type of industry can encourage the development of other industries. This type of industry is quite indispensable in developing other industries.

Category Ⅲ：Both the index values of sensitivity of dispersion and the index of power of dispersion are smaller than the mean value. This type of industry itself is less likely to drive other industries, and also less likely to be affected by the

development of other industries, and is the industry with the lowest relevancy effect.

Category Ⅳ：The index value of power of dispersion is high, but the index value of sensitivity of dispersion is low. The industry itself is less prone to be affected by other industries, but is highly likely to drive the development of other industries.

Table 3-2 Inter-industry Interdependency Linkage

Source: Hirschman, 1958.

In this study, industries performing simultaneously high backward and forward linkage indices are determined as candidate core industries in the study area. Accordingly, the core industries are selected from industries i with both high forward and backward linkages. To combine the forward (RF) and backward linkage (RB) as one indicator, this study simply adds them up to one indicator, namely total linkage (TL). The higher the total linkage, the higher potential to be selected as the core industry.

i i

i RB RF

TL = + (9)

Backward Linkage Impact

（index of power of dispersion）

Type of Linkage Impact

High Low

High Ⅰ Ⅱ

Forward Linkage Impact

(index of sensitivity dispersion） Low Ⅳ Ⅲ

在文檔中 自由貿易港區核心產業的選擇 (頁 52-58)