Journal of Economic Structures

The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

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Open Access

Inter-industry analysis in the Korean flow-of-funds accounts

Journal of Economic StructuresThe Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)20176:25

https://doi.org/10.1186/s40008-017-0084-9

Received: 30 May 2017

Accepted: 15 August 2017

Published: 18 October 2017

Abstract

This study mainly aims to provide an inter-industry analysis through the subdivision of various industries in flow-of-funds (FOF) accounts. Combined with the Financial Statement Analysis data from 2004 and 2005, the Korean FOF accounts are reconstructed to form “from-whom-to-whom” basis FOF tables, which are composed of 115 institutional sectors and correspond to tables and techniques of input–output (I–O) analysis. First, power of dispersion indices are obtained by applying the I–O analysis method. Most service and IT industries, construction, and light industries in manufacturing are included in the first-quadrant group, whereas heavy and chemical industries are placed in the fourth quadrant since their power indices in the asset-oriented system are comparatively smaller than those of other institutional sectors. Second, investments and savings, which are induced by the central bank, are calculated for monetary policy evaluations. Industries are bifurcated into two groups to compare their features. The first group refers to industries whose power of dispersion in the asset-oriented system is greater than 1, mainly light industries, IT, and service. On the other hand, the second group indicates that their index is less than 1, mostly heavy and chemical industries. We found that the net induced investments (NII)–total liabilities ratios of the first group show levels half those of the second group since the former’s induced savings are obviously greater than the latter.

Keywords

Inter-industry analysisFlow-of-fundsMonetary policy evaluation

JEL Classification

C67E01E58G30

1 Background

Flow-of-funds (FOF) accounts indicate the interrelations between the various institutional sectors of each nation, including overseas sectors, in a systematic and coherent manner. The FOF system adopts a quadruple-entry system proposed by Copeland (1952), wherein each transaction is recorded with a double entry. On the other hand, the input–output (I–O) table, which indicates production in the real economy, is composed of various industries. Transactions of production always involve funds transactions. Klein (2003) indicated a need for the “from-whom-to-whom” basis FOF table’s construction, which corresponds to tables and techniques of I–O analysis. However, it is difficult to link the I–O table and FOF accounts. The economic agents in the I–O table are separated into hundreds of industries. Though the FOF accounts comprise all economic agents in one country, data on only two types of institutional sectors, namely nonfinancial public corporations and nonfinancial private corporations, are announced in the FOF accounts. In other words, most economic agents in the I–O table are aggregated in the FOF accounts.

Numerous studies have explored inter-industry or firm financing, for example, studies by Corbett and Jenkinson (1996), Braun and Larrain (2005), and Marozzi and Cozzucoli (2016). Some previous researches have disaggregated the nonfinancial corporation sector of the FOF accounts into several institutional sectors. Nishiyama (1991) used the balance sheets and income statements of each industry to subdivide nonfinancial corporations in FOF accounts into 37 industries. In this paper, the power indices of 44 institutional sectors are reported. According to this study, using balance sheets and income statement data for each industry, it is possible to generate expanded FOF accounts that indicate the financial transactions of each industry. Kim (2014) examined the division of the nonfinancial private corporation sector into the chaebol sector, which indicates groups of large-scale and family-run management enterprises, and the private corporation (small- and middle-scale) sector.

Not only inter-industry analysis of FOF accounts has been intensified but also international money flow analysis has deepened, which corresponds with the international I–O table. Zhang (2005, 2009) built global FOF and estimated multiple-equation models. Tsujimura and Tsujimura (2008) constructed a financial transaction table between multiple countries. Kim and Song (2012) built a flow-of-FX-funds table for Korea based on the balance of payments (BOP), external debt, assets, and international investment position (IIP) tables.

There are some preliminary studies that link analysis methods between I–O tables and financial information. Ogawa et al. (2012) attempted to link the unique I–O table of Japan, which is augmented by firm size dimension, with balance sheet conditions. This paper uses Financial Statistics of Corporations data, which are published by the Ministry of Finance. Manabe (2014) estimated a production function with net induced investments (NII), which are computed from the US FOF accounts. This paper adopted an evaluation method using an asset–liability–matrix (ALM), which is derived by Tsujimura and Mizoshita (2003), though many literatures have evaluated monetary and financial policies (De Haan and Sterken 2006). Tsujimura and Mizoshita (2002a, b) devised the FOF analysis methods by applying I–O analysis methods. Originally, Stone (1966) and Klein (1983) proposed the concept of the Leontief inverse, which is applied to the ALM. Furthermore, Tsujimura and Mizoshita (2003), Tsujimura and Tsujimura (2006) estimated the induced amount of the supply and demand of funds to analyze the effect of central bank monetary policies, through financial transactions between institutional sectors represented in the Leontief inverse. Adopting this analysis method, Manabe (2009) also tried policy evaluations of public financial institutions using the FOF accounts of Japan.

However, the subdivision of industries was not examined by Manabe (2009). Therefore, only one production function is estimated in this paper, though the I–O table has around 400 industries (for example, in the cases of Korea and Japan). Kim et al. (2017) used a system of multi-sector, multifactor production functions to derive technological structure transitions associated with cost changes induced by an innovation. Applying this model, production functions of each industry can be estimated using the linked I–O tables. Therefore, it is possible to link the FOF accounts and I–O table by obtaining the expanded FOF accounts, which are subdivided into various industries. Furthermore, productivity changes in every industry caused by monetary or financial policies can be estimated. First, each industry’s NII, which is implemented by the policy authority, are calculated in the expanded FOF accounts. Second, each industry’s productivity changes caused by the monetary or financial policies can be estimated using their NII. In other words, it is possible to combine the expanded FOF accounts and I–O table.

This study mainly aims to conduct an inter-industry analysis through the subdivision of the various industries in the FOF accounts. Using the expanded FOF tables, we examine the central bank’s monetary policy evaluations. Previous studies have indicated that by obtaining the NII of each industry, which are caused by any kind of monetary or financial policy, it is possible to link the I–O table and NII from the FOF accounts. In this study, we will adopt the I–O analysis method, which is applied to the FOF accounts devised by Tsujimura and Mizoshita (2002a, b). Applying the I–O analysis method to the ALM derived from the FOF accounts, Y and Y* matrices (ALM of institutional sector-by-institutional sector) are obtained. Using the Leontief inverse matrix, four kinds of indices (power of dispersion index in the liability-oriented system, power of dispersion index in the asset-oriented system, sensitivity of dispersion index in the liability-oriented system, and sensitivity of dispersion index in the asset-oriented system) are estimated. Furthermore, by employing ALM, it is possible to evaluate the effectiveness of a monetary policy by applying the Leontief inverse. In summary, if the expanded FOF accounts, which are separated into various inter-industries, are obtained, (1) a financial transaction table of each inter-industry by inter-industry, which are deeply related to the I–O table, is created; (2) the power index and sensitivity index of each industry are computed; and (3) an analysis method connected to the I–O table and the FOF accounts can be applicable. To subdivide the nonfinancial corporation sector of the FOF accounts into different types of industries, we adopt the Financial Statement Analysis (FSA) data compiled by the Bank of Korea (BOK). Since the FSA data announce annual balance sheets and income statements for each industry, it is possible to create expanded FOF accounts whose institutional sectors are divided into about 100 types of industry. This study aims to (1) analyze various inter-industries from the viewpoint of the FOF accounts, (2) examine policy evaluation methods and suggest monetary market operations, and (3) derive a new analysis tool to link the I–O table and FOF accounts for future works.

This paper contains five sections: The second section describes the data adopted for this analysis and explains the methodologies. The subdivision of industries and analysis results are reported in the third section. In this part, data for 2004 and 2005 are adopted. For future works, we will try to link the 2005 I–O table and the expanded FOF accounts. The reason for the data selection is that the 2005 I–O table is linked to the 2000 I–O table, and the linked I–O tables for 2000–2005 and 2005–2010 will be announced by the BOK in the near future. We need to choose the linked I–O tables to estimate production functions as a next step. Evaluations of BOK’s monetary policies are presented in the fourth section. The conclusions of this paper are presented in the last section.

2 Data and methodology

2.1 Data

To achieve the first purpose of this analysis, the FOF accounts are used. The BOK (2001, 2005, 2006) publishes Korean FOF accounts both quarterly and yearly; these accounts contain (1) financial transactions (flows) and (2) financial assets and liabilities (stocks). Table 1 shows the number of institutional sectors and financial instruments in the Korean FOF accounts in the 1968, 1993, and 2008 Systems of National Accounts (SNA). The FOF account data in the 1993 SNA, which contain 22 institutional sectors and 35 financial instruments, are retroacted to 2002. Furthermore, the 2008 SNA data, which contains 23 institutional sectors and 46 financial instruments, have existed since 2008. We used the FOF accounts of the 1993 SNA to subdivide nonfinancial corporations into each industry since the 1968 SNA data have only nine institutional sectors.
Table 1

Flow-of-funds accounts in Korea

 

1968 SNA

1993 SNA

2008 SNA

Institutional sectors

9 sectorsa

18 sectorsb

23 sectors

Financial instruments

34 items

35 items

46 items

Period

1975Q1–2005Q4 (quarterly/annual)

2002Q4–2013Q4 (quarterly/annual)

2008Q4–present (quarterly/annual)

aFive sectors on the BOK Web site, Economic Statistics System

bAvailable to extend to 22 sectors

To subdivide the various industries, the FSA data, which are compiled annually by the BOK, are available. Balance sheets and income statements of enterprises are represented by industries in these data. However, the construction of the expanded funds transaction table subdivided into a hundred industries for each year of the FOF data is not a simple task. We adopted data from 2004 and 2005 to expand institutional sectors in the FOF accounts into various industries since it is useful to conduct the analysis with linked I–O tables for future challenges. As a framework for expanding the FOF accounts, seven financial instruments were chosen. Table 2 presents the financial instruments for the correspondence between the FOF accounts and the FSA data. In the FSA data, securities assets have adopted market values since the end of 1997, whereas capital stock in stockholders’ equity takes face value. Since the FOF accounts in the 1993 SNA adopted market values for both assets and liabilities accounts, capital stocks from the FSA data need to be adjusted to reflect market value. Using listed capital stock and total market capitalization by industry group, which are announced by the Korea Exchange (2005), the capital stock of each industry is adjusted. Institutional sectors for the FOF accounts and industries in the FSA data are represented in Tables 3 and 4. There are 22 institutional sectors in the FOF accounts and 94 industries in the FSA data. We use the term “residual industry” to refer to the results obtained by subtracting all industries in the FSA data from the nonfinancial corporations in the FOF accounts. Since the total amount of financial assets or liabilities in the FSA data is not exactly equal to the total nonfinancial corporations in the FOF accounts, the variable residual industry is inserted in the expanded FOF accounts as a new sector. Therefore, residual industry includes items not included in the FSA data but included in the FOF accounts.
Table 2

Correspondence between FOF accounts and FSA data

Financial instruments selected for this study

The FOF accounts in 1993 SNA

The FSA data

Currency and deposits

Currency and deposits

Assets

 Cash and deposits

Insurance and pension reserves

Insurance and pension reserves

n/a

Securities

Securities other than shares, shares and other equities, financial derivatives

Assets

 Short-term securities, long-term securities and investments

Liabilities

 Current maturities of bonds payable, bonds payable, capital stock (adjusted by market capitalization)

Loans

Loans, government loans

Liabilities

 Short-term borrowings from banking institutions, current maturities of long-term borrowings, other short-term borrowings, long-term borrowings from banking institutions, other long-term borrowings

Trade credits

Trade credits

Assets

 Trade receivables

Liabilities

 Trade payables

Foreign exchange holdings

Foreign exchange holdings

n/a

Call loans and money

Call loans and money

n/a

Other claims and debts

Foreign direct investment, other foreign claims and debts, miscellaneous

Assets

 Nontrade accounts and notes receivable, other quick assets

Liabilities

 Nontrade accounts and notes payable, liability provisions, other liabilities

Table 3

Institutional sectors in the FOF accounts

1

Central bank

2

Domestically licensed banks

3

Specialized banks

4

Other banks

5

Collectively managed trusts

6

Small loan financial companies for households and small businesses

7

Investment institutions

8

Other nonbanks

9

Life insurance companies

10

Nonlife insurance companies

11

Cooperative society

12

Pension funds

13

Securities institutions

14

Credit-specialized financial institutions

15

Public financial institutions

16

Other financial intermediaries

17

Financial auxiliaries

18

General government

19

Nonfinancial public corporations

20

Nonfinancial private corporations

21

Households and nonprofit organizations

22

Rest of the world

Table 4

Industries in the FSA data

1

B Fishing

2

C Mining and quarrying

3

D151 Production, processing, and preserving of meat, fish, fruits, vegetables, oils, and fats

4

D152 Dairy products and ice cream

5

D153 Grain mill products, starch products, and prepared animal feeds

6

D1541 Bakery and dry bakery

7

D1545 Condiments and food additive products

8

D1542,3,4,9 Sugar, cocoa, and chocolate, noodles, other food products

9

D1551-3 Distilling and blending of spirits, fermented alcoholic beverages, and malt liquors

10

D1554 Ice and nonalcoholic beverages, production of mineral waters

11

D171 Preparation and spinning of textile fibers

12

D172 Weaving of textile fibers

13

D173,4,9 Other textiles

14

D18 Sewn wearing apparel and fur articles

15

D191,2 Leather, luggage, handbags, saddlery, and harnesses

16

D193 Footwear

17

D20 Wood and products of wood and cork, except furniture

18

D21 Pulp, paper, and paper products

19

D221 Publishing

20

D222,3 Printing and reproduction of recorded media

21

D23 Coke, refined petroleum products, and nuclear fuel

22

D2411-3 Basic chemicals, except fertilizers

23

D2414 Fertilizers and nitrogen compounds

24

D2415 Synthetic rubber and plastics in primary forms

25

D242 Pharmaceuticals, medicinal chemicals, and botanical products

26

D2431 Pesticides and other agrochemical products

27

D2432 Paints, varnishes, and similar coatings, printing ink and mastics

28

D2433 Soap, cleaning compounds, and toilet preparations

29

D2434,9 Other chemical products

30

D244 Man-made fibers

31

D2511 Rubber tires and tubes

32

D2519 Other rubber products

33

D252 Plastic products

34

D261 Glass and glass products

35

D262 Ceramic ware

36

D2631 Cement, lime, and plaster

37

D2632 Articles of concrete, cement, and plaster

38

D269 Other nonmetallic mineral products

39

D271 Basic iron and steel

40

D272 Basic precious and nonferrous metals

41

D273 Cast of metals

42

D281 Structural metal products, tanks, reservoirs, and steam generators

43

D289 Other fabricated metal products and metal treating services

44

D2916 Work trucks, lifting, and handling equipment

45

D2911-5,7 Other general-purpose machinery

46

D292 Machine tools

47

D2931 Agricultural and forestry machinery

48

D2933 Machinery for mining, quarrying, and construction

49

D2932,4-6,9 Other special-purpose machinery

50

D295 Other domestic appliances

51

D30 Computers and office machinery

52

D311 Electric motors, generators, and transformers

53

D312 Electricity distribution and control apparatuses

54

D313 Insulated wires and cables

55

D314,5,9 Other electrical equipment

56

D321 Semiconductors and other electronic components

57

D322 Television and radio transmitters and apparatuses for line telegraphy

58

D323 TV and radio receivers, sound or video recording or reproducing apparatuses

59

D33 Medical, precision, and optical instruments, watches and clocks

60

D341 Motor vehicles and engines

61

D342,3 Bodies for motor vehicles, trailers, and semitrailers, and parts and accessories

62

D351 Building of ships and boats

63

D352,3,9 Railway locomotives, aircraft, and transport equipment

64

D361 Furniture

65

D369 Other manufacturing

66

D37 Recycling

67

E401 Electricity

68

E402,3 Gas, steam, and hot water supply

69

F Construction

70

G50 Sale of motor vehicles, retail sale of automotive fuel

71

G51 Wholesale trade and commission trade

72

G5211-9 Retail sale in nonspecialized stores except department stores

73

G52111 Department stores

74

G5280 General retail trade except retail sales via mail-order houses

75

G5281 Retail sales via mail-order houses

76

H551 Accommodation

77

I602 Transit and ground passenger transportation

78

I603 Road freight transport

79

I61 Water transport

80

I62 Air transport

81

I63 Supporting, auxiliary transport activities, and travel agencies

82

J642 Telecommunications

83

L70 Real estate, renting, and leasing

84

M722 Software consultancy and supply

85

M724 Database activities and online information provision services

86

M721,3,9 Other computer activities

87

M743 Architectural, engineering services

88

M745 Advertising

89

M741,2,4,9 Other professional, scientific, and technical services

90

M75 Business support services

91

Q872 Broadcasting

92

Q871,3 Motion picture and performing arts

93

Q88 Other recreational, cultural, and sporting activities

94

R90 Sewage and refuse disposal, sanitation, and similar activities

2.2 Basic methodologies for the asset–liability–matrix model

2.2.1 Construction of the Y and Y* tables (financial transaction matrices)

In this analysis, we adopt the I–O analysis method devised by Tsujimura and Mizoshita (2002a, b)1 for the FOF accounts. First of all, the two tables should be constructed for this procedure. The E table is a matrix that represents the fund-employment portfolio of each institutional sector, whereas the R table shows fund-raising in each institutional sector. By applying a method widely used in I–O analysis, it is possible to make two types of square matrices, Y and Y*, using the E and R tables. The Y table is based on a fund-employment portfolio, whereas the Y* table is founded on a fund-raising portfolio. The coefficient matrices, B and B*, are constructed from R and E tables by dividing the column sums T vector, which consists of the sum of either assets or liabilities, whichever is greater.
$$ b_{ij} = r_{ij} /t_{j} $$
$$ b_{ij}^{*} = e_{ij} /t_{j} $$
Likewise, the coefficient matrices D and D*, which are obtained from \( E{\prime } \) and \( R{\prime } \) by dividing \( T^{E} \) and \( T^{R} , \) indicate the sums of the financial instruments. \( t_{j}^{E} \) represents the sum of assets, whereas \( t_{j}^{R} \) indicates the sum of liabilities for financial instrument j.
$$ d_{ij} = e'_{ij} /t_{j}^{E} $$
$$ d_{ij}^{*} = r'_{ij} /t_{j}^{R} $$
The m × m (m = number of institutional sectors) coefficient matrices C and C* are estimated under the institutional sector portfolio assumption.
$$ C = DB $$
$$ C^{*} = D^{*} B^{*} $$
Then, each element of transaction quantity matrices Y and Y* is obtained as follows:
$$ y_{ij} = c_{ij} t_{j} $$
$$ y_{ij}^{*} = c_{ij}^{*} t_{j} $$
where \( t_{j} \) represents the sum of either assets or liabilities; \( y_{ij} \) is the amount of funds provided from the ith institutional sector to the jth institutional sector; and \( y_{ij}^{*} \) identifies the amount of funds from the jth to the ith institutional sector. Y is founded on the assumption that each institutional sector’s fund-raising portfolio is settled. In contrast, Y* is based on the assumption that the fund-employment portfolio of each institutional sector is fixed.
In this analysis, we created the FOF accounts that are combined with the FSA data. Figure 1 displays the prototype of the expanded Y table whose nonfinancial corporation sector is subdivided into many types of industry for this paper. Therefore, it contains additional blue-colored blocks of information compared with the original Y table, which is not separated into industries.
Fig. 1

Expanded financial transaction table (Y table)

2.2.2 Power of dispersion index and sensitivity of dispersion index

Next, we will apply the Leontief inverse to obtain the indices of the power and sensitivity of dispersion to the ALM. The Y table can be expressed as follows in matrix terms, where \( \varepsilon^{Y} \) represents excess liabilities:
$$ CT^{Y} + \varepsilon^{Y} = T^{Y} $$
Solving each equation for \( T^{Y} \) yields
$$ T^{Y} = \left( {I - C} \right)^{ - 1} \varepsilon^{Y} $$
$$ T^{Y} = I\varepsilon^{Y} + C\varepsilon^{Y} + C^{2} \varepsilon^{Y} + C^{3} \varepsilon^{Y} + \cdots $$
where I denotes the m × m unit matrix and \( \left( {I - C} \right)^{ - 1} \) is Leontief inverse matrix. Matrix \( \varGamma \) is described as follows:
$$ \varGamma = \left( {I - C} \right)^{ - 1} = \left[ {\begin{array}{*{20}c} {\gamma_{11} } & {\begin{array}{*{20}c} {\gamma_{12} } & \cdots \\ \end{array} } & {\gamma_{1m} } \\ {\begin{array}{*{20}c} {\gamma_{21} } \\ \vdots \\ \end{array} } & {\begin{array}{*{20}c} {\begin{array}{*{20}c} {\gamma_{22} } \\ \vdots \\ \end{array} } & {\begin{array}{*{20}c} \ldots \\ \ddots \\ \end{array} } \\ \end{array} } & {\begin{array}{*{20}c} {\gamma_{2m} } \\ \vdots \\ \end{array} } \\ {\gamma_{m1} } & {\begin{array}{*{20}c} {\gamma_{m2} } & \ldots \\ \end{array} } & {\gamma_{mm} } \\ \end{array} } \right] $$
It is possible to calculate indices for both power of dispersion and sensitivity of dispersion in the liability-oriented system. The power of dispersion index, \( \omega_{j}^{Y} \), and the sensitivity of dispersion index, \( {\text{z}}_{\text{i}}^{\text{Y}} \), are expressed as follows.
$$ \omega_{j}^{Y} = \frac{{\sum\nolimits_{i = 1}^{m} {\gamma_{ij} } }}{{\frac{1}{m}\sum\nolimits_{j = 1}^{m} {\sum\nolimits_{i = 1}^{m} {\gamma_{ij} } } }} $$
$$ z_{i}^{Y} = \frac{{\sum\nolimits_{i = 1}^{m} {\gamma_{ij} } }}{{\frac{1}{m}\sum\nolimits_{i = 1}^{m} {\sum\nolimits_{i = 1}^{m} {\gamma_{ij} } } }} $$

Based on the same method of Y*, the power of dispersion index, \( \omega_{j}^{{Y^{*} }} \), and the sensitivity of dispersion index, \( z_{i}^{{Y^{*} }} \), in the asset-oriented system are also obtained.

3 Subdivision of the FOF accounts into types of industry

3.1 Inter-industry liability and asset portfolio

The nonfinancial corporation sector in the FOF accounts will now be subdivided into types of industry. Tables 5 and 6 show the liability portfolio and financial asset portfolio of 25 industries2 in 2005. In Table 5, industries’ liabilities consist of loans, securities, trade credits, and other foreign debts, which mean instruments for fund-raising. The average3 portfolio liabilities consist of is 14.4% loans, 61.8% securities, 9.1% trade credits, and 14.7% other foreign debts. Overall, industries raised more than half of their funds through securities, which is one form of direct financing, except three industries as follows: (1) fishing (41.8% of loans, 32.9% of securities), (2) textiles, apparel, and leather (29.5% of loans, 42.2% of securities), and (3) rubber and plastic products (22.5% of loans, 48.0% of securities) all show fewer securities and more loans than other industries. On the other hand, the telecommunications (81.4%); coke, refined petroleum products and nuclear fuel (74.5%); and business activities (73.1%) industries are mainly dependent on securities. Trade credits for gas, steam, and hot water (14.1%) and wholesale and retail trade (13.6%) are greater than in other industries. On the contrary, accommodation (0.7%) and real estate, renting, and leasing (1.0%) have a low level of trade credits. Lastly, real estate, renting, and leasing (29.5%) and motor vehicles, railway, and transport equipment (21.7%) show more fund-raising from overseas sector than other industries.
Table 5

Inter-industry liability portfolio in 2005

Industry

Total liabilities (Bil. KRW)

Loans (%)

Securities (%)

Trade credits (%)

Other foreign debt (%)

Fishing

909.7

41.8

32.9

6.1

19.1

Mining and quarrying

2633.2

15.3

71.3

3.7

9.6

Food and beverages

67,318.7

13.8

71.0

4.9

10.3

Textile, apparel, and leather

30,838.1

29.5

42.2

12.9

15.4

Wood, pulp, and paper products

34,742.8

21.6

58.3

6.5

13.5

Coke, refined petroleum products, and nuclear fuel

32,794.2

10.7

74.5

8.1

6.8

Chemicals and chemical products

77,407.7

11.8

72.1

7.4

8.7

Rubber and plastic products

20,079.6

22.5

48.0

12.0

17.5

Nonmetallic mineral products

24,063.5

23.0

53.8

11.3

11.9

Metals and metal product except machinery and equipment

96,925.1

16.6

66.0

8.8

8.5

Machinery and equipment, computers

190,587

15.2

57.6

10.9

16.3

Motor vehicles, railway, and transport equipment

174,943.9

10.2

56.6

11.6

21.7

Other manufacturing, furniture, medical, precision, optical instruments, watches, and clocks

17,844

17.4

61.1

10.5

11.0

Recycling

1903

31.6

56.5

3.9

8.0

Electricity

82,564.6

7.7

71.9

3.9

16.6

Gas, steam, and hot water supply

22,648.1

23.4

56.5

14.1

6.1

Construction

179,571.4

15.0

57.1

10.9

17.0

Wholesale and retail trade

260,203.7

15.2

59.4

13.6

11.8

Accommodation

23,046.4

17.3

62.5

0.7

19.4

Transport

78,220.5

26.1

54.8

5.7

13.4

Telecommunications

73,651.6

1.9

81.4

2.5

14.2

Real estate, renting, and leasing

29,472.8

18.4

51.1

1.0

29.5

Business activities

36,046.3

7.1

73.1

6.9

13.0

Recreational, cultural, broadcasting, and performing arts

50,004.6

8.9

68.5

1.4

21.2

Sewage and refuse disposal, sanitation, and similar activities

2875.3

16.0

71.6

1.2

11.1

Total

1,611,295.6

14.4

61.8

9.1

14.7

Table 6

Inter-industry financial asset portfolio in 2005

Industry

Total financial assets (Bil. KRW)

Currency and deposits (%)

Securities (%)

Trade credits (%)

Other foreign claims (%)

Fishing

909.7

33.6

34.7

9.9

21.8

Mining and quarrying

2633.2

16.9

17.1

35.0

31.0

Food and beverages

67,318.7

24.2

45.3

23.3

7.2

Textile, apparel, and leather

30,838.1

20.2

27.8

40.9

11.1

Wood, pulp, and paper products

34,742.8

24.4

28.9

37.6

9.1

Coke, refined petroleum products, and nuclear fuel

32,794.2

22.5

35.5

36.8

5.2

Chemicals and chemical products

77,407.7

15.3

39.5

39.0

6.2

Rubber and plastic products

20,079.6

16.8

26.8

37.1

19.3

Nonmetallic mineral products

24,063.5

17.5

33.6

38.9

10.0

Metals and metal product except machinery and equipment

96,925.1

14.4

42.3

37.9

5.4

Machinery and equipment, computers

190,587

21.9

38.8

29.3

10.0

Motor vehicles, railway, and transport equipment

174,943.9

24.2

32.2

32.1

11.5

Other manufacturing, furniture, medical, precision, optical instruments, watches, and clocks

17,844

22.3

27.3

40.0

10.4

Recycling

1903

11.7

16.5

50.0

21.9

Electricity

82,564.6

5.4

81.2

11.4

2.0

Gas, steam, and hot water supply

22,648.1

10.3

21.8

63.6

4.2

Construction

179,571.4

16.6

26.1

35.7

21.7

Wholesale and retail trade

260,203.7

14.4

39.3

34.1

12.2

Accommodation

23,046.4

16.1

73.2

4.4

6.2

Transport

78,220.5

23.2

32.5

27.1

17.1

Telecommunications

73,651.6

15.0

45.5

26.9

12.7

Real estate, renting, and leasing

29,472.8

20.7

54.0

9.7

15.5

Business activities

36,046.3

28.9

24.6

31.6

15.0

Recreational, cultural, broadcasting, and performing arts

50,004.6

26.8

48.6

12.6

11.9

Sewage and refuse disposal, sanitation, and similar activities

2875.3

40.3

18.0

17.7

24.1

Total

669,764.1

18.3

38.2

31.5

11.9

Table 6 demonstrates the inter-industry fund employment. It is composed of currency and deposits, securities, trade credits, and other foreign claims. The average4 financial asset portfolio contains 18.3% currency and deposits, 38.2% securities, 31.5% trade credits, and 11.9% other foreign claims. Fishing (33.6%), sewage, refuse disposal, sanitation, and similar activities (40.3%) have more currency and deposits than average. In contrast, electricity (5.4% currency and deposits, 81.2% securities) and accommodation (16.1% currency and deposits, 73.2% securities) held more securities than other financial assets. Gas, steam, and hot water (63.6%) and recycling (50.0%) held larger portions of trade credits than other industries. On the other hand, accommodation (4.4%) and real estate, renting, and leasing (9.7%) show small portions of trade credits in common with a liability portfolio. Sewage, refuse disposal, sanitation, and similar activities (24.1%), recycling (21.9%), and construction (21.7%) invested more in foreign countries than other industries. In contrast, electricity (2.0%), gas, steam, and hot water (4.2%) held less foreign claims than the others.

The real assets term is obtained by subtracting total financial assets from total liabilities. Real assets are composed of inventories, tangible assets, and intangible assets in the FSA data. Finished or semi-finished goods, raw materials, and other inventories are included in these inventories. Land, buildings and structures, machinery and equipment, ship vehicles and transportation equipment, construction in progress, and other tangible assets are considered tangible assets. Lastly, intangible assets contain development costs and the like. Table 7 represents the component ratio of real assets. On average,5 the real assets term is composed of 18.9% inventories, 76.4% tangible assets, and 4.7% intangible assets. Each of the top four distinguished industries that show greater than 43% inventories, 95% tangible assets, or 25% intangible assets are listed in this table. First, two manufacturing industries ([1] sewn wearing apparel and fur articles, and [2] leather, luggage, handbags, saddlery, and harnesses), which are related to apparel, are ranked first and fourth in their share of inventories. The largest inventory of these industries is finished or semi-finished goods, since these industries need finished goods, for example, textile products, threads, and yarn to produce clothing. Likewise, most inventories in wholesale trade and commission trade are finished or semi-finished goods, since this industry conducts trade rather than manufacture products. On the other hand, the greatest component of construction inventories is the raw materials item, comprising 33.4%. Second, (1) sewage and refuse disposal, sanitation, and similar activities; (2) other recreational, cultural, and sporting activities; (3) air transport; and (4) electricity sectors all demonstrate tangible asset ratios greater than 95%. Of these four industries, air transport in particular shows a tangible asset ratio of 83.0% machinery, transportation equipment, and other. Finally, the four industries of (1) real estate, renting, and leasing; (2) software consultancy and supply; (3) support and auxiliary transport activities, and travel agencies; and (4) database activities and online information provision services all have intangible asset ratios above 25%. This table reflects well the characteristics of each industry. The composition rate of real assets depends on an industry’s features. For example, development costs, one component of intangible assets, are almost a necessity for software and database activity-related industries. To understand the general peculiarities of different industries, composition rates of real assets by type and level are listed in Table 8. Domestic enterprises, light industries, and living and other industries have greater inventories and smaller tangible assets than export enterprises and heavy and chemical industries. Similarly, high- and medium–high-technology industries possess larger portions of tangible assets and lower inventories than low- and medium–low-technology industries. Finally, information and communication technology industries (services) have greater intangible assets than any other industry.
Table 7

Component ratio of real assets of distinguished industries in 2005

Industry

A (%)

B (%)

C (%)

A + B + C (%)

D (%)

E (%)

F (%)

D + E +F (%)

G (%)

Total (%)

D18 Sewn wearing apparel and fur articles

37.6

9.4

12.3

59.2

18.6

16.6

5.4

40.5

0.2

100.0

F Construction

9.2

33.4

14.5

57.0

16.2

16.4

9.0

41.6

1.4

100.0

G51 Wholesale trade and commission trade

38.9

2.7

2.0

43.6

23.4

18.7

12.0

54.1

2.3

100.0

D191,2 Leather, luggage, handbags, saddlery, and harnesses

25.9

10.8

6.3

43.0

31.5

17.0

8.2

56.7

0.3

100.0

R90 Sewage and refuse disposal, sanitation, and similar activities

0.2

0.1

0.3

0.6

41.7

25.0

31.5

98.2

1.1

100.0

Q88 Other recreational, cultural, and sporting activities

0.4

0.5

0.8

1.6

24.8

36.0

37.2

98.0

0.4

100.0

I62 Air transport

0.1

0.2

1.6

1.9

5.1

8.5

83.0

96.6

1.5

100.0

E401 Electricity

0.0

1.6

1.1

2.7

8.7

53.0

34.1

95.8

1.5

100.0

L70 Real estate, renting, and leasing

2.1

1.3

3.3

6.7

19.5

20.3

4.4

44.2

49.0

100.0

M722 Software consultancy and supply

10.9

5.1

0.7

16.7

12.6

15.5

25.7

53.8

29.5

100.0

I63 Supporting, auxiliary transport activities, and travel agencies

2.2

0.4

0.3

3.0

18.7

14.0

35.4

68.1

28.9

100.0

M724 Database activities and online information provision services

3.0

2.3

0.0

5.3

17.5

7.2

44.2

68.9

25.8

100.0

Average

9.2

6.3

3.4

18.9

18.3

28.3

29.8

76.4

4.7

100.0

A, finished or semi-finished goods; B, raw materials; C, other inventories; A + B + C, inventories; D, land; E, buildings, structures, construction in progress; F, machinery, transportation equipment, and others; D + E + F, tangible assets; G, intangible assets

Table 8

Composition rates of real assets by type and technology level in 2005

Industry

A (%)

B (%)

C (%)

A + B + C (%)

D (%)

E (%)

F (%)

D + E + F (%)

G (%)

Total (%)

Export enterprises (manufacturing)

7.9

5.9

4.0

17.7

15.7

27.9

35.3

78.9

3.4

100.0

Domestic enterprises (manufacturing)

11.9

8.2

3.6

23.6

22.1

26.5

25.1

73.7

2.6

100.0

Heavy and chemical industries (manufacturing)

9.2

6.8

3.8

19.7

17.5

27.5

32.1

77.0

3.2

100.0

Light industries (manufacturing)

13.9

8.5

3.6

26.0

26.4

25.8

19.8

71.9

2.0

100.0

High-technology industries (manufacturing)

5.9

5.7

4.6

16.2

10.7

28.5

39.7

78.8

4.9

100.0

Medium–high-technology industries (manufacturing)

9.7

6.0

3.7

19.4

20.3

26.0

30.6

76.9

3.7

100.0

Medium–low-technology industries (manufacturing)

11.3

8.5

3.3

23.1

19.9

28.5

26.9

75.3

1.6

100.0

Low-technology industries (manufacturing)

13.4

8.3

3.6

25.4

26.4

25.1

21.3

72.8

1.8

100.0

Primary material industries (manufacturing)

12.3

7.7

2.9

22.9

20.0

26.1

29.5

75.6

1.5

100.0

Processing assembly industries (manufacturing)

7.0

6.0

4.5

17.5

16.7

28.5

32.9

78.1

4.4

100.0

Living and other industries (manufacturing)

14.4

9.5

4.1

28.0

26.1

25.3

18.1

69.5

2.5

100.0

Information and communication technology industries (goods)

5.4

5.3

4.6

15.3

10.7

28.0

41.1

79.9

4.8

100.0

Information and communication technology industries (services)

2.7

0.8

0.3

3.7

7.2

18.3

52.5

78.0

18.2

100.0

A, finished or semi-finished goods; B, raw materials; C, other inventories; A + B + C, inventories; D, land; E, buildings, structures, construction in progress; F, machinery, transportation equipment, and others; D + E+F, tangible assets; G, intangible assets

3.2 Analysis of financial transactions and the power of dispersion indices

It is possible to construct a Y table that represents financial transactions and the coefficient C matrix by 115 institutional sectors combined with FSA data. In this subsection, we describe the structure of financial markets using the financial transaction matrix (Y table) and power of dispersion indices calculated from the Leontief inverse matrix. The Y table displays financial transactions on a “from-whom-to-whom” basis, which corresponds to the I–O table. Table 9 shows a fund-raising portfolio of total industries. In other words, nonfinancial corporations raised approximately 2051 trillion Korean won from other institutional sectors in 2005. Among this, funds from nonfinancial corporations (19.6%) is the largest. There are four specific industries on average that have larger ratios than the other industries.6 Funds from wholesale trade and commission trade (2.4%), construction (2.3%), semiconductors and other electronic components (1.0%), and electricity (0.9%) to nonfinancial corporations are comparatively larger than for other industries. Among these, only semiconductors and other electronic components are included in manufacturing. Semiconductors are one of the Korea’s leading export industries.7 Except fund-raising from nonfinancial corporations by themselves, funds from domestically licensed banks (14.3%), the rest of the world (11.3%), households and nonprofit organizations (10.3%), and the general government (10.1%) are remarkable. Since the rest of the world provided more than 10% of the funds, Korean industries have a high level of dependence on foreign funds. Industries highly dependent on foreign funds are listed in Table 10.8 Retail sales via mail-order houses (18.0%) are the highest foreign fund-dependent industry. Electricity, telecommunications, services, and arts and cultural activities also have high ratios. In manufacturing, only three industries, namely the building of ships and boats (17.8%), semiconductors and other electronic components (14.2%), and motor vehicles and engines (14.0%), are shown in this table. The building of ships and boats, motor vehicles, and engines are categorized as traditional core industries of Korea in the Korea Development Bank (KDB) (2005a). According to the KDB (2005b), the ratio of the electronic components industry9 in Korean manufacturing has increased steadily owing to the development of the semiconductor and other electronic components industry. After 2003, Korean semiconductor firms had driven aggressive investment into facilities and equipment to expand their market power. Domestic demand for semiconductors rose owing to an upswing in the export of mobile phones, MP3 players, and digital televisions. In contrast, investments by foreign competitors were conservative in that period by fall in semiconductor prices. As a result, Korean semiconductor companies could expand their market share in the global market.10
Table 9

Fund-raising portfolio of total industries in 2005

Institutional sectors (from-whom-to industries)

Amount (Bil. KRW)

Ratio (%)

Central bank

12,476

0.6

Domestically licensed banks

292,575

14.3

Specialized banks

133,441

6.5

Other banks

47,597

2.3

Collectively managed trusts

41,523

2.0

Small loan financial companies for households and small businesses

100,937

4.9

Investment institutions

88,384

4.3

Other nonbanks

17,774

0.9

Life insurance companies

17,271

0.8

Nonlife insurance companies

115,310

5.6

Cooperative society

1785

0.1

Pension funds

13,778

0.7

Securities institutions

25,501

1.2

Credit-specialized financial institutions

20,956

1.0

Public financial institutions

38,892

1.9

Other financial intermediaries

25,859

1.3

Financial auxiliaries

6072

0.3

General government

207,298

10.1

Credit-specialized financial institutions

402,166

19.6

 D321 Semiconductors and other electronic components

20,296

1.0

 E401 Electricity

17,866

0.9

 F Construction

48,183

2.3

 G51 Wholesale trade and commission trade

48,330

2.4

Households and nonprofit organizations

210,624

10.3

Rest of the world

230,780

11.3

Total

2,050,999

100.0

Table 10

Industries highly dependent on foreign funds

Industry

Ratio (%)

D321 Semiconductors and other electronic components

14.2

D341 Motor vehicles and engines

14.0

D351 Building of ships and boats

17.8

E401 Electricity

14.5

G5281 Retail sale via mail-order houses

18.0

I602 Transit and ground passenger transportation

15.6

J642 Telecommunications

15.0

L70 Real estate, renting, and leasing

15.3

M724 Database activities and online information provision services

14.8

M743 Architectural, engineering services

14.9

M75 Business support services

14.6

Q871,3 Motion picture and performing arts

14.1

Q88 Other recreational, cultural, and sporting activities

17.3

Figures 2 and 3 display the power of dispersion index for each institutional sector in 2004 and 2005. The index baseline is 1, which is used to identify the extent of dispersion. The major benefit of these indices is that they identify the relative position of each institutional sector in a financial market where these institutional sectors are inter-dependent, either directly or indirectly. The power of dispersion index in the liability-oriented system is displayed in the rows, whereas the columns show the power of dispersion index in the asset-oriented system. Each institutional sector is placed in the four-quadrant graph. For example, households with excess savings are generally located in the second quadrant since they exercise more power over assets and less power over liabilities. Meanwhile, corporations with excess investment are generally displayed in the fourth quadrant since they hold more power over liabilities and less power over assets. In Figs. 2 and 3, most institutional sectors other than nonfinancial corporations are located in the second quadrant. Financial auxiliaries and only a few financial institutions are included in the first quadrant. Most nonfinancial corporations are located in the fourth quadrant. However, 27 industries in 2004 and 22 industries in 2005 are sited in the first quadrant, indicating that both of their power indices are greater than 1.
Fig. 2

Power of dispersion index in 2004

Fig. 3

Power of dispersion index in 2005

Table 11 lists industries included in the first quadrant in 2004 or 2005. Most service and IT industries, as well as construction industries, are included in the first-quadrant group. In manufacturing and light industries, for example, food products, textile fibers and apparel, and glass and ceramics are located in the first quadrant, whereas fishing, mining, and quarrying as well as heavy and chemical industries including metal products and petroleum represent the fourth quadrant, since their power indices of fund employment are comparatively smaller than those of other institutional sectors. Furthermore, four industries, namely (1) support and auxiliary transport activities and travel agencies, (2) telecommunications, (3) other professional, scientific, and technical services, and (4) broadcasting, are included in the third-quadrant group in 2004 as listed in Table 12, which means that both of their power indices are very small. Three industries moved to the fourth-quadrant group in 2005, although broadcasting remained in the third quadrant.
Table 11

Industries whose dispersion power indices are in the first quadrant

2004

2005

 

B Fishing

D152 Dairy products and ice cream

D152 Dairy products and ice cream

D1545 Condiments and food additive products

D1541 Bakery and dry bakery

D1542,3,4,9 Sugar, cocoa, and chocolate, noodles, other food products

D171 Preparation and spinning of textile fibers

D18 Sewn wearing apparel and fur articles

D18 Sewn wearing apparel and fur articles

D193 Footwear

D191,2 Leather, luggage, handbags, saddlery, and harnesses

D221 Publishing

 

D2432 Paints, varnishes, and similar coatings, printing ink, and mastics

D2432 Paints, varnishes, and similar coatings, printing ink and mastics

D2519 Other rubber products

D2519 Other rubber products

D252 Plastic products

D252 Plastic products

D261 Glass and glass products

D261 Glass and glass products

D262 Ceramic ware

 

D2632 Articles of concrete, cement, and plaster

D2632 Articles of concrete, cement, and plaster

D269 Other nonmetallic mineral products

D269 Other nonmetallic mineral products

D312 Electricity distribution and control apparatuses

 

D313 Insulated wire and cable

D313 Insulated wire and cable

D321 Semiconductors and other electronic components

D321 Semiconductor and other electronic components

D341 Motor vehicles and engines

D322 Television and radio transmitters and

D351 Building of ships and boats

D323 TV and radio receivers, sound or video

F Construction

F Construction

G51 Wholesale trade and commission trade

G51 Wholesale trade and commission trade

G5281 Retail sale via mail-order houses

G5281 Retail sale via mail-order houses

M722 Software consultancy and supply

M722 Software consultancy and supply

M724 Database activities and online information provision services

 

M721,3,9 Other computer activities

 

M743 Architectural, engineering services

M743 Architectural, engineering services

M745 Advertising

 

Q871,3 Motion pictures and performing arts

Q871,3 Motion pictures and performing arts

Table 12

Industries whose dispersion power indices are in the third quadrant

2004

2005

I63 Supporting and auxiliary transport activities and travel agencies

 

J642 Telecommunications

 

M741,2,4,9 Other professional, scientific and technical services

 

Q872 Broadcasting

Q872 Broadcasting

Nishiyama (1991) subdivided Japanese FOF accounts into 37 industries and calculated one type of power of dispersion index. Nishiyama (1991) demonstrated that food products, textile fibers and apparel, pulp, paper, and paper products, publishing and printing, metal products, retail sale, real estate, construction, broadcasting, transport, motion pictures, and recreational activities have relatively smaller indices than other industries in some periods. The results of this paper are not comparable exactly with Nishiyama’s (1991) results, since two types of indices based on liability and asset approaches are obtained for the Korean case. However, we could say that some industries located in the first or third quadrants, for example, most of the service and IT industries, light industries, construction, and broadcasting in Korea, overlap with the smaller Japanese power of dispersion indices’ industries.

4 Inter-industry monetary policy evaluations in the FOF accounts

In the SNA, the difference between assets and liabilities in the FOF accounts reflects net investments, i.e., the difference between savings and investments, in the real economy. Earlier, the Y and Y* tables showing the financial transactions between institutional sectors were calculated. Using the Y and Y* tables, Tsujimura and Mizoshita (2003), Tsujimura and Tsujimura (2006) examined the effectiveness of the central bank’s monetary policies, namely the so-called quantitative easing policy introduced by the Bank of Japan (BOJ). To evaluate this monetary policy, the central bank is treated as an exogenous institutional sector in the Y and Y* tables. In this section, we adopt the evaluations method by Tsujimura and Mizoshita (2003).11 The previous study estimated the induced amount of fund demand and supply to analyze the effect of the central bank’s monetary policies through the financial transactions between institutional sectors, which are represented in Leontief inverse.

In this subsection, each industry’s net investments induced by the BOK in 2004 and 2005 are calculated. Table 13 demonstrates only distinguished industries in NII or changes in NII. First, industries that had greater NII than other industries are listed in this table. Since the NII of some particular industries on a large scale might be larger than others, the fourth and fifth columns display NII divided by its total liabilities, whereas the second and third columns show NII denominated in billions of Korean won. Next, industries in red have negative changes in NII when subtracting NII in 2004 from 2005. In other words, industries in red saw their NII in 2005 shrink as compared with that in the previous year. Consequently, this table shows a distinguished group that includes (1) industries having positive and greater NII than their 15% of total liabilities in either 2004 or 2005 and (2) industries whose NII fell during that 1 year.
Table 13

Industries with remarkable NII or changes in NII

Inter-industry

NII (Bil. KRW)

NII/total liabilities (%)

ΔNII (Bil. KRW)

2004

2005

2004

2005

B Fishing

114

60

12.6

6.6

−54

C Mining and quarrying

622

409

23.6

15.5

−214

D151 Production, processing, and preserving of meat, fish, fruits, vegetables, oils, and fats

670

1513

6.7

15.2

843

D1554 Ice and nonalcoholic beverages, production of mineral waters

621

939

10.5

15.9

318

D172 Weaving of textile fibers

1261

1012

16.6

13.3

−249

D191,2 Leather, luggage, handbags, saddlery, and harnesses

298

266

11.0

9.8

−32

D23 Coke, refined petroleum products, and nuclear fuel

5163

4787

11.9

11.0

−377

D2411-3 Basic chemicals, except fertilizers

5396

4547

16.5

13.9

−849

D2415 Synthetic rubber and plastics in primary forms

40,99

3619

15.1

13.3

−480

D2432 Paints, varnishes, and similar coatings, printing ink and mastics

467

386

9.0

7.4

−80

D2433 Soap, cleaning compounds, and toilet preparations

811

652

15.3

12.3

−160

D244 Man-made fibers

1152

627

24.0

13.1

−524

D2631 Cement, lime, and plaster

1077

1005

14.1

13.1

−72

D2931 Agricultural and forestry machinery

205

315

10.3

15.9

110

D2933 Machinery for mining, quarrying, and construction

653

1187

8.9

16.2

534

D30 Computers and office machinery

938

864

11.8

10.9

−74

D351 Building of ships and boats

5361

8291

10.0

15.4

2930

D352,3,9 Railway locomotives, aircraft, and transport equipment

767

1454

8.5

16.1

687

D369 Other manufacturing

639

568

13.1

11.6

−71

E401 Electricity

9437

9426

11.4

11.4

−11

G5211-9 Retail sale in nonspecialized stores except department stores

2193

5286

6.9

16.7

3093

G52111 Department stores

3541

4223

12.8

15.2

682

H551 Accommodation

1979

3802

8.6

16.5

1824

I602 Transit and ground passenger transportation

824

842

16.8

17.1

18

I62 Air transport

3073

2985

15.2

14.8

−88

I63 Supporting and auxiliary transport activities and travel agencies

9318

1834

61.7

12.2

−7484

J642 Telecommunications

13,904

10,852

18.9

14.7

−3052

L70 Real estate, renting, and leasing

1702

4465

5.8

15.2

2764

Q88 Other recreational, cultural, and sporting activities

3771

4015

15.9

17.0

244

Residual industry

56,722

10,191

15.5

2.8

−46,531

Italic values: negative NII (NII < 0)

Compared with Table 11, wherein both of their power of dispersion indices are greater than 1, Table 13 exhibits an interesting feature. Most industries that are listed in Table 11 are not included in Table 13.12 In other words, the NII of the first-quadrant industries group are computed comparatively smaller than that of other industries listed in Table 11. Industries whose power of dispersion index in the asset-oriented system is greater than 1 are included in the first quadrant.

Table 14 shows GIS, GII, and NII bifurcated by the sign of the power of dispersion index in the asset-oriented system. They are expressed as proportions of GIS, GII, and NII to total liabilities. It is clear that for the GIS–total liabilities ratios of the first group, the index is larger than 1 and mainly includes light industry, and the ratios are obviously smaller than in the second group. Since no significant gap exists between the GII of the two industry groups, the NII–total liabilities ratio of the first group is half that of the second group.
Table 14

GIS, GII, and NII bifurcated by sign of power of dispersion index in the asset-oriented system

Inter-industries

Year

GIS/total liabilities (%)

GII/total liabilities (%)

NII/total liabilities (%)

First group: index > 1

2004

9.1

15.4

6.2

 

2005

11.5

18.5

7.1

Second group: index < 1

2004

6.1

18.4

12.3

 

2005

5.3

19.6

14.2

To clarify the distinction between these two groups, let us explain using industry asset portfolios. Table 15 demonstrates the asset–total liabilities ratios of two groups using a Y table, which represents transactions between institutional sectors; the central bank column and row are not removed. The same method of grouping industries is used in Table 14. It is obvious that (1) financial assets, in other words, funds from each group, have inter-industry flows, and (2) excess liabilities show large differences. It is clear that the assets of the first group, which are invested in other nonfinancial corporations and which comprised 30.4% in 2004 and 27.2% in 2005, are greater than those of the second group, which were 16.9 and 16.8%. On the other hand, the second group’s excess liabilities, which run to nearly 60%, are significantly greater than those of the first group. Since excess liabilities are calculated by subtracting total financial assets from total liabilities, substantial excess liabilities imply that the industry has carried out large-scale real investments. There is not much difference between the two groups in other institutional sectors, with the exception of funds supplied to domestically licensed banks from the first group, which edged up to 10.0% in 2004.
Table 15

Asset–total liabilities ratios of industries bifurcated into two groups

Institutional sector

2004

2005

First group (%)

Second group (%)

First group (%)

Second group (%)

Central bank

2.8

1.9

1.9

1.8

Domestically licensed banks

10.0

6.4

6.8

6.8

Specialized banks

3.8

2.5

2.6

2.6

Other banks

1.3

0.9

1.0

0.9

Collectively managed trusts

1.6

1.1

1.1

1.0

Small loan financial companies for households and small businesses

3.5

2.2

2.3

2.4

Investment institutions

2.6

1.8

2.1

1.8

Other nonbanks

0.7

0.4

0.5

0.5

Life insurance companies

0.1

0.1

0.2

0.1

Nonlife Insurance Companies

0.2

0.1

0.1

0.1

Cooperative society

0.0

0.0

0.0

0.0

Pension funds

0.0

0.0

0.0

0.0

Securities Institutions

0.9

0.6

0.8

0.8

Credit-specialized financial institutions

0.7

0.5

0.5

0.4

Public financial institutions

1.5

1.0

0.9

0.8

Other financial intermediaries

1.0

0.7

0.7

0.6

Financial auxiliaries

0.2

0.2

0.2

0.1

General government

2.2

1.6

1.8

1.5

Households and nonprofit organizations

4.0

1.7

3.5

1.5

Rest of the world

2.6

1.6

2.3

1.6

Inter-industry (nonfinancial corporations)

30.4

16.9

27.2

16.8

Excess liabilities

29.8

57.9

43.5

58.0

Total liabilities

100.0

100.0

100.0

100.0

Table 14 implies two primary features of Korean industries. Mainly light industries, IT, and service industries are included in the first group, while the second group consists of heavy and chemical industries in the main. NII–total liabilities ratios of the second group are higher than the first group, since GIS–total liabilities ratios of the first group are larger than the second group. First feature is that intuitionally heavy and chemical industries need huger plant and equipment investment than light industries. A high level of the real investment is able to cause small savings, in other words, lower GIS–total liabilities ratios. Second characteristic is possibility of compensatory balance, in other words, forced deposits in return for bank loans. According to Park (2003), the compensating balance is useful when banks make loans to informationally opaque firms. This paper argues that banks in Korea came to exercise power after onset of the financial crisis in late 1990s based on anecdotal and empirical evidence. Historically, small and medium enterprises have been forced more than large firms to make a deposit when they get a bank loan in Korea. Thus, forced deposits can reduce adverse selection problems of banks. A high compensating balance brings about bigger savings and higher GIS–total liabilities ratios. Therefore, huger compensating balance can be one of the reasons of higher GIS–total liabilities ratios of the first group.

5 Concluding remarks

Expanded FOF accounts, which contain a range of industries, are developed in this paper. Combined with 2004 and 2005 FSA data, the FOF accounts are subdivided into 115 institutional sectors, including 95 types of inter-industries. First, inter-industry analysis of the FOF accounts was examined. Liability and financial asset portfolios and real assets ratios of industries were explained. Domestic enterprises, light industries, and medium–low-technology industries show larger inventories and fewer tangible assets than export enterprises, heavy and chemical industries, and high and medium–high-technology industries. Liability portfolios of Korean core industries (semiconductors and other electronic components and the building of ships and boats, motor vehicles, and engines) are more dependent on foreign funds than other manufacturing industries. Power of dispersion indices were then presented, which showed that most service and IT industries, construction, and light industries in manufacturing are included in the first-quadrant group, whereas heavy and chemical industries are placed in the fourth quadrant since their power indices in the asset-oriented system are comparatively smaller than those of other institutional sectors. Second, inter-industry policy evaluations in the FOF accounts are derived in the fourth section. The evaluation results of monetary policies implemented by the central bank are reported. Industries are bifurcated into two groups to compare their features. The first group contains industries whose power of dispersion in the asset-oriented system is greater than 1, whereas the second group contains those whose index is less than 1. We found that the NII–total liabilities ratios of the first group were half those of the second group, since GIS–total liabilities ratios of the former are obviously greater than the latter.

The FOF table has a weakness in that it does not correspond to the I–O table, since it is not subdivided into various industries. Previous researches, for example, Tsujimura and Mizoshita (2003), Tsujimura and Tsujimura (2006), and Manabe (2009), examined policy evaluations using the FOF accounts that were not separated into industries. In this respect, the main contribution of this study is demonstrating the possibility of constructing “from-whom-to-whom” tables that correspond to the I–O tables and a technical I–O analysis, as Klein (2003) mentioned. Though Nishiyama (1991) tried to build “from-whom-to-whom” tables with 44 institutional sectors including 37 industries and obtained one type of power of dispersion index, this paper aimed to design more detailed tables and calculate two power of dispersion indices based on the liability approach and asset approach to evaluate the central bank’s monetary policy.

There are many possibilities and potentialities to suggest desirable economic policies by applying and extending these analytical methods. For future work, we consider an analysis method to link the I–O table and FOF accounts separated into various types of industries, for example, an estimation of production functions using inter-industry data from the linked I–O table and the NII calculated from the FOF accounts.

It has been shown here that 18 industries showed negative NII changes in 2004 and 2005, for example, fishing, mining and quarrying, certain manufacturing industries, electricity, air transport, support and auxiliary transport activities and travel agencies, and telecommunications. Challenges for the future include estimations of production functions for every industry, including a variable for changes in NII. This work will enable us to analyze how negative or positive changes in each industry’s NII, which are caused by the central bank’s monetary policy, affect the real economy. In addition, policymakers will be able to refer to these estimation and simulation results as indicators to evaluate policies and make decisions for both the financial market and the real economy.

Footnotes
1

For details, refer to Tsujimura and Mizoshita (2002a) in English and Tsujimura and Mizoshita (2002b), pp. 32–43 and pp. 116–129 in Japanese.

 
2

Some industries are aggregated since there are 94 industries in the expanded FOF accounts in Tables 5 and 6.

 
3

Residual industry is excluded.

 
4

Residual industry is excluded.

 
5

Residual industry is excluded.

 
6

The other industries show less than 0.6%.

 
7

According to market research firm IC insights, Korea became the world’s second-largest semiconductor manufacturer in 2013.

 
8

The ratios of industries having more than 14% of funds raised from foreign countries to total liabilities are listed in Table 9.

 
9

According to the KDB (2005b), there are two groups in the electronic components industries: one is a technology-intensive industry and the other is a labor-intensive industry. Semiconductors and LCDs, which are capital- and technology-intensive industries, are led by large firms with mass production systems. On the other hand, other electronic components are led by labor-intensive industries dominated by small and medium enterprises with small quantity batch production methods.

 
10

Korea ranked fourth with the largest share at 10.0% (Korea’s production was recorded as US$39,904 million out of the world total of US$398,826 million) in global electronic component production in 2005. Japan ranked first with US$95,604 million, whereas the USA and China ranked second (US$61,236 million) and third (US$41,368 million) (source: Reed Electronic Research 2005).

 
11

For details, please see Appendix.

 
12

Only two industries, namely (1) paints, varnishes, and similar coatings, printing ink and mastics, and (2) the building of ships and boats, are duplicated, since the NII of the former industry shrank in this period, whereas the latter industry had a NII-to-total liabilities ratio greater than 15% in 2005.

 

Declarations

Acknowledgements

Not applicable.

Competing interests

The author declares that she has no competing interests.

Availability of data and materials

The Bank of Korea, Economic Statistics System (http://ecos.bok.or.kr/).

Consent for publication

Not applicable.

Ethics approval and consent to participate

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Funding

Not applicable.

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Authors’ Affiliations

(1)
Global Value Chains Studies Group, Inter-disciplinary Studies Center, Institute of Developing Economies, Japan External Trade Organization (IDE-JETRO)

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