For presentation at WIOD Conference (2012) Groningen (The Netherlands), April 24-26, 2012 The Impact of Financial Crises and IT Revolution on Income Distribution in Korea: Evidence from Social Accounting Matrices Hak K. Pyo 1 , Keun Hee Rhee** and Gong Lee*** Abstract In recent years, Korea had experienced two financial crises in 1997-1998 and 2007-2008 respectively and the IT revolution during the interval between the two financial crises. We have constructed Social Accounting Matrix (SAM) in 2000 and 2009 for Korea by combining Input-output tables used in WIOD project in Korea with data from 10-decile Urban Household Income Survey. We analyze gross income effect and income redistribution effect of financial crises and IT revolution by adopting a SAM framework following Pyatt and Round (2004) and Saari, Dietzenbacher and Los (2010). Both financial crises and IT revolution have generated larger income multiplier effect on Higher IT- intensive Manufacturing sector but have affected negatively on income redistribution of lower income groups. 1 Director, Center for National Competitiveness and Faculty of Economics, Seoul National University, Seoul 151-746, Korea. Tel: +82-2-880-6395. Fax: +82-2-886-4231. E-mail: [email protected]. Corresponding author. ** Senior Researcher, Korea Productivity Center, E-mail:[email protected]*** Ph.D. Candidate, Department of Economics, Seoul National University, Seoul 151-746, Korea. Tel: +82-10-9586-2020, Fax: +82-2-886-4231, E-mail: [email protected]
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For presentation at WIOD Conference (2012) Groningen (The Netherlands), April 24-26, 2012
The Impact of Financial Crises and IT Revolution on Income Distribution in Korea:
Evidence from Social Accounting Matrices
Hak K. Pyo1, Keun Hee Rhee** and Gong Lee***
Abstract
In recent years, Korea had experienced two financial crises in 1997-1998 and 2007-2008 respectively and the IT revolution during the interval between the two financial crises. We have constructed Social Accounting Matrix (SAM) in 2000 and 2009 for Korea by combining Input-output tables used in WIOD project in Korea with data from 10-decile Urban Household Income Survey. We analyze gross income effect and income redistribution effect of financial crises and IT revolution by adopting a SAM framework following Pyatt and Round (2004) and Saari, Dietzenbacher and Los (2010). Both financial crises and IT revolution have generated larger income multiplier effect on Higher IT-intensive Manufacturing sector but have affected negatively on income redistribution of lower income groups.
1 Director, Center for National Competitiveness and Faculty of Economics, Seoul National University, Seoul 151-746, Korea. Tel: +82-2-880-6395. Fax: +82-2-886-4231. E-mail: [email protected]. Corresponding author. ** Senior Researcher, Korea Productivity Center, E-mail:[email protected] *** Ph.D. Candidate, Department of Economics, Seoul National University, Seoul 151-746, Korea. Tel: +82-10-9586-2020, Fax: +82-2-886-4231, E-mail: [email protected]
1. INTRODUCTION Social Accounting Matrix (SAM) provides a useful framework to analyze the composition of national income and product and the process of income distribution. Even though SAM is a static analytical tool, we can examine the dynamic aspects of national income composition and its distribution by comparing two SAM over a time interval. We have shown in a recent report (Pyo, Kim and Lee (2011)) using SAM with the household micro cell by age group that exogenous injections of income to government mostly benefit the relative income of the pensioners, the age group over 60. The empirical result is consistent with that of Llop and Manresa (2004) who have found that exogenous injections of income to government mostly benefit the relative income of inactive households, which are mainly those of pensioners and have argued that such empirical evidence is important for policy making if the policies are aimed at modifying the distribution of income among economic agents. The purpose of the present paper is to analyze the impacts of IT revolution and two financial crises on the determination of national income and changes in the level of income of endogenous sectors through multiplier analysis and the structure of income distribution by constructing micro cell of household sector. As analyzed in Pyo (2000), the growth rate of real GDP which had averaged 7.1 percent during the pre-crisis period of 1993-1997 declined sharply recording – 6.9 percent in year 1998 when the so-called twin crises had hit the Korean economy. The financial crisis of 1997-1998 was called twin crises because there were both domestic banking crisis and foreign exchange crisis. As documented in Pyo (2004a) and Otsu and Pyo (2009), the macroeconomic adjustment during the post-crisis period was a successful one largely due to remarkable export performance helped by Won depreciation. During the period of 1998-2007, the export performance led by IT-intensive products has helped the economy to make a sustainable growth. When the global financial crisis occurred in 2007-2008, the recovery pattern of the Korean economy has been quite similar to the recovery pattern after the 1997-1998 crises. Korean Won was depreciated against not only US dollar but also Yen, Euro and Yuan and strong export performance in IT-intensive manufactures followed. We have attempted to analyze the impact of large scale depreciations immediately after two financial crises and export drive of IT-intensive products during the two post-crisis periods by multiplier and redistribution analysis based on SAM. For the purpose, we have constructed SAM for two discrete years of 2000 and 2009 in which we have treated Capital Accounts, External Accounts and Government Accounts as exogenous sectors and four-category production sectors as endogenous sectors:(1) Higher IT-intensive Manufacturing (2) Lower IT- intensive Manufacturing with Agriculture and Mining (3) Higher IT-intensive Services and (4) Lower IT-intensive Services following Ha and Pyo (2004) and Pyo and Ha (2007). We have also added a micro-SAM by decomposing the household sector to 10-decile units of income distribution by using KLIPS (Korea Labor Income Panel Study) dataset (1998-2008). There are major findings of the present study. The first finding is that the impact of both IT technology and two drastic depreciations have generated significant multiplier effects and redistributed income effects on Higher IT-intensity endogenous production sectors. The second finding is that the lowest income group (Unit 1) was the largest beneficiary group in terms of multiplier contribution and the highest income group (Unit 10) was the largest beneficiary group in terms of redistributed income effect. The last finding is consistent with the empirical finding by Llop and Manresa (2004) that injections of income to activities mostly benefit the relative income of the richest active households and the finding by Noh and Nam (2006).
In Section 2, we outline a SAM model of multiplier effects and redistribution effects. Section 3 presents empirical results after constructing SAM for the Korean economy of 2000 and 2009. The last section concludes the paper.
2. THE MULTIPLIER EFFECT AND RELATIVE INCOME DISTRIBUTION Following Roland-Holst and Sancho (1992) Llop and Manresa (2004) and Saari, Dietzenbacher and Los (2010), we can specify the multiplier analysis by dividing the accounts of a SAM into two separate sectors: endogenous and exogenous accounts as shown in Table 1. If we consider m endogenous sectors and z exogenous sectors, a SAM can be written as follows:
1
where Aij are partitioned sub-matrices that contain the expenditure share coefficients calculated by dividing the transactions in the SAM by the corresponding sum column. The multiplier analysis assumes that the expenditure coefficients are constant, so sub-matrices Aij assumed to be invariant over time. Income from endogenous accounts (Ym) can be obtained as follows from the top first row equations:
2
where I is the identity matrix, M = (I – Amm)-1 is a multiplier matrix and x = AmzYz is a vector of exogenous variables. The multiplier matrix M shows the overall effects of a unitary increase in the
exogenous components on the endogenous accounts. Therefore, the element ijm of M quantifies the
changes in the income of the sector I ( mdY ), i.e. gross income effect as a consequence of a unitary and
exogenous injection received by the sector j( mdY ).
From expression (2), the analysis of multipliers corresponding to endogenous sectors illustrates the changes in the absolute levels of income. Roland-Holst and Sancho (1992) presented an overall context for distributive incidence based on the SAM model. To identify the changes in the relative incomes2 of every endogenous sector, expression (2) can be normalized as follows:3
3 where e is a unitary row vector. From (3), the changes in the relative income of the endogenous
2 By relative income we mean relative to total income of all the endogenous accounts. 3 See Roland-Holst and Sancho (1992).
sectors generated by a modification in the exogenous injections are equal to: Table 1. Simplified Schematic Social Accounting Matrix4 Expenditures
Production activities 3 0 T32 T33 x3 y3 Exogenous sectors
Sum of other accounts 4 l′1 l′2 l′3 t yx
Totals 5 y′1 y′2 y′3 y′x
1
4
In this expression, R is defined as the m by m redistribution matrix. It shows the change (positive or
negative) in the relative income of the endogenous sectors ( mdY ) caused by unitary modifications in
the exogenous injections of income received( mdX ). An individual element of this matrix, ijr ,
determines the magnitude (positive or negative) of the percentage change in the relative income of the sector i as a result of a unitary inflow in the sector j. This way of calculating the distribution process involves a set of bilateral connections between the endogenous sectors that tell us how one account influences the relative status of another. It is interesting that, irrespective of which endogenous components are chosen in the model, the sum of the columns in the matrix of redistribution is zero.5 This mathematical property means that the distribution process between the endogenous accounts can be interpreted as a game of winners and losers. If we take expression (4), we can identify three multiplicative components in the structure of R :
5 4 Thorbecke, E. and H.S. Jung, Journal of Development Economics 48 (1996) 279-300 5 By relative income we mean relative to total income of all the endogenous accounts.
The first component, 1( ' )b e Mx , is the inverse of the total income of the endogenous sectors and
is a scalar. The second matrix, 1( ' ) ( ) 'D I e Mx Mx e , has two parts: the first part ( I ) is the
initial and exogenous injection of income that activates the multiplier process, and the second part 1( ' ) ( ) 'e Mx Mx e is the matrix of the initial relative income of every endogenous sector (with a
negative sign). Finally, M is the matrix of standard multipliers. Expression (5) represents matrix R in a multiplicative form and can be transformed into an additive expression. This transformation will make it easier to interpret the effects involved in the income distribution process. Specifically, we can define the redistribution matrix as follows:
6
This representation of R reveals the underlying components of the income distribution process and displays the sequential terms involved. In expression (6), b is the inverse of the total income of the endogenous sectors or the factor of normalization. The terms in the bracket show the initial and exogenous injection that starts the multiplier effect and the distribution process. Also, the matrix
( )I D tells us the endogenous sectors’ relative position. The last term in the bracket, ( )D M I ,
is the net multiplier effect on relative income and represents the additive contribution of the net
multipliers to the distribution process. Notice that ( )D M I contains the cross multiplier effects
among the endogenous sectors and its effects on relative income determination. An arbitrary element
( , )i j of this matrix, which can be either positive or negative, is equal to:
. .
where ijmn are the components of the matrix M I of net multipliers and . jmn are the sum of
the elements of the j th column of M I . The multiplier contribution to income distribution is,
therefore, equal to its net multiplier minus the distribution generated by the account j to the other
endogenous institutions. The additive formulae of the redistribution matrix R clarify the direction and magnitude of the changes in the relative position of the accounts. Specifically the distribution procedure among economic agents when there are exogenous inflows of income is shown as the result of combining effects with different meaning. The initial and exogenous injection received by the sectors ( I ) affects
their relative status positively. The net multiplier effects on relative income ( )D M I have either a
positive or negative effect within the income distribution process. Finally, the initial relative income
of the endogenous sectors ( )I D contributes to the changes in the relative income negatively.
In particular, it is interesting to determine the contribution of the net multipliers (i.e., the matrix
( )D M I ) to income distribution because this provides information about the changes in the relative
position of the sectors as a result of the multiplier process. It is important to decompose the matrix of redistribution into different additive components. In our case we will see, through the elements of
matrix ( )D M I , a poor multiplier capacity for modifying the relative income of the endogenous
accounts.
3. Multiplier Effects and Income Redistribution Effects in Korea (2000 and 2009)
(1) Financial Crises and IT Revolution: An Overview
In order to assess macroeconomic fundamentals during the period of 2000-2011, we have decomposed production industries into 4 sectors as shown in Table 2: (1) Higher IT-intensive Manufacturing (2) Lower IT -intensive Manufacturing and Primary (3) Higher IT-intensive Services and (4) Lower IT-intensive Services following Ha and Pyo (2004) and Pyo and Ha (2007). We have summarized major final demand indicators (Figure 1), exchange rate and stock market index (Figure 2) and production and export performance by industries (see Table 3). The trend in final demand indicators clearly marks two points of financial crises in 1997-1998 and 2007-2008 when both real GDP and real investment fell sharply. After the two financial crises, export not investment had led the way for recovery. The movement in exchange rate (Won/dollar rate) shows large-scale abrupt depreciation of Won and drop in KOSPI stock index just after each financial crisis in 1997 and 2007. The decomposed industrial performances of output and exports over the period of 2000-2011 clearly indicate the strong performance by higher IT-intensity manufacturing with output growth (7.8 %) and export growth (11.9 %) respectively. Table 2 Industrial Classification by IT-Intensity
Main Category
intensity Sub Category Main
Category intensity Sub Category
manufacturing sector
lower IT- intensity
1 agriculture and fishing
service sector
lower IT-intensity
22 construction
2 mining 26 transportation, storage
3 food 29 real estate
4 textile, apparels, leather 32 government
5 wood
higher IT-
intensity
23 electicity, gas, water
6 paper allied 24 trade
10 rubber and plastic 25 hotels and restaurants
11 stone, clay, glass 27 communication
13 fabricated metal 28 finance, insurance
14 machinery 30 business services
16 electrical machinery 31 social and personal services
19 instrument
21 furniture and misc. manufacturing
higher IT-
intensity
7 printing and publishing
8 coal and petroleum product
9 chemicals
12 primary metal
15 computer and peripherals
17 electric components
18 sound, video, communication equipment
20 transportation equipment
Sources: Ha and Pyo (2004) and Pyo and Ha (2007)
Figure 1. GDP, Investment and Export Growth Rate: Korea 1995-2011
Source: The Bank of Korea Figure 2. Exchange Rate and Stock Price Index: Korea 1995-2011
Source: The Bank of Korea
Table 3. Average Growth Rate for different periods
average growth rate 2000-2007 2008-2011 2000~2011
Gross Domestic Product 5.21 3.13 4.52
Agriculture, Forestry and Fishing 1.38 0.60 1.12
Mining, Quarrying and Manufacturing 8.10 5.73 7.31
Manufacturing 8.18 5.83 7.39
Services 4.53 2.63 3.89
Final Consumption Expenditure 4.71 2.38 3.93
Private 4.76 2.00 3.84
Government 4.53 3.73 4.26
Gross Fixed Capital Formation 4.46 0.45 3.13
Construction 2.95 -2.03 1.29
Facilities Investment 7.13 4.65 6.30
Intangible Fixed Assets 7.95 3.40 6.43
Exports of Goods and Services 11.60 7.40 10.20
Imports of Goods and Services 10.69 5.05 8.81
average growth rate 2000-2007 2008-2011 2000-2011
Gross Domestic Product 5.21 3.13 4.52
(1)manufacturing high IT-intensity industries 7.76 3.60 5.20
(2)Primary and manufacturing low IT-intensity industries 5.11 2.67 3.86
(3)service high IT-intensity industries 6.62 4.13 5.98
(2) Macro-SAM in 2000 and 2009 with Micro-SAM In order to identify multiplier effects and income redistribution effects for the period of 2000-2009, we have constructed SAM of Korea as shown in Table 4 for year 2000 and Table 5 for year 2009 by combining Input-Output Tables and National Accounts by the Bank of Korea in respective years. In order to construct a supplementary Micro-SAM, we have used Korea Labor & Income Panel Study (KLIPS) Database (1998-2008) to decompose household sector into 10-Decile units. They are presented in Table 6 for year 2000 and Table 7 for year 2009 which show sources of income (wage, profit, business transfer, government transfer and foreign transfer income). When we compare Table 6
with Table 7, the income distribution of profit has been markedly skewed in favor of higher income deciles. For example, 40.5 percent of the profit income was distributed to the highest income decile (Unit of 10) in 2000 but 52.7 percent in 2009. On the other hand, the business transfer income moved to the opposite direction; the highest income decile received 73.9 percent in 2000 but only 45.4 percent in 2009. In general, the distribution structure of gross income by 10 deciles changed in favor of lower income groups. The ratio of upper 20 % income to lower 20 % income was 17.1 in 2000 and 12.1 in 2009 respectively. The wage income of the lower income deciles (Unit of 1 and 2) was only 3.1 percent in 2000 but improved to be 3.7 percent in 2009. It was mainly due to the welfare policies for the lower income groups by President Kim (1998-2002) and President Roh administration (2003-2007). On the household expenditure side, we have used 2001 KLIPS data for Micro-SAM (2000) and 2008 KLIPS data for Micro-SAM (2009) as shown in Table 8 and 9 respectively. In general, the distribution structure of gross expenditure by 10 deciles changed in favor of lower income groups too. The ratio of upper 20 % expenditure to lower 20 % expenditure was 4.5 in 2000 and 3.6 in 2009 respectively. The gross expenditure of the lower income deciles (Unit of 1 and 2) was only 8.3 percent in 2000 but improved to be 10.1 percent in 2009. Because of differences in survey items between 2001 KLIPS data and 2008 KLIPS data, we had to use the same proportions of income and expenditure in both 2000 and 2009.
Table 4 Macro Social Accounting Matrix (South Korea, Year 2000, 1 billion won)
Income/ Expenditure
Production Activities
Production Commodities
Labor Capital HouseholdCorporateenterprise
GovernmentCombinedenterprise
Rest of World
Error term Total
Production Activities 1,155,961 236,966 1,392,928
Production Commodities 793,283 352,371 61,653 188,443 1,395,750
Table 6 Household Income Sources by Decile ( Year 2000 )
Equalization income by decile(2000)
Gross income
Wage Profit
income
Businesstransfer income
Government transfer income
Foreign transfer income
Errorterm
Unit of 1 0.004 0.008 0.011 0.009 0.255 0.061 0.002
Unit of 2 0.023 0.023 0.036 0.022 0.233 0.266 0.009
Unit of 3 0.043 0.043 0.036 0.015 0.119 0.134 0.021
Unit of 4 0.059 0.061 0.059 0.029 0.113 0.084 0.026
Unit of 5 0.075 0.077 0.098 0.016 0.064 0.068 0.037
Unit of 6 0.093 0.099 0.034 0.017 0.056 0.044 0.042
Unit of 7 0.112 0.117 0.079 0.043 0.044 0.054 0.044
Unit of 8 0.130 0.133 0.109 0.039 0.046 0.063 0.043
Unit of 9 0.164 0.168 0.133 0.072 0.042 0.052 0.083
Unit of 10 0.297 0.273 0.405 0.739 0.028 0.174 0.692
Total 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Table 7 Household Income Sources by Decile ( Year 2009 )
Equalization income by decile(2009)
Gross income
Wage Profit
income
Businesstransfer income
Government transfer income
Foreign transfer income
Errorterm
Unit of 1 0.010 0.011 0.015 0.009 0.255 0.093 0.005
Unit of 2 0.026 0.026 0.043 0.015 0.233 0.129 0.022
Unit of 3 0.044 0.043 0.051 0.036 0.119 0.095 0.015
Unit of 4 0.060 0.060 0.050 0.033 0.113 0.082 0.050
Unit of 5 0.074 0.077 0.038 0.084 0.064 0.064 0.026
Unit of 6 0.090 0.095 0.050 0.022 0.056 0.044 0.022
Unit of 7 0.108 0.113 0.044 0.075 0.044 0.065 0.062
Unit of 8 0.131 0.137 0.085 0.052 0.046 0.063 0.157
Unit of 9 0.165 0.169 0.097 0.219 0.042 0.105 0.121
Unit of 10 0.293 0.270 0.527 0.454 0.028 0.260 0.521
Total 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Table 8. Household Expenditure Distribution by Decile ( Year 2000 )
Equalization expenditure by decile (2000)
Gross expenditure
Consumption Business transfer
expenditure
Government transfer
expenditure Savings
Foreign transfer
expenditure
Unit of 1 0.043 0.050 0.021 0.015 0.010 0.025
Unit of 2 0.040 0.047 0.021 0.023 0.017 0.037
Unit of 3 0.057 0.064 0.043 0.044 0.031 0.056
Unit of 4 0.072 0.079 0.058 0.063 0.036 0.076
Unit of 5 0.084 0.089 0.082 0.083 0.056 0.087
Unit of 6 0.098 0.101 0.101 0.100 0.084 0.096
Unit of 7 0.112 0.112 0.131 0.119 0.091 0.112
Unit of 8 0.122 0.126 0.130 0.145 0.110 0.135
Unit of 9 0.151 0.140 0.157 0.170 0.188 0.151
Unit of 10 0.221 0.192 0.258 0.238 0.379 0.224
Total 1.000 1.000 1.000 1.000 1.000 1.000
Table 9. Household Expenditure Distribution by Decile (Year 2009 )
Equalization expenditure by decile (2009)
Gross expenditure
Consumption Business transfer
expenditure
Government transfer
expenditure Savings
Foreign transfer
expenditure
Unit of 1 0.053 0.038 0.008 0.015 0.002 0.025
Unit of 2 0.048 0.049 0.015 0.023 0.009 0.037
Unit of 3 0.055 0.064 0.031 0.044 0.025 0.056
Unit of 4 0.069 0.078 0.049 0.063 0.040 0.076
Unit of 5 0.080 0.089 0.070 0.083 0.059 0.087
Unit of 6 0.095 0.099 0.097 0.100 0.097 0.096
Unit of 7 0.112 0.118 0.115 0.119 0.120 0.112
Unit of 8 0.126 0.130 0.151 0.145 0.115 0.135
Unit of 9 0.145 0.143 0.182 0.170 0.189 0.151
Unit of 10 0.217 0.193 0.282 0.238 0.343 0.224
Total 1.000 1.000 1.000 1.000 1.000 1.0
(3)Multiplier Effects In both Table 10 and Table 11, we have shown multiplier income effects on four sectors of industries, (1) Manufacturing with low IT-intensity, (2)Manufacturing with high IT-intensity, (3)Service with low IT-intensity and (4)Service with high IT-intensity. The row average of each table represents average sensitivity of the sector when expenditure is injected from each of four sectors. In both Table 9 and 10, the multiplier effect is highest with the Sector (2) and lowest with the Sector (3). By comparing two tables, we can see that each sector’s income multiplier sums in all four sectors have become larger in 2009 than in 2000. The sector (3) Service with low IT-intensity had the lowest average sensitivity, 0.433 and 0.411 respectively. Therefore, we can conclude that sector (3) Service with low-IT intensity has both the lowest average sensitivity and the lowest income multiplier effect, which is a typical nature of the non-tradable service sector.
Table 10 Multiplier Contribution ( nnM ) to Income Distribution in Production (Year 2000)
Expenditure
Income
Production Activities Average
Sensitivity (1)
Manufacturinglow IT-intensity
(2) Manufacturinghigh IT-intensity
(3) Service low IT-intensity
(4) Service high IT-intensity
Production Activities
(1) Manufacturing low IT-intensity
1.791 0.770 0.639 0.661 0.965
(2) Manufacturing high IT-intensity
0.447 1.527 0.426 0.438 0.709
(3) Service low IT-intensity
0.172 0.170 1.188 0.203 0.433
(4) Service high IT-intensity
0.563 0.553 0.579 1.639 0.833
Total Effect 2.974 3.020 2.831 2.942
Table 11 Multiplier Contribution ( nnM ) to Income Distribution in Production (Year 2009)
Expenditure
Income
Production Activities Average
Sensitivity (1)
Manufacturinglow IT-intensity
(2) Manufacturinghigh IT-intensity
(3) Service low IT-intensity
(4) Service high IT-intensity
Production Activities
(1) Manufacturing low IT-intensity
1.807 0.794 0.659 0.682 0.985
(2) Manufacturing high IT-intensity
0.427 1.554 0.417 0.411 0.702
(3) Service low IT-intensity
0.154 0.145 1.165 0.182 0.411
(4) Service high IT-intensity
0.635 0.624 0.667 1.759 0.921
Total Effect 3.022 3.116 2.907 3.034
In Table 12 and 13, we have summarized multiplier contribution of production to household income by 10-decile groups. In both 2000 and 2009, total multiplier contribution was highest in sector (3) lower IT-intensity service sector such as Construction, Transportation and Storage, Real Estate and Government. These industries have lower intermediate inputs and higher value-added and therefore, when the production activity increases, the income generation through multiplier effects is relatively larger than other sectors. In general though, the multiplier effects of production to household income have become smaller in 2009 than in 2000. In addition, we can point out that the average sensitivity by each unit of household is larger as we move to upper income units. It also became smaller in 2009 than in 2000.
Table 12 Multiplier Contribution ( nnM ) of Production to Household Income (Year 2000)
Expenditure
Income
Production Activities Average
Sensitivity(1)
Manufacturing low IT-intensity
(2) Manufacturing high IT-intensity
(3) Service low IT-intensity
(4) Service high IT-intensity
Equalization household by decile
Unit 1 0.006 0.005 0.007 0.007 0.006
Unit 2 0.017 0.015 0.021 0.020 0.018
Unit 3 0.026 0.024 0.033 0.032 0.029
Unit 4 0.039 0.036 0.049 0.048 0.043
Unit 5 0.051 0.047 0.065 0.063 0.056
Unit 6 0.050 0.047 0.067 0.063 0.057
Unit 7 0.067 0.062 0.087 0.083 0.075
Unit 8 0.078 0.073 0.102 0.098 0.088
Unit 9 0.100 0.093 0.130 0.125 0.112
Unit 10 0.224 0.206 0.275 0.271 0.244
Total Effect 0.657 0.607 0.835 0.810
Table 13 Multiplier Contribution ( nnM ) of Production to Household Income (Year 2009)
Expenditure
Income
Production Activities Average
Sensitivity(1)
Manufacturing low IT-intensity
(2) Manufacturing high IT-intensity
(3) Service low IT-intensity
(4) Service high IT-intensity
Equalization household by decile
Unit 1 0.007 0.006 0.009 0.009 0.008
Unit 2 0.016 0.015 0.021 0.021 0.018
Unit 3 0.025 0.023 0.034 0.032 0.028
Unit 4 0.031 0.029 0.043 0.041 0.036
Unit 5 0.039 0.036 0.054 0.051 0.045
Unit 6 0.045 0.042 0.064 0.060 0.053
Unit 7 0.054 0.050 0.077 0.071 0.063
Unit 8 0.067 0.062 0.094 0.088 0.078
Unit 9 0.089 0.082 0.123 0.116 0.102
Unit 10 0.186 0.170 0.244 0.238 0.210
Total Effect 0.559 0.515 0.763 0.727
In Table 14 and 15, we have presented inter-household multiplier contribution to income in 2000 and 2009. It shows that when an income is injected into the lowest income unit (Unit of 1), it has the largest income effect. The effect becomes smaller as we move toward higher income deciles. It is not a surprising result because the consumption expenditure to income increase is more sensitive in lower income brackets than in higher income brackets. On the other hand, if an equal amount was injected across all units of income decile, the highest income unit gains the most. It should also be pointed out that the column sum of two tables, Table 14 and 15, is the largest in Unit of 1 in both 2000 and 2009. This is because the lower income groups pay lower taxes and pension contributions while they tend to have higher propensity to consume. Since the government sector is regarded as an exogenous account and the higher income groups pay higher taxes and pension contributions, their propensity to consume will be less than lower income groups. On the other hand, average sensitivity becomes higher as we move from lower income units to higher income units in both 2000 and 2009. It indicates that higher income units derive higher multiplier effects and marginal benefits.
Table 14 Multiplier Contribution ( nnM ) to Income Distribution in Household (Year 2000)
Expenditure Income
Equalization household by decile Average Sensitivity Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Unit 7 Unit 8 Unit 9 Unit 10
(4) Redistribution Effects When there is income generated by one account, the income generated is distributed not equally to each sector and it becomes a zero-sum game. We can analyze how income generated by each production sector gets redistributed across different income units of household. For example, we can identify the induced production activities of four endogenous sectors by increased final demand in three exogenous accounts. Then we can trace how the income generated in endogenous sectors gets redistributed among household income units. Table 16 and 17 is the matrix of such redistributed income which sums up to zero. The result is different from multiplier effects. When income is generated from the entire production sectors, the Higher IT-intensity Manufacturing sector has the lowest income redistribution effect. In particular, the sector’s redistribution effect became negative (-1.019) while all other three sectors have had positive redistribution effects. On the other hand, when income is injected to production activity, the lower IT-intensity Service sector has the lowest column sum like the sector’s multiplier effects and it became lower in 2009 than in 2000.
Table 16 Redistributed Income Matrix ( ( ' )n nne Y R ) in Production (Year 2000)
Expenditure
Income
Production Activities Average
Sensitivity (1)
Manufacturinglow IT-intensity
(2) Manufacturinghigh IT-intensity
(3) Service low IT-intensity
(4) Service high IT-intensity
Production Activities
(1) Manufacturing low IT-intensity
0.858 -0.155 -0.294 -0.299 0.027
(2) Manufacturing high IT-intensity
-0.346 0.739 -0.367 -0.378 -0.088
(3) Service low IT-intensity
-0.062 -0.063 0.954 -0.038 0.198
(4) Service high IT-intensity
-0.080 -0.086 -0.065 0.977 0.187
Total Effect 0.369 0.436 0.227 0.262
Table 17 Redistributed Income Matrix ( ( ' )n nne Y R ) in Production (Year 2009)
Expenditure
Income
Production Activities Average
Sensitivity (1)
Manufacturinglow IT-intensity
(2) Manufacturinghigh IT-intensity
(3) Service low IT-intensity
(4) Service high IT-intensity
Production Activities
(1) Manufacturing low IT-intensity
0.910 -0.112 -0.252 -0.259 0.072
(2) Manufacturing high IT-intensity
-0.512 0.605 -0.537 -0.575 -0.255
(3) Service low IT-intensity
-0.040 -0.050 0.968 -0.021 0.214
(4) Service high IT-intensity
-0.027 -0.045 -0.006 1.065 0.247
Total Effect 0.330 0.397 0.173 0.210
Table 18 and 19 are the results of redistributed income effects of production activity in four sectors on household income by ten income units. In both 2000 and 2009, the lower IT-intensity Service sector has generated the largest total income redistribution effect (0.034 and 0.081). In terms of average sensitivity, the highest income group, Unit 10, has had the largest negative sensitivity (-0.040) in 2000 but it became the only positive sensitivity (0.002) in 2009.
Table 18 Redistributed Income Effect ( ( ' )n nne Y R ) of Production on Household Income (Year
2000)
Expenditure
Income
Production Activities Average
Sensitivity (1)
Manufacturinglow IT-intensity
(2) Manufacturinghigh IT-intensity
(3) Service low IT-intensity
(4) Service high IT-intensity
Equalization household by decile
Unit 1 -0.006 -0.007 -0.005 -0.006 -0.006
Unit 2 -0.010 -0.011 -0.006 -0.007 -0.008
Unit 3 -0.008 -0.009 0.000 -0.002 -0.005
Unit 4 -0.008 -0.010 0.003 0.000 -0.004
Unit 5 -0.008 -0.012 0.006 0.002 -0.003
Unit 6 -0.010 -0.013 0.007 0.002 -0.003
Unit 7 -0.010 -0.015 0.010 0.004 -0.003
Unit 8 -0.011 -0.016 0.012 0.006 -0.002
Unit 9 -0.015 -0.021 0.015 0.006 -0.004
Unit 10 -0.059 -0.075 -0.008 -0.020 -0.040
Total Effect -0.145 -0.189 0.034 -0.015
Table 19 Redistributed Income Effect ( ( ' )n nne Y R ) of Production on Household Income (Year
2009)
Expenditure
Income
Production Activities Average
Sensitivity (1)
Manufacturinglow IT-intensity
(2) Manufacturinghigh IT-intensity
(3) Service low IT-intensity
(4) Service high IT-intensity
Equalization household by decile
Unit 1 -0.012 -0.013 -0.010 -0.011 -0.011
Unit 2 -0.013 -0.014 -0.008 -0.009 -0.011
Unit 3 -0.009 -0.011 0.000 -0.003 -0.006
Unit 4 -0.009 -0.012 0.002 -0.002 -0.005
Unit 5 -0.009 -0.012 0.006 0.001 -0.003
Unit 6 -0.009 -0.013 0.009 0.003 -0.003
Unit 7 -0.010 -0.015 0.011 0.004 -0.002
Unit 8 -0.011 -0.016 0.015 0.007 -0.001
Unit 9 -0.014 -0.021 0.019 0.009 -0.002
Unit 10 -0.018 -0.036 0.037 0.024 0.002
Total Effect -0.113 -0.163 0.081 0.022
Lastly we examine the redistribution effect of household income injected on household income redistributed among different income units. Table 20 and 21 are redistributed household income among different income units in 2000 and 2009 respectively. In general the total effects are positive across all income units. But within each unit, the redistribution effect is positive only to itself and negative in all other sectors. In other words, the redistributed income matrix of households has positive diagonal elements but all negative off-diagonal elements. The total redistribution effect is the largest in the highest income unit and became smaller as we move lower income units in both 2000 and 2009. The total effect was larger in 2009 than in 2000 across all units. On the other hand average sensitivity moved in the opposite direction becoming lower as we move from lower income units to higher ones. It implies when income is injected to household account, the higher income units derive higher redistribution effects. On the other hand, the redistribution effect becomes more sensitive with lower income groups because their income level are at lower level and therefore, they are affected marginally more. It is noted that average sensitivity of Unit 10 was negative (-0.002) in 2000 but it became positive (0.049) in 2009 which implies after the global financial crisis of 2007-2008, the highest income unit (Unit 10) has become net gainers than net losers in redistribution of household income.
Table 20. Redistributed Income Effect ( ( ' )n nne Y R ) in Household (Year 2000)
Expenditure Income
Equalization household by decile Average Sensitivity Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Unit 7 Unit 8 Unit 9 Unit 10
In the present paper, we have examined the impact of two financial crises and IT revolution on multiplier effects and redistribution effects of both production sectors and household income units in Korea for the period of 2000-2011. We have decomposed the entire production activities into four sectors by the ranking of It-intensity. As a consequence of depreciation in the post-crisis periods, there was a strong export performance in higher IT-intensity Manufacturing Sector. This has generated a strong exogenous impact on four endogenous sectors of production. It also has generated income redistribution effects among different sectors of production and across different income units. In general, the distribution structure of gross income by 10 deciles changed in favor of lower income groups. The ratio of upper 20 % income to lower 20 % income was 17.1 in 2000 and 12.1 in 2009 respectively. The wage income of the lower income deciles (Unit of 1 and 2) was only 3.1 percent in 2000 but improved to be 3.7 percent in 2009. It was mainly due to the welfare policies for the lower income groups by President Kim (1998-2002) and President Roh administration (2003-2007). In analysis of multiplier effects, we have found that the sector (3) Service with low IT-intensity had the lowest average sensitivity, 0.433 and 0.411 respectively. Therefore, we can conclude that sector (3) Service with low-IT intensity has both the lowest average sensitivity and the lowest income multiplier effect, which is a typical nature of the non-tradable service sector. On the other hand, the multiplier effects of production to household income have become smaller in 2009 than in 2000. In addition, we can point out that the average sensitivity by each unit of household is larger as we move to upper income units. It also became smaller in 2009 than in 2000. In analysis of redistribution effects, the lower IT-intensity Service sector has generated the largest total income redistribution effect (0.034 and 0.081) in both 2000 and 2009,. In terms of average sensitivity, the highest income group, Unit 10, has had the largest negative sensitivity (-0.040) in 2000 but it became the only positive sensitivity (0.002) in 2009. In general the total redistribution effects are positive across all income units. But within each unit, the redistribution effect is positive only to itself and negative in all other sectors. In other words, the redistributed income matrix of households has positive diagonal elements but all negative off-diagonal elements. The total redistribution effect is the largest in the highest income unit and became smaller as we move lower income units in both 2000 and 2009. The total effect was larger in 2009 than in 2000 across all units. On the other hand average sensitivity moved in the opposite direction becoming lower as we move from lower income units to higher ones. It implies when income is injected to household account, the higher income units derive higher redistribution effects. These finding provide evidence increasing globalization of production activities in Korea and IT-intensity deepening has generated more redistribution effect in favor of higher income units. However the overall indicator of income disparity has not been worsened between 2000 and 2009 after two financial crises. When we measure it as the ratio of upper 20 percent income divided by lower 20 % income, the income disparity has been narrowed.
References
Dadush, U., Dasgupta, D. & Uzan, M. (2000), Private Capital Flows in the Age of Globalization: the Aftermath
of the Asian Crisis", Edward Elgar, Cheltenham
Defourny, J. & Thorbecke, E (1984), "Structural path analysis and multiplier decomposition within a social
8 Nonmetallic minerals 8 Non-metallic minerals 38 Glass products 32 Glass products 39 Pottery and clay products 33 Ceramic ware 40 Cement and concrete products 34 Cement and concrete products 41 Other nonmetallic mineral products 35 Other nonmetallic mineral products
13 fabricated metal
42 Pig iron and crude steel 36 Pig iron and crude steel 43 Primary iron and steel products 37 Primary iron and steel products
44 Nonferrous metal ingots and
primary nonferrous metal products 38
Nonferrous metal ingots and primary nonferrous metal products
21 furniture and misc. manufacturing 58 Other manufacturing products 52 Other manufactured products
High IT- intensity
7 printing and publishing 26 Printing, publishing and
reproduction of recorded media 21
Printing and reproduction of recorded media
8 coal and petroleum product 27 Coal products 22 Coke and hard-coal 28 Petroleum refinery products 23 Refined petroleum products
9 chemicals
29 Organic basic chemical products 24 Basic chemical products 30 Inorganic basic chemical products 25 Synthetic resins and synthetic rubber 31 Synthetic resins and synthetic rubber 26 Chemical fibers 32 Chemical fibers 27 Fertilizers and agricultural chemicals 33 Fertilizers and agricultural chemicals 28 Drugs, cosmetics, and soap 34 Drugs, cosmetics, and soap 29 Other chemical products 35 Other chemical products
12 primary metal 45 Fabricated metal products 39 Fabricated metal products except
machinery and funiture 15 computer and peripherals 45 Computer and office equipment 17 electric components 49 Electronic components and accessories 43 Electronic components and accessories
18 sound, video, communication
equipment 50
Radio, television and communications equipment
44 Audio, video and communications
equipment
20 transportation equipment 54 Motor vehicles and parts 48 Motor vehicles and parts 55 Ship building and repairing 49 Ship building and repairing 56 Other transportation equipment 50 Other transportation equipment
service sector
Low IT- intensity
22 construction 61 Building construction and repair 55 Building construction and repair 62 Civil Engineering 56 Civil engineering
26 transportation, storage
65 Transportation and warehousing 59 Land transport 60 Water and air transport
61 Strorage and support activities
for transportation 29 real estate 68 Real estate agencies and rental 65 Real estate
32 gonerment
70 Public administration and defense 69 Public administration and defense 71 Educational and research services 70 Education
72 Medical and health services,
and social security 71 Medical and health services
72 Social work activities 73 Sanitary services
High IT- intensity
23 electicity, gas, water 59 Electric services 53 Electric utilities 60 Gas and water supply 54 Gas and water supply
24 trade 63 Wholesale and retail trade 57 Wholesale and retail trade
25 hotels and restaurants 64 Eating and drinking places, and hotels and other lodging places
58 Accommodation and food services
27 communication 66 Communications and broadcasting 62 Communications services 28 finance, insurance 67 Finance and insurance 64 Finance and insurance
30 business services
51 Computer and office equipment 5 Agriculture, forestry and fishing related services
69 Business services 66 Research and development 75 Office supplies 67 Business services
68 Other business services
31 social and personal services
73 Culture and recreational services 63 Broadcasting 74 Other services 74 Publishing and cultural services 76 Business consumption expenditure 75 Amusement and sports activities 77 Nonclassifiable activities 76 Social organizations
77 Other services 78 Dummy sectors
Table B-1 Micro Social Accounting Matrix 25 25 (South Korea, Year 2000, 1 billion won)
Income/Expenditure
Production Activities Production Commodities
Labor Capital
Equalization household by decile Corporateenterprise