Output Quality, Skill Intensity, and Factor
Contents of Trade:
An Empirical Analysis Based on Micro-Data of
the Census of Manufactures
WIOD Conference on Industry‐Level Analyses of
Globalization and its Consequences
Technische Universitaet Wien, Vienna
May 26-28, 2010
Kyoji Fukao (Hitotsubashi University and RIETI)
Keiko Ito (Senshu University)
1
1. Introduction
• Recent studies on intra-industry trade (IIT) have
brought to light rapid increases in vertical IIT (VIIT)
(for example, see Fukao, Ishido and Ito (2003) and
Schott (2004)).
• Many theoretical models assume that developed
economies export physical and human capitalintensive products of high quality and import
unskilled labor-intensive products of low quality
from developing economies.
• Through this mechanism, an increase in vertical IIT
may have a large impact on factor demand and
factor prices.
2
1. Introduction (Contd.)
• Empirical studies use information on the unit value
of commodities as a proxy for product quality.
• Most of empirical studies based on the unit value
data take the positive relationships between
commodity prices and factor intensities as given.
• Yet, to the best of our knowledge, few studies have
empirically examined the relationship between unit
values of commodities and their factor contents at
the factory level.
(Notes: Some recent studies try to take quality difference among firms
(plants) into account. <Quality Heterogenous-Firms model> Baldwin &
Harrigan 2007; Kugler & Verhoogen 2008; Hallak & Sivadasan 2009, etc.)
3
1. Introduction (Contd.)
• The Purposes of this study is:
- To develop a theoretical framework to estimate
the relationship between the unit values of gross
output and factor intensities
- To test whether factories that produce goods with
a higher unit value tend to input more skilled labor
and capital stock services, using micro data of the
Census of Manufactures for Japan
-To calculate the factor contents of trade for Japan,
based on the estimated relationship between the
unit values and factor intensity.
4
2. Theoretical Analysis of Factor Contents in VIIT
• We assume that factories, in order to produce
commodities of a high quality, engage in production
processes that are intensive in both skilled labor and
capital.
• Suppose that N commodities are produced in an
industry. For each commodity, there is a continuum
of different qualities.
• Each “commodity” in our model corresponds to one
product item in the most detailed commodity
classification of production and trade statistics and
that products that differ only in quality are not
recorded as different products in the statistics.
5
2. Theoretical Analysis of Factor Contents in VIIT (Contd.)
• Each commodity, (n, q), is produced by a Leontief-type
constant-returns-to-scale production function.
Yq , i , t
 LU , q , i , t L S , q , i , t
K q ,i ,t
M q ,i ,t 

min 
,
,
,

e ( qi ,t )
 f ( q i , t )  g ( q i , t )  h ( q i , t ) 
 
ai ,t cn ,t
where LU, q, i, t, LS, q, i, t, Kq, i, t and Mq, i, t denote unskilled
(blue-collar) labor, skilled (white-collar) labor, capital, and
intermediate input. Yq, i, t denotes the gross output of
factory i. a i, t denotes factory i’s total factor productivity
(TFP) level in comparison with the industry average TFP
level in year t.
We express elasticity values by ηY=(qi, t de(qi, t))/(e(qi, t) dqi,
t), ηS=(qi, t df(qi, t))/(f(qi, t) dqi, t), ηK=(qi, t dg(qi, t))/(g(qi, t) dqi,
6
t), ηM=(qi, t dh(qi, t))/(h(qi, t) dqi, t), respectively.
2. Theoretical Analysis of Factor Contents in VIIT (Contd.)
From our production function, we have the following
factor demand relationships:
L S , q ,i ,t

LU , q , i , t
K q ,i ,t




LU , q , i , t

M q ,i ,t


LU , q , i , t
LU , q , i , t
Yq , i , t


f ( q i ,t )
(3.3)
g ( qi ,t )
(3.4)
h ( qi ,t )
(3.5)

ai ,t c n ,t
e ( qi ,t )
(3.6)
We assume that the price elasticity of demand for each
factory’s output in this industry is constant and takes the
same value for all factories producing commodity n.
7
2. Theoretical Analysis of Factor Contents in VIIT (Contd.)
From an unit production cost function and constant
mark-up ratio, we have the following relationship
between p and q.
ln  p q , i , t   ln e q i , t   ln  wU , t   f ( q i , t ) w S , t   g ( q i , t ) rt   h ( q i , t ) p M , t 
 ln a i , t   ln c n , t   ln 1   
By making a linear approximation of equation (3.3) and
subtracting average values across all factories from both
sides of the equation, we have
 LS , q ,i ,t
ln 
L
 U , q ,i ,t

 LS ,t
  ln 
L

 U ,t


   S ln  p q , i , t   ln  p t    S ln a i , t 


8


2. Theoretical Analysis of Factor Contents in VIIT (Contd.)
 K q ,i ,t
ln 
L
 U , q ,i ,t

 Kt 
  ln 
   K ln  p q , i , t   ln  p t    K ln a i , t 
L 

 U ,t 

 M q ,i ,t
ln 
L
 U , q ,i ,t

 Mt
  ln 
L

 U ,t

 LU , q , i , t
ln 
 Y
 q ,i ,t


 LU , t
  ln 
 Y

 t



   M ln  p q , i , t   ln  p t    M ln a i , t 





   Y ln  p q , i , t   ln  p t    Y ln a i , t   ln a i , t 




9
2. Theoretical Analysis of Factor Contents in VIIT (Contd.)
where
S 
K 
M 
Y 
S

 f ( q t *) w S , t S   g ( q t *) rt K   h ( q t *) p M , t M
 Y 

 wU , t   f ( q t *) w S , t   g ( q t *) rt   h ( q t *) p M , t





K

 f ( q t *) w S , t S   g ( q t *) rt K   h ( q t *) p M , t M
 Y 

 wU , t   f ( q t *) w S , t   g ( q t *) rt   h ( q t *) p M , t





M

 f ( q t *) w S , t S   g ( q t *) rt K   h ( q t *) p M , t M
 Y 

 wU , t   f ( q t *) w S , t   g ( q t *) rt   h ( q t *) p M , t





Y

 f ( q t *) w S , t S   g ( q t *) rt K   h ( q t *) p M , t M
 Y 

 wU , t   f ( q t *) w S , t   g ( q t *) rt   h ( q t *) p M , t





10
2. Theoretical Analysis of Factor Contents in VIIT (Contd.)
Since we assume constant returns to scale and a constant
mark-up ratio, we have the following identity among the
coefficients of the above four equations.
Y 


 f ( q t *) w S , t
 wU , t   f ( q t *) w S , t   g ( q t *) rt   h ( q t *) p M , t
 g ( q t *) rt
 wU , t   f ( q t *) w S , t   g ( q t *) rt   h ( q t *) p M , t
 h ( q t *) p M , t
 wU , t   f ( q t *) w S , t   g ( q t *) rt   h ( q t *) p M , t
S
K
(3.19)
M  1
This constraint means that a one percent increase in the
unit price of output corresponds to a one percent
increase in the unit production cost.
We estimate the four equations under the constraint
11
(3.19) using SUR techniques.
3. Factor Contents in Japan’s VIIT
• Using equations (3.15) and (3.18), we can express the
ratio of the white-collar labor input to the output
quantity for a factory which produces commodity (n,
q) as follows:
LS , n ,t ( pt )
 c n , t p t
 S  Y
Yn , t ( p t )
where c’n, t denotes a commodity- and year-specific
constant term.
Let φD, n, t(pt) denote the distribution function of output
quantity by all the factories producing commodity n in
Japan over unit value p. Then, we can derive the
following equation from the above equation:
12
3. Factor Contents in Japan’s VIIT (contd.)
L S , D , n , t  Y D , n , t c n , t 

pt  0
pt
 S  Y
 D , n , t ( p t ) dp t
• In a similar way, we can derive
L S , E , n ,t  YE , n ,t
L S , I , n ,t  YI , n ,t
L S , D , n ,t
YD , n ,t
L S , D , n ,t
YD , n ,t





pt  0
 S  Y
 E , n , t ( p t ) dp t
 S  Y
 D , n , t ( p t ) dp t
 S  Y
 I , n , t ( p t ) dp t
 S  Y
 D , n , t ( p t ) dp t
pt

pt  0
pt

pt  0
pt

pt  0
pt
13
4. Data
• Census of Manufactures for Japan
- Larger Establishment Sample: all mfg. plants with
30 or more employees
- 6-digit commodity level information on shipment
and quantity  Unit values can be calculated for
800 commodities out of 2,000 commodities
- Number of blue-collar and white-collar workers
available for years 1981, 1984, 1987, and 1990.
• Trade Statistics for Japan
- Values and quantities of exports and imports at
the HS 9-digit commodity level (7,000 commodities
for exports and 9,000 commodities for imports)
14
4. Data (contd.)
• To estimate the relationship between the unit value
of gross output and factor intensities, we select
only single-product establishments, which we
define as establishments where one commodity
accounts for more than 60% of total shipments.
• By using the unit value and factor intensity
information taken form the CM and the TS, we
calculate the factor contents of Japan’s trade, taking
account of quality (price) difference between
exported and imported goods.
15
Table 5. Summary table of the unit value analysis: The case of cotton tubular knit fabric
Unit value data of the Census of Manufactures 1990
Commodity classification name in the Census of Manufactures
Commodity code
Number of factories whose data were used
Number of white-collar workers per one million yen gross output
Number of blue-collar workers per one million yen gross output
Capital stock (in million yen) per one million yen gross output
Average unit value (million yen per ton)
Standard deviation of unit value (million yen per ton)
Average of natural log of unit value
Standard deviation of natural log of unit value
Cotton tubular knit fa
1451-11
14
0.0066
0.0167
0.1257
1.3571
1.6016
0.0393
0.6073
Total value of exports (million yen)
Total value of exports/total volume of exports (million yen)
Weighted average of unit value of exports (weight: value of exports)
Total value of imports (million yen)
Unit value (Total value of imports/total volume of imports, million yen per ton)
Weighted average of unit value of imports (weight: value of imports)
203.110
2.482
2.488
543.365
1.344
1.400
16
5. Empirical Results on the Relationship between
Output Unit Values and Factor Intensities
• Estimate equations (3.15)-(3.18) by SUR estimations
subject to the constraint expressed by equation
(3.19)----using factory-level data from the Census
 LS , q ,i ,t
ln 
 L
 U , q ,i ,t

 L
  ln  S , t
 L

 U ,t

 K q ,i ,t
ln 
 L
 U , q ,i ,t

 Kt
  ln 
 L

 U ,t

 M q ,i ,t
ln 
 L
 U , q ,i ,t
 LU , q , i , t
ln 
 Y
 q ,i ,t

 Mt
  ln 
 L

 U ,t


L
  ln  U , t
 Y

 t


   S ln


 p
q ,i ,t
  ln  p   
t

   K ln  p q , i , t   ln



S
ln a i , t
 p t    K

ln a i , t


   M ln  p q , i , t   ln  p t    M ln a i , t 





   Y ln  p q , i , t   ln  p t    Y ln a i , t   ln a i , t 




17
5. Empirical Results on the Relationship between Output
Unit Values and Factor Intensities: Basic Result
Table 1. Relationship between factor intensity and unit price: Seemingly Unrelated Regression estimations with constraint
Equation
number
Dependent
variable
Food
Textiles
Wood
(1)
(2)
(3)
(3.15)
dvlnWBratio
0.088**
(0.040)
(3.16)
dvlnKBratio
(3.17)
(3.18)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
0.165*** 0.097*** 0.056*** 0.115*** 0.117*** -0.002
(0.022)
(0.027)
(0.015)
(0.014)
(0.020)
(0.023)
0.315***
(0.058)
-0.248*** 0.073*** -0.051
(0.044)
(0.018)
(0.048)
0.132*** 0.004
(0.029)
(0.031)
-0.110*** 0.048*** 0.155*** 0.051
(0.020)
(0.015)
(0.025)
(0.034)
0.140**
(0.068)
dvlnMBratio
-0.282*** 0.131*** -0.026
(0.035)
(0.018)
(0.033)
-0.037*
(0.020)
-0.179*** 0.079*** 0.051*** 0.025
(0.015)
(0.013)
(0.017)
(0.026)
0.067
(0.044)
dvlnBYratio
1.217*** 0.897*** 1.021*** 1.007*** 1.022*** 1.134*** 0.931*** 0.946*** 0.979*** 0.928***
(0.029)
(0.014)
(0.028)
(0.016)
(0.016)
(0.012)
(0.010)
(0.015)
(0.021)
(0.034)
Number of
observations
3006
0.119*** 0.050
(0.017)
(0.033)
Chemicals Ceramics
Electrical
Transpor- Miscellane
General
and
Metals
tation
-ous
machinery precision
equipment products
machinery
6712
1942
4331
-0.047**
(0.021)
5515
8270
2267
1736
906
1074
18
5. Empirical Results on the Relationship between Output
Unit Values and Factor Intensities: TFP controlled
Table 2. Relationship between factor intensity and unit price: Seemingly Unrelated Regression estimations with constraint, TFP controlled
Equation Dependent variable
number
Explanatory
variables
(3.15)
dvlnWBratio
dvlnUV
lnTFP
(3.16)
dvlnKBratio
dvlnUV
lnTFP
(3.17)
dvlnMBratio
dvlnUV
lnTFP
(3.18)
dvlnBYratio
dvlnUV
lnTFP
Number of observations
Food
Textiles
Wood
(1)
(2)
(3)
Chemicals Ceramics
(4)
(5)
Metals
(6)
Electrical
Transpor- Miscellane
General
and
tation
-ous
machinery precision
equipment products
machinery
(7)
(8)
(9)
(10)
0.097**
(0.041)
0.111**
(0.052)
0.087***
(0.017)
0.909***
(0.049)
0.049
(0.033)
0.137*
(0.081)
0.154***
(0.022)
0.109**
(0.050)
0.092***
(0.027)
0.062
(0.041)
0.051***
(0.015)
0.319***
(0.038)
0.116***
(0.014)
0.210***
(0.074)
0.093***
(0.020)
0.975***
(0.090)
-0.005
(0.023)
0.345***
(0.097)
0.302***
(0.058)
0.411***
(0.121)
-0.268***
(0.044)
0.207***
(0.057)
0.062***
(0.018)
0.296***
(0.053)
-0.045
(0.049)
-0.335***
(0.120)
0.120***
(0.029)
-0.119*
(0.065)
0.017
(0.032)
-0.163***
(0.048)
-0.101***
(0.020)
0.151***
(0.049)
0.056***
(0.016)
-0.181**
(0.081)
0.131***
(0.025)
1.248***
(0.112)
0.057*
(0.034)
-0.049
(0.142)
0.150**
(0.070)
-0.120
(0.145)
-0.298***
(0.035)
0.545***
(0.045)
0.082***
(0.017)
0.991***
(0.050)
-0.025
(0.032)
0.409***
(0.080)
-0.073***
(0.019)
0.297***
(0.043)
-0.045**
(0.022)
0.404***
(0.033)
-0.189***
(0.015)
0.349***
(0.038)
0.077***
(0.013)
0.118*
(0.066)
0.022
(0.016)
0.864***
(0.074)
0.023
(0.026)
0.220**
(0.108)
0.040
(0.044)
0.195**
(0.093)
1.229***
(0.029)
-0.983***
(0.041)
2940
0.934***
(0.013)
-1.438***
(0.038)
6665
1.019***
(0.027)
-1.177***
(0.070)
1931
1.035***
(0.016)
-0.937***
(0.038)
4292
1.020***
(0.016)
-1.092***
(0.028)
5461
1.141***
(0.012)
-1.093***
(0.032)
8223
0.932***
(0.010)
-0.857***
(0.055)
2248
0.971***
(0.014)
-1.504***
(0.063)
1716
0.980***
(0.021)
-0.928***
(0.090)
893
0.949***
(0.035)
-0.895***
(0.074)
1066
19
5. Empirical Results on the Relationship between Output Unit
Values and Factor Intensities: Estimation without the Constraint
Table 3. Relationship between factor intensity and unit price: Seemingly Unrelated Regression estimations without constraint
Equation
number
Dependent
variable
Food
Textiles
Wood
(1)
(2)
(3)
Chemicals Ceramics
(4)
(5)
Metals
(6)
Electrical
Transpor- Miscellane
General
and
tation
-ous
machinery precision
equipment products
machinery
(7)
(8)
(9)
(10)
(3.15)
dvlnWBratio
0.032
(0.040)
0.125***
(0.017)
0.050
(0.033)
0.168***
(0.022)
0.104***
(0.027)
0.057***
(0.015)
0.114***
(0.014)
0.120***
(0.020)
-0.002
(0.023)
0.340***
(0.058)
(3.16)
dvlnKBratio
-0.185*** 0.073***
(0.044)
(0.018)
-0.056
(0.049)
0.131***
(0.029)
-0.013
(0.031)
-0.107*** 0.047***
(0.020)
(0.015)
0.166***
(0.026)
0.051
(0.034)
0.152**
(0.068)
(3.17)
dvlnMBratio
-0.169*** 0.127***
(0.036)
(0.018)
-0.025
(0.033)
-0.047**
(0.020)
-0.006
(0.022)
-0.178*** 0.079***
(0.015)
(0.013)
0.047***
(0.017)
0.024
(0.026)
0.052
(0.044)
(3.18)
dvlnBYratio
0.933***
(0.035)
0.879***
(0.014)
1.001***
(0.031)
0.933***
(0.018)
0.890***
(0.021)
1.115***
(0.014)
0.920***
(0.011)
0.924***
(0.016)
0.976***
(0.023)
0.844***
(0.038)
3006
6712
1942
4331
5515
8270
2267
1736
906
1074
Number of
observations
20
5. Empirical Results on the Relationship between Output Unit
Values and Factor Intensities: based on data of factories belonging
to firms with no additional factory and whose headquarters are
located in the same place
Table 4. Relationship between factor intensity and unit price: Seemingly Unrelated Regression estimations without constraint, based on data of
factories belonging to firms with no additional factory and whose headquarters are located in the same place
Equation
number
Dependent
variable
Food
Textiles
Wood
(1)
(2)
(3)
Chemicals Ceramics
(4)
(5)
Metals
(6)
(3.15)
dvlnWBratio
0.122**
(0.058)
0.123***
(0.022)
-0.001
(0.042)
0.224***
(0.038)
0.157***
(0.044)
0.064***
(0.022)
(3.16)
dvlnKBratio
-0.199*** 0.078***
(0.067)
(0.024)
-0.039
(0.058)
0.049
(0.051)
-0.140**
(0.056)
(3.17)
dvlnMBratio
-0.186*** 0.091***
(0.052)
(0.023)
-0.063
(0.046)
-0.053
(0.036)
(3.18)
dvlnBYratio
1.055***
(0.050)
0.903***
(0.019)
1.051***
(0.042)
1578
3547
963
Number of
observations
Electrical
Transpor- Miscellane
General
and
tation
-ous
machinery precision
equipment products
machinery
(7)
0.117***
(0.020)
(8)
(9)
(10)
0.132***
(0.036)
-0.010
(0.030)
0.209***
(0.075)
-0.076*** 0.051**
(0.029)
(0.023)
0.079*
(0.043)
-0.021
(0.051)
0.219**
(0.096)
-0.001
(0.037)
-0.167*** 0.106***
(0.023)
(0.019)
0.011
(0.030)
-0.044
(0.034)
0.020
(0.053)
0.964***
(0.028)
0.880***
(0.035)
1.127***
(0.020)
0.893***
(0.016)
0.962***
(0.027)
1.031***
(0.030)
0.875***
(0.046)
1448
2245
3766
1050
601
468
561
21
5. Robustness checks: Relationship between Unit Production
Costs and Factor Intensities
Appendix Table 2. Relationship between factor intensity and unit production cost: Seemingly Unrelated Regression estimations without constraint
Equation
number
Dependent
variable
Food
Textiles
Wood
(1)
(2)
(3)
(3.15)
dvlnWBratio
0.109***
(0.038)
(3.16)
dvlnKBratio
(3.17)
(3.18)
(4)
(5)
Metals
(6)
(7)
(8)
(9)
(10)
0.091***
(0.030)
0.161***
(0.021)
0.127***
(0.024)
0.058***
(0.014)
0.111***
(0.014)
0.113***
(0.021)
-0.008
(0.023)
0.277***
(0.055)
-0.132*** 0.089***
(0.042)
(0.018)
0.073
(0.045)
0.148***
(0.027)
0.092***
(0.028)
-0.062*** 0.055***
(0.019)
(0.015)
0.140***
(0.026)
0.055
(0.034)
0.155**
(0.064)
dvlnMBratio
-0.241*** 0.132***
(0.034)
(0.017)
0.013
(0.031)
-0.015
(0.019)
-0.024
(0.019)
-0.093*** 0.088***
(0.015)
(0.012)
0.062***
(0.017)
0.040
(0.025)
0.133***
(0.041)
dvlnBYratio
1.164***
(0.030)
0.896***
(0.014)
0.980***
(0.028)
0.972***
(0.016)
0.946***
(0.017)
1.067***
(0.013)
0.919***
(0.011)
0.943***
(0.015)
0.978***
(0.022)
0.842***
(0.034)
3006
6712
1942
4331
5515
8270
2267
1736
906
1074
Number of
observations
0.089***
(0.017)
Chemicals Ceramics
Electrical
Transpor- Miscellane
General
and
tation
-ous
machinery precision
equipment products
machinery
22
5. Robustness checks: Relationship between Output Unit Values and
Factor Intensities including Multi-Product Establishments
Appendix Table 3. Relationship between factor intensity and unit price: Seemingly Unrelated Regression estimations with constraint
--- Including multi-product establishments ---
Equation
number
Dependent
variable
Food
Textiles
Wood
(1)
(2)
(3)
Chemicals Ceramics
(4)
(5)
Electrical
Transpor- Miscellane
General
and
Metals
tation
-ous
machinery precision
equipment products
machinery
(6)
(7)
(8)
(9)
(3.15)
dvlnWBratio
0.084**
(0.037)
(3.16)
dvlnKBratio
-0.224*** 0.077*** -0.117*** 0.150*** -0.006
(0.043)
(0.017)
(0.038)
(0.028)
(0.030)
-0.116*** 0.060*** 0.175*** 0.066**
(0.018)
(0.014)
(0.024)
(0.031)
0.147**
(0.065)
(3.17)
dvlnMBratio
-0.205*** 0.137*** -0.021
(0.036)
(0.017)
(0.027)
-0.179*** 0.085*** 0.054*** 0.045*
(0.014)
(0.012)
(0.016)
(0.025)
0.073*
(0.042)
(3.18)
dvlnBYratio
1.158*** 0.893*** 1.019*** 1.019*** 1.012*** 1.135*** 0.926*** 0.942*** 0.963*** 0.924***
(0.029)
(0.013)
(0.022)
(0.016)
(0.015)
(0.011)
(0.009)
(0.014)
(0.020)
(0.033)
Number of
observations
3450
0.125*** 0.073*** 0.182*** 0.124*** 0.051*** 0.111*** 0.127*** -0.002
(0.016)
(0.028)
(0.021)
(0.025)
(0.014)
(0.013)
(0.019)
(0.022)
(10)
8508
3042
-0.056*** -0.034*
(0.019)
(0.020)
5085
6255
9475
2675
2076
1187
0.318***
(0.058)
1158
23
6. Calculated Factor Contents in VIIT (contd.)
• Using unit value information taken from the CM and
the trade statistics as well as data on factor
intensities, we estimate the factor contents of
Japan’s VIIT.
• For number of white-collar workers embodied in
Japan’s exports for commodity n are calculated as:
Unknown
L S , E , n ,t  YE , n ,t
L S , D , n ,t
YD , n ,t



pt  0
pt

pt  0
pt
 S  Y
 E , n , t ( p t ) dp t
 S  Y
 D , n , t ( p t ) dp t
Estimated
24
6. Calculated Factor Contents in VIIT (contd.)
• Assuming that φE, n, t(pn, t) and φI, n, t(pt) follow a log
normal distribution,
L S , E , n ,t  Y E , n ,t
L S , D , n ,t
Y D ,n ,t

exp   S   Y

  E
2
2
2 




 D 
 S   Y  E   D 
1

2
μE : log of unit value of Japan’s exports
μD : average of the factory-level unit values in logarithm
σE : standard deviation of the distribution functions of
exports
σD : standard deviation of the distribution functions of
all shipments by single-product factories
25
6. Calculated Factor Contents in VIIT (contd.)
• Due to data constraints, we use average unit value
differences (μE – μD, μI – μD) at the broad industry
level.
• Average difference between ln(unit value for
exporting factories) and ln(unit value for nonexporting factories), calculated using the CM for
2001-2004.
• Average difference between ln(export unit value)
and ln(import unit value), calculated using the TS
• We assume that σE = σI = σD
L S , E , n , t  NY E , n , t
LS , D ,n ,t
NY D , n , t
exp  S   Y  1   E   D 
26
Table 7. Difference in average unit values
Estimate relative unit values for domestic shipments, exports,
and imports
2001-2004 Average
1990
2000
Census of Manufactures
Trade Statistics
Trade Statistics
Export Export Export No. of
No. of
No. of
commodities Domestic* commodities Import** commodities Import**
Industry
1 Food
32
-0.003
304
0.389
332
0.444
2 Textiles
45
-0.042
716
0.342
738
0.964
3 Wood
21
-0.111
184
0.402
209
0.985
4 Chemicals
176
0.067
915
0.238
943
0.380
5 Ceramics
28
-0.104
140
-0.045
149
0.723
6 Metals
111
0.122
504
0.149
581
0.348
7 General machinery
70
0.000
442
-0.104
448
0.444
8 Electrical & precision machinery
48
0.095
379
-0.281
434
0.277
9 Transportation equipment
32
-0.078
95
-0.367
101
0.089
10 Miscellaneous products
12
0.099
230
0.293
233
0.561
Notes: * Average value of 6-digit commodity-level "ln(unit value for exporting factories)-ln(unit value for non-exporting
factories)" using the 6-digit commodity-level shipments as weights.
** Average value of HS 6-digit commodity-level "ln(export unit value)-ln(import unit value)" using the HS 6-digit
commodity-level trade values as weights.
27
Tables 8 & 9. Estimated factor contents of net exports
Table 8: Year 1990
Industry
1 Food
2 Textiles
3 Wood
4 Chemicals
5 Ceramics
6 Metals
7 General machinery
8 Electrical & precision machinery
9 Transportation equipment
10 Miscellaneous products
Manufacturing total
Taking account of VIIT
Not taking account of VIIT
LS(persons) LU(persons) K(mil.yen) LS(persons) LU(persons) K(mil.yen)
-23,315
-118,978 -1,008,666
-26,576
-130,497
-995,626
-16,295
-119,305
-576,299
-16,466
-111,666
-565,202
-13,619
-64,136
-456,310
-14,225
-64,946
-446,925
8,298
27,373
251,845
7,299
27,284
179,786
3,243
15,527
125,142
3,307
15,580
125,724
-11,126
-20,594
-40,230
-11,791
-22,045
-47,041
75,763
154,040
2,335,483
75,851
153,777
2,334,241
144,037
363,532
5,576,039
143,900
363,839
5,568,235
45,749
153,803
4,926,502
45,545
153,253
4,949,640
-7,592
-48,526
-253,930
-8,683
-46,957
-264,206
205,144
342,736 10,879,577
198,161
337,620 10,838,626
LS +7,000 persons (1.5%)
LU +5,000 persons (2%)
K +41 bil. yen (0.4%)
Table 9: Year 2000
Industry
1 Food
2 Textiles
3 Wood
4 Chemicals
5 Ceramics
6 Metals
7 General machinery
8 Electrical & precision machinery
9 Transportation equipment
10 Miscellaneous products
Manufacturing total
Taking account of VIIT
Not taking account of VIIT
LS(persons) LU(persons) K(mil.yen) LS(persons) LU(persons) K(mil.yen)
-27,056
-153,827 -1,617,631
-31,321
-170,601 -1,594,079
-34,241
-268,381 -2,223,632
-34,984
-234,334 -2,136,546
-17,133
-83,518
-866,133
-18,752
-85,739
-829,682
13,772
33,510
999,926
11,983
33,333
822,006
4,874
18,116
287,782
4,511
17,847
282,194
-3,932
-5,167
370,522
-5,780
-9,078
345,749
97,075
201,622
4,841,347
96,627
203,057
4,851,651
106,933
246,542
7,310,621
104,482
251,522
7,073,300
57,630
173,209
7,080,518
57,554
172,994
7,093,613
-8,680
-69,130
-435,466
-11,143
-65,169
-469,619
189,242
92,975 15,747,853
173,178
113,830 15,438,58728
LS +16,000 persons (10%)
LU -21,000 persons (-18%)
K +310 bil. yen (2%)
7. Conclusion
• As for the relationship b/w the unit value of a
product and its white-collar labor intensity, the
significant and positive relationship we find is
important empirical evidence which supports the
assumption widely employed in theoretical models.
• On the other hand, we find that the widely employed
assumption that commodities with higher prices are
more physical capital-intensive does not always hold.
• The results of the factor contents of trade estimation
suggests that the implication of international trade
on the domestic factor markets should be very
different if we take account of VIIT.
29
8. Extensions
• Incorporate headquarter-level data in our analysis in
order to take account of white-collar tasks provided
by headquarters.  Using information from “Kigyo
Katsudo Kihon Chosa (BSBSA)?
• What is the definition of “skill”?  We may use the
wage information as a proxy of skill.
• Distinguish between intra-firm trade and inter-firm
trade?  If we can match the trade statistics with
the firm- or plant-level data, it would be possible. (c.f.
France, U.S.)
30