Vertical Intra-Industry Trade, Unit Values of Commodities, and Factor Contents:
An Empirical Analysis Based on Micro-Data of the Census of Manufactures

Paper prepared for the Hitotsubashi COE Conference on International Trade and FDI

December 12-13, 2009, Hitotsubashi University

Kyoji Fukao (Hitotsubashi University and RIETI)*

Keiko Ito (Senshu University)**

This research was conducted as part of a project “Japan’s productivity and Economic Growth” at the
Research Institute of Economy, Trade and Industry (RIETI). The authors would like to thank Alan Deardorff
and participants of the CGP conference “Quantitative Analysis of Newly Evolving Patterns of Japanese andU.S. International Trade: Fragmentation; Offshoring of Activities; and Vertical Intra-Industry Trade” held atUniversity of Michigan on October 16-17, 2009, for helpful discussions and comments. The authors are also
grateful to Hyoeg Ug Kwon for his instruction on the dataset.
* Corresponding author: Institute of Economic Research, Hitotsubashi University, 2-1, Naka, Kunitachi,
Tokyo 186-8601 JAPAN. Tel: +82-42-580-8359, FAX.: +81-42-580-8333, e-mail: k.fukao@srv.cc.hit-u.ac.jp** Faculty of Economics, Senshu University, 2-1-1, Higashi-Mita, Tama-ku, Kawasaki 214-8580 JAPAN.
Tel.: +81-44-900-7818, Fax.: +81-44-911-0467, e-mail: keiko-i@isc.senshu-u.ac.jp.

1. Introduction
Recent studies on intra-industry trade (IIT) have brought to light rapid increases invertical IIT (VIIT), i.e., intra-industry trade where goods are differentiated by quality. Falvey(1981) pointed out in his seminal theoretical paper that commodities of the same statisticalgroup but of different quality may be produced using different mixes of factor inputs. Basedon this idea, empirical studies have typically used information on the unit value ofcommodities as a proxy for product quality and, employing such unit value data, haveexamined patterns of IIT or the international division of labor (e.g., Greenaway et al. 1994,
Fontagné et al., 1997). Research has also shown that developed economies tend to export
commodities at higher prices than developing economies (Schott, 2004, Hummels andKlenow, 2005, etc.). These studies suggest that an increase in VIIT may have a large impact
on factor demand and factor prices in both developed and developing countries if there exists
a positive relationship between commodity prices or quality and physical and humancapital-intensities. For example, Widell (2005), addressing this issue, and calculated the factor
contents of Swedish trade, adjusting for difference between export unit values and import unit
values.1
On the other hand, many studies have investigated the impact of increasing imports
from developing countries on developed countries, focusing on issues such as domestic
skill-upgrading, capital deepening, firm dynamics, and so on (Feenstra and Hanson, 1999,
2001, etc.). Although such studies do not rely on unit value or price information, their ideas
are founded on the assumption that developed economies export physical and humancapital-intensive products of high quality and import unskilled labor-intensive products of low
quality from developing economies. Thus, many theoretical and empirical studies have incommon that they take the positive relationships between commodity prices or quality andphysical and human capital-intensities as given. Yet, to the best of our knowledge, there are
no studies that have empirically examined the relationship between unit values ofcommodities and their factor contents at the commodity level.
Against this background, in this study, using micro-data of the Census of Manufactures(CM) for Japan and comparing the factor inputs of factories producing the same goods, we
1

There are increasing number of studies which use unit value information as a proxy for product quality. For
example, Baldwin and Harrigan (2007) find that export unit values are positively related to distance, which isconsistent with the prediction of their quality heterogenous-firms model where only firms with sufficiently
high-price/high-quality goods find it worthwhile to see to distant markets. Kugler and Verhoogen (2008) findthat output and input prices are positively correlated with plant size within industries and that exporters tend
to have higher output and input prices, using Colombian manufacturing plants data. Their interpretation of theresults is that input quality and plant productivity are complementary in generating output quality. Hallak andSivadasan (2009), using manufacturing establishment datasets for India, the U.S., Chile, and Colombia, showthat conditional on size, exporters are predicted to sell products of higher quality and at higher prices, pay
higher wages and use capital more intensively.

1estimate the relationship between the unit values of gross output and factor contents and test
whether factories that produce goods with a higher unit value tend to input more skilled laborand capital stock services. To do so, we treat factories producing the same commodity
according to detailed commodity classifications as producing the “same” goods. Although weshould use information on commodity-level factor intensities ideally, we use factory-levelfactor intensity information as a proxy for the commodity-level factor intensity information
because the commodity-level factor intensities are not available. Using the results of therelationship between unit values and factor intensity, we then estimate the factor contents ofJapan’s trade with the rest of the world. For the analysis, we use micro-data of the CM andJapanese trade statistics. Factor intensities such as capital-labor ratios and skilled-unskilledlabor ratios are calculated at the 6-digit commodity-level using the micro-data of the CM, anestablishment-level annual survey conducted by the Ministry of Economy, Trade and Industry.
Commodity-level unit values for products made domestically are calculated using the
micro-data of the CM, while unit values for exports and imports are calculated using Japan’s
trade statistics. Then, we match such commodity-level information calculated from the CMwith that calculated from the trade statistics. Using this matched dataset, we can measure thefactor contents of trade, taking account of differences in unit values of shipments of aparticular product by establishments in Japan, of exports, and of imports.
The remainder of the paper is organized as follows. In Section 2, we present a simple
theoretical model for the estimation and in Section 3, we describe the data sources for our
variables and how our dataset is constructed. In Section 4, we provide econometric evidence
on the relationship between output unit values and factor intensities, while in Section 5, weestimate the factor contents of Japan’s VIIT. Section 6 concludes the paper.

2. Theoretical Analysis of Factor Contents in VIIT
In this section, we present a simple theoretical model to examine factor contents invertical intra-industry trade. We begin by providing a model in which factories, in order toproduce commodities of a high quality, engage in production processes that are intensive inboth skilled labor and capital. Next, using this framework, we derive an econometric model toestimate the relationship between output unit values and factor contents. Finally, we estimate
factor contents in Japan’s vertical intra-industry trade with the rest of the world.
We assume the existence of four factors, skilled (white-collar) labor (LS), unskilled(blue-collar) labor (LU), capital (K) and intermediate input (M).2 We focus on a certain2

In the Census of Manufactures, data on the number of skilled and unskilled workers are not available. What
are available, however, are data on the number of non-production and production workers. Since
non-production workers tend to be more highly educated and in charge of relatively sophisticated tasks, such2

manufacturing industry, such as the electrical and precision machinery or the generalmachinery industry. Suppose that N commodities are produced in this industry. For eachcommodity, there is a continuum of different qualities [q, q ]. We assume that 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.
Each commodity is produced by a Leontief-type constant-returns-to-scale production
function. We examine the profit maximization behavior of factory i in year t, which produces
commodity (n, q), that is, commodity n of quality q. The production function of this factory isdefined by

Yq ,i ,t =

⎡L
⎤
L
K
M
min ⎢ U , q ,i ,t , S , q ,i ,t , q ,i ,t , q ,i ,t ⎥e(qi ,t )βf (qi ,t ) γg (qi ,t ) δh(qi ,t ) ⎥⎦⎢⎣ α
ai ,t cn ,t(3.1)

where LU, q, i, t, LS, q, i, t, Kq, i, t and Mq, i, t denote blue-collar labor, white-collar labor, capital, andintermediate input. Yq, i, t denotes the gross output of factory i. a i, t denotes factory i’s totalfactor productivity (TFP) level in comparison with the industry average TFP level in year t.
To simplify our notation, we omit suffix n for variables except for the commodity-specificterm cn, t. We normalize values a i, t and cn, t so that the average value of ln(a i, t) across all
factories producing commodity n is zero for any t. The parameters α, β, γ and δ are constantpositive values satisfying α + β + γ + δ = 1, and do not depend on n.
In order to raise output quality, factories need to change their amount of factor inputs.
The relationship between output quality and factor inputs is determined by four functions, e(qi,t), f(qi, t), g(qi, t), and h(qi, t). These functions are continuously differentiable in q, take positive

values for any q ∈ [q, q ], 0 < q < 1 < q , and satisfy e(1)=1, f(1)=1, g(1)=1 and h(1)=1.
What is of key interest in our analysis are the signs of f’(qi, t) and g’(qi, t). If these derivatives
are positive, we will have the relationship that as qi, t approaches q , the commodity becomes
more white-collar labor and physical-capital intensive. To simplify our analysis, we also
assume that the elasticities of these functions in qi, t are constant. We express these 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, t), ηM=(qi, t dh(qi, t))/(h(qi, t) dqi, t), respectively.
We assume that all factories are price takers in factor markets. Let wU, t, wS, t, rt and pM, t
as management, monitoring of production processes, planning, and research and development (R&D), we use
the ratio of non-production to production workers as a proxy for ratio of skilled to unskilled workers and referto this variable as the white-collar/blue-collar labor ratio.

3

denote the wage rate for blue-collar workers, the wage rate for white-collar workers, the costof capital, and the price of intermediate input in year t. From cost minimization conditions, wehave the following relationships:

LS , q ,i ,t
LU , q ,i ,tK q ,i ,t
LU , q ,i ,tM q ,i ,t
LU , q ,i ,t=βf ( qi , t )
α

(3.3)

=

γ
g (qi ,t )
α

(3.4)

=

δ
h(qi ,t )α(3.5)

From the above relationships and our production function, we have the following factor
demand functions:

LU , q ,i ,tYq ,i ,tLS , q ,i ,tYq ,i ,tK q ,i ,t
Yq ,i ,tM q ,i ,t
Yq ,i ,t

=
=

=
=

α

e(qi ,t )(3.6)

e(qi.t ) f (qi ,t )(3.7)

e(qi ,t ) g (qi ,t )(3.8)

e(qi ,t )h(qi ,t )(3.9)

ai ,t cn ,tβ
ai ,t cn ,tγ
ai ,t cn ,tδ
ai ,t cn ,tWe assume monopolistic competition. 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. This means that the mark-up ratio will be the same for all factories and we will
have the following relationship between factory i’s unit production cost, uq, i, t, and the unit
value of its output, pq, i, t:

pq ,i ,t = (1 + λn )u q.i ,t(3.10)

Unit production cost is determined by4

uq ,i ,t = wU ,t=

LU , q ,i ,te(qi ,t )ai ,t cn ,tYq ,i ,t(αw

+ wS ,tU ,tLS , q ,i ,tYq ,i ,t+ rt

K q ,i ,t
Yq ,i ,t

+ pM , t

M q ,i ,t
Yq ,i ,t(3.11)+ β f (qi ,t ) wS ,t + γg (qi ,t )rt + δh(qi ,t ) pM ,t )We assume that most of the four elasticity parameters, ηY, ηS, ηK, ηM, do not take large
negative values, so that uq,i is an increasing function of q.
If we take the logarithm of both sides of the above equation and use equation (3.10),
we obtainln( pq ,i ,t ) = ln(e(qi ,t )) + ln(αwU ,t + βf (qi ,t )wS ,t + γg (qi ,t )rt + δh(qi ,t ) pM ,t )− ln(ai ,t ) − ln(cn,t ) + ln(1 + λ )(3.12)

We make a linear approximation of each term on the right-hand side of the above
equation around a certain value of qt, which we denote by qt *. If we subtract the averagevalues of each term of equation (3.12) across all factories from both sides of equation (3.12),
we obtainln ( pq ,i ) − ln( pt ) =⎛βf (qt *)wS ,tη S + γg (qt *)rtη K + δh(qt *) pM ,tη M ⎞⎟ ln (qi ,t ) − ln(qt )
⎜ηY +
⎟
⎜
w
f
(
q
*)
w
g
(
q
*)
r
h
(
q
*)
p
α
β
γ
δ
+
+
+
U ,t
t
S ,t
t
t
t
M ,t ⎠⎝− ln (ai ,t )() (3.13)

Variables with upper bars denote average values. To derive the above equation, we used thefact that the average value of ln(ai, t) is equal to zero as a result of our normalization of ai, t andcn, t.
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 ⎞⎛L ⎞⎟ − ln⎜ S ,t ⎟ = η S ln (qi ,t ) − ln (qt )
ln⎜
⎜L ⎟
⎟
⎜L
⎝ U ,t ⎠⎝ U , q ,i ,t ⎠()(3.14)

From equations (3.13) and (3.14), we obtain the relationship between the unit value of a5

product and its white-collar labor intensity:

⎛L
⎞
⎛L ⎞
ln⎜ S , q ,i ,t ⎟ − ln⎜ S ,t ⎟ = θ S ln ( pq ,i ,t ) − ln( pt ) + θ S ln (ai ,t )
⎜L ⎟
⎜L
⎟
⎝ U ,t ⎠⎝ U , q ,i ,t ⎠()(3.15)

By using equation (3.13) and one of the equations (3.4), (3.5) or (3.6), we also obtain thefollowing equations:

⎛ K
⎞
⎛ K ⎞
ln⎜ q ,i ,t ⎟ − ln⎜ t ⎟ = θ K ln( pq ,i ,t ) − ln( pt ) + θ K ln(ai ,t )
⎜L ⎟
⎜L
⎟
⎝ U ,t ⎠⎝ U , q ,i ,t ⎠(3.16)⎛M⎞⎛M ⎞ln⎜ q ,i ,t ⎟ − ln⎜ t ⎟ = θ M ln ( pq ,i ,t ) − ln( pt ) + θ M ln(ai ,t )
⎜L ⎟
⎜L
⎟
⎝ U ,t ⎠⎝ U , q ,i ,t ⎠(3.17)⎛L⎞⎛L ⎞ln⎜ U , q ,i ,t ⎟ − ln⎜⎜ U ,t ⎟⎟ = θY ln( pq ,i ,t ) − ln( pt ) + θY ln(ai ,t ) − ln(ai ,t )⎜ Y⎟
⎝ Yt ⎠⎝ q ,i ,t ⎠(3.18)

(

)

(

)

(

)

where

θS =

θK =

θM =

θY =

ηS
⎛
βf (qt *)wS ,tη S + γg (qt *)rtη K + δh(qt *) pM ,tη M ⎞
⎜ηY +
⎟
⎜
⎟
α
w
β
f
(
q
*)
w
γ
g
(
q
*)
r
δ
h
(
q
*)
p
+
+
+
U
,
t
t
S
,
t
t
t
t
M
,
t
⎝
⎠

ηK
⎛
βf (qt *)wS ,tηS + γg (qt *)rtη K + δh(qt *) pM ,tηM ⎞
⎜ηY +
⎟
⎜
⎟
α
w
β
f
(
q
*)
w
γ
g
(
q
*)
r
δ
h
(
q
*)
p
+
+
+
U ,t
t
S ,t
t
t
t
M ,t ⎠⎝ηM⎛
βf (qt *)wS ,tηS + γg (qt *)rtη K + δh(qt *) pM ,tη M ⎞
⎜ηY +
⎟
⎜
⎟
α
w
β
f
(
q
*)
w
γ
g
(
q
*)
r
δ
h
(
q
*)
p
+
+
+
U ,t
t
S ,t
t
t
t
M ,t ⎠⎝ηY⎛
βf (qt *)wS ,tη S + γg (qt *)rtη K + δh(qt *) pM ,tη M ⎞
⎟
⎜ηY +
⎟
⎜
α
w
β
f
(
q
*)
w
γ
g
(
q
*)
r
δ
h
(
q
*)
p
+
+
+
U
,
t
t
S
,
t
t
t
t
M
,
t
⎠
⎝

These are the four equations that we estimate in order to examine the relationship6

between output unit values and factor contents. Since we assume constant returns to scale anda constant mark-up ratio, we have the following identity among the coefficients of(3.15)-(3.18):

θY +β f (qt *)wS ,tθS
αwU ,t + β f (qt *)wS ,t + γg (qt *)rt + δh(qt *) pM ,t+γg (qt *)rt
θK
αwU ,t + βf (qt *)wS ,t + γg (qt *)rt + δh(qt *) pM ,t+δh(qt *) pM ,tθM = 1
αwU ,t + βf (qt *)wS ,t + γg (qt *)rt + δh(qt *) pM ,t(3.19)

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 equations (3.15)-(3.18) under the constraint (3.19). For the constraint(3.19), we use the sample average cost share of white-collar workers as the value of βf(qt*)wS,t/{αwU, t+βf(qt*)wS, t+γg(qt*)r t+δh(qt*)pM, t}. We also use the sample average cost share ofcapital service input as the value of γg(qt*)r t/{αwU, t +βf(qt*)wS, t+γg(qt*)r t+δh(qt*) pM, t} andthe sample average cost share of intermediate input as the value of δh(qt*)pM, t/{αwU,
t+βf(qt*)wS, t+γg(qt*)r t+δh(qt*)pM, t}.

3. Data
The core empirical part of this paper estimates the relationship between output unit
values and factor intensities, and calculates the factor contents embodied in Japan’s VIIT
using this relationship. We first describe the data sources for our variables and then explain
how our dataset was constructed.
As a first step, using micro-data of the Census of Manufactures for Japan andcomparing the factor inputs of factories producing the same good, we estimate therelationship between the unit value of gross output and factor intensities based on commodityand factory-level data. The CM is an annual survey conducted by the Ministry of Economy,
Trade and Industry. We use the establishment-level data of the Larger Establishment Sampleof the CM that covers all manufacturing establishments with 30 or more employees.3 The
CM includes information on shipments by commodity for each establishment as well as other

3

The CM consists of two samples, the Larger Establishment Sample and the Smaller Establishment Sample,
which includes data on factories with less than 30 employees. Because data on the number of white-collar andblue-collar workers are not available in the Smaller Establishment Sample, we use the data of the Large
Establishment Sample for the analysis in this paper. Moreover, in the Smaller Establishment Sample, tangible
assets data are missing for many establishments.

7

establishment-level data such as the book value of capital, intermediate input, the number of
production and non-production workers, the wage bill, and so on. Using the micro-data of theCM,

wecalculatefactorintensities

at

the

establishment

level

such

as

the

white-collar/blue-collar labor ratio, the capital/blue-collar labor ratio, the intermediate
input/blue-collar labor ratio, and the blue-collar labor/output ratio.4 Moreover, using theinformation on a 6-digit commodity classification basis, we select only single-productestablishments, which we define as establishments where one commodity accounts for morethan 60 percent of total shipments. In the CM, there are approximately 2,000 commodities,out of which quantity information is available for approximately 800 commodities. Based on
the 60 percent threshold, we calculate the unit value of a commodity (commodity-level
shipments divided by quantity) and various factor intensities at the establishment level. As aresult, we obtain information both on unit values and factor intensities for approximately
500+ commodities for each year. However, data on the number of production andnon-production workers are available only for 1981, 1984, 1987, and 1990, and we cannot
distinguish between production and non-production workers after 1990. Therefore, in thispaper, we mainly use the micro-data of the CM for these four years to estimate therelationship between the unit value of output and factor intensities. By estimating equations(3.15)-(3.18), we can derive the relationship between the unit value of output and factor
intensities. For the estimation, we employ seemingly unrelated regression (SUR) estimations
subject to the constraint expressed by equation (3.19). The estimation results will be presented
in Section 4.
After obtaining the relationship between the unit value of output and factor intensities,
we calculate the factor contents of Japan’s VIIT by matching the trade statistics with thecommodity-level unit value and factor intensity data taken from the CM. In the case ofJapan’s trade statistics, classification at the 9-digit commodity level is available, which is

4Some people may argue that the production- and non-production-job category does not appropriately
capture skill levels of workers. Some production workers with a long working experience may be much moreskilled than non-production workers with less working experience. Or, education level may determine skillsof workers to some extent. However, data on working experience or education levels are not available in the
CM and only available data are number of production workers and non-production workers. Moredisaggregated job categories are not available in the CM. According to the Basic Survey on Wage Structure
for Japan, production workers are clearly less educated than non-production workers. In the manufacturingsector in 1990, 96 percent of the production workers received only primary and secondary education while 42
percent of the non-production workers received tertiary education. Moreover, average hourly wage for male
non-production workers with secondary education was 36 percent higher than that for male production
workers with secondary education in the manufacturing sector in 1990. Comparing hourly wages for maleworkers with approximately 14 years experience in the company, non-production workers received 23 percent
higher hourly wage than production workers on average. Therefore, we interpret that the productionnon-production-job category can be a proxy for skill levels and we use this job category in the empiricalanaylysis in this paper.

8

much more detailed than the commodity classification for the CM.5 Therefore, we match thetrade statistics with the CM data at the CM’s 6-digit commodity level. We do so for 1990,
because at this stage, the commodity-level correspondence between the trade statistics and the
CM is available only for 1990.6 Using the trade statistics, we calculate the unit values forJapan’s export and import commodities and then match the commodity-level unit value data
for exports and imports with the commodity-level unit value data and factor intensity datataken from the CM. We should note that the matched data are limited to commodities forwhich the unit of quantity is the same in both the CM and the trade statistics.7 As a result, forthe year 1990, we obtain unit value and factor intensity data for 635 commodities from theCM, out of which 354 commodities have export unit value information while 336
commodities have import unit value information.

4. Empirical Results on the Relationship between Output Unit Values and Factor
Intensities
In this section, we report our estimation results on the relationship between output unit
values and factor intensities. We estimate the system of equations (3.15)-(3.18) under theconstraint expressed by equation (3.19), using SUR techniques. In order to take account of thepossibility that factor intensities and production technologies may differ across industries, weestimated the system of equations separately for the following ten manufacturing subsectors:
food, textiles, wood, chemicals, ceramics, metals, general machinery, electrical and precision
machinery, transportation equipment, and miscellaneous products. A full set of year dummies
is included in order to capture industry-level productivity shocks over time.
The estimation results are reported in Table 1. The most important result is that in thecase of the relationship between unit values and the white-collar/blue-collar labor ratio, thecoefficient was positive for all ten subsectors and statistically significant for eight. That is, toproduce high unit-value products, factories need a high white-collar/blue-collar labor ratio.
White-collar labor tends to be more abundant and therefore relatively cheap in developed
economies, so that developed economies are expected to have a comparative advantage inwhite-collar labor intensive products. Our finding that more expensive products are morewhite-collar labor intensive is consistent with the well known stylized fact that developed
5

For example, for 1990, we identified 6,716 export commodities and 8,744 import commodities at the
9-digit commodity level in the Trade Statistics compared with only 1,853 commodities at the 6-digit level in
the CM.
6
We hope to construct correspondence tables for other years and extend our analysis in the near future.
7
There are various quantity units reported in the CM and the Trade Statistics. In the case of the Trade
Statistics, approximately 90 percent of commodities with quantity information are reported in terms ofkilograms or tons. However, in the case of the CM, the unit “number” is the most frequent quantity unit,
although there are also many commodities that are reported in terms of tons.

9economies tend to export products with higher unit values and import products with lower
unit values (Fukao et al., 2003; Schott 2004).
Insert Table 1
The relationship between the capital/blue-collar labor ratio and unit values and thatbetween the intermediate input/blue-collar labor ratio and unit values differ across subsectors.
For example, the unit value coefficient in the capital/blue-collar labor ratio equation ispositive in six subsectors (textiles, chemicals, general machinery, electrical and precision
machinery, transportation equipment, and miscellaneous products) but negative in the otherfour subsectors.
It is interesting to note that the coefficient in the blue-collar labor/gross output ratio
equation is greater than 0.9 in all subsectors. This result implies that in order to raise the unit
value of their output by 10 percent, factories need to increase their blue-collar labor input per
output by more than 9 percent. In other words, in order to produce higher unit value products,
an increase only of white-collar labor input or of capital is not sufficient. Our estimation
results show that even if factories increase their white-collar/blue-collar labor ratio, they alsoneed to increase the input/output ratio for all other inputs simultaneously.8
In order to check the robustness of our results, we also estimate the system of four
equations (3.15)-(3.18) without the constraint (3.19). The results are reported in Table 2. The
results are very similar to those in Table 1 in most of the subsectors.9
Insert Table 2
One caveat regarding the CM data is that they do not cover the activities ofheadquarters if these are not located in the same place as the factory. This means that
headquarter activities, such as research and development, design, and advertising, which tend
to be white-collar labor and capital-intensive and are necessary to produce and sell
8

From equations (3.17) and (3.18), we have the following relationship:

⎞
⎛K
⎛K ⎞
ln⎜ U ,q ,i ,t ⎟ − ln⎜⎜ U ,t ⎟⎟ = (θ K + θY )θY ln ( pq ,i ,t ) − ln( pt ) + θ K ln (ai ,t ) + θY ln (ai ,t ) − ln (ai ,t )⎟⎜ Y
⎝ Yt ⎠⎝ q ,i ,t ⎠()

Taking the electrical machinery industry as an example, this implies that in order to raise the unit value of
output by 10 percent, factories need to increase their capital input per output by 1.62+9.29=10.91 percent (seecolumn (9) in Table 1).
9
We should note that high output prices may reflect high mark-ups rather than high product quality. We
jointly estimated equations (3.15)-(3.18), using unit production costs instead of unit output prices. We
obtained very similar results to those in Tables 1 and 2. Therefore, we interpret that high output prices shouldreflect high product quality.

10

high-quality products, are included for some observations but not for others. This means thatthe coefficients in the regressions for the white-collar/blue-collar labor ratio and thecapital/blue-collar labor ratio may be biased. Another potential problem of our estimation isthat the unit value of output could be arbitrary and not convey meaningful information if theoutput is traded within the firm. In order to examine whether our estimates are affected bythese potential issues, we re-estimate the system of four equations (without the constraint)using only data of factories belonging to firms with no additional factory and whose
headquarters are located in the same place. As Table 3 shows, the results are largely similar to
those in Tables 1 and 2.
Insert Table 3

5. Factor Contents in Japan’s VIIT
In this section we estimate the factor contents of Japan’s VIIT. We first present ourtheoretical framework and then, using concrete examples, show how we obtain the necessary
data for the factor content analysis. Finally, we calculate the factor contents.
We can derive factor contents of international trade from our estimators of elasticity
values as well as the factor demand functions. We assume that ai, t is close to one for any i andany t. 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 )Yn ,t ( pt )θ +θ Y= c′n ,t pt S

(3.20)

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 (3.20):

LS , D , n ,t = YD , n ,t cn′ ,t ∫+∞pt = 0θ +θ Ypt S

ϕ D , n ,t ( pt ) dpt(3.21)

where LS, D, n, t denotes the total input of white-collar labor in products made in Japan of n and
YD, n, t denotes the total domestic output quantity of n. Finally, white-collar labor embodied inJapan’s exports of commodity n, LS, E, n, t, and imports, LS, I, n, t, is given by

11

+∞

LS , E , n ,t = YE , n ,tLS , D , n ,t ∫pt = 0 pt
YD , n ,t∫+∞pt = 0LS , D , n ,t ∫pt = 0 pt
YD , n ,t∫+∞

ϕ E , n ,t ( pt )dptθ +θ Yϕ D , n ,t ( pt )dpt

pt S

+∞

LS , I , n ,t = YI , n ,t

θ S +θ Y

θ S +θ Y

ϕ I , n ,t ( pt )dptθ +θ Yϕ D , n ,t ( pt )dpt

pt S

pt = 0(3.22)(3.23)

where YE,n,t and YI,n,t denote the total export volume and total import volume of commodity n.
φE, n, t(pt) and φI, n, t(pt) denote the distribution functions of export and import quantity overunit value. Usually, we do not know these distribution functions. But we do know the average
unit value of exports and imports:

pt = ∫
E+∞

pt = 0pt = ∫
I+∞

pt = 0ptϕ E , n ,t ( pt ) dpt(3.24)

ptϕ I , n ,t ( pt ) dpt(3.25)

If we assume that φE, n, t(pn, t) and φI, n, t(pt) follow a log normal distribution and their standard
deviations are, say, one half of φD, n, t(pt), we can derive LS, E, n, t and LS, I, n, t.
By using equations (3.16)-(3.18), we also obtain the following equations:+∞K D , n,t ∫pt = 0 pt

K E , n ,t = YE , n,t

YD , n,t∫+∞pt = 0K D , n ,t ∫pt = 0 pt
K n ,t∫+∞pt = 0ϕ D , n ,t ( pt )dpt

ϕ I , n ,t ( pt )dptθ +θ Yϕ D , n ,t ( pt )dpt

pt K

YLU , D , n,t ∫pt = 0 pt ϕ E , n ,t ( pt )dpt

YD , n,t∫+∞pt = 0+∞

LU , I , n ,t = YI , n,tθ +θ Yθ K +θ Y+∞

LU , E , n,t = YE , n ,t

ϕ E , n ,t ( pt )dpt

pt K

+∞

K I , n ,t = YI , n,tθ K +θ Y∫+∞

pt = 0(3.27)θθ

pt Y ϕ D , n,t ( pt )dpt

Y
LU , D , n,t ∫pt = 0 pt ϕ I , n,t ( pt )dpt

YD , n,t(3.26)(3.28)θθ

pt Y ϕ D , n ,t ( pt )dpt

12(3.29)

Next, using concrete examples, we show how we obtain the necessary data for ourfactor content analysis, such as the unit value of the shipments of a particular product by firms
in Japan, of exports and of imports of that product, and the standard deviation of the unit
values of shipments of that product. 10
Table 4 provides summary information of our unit value analysis for the case of “cotton
tubular knit fabric,” a category at the most disaggregated, 6-digit commodity category level of
the CM. We can calculate unit values and factor contents for 14 factories for 1990. The
average unit value of the gross output of these single-product factories is 1.36 million yen per
ton. The standard deviation of the natural log of unit values across factories is 0.607. “Cotton
tubular knit fabric” cover three commodity categories in the 9-digit commodity classificationof the Harmonized System (HS) in the case of Japan’s exports and six commodity categories
in the case of Japan’s imports.
Insert Table 4
It is interesting to note that the unit value of Japan’s exports (2.48 million yen per ton),which is calculated as the total value of exports over the total volume of exports, is more than
50 percent higher than the unit value of total shipments by single-product factories (1.36
million yen per ton). Probably, two factors contribute to this gap in unit values. One is thatamong factories in Japan, only those factories that are white-collar labor-intensive andproducing output with a high unit value may be engaged in exporting. The other factor is thatthe observations for our unit value analysis consist only of single-product factories, which
may be less white-collar labor-intensive and produce cheaper products than the averagefactory in Japan. On the other hand, the unit value of Japan’s imports (1.34 million yen perton) is almost the same as the unit value of the total shipments by single-product factories.
Next, Table 5 provides summary information of our unit value analysis for the case of“light and small passenger cars,” another category at the 6-digit commodity level of the CM.
We can calculate unit values and factor contents for 9 factories for 1990. The average unit
value of the gross output of these single-product factories is 0.943 million yen per unit, andthe standard deviation of the natural log of unit values across factories is 0.237. “Light and10
In the CM, we cannot distinguish between shipments for the domestic market and shipments for the export
market. Moreover, there is no information on exports by each establishment and we cannot distinguishwhether an establishment is involved in exporting/importing or not. In 2001, however, a question was addedin the CM asking for the export-shipment ratio of each establishment. Thus, for years after 2001, it may bepossible to distinguish between the unit value of products made and sold in Japan and the unit value of
products made in Japan but exported.

13

small passenger cars” cover seven commodity categories in the 9-digit commodity
classification of the Harmonized System (HS) in the case of Japan’s exports and fivecommodity categories in the case of Japan’s imports.
Insert Table 5
In the case of this type of cars, the unit value of Japan’s exports (0.981 million yen
unit) is almost equal to the unit value of all shipments by single-product factories in Japan(0.943 million yen per unit). On the other hand, the unit value of Japan’s imports (2.66 million
yen per unit) is much higher than the unit value of all shipments by single-product factories
and the unit value of exports. A probable reason is that Japan imports mainly luxury cars.
Using such unit value information taken from the CM and the trade statistics as well as
data on factor intensities for each commodity, we estimate the factor contents of Japan’s VIIT
based on equations (3.22), (3.23) and (3.26)-(3.29). For pt, we use the log of the unit value ofall shipments by single-product factories. For ptE and ptI, we use the log of the unit value ofJapan’s exports and the log of the unit value of Japan’s imports, respectively.11 We also obtainfrom the CM factor inputs and the total domestic output quantity for each commodity, LS, D, n, t,
K D, n, t, LU, D, n, t, YD, n, t, while total export and import volumes for each commodity, YE,n,t and
YI,n,t, are obtained from the trade statistics.
As already mentioned, we do not know the distribution functions of export and importquantities over unit values, φE, n, t(pt) and φI, n, t(pt), but we do know the average unit value ofexports and imports. Therefore, we assume that φE, n, t(pn, t) and φI, n, t(pt) follow a log normal
distribution and their standard deviations are equal to: (1) the standard deviation of the
distribution function of output quantity by all the factories producing commodity n in Japanover unit value p, φD, n, t(pt); (2) one half of the standard deviation of φD, n, t(pt); or (3) twice
the standard deviation of φD, n, t(pt). If we assume that φE, n, t(pn, t) and φI, n, t(pt) follow a log
normal distribution, we can simplify equations (3.22), (3.23) and (3.26)-(3.29). For example,
equation (3.22) can be rewritten as:

LS , E ,n ,t = YE ,n ,tLS ,D ,n ,t1⎧⎫
2
exp ⎨(θ S + θ Y )(μ E − μ D ) + (θ S + θ Y ) (σ E2 − σ D2 )⎬Y D , n ,t2⎩⎭
(3.22)’

11

In the case of the log of the unit value of all shipments by single-product factories, the log of the unit value
is the simple mean of factory-level unit values in logarithm for each 6-digit commodity in the CM. In the caseof the log of the unit value of Japan’s exports and imports, we calculate the log of the sum of exports(imports) in the 9-digit commodities in the trade statistics corresponding to the 6-digit commodity category in
the CM divided by the sum of the quantities in the 9-digit commodities in the trade statistics.

14

where μE and μD denote the log of the unit value of Japan’s exports and the average of thefactory-level unit values in logarithm, respectively, for commodity n. σE and σD denote thestandard deviation of the distribution functions of exports and of all shipments bysingle-product factories, respectively, for commodity n. For σD, we use the standard deviation
calculated from the unit value data of the CM. For σE, we assume above three cases. Similarly,
we can rewrite equations (3.23) and (3.26)-(3.29) using μE, μI, μD,σE, σI, and σD.
We calculate the factor contents of exports or imports for 1990 at the 6-digit
commodity level for commodities for which unit value information can be calculated and the
CM data and the trade statistics can be matched.12 The values are then aggregated at thebroad industry level and the results shown in Table 6. We find, first, that estimated factor
contents vary depending on our assumption with regard to the standard deviation, suggestingthat finding a plausible assumption regarding the standard deviation is one of the key issues
for improving our factor contents estimation. Second, we were not able to obtain a unit value
and match the CM data with the trade data for many commodities. Therefore, the estimatedfactor contents of trade shown in Table 6 are subject to serious underestimation. In fact, asshown in Table 7, the coverage ratios of the matched commodities are not very high. The firstcolumn of Table 7 shows the coverage ratio based on the shipment value taken from the CM.
The coverage ratio is defined as sum of shipments of matched commodities divided by total
domestic shipments. The second and third columns of Table 7 show the coverage ratio
calculated from the trade statistics as the amount of exports (imports) of matchedcommodities divided by total amount of exports (imports). The coverage ratio for all
manufacturing sectors together is 10.4 percent based on shipments, 32.0 based on exports, and21.6 percent based on imports. Although the coverage ratios for some industries, such as
metals and transportation equipment, are relatively high, those for general machinery andelectrical and precision machinery are extremely low.
In fact, VIIT is most prominent in the machinery industries in Japan and East Asia.13
Therefore, in order to improve our estimation, it is critical to find a way to estimate factor
contents of VIIT for the machinery industries. Moreover, given the fact that VIIT in East Asia
increased rapidly in the 1990s, an urgent task is to apply our methodology to data for later
years, for example 2000 or 2005, in order to examine the impact of increasing VIIT on
12
In Japan’s Trade Statistics, exports are recorded on an f.o.b. basis while imports are on c.i.f. basis.
Moreover, insurance and freight cannot be separated from the cost of imported goods. Therefore, if the valueof imports is simply divided by the quantity of imports, import unit values will be overestimated. In order tomitigate this problem, we subtract 10 percent from all import values, a percentage that is approximately
equivalent to the cost of insurance and freight, as suggested by Fukao et al. (2003), who estimate thedifference between c.i.f. and f.o.b. values and report that the difference is 12.35 percent in the case ofelectrical machinery.
13
See Fukao et al. (2003), for example.

15

changes in the factor content of Japan’s trade.
Insert Tables 6 & 7

6. Conclusion (Tentative)
This paper aimed to contribute to the development of a new analytical framework forthe empirical study of factor contents of VIIT. To this end, we first examined whether or not
the widely used assumption of a positive relationship between unit values and human- or
physical-capital intensities holds.
We found significant and stable relationships between factor intensities and unit values
for many industries. As for the relationship between the unit value of a product and its
white-collar labor intensity, the significant and positive relationship we found is important
empirical evidence which supports the assumption widely used in theoretical models that
commodities with higher prices are of higher quality and more human capital-intensive. On
the other hand, we found that the relationship between the unit value of a product and its
capital intensity is not always positive and that the relationship is significantly negative insome sectors. That is, we find that the widely used assumption that commodities with higher
prices are more physical capital-intensive does not always hold.
After confirming the significant relationships between unit values and factor intensities,
we tried to estimate the factor contents of trade, taking account of differences in unit values ofshipments of a particular product by establishments in Japan, of exports, and of imports.
However, at this stage, we face the following difficulties. First, estimated factor contents
varied depending on our assumption with regard to the standard deviation of distributionfunctions of export and import quantities over unit values. This suggests that finding aplausible assumption regarding the standard deviation is one of the key issues for improving
our factor contents estimation. Second, we were not able to obtain a unit value and match the
CM data with the trade data for many commodities. Particularly, the coverage problem isserious in the machinery industries where VIIT is most prominent in East Asia. We need tofind a way to estimate factor contents of VIIT for the machinery industries. Third, the factor
contents analysis is limited to the year 1990. Extending our analysis to 2000 or 2005 onwards
certainly is an urgent task to capture the rapid increase in VIIT in the 1990s.
If we can resolve some of the difficulties we are currently facing, our methodology
may enable us to conduct various pioneering analyses on VIIT and factors embodied in trade.
Given that in many parts of the world, intra-industry trade is more prominent than traditional
inter-industry trade, there is an urgent need to develop a new research framework to gain a16

better and more detailed understanding of the impact of intra-industry trade.

17References
Baldwin, Richard, and James Harrigan (2007) “Zeros, Quality and Space: Trade Theory andTrade Evidence,” NBER Working Paper 13214, National Bureau of Economic Research.
Falvey, Rodney E. (1981) “Commercial Policy and Intra-Industry Trade,” Journal ofInternational Economics, Vol. 11, pp. 495-511.
Feenstra, Robert C. and Gordon H. Hanson (1999) "The Impact of Outsourcing andHigh-Technology Capital on Wages: Estimates for the U.S., 1972-1990," Quarterly
Journal of Economics, 114(3): 907-940.
Feenstra, Robert C. and Gordon H. Hanson (2001) “Global Production Sharing and Rising
Inequality: A Survey of Trade and Wages,” NBER Working Paper No. 8372, July,
National Bureau of Economic Research.
Fontagné, Lionel, Michael Freudenberg, and Nicholas Péridy (1997) “Trade Patterns Inside
the Single Market,” CEPII Working Paper No. 1997-07, April, Centre D’Etudes
Prospectives et D’Informations Internationales.
Retrieved from [http://www.cepii.fr/anglaisgraph/workpap/pdf/1997/wp97-07.pdf]
7 June 2002.
Fukao, Kyoji, Hikari Ishido, and Keiko Ito (2003) "Vertical Intra-Industry Trade and Foreign
Direct Investment in East Asia," Journal of the Japanese and International Economies,
vol.17, pp.468-506.
Greenaway, David, Robert Hine, and Chris Milner (1994) “Country-Specific Factors and thePattern of Horizontal and Vertical Intra-Industry Trade in the UK,” Weltwirtschaftliches
Archiv, Vol. 130, No. 1, pp. 77-100.
Hallak, Juan Carlos, and Jagadeesh Sivadasan (2009) “Firms’ Exporting Behavior Under
Quality Constraints,” NBER Working Paper No. 14928, April, National Bureau of
Economic Research.
Hummels, David, and Peter J. Klenow (2005) “The Variety and Quality of a Nation’s
Exports,” American Economic Review, vol. 95, no. 3, pp. 704-723.
Kugler, Maurice, and Eric Verhoogen (2008) “The Quality-Complementarity Hypothesis:
Theory and Evidence from Colombia,” NBER Working Paper No. 14418, October,
National Bureau of Economic Research.
Schott, Peter K. (2004) “Across-Product versus Within-Product Specialization in International
Trade,” the Quarterly Journal of Economics, vol. 119, no. 2, pp. 647-678.
Widell, Lars M. (2005) “Product Quality and the Human Capital Content of Swedish Trade in
the 1990s,” a paper prepared for the ETSG conference in Dublin, September.

18

Table 1. Relationship between factor intensity and unit price: Seemingly Unrelated Regression estimations with constraint
ElectricalTranspor- Miscellane
GeneralandFoodTextilesWood
Chemicals Ceramics
Metalstation-ous
machinery precisionequipment products
machinery
Equation
numberDependent
variable

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(3.15)

dvlnWBratio

0.144***(0.040)0.108***(0.016)0.057(0.033)0.161***(0.022)0.087**(0.027)0.044**(0.015)0.119***(0.015)0.138***(0.022)0.010(0.025)0.259***(0.056)(3.16)

dvlnKBratio-0.245*** 0.066***(0.043)(0.017)-0.069(0.047)0.048(0.028)-0.005(0.031)-0.094*** 0.058***(0.020)(0.016)

0.162***(0.027)0.066(0.036)0.151*(0.068)(3.17)

dvlnMBratio-0.283*** 0.116***(0.033)(0.017)-0.028(0.032)-0.061**(0.019)-0.064**(0.021)-0.177*** 0.081***(0.015)(0.013)0.070***(0.019)0.032(0.027)0.058(0.041)(3.18)

dvlnBYratio

1.212***(0.028)1.022***(0.027)1.031***(0.016)1.035***(0.016)

1.132***(0.012)0.929***(0.016)

0.972***(0.022)0.938***(0.032)

Notes:

0.909***(0.013)0.928***(0.010)

Number of
3006
6712
1942
4334
5515
8270
2267
1736
906
observations
1. The dependent variables are factor intensities expressed in logarithmic form (deviation from the commodity-year mean).
2. Standard errors are in parentheses, with ***, ** and * indicating significance at the 1, 5 and 10 percent level, respectively.
3. Constant terms and year dummies are included, but estimated coefficients are not reported.
4. For the estimation, pooled data of factories with 30 or more employees in 1981, 1984, 1987, 1990 were used.

19

1074

Table 2. Relationship between factor intensity and unit price: Seemingly Unrelated Regression estimations without constraint

Equation
numberDependent
variable

Food

Textiles

Wood

(1)

(2)

(3)

Chemicals Ceramics

(4)

(5)

Metals(6)

ElectricalTranspor- Miscellane
Generalandtation-ous
machinery precisionequipment products
machinery
(7)

(8)

(9)

(10)

(3.15)

dvlnWBratio

0.098*(0.040)0.112***(0.016)0.057(0.033)0.162***(0.022)

0.097***(0.027)0.046**(0.015)0.119***(0.015)0.143***(0.022)0.011(0.025)0.284***(0.056)(3.16)

dvlnKBratio-0.212*** 0.065***(0.043)(0.017)-0.073(0.047)0.045(0.028)-0.021(0.032)-0.091*** 0.057***(0.020)(0.016)0.176***(0.027)0.065(0.036)

0.162*(0.068)(3.17)

dvlnMBratio-0.202*** 0.109***(0.034)(0.017)-0.028(0.032)-0.072*** -0.021(0.019)(0.022)-0.176*** 0.081***(0.015)(0.013)0.066***(0.019)0.031(0.027)0.038(0.041)(3.18)

dvlnBYratio

0.967***(0.033)1.004***(0.030)0.978***(0.018)1.107***(0.013)0.902***(0.017)0.962***(0.024)0.856***(0.036)

Notes:

0.895***(0.013)0.895***(0.021)0.920***(0.011)

Number of
3006
6712
1942
4334
5515
8270
2267
1736
906
observations
1. The dependent variables are factor intensities expressed in logarithmic form (deviation from the commodity-year mean).
2. Standard errors are in parentheses, with ***, ** and * indicating significance at the 1, 5 and 10 percent level, respectively.
3. Constant terms and year dummies are included, but estimated coefficients are not reported.
4. For the estimation, pooled data of factories with 30 or more employees in 1981, 1984, 1987, 1990 were used.

20

1074

Table 3. Relationship between factor intensity and unit price: Seemingly Unrelated Regression estimations without constraint, based on data offactories belonging to firms with no additional factory and whose headquarters are located in the same place

Equation
numberDependent
variable

Food

Textiles

Wood

(1)

(2)

(3)

Chemicals Ceramics

(4)

(5)

Metals(6)

ElectricalTranspor- Miscellane
Generalandtation-ous
machinery precisionequipment products
machinery
(7)

(8)

(9)

(10)

(3.15)

dvlnWBratio

0.157**(0.058)0.114***(0.022)-0.003(0.042)0.226***(0.037)0.163***(0.045)0.067**(0.022)0.120***(0.019)0.116***(0.035)-0.001(0.030)0.190**(0.073)(3.16)

dvlnKBratio-0.186**(0.063)0.058*(0.023)-0.040(0.057)0.005(0.046)-0.128*(0.055)-0.046(0.029)0.044*(0.021)

0.075(0.042)0.015(0.050)0.215*(0.091)(3.17)

dvlnMBratio-0.187*** 0.081***(0.048)(0.022)-0.045(0.044)-0.041(0.035)-0.001(0.038)-0.144*** 0.093***(0.023)(0.018)0.022(0.029)-0.027(0.035)0.013(0.052)(3.18)

dvlnBYratio

1.039***(0.047)1.041***(0.040)0.951***(0.028)0.870***(0.036)

1.105***(0.020)0.962***(0.027)1.002***(0.030)0.881***(0.046)

Notes:

0.912***(0.018)0.904***(0.015)

Number of
1578
3548
963
1452
2245
3766
1050
601
468
observations
1. The dependent variables are factor intensities expressed in logarithmic form (deviation from the commodity-year mean).
2. Standard errors are in parentheses, with ***, ** and * indicating significance at the 1, 5 and 10 percent level, respectively.
3. Constant terms and year dummies are included, but estimated coefficients are not reported.
4. For the estimation, pooled data of factories with 30 or more employees in 1981, 1984, 1987, 1990 were used.

21

561

Table 4. 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 valueStandard deviation of natural log of unit value

Cotton tubular knit fabric
1451-11140.00660.01670.12571.35711.60160.03930.6073

Corresponding Trade Statistics for 1990
Exports
HS 9-digit code
HS 9-digit name600210190 Knitted or crocheted fabrics of a width not exceeding 30 cm, containing by weight 5% or more of elastomeric yarn or rubber thread, made of cotton
Unit value of exports (million yen per ton)
2.240
Quantity of exports (ton)
15.497
Value of exports (million yen)34.709600220190 Knitted or crocheted fabrics of a width not exceeding 30 cm, made of cotton, other than those of heading 600210
Unit value of exports (million yen per ton)
2.583
Quantity of exports (ton)
13.849
Value of exports (million yen)
35.768600230190 Knitted or crocheted fabrics of a width exceeding 30 cm, containing by weight 5% or more of elastomeric yarn or rubber thread, made of cotton
Unit value of exports (million yen per ton)
2.527
Quantity of exports (ton)52.484
Value of exports (million yen)
132.633
Total value of exports (million yen)203.110
Total volume of exports
81.830Total value of exports/total volume of exports (million yen)
2.482
Weighted average of unit value of exports (weight: value of exports)
2.488
Imports
HS 9-digit code
HS 9-digit name600210031 Knitted or crocheted fabrics of a width not exceeding 30 cm, containing by weight 5% or more of rubber thread, not figured, made of cotton
Unit value of imports (million yen per ton)
1.903
Quantity of imports (ton)7.579
Value of imports (million yen)
14.423600210092 Knitted or crocheted fabrics of a width not exceeding 30 cm, containing by weight 5% or more of elastomeric yarn, not figured, made of cotton
Unit value of imports (million yen per ton)n.a.
Quantity of imports (ton)0Value of imports (million yen)
0.000
600220022 Knitted or crocheted fabrics of a width not exceeding 30 cm, not figured, made of cotton, other than those of heading 600210
Unit value of imports (million yen per ton)
0.731
Quantity of imports (ton)32.095
Value of imports (million yen)
23.469600230031 Knitted or crocheted fabrics of a width exceeding 30 cm, containing by weight 5% or more of rubber thread, not figured, made of cotton
Unit value of imports (million yen per ton)
5.614
Quantity of imports (ton)0.057
Value of imports (million yen)
0.320
600230092 Knitted or crocheted fabrics of a width exceeding 30 cm, containing by weight 5% or more of elastomeric yarn, not figured, made of cotton
Unit value of imports (million yen per ton)
9.790
Quantity of imports (ton)
0.200
Value of imports (million yen)
1.958
600292020 Knitted or crocheted fabrics, not figured, made of cotton, other than those of heading 600210, 600220, and 600230
Unit value of imports (million yen per ton)
1.382
Quantity of imports (ton)364.215
Value of imports (million yen)503.195
Total value of imports (million yen)543.365
Total volume of imports (ton)404.146
Unit value (Total value of imports/total volume of imports, million yen per to
1.344
Weighted average of unit value of imports (weight: value of imports)
1.400

22

Table 5. Summary table of the unit value analysis: The case of light and small passenger cars
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 unit)
Standard deviation of unit value (million yen per unit)
Average of natural log of unit valueStandard deviation of natural log of unit value

Light and small passenger cars, less than 2000ml cylinder capacity, including chassis3111-1190.00240.00650.0824
0.94310.20694.52290.2374

Corresponding Trade Statistics for 1990
Exports
HS 9-digit code HS 9-digit name
870321910 Passenger automobiles, with spark-ignition internal combustion reciprocating piston engine, of a cylinder capacity not exceeding 550cc, excluding knock down products
Unit value of exports (million yen per unit)
0.302
Quantity of exports (unit)
12,730
Value of exports (million yen)
3,848
870321920 Passenger automobiles, with spark-ignition internal combustion reciprocating piston engine, of a cylinder capacity exceeding 550cc and not exceeding 1,000cc, excluding knock down products
Unit value of exports (million yen per unit)
0.587
Quantity of exports (unit)
215,033
Value of exports (million yen)
126,218870322900 Passenger automobiles, with spark-ignition internal combustion reciprocating piston engine, of a cylinder capacity exceeding 1,000cc and not exceeding 1,500cc, excluding knock down products
Unit value of exports (million yen per unit)
0.814
Quantity of exports (unit)
1,027,269
Value of exports (million yen)
836,088870323910 Passenger automobiles, with spark-ignition internal combustion reciprocating piston engine, of a cylinder capacity exceeding 1,500cc and not exceeding 2,000cc, excluding knock down products
Unit value of exports (million yen per unit)
1.152
Quantity of exports (unit)
1,589,365
Value of exports (million yen)
1,831,106870331910 Passenger automobiles, with compression-ignition internal combustion reciprocating piston engine, of a cylinder capacity not exceeding 1,000cc, excluding knock down products
Unit value of exports (million yen per unit)
0.679
Quantity of exports (unit)
2,688
Value of exports (million yen)
1,826
870331920 Passenger automobiles, with compression-ignition internal combustion reciprocating piston engine, of a cylinder capacity exceeding 1,000cc and not exceeding 1,500cc, excluding knock down products
Unit value of exports (million yen per unit)
0.769
Quantity of exports (unit)
2,425
Value of exports (million yen)
1,866
870332910 Passenger automobiles, with compression-ignition internal combustion reciprocating piston engine, of a cylinder capacity exceeding 1,500cc and not exceeding 2,000cc, excluding knock down products
Unit value of exports (million yen per unit)
0.929
Quantity of exports (unit)
79,611
Value of exports (million yen)
73,921
Total value of exports (million yen)
2,874,872
Total volume of exports
2,929,121
Total value of exports/total volume of exports (million yen)
0.981
Weighted average of unit value of exports (weight: value of exports)
1.021
Imports
HS 9-digit code HS 9-digit name
870321000 Passenger automobiles, with spark-ignition internal combustion reciprocating piston engine, of a cylinder capacity not exceeding 1,000cc
Unit value of imports (million yen per unit)
0.842
Quantity of imports (unit)
17,974
Value of imports (million yen)
15,140870322000 Passenger automobiles, with spark-ignition internal combustion reciprocating piston engine, of a cylinder capacity exceeding 1,000cc and not exceeding 1,500cc
Unit value of imports (million yen per unit)
1.064
Quantity of imports (unit)
9,300
Value of imports (million yen)
9,895
870323000 Passenger automobiles, with spark-ignition internal combustion reciprocating piston engine, of a cylinder capacity exceeding 1,500cc and not exceeding 3,000cc
Unit value of imports (million yen per unit)
2.951
Quantity of imports (unit)
171,001
Value of imports (million yen)504,628
870331000 Passenger automobiles, with compression-ignition internal combustion reciprocating piston engine, of a cylinder capacity not exceeding 1,500cc
Unit value of imports (million yen per unit)
1.772
Quantity of imports (unit)
3
Value of imports (million yen)
5870332000 Passenger automobiles, with compression-ignition internal combustion reciprocating piston engine, of a cylinder capacity exceeding 1,500cc and not exceeding 2,500cc
Unit value of imports (million yen per unit)
2.044
Quantity of imports (unit)
2,740
Value of imports (million yen)
5,600
Total value of imports (million yen)
535,269
Total volume of imports (unit)
201,018
Unit value (Total value of imports/total volume of imports, million yen
2.663
Weighted average of unit value of imports (weight: value of imports)
2.847

23

Table 6. Estimated factor contents of trade: Matched commodities(a) Assumption 1: σE=σI=σD
Industry
1 Food2 Textiles
3 Wood4 Chemicals
5 Ceramics
6 Metals
7 General machinery
8 Electrical & precision machinery
9 Transportation equipment
10 Miscellaneous products
Manufacturing total(b) Assumption 2: σE=σI=0.5*σDIndustry
1 Food2 Textiles
3 Wood4 Chemicals
5 Ceramics
6 Metals
7 General machinery
8 Electrical & precision machinery
9 Transportation equipment
10 Miscellaneous products
Manufacturing total(c) Assumption 3: σE=σI=2*σDIndustry
1 Food2 Textiles
3 Wood4 Chemicals
5 Ceramics
6 Metals
7 General machinery
8 Electrical & precision machinery
9 Transportation equipment
10 Miscellaneous products
Manufacturing total

EXPORTS
LU
K
232
701
6,185
2,327
8,923
46,755568
1,634
58,805
8,716
12,833
301,346
2,186
5,233
74,253
10,44727,516393,667
1,614
2,370
15,556
13,78915,658
182,639
32,48782,460414,53528
108
544
72,395
157,436 1,494,285

LS
1,794
1,299
3,039
5,930
706
4,851
117
2,246
2,169
1
22,152IMPORTS
LU
3,443
7,160
7,264
15,242
1,411
11,652
166
588
7,704
12
54,642

K
53,854
44,153
132,163
211,145
23,469306,520
1,070
36,03444,542
44852,995

NET EXPORTS
LS
LU
K-1,562-2,742-47,669
1,028
1,764
2,602
-2,471-5,630-73,358
2,787-2,40990,201
1,481
3,821
50,785
5,596
15,86487,1471,497
2,204
14,485
11,543
15,070146,605
30,31874,756369,99327
96
500
50,243
102,794641,290

EXPORTS
LU
K
215
670
6,011
2,093
8,365
42,169522
1,511
55,455
7,435
11,293
270,394
1,905
4,609
68,014
9,105
24,123359,715
1,233
1,899
12,051
9,978
12,821
130,676
27,372
70,149369,1352293
479
59,881
135,534 1,314,098

LS
1,557
1,172
2,731
5,072
612
4,605
75
1,292
1,964
1
19,082IMPORTS
LU
3,127
6,650
6,342
14,117
1,240
11,108
120
399
6,831
10
49,944

K
51,27740,401126,097
193,581
21,226300,332
738
20,26342,996
34
796,944

NET EXPORTS
LS
LU
K-1,342-2,458-45,266
921
1,714
1,768
-2,209-4,830-70,643
2,363-2,82476,813
1,293
3,369
46,7894,500
13,01659,383
1,158
1,779
11,313
8,686
12,422
110,41325,40863,318326,13921
84
446
40,79885,590517,154EXPORTS
LU
K
339
864
6,994
4,091
12,30378,725874
2,430
77,42247,30037,579800,787
4,336
9,437
115,376
21,81455,081
638,835
6,350
6,726
51,294
67,62839,691
989,147
71,224
171,718
793,460
84
207
1,028
224,039
336,036 3,553,068

LS
3,399
2,143
5,356
38,856
1,501
6,526
784
20,7703,497
582,836IMPORTS
LU
K
5,317
66,195
9,896
66,202
13,847
169,824
35,291
520,583
2,712
39,514
15,335339,962
657
5,182
2,990
367,900
13,42352,89926
12999,493 1,628,391

NET EXPORTS
LS
LU
K-3,060-4,453-59,201
1,947
2,407
12,523-4,482-11,417-92,4028,444
2,288
280,204
2,835
6,725
75,862
15,28839,746
298,873
5,566
6,069
46,112
46,85836,701
621,248
67,727
158,296
740,56179
181
899
141,203
236,543 1,924,677

LS

LS

LS

Notes: σE: standard deviation of log of unit value for exports; σI: standard deviation of log of unit value for imports.
σD: standard deviation of log of unit value for domestically produced goods.
Ls: white-collar labor (number of workers) embodied in trade.
Lu: blue-collar labor (number of workers) embodied in trade.
K: Capital stock (million yen) embodied in trade.

24

Table 7. Coverage ratio for the matched commodities

Industry
1 Food2 Textiles
3 Wood4 Chemicals
5 Ceramics
6 Metals
7 General machinery
8 Electrical & precision machinery
9 Transportation equipment
10 Miscellaneous products
Manufacturing total

CensusDomestic
shipments
14.8
6.0
6.9
12.4
14.9
21.2
1.1
4.0
13.3
0.4
10.4

(%)
Trade Statistics
Exports
16.2
25.9
30.8
33.0
38.6
67.1
2.2
10.4
67.6
0.2
32.0

Imports
8.0
9.8
34.0
30.6
19.5
41.6
0.9
4.4
51.9
0.0
21.6

Notes: The coverage ratio calculated from the Census data is defined as shipments ofmatched commodities divided by total domestic shipments.
The coverage ratio calculated from the Trade Statistics is defined as exports (imports) ofmatched commodities divided by total exports (imports).

25