The structure and the determinants of the tradeof SME products
Sawako Maruyama∗

Abstract
This paper aims to investigate the structure and the determinantsof the trade of products manufactured by small- and medium-sized enterprises. For this purpose, trade database for selected SME-based industries
is prepared. Analyzing this database, the following three ﬁndings are obtained. First, ﬁrms in SME-based industries are facing a large inﬂow
of imported goods, while the volume of their export is relatively small.
Secondly, the share of Asian countries in the trade of SME products islarger than overall trade. Thirdly, the gravity model can be applied forthe trade of SME products. In some cases, distance and diﬀerence ofincome level tend to be more sensitive for SME products than overall
trade. These results are consistent with the labor-intensive characteristics of SME products.
Keywords: Trade; Gravity model; Small and Medium-sized Enterprises (SMEs); Manufacturing

1

Introduction

This paper investigates the structure and the determinants of the trade of the
products manufactured by small- and medium-sized enterprises, SMEs. SMEs
play important roles in the business activities in Japan. They dominate largesized enterprises with more than 90 percent of total number of ﬁrms, and theirproduction accounts for nearly half of the shipment value of manufacturing.
Meanwhile, SMEs have been facing stiﬀ competition since the 1980s. The rapid
Yen appreciation after the Plaza Accord in 1985 lowered competitiveness ofJapanese SME products. In addition, the inﬂow of products from other Asian∗ Graduate School of Economics, Kobe University, 2-1 Rokkodai-cho, Nada-ku, Kobe, Japan

657-8501, Tel: +81-78-803-6826, E-mail: maruyama@econ.kobe-u.ac.jp

1

countries has declined domestic production of Japanese SMEs. Furthermore, recent Yen appreciation and the shrinking domestic market because of the aging
population are enhancing these long-term trends. In contrast to the increased
globalization of circumstances, globalization of the activities of Japanese SMEs
is progressing slowly. An increasing number of SMEs engage in overseas activities, however, they are only a small part of total SMEs: only 2.8% of SMEs
export their products abroad directly, and 1% of them make foreign direct investment(FDI)1 .
Globalization has changed business circumstances for Japanese SMEs, but
to what extent have they changed? This paper attempts to estimate the volumeof trade related to SMEs, since trade is most relevant among overseas activities
for SMEs. A database of SME products is prepared with concordance between
Census of Manufactures and Trade Statistics. This database makes it possible toanalyze the characteristics and the trade structure of SME products. Moreover,this paper examines determinants of trade ﬂow. How is the trade of SME
products composed by industry and by region? What determines the trade
ﬂow of SME products? Do those determinants aﬀect trade of SME products
diﬀerently from overall trade? The aim of this paper is to answer these questions.
The rest of the paper is organized as follows. In the next section, we review
literature which analyzes the overseas activities of SMEs in Japan. Section 3
explains the methodology of concordance between Census of Manufactures andTrade Statistics. In section 4, the determinants of trade of SME products are
examined using the gravity model. The result of the analysis is summarized in
section 5.

2

Literature

Firm size is recognized as one of the important factors for internationalization.
It has been employed as a determinant of overseas expansion of ﬁrms in many
empirical analyses. However, most empirical literature has focused on largeﬁrms. Generally, SMEs engage less in overseas activities in comparison to large
enterprises. One reason is that SMEs are lacking their resources such as technology, human resources, and ﬁnance. Those resources are related to what Dunningcalled ’Ownership advantages’ in his eclectic theory2 . Internationalized SMEs
are a small portion of all SMEs. On the other hand, more SMEs are aﬀected by
the globalized economy regardless whether or not they engage in international
1 Small and Medium Enterprise Agency [21], p.71.
2 See for example Dunning [8].

2business. Therefore, when we discuss globalization and SMEs, it is important
to distinguish the following issues: the globalization of SMEs themselves andglobalization of circumstances surrounding SMEs. We summarize the recent
literature concerning these issues.

2.1

Empirical analysis for SMEs

With regard to the globalization of SMEs, a growing number of empirical analyses have been made using ﬁrm-level data. For example, empirical analysis byHollenstein [12] reveals that ﬁrm size aﬀects the probability and the degree ofinternationalization, especially to cross a threshold of internationalization. Similar results are presented in the survey of empirical analyses during late 1980s
and 1990s by Coviello and McAuley [5]. They point out that ﬁrm size appears
to aﬀect the internationalization of ﬁrms. Meanwhile, they also mention somediﬃculties for empirical investigation. One of those diﬃculties is that internationalization is not explained using only one framework. A number of frameworks need to be integrated for the investigation. Another diﬃculty is biased
samples of the manufacturing sector and focusing too much on country-speciﬁcfactors.
Similarly, most empirical analyses for Japanese SMEs focus on a speciﬁc
industry or region. They often use questionnaires or interview surveys withqualitative factors, and there is only a small number of quantitative analyses
for internationalization of Japanese SMEs. Urata and Kawai [27] is one ofthose quantitative analyses. They examine the determinants of FDI location
by Japanese SME. They use a dataset including both SMEs and large ﬁrms.
Their empirical results show that SMEs are more sensitive to local conditions
in making decision for FDI.
Some empirical analyses suggest that international SMEs are more productive and proﬁtable than domestic SMEs, and this is in line with the results
obtained from the analyses done on large enterprises. For example, an analysis
of Small and Medium Enterprise Agency [20] examines the eﬀect of FDI on
the performance of ﬁrms. They conclude that FDI by SMEs results in higher
performance of domestic operations by shifting into the production of more sophisticated goods or operation of higher value-added ﬁelds3 . Kawai [16] andTodo [23] also examine the diﬀerence of performance between international anddomestic SME with ﬁrm-level data. Kawai investigates the determinants ofglobal strategies by SMEs and divides these into four stages: trade, outsourcing, subsidiary by joint venture, and wholly-owned subsidiary. He concludes
3 Small and Medium Enterprise Agency [20], pp.88-91.

3

that activities of SMEs are more globalized when SMEs possesses ﬁrm-speciﬁc
assets such as R&D investment or technology, introducing IT, or participating
in a joint project. Todo focuses on the overseas outsourcing including bothinter-ﬁrm and intra-ﬁrm contracts. He ﬁnds that outsourcing to Asian ﬁrms
improves the performance of headquarters in Japan.
These studies succeed in revealing the features of globalization by SMEs.
However, the number of empirical analysis for SMEs is still fewer in comparison to large enterprises and the globalization of SMEs has not been examined
suﬃciently. Moreover, we have to pay attention for some limitations of analysis using ﬁrm-level data. Firstly, those studies investigate internationalized
SMEs which account for only a small part of total SMEs. The vast majority ofJapanese SMEs are not engaging in overseas activities, and the estimated result cannot be generalized to the whole of SMEs. Secondly, there is a potentialof underestimation of trade since only direct trade is reported in the survey.
A lot of manufacturing SMEs in Japan use trading companies for export, ortrading companies independently export the products of manufacturing SMEs.
Recently, there are studies emphasizing the importance of intermediaries on
trade4 . Thirdly, competing import goods are not observed in the survey which
reports only direct import. To show the inﬂuence of trade on both internationaland domestic SMEs, it is necessary to estimate the trade eﬀect at the industry
level.

2.2

Import competition

For SMEs in developed countries, import competition with products from developing countries is one of the major impacts induced by globalization. Eﬀect ofimport competition on wages and employment has been investigated, although
the eﬀect is not limited to SMEs. Freeman and Katz [11], Sachs and Shatz [18]are examples for these types of analyses. They use industry-level data aggregated at 2-digit level of SIC. More detailed data are prepared by Feenstra [9], andFeenstra et al. [10]. They oﬀer a concordance dataset between industrial census
using SIC code and trade statistics using HS code. This concordance enables us
to test the impact of imports at a disaggregated level. Moreover, the ’importpenetration ratio’ can be calculated using shipment value and trade value byindustry. In the Japanese case, a series of studies such as done by Tomiura
and Uchida [26], Tomiura [24], and Tomiura [25] are examples which prepare aconnected database between 4-digit industrial code for Census of Manufactures

4 See Ahn et al. [1] and Bernard et al. [3]4and HS 9-digit code for Trade Statistics 5 .
These studies reveal the change of circumstances by globalization. They suggest that increasing import from developing countries aﬀect domestic economy.
This is true for Japanese SMEs which experienced rapid change of demand after
the appreciation of the yen in 1980s. However, the eﬀect of import has not been
examined suﬃciently due to a lack of appropriate data. For example, studies investigating entry and exit of SMEs as Doi [7], Honjo and Harada [13], Kawai and
Urata [17], Small and Medium Enterprise Agency [19]6 do not include importor export as explanatory variables.
Therefore, this paper attempts to combine these two issues of the globalization of SMEs and globalization of circumstances surrounding SMEs using adataset for trade of SME products.

3

The methodology

3.1

Selection of SME-based industry

To start analyzing the trade of products manufactured by SMEs, we have to deﬁne SME products and to distinguish them from others. In order to distinguish
SME products, we select industries whose production is dominated by SMEs.
Census of Manufactures by METI, the ministry of economy, trade and industryof Japan, oﬀers the aggregation of shipment value by ﬁrm size for the 3-digit
code of Japan Standard Industrial Classiﬁcation, JSIC7 . According to the Act
for Small- and Medium-sized Enterprises, SMEs are deﬁned with the criteriaof less than 300 employees or capital of 300 million yen for manufacturing. In
this analysis, we employ a criterion of 300 employees. We aggregate shipment
value of ﬁrms with less than 300 employees in 3-digit JSIC. When the aggregated value of an industry i accounts for more than 70% of total shipment, theindustry i is regarded as an SME-based industry. We use the average of SME
shipment ratio from 2002 to 2009.
The result of calculation shows that 63 industries out of 150 in 3-digit code
are classiﬁed as SME-based industry (See Appendix Table 1). The numberof SMEs in those 63 industries is 99,796 in average during 2002-2007, and it
5 In order to connect these diﬀerent codes, an appendix table of Input-Output table forrearranging trade statistics is used. This appendix table is originally used when trade sectorof I-O table is estimated. Detail of the methodology is described in the appendix of Tomiura
and Uchida [26].
6 Small and Medium Enterprise Agency [19], 1.2.1.
7 JSIC code has changed since 2008, therefore data after 2008 is adapted to the old classiﬁcation until 2007.

5

accounts for 40.4% of the total number of SMEs. The rest of SMEs are included
in large-ﬁrm-based industries or mixed industries of both sizes. A number oflight industries such as textile, apparel and leather are included in SME-basedindustries. The largest SME-based industry concerning the number of ﬁrms is’Fabricated constructional and architectural metal products’ (254) with 15,187
ﬁrms. In addition to this, industries related to intermediate goods or inputs forconstruction such as ’Sawing, planning mills and wood products’ (131), ’Cement
and its products’ (222), ’Furniture’ (141), and ’Sliding doors and screens’ (143)have a large number of ﬁrms.
What are the features of SME-based industries? Figure 1 shows the relationship between SME shipment ratio and three industrial characteristics including
average wage, capital equipment ratio, and labor distribution ratio. Each index of industrial characteristics is expressed as a ratio to total manufacturing.
The ﬁrst ﬁgure (a) shows that the average wage of SME-based industries tend
to be low8 . 53 out of 63 SME-based industries indicate lower values than the
average of total manufacturing. The correlation coeﬃcient between SME shipment ratio and average wage is -0.734, which suggests a negative and strong
relationship. The second ﬁgure (b) shows the relationship with capital equipment ratio9 . A higher index indicates that an industry is more capital intensive.
The correlation coeﬃcient with SME shipment ratio is -0.345, and this meansthat SME-based industries tends to indicate a lower capital equipment ratio andbe of labor-intensive nature. In addition, 51 SME-based industries show lower
values of capital equipment ratio than average. The third ﬁgure, (c), shows alabor distribution ratio calculated as the ratio of total cash wage and salary
to gross value added. An industry with high labor distribution ratio tends tobe labor-intensive, and 55 SME-based industries indicate higher values than total manufacturing. The correlation with SME shipment ratio is positive withthe value of 0.390. These facts suggest that most of SME-based industries arecharacterized as low-wage, labor-intensive industry.

3.2

Concordance between SME-based industry and trade
statistics

In the next step to estimate trade value of SME products, it is necessary to use
a concordance table between Census of Manufactures and Trade Statistics. In
this analysis, the concordance table from the Input-Output Table (hereafter I-O
8 Average wage is calculated as total cash wages and salaries per employee.
9 Capital equipment ratio is calculated as the value of tangible ﬁxed assets per employee

using available data for enterprises with more than 30 employees.

6Figure 1: Characteristics of SME-based industries
(a)

(b)

(c)

(Source) Own calculation.
(Notes) ’Other industry’ includes both large-ﬁrm based industries and mix-sized industries.
Average wage (mil. yen) = Total cash wage and salary (mil. yen)
/ The number of employees
Capital equipment ratio = Value of tangible ﬁxed assets (end of the year, mil. yen)
/ The number of employees(enterprises with more than 30 employees)
Labor distribution ratio = Total cash wage and salary/ Gross value added (mil. yen) *100

7

Figure 2: Trade value of SME products and its ratio to overall trade(Source) Own calculation.

table) by Statistics Bureau is used as described in the analysis by Tomiura and
Uchida [26]. The concordance table is a list prepared for the I-O table of baseyear 2005. We match 3-digit JSIC code of SME-based industries to 4-digit codeof I-O table and select correspondent sectors on I-O table. From the concordancetable, we obtain HS 9-digit trade goods included in selected 4-digit sectors on IO table. Trade data of HS 9-digit from Trade Statistics by Ministry of Finance,
MOF, is aggregated according to 3-digit JSIC10 . Trade statistics from 2001 to2010 are connected, reﬂecting the changes of HS 9-digit code. Three SMEbased industries (104, 169, 256) are excluded from the analysis because of theabsence of corresponding trade goods, and one industry (242) is excluded on
the grounds of being unable to distinguish from non-SME-based industry (241)in Trade Statistics. The number of exported goods included in SME products
is 1,865, while the number of imported goods is 3,595 in 2005.
Aggregated import value of SME-products is about four times as large asexport (Figure 2). In 2007, import of SME products was 11,122 billion yen,while export was 2,768 billion yen. Both aggregated import and export value
for SME products tend to change along with business cycle. After the peak at
2007, both import and export values fell drastically. The share of SME products
to overall export accounts for around 3.5% each year, while, concerning import,
it accounts for around 20% of overall import.
These trade values are comparable to domestic production. As a production
data, shipment values from 2002 to 2009 are available. Using average value of
10 3-digit JSIC code is not fully corresponding to 4-digit code of I-O table. Thus the corre-

spondence between 3-digit JSIC code and 9-digit HS code is ﬁnally checked.

8

trade and shipment during the period, the following three indices are calculated:
export ratio to shipment, import ratio to shipment, and import penetration
ratio (See Appendix Table 2). The import penetration ratio is calculated as theshare of import to domestic demand, that is shipment value plus import minus
export. From export ratio to shipment, it is obvious that most of the SMEbased industries show a low level of export. Only in four industries (114, 115,
209, 323), the export ratio to shipment indicates a value larger than 0.50, thatis to say, more than half amount of domestic production in these industries areexported. In addition, seven industries take values between 0.25 and 0.50, whichmeans that more than a quarter of the amount of production are exported. On
the other hand, in fourteen industries the import ratio to shipment is larger
than 1, which means that the value of import exceeds the domestic production.
Apparel industries (121, 122), of which domestic production is large, are one ofthose industries.
The import penetration ratio compares import to domestic production. 18
industries out of 59 industries indicate a value larger than 0.50 concerning import penetration ratio. The criterion of 0.50 means that import is equal to the
supply by domestic ﬁrms. That suggests one-third of SME-based industries arefacing the dominance of import goods in the domestic market.
Figure 3 shows the relationship between import penetration ratio and threeindustrial characteristics. The Y-axis is import penetration ratio in all graphs.
The X-axis of each graph shows respectively average wage in (a) and (b), capital
equipment ratio in (c) and (d), and labor distribution ratio in (e) and (f). The
size of each circle represents the value of export in (a), (c), (e), and import in(b), (d), (f). These ﬁgures show the following two tendencies. First, low-wageor labor-intensive industries tend to show a higher import penetration ratio,
although the correlation is not so strong. The correlation coeﬃcient with importpenetration ratio is -0.334 for average wage, -0.259 for capital equipment ratio,
and 0.163 for labor distribution ratio. Secondly, the volume of import tends
to be large in low-wage or labor intensive industries. Meanwhile, the volume
of export tends to be large in industries taking value around 1.0, which means
close to total manufacturing and less labor-intensive.
For the convenience of analysis, 59 SME-based industries are classiﬁed in 10
industrial groups based upon a 2-digit classiﬁcation: food and beverages, textile,
apparel, lumber and furniture, paper and printing, manufactured goods classiﬁed by materials, leather, ceramic, fabricated metal, and miscellaneous11 . To
11 Respective industrial groups include following 3-digit industry: [Food and beverages] 92,

93, 96, 103, 106 [Textile] 111, 113, 114, 115, 117, 118, 119 [Apparel] 121, 122, 123, 124, 125,9Figure 3: Trade status and industrial characteristics of SME-based industries
(a)

(b)

(c)

(d)

(e)

(f)

(Source) Own calculation.
(Note) The location of each circle is the same between (a) and (b), (c) and (d), (e) and (f),but the size is diﬀerent within each pair. The circle is expressed as relative size within export
or import, therefore the absolute size of export is non-comparable to that of import in thesegraphs.

10Figure 4: Trade structure by industrial group (average during 2001-2010, %)compare trade values among these industrial groups, we use the average during
2001-2010. Among the 10 groups, textile is the only one group in which export exceeds import (Figure 4). In other groups import value dominates export
value. Import value is especially large in the apparel industry, which indicatesthat it is more than ﬁfty times of export value. This result is interesting; theapparel industry, which is located at the downstream of the value-chain shows
opposite trade ﬂow with textile industry at the upper stream. On the other
hand, the diﬀerence between import and export value is not large among industrial groups which produce materials such as ceramic, fabricated metal, andmanufactured goods classiﬁed by materials.

4

The determinants of trade of SME products

4.1

The structure of trade of SME products

In this section, we investigate trade structure and determinants of SME products12 . For most of the SME products, the share of East and Southeast Asia islarge in both export and import. This is obvious when it is compared with the
129 [Lumber and furniture] 131, 132, 133, 139, 141, 142, 143 [Paper and printing] 151, 154,
162, 163 [Manufactured goods classiﬁed by materials] 171, 182, 183, 189, 194, 195, 202, 209[Leather] 211, 212, 213, 214, 215, 216, 217, 218, 219 [Ceramic] 222, 227, 228 [Fabricated metal]
239, 252, 254, 257, 258 [Miscellaneous] 316, 321, 323, 326, 327.
12 We have to pay attention that SME products do not cover all of the trade concerning
SMEs; it only includes the industries dominated by SMEs. SMEs manufacturing large-sizebased products and mixed-size-based products are excluded.

11

regional composition of overall trade. Table A3.1 and A3.2 of Appendix show
the composition of trade by region for SME products and overall trade (columns
noted ’all’) using the average during 2001-2010. The sum of China, NIEs and
ASEAN4 is higher in 46 products for export and 44 products for import thanoverall trade. Focusing on trade with China, the share in 27 products for export and in 43 products for import is larger than the overall export respective
import. At the same time, the share of developed countries such as US and EU
is smaller for SME products. It suggests that trade of SME products tends tobe large with geographically approximate partners or developing countries.

4.2

The determinants of trade

4.2.1

The model

We examine the determinants of trade of SME products employing the gravity
model. The gravity model is a model which explains the bilateral trade volume inspired from the law of universal gravitation. For variables it contains
the economic size of countries of both origin and destination, and geographicaldistance. This model was used in the early study by Tinbergen [22], and has
been applied in a lot of empirical analyses of trade. The basic model for theestimation is expressed as follows:β3
Eij = Yiβ1 Yjβ2 Dij(1)

Eij is bilateral trade ﬂow from country i to country j. Yi is an economic
size of export country and indicates the capacity of supplying. Generally, GDPis used as a proxy variable. The larger the GDP of the export country is,
the more goods are supplied and traded. Yj is an economic size of importcountry which indicates the market size, and for this GDP is also used as aproxy variable. The larger the GDP of the import country is, the more goods
are demanded and traded. Both coeﬃcients of β1 and β2 are expected to bepositive. Geographical distance Dij indicates transportation cost and aﬀects
negatively upon trade ﬂow.
In this paper, both export and import between Japan and each trade partner
are tested as dependent variable. In our estimation, trade preferential factor,
diﬀerence of income level, and the exchange rates are added to expand themodel13 . The equation is expressed as follows using log-formation.
ln Eij = β0 +β1 ln Yi +β2 ln Yj +β3 ln Dij +β4 ln Pij +β5 ln yij +β6 ln xij +ij (2)13 The gravity model has been expanded to include GDP per capita by Linnemann [15] totest the eﬀect of income level.

12

Trade preferential factor Pij is a dummy variable for free trade agreement,
FTA, in eﬀect between Japan and trade partners. β4 is expected to be positive.
yij is a variable indicating the diﬀerence of income level. xij is exchange rate.
Appreciation of the yen increases import (positive sign) and decreases export(negative sign).
Among these gravity factors, in which do the characteristics of SME products appear? The products of SMEs tend to be labor-intensive and low-wage
goods, as shown with related indices in Table 1. From these characteristics, twohypotheses are raised as follows:
Hypothesis 1 Import from the countries with lower income level tends to belarger for SME products in comparison with overall import.
As the factor proportion theory suggests, it is a developing country whichholds comparative advantage in labor-intensive industries. Import of labor intensive goods including SME products to Japan is expected to be large fromdeveloping countries, as Japan is relatively capital abundant. Hypothesis 1 istested using the diﬀerence of income level between Japan and each trade partner. The eﬀect of income diﬀerence on import, β5 in equation (2), is expected
to be positive, and the coeﬃcient to be larger than overall import. In otherwords, SME products are imported more from the country with larger income
diﬀerence. While, the eﬀect of income diﬀerence on export is not clear, since
Japan has comparative disadvantage in those labor-intensive industries.
The other hypothesis is regarding an eﬀect of transport cost. For an importer, a transport cost is an additional cost to a price of imported goods. A
large transport cost relative to the price diminishes the beneﬁts of importing,and the importer tends to choose to import from geographically closer countries.
This is more likely to happen for low-price, low valued-added goods than highprice, high value-added goods, because the relative transport cost is higher for
low-price goods. Therefore, trade with distant countries is expected to be less for
low-price goods. There are some discussions in recent literature with regard to
the relationship between price/quality and distance. Baldwin and Harrigan [2]show that high-price/high-quality goods are exported to distant markets, while
low-price goods are not. Similarly, Hummels and Skiba [14] empirically examine
the ’Alchian-Allen eﬀect’, and show a positive relationship between prices andtransportation costs.
SME products are assumed to be low-price, low value-added goods fromtheir labor-intensive characteristics. Therefore, trade with distant countries is
expected to be smaller.

13

Hypothesis 2 Trade of SME products tend to be susceptible to transport
costs.
The geographical distance is a proxy for transport cost and has a larger
eﬀect on SME products than overall trade14 . The coeﬃcient of distance, β3
in equation (2), is expected to be larger for SME products in comparison withoverall trade.
4.2.2

Data

In regression analysis, the panel data of bilateral trade of Japan and each trade
partner during 2001-2010 are employed. In order to make the characteristic of
SME products clear, both trade value of SME products and overall trade aretested and compared. In addition, SME products are divided into 10 industrial groups shown in Figure 4 and tested. The bilateral trade value of SME
products, both aggregated value and respective values for ten industrial groups,
are prepared from the database prepared in the previous section. Overall export/import value used as dependent variable is obtained from Trade Statistics.
These data are deﬂated using Corporate Goods Price Index 2005 by the Bankof Japan.
As shown in equation (2), a logarithm of trade value is used as a dependent
variable. However, disaggregated trade data includes zero observations which
are impossible to transform into a logarithm. In such a case, we need to employ
some methods suitable for data with zero observation. Therefore, we use threemethods in this analysis. The ﬁrst method is to use logarithm of the value
added one to each trade value (ln(Eij + 1)). Then, it is possible to employ panel
regression with the random eﬀect which is used most commonly in the test of thegravity model. The second method is Tobit regression with random eﬀect. Tobit
model is employed when a dependent variable is truncated and contains a lotof zero observation. Also with this method, a logarithm of trade value plus one
is used. The third method is negative binomial regression with ﬁxed/random
eﬀect. Negative binomial model (hereafter NB model) is employed when adependent variable is count data and it also allows the dependent variable toinclude zero observation15 . In regression of the NB model, dependent variable
is expressed in level, Eij , and independent variables are expressed with linear
14 For apparel products,

there is another reason. Dicken [6] explains the importance ofgeographical proximity with a rapid rate of product turnover. He points that low-cost countries
located close to the major consumer markets have a relative advantage in industries wheretime to delivery is critical.
15 For details of NB model, see Cameron and Trivedi [4].

14Table 1: Summary statistics(Export)PARTRGDP
JAPANRGDPDIFGDPPCDISTANCE
EXCHRATE(Import)PARTRGDP
JAPANRGDPDIFGDPPCDISTANCE
EXCHRATE

n=1930Mean
28.03896
33.84453
2.378161
9.150367
6.19e-08
n=1890Mean
28.08149
33.84453
2.381252
9.14526-2.17e-06

Std. Dev.
2.551129
0.0319602
1.711203
0.4343674
1.000259

Min
18.70462
33.79756-1.030336
7.057898-2.60295Max
34.97562
33.89191
11.85857
9.829572
2.61194

Std. Dev.
2.557016
0.0319604
1.705538
0.4370898
1.000268

Min
18.70462
33.79756-1.030336
7.057898-2.6029Max
34.97562
33.89191
11.85857
9.829572
2.6119relationship as equation (2). Among these three methods, Tobit model and NB
model are used for the regression of disaggregated data which includes a lot ofzero observations.
GDP data is obtained from the National Accounts database of the United
Nations. Real GDP in national currency of each trade partner is transformed toa yen-basis using exchange rates crossrates prepared by UNCTAD. As a proxyof transport cost, direct distance between the capital cities is used16 . All ofthese variables are transformed to logarithm.
The exchange rates are also introduced as an independent variable. We areinterested in the volatility of currency, not in the cross-country diﬀerence, therefore normalized exchange rates for each trade partner are employed. A larger
value means the appreciation of the yen, while a smaller value means depreciation. FTA dummy variable is introduced as a variable which indicates one since
the year when FTA enters into eﬀect. In case that FTA goes into eﬀect during
the last quarter of the year, FTA dummy starts to take one from the following
year. In addition, all regressions include the year dummy. Summary statistics
and correlation matrix of variables are listed in Table 1 and 2 respectively.
4.2.3

The results of estimation

We estimate determinants of trade using panel data of 193 countries for export
and 189 countries for import. The results of estimation for export and import are
shown in Table 3 and 4 respectively. Hausman test for NB regression supports
the estimation with ﬁxed eﬀect, therefore the results of NB regression withrandom eﬀect are omitted. In addition, export and import of SME products by
16 Distance is calculated using a calculator of the website of Geospatial Information Author-

ity of Japan and the latitude and longitude of each capital city.

15

Table 2: Correlation matrix(Export)PARTRGDP
JAPANRGDPDIFGDPPCDISTANCE
EXCHRATEFTADUMMY(Import)PARTRGDP
JAPANRGDPDIFGDPPCDISTANCE
EXCHRATEFTADUMMYPARTRGDP JAPANRGDP DIFGDPPC DISTANCE EXCHRATE FTADUMMY
1.00000.03811.0000-0.4484-0.02021.0000-0.10200.00000.03321.0000-0.0517-0.10400.08710.00001.00000.13850.0670-0.0740-0.1138
0.05331.0000PARTRGDP JAPANRGDP DIFGDPPC DISTANCE EXCHRATE FTADUMMY
1.00000.03781.0000-0.4578-0.0199
1.0000-0.09400.00000.03731.00000.0519-0.1071
0.08800.00001.00000.13730.0677-0.0753-0.1127
0.05381.0000industrial groups are tested using NB model with ﬁxed eﬀect, random eﬀect andTobit model with random eﬀect. Results of these estimation with disaggregateddata are listed Table A4.1 to A 4.6 in Appendix.
With regard to export, most coeﬃcients of P ART RGDP and DIST AN CE
show expected signs for both overall export and SME products. Meanwhile, coeﬃcients of JAP AN RGDP on SME products show unexpected negative signs inpanel regression and Tobit regression with random eﬀect, although they are insigniﬁcant. These three variables are fundamental factors for the gravity model,
and they indicate similar tendency in the results of SME products by industrial
groups. Concerning other variables, the signs of coeﬃcient of DIF GDP P C
are diﬀerent by estimating method; positive for Tobit model, and negative for
NB model. Trade preferential factor F T ADU M M Y is with a positive sign as
expected, however, some cases are statistically insigniﬁcant. All coeﬃcients of
EXCHRAT E are positive, although expected signs are negative.
Regarding import, we obtain statistically signiﬁcant coeﬃcients for P ART RGDP
with positive signs and DIST AN CE with negative signs for both overall imports and SME products with any methods of estimation. The coeﬃcients of
JAP AN RGDP are signiﬁcant but negative. EXCHRAT E and F T ADU M M Y
also show expected positive signs, however, only the results using NB model are
signiﬁcant for SME products. The coeﬃcients of DIF GDP P C are positive inpanel regression and Tobit regression.
Among three fundamental variables of the gravity model, eﬀects of P ART RGDP
and DIST AN CE are clear; coeﬃcients of them show expected signs and are statistically signiﬁcant for both export and import, and using any method. More-

16

Table 3: Determinants of Export
Panel: random eﬀect
OverallSME
exportproducts
PARTRGDP
0.768
1.116
(19.409) *** (17.674) ***JAPANRGDP
4.670-2.398(2.350) ** (-0.888)
DIFGDPPC
0.062
0.290
(1.116)(3.334) ***DISTANCE-1.314-2.447(-6.278) *** (-7.310) ***EXCHRATE
0.132
0.094
(3.323) *** (1.739) *FTADUMMY
0.585
0.435
(2.163) ** (1.174)
Constant-151.705
83.130(-2.259) ** (0.911)

Tobit: random eﬀect
OverallSME
exportproducts
0.767
1.115
(18.909) *** (17.516) ***4.712-2.375(2.384) **(-0.883)
0.068
0.294
(1.179)(3.297) ***-1.316-2.448(-6.149) *** (-7.264) ***
0.130
0.093
(3.287) ***(1.724) *
0.576
0.431
(2.135) **(1.168)-153.092
82.399(-2.292) **(0.907)

Negative binomial: ﬁxed eﬀect
OverallSME
exportproducts
0.164
0.511
(11.356) ***(34.025) ***
4.168
3.302
(3.500) ***(5.058) ***-0.108-0.115(-5.364) ***(-5.291) ***0.314-1.150(3.735) *** (-13.427) ***
0.046
0.079
(1.958) *(5.670) ***
0.473
0.195
(3.444) ***(3.018) ***-148.258-114.763(-3.680) ***(-5.196) ***No of obs.
1930
1930
1930
1930
1930
1930
Wald-chi2
667.773488.521388.208
200.63321.4992774.063
loglikeihood-3333.216-3952.281-30396.051-21970.602
Hausman3795.32 ***98.38 ***
(Note) The numbers in parentheses are z-values. *, **, *** indicate signiﬁcant level at 10%, 5%, and 1%
respectively.
Negative binomial regression is tested using Stata command ’xtnbreg’ with FE option.
Fixed eﬀect is chosen from the result of Hausman test for FE vs RE.

17

Table 4: Determinants of Import
Panel: random eﬀect
Overall
SME
importproducts
PARTRGDP
1.168
1.231
(17.490) *** (11.712) ***JAPANRGDP -4.825-14.382(-2.131) ** (-4.164) ***DIFGDPPC
0.338
0.582
(3.781) *** (4.161) ***DISTANCE-2.173-2.547(-6.170) *** (-4.591) ***EXCHRATE
0.128
0.047
(2.790) *** (0.665)
FTADUMMY
0.265
0.224
(0.854)(0.473)
Constant
164.575
485.935(2.152) ** (4.166) ***No. of obs.
Wald-chi2

1890
442.6901890
219.197

Tobit: random eﬀect
Overall
SME
importproducts
1.163
1.223
(16.693) *** (11.216) ***-4.704-14.229(-2.090) **(-4.141) ***
0.357
0.606
(3.757) ***(4.110) ***-2.179-2.555(-5.949) *** (-4.457) ***
0.123
0.040
(2.663) ***(0.566)0.253
0.216(0.821)(0.460)160.655
480.991(2.114) **(4.147) ***Negative binomial: ﬁxed eﬀect
Overall
SME
importproducts
0.460
0.512
(33.100) ***(31.627) ***-1.486-7.398(-2.495) **(-7.495) ***-0.091
0.036(-4.444) ***(1.613)-1.339-1.080(-16.374) *** (-13.440) ***
0.064
0.076
(5.201) ***(3.905) ***
0.043
0.207
(0.715)(2.126) **50.672245.449(2.508) **(7.342) ***1890
623.5931890
1750.808

1890
473.395

1890
1448.207

loglikeihood-3558.230-4361.436-27345.731-22931.229
Hausman239.43 ***23.06 *
(Note) The numbers in parentheses are z-values. *, **, *** indicate signiﬁcant level at 10%, 5%, and 1%
respectively.
Negative binomial regression is tested using Stata command ’xtnbreg’ with FE option.
Fixed eﬀect is chosen from the result of Hausman test for FE vs RE.

over, all of the results for disaggregated export/import by industrial groups support it. Meanwhile, JAP AN RGDP shows unexpected or insigniﬁcant results.
A possible reason of this is that JAP AN RGDP do not adequately express thediﬀerence of supply capacity or demand size of Japan – rather it represents atime-variant factor such as business ﬂuctuation. Taking this into account, results of JAP AN RGDP are less important as a gravity factor. From the resultsof P ART RGDP and DIST AN CE, we can conclude that the gravity model isapplicable for the trade of SME products; the longer the distance with a trade
partner, the less SME products are traded.
Then, how about two hypotheses for the characteristics of SME products?
From the ﬁrst hypothesis concerning DIF GDP P C, we expect that the coefﬁcient of import of SME products shows a larger value than that of overall
import. In Table 4, the results of panel regression and Tobit model with random eﬀect support the hypothesis. For the result using NB model, the testfor overall import results in an unexpected negative sign, while SME products
holds a positive sign. From these results, it is concluded that the import of SME
products tends to be larger with developing countries.

18

The second hypothesis is concerning the eﬀect of DIST AN CE. We expectthat the coeﬃcient of SME products is larger than overall export/import. In
the result of export in Table 3, coeﬃcients of SME products exceed those of
overall export in panel regression and the Tobit model. In NB model it is
impossible to compare the strength of eﬀect, because the signs are opposite
between overall export and SME products. The results are similar in the caseof import. Coeﬃcients using panel regression and Tobit regression with random
eﬀect support the hypothesis 2. However, the coeﬃcient of overall import islarger than SME products in NB model.
As a whole, hypothesis 1 is supported from our regression results. Hypothesis
2 is partly supported, and it depends on the method of regression.

5

Concluding remarks

This paper investigates the structure and the determinants of trade of SME
products using the trade database prepared for selected SME-based industries.
The main ﬁndings of this paper are summarized as follows. Firstly, the estimatedtrade value for SME products shows that ﬁrms in SME-based industries arefacing a large inﬂow of imported goods, while the volume of their export isrelatively small. In a third of SME-based industries, import exceeds domestic
production. Secondly, the share of Asian countries in SME products is larger
than those shares in overall trade. Thirdly, the gravity model explains the trade
ﬂow of SME products. The susceptibility to the distance and the diﬀerence ofincome level is consistent with the labor-intensive character of SME products.
The results from our analysis suggest that SMEs in Japan are facing a severe
situation in competing with products from developing countries in the domestic
market. This paper focuses on the trade during 2001-2010, however, the results
should be interpreted in the context of a long-term change of industrial andtrade structure since 1980s in Japan and the world economy. It illustrates aconsequence of the changes of the circumstances for Japanese SMEs. Further
research is necessary to examine quantitatively whether the trade has aﬀect
for SMEs concerning their domestic production, employment, and entry/exit.
These issues should be analyzed in future studies.

Acknowledgement
An earlier version of this paper was presented at the 71th annual conferenceof The Japan Society of International Economics held on 13-14th October at

19

Konan University. I would like to thank Kozo Kiyota, Eiichi Tomiura, Taiji
Furusawa, Tetsuya Saito, Takamune Fujii, Tadashi Ito and other participants
for helpful comments and suggestions.

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22

Appendix Table 1. SME shipment ratio and thenumber of the ﬁrms in SME-based industries
JSIC
3
Industry
digit
code
92
Seafood products
93
Canned and preserved fruit and vegetable products
96
Flour and grain mill products
103 Tea and coﬀee
104 Manufactured ice
106 Prepared animal foods and organic fertilizers
111 Silk reeling plants
113 Twisting and bulky yarns
114 Woven fabric mills
115 Knit fabrics mills
117 Rope and netting
118 Lace and other textile goods
119 Miscellaneous textile mill products
121 Textile outer garments and shirts, including
bonded fabrics and lace, except Japanese style
122 Knitted garments and shirts
123 Underwear
124 Japanese style apparel and Tabi -sock
125 Other textile apparel and accessories
129 Miscellaneous fabricated textile products
131 Sawing, planning mills and wood products
132 Millwork, plywood and prefabricated structural
wood products
133 Wooden, bamboo and rattan containers
139 Miscellaneous manufacture of wood products,
including bamboo and rattan
141 Furniture
142 Furniture for religious purposes
143 Sliding doors and screens
151 Pulp
154 Paper products
162 Plate making for printing
163 Bookbinding and printed matter
169 Service industries related to printing trade
171 Chemical fertilizers
182 Lubricating oils and greases (not made inpetroleum reﬁneries)
183 Coke
189 Miscellaneous petroleum and coal products
194 Formed and reinforced plastic products
195 Compounding plastic materials, including
reclaimed plastic
202 Rubber and plastic footwear and its ﬁndings
209 Miscellaneous rubber products
211 Leather tanning and ﬁnishing
212 Mechanical leather products, except gloves andmittens
213 Cut stock and ﬁndings for boots and shoes

23

SME Number Number Ratio
shipofofofment enter- SMEs SMEs
ratio prises
(%)
87.1
7,401
7,363
99.5
90.9
2,048
2,039
99.6
74.4
690
685
99.3
73.3
1,320
1,317
99.8
100.0
176
176
100.0
78.7
692
690
99.7
86.6
6
6
100.0
86.6
791
791
100.0
84.8
1,998
1,992
99.7
96.1
447
447
100.0
92.2
309
309
100.0
94.2
699
698
100.0
82.1
1,361
1,354
99.4
86.3
5,730
5,712
99.7
93.0
77.1
94.9
86.3
90.1
79.3
81.7

2,372
804
472
1,164
3,828
5,062
1,910

2,371
801
472
1,160
3,825
5,056
1,898

100.0
99.6
100.0
99.6
99.9
99.9
99.4

96.5
92.0

768
1,197

768
1,197

100.0
100.0

74.3
96.8
84.0
95.6
74.6
78.0
78.2
100.0
84.8
100.0

4,403
425
3,048
21
984
1,419
2,376
104
113
82

4,386
425
3,045
20
975
1,412
2,371
104
112
82

99.6
100.0
99.9
99.2
99.1
99.5
99.8
99.5
99.1
100.0

100.0
100.0
85.2
75.8

6
84
1,419
774

5
84
1,410
768

87.9
99.6
99.4
99.2

75.4
82.8
86.0
100.0

573
460
306
42

571
458
305
42

99.7
99.5
99.8
100.0

100.0

234

234

100.0

JSIC
SME Number Number Ratio
3
Industry
shipofofof
digit
ment enter- SMEs SMEs
code
ratio prises(%)
214 Leather footwear
100.0
607
607
100.0
215 Leather gloves and mittens
100.0
43
43
100.0
216 Baggage
100.0
357
357
100.0
217 Handbags and small leather cases
97.7
678
678
100.0
218 Fur skins
100.0
4
4
100.0
219 Miscellaneous leather products
100.0
112
112
100.0
222 Cement and its products
83.1
4,813
4,794
99.6
227 Abrasive products
76.1
232
229
98.6
228 Aggregated and stone products
98.0
2,308
2,308
100.0
239 Miscellaneous iron and steel
91.5
2,266
2,257
99.6
242 Secondary smelting and reﬁning of non-ferrous
87.6
313
310
99.0
metals, including non-ferrous alloys
252 Tableware (occidental type), cutlery, hand tools
85.7
2,411
2,404
99.7
and hardware
254 Fabricated constructional and architectural
74.3 15,187 15,143
99.7
metal products, including fabricated plate workand sheet metal work
256 Metal coating, engraving and heat treating,
80.2
5,604
5,580
99.6
except enameled ironware
257 Fabricated wire products
78.2
911
910
99.9
258 Bolts, nuts, rivets, machine screws and wood screws 73.0
1,711
1,698
99.2
316 Ophthalmic goods, including frames
76.3
349
346
99.0
321 Precious metal products, including jewel
79.8
574
573
99.8
323 Toys and sporting goods
77.1
1,437
1,431
99.6
326 Lacquer ware
100.0
513
513
100.0
327 Sundry goods of straw, Tatami mats,
100.0
1,568
1,568
100.0
umbrellas and other daily commodities
Manufacturing− 250,074 246,938
98.7(Source) Own Calculation using METI Census of Manufactures.
(Notes) Industries with shaded code are classiﬁed in basic materials, otherwise in daily necessities.
Number of enterprises and business establishments are average between 2002-2007.

24

Appendix Table 2. Shipment value, trade value
and import penetration ratio (average during 20022009)
JSIC Shipment Export
ExportImportImportImport
code
value
value
ratio tovalue
ratio to penetration(mil. yen) (mil. yen) shipment (mil. yen) shipment
ratio
92 3,296,077
117,071
0.036 1,291,769
0.392
0.289
93
787,8728,736
0.011
429,5320.545
0.355
96 1,317,288
9,508
0.007
45,992
0.035
0.034
103
585,1216,545
0.011
41,862
0.072
0.067
106 1,095,638
8,027
0.007
146,137
0.133
0.118
111
623
213
0.341
3,725
5.983
0.901
113
83,161
877
0.011
1,627
0.020
0.019
114
449,751
366,0770.814
108,479
0.241
0.565
115
122,250
64,3770.527
11,072
0.091
0.161
117
78,4808,586
0.109
15,840
0.202
0.185
118
103,320
12,834
0.124
8,313
0.080
0.084
119
670,640
159,3220.238
149,216
0.222
0.226
121
749,419
15,3390.020 1,107,499
1.478
0.601
122
307,308
15,4600.050
877,634
2.856
0.750
123
183,367
5,218
0.028
196,259
1.070
0.524
124
44,043745
0.017
24,710
0.561
0.363
125
232,704
8,358
0.036
238,405
1.025
0.515
129
641,577
14,521
0.023
345,0250.538
0.355
131 1,004,126
3,044
0.003
558,6430.556
0.358
132 1,266,338
1,480
0.001
294,047
0.232
0.189
133
105,534
312
0.003
3,917
0.037
0.036
139
157,656
2,705
0.017
99,7030.632
0.392
141 1,389,610
81,1670.058
426,225
0.307
0.246
142
45,282
12,551
0.277
0.217
143
313,927
3,449
0.011
30,988
0.099
0.091
151
31,351
10,5220.336
144,998
4.625
0.874
154
516,887
39,4490.076
55,369
0.107
0.104
162
447,180
193
0.000
6
0.000
0.000
163
323,710
978
0.003
239
0.001
0.001
171
259,490
14,4960.056
94,374
0.364
0.278
182
167,290
575
0.003
0.003
183
152,468
39,151
0.257
61,005
0.400
0.350
189
85,67834,1400.398
74,017
0.864
0.590
194
784,173
43,421
0.055
21,822
0.028
0.029
195
570,869
53,844
0.094
154,200
0.270
0.230
202
103,5962,524
0.024
269,170
2.598
0.727
209
220,933
141,137
0.639
78,244
0.354
0.495
211
70,48014,0140.199
18,325
0.260
0.245
212
30,559
35
0.001
29
0.001
0.001
213
18,6382,034
0.109
21,496
1.153
0.564
214
187,352
1,357
0.007
115,152
0.615
0.382
215
9,066
31
0.003
11,122
1.227
0.552
216
64,3633,544
0.055
311,767
4.844
0.837
217
91,826689
0.007
111,174
1.211
0.550
218
436
94
0.216
1,246
2.856
0.785
219
12,892841
0.065
26,2872.039
0.686
222 2,937,353
30,1150.010
15,369
0.005
0.005
227
196,11355,132
0.281
9,201
0.047
0.061

25

JSIC Shipment Export
ExportImportImportImport
code
value
value
ratio tovalue
ratio to penetration(mil. yen) (mil. yen) shipment (mil. yen) shipment
ratio
228
516,171304
0.001
91,511
0.177
0.151
239 2,960,942
16,233
0.005
111,404
0.038
0.036
252
700,269
69,2880.099
101,195
0.145
0.138
254 5,994,761
56,7980.009
184,971
0.031
0.030
257
286,095
59,3930.208
44,355
0.155
0.164
258
867,934
201,6280.232
65,954
0.076
0.090
316
134,577
45,261
0.336
129,373
0.961
0.592
321
202,559
76,921
0.380
274,602
1.356
0.686
323
520,881
293,506
0.563
441,965
0.848
0.660
326
38,442
73
0.002
0.000
327
209,803
19,4260.093
87,5620.417
0.315(Note) ’-’ is denoted in industries lacking the corresponding code in either export of import.

26

Appendix Table 3. Trade structure by region,(average during 2001-2010, %)A3.1 Export
JSIC
All 92 93 96 103 106 111 113 114 115 117 118 119 121 122
China
14.3 18.9 7.7 2.7 4.3 8.4 1.6 54.3 52.9 61.2 10.5 54.4 31.8 7.5 6.0
NIEs
23.1 40.1 36.9 67.2 30.1 44.4 1.2 12.0 15.0 17.6 26.1 16.7 26.0 49.6 80.4
ASEAN4
9.0 11.6 4.8 7.8 7.1 13.5 2.5 5.8 4.8 5.8 10.5 7.9 10.0 1.0 0.6
North America 23.0 14.5 31.7 1.7 26.3 13.2 0.3 7.6 4.6 3.5 16.3 3.4 13.8 22.0 5.0
EU14.5 1.6 11.9 0.6 10.6 11.5 4.0 4.3 5.0 2.2 6.2 5.1 9.0 15.9 6.9Others
16.1 13.2 7.0 20.0 21.6 9.1 90.4 16.0 17.9 9.6 30.5 12.6 9.6 3.9 1.1
JSIC
123 124 125 129 131 132 133 139 141 143 151 154 162 163 171
China
11.2 43.8 35.8 16.9 20.8 59.4 9.9 8.9 20.7 32.3 53.7 27.2 17.8 5.6 2.4
NIEs
71.9 18.3 28.4 34.7 12.6 13.3 29.4 21.6 10.0 19.4 35.0 25.6 34.9 16.6 19.8
ASEAN412.4 11.2 3.1 9.3 38.6 17.0 14.9 8.0 12.6 12.6 6.7 19.4 30.1 14.4 42.6
North America 1.4 4.6 12.2 12.6 14.3 1.3 19.0 36.2 29.3 5.6 0.2 10.6 2.5 30.9 12.9EU
0.9 11.1 10.8 11.2 4.8 0.9 11.1 15.4 12.2 13.6 0.1 9.8 10.4 21.8 2.3
Others2.1 11.0 9.7 15.4 8.9 8.3 15.6 9.7 15.1 16.4 4.4 7.4 4.3 10.7 20.0
JSIC
183 189 194 195 202 209 211 212 213 214 215 216 217 218 219
China
10.1 37.2 14.9 31.1 13.6 15.7 44.1 13.0 53.4 6.5 13.6 3.8 2.6 52.3 30.2
NIEs
6.2 16.5 27.7 59.5 70.5 13.9 40.1 31.0 37.4 72.4 31.3 67.9 80.0 28.9 33.2
ASEAN4
3.9 8.6 7.6 1.5 5.3 13.7 10.7 18.2 3.2 2.0 7.2 2.6 0.7 0.4 14.9
North America 34.6 14.7 23.5 4.7 2.5 22.3 0.4 9.9 0.2 5.4 3.8 12.8 6.9 0.8 6.5
EU
14.3 11.5 19.8 1.4 4.1 17.4 0.8 11.4 0.6 9.7 21.5 10.0 7.2 16.8 5.5
Others30.9 11.6 6.6 1.8 4.1 16.9 3.7 16.4 5.2 3.9 22.7 2.9 2.8 0.8 9.8
JSIC
222 227 228 239 252 254 257 258 316 321 323 326 327
China
9.4 13.2 24.8 13.3 9.1 13.5 18.8 16.5 3.3 6.9 7.5 6.7 7.9
NIEs
43.3 38.0 32.7 27.2 26.7 40.7 24.7 9.2 15.0 67.4 28.3 27.4 19.9
ASEAN4
7.4 22.2 1.6 7.9 11.4 12.1 13.9 22.1 1.5 8.6 2.6 2.9 9.5
North America 1.9 12.5 20.6 6.6 23.3 12.4 18.9 29.1 43.2 8.4 33.7 26.2 23.2
EU
0.9 7.5 6.8 5.0 16.9 6.2 8.7 11.2 33.2 3.3 24.3 30.3 14.8
Others37.1 6.6 13.5 40.0 12.5 15.1 15.1 11.9 3.7 5.4 3.5 6.4 24.8
(Notes) NIEs include South Korea, Taiwan, Hong Kong, and Singapore.
ASEAN4 includes Thailand, Malaysia, Philipine and Indonesia.

27

A3.2 ImportJSIC
All 92 93 96 103 106 111 113 114 115 117 118 119 121 122
China
20.1 19.9 44.4 14.8 28.4 8.1 69.8 56.2 40.2 19.7 54.7 41.2 35.1 80.7 85.7
NIEs
9.4 8.2 5.1 0.4 1.9 1.6 0.0 1.6 8.2 32.5 8.4 15.5 15.2 0.7 2.8
ASEAN411.7 16.3 6.6 18.8 4.7 17.7 0.5 22.1 13.9 4.3 8.3 21.1 10.9 2.1 2.0
North America 14.4 14.5 20.6 53.7 7.7 29.9 0.0 3.2 1.1 9.2 3.9 3.4 12.4 1.1 1.2
EU
11.1 3.2 8.8 2.5 11.7 8.2 0.0 15.9 29.4 31.9 3.4 16.2 13.6 7.4 5.1
Others33.3 37.8 14.4 9.9 45.5 34.5 29.6 1.0 7.3 2.3 21.3 2.5 12.9 8.0 3.2
JSIC
123 124 125 129 131 132 133 139 141 142 143 151 154 162 163
China
88.2 74.5 67.7 83.6 6.3 9.9 60.0 70.0 42.6 65.1 40.5 0.4 34.2 0.0 20.9
NIEs
1.3 14.3 5.5 2.9 0.6 1.0 1.9 2.4 9.0 0.7 4.2 0.0 7.6 4.4 14.9
ASEAN4
3.1 6.3 5.9 3.0 7.9 69.2 4.3 18.3 20.2 17.5 33.0 7.0 22.4 2.9 6.0
North America 0.2 1.1 1.8 2.1 25.7 4.1 6.7 1.8 5.1 0.0 12.7 64.0 5.8 50.2 27.8
EU
1.4 1.0 14.9 3.5 15.8 11.5 11.2 4.6 12.8 0.3 8.8 4.2 7.7 30.6 26.9
Others
5.9 2.8 4.2 4.9 43.7 4.3 16.0 2.9 10.5 16.3 0.7 24.4 22.3 11.9 3.6
JSIC
171 182 183 189 194 195 202 209 211 212 213 214 215 216 217
China
14.0 18.1 91.1 12.8 31.5 44.4 86.4 30.0 6.5 5.7 79.4 29.6 76.1 56.6 40.1
NIEs
5.6 6.4 1.7 12.2 16.3 11.9 2.1 8.4 7.0 5.6 6.3 1.4 1.1 2.2 1.0
ASEAN4
11.4 0.4 0.0 2.4 17.2 8.2 4.0 41.8 4.1 0.0 4.7 5.1 11.1 1.7 1.0
North America 45.3 33.0 0.1 63.0 15.0 18.1 0.3 7.0 5.0 13.8 1.5 2.6 0.6 2.2 1.1EU
4.6 37.5 0.1 3.8 18.5 14.2 3.4 8.8 25.5 37.8 2.7 36.9 8.7 34.4 55.4
Others19.3 4.6 7.0 5.9 1.6 3.0 3.9 4.0 51.9 37.2 5.4 24.5 2.5 2.9 1.5
JSIC
218 219 222 227 228 239 252 254 257 258 316 321 323 327
China
44.4 47.0 30.5 27.0 89.0 53.5 48.2 42.5 47.4 30.3 15.1 4.0 73.6 76.6
NIEs
2.0 3.8 33.0 17.4 1.2 12.5 12.4 18.8 28.3 26.6 6.4 7.7 5.6 3.3
ASEAN4
0.3 2.1 10.6 20.6 0.8 6.7 4.1 25.6 5.9 6.6 13.6 5.7 5.6 9.5
North America 5.6 3.7 8.4 18.6 0.2 9.6 13.8 5.2 7.2 25.7 15.2 17.4 7.9 3.7
EU
32.3 39.4 15.2 14.0 6.1 15.1 17.3 5.6 8.8 8.1 47.6 35.3 5.6 5.1
Others
15.4 4.1 2.2 2.4 2.7 2.5 4.1 2.3 2.3 2.7 2.1 29.8 1.7 1.9(Notes) NIEs include South Korea, Taiwan, Hong Kong, and Singapore.
ASEAN4 includes Thailand, Malaysia, Philipine and Indonesia.

28

Appendix Table 4. Determinants of trade ﬂow
for SME products
A4.1 Export, Negative binomial regression: ﬁxed eﬀect
Food andTextileApparelLumber andPaper andBeverages
Furniture
Printing
PARTRGDP
0.406
0.593
0.586
0.622
0.678
(17.746) ***(28.693) ***(27.435) ***(28.139) ***(28.613) ***JAPANRGDP
2.742-7.069-0.858
4.829
2.097(1.825) *(-7.303) ***(-0.634)(3.445) ***(1.531)DIFGDPPC-0.446-0.227-0.057-0.082-0.027(-15.433) ***(-8.441) ***(-2.128) **(-3.141) ***(-0.947)
DISTANCE-1.414-1.398-1.585-1.347-0.966(-16.350) *** (-15.087) *** (-13.860) *** (-15.890) *** (-10.668) ***EXCHRATE
0.113
0.113
0.030
0.093
0.021
(3.391) ***(5.542) ***(1.082)(3.125) ***(0.760)FTADUMMY
0.614
0.257
0.307
0.298
0.175
(4.112) ***(2.475) **(2.348) **(2.184) **(1.311)
Constant-90.52235.0523.617-169.633-82.559(-1.781) *(7.164) ***(0.515)(-3.570) ***(-1.784) *No. of obs.
Wald-chi2
loglikeihood
Hausman

1550
1760
1710
1770
1610
1167.606
1845.919
1455.239
1671.945
1459.601-12200.92-15736.003-11849.394-12000.255-11202.661
1.44
193.51 ***82.94 ***
20.3
80.83 ***
Manuf. Goods Leather
Ceramic
FabricatedMiscellaneousby Material
Metal
PARTRGDP
0.611
0.863
0.799
0.545
0.403
(35.822) ***(26.316) ***(31.680) ***(31.813) ***(23.100) ***JAPANRGDP
4.408-8.5663.933-1.8980.457(5.242) ***(-5.536) ***(2.835) ***(-2.150) **(0.387)
DIFGDPPC-0.082-0.068-0.141-0.142-0.334(-3.647) ***(-1.766) *(-4.462) ***(-6.634) *** (-14.215) ***DISTANCE-0.856-1.474-1.068-1.164-1.267(-9.530) *** (-16.084) *** (-11.714) *** (-14.130) *** (-15.809) ***EXCHRATE
0.102
0.087
0.065
0.069
0.160
(5.981) ***(2.759) ***(2.226) **(3.754) ***(6.531) ***FTADUMMY
0.039
0.182
0.356
0.029
0.121
(0.460)(1.147)(2.560) **(0.310)(0.988)Constant-158.426
276.382-147.514
59.629-15.354(-5.602) ***(5.273) ***(-3.160) ***(1.999) **(-0.385)
No. of obs.
1900
1310
Wald-chi2
2183.564
1334.523
loglikeihood -18115.906-7161.189
Hausman
118.75 ***
18.1

1570
1880
1760
1929.326
1884.063
1648.201-10516.528-18649.192-14931.381
49.61 ***
152.23 ***468.63 ***(Note) The numbers in parentheses are z-values. *, **, *** indicate signiﬁcant level at 10%,5%, and 1% respectively.
Negative binomial regression is tested using Stata command ’xtnbreg’ with FE option.
Groups with all zero outcomes are dropped.

29

A4.2 Export, Negative binomial regression: random eﬀect
Food andTextileApparelLumber andPaper andBeverages
Furniture
Printing
PARTRGDP
0.540
0.691
0.657
0.658
0.771
(25.594) ***(36.590) ***(34.571) ***(32.831) ***(37.201) ***JAPANRGDP
1.661-8.027-2.1234.852
1.986(1.127)(-8.911) ***(-1.703) *(3.564) ***(1.555)
DIFGDPPC-0.313-0.170-0.046-0.058-0.005(-12.004) ***(-7.061) ***(-1.850) *(-2.450) **(-0.184)
DISTANCE-1.653-1.472-1.524-1.441-1.060(-17.037) *** (-16.217) *** (-16.624) *** (-17.584) *** (-12.742) ***EXCHRATE
0.123
0.123
0.047
0.102
0.036
(3.787) ***(6.497) ***(1.802) *(3.544) ***(1.352)FTADUMMY
0.639
0.270
0.366
0.317
0.204
(4.302) ***(2.728) ***(2.992) ***(2.401) **(1.605)Constant-57.704
264.9265.834-171.164-80.783(-1.157)(8.670) ***(1.557)(-3.711) ***(-1.872) *No. of obs.
1930
1930
1930
1930
1930
Wald-chi21523.007
2711.994
2238.987
2158.019
2378.833
loglikeihood -14766.717-18459.605-14066.604-14360.799-13301.513
Manuf. Goods Leather
Ceramic
FabricatedMiscellaneousby Material
Metal
PARTRGDP
0.651
0.922
0.825
0.607
0.498
(40.067) ***(33.789) ***(37.003) ***(38.037) ***(30.726) ***JAPANRGDP
4.391-9.4633.885-1.9780.364(5.459) ***(-6.547) ***(2.879) ***(-2.353) **(0.323)
DIFGDPPC-0.075-0.063-0.167-0.101-0.298(-3.537) ***(-1.910) *(-5.905) ***(-4.981) *** (-13.546) ***DISTANCE-0.902-1.569-1.245-1.199-1.395(-10.451) *** (-18.696) *** (-15.003) *** (-15.195) *** (-18.295) ***EXCHRATE
0.106
0.093
0.083
0.072
0.157
(6.459) ***(3.106) ***(2.888) ***(4.075) ***(6.706) ***FTADUMMY
0.039
0.237
0.395
0.036
0.127
(0.474)(1.540)(2.889) ***(0.406)(1.081)
Constant-158.635
305.792-145.122
60.75-13.992(-5.867) ***(6.240) ***(-3.194) ***(2.138) **(-0.369)
No. of obs.
1930
Wald-chi22623.530
loglikeihood -20991.321

1930
2204.701-8730.880

1930
2742.600-12730.842

1930
2349.419-21616.950

1930
2381.522-17558.304(Note) The numbers in parentheses are z-values. *, **, *** indicate signiﬁcant level at 10%,5%, and 1% respectively.
Negative binomial regression is tested using Stata command ’xtnbreg’ with RE option.

30

A4.3 Export, Tobit regression: random eﬀect
Food andTextileApparelLumber and Paper andBeverages
Furniture
Printing
PARTRGDP
1.040
1.355
1.191
1.333
1.339
(9.760) *** (13.791) *** (16.475) *** (18.310) *** (18.134) ***JAPANRGDP-2.383-12.444
0.175-1.592-2.849(-0.482)(-3.561) ***(0.047)(-0.376)(-0.856)
DIFGDPPC-0.100
0.233
0.136
0.176
0.239
(-0.664)(1.737) *(1.320)(1.673) *(2.279) **DISTANCE-3.387-3.961-3.665-3.581-3.142(-6.010) ***(-7.604) *** (-9.626) *** (-9.324) *** (-8.065) ***EXCHRATE
0.088
0.053
0.062
0.085
0.104
(0.888)(0.746)(0.836)(1.018)(1.557)FTADUMMY
0.946
0.288
0.228
0.804
0.026
(1.399)(0.598)(0.452)(1.407)(0.056)
Constant
88.777426.785-0.15754.59092.497(0.532)(3.616) *** (-0.001)(0.382)(0.822)
No. of obs.
1930
1930
1930
1930
1930
Wald-chi2
127.425
195.421163.573
176.808144.281loglikeihood -5111.513-4485.460-4539.329-4773.261-4352.184
Manuf. Goods Leather
Ceramic
FabricatedMiscellaneousby Material
Metal
PARTRGDP
1.325
1.068
1.311
1.255
1.123
(16.350) *** (14.847) *** (14.085) *** (15.777) *** (12.469) ***JAPANRGDP-0.362-6.45614.810-4.986-10.777(-0.121)(-2.049) **(3.817) *** (-1.475)(-3.239) ***DIFGDPPC
0.370
0.104
0.149
0.283
-0.029(3.301) ***(1.022)(1.141)(2.556) **(-0.223)
DISTANCE-2.189-3.605-3.519-3.011-3.760(-5.090) ***(-9.486) *** (-7.149) *** (-7.151) *** (-7.930) ***EXCHRATE
0.083
0.184
-0.078-0.0340.339(1.371)(2.897) *** (-0.994)(-0.505)(4.983) ***FTADUMMY
0.291-0.029
0.375
0.183
0.403
(0.706)(-0.066)(0.702)(0.394)(0.882)
Constant
3.528
224.619-500.518
170.104374.802(0.035)(2.110) **(-3.819) ***(1.489)(3.336) ***No. of obs.
1930
Wald-chi2
238.261
loglikeihood -4180.989

1930
232.349-4250.635

1930
257.574-4660.895

1930
272.672-4393.114

1930
272.021-4379.650(Note) The numbers in parentheses are z-values. *, **, *** indicate signiﬁcant level at 10%,5%, and 1% respectively.

31

A4.4 Import, Negative binomial regression: ﬁxed eﬀect
Food andTextileApparelLumber andPaper andBeverages
Furniture
Printing
PARTRGDP
0.557
1.155
0.587
0.715
0.856
(25.896) ***(34.975) ***(26.521) ***(29.284) ***(20.376) ***JAPANRGDP-7.543-11.281-10.164-8.595-10.381(-5.007) *** (-12.209) ***(-7.029) ***(-5.203) ***(-5.506) ***DIFGDPPC
0.103
0.167
-0.0470.160-0.229(3.854) ***(4.454) ***(-1.758) *(5.497) ***(-5.216) ***DISTANCE-0.603-1.414-0.843-0.646-1.031(-6.836) *** (-13.159) ***(-9.224) ***(-6.386) ***(-8.425) ***EXCHRATE
0.028
0.128
0.058
0.098
0.200
(0.944)(6.470) ***(2.121) **(3.132) ***(5.050) ***FTADUMMY
0.286
0.238
0.298
0.505
0.340
(1.967) **(2.082) **(2.201) **(3.416) ***(1.926) *Constant
243.620359.116333.892
274.356333.857(4.778) ***(11.578) ***(6.821) ***(4.911) ***(5.238) ***No. of obs.
1700
1240
1730
1650
900
Wald-chi2
833.7711686.016
1157.775
1077.873
658.600
loglikeihood -18016.149-9767.067-15203.597-13440.018-6964.079
Hausmann
7.36
7.67
181.02 ***-109.425.72 **
Manuf. Goods Leather
Ceramic
FabricatedMiscellaneousby Material
Metal
PARTRGDP
0.691
0.732
1.098
0.898
0.729
(29.366) ***(26.461) ***(24.187) ***(30.003) ***(25.393) ***JAPANRGDP-5.492-6.794-13.645-12.212-9.303(-4.556) ***(-4.099) ***(-7.578) ***(-9.438) ***(-6.035) ***DIFGDPPC-0.3220.007
0.110-0.057-0.080(-9.400) ***(0.229)(2.330) **(-1.607)(-2.334) **DISTANCE-1.597-0.593-1.071-1.790-1.178(-15.938) ***(-6.242) ***(-8.272) *** (-15.020) *** (-12.307) ***EXCHRATE
0.167
0.133
0.084
0.140
0.104
(6.697) ***(4.205) ***(2.480) **(5.166) ***(3.455) ***FTADUMMY
0.465
0.407
0.385
0.287
0.504
(3.540) ***(2.691) ***(2.331) **(1.990) **(3.479) ***Constant
179.727212.667436.829401.660
303.054(4.387) ***(3.795) ***(7.189) ***(9.188) ***(5.809) ***No. of obs.
1360
Wald-chi2
1811.436
loglikeihood -11032.171
Hausman
3.73

1420
1082.723-11614.150-428.48

1010
1360
1530
890.0541241.993
1303.366-6809.697-9405.027-11450.538
51.72 ***
64.08 ***
35.03 ***

(Note) The numbers in parentheses are z-values. *, **, *** indicate signiﬁcant level at 10%,5%, and 1% respectively.
Negative binomial regression is tested using Stata command ’xtnbreg’ with FE option.
Groups with all zero outcomes are dropped.

32

A4.5 Import, Negative binomial regression: random eﬀect
Food andTextileApparelLumber andPaper andBeverages
Furniture
Printing
PARTRGDP
0.561
1.171
0.609
0.738
0.918
(26.513) ***(37.838) ***(29.767) ***(32.533) ***(23.062) ***JAPANRGDP-7.673-11.333-10.580-8.725-10.748(-5.125) *** (-12.713) ***(-7.455) ***(-5.289) ***(-5.986) ***DIFGDPPC
0.104
0.167
-0.0660.158-0.243(3.892) ***(4.570) ***(-2.622) ***(5.603) ***(-5.646) ***DISTANCE-0.620-1.479-0.957-0.661-1.054(-7.079) *** (-14.235) *** (-10.866) ***(-6.630) ***(-8.818) ***EXCHRATE
0.031
0.128
0.060
0.099
0.208
(1.039)(6.696) ***(2.209) **(3.167) ***(5.543) ***FTADUMMY
0.288
0.241
0.311
0.540
0.322
(1.996) **(2.137) **(2.303) **(3.649) ***(1.839) *Constant
248.080361.016348.314278.134344.603(4.895) ***(12.065) ***(7.249) ***(4.986) ***(5.675) ***No. of obs.
1890
1890
1890
1890
1890
Wald-chi2
869.952
1999.999
1401.045
1302.095
835.107loglikeihood -21135.399-11734.803-17944.559-16013.819-8405.655
Manuf. Goods Leather
Ceramic
FabricatedMiscellaneousby Material
Metal
PARTRGDP
0.699
0.767
1.185
0.925
0.797
(30.671) ***(28.608) ***(32.716) ***(33.647) ***(31.314) ***JAPANRGDP-5.621-7.264-15.794-12.943-9.891(-4.737) ***(-4.509) ***(-9.452) ***(-9.979) ***(-6.618) ***DIFGDPPC-0.3280.012
0.018-0.115-0.047(-9.707) ***(0.377)(0.430)(-3.476) ***(-1.504)
DISTANCE-1.618-0.609-1.309-1.790-1.221(-16.368) ***(-6.543) *** (-11.710) *** (-16.027) *** (-13.442) ***EXCHRATE
0.167
0.136
0.119
0.155
0.106
(6.789) ***(4.452) ***(3.744) ***(5.676) ***(3.608) ***FTADUMMY
0.465
0.418
0.517
0.361
0.443
(3.572) ***(2.816) ***(3.117) ***(2.440) **(3.049) ***Constant
184.076227.648508.913425.505
321.149(4.564) ***(4.179) ***(9.013) ***(9.712) ***(6.349) ***No. of obs.
1890
Wald-chi2
1985.427
loglikeihood -13259.894

1890
1218.035-13870.707

1890
1745.188-8319.024

1890
1706.898-11436.235

1890
1680.093-13797.100(Note) The numbers in parentheses are z-values. *, **, *** indicate signiﬁcant level at 10%,5%, and 1% respectively.
Negative binomial regression is tested using Stata command ’xtnbreg’ with RE option.

33

A4.6 Import, Tobit regression: random eﬀect
Food andTextileApparelLumber and Paper andBeverages
Furniture
Printing
PARTRGDP
1.282
1.468
1.296
1.544
1.176
(8.294) *** (11.393) ***(9.715) *** (10.995) ***(9.283) ***JAPANRGDP -17.279-15.527-9.976-19.057-11.867(-4.125) ***(-5.223) *** (-2.973) *** (-5.309) *** (-4.405) ***DIFGDPPC
0.651
0.918
0.692
0.927
0.461
(3.251) ***(5.546) ***(4.103) ***(5.083) ***(2.933) ***DISTANCE-2.113-3.605-3.772-2.369-2.657(-2.580) ***(-5.269) *** (-5.383) *** (-3.184) *** (-3.936) ***EXCHRATE
0.015
0.178
0.065
0.128
0.170
(0.175)(2.847) ***(0.928)(1.715) *(3.013) ***FTADUMMY
0.371
0.166
-0.1940.292
0.008(0.646)(0.409)(-0.424)(0.594)(0.022)
Constant
575.661519.956
341.576627.444395.195(4.072) ***(5.188) ***(3.018) ***(5.181) ***(4.353) ***No. of obs.
1890
1890
1890
1890
1890
Wald-chi2
207.616346.894487.946
571.067
515.469
loglikeihood -4762.029-4132.076-4349.538-4476.284-3959.893
Manuf. Goods Leather
Ceramic
FabricatedMiscellaneousby Material
Metal
PARTRGDP
1.372
1.527
1.255
1.410
1.476
(11.335) *** (12.660) *** (12.512) *** (12.868) *** (12.531) ***JAPANRGDP-9.728-6.378-11.019-9.695-10.758(-2.754) ***(-1.859) *(-3.580) *** (-2.732) *** (-2.899) ***DIFGDPPC
0.179
0.596
0.332
0.288
0.356
(1.108)(3.779) ***(2.446) **(1.893) *(2.197) **DISTANCE-3.721-2.929-2.720-3.860-3.755(-5.878) ***(-4.606) *** (-5.147) *** (-6.709) *** (-6.055) ***EXCHRATE
0.480
0.176
0.181
0.155
0.211
(6.565) ***(2.493) **(2.857) ***(2.125) **(2.765) ***FTADUMMY
0.372
0.573
0.628
-0.2700.946(0.771)(1.220)(1.491)(-0.555)(1.865) *Constant
329.655
203.863364.764327.755
361.630(2.766) ***(1.760) *(3.511) ***(2.736) ***(2.887) ***No. of obs.
1890
Wald-chi2
381.470
loglikeihood -4421.892

1890
419.624-4376.019

1890
404.761-4157.325

1890
401.028-4415.960

1890
309.144-4506.055(Note) The numbers in parentheses are z-values. *, **, *** indicate signiﬁcant level at 10%,5%, and 1% respectively.

34