KEO Discussion Paper No. 150

Multinationals, Intrafirm Trade, and Employment Volatility

Kozo Kiyota
Toshiyuki Matsuura
Yoshio HiguchiAugust 9, 2019

Multinationals, Intrafirm Trade, and Employment Volatility∗
Kozo Kiyota†

Toshiyuki Matsuura‡

Yoshio Higuchi§

Abstract
This paper examines the theoretically ambiguous relationship between thevolatility of employment growth and the foreign exposure of firms. We employ unique Japanese firm-level data over the period 1994–2012. This allows
us to investigate any differences in this relationship across multinational firms
and trading and nontrading firms, manufacturing and wholesale trade, andintrafirm and interfirm trade. One major finding is that in manufacturing, employment volatility increases as the share of intrafirm exports to total sales
increases. In contrast, in wholesale trade, employment volatility declines as
the share of intrafirm imports to total imports increases. One possible interpretation of these results is that the transmission of foreign supply and demand shocks could be through not only manufacturing, but also wholesaletrade firms. Further, a higher share of intrafirm trade could magnify foreign
demand shocks in manufacturing, and could mitigate foreign supply shocks
in wholesale trade.
Key words: Employment volatility; Multinational firm; Intrafirm trade;
Wholesale trade
JEL classification codes: F1; F16; L25; L81∗
This study was conducted as part of the “Microeconomic Analysis of Firm Growth” project undertaken at the Research Institute of Economy, Trade, and Industry (RIETI). This study utilizes the micro
data from the Basic Survey of Japanese Business Structure and Activities (BSJBSA) conducted by the
Ministry of Economy, Trade, and Industry (METI). The authors thank Philippe Chereau, Kyoji Fukao,
Taiji Furusawa, Yoshihisa Godo, Kaoru Hosono, Sebastien Jean, Ayako Kondo, Hiroshi Mukunoki, Sanae
Ohno, Hideo Owan, Chisato Shibayama, Michio Suzuki, Yui Suzuki, Mari Tanaka, Yasuyuki Todo, Eiichi Tomiura, Nobuaki Yamashita, and Uraku Yoshimoto for helpful suggestions on previous versions of
this paper. The authors also thank seminar participants at EHESS (École des Hautes Études en Sciences
Sociales), Musashi University, Okinawa University, Skema Business School, and RIETI, attendees at theTokyo Labor Economics Workshop, and the Japan Society of International Economics meeting for theiruseful comments. Kiyota and Matsuura gratefully acknowledge the financial support of the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid (JP18H03637, JP19H00598). Kiyota acknowledges
financial support from JSPS Grant-in-Aid (JP26220503). The usual disclaimers apply.
†
Keio University, University of Hawai‘i, Mānoa, and RIETI; E-mail: kiyota@sanken.keio.ac.jp‡
Keio University; E-mail: matsuura@sanken.keio.ac.jp§
Keio University and the Japan Institute for Labour Policy and Training; E-mail: higuchi@fbc.keio.ac.jp

“In an economy that is more open to foreign trade and investment, the demand for labor will
generally be more responsive to changes in the price of labor, or more elastic. ... The flatteningof labor demand curves as a consequence of globalization results in greater instability in labormarket outcomes.” – Rodrik (1997, p. 16 and p. 19)

1

Introduction

Increased labor demand elasticities have important labor market consequences. As
Rodrik (1997) noted, one of the main concerns is the relationship between foreign exposure and employment volatility, such that firms exposed to foreign demand and/or
supply are expected to have higher labor demand elasticities. For example, trade liberalization could result in greater product market competition, which results in higher
labor demand elasticities (e.g., Rodrik, 1997). Offshoring could also increase the substitution between foreign and domestic workers, which further flattens the labor demand
curve (e.g., Senses, 2010). Thus, it is widely believed by the public that employment infirms with greater foreign exposure tends to be more volatile than the employment ofdomestic firms.
If firms are risk neutral, whether employment volatility is high or not does not seemto be a problem, providing that there are no labor adjustment costs. However, whenfirms face high labor adjustment costs, higher employment volatility will certainly be
an issue because it will generate large adjustment costs to the economy as a whole.
Indeed, OECD (2005) featured labor adjustment costs as one of its concerns related tothe expansion of international trade and foreign direct investment (FDI). Therefore, theadjustment of labor in response to foreign exposure is then an important concern forpolicymakers.
Despite this, the relationship between foreign exposure and employment volatility
is theoretically ambiguous. For exports, employment volatility will be higher for exporters than for nonexporters if the volatility of the shocks is significantly higher forthe trading partners than for the home country, or if the export activity itself is volatileowing to changes in the exchange rate, for example. Conversely, exporters may be able
to absorb demand shocks in one country by diversifying their activities across othercountries.
Similarly, for imports, a firm that sources inputs from many countries can moreeasily absorb shocks to a particular input by switching its sources to another country
compared with a firm that sources inputs only from the domestic market. In contrast,
importers could have higher employment volatility if imported intermediate inputs
are easily substitutable for labor inputs. A similar argument applies to the case of
FDI. Because the relationship between foreign exposure and employment volatility istheoretically ambiguous, a need exists for empirical analysis to clarify the relationshipthat appears strongest in reality.
A number of studies have examined the causes and effects of sales volatility.1 For
example, Comin et al. (2009) examined the relationship between sales and wage volatilities among U.S. firms and found a positive relationship. However, they did not distinguish between domestic sales and exports. Elsewhere, Buch et al. (2009) examined therelationship between export openness and output volatility using firm-level data on
1

Another related strand of study is the estimation of labor demand functions, usually by focusing on
the differences between multinational and domestic firms (e.g., Barba Navaretti et al., 2003; Fabbri et al.,
2003; Kiyota and Matsuura, 2006; Murakami and Fukao, 2007; Buch and Lipponer, 2010). However, it
should be noted that increases in labor demand elasticity are not necessarily sufficient to explain increases
in employment volatility because high output volatility (for instance, through productivity shocks) could
also result in high employment volatility.

1German manufacturing firms for the period 1980–2011. They found that exporters had
a lower volatility of sales than nonexporters, although they did not focus on employment volatility. Lastly, Vannoorenberghe (2012) examined the relationship between
sales volatility and the export intensity of firms, as measured by the share of exports tototal sales. Using French firm-level data, they found that export intensity had a positiveand substantial effect on sales volatility. Nevertheless, they did not address the labormarket consequences.
To our knowledge, only Kurz and Senses (2016) have examined the relationshipbetween foreign exposure and employment volatility. Using firm- and transactionlevel data from U.S. manufacturing firms between 1991 and 2005, they found that theemployment of exporters was less volatile than that of domestic firms, whereas that ofimporters was more volatile. Their study also identified a nonmonotonic relationshipbetween export intensity and employment volatility, such that the effects of exports
could be more or less volatile, depending on the share of exports to total sales. On
this basis, they concluded that “as long as a firm’s overall exposure is not too large,
exporting affords firms the ability to diversify their demand sources across countries
and products” (p. 174).
Building on Kurz and Senses (2016), this paper examines the relationship between
FDI and employment volatility and between international trade and employment volatility using large-scale, firm-level data from Japan. The major contributions of the paper
are threefold. First, we distinguish among multinational firms, trading and nontrading firms when analyzing the relationship between foreign exposure and employment
volatility. Although Kurz and Senses (2016) made significant contributions to this literature, the scope of their study is limited in that they did not consider the relationshipwith FDI, even though it is an important globalization channel for most firms. Indeed,a recent study by Dobbelaere and Kiyota (2018) used firm-level data in Japan and foundthat exporters and multinational enterprises (MNEs) face a different type and degreeof labor market imperfection. Therefore, our study clarifies the heterogenous relationship between the mode of foreign exposure and employment volatility in a much morecomprehensive manner.
Second, we expand the industry coverage of the analysis. Our data cover not only
manufacturing, but also wholesale trade firms. As Bernard et al. (2010b) emphasized,
not only producers, but also wholesale traders engage in international trade. In addition, they found that wholesale traders behaved differently from producers. For example, trade by wholesale traders was less sensitive to market size compared with tradeby manufacturing firms. Similarly, Comin et al. (2009) found that the relationship between sales and wage volatility was stronger in services firms than in manufacturingfirms. This is because in the service sector, it can be difficult to monitor or assess performance, which makes it difficult to relate a worker’s individual performance andincentives to firm goals. As a result, when firms set wages, they need to relate wages
to observable firm-level performance (i.e., sales). The distinction between these typesof firms is important for a deeper understanding of international trade.
Third, we consider the difference between intrafirm and interfirm trade. The relationship between intrafirm trade and employment volatility is also ambiguous. On
the one hand, because intrafirm trade is, by definition, a transaction within a firm, we
expect intrafirm trade uncertainty to be weaker than that of interfirm trade. As a result, firms with a greater intensity of intrafirm trade could experience less employment
volatility, all else being equal.2 On the other hand, if intrafirm trade depends on the2
Kiyota et al. (2008) found that the intrafirm trade of Japanese MNEs increased as exchange rate uncertainty increased. This suggests that intrafirm trade helps make adjustments within the firm so it is able toabsorb exchange rate shocks.

2

supply chain of certain specific products, the firm could lack flexibility when unexpected shocks affect foreign demand or the supply chain itself. For example, whensevere flooding affected the Thai economy in 2011, Honda needed to halve its production in its Japanese and North American plants. This was not because the floods
directly influenced these plants, but because the affected plants in Thailand disrupted
its global supply of parts and components (Toyokeizai, Japanese version, November 14,2011). Thus, distinguishing between intrafirm and interfirm trade allows us to examine
the precise channel that transmits foreign shocks to domestic employment.
These extensions are simple but nontrivial because, as we will see, these extensions
enable us to identify the possible transmission mechanisms of foreign shocks throughinternational trade. In addition to these contributions, this paper is the first to addresses the relationship between foreign exposure and the employment volatility offirms in Japan.3 Thus, our study contributes to the literature by adding another national perspective to the available evidence.
Our major findings are summarized as follows. First, in manufacturing, the relationship between exports and employment volatility varies depending on the share of
intrafirm exports to total sales (intrafirm export intensity). In contrast, in wholesaletrade, exports and employment generally have no significant relationship. Second,in both manufacturing and wholesale trade, employment volatility tends to become
higher as the share of imports to total purchases increases. In wholesale trade, however, intrafirm imports tend to offset such shocks.
Third, MNEs exhibit higher employment volatility in manufacturing while, in wholesale trade, MNEs do not necessarily exhibit higher employment volatility. Fourth,
employment adjustments by trading firms are more likely to occur through temporary workers. Finally, we find negative correlations between the contractibility index
and intrafirm export intensity and between the contractibility index and employment
volatility. The implication of these results is discussed in more detail in the following
sections.
The remainder of the paper is organized as follows. Section 2 explains the analyticalframework and the data used. We present the baseline regression results in Section 3.
Section 4 discusses the robustness of our results and Section 5 presents more detailed
discussions on part-time/temporary workers and intrafirm/interfirm trade. Section 6
provides concluding remarks.

2

Analytical Framework

2.1

Methodology

To measure employment volatility, following Kurz and Senses (2016), we employ a“residual” approach. Let i, j, and t denote the firm, industry, and year, respectively. Let
γijt denote the growth of employment Eit . We define γijt as the conditional (residual)
growth rate of employment estimated from the following specification:
γijt = ln(Eit ) − ln(Eit−1 ) = φi + µjt + υijt ,(1)

where φi are the firm fixed effects that capture the unobserved firm-specific characteristics, including the employment system used, µjt are the industry and year fixed
3

Using firm-level data for Japan from the Basic Survey of Japanese Business Structure and Activities(BSJBSA), Tanaka (2013) examined the effects of trade on sales volatility but not employment volatility.
Similarly, Yokoyama et al. (2019) utilized BSJBSA firm-level data to examine the effects of the exchange
rate on employment. However, they did not explicitly focus on employment volatility.

3

effects, that capture industry-year-specific shocks, and υijt is the deviation of employment from the firm-average and the industry-average in year t. The volatility σ canbe interpreted as the standard deviation of the residual growth rates for a window oflength w:4
s
1 X 2
w
υ .
σij =
(2)
w − 1 t ijt
To formally test the linkage between the firm’s foreign exposure and its employment volatility, Kurz and Senses (2016) employed the following specification:
w
w
w
w
w
ln σij= β0 + β1 Bothwi + β2 Xi + β3 Mi + β4 xi + β5 mi+αZiw + θYjw + εw
ij ,

(3)

w
where Bothw
i is an importer and exporter dummy, Xi is an exporter (but not importer)
dummy, Miw is an importer (but not exporter) dummy, xw
i is the share of exports relative to sales, mw
is
the
share
of
imports
relativetopurchases,
Ziw and Yjw are the firmi
and industry control variables, respectively, and εw
ij is the error term. The firm andindustry control variables are calculated as the average over w, the window of interest. The trade status dummy variables along with the share of exports and imports areincluded to capture the nonmonotonic relationship between trade and volatility (e.g.,
Kurz and Senses, 2016; Vannoorenberghe, 2012).
Our study addresses the heterogenous relationship between the mode of foreign
exposure and employment volatility by extending equation (3) to the following specification:
w
w
w
w
w
ln σij= β0 + β1 Bothwi + β2 Xi + β3 Mi + β4 xi + β5 mi+β6 Bothint,w+ β7 Xiint,w + β8 Miint,w + β9 xint,w+ β10 mint,wiii+β11 M N Eiw + αZiw + θYjw + εw
ij ,

(4)

where Bothint,w
is an intrafirm importer and exporter dummy, Xiint,w is an intrafirm exi
porter (but not intrafirm importer) dummy, Miint,w is an intrafirm importer (but not intrafirm exporter) dummy, xint,w
is the intrafirm export intensity, mint,w
is the intrafirmiiimport intensity (i.e., the share of intrafirm imports to total purchases), and M N Eiw is adummy for firms that either engage in FDI or are foreign-owned firms. Other variables
are the same as those in equation (4).

2.2

Data

2.2.1

Source and industry classification

Our data are from the Basic Survey of Japanese Business Structure and Activities (BSJBSA)
compiled by the Ministry of Economy, Trade, and Industry (METI), Japan. The purpose of this survey is to capture an overall picture of Japanese corporate activities,
including globalization and diversification, along with basic corporate characteristics,
including sales, costs, profits, employment, trade, and FDI. The strength of this survey
is its coverage and reliability. As evidence, the survey is compulsory for firms in bothmanufacturing and nonmanufacturing industries with more than 50 employees and4

Our measure of employment volatility is based on employment growth at the firm level. Therefore,
the employment volatility caused by the entry and exit of firms is beyond the scope of our analysis,
although it is an important aspect of employment volatility at the aggregate level.

4with capital exceeding 30 million yen.5
The weakness of this survey is that some nonmanufacturing industries, such as construction, medical services, and transportation services, are not included. Small firms
with less than 50 employees or with capital less than 30 million yen are also not covered. In addition, the information on exports and imports is disaggregated by neither
destination country nor product. In this analysis, we focus on manufacturing and thewholesale trade industry because data for these industries are available throughout oursample period.6
In the BSJBSA, a three-digit industry classification code is assigned to each firmbased on their main activities. For example, suppose that a firm engages in both manufacturing and wholesale trade. If its largest revenue is from wholesale trade, the
BSJBSA classifies it as a wholesale trade firm, implying that firms in the wholesale tradeindustry do not always specialize in wholesale trade activities. Moreover, some firms
switched from one industry to another during our sample period. Although firms’switching behavior is an important issue in itself, we assign each firm the industry
classification to which it belongs most frequently during our sample period.7
2.2.2

Sample selection

We use the BSJBSA covering the period 1994–2012. Following Kurz and Senses (2016),
we first delete outlier observations from the top and bottom first percentiles of employment level and growth rate. We then restrict the sample to firms that report employment for at least five consecutive years to obtain sufficient observations to calculate
firm-level volatility. The BSJBSA as a whole has 36,074 manufacturing and wholesaletrade firms. We exclude 12,518 firms that report employment for less than five years.
As a result, our final sample consists of 23,556 firms (15,978 manufacturing and 7,578
wholesale trade firms). Since the data for 1994 are used to calculate the employment
growth rate for 1995, the volatility measure is available from 1995 to 2012, an 18-year
window.
2.2.3

Employment

The number of permanent workers measures employment. In the BSJBSA, permanent
workers are those with a contract period that extends for one month or longer, or an employee who worked for 18 days or more in each of the last two months in the previous
fiscal year. Accordingly, permanent workers comprise regular workers (i.e., Seishain
or Seikishokuin in Japanese) and part-time workers (i.e., Pāto or Arubaito in Japanese),but not daily workers (i.e., Hiyatoi in Japanese) and dispatched workers (i.e., Haken in
Japanese).8
Other than regular and part-time workers, there are two additional worker classifications, namely daily and dispatched workers. As noted, daily workers are not in5
In 2012, the BSJBSA covered approximately 13.4 million permanent workers and 112.7 trillion yen ofvalue added, which is approximately 50.1 percent and 82.2 percent of total permanent workers and value
added, respectively. Total numbers come from the Japanese Economic Census in 2012.
6
Some manufacturing firms own subsidiaries that engage in wholesale activities to distribute theirproducts. Different from independent wholesale trade firms, these wholesale trade firms may react toforeign shocks as do their parent manufacturing firms. However, we confirmed that these firms are not
the majority in our sample. Specifically, we found out of 7,585 wholesale trade firms, 1,368 firms are
subsidiaries of manufacturing firms, of which 625 firms are wholly owned subsidiaries.
7
For firms’ product-switching behavior, see Bernard et al. (2010c), Kawakami and Miyagawa (2010),
and Bernard and Okubo (2013).
8
The use of permanent and regular workers in this paper follows Yokoyama et al. (2019). In Section
5.1, we discuss the employment volatility of part-time workers in more detail.

5

cluded as permanent workers because their contract period is shorter than one month.
We also exclude dispatched workers because they have no direct employment contract
with the firm, but do so with temporary worker agencies. We refer to daily and dispatched workers as temporary workers.9 Importantly, although we can disaggregate
the number of workers by sector for a firm, such as the research and development andmanufacturing sectors, wage bills are only available at the firm level.
2.2.4

Trade and multinational enterprise (MNE) status

From the BSJBSA, we obtain variables for trade status, MNE status, and export and import intensity. Trade status includes four categories: firms that do not engage in trade(Nontrader), firms that engage only in exports (Exports only), firms that engage only inimports (Imports only), and firms that engage in both exports and imports (Both). We
define Imports only (Exports only) firms as those that engage in importing (exporting) inat least one year during our sample period, but do not engage in exporting (importing).
Both firms are defined as those that engage in exporting and importing in at least one
year during an 18-year window.10 The remaining firms are Nontraders. Export and import intensities are defined as the ratio of exports to total sales and the ratio of imports
to total purchases (i.e., total costs of intermediate inputs), respectively.11
One could ask which wholesale trade firms engage in exports and/or imports. A
typical example in wholesale trade is trading companies. Note also that firms could
engage in both manufacturing and wholesale trade activities. We classify firms thatengage in both manufacturing and wholesale trade activities as wholesale trade firms
if their primary sales are from wholesale trade activity.
In the BSJBSA, MNEs comprise two types of firms: foreign-owned firms and Japanesefirms that engage in FDI, which we refer to as Japanese FDI firms. A foreign-owned
firm has a share of foreign capital greater than 50 percent and headquarter outside ofJapan. A Japanese FDI firm that engages in FDI is a firm that has at least one foreign
affiliate.12 In our sample, the share of foreign-owned firms is rather small, just 1.3 percent of firms in manufacturing and 3.0 percent in wholesale trade. Given this, and toensure consistency with the existing literature, we combine these two types of firms.13
We classify the remaining firms as non-MNEs. We define MNE status similarly as tradestatus. Japanese FDI firms are then firms with foreign subsidiaries for at least one year
in the 18-year window. Similarly, foreign-owned firms have foreign parent firms atleast once during 18 years. The remaining firms are non-MNEs.
The other feature of this survey is the availability of the data for intrafirm trade. The
BSJBSA reports between exports and imports to/from firms’ majority-owned foreign
affiliates. To distinguish between intrafirm and interfirm trade, we construct intrafirm
9

The number of dispatched workers is available after 2000. In Section 5.1, we discuss the employment
volatility of temporary workers (i.e., daily and dispatched workers) in more detail.
10
Both then includes firms that export in one year and import in another year.
11
For 1995 and 1996, the value of exports and imports is not available. Instead, we obtain sales to andpurchases from foreign countries. These variables include transactions between foreign branches andforeign sales or purchases through trading companies along with conventional exports and imports. As
both export and foreign sales (imports and purchases from foreign countries) are available for 1997, we
adjust the value of foreign sales (purchases from foreign countries) in 1995 and 1996 using the ratio ofexports to foreign sales (imports to purchases from foreign countries) at the industry level. We modify
intrafirm export and import intensity in the same manner.
12
If foreign-owned firms also have foreign affiliates outside Japan, they are classified not as Japanese
FDI firms, but as foreign-owned firms. In the BSJBSA, a Japanese foreign affiliate is an affiliate with acapital share of more than 20 percent.
13
For example, Bernard et al. (2009) defined firms that have a related-party transaction during a particular year as multinationals.

6

export and import intensity variables (Intrafirm export intensity and Intrafirm import intensity, respectively) and intrafirm trade status variables (Intrafirm both, Intrafirm exports
only, and Intrafirm imports only). Intrafirm export and import intensities are the ratios of
intrafirm exports to total sales and intrafirm imports to total procurement, respectively.
Firms that engage in intrafirm trade are a subset of MNEs and trading firms (either
exporters or importers).
2.2.5

Control variables

To control for firm characteristics (i.e., Ziw in equation (4)), we use the log of the number of employees (Employment), the log of the number of establishments (Number of
establishments), the R&D–sales ratio (R&D–sales ratio), firm age (Age), and the share ofnonproduction workers (Share of nonproduction workers). We define the share of nonproduction workers as the ratio of nonproduction workers to total employees at the firmlevel.14
The industry control variables (i.e., Yjw in equation (4)) include the industry-level
share of nonproduction workers (Industry nonproduction worker share), the size of the industry (Industry size), the import penetration ratio (Import penetration), and the capital–
labor ratio (Industry capital–labor ratio). We calculate the industry skill share by aggregating the firm-level share of nonproduction workers. The size of the industry is the logof the aggregate number of employees by industry. The import penetration ratio andthe capital–labor ratio are from the Japan Industry Productivity (JIP) database.15 The
import penetration ratio is the ratio of imports to total domestic demand. The capital–
labor ratio is the ratio of net capital stock to person-hour labor inputs. We calculatethese control variables as an average over the 18-year window.16

2.3

Descriptive statistics

Table 1 provides basic descriptive statistics for the 18-year window from 1994 to 2012
for the full sample of firms and by trade and MNE status for all industries.17 Column (1) provides the number of firms. Column (2) shows the shares of firms, in termsof the number of firms, by trade and MNE status. Column (3) indicates the average
employment size. Columns (5) and (6) detail the mean and standard deviation of employment volatility, respectively, as measured by equation (2). Our sample consists of
23,556 firms, of which 52.3 percent (12,324 firms) engage in international trade and 28.9
percent (6,814 firms) are MNEs.
Four findings stand out from Table 1. First, there is a systematic relationship between firm size and trade status. Firms that engage in either exports or imports arelarger than those that do not. Moreover, firms that engage in both exports and imports
14

To calculate the share of nonproduction workers, we first obtain the number of employees who workin the manufacturing plant or engage in manufacturing activities at the firm headquarters. We then subtract this from the total number of employees, which implies the number of nonproduction workers. The
share of nonproduction workers is the ratio of this figure to the total number of employees.
15
In the JIP database, industry cannot be disaggregated within the wholesale trade industry. We
thus use industry control variables only for manufacturing firms. The database is downloadable fromhttp://www.rieti.go.jp/en/database/JIP2014/index.html. For more details about the JIP
database, see Fukao et al. (2007).
16
For the log value, we first compute the period average of each control variable and then take thelogarithm.
17
Table A1 in the Appendix presents the number of firms, by sector and year. The summary statisticsof the variables used in the regression analysis are provided in Table A2. We take each two-digit industry
category (i.e., SNA intermediate level classification) as representing a “sector”, whereas each three-digit
industry category (i.e., JIP industry classification) is an “industry”. All industry characteristics are at theindustry (three-digit) level.

7

Table 1: Basic Statistics, by Trade and MNE Status: All Industry
(1)
(2)
(3)
(4)
(5)
# of Share
Average Employment
firms(%) employment size
volatility
Mean
S.D.
Total
23,556 100.0
265 0.083 0.044
Non-trader
11,23247.7
185 0.083 0.045
Both
7,898
33.5
397 0.084 0.044
Exports only
2,016
8.6
251 0.078 0.042
Imports only
2,410
10.2
218 0.089 0.045
Non-MNEs
16,74271.1
182 0.083 0.044
MNEs
6,814
28.9
470 0.085 0.044
Source: Authors’ calculation based on the BSJBSA.
are even larger than those that engage in either exports or imports. Second, there is asystematic relationship between firm size and MNE status. On average, MNEs are approximately two-and-a-half times larger than other firms. This indicates that the firmsthat engage in international trade and MNEs are generally larger.
Third, the employment volatility of firms that import only is larger than that offirms that do not trade. In contrast, the employment volatility of firms that engage inexports only is smaller than that of firms that do not trade. These results suggest thatthe relationship between imports and employment volatility is different from that between exports and employment volatility. Finally, the employment volatility of MNEs
is larger than that of non-MNEs. This implies that employment volatility could also
vary by MNE status.18
Table 2 decomposes these statistics by manufacturing and wholesale trade. Our
sample consists of 15,978 manufacturing and 7,578 wholesale trade firms. Interestingly,
while 54.1 percent (8,646 firms) of firms engage in international trade in manufacturing,
48.5 percent (3,678 firms) of firms do so in wholesale trade. Similarly, the share of
MNEs is 30.9 percent (4,939 firms) in manufacturing, whereas it is 24.7 percent (1,875
firms) in wholesale trade. These figures indicate that the share of firms that engage ininternational trade or as MNEs are comparable for manufacturing and wholesale trade,
although wholesale trade firms are more likely than manufacturing firms to focus theirsales only on the domestic market.
We highlight three main findings. First, in manufacturing, we observe a similar
relationship between firm size and trade status to the relationship for all industries. On
average, firms that engage in both exports and imports are largest, followed by thosethat engage in either exports or imports only. Firms that do not engage in international
trade tend to be smaller in terms of employment. We confirm a similar relationshipin wholesale trade. These results indicate that a relationship between trade status andfirm size is common in both manufacturing and wholesale trade.
Second, in both manufacturing and wholesale trade, the employment volatility offirms that import only is higher than that of firms that do not trade, and the employment volatility of firms that engage in exports only is smaller than that of firms that do
not trade. Third, employment volatility is almost identical for MNEs and non-MNEs
18

Another interesting finding is that, on average, employment volatility is smaller in Japan (0.08 for fullsample) than in the United States (0.35 for full sample in Kurz and Senses (2016)). Part of this may be
because job mobility remains considerably lower in Japan than in the United States (Ono, 2010). Althoughcaution may be needed given the differences in the coverage of the data in Japan and the United States,
this suggests an interesting difference between Japanese and U.S, labor markets.

8

Table 2: Basic Statistics, by Trade and MNE Status: Manufacturing and Wholesale
Trade
(1)
(2)
(3)
(4)
(5)
# of Share
Average Employment
firms(%) employment size
volatility
Mean
S.D.
Manufacturing
Total
15,978 100.0
289 0.081 0.041
Non-trader
7,332
45.9
186 0.082 0.042
Both
5,552
34.7
450 0.081 0.040
Exports only
1,629
10.2
257 0.077 0.040
Imports only
1,465
9.2
231 0.085 0.042
Non-MNEs
11,03969.1
187 0.081 0.041
MNEs
4,939
30.9
517 0.082 0.041
Wholesale tradeTotal
7,578 100.0
215 0.088 0.050
Non-trader
3,900
51.5
184 0.084 0.050
Both
2,346
31.0
272 0.091 0.050
Exports only
387
5.1
226 0.081 0.048
Imports only
945
12.5
198 0.095 0.049
Non-MNEs
5,703
75.3
173 0.086 0.050
MNEs
1,875
24.7
345 0.093 0.051
Source: Authors’ calculation based on the BSJBSA.
in manufacturing. In contrast, MNEs’ employment volatility is higher than that of
non-MNEs in wholesale trade. Moreover, employment volatility is generally higher inwholesale trade than in manufacturing. Together, these results suggest that the relationship between trade, MNEs, and employment volatility differs between manufacturing and wholesale trade.
Note that export or import status does not necessarily infer a high degree of foreign exposure because, in some cases, export or import intensities may be very small.
Thus, examining the export and import intensities of the firms may be useful.19 The
upper part of Table 3 presents the export and import intensities. Table 3 shows thatthe average export and import intensities are small, amounting to about three percent
for exports and five percent for imports in all industries. We also report the shares of
intrafirm exports and imports to total sales. These are also small, amounting to approximately one percent for intrafirm exports and two percent for intrafirm imports in allindustries.20
One could argue that the existence of zero trade affects these results in that the figures in Table 1 confirmed that nearly half of the firms do not trade, as a result, theaverage share of exports and imports is zero. Therefore, we compute the export andimport intensities, conditional on positive exports and imports, respectively. The re19

For the definition of export and import intensity, see Section 2.2. In this sense, we could argue that not
only the intensity of trade, but also the share of foreign production to total production may affect employment volatility. While this may be true, it is difficult to obtain such information for foreign-owned firms.
Even if we were to focus only on Japanese multinationals, the sample size would decline substantiallygiven the limited data availability. For this reason, we do not further pursue this topic.
20
The proportion of MNEs that engage in intrafirm trade is high. In both manufacturing and wholesaletrade, 78 percent of firms (3,876 firms in manufacturing and 1,496 firms in wholesale trade) engage inintrafirm trade.

9

Table 3: Export and Import Intensity, by Trade Status and by Industry
All industry
Manufacturing
Wholesale trade
Unonditional# of Intensity# of Intensity# of Intensity
intensity
firms
firms
firms
Exports
23,5560.03 15,978
0.04
7,578
0.02
Imports
23,5560.05 15,978
0.04
7,578
0.07
Intrafirm exports 23,556
0.01 15,978
0.01
7,578
0.00
Intrafirm imports 23,556
0.02 15,978
0.02
7,578
0.02
All industry
Manufacturing
Wholesale trade
Conditional# of Intensity# of Intensity# of Intensity
intensity
firms
firms
firms
Exports
9,914
0.08
7,181
0.08
2,733
0.06
Imports
10,308
0.11
7,017
0.10
3,291
0.15
Intrafirm exports
9,914
0.02
7,181
0.02
2,733
0.01
(5,619)(4,187)(1,432)Intrafirm imports 10,308
0.04
7,017
0.04
3,291
0.05
(5,423)(3,836)(1,587)
Notes: Intensities are average over the firms. Conditional intensity
shows the averages for firms with non-zero exports or imports. Figures in parentheses indicate the number of firms with non-zero intrafirm exports (or imports).
Source: Authors’ calculation based on the BSJBSA.
sults are shown in the lower part of Table 3. If we exclude zero-trade firms, the average
share of exports and imports is slightly higher, in all industries, approximately eightpercent for exports and 11 percent for imports. Similar results are confirmed when
we focus on manufacturing and wholesale trade. Thus, how exports and imports affect employment volatility is not clear. To better test the linkage between firm foreign
exposure and its employment volatility, we now turn to the regression analysis.

3

Globalization and Employment Volatility in Japan

3.1

Preliminary analysis

Before presenting our baseline results, we begin by estimating equation (3) to compare our results with those of Kurz and Senses (2016). Columns (1) and (2) in Table
4 presents the results, estimated using ordinary least squares.21 For the categoricalvariables, the coefficients for trade status (i.e., Both, Exports only, and Imports only) are
relative to Nontrader. As pointed out by Guadalupe and Wulf (2010), this is a standard
difference-in-differences specification that exploits the foreign exposure where exports
and imports (the “treatment”) are continuous. We estimate employment volatility using the residual approach over the 18-year window, as in equation (2).22
We highlight two main findings. First, for manufacturing in column (1), our results
are similar to the results presented in Kurz and Senses (2016). We find a significantly
21

Table 4 includes firm- and industry-level control variables but their coefficients are not reported. The
full results for manufacturing and wholesale trade are provided in Tables A3 and A4 in the Appendix,
respectively.
22
While the industry characteristic variables are at the industry (i.e., three-digit) level, the sectorwindow fixed effect is at the sector (i.e., two-digit) level because of the perfect collinearity between them.
Note also that we control for industry–year-specific shocks in computing employment volatility.

10

Both
Exports only
Imports only
Export intensity
Import intensity
MNEs
Intrafirm both
Intrafirm exports
onlyIntrafirm imports
onlyIntrafirm export
intensity
Intrafirm importintensity
N
R2
Characteristics
Firm
Industry

Table 4: Regression Results
Kurz and Senses (2016)
Baseline
specification
specification
(1)
(2)
(3)
(4)
Manufacturing Wholesale Manufacturing Wholesale
trade
trade
0.001
0.057***-0.0140.010(0.011)(0.017)(0.014)(0.021)-0.042***-0.017-0.047***-0.032(0.014)(0.029)(0.015)(0.030)0.030**
0.111***
0.027*
0.091***(0.014)(0.020)(0.015)(0.021)0.115**
0.178*-0.0450.049(0.048)(0.096)(0.059)(0.122)0.182***
0.179***
0.136**
0.324***(0.040)(0.040)(0.057)(0.057)0.038***
0.019(0.013)(0.022)0.001
0.056*(0.018)(0.028)0.010
0.057*(0.018)(0.035)0.004
0.052*(0.021)(0.030)0.486***
0.304(0.134)(0.288)0.026-0.379***(0.094)(0.089)15,978
0.041

7,578
0.045

15,978
0.043

7,578
0.048

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Notes: Standard errors clustered by firm are in parentheses. ***, **,and * indicate statistically significant at 1%, 5%, and 10% levels, respectively. Firm-level control variables are the log of the number of
employees, the log of the number of establishments, the R&D–sales ratio, firm age, and the share of nonproduction workers. The industrylevel control variables are the industry-level share of nonproduction
workers, the size of the industry, the import penetration ratio, andthe capital–labor ratio. For the log value, we first compute the period
average of each control variable and then take the logarithm. Firmand industry-level control variables are included but not reported inthis table. The full results for manufacturing and wholesale trade areprovided in Tables A3 and A4 in the Appendix, respectively.
Sources: Authors’ calculation based on the BSJBSA and the JIP
database.

11

negative coefficient for Exporter only and significantly positive coefficients for Export
Intensity and Import Intensity. The only difference is the coefficient for Both that is insignificant in our study while significantly negative in Kurz and Senses (2016). Thus,
our results for Japanese manufacturing firms are generally consistent with the resultsfor U.S. manufacturing firms.
Second, the coefficient for wholesale trade is similar to those for manufacturing except for Both and Exports only. The coefficient for Both becomes significantly positive
whereas that of Exports only turns insignificant. Because Kurz and Senses (2016) focused only on manufacturing firms, we cannot directly compare our results with theirs.
Nevertheless, our results suggest that the relationship between foreign exposure andemployment volatility is different between manufacturing and wholesale trade. We
now turn to our baseline results of equation (4).

3.2

Baseline results

Columns (3) and (4) in Table 4 present our baseline results for manufacturing andwholesale trade, respectively, based on equation (4). For the categorical variables, thecoefficients for trade status (i.e., Both, Exports only, Imports only, Intrafirm both, Intrafirm
exports only, and Intrafirm imports only) are relative to Nontrader as in the preliminary
analysis. The coefficient for MNE status is relative to that of Non-MNEs. We first examine the results for manufacturing and then discuss the results for wholesale trade.
For manufacturing, four findings are evident from the baseline results in Table 4.
First, the estimated coefficient for Exports only is significantly negative. This implies
that the employment of firms that engage in exports only is less volatile than that offirms that do not engage in international trade. This result is consistent with the findingof Kurz and Senses (2016), for which the number of products and destination countries
for exports display negative relationships with employment volatility. This result alsosuggests that the diversification of products and/or destinations occurs in Japan, eventhough the firm-level data cannot identify the number of products or the destination
countries.
Second, the coefficient for Intrafirm export intensity is significantly positive. Thisfinding may imply that products exported from Japanese parent firms to their foreign
affiliates are relation specific; therefore, shocks are transmitted through intrafirm rather
than interfirm trade.23 These results together indicate that the relationship between
exports and employment volatility depends on intrafirm export intensity.
Third, the coefficients for both Imports only and Import intensity are significantly
positive. This implies that employment volatility becomes higher as import intensity
increases. Note that the coefficient for Intrafirm import intensity is insignificant. Accordingly, unlike exports, the relationship between imports and employment volatility
becomes evident regardless of interfirm or intrafirm trade. This result implies that theimported intermediate inputs are generally not easy to substitute with domestic intermediate inputs. Finally, the coefficient for MNEs is significantly positive, suggestingthat the employment of MNEs is more volatile than that of non-MNEs.
For wholesale trade, we highlight four main findings. First, none of the coefficientsfor Both, Exports only, Export intensity, or Intrafirm exports only, is statistically significant. This implies that, unlike manufacturing, exports do not relate significantly with
employment volatility in general. This is probably because wholesale trade firms do
not have a production facility and do not have to adjust their domestic employment
worker. Second, the coefficients for Imports only, Import intensity, Intrafirm both, and
23
In Section 5.2, we discuss the relationship between intrafirm trade and relationship-specificity in moredetail.

12

Intrafirm imports only are significantly positive. However, it should be noted that thecoefficient for Intrafirm import intensity is significantly negative. Consequently, employment volatility increases alongside import intensity, but somewhat offsets as intrafirm
import intensity increases. This result may suggest importing wholesale trade firms
adjust their domestic sales staff depending on import demand. In contrast, multinational wholesalers that engage in intrafirm imports are able to absorb shocks withinsubsidiaries and mitigate the impact on employment.
Third, the coefficient for MNEs is insignificant. This indicates that there is no significant difference in employment volatility between MNEs and non-MNEs. Note that, inTable 2, we confirmed the higher employment volatility of MNEs in wholesale trade.
Once we control for various firm and industry characteristics, the employment volatility of MNEs is almost the same as that of non-MNEs in wholesale trade. Finally, thecoefficient for Intrafirm import intensity is significantly negative. This indicates that employment volatility decreases as intrafirm import intensity increases.
Among these findings, the distinction between interfirm and intrafirm trade seems
particularly interesting because it is entirely new in the literature on employment volatility and trade. Therefore, it is worth discussing in more detail. However, before continuing on the discussion, we examine the robustness of these results to test their credibility.

4

Robustness check

4.1

IssuesThere could be some concern that our results are sensitive to the measurement of employment volatility, trade and MNE status, sample period, etc. To confirm the robustness of our results, we address six issues. The first is the measurement of employment
volatility. Following Kurz and Senses (2016), we employ two alternative measures of
employment volatility. One utilizes shorter windows, as we split the original 18-year
sample period into three 6-year subperiods. We then calculate the employment volatility for each subperiod, which implies that the analysis focuses on shorter-run relationships relative to the baseline model. The other measure of employment volatility utilizes the actual rather than the residual growth rate (i.e., equation (1)). Here, we measure employment volatility as the standard deviation of actual employment growth,where the employment growth rate is the log difference in employment between years
t and t − 1:
"
#1/2
w
X
1
w
σijt
=
(γij,t+τ − γ̄ij )2
,
(5)
w−1
τ =0

where w is the length of the window and γ̄ij is the average growth rate over windoww.
The second is the measurement of trade and MNE status. Trade and MNE status
take values of one if firms engage in trade and multinational activities for at least one
year during our sample period. This implies that some exporters or MNEs may engage
in trade or multinational activities only once during the 18 years. We follow Kurz and
Senses (2016) when using this definition. However, our results might change with analternative indicator of trade and MNE status. To address this possibility, we measuretrade and MNE status based on the mode of the status. For example, if a firm is an
MNE in only one year during the sample period, it is now a domestic firm. In contrast,
if a firm is an MNE during most of the sample period, it remains an MNE. We apply
this measure to all trade and MNE status firms and re-estimate equation (3).24
24

In this connection, we also examined the year-to-year transition of trade and MNE status. Table A5 in

13The third issue is the effects of productivity shocks and output growth. Productivity shocks and output change may also affect employment. Although we include
industry–year fixed effects to measure employment volatility (as in equation (2)), suchproductivity shocks and output changes could be heterogeneous across firms. To address this issue, we include the volatility of total factor productivity (TFP) and output as additional control variables. The volatility of TFP is calculated using the same
methodology as that used to calculate the employment volatility (i.e., equation (2)).
To estimate TFP, we employ the Wooldridge–Levinsohn–Petrin method (Wooldridge,2009).
The fourth issue is related to the sample period. There is the possibility shocks
caused by the global financial crisis in 2009, the 2011 Great East Japan earthquake, andthe 2011 Thailand floods affect our results. Employment volatility may then increase
purely or in part because of these unexpected domestic and foreign shocks. Thus, ourresults may be sensitive to the choice of the sample period. To address this concern, we
rerun the regression for the period 1994–2008, prior to these events.
The fifth is attrition: the entry and exit of firms. The relationship between foreign
exposure and employment volatility may also be sensitive to the period of firm survival because a firm’s employment could be less volatile conditional on survival. Note,however, that it is impossible to distinguish the difference between a firm that dropped
below the threshold employment level of the BSJBSA (i.e., 50 workers) and that exitedfrom the market in a precise manner. As a short cut, we employ two approaches toaddress this issue. First, we include in the regression equation as an additional control
variable the number of years in which the firm is in the sample. Second, we change thethreshold employment level from 50 workers as set by the BSJBSA to 60 workers to beable to confirm how the results are sensitive to the threshold employment level.
The last issue is concerning the unobserved industry heterogeneity. Our baseline
analysis includes the industry characteristics variables, and it may be more appropriate for the industry fixed effects to control for the unobserved industry heterogeneity.
Thus, we include the three-digit industry fixed effects, dropping the industry characteristics variables.25

4.2

Results of the robustness check

Tables 5–8 present the results of the robustness check. Tables 5 and 6 present the results
for manufacturing and Tables 7 and 8 indicate the results for wholesale trade.
In Tables 5 and 7, column (1) presents the results for the 6-year windows for manufacturing and wholesale trade, respectively. Column (2) in these tables shows theresults for actual employment growth. Column (3) indicates the results of alternative trade and MNE status whereas columns (4)–(5) present the results in which thevolatility of productivity and output is included as an additional control variable, respectively.
In Tables 6 and 8, column (1) shows the results for the period before 2009 for manufacturing and wholesale trade, respectively. Columns (2) and (3) present the resultsthat include the number of years of survival as an additional control variable and thethe Appendix presents the results and indicates that 72.9–98.3 percent of firms are of the same status. The
result suggests that both trade and MNE status are relatively stable throughout the period.
25
Table A6 in the Appendix presents the number of firms, by three-digit industry. In this connection, it
is also possible to include firm fixed effects when we split the original 18-year sample period into three
6-year subperiods. However, once we include firm fixed effect, most of the coefficients for the tradeand MNE dummies become insignificant. Noting that all time-invariant effects are absorbed by the firm
fixed effect, this result implies that significant coefficients are mainly obtained for firms that continue asexporters, importers, and/or MNEs rather than firms that switch their trade and/or MNE status.

14

Both
Exports only
Imports only
Export intensity
Import intensity
MNEs
Intrafirm both

Table 5: Robustness Check 1: Manufacturing
(1)
(2)
(3)
(4)
6-yearActualAlternative
Adds
window growth trade / MNE
TFP
statusshocks-0.003-0.002-0.124***-0.019(0.015)(0.001)(0.016)(0.014)-0.020-0.003***-0.095***-0.054***(0.015)(0.001)(0.016)(0.015)0.043***
0.001-0.056***0.022(0.016)(0.001)(0.020)(0.016)-0.018-0.0020.112*-0.129**(0.057)(0.005)(0.061)(0.061)0.019
0.014***
0.294***
0.110*(0.052)(0.005)(0.060)(0.060)0.043*** 0.003***-0.0080.024*(0.014)(0.001)(0.014)(0.013)0.020-0.0000.096***
0.000(0.020)(0.001)(0.018)(0.018)0.001-0.0000.042***
0.017(0.020)(0.001)(0.015)(0.019)-0.027-0.001
0.058***-0.003(0.025)(0.002)(0.018)(0.022)0.297*** 0.045***0.367***
0.533***(0.114)(0.013)(0.135)(0.146)
0.196**-0.006-0.1110.050(0.086)(0.008)(0.097)(0.100)31,174
15,97815,978
14,213
0.048
0.045
0.046
0.073

Intrafirm exports
onlyIntrafirm imports
onlyIntrafirm export
intensity
Intrafirm importintensity
N
R2
Characteristics
Firm
Yes
Yes
Industry
Yes
Yes
Sector (2-digit) window-fixed effectYes
No

For notes and sources, see those of Table 4.

15

(5)
Adds
outputgrowth-0.026*(0.014)-0.057***(0.014)0.018(0.015)-0.257***(0.060)0.168***(0.056)
0.034***(0.013)-0.004(0.017)0.009(0.018)0.001(0.021)
0.692***(0.130)0.005(0.092)
15,496
0.082

Yes
Yes

Yes
Yes

Yes
Yes

No

No

No

Table 6: Robustness Check 2: Manufacturing
(1)
(2)
(3)
(4)
BeforeAdds
Different
Three2009
numbersize
digit fixedof years threshold
effectBoth-0.008-0.002-0.013-0.003(0.015)(0.014)(0.015)(0.014)
Exports only
-0.042*** -0.040*** -0.043***-0.036**(0.016)(0.015)(0.017)(0.015)
Imports only
0.043**
0.032**
0.019
0.021(0.017)(0.015)(0.018)(0.015)
Export intensity-0.098-0.059-0.051-0.017(0.069)(0.059)(0.063)(0.060)
Import intensity
0.219***
0.107*
0.150**
0.142**(0.064)(0.056)(0.066)(0.058)MNEs
0.050*** 0.037***0.044***
0.046***(0.014)(0.013)(0.014)(0.013)Intrafirm both-0.0180.001-0.0030.001(0.020)(0.017)(0.019)(0.017)Intrafirm exports-0.0020.008-0.0030.019
only(0.021)(0.018)(0.020)(0.018)Intrafirm imports-0.031
0.004
0.004
0.001
only
(0.025)(0.021)(0.023)(0.021)
Intrafirm export
0.595*** 0.498***
0.483***
0.360***
intensity(0.149)(0.133)(0.138)(0.132)Intrafirm import-0.0290.031
0.052-0.037intensity(0.107)(0.093)(0.102)(0.094)N
14,493
15,97812,886
15,978
R2
0.041
0.052
0.050
0.073
Characteristics
Firm
Yes
Yes
Yes
Yes
Industry
Yes
Yes
Yes
Yes
For notes and sources, see those of Table 4.

16

Both
Exports only
Imports only
Export intensity
Import intensity
MNEs
Intrafirm both

Table 7: Robustness Check 1: Wholesale Trade
(1)
(2)
(3)
(4)
(5)
6-yearActualAlternative
Adds
Adds
window growth trade / MNE
TFP
outputstatusshocksgrowth-0.0200.000-0.096***-0.007-0.008(0.023)(0.002)(0.024)(0.023)(0.021)-0.040-0.003-0.116***-0.045-0.052*(0.031)(0.003)(0.037)(0.031)(0.030)0.116*** 0.006***
0.018
0.083*** 0.090***(0.022)(0.002)(0.024)(0.022)(0.021)-0.1250.008
0.234*
0.070-0.203(0.119)(0.012)(0.124)(0.126)(0.131)0.245*** 0.030***
0.394***
0.356*** 0.341***(0.062)(0.006)(0.060)(0.062)(0.057)0.051**
0.001
0.001-0.0020.006(0.024)(0.002)(0.025)(0.023)(0.022)0.078**
0.004
0.095***
0.049
0.042
(0.033)(0.003)(0.031)(0.030)(0.029)0.074*
0.003
0.090***
0.048
0.056
(0.039)(0.003)(0.030)(0.036)(0.035)0.024
0.003
0.127***
0.078**
0.061**(0.037)(0.003)(0.027)(0.031)(0.030)
0.247
0.029
0.285
0.288
0.655**(0.299)(0.029)(0.289)(0.323)(0.292)-0.280*** -0.035***-0.461***-0.454*** -0.477***(0.095)(0.009)(0.089)(0.096)(0.086)
13,856
7,578
7,578
6,454
7,279
0.035
0.050
0.049
0.076
0.074

Intrafirm exports
onlyIntrafirm imports
onlyIntrafirm export
intensity
Intrafirm importintensity
N
R2
Characteristics
Firm
Yes
Yes
Sector (2-digit) window-fixed effectYes
No

For notes and sources, see those of Table 4.

17

Yes

Yes

Yes

No

No

No

Both
Exports only
Imports only
Export intensity
Import intensity
MNEs
Intrafirm both
Intrafirm exports
onlyIntrafirm imports
onlyIntrafirm export
intensity
Intrafirm importintensity
N
R2
Characteristics
Firm
Industry

Table 8: Robustness Check 2: Wholesale Trade
(1)
(2)
(3)
(4)
(5)
BeforeAdds
Different
Three“Pure”
2009
numbersize
digit fixed wholesaleof years threshold
effect
trade
0.031
0.021
0.007
0.017
0.027
(0.023)(0.021)(0.025)(0.021)(0.029)-0.055*-0.021-0.038-0.021-0.026(0.033)(0.030)(0.037)(0.029)(0.039)0.114*** 0.088***
0.096***
0.065***
0.107***(0.023)(0.021)(0.024)(0.021)(0.028)-0.0170.022-0.1570.191
0.154(0.136)(0.121)(0.139)(0.121)(0.169)0.341*** 0.305***
0.325***
0.258***
0.346***(0.068)(0.056)(0.068)(0.057)(0.072)
0.010
0.019
0.007
0.015
0.049*(0.025)(0.022)(0.024)(0.022)(0.029)0.033
0.056**
0.071**
0.079***
0.019(0.033)(0.028)(0.032)(0.028)(0.038)0.039
0.058*
0.050
0.073**
0.030(0.040)(0.035)(0.039)(0.034)(0.046)0.020
0.059**
0.093***
0.036
0.045
(0.034)(0.030)(0.035)(0.030)(0.042)0.539*
0.332
0.569*
0.242-0.173(0.316)(0.287)(0.314)(0.286)(0.587)-0.393*** -0.389*** -0.428***-0.246***-0.485***(0.106)(0.089)(0.102)(0.089)(0.114)
6,877
7,578
5,666
7,578
4,773
0.044
0.058
0.046
0.071
0.049
Yes
Yes

Yes
Yes

Yes
Yes

For notes and sources, see those of Table 4.

18

Yes
Yes

Yes
Yes

results for firms with more than 60 workers for manufacturing and wholesale trade,
respectively. Column (4) presents the results with three-digit industry fixed effect.
For manufacturing, we find that the coefficient for Exports only is significantly negative in eight out of nine specifications, whereas the coefficient for Intrafirm export intensity is significantly positive in all specifications. In addition, the coefficient for Import intensity continues to be significantly positive in eight out of nine specifications, although
the relationship between Imports only and employment volatility is not necessarily robust. These results together suggest that the relationship between trade and employment volatility in the baseline results for manufacturing mostly continue to hold inthese specifications.
Moreover, the coefficient for MNEs is significantly positive in eight out of nine specifications. This implies that employment volatility is higher for MNEs than for nonMNEs. Note that firms that engage in intrafirm trade are a subset of MNEs; therefore,
not only whether firms engage in multinational activities, but also the degree to which
they engage in intrafirm exports (relative to their total exports) is important when discussing MNEs’ employment volatility.
For wholesale trade, regarding the baseline results, the coefficients for Both, Exports
only, Export intensity, and Intrafirm export intensity continue to be insignificant in most
specifications. Besides, the coefficients for both Imports only and Import intensity are significantly positive in eight and all specifications, respectively. While the coefficient for
Intrafirm imports only is insignificant in most specifications, the coefficient for Intrafirm
import intensity continues to be significantly negative in all specifications. These results
together suggest that the relationship between trade and employment volatility in thebaseline results for wholesale trade is mostly robust in these specifications. Furthermore, the coefficient for MNEs is insignificant in eight out of nine specifications. Thisimplies that MNEs do not necessarily exhibit higher employment volatility.
As noted, we classify firms that engage in both manufacturing and wholesale tradeactivities as wholesale trade firms if their primary sales are from wholesale trade. One
may question how the results would change if we focus on wholesale trade firms that
do not engage in manufacturing activities, which we refer to as “pure” wholesale trade
firms. Column (5) in Table 8 presents the results. Even when we focus on wholesaletrade firms that do not employ manufacturing workers, we continue to find qualitatively the same results as the baseline model. Exports and employment volatility haveno significant relationship. Employment volatility is higher for importers and increases
as import intensity increases and lower when intrafirm import intensity increases. Our
main messages are mostly unchanged even when we focus only on “pure” wholesaletrade firms.

4.3

Summary of the robustness check

In sum, the relationship between trade and employment volatility in the baseline results mostly continues to hold for both manufacturing and wholesale trade. Morespecifically, our main findings are threefold. First, in manufacturing, the relationshipbetween exports and employment volatility varies depending on intrafirm export intensity. One possible interpretation is that the transmission of foreign demand shocks
appears through intrafirm exports. In wholesale trade, there is no significant relationship between exports and employment volatility in general. Unlike manufacturing, foreign demand shocks may not be significantly related to domestic employment
volatility.
Second, in both manufacturing and wholesale trade, employment volatility tends
to increase alongside the share of imports to total purchases. In contrast, in wholesale

19trade, intrafirm imports tend to offset these shocks. This possibly implies that the transmission of foreign supply shocks to domestic employment appears through interfirm
imports for wholesale trade.
Finally, MNEs exhibit higher employment volatility for manufacturing. Therefore,
multinational activities could account for higher employment volatility. In contrast, inwholesale trade, MNEs do not necessarily exhibit higher employment volatility. Notethat firms that engage in intrafirm trade are a subset of MNEs. In wholesale trade,
MNEs may successfully mitigate the transmission of foreign supply shocks by intrafirm
trade.

5

Discussions

5.1

Adjustment through part-time/temporary workers or wages

We measure employment as the number of permanent workers. Because the definition of permanent workers does not include temporary workers, but includes parttime workers, it may be a concern that employment volatility could vary if we include
temporary workers or exclude part-time workers (i.e., if we focus on regular workers
only).26 Indeed, regular and nonregular workers have different degrees of employment
protection (OECD, 2014, Chapter 4).27 As a result, employment could be less volatilein response to foreign exposure for regular than for nonregular workers.
It is interesting to examine the employment volatility of regular workers (= permanent workers − part-time workers), that of part-time workers, and that of temporary
workers separately. However, some firms employ neither part-time nor temporary
workers. Moreover, the information on temporary workers is available only after 2000
in the BSJBSA. As a compromise, we utilize two alternative measures of employment:
the number of permanent and temporary workers, and the number of regular workers, which excludes part-time workers from the permanent worker category. We thencompute the employment volatility and run the same regression as the baseline model.
In Tables 9 and 10, column (1) provides the results whereby employment excludes
part-time workers for manufacturing and wholesale trade, respectively. For manufacturing, we continue to find a significantly negative coefficient for Exports only andsignificantly positive coefficients for Imports only, Import Intensity, and Intrafirm export
intensity. One notable difference is that the coefficient of MNEs turns out to be insignificant. This result implies that employment adjustments by MNEs occur mainly among
part-time workers. For wholesale trade, we continue to find a significantly positive coefficients for Imports only and Import Intensity and significantly negative coefficients for
Intrafirm import intensity. The results suggest that, for wholesale trade, both part-timeand regular workers face similar employment adjustments as a result of imports.
Column (2) presents the results whereby employment includes temporary workers
for manufacturing and wholesale trade, respectively. Even though the sample periodchanged from 1995–2012 to 2000–2012, the results are qualitatively similar to the baseline results.
Note, however, that even if employment volatility is similar whether or not temporary workers are included, their magnitudes may differ. To further address this issue,
we investigate the volatility of the ratio of temporary workers to the total number of26

For the definition of temporary and part-time workers, see Section 2.2.3.
In this connection, Sala et al. (2012) examined differences in the employment protection registration
between permanent worker and temporary worker and their impact on employment volatility. Similarly,
Jahn and Weber (2016) found that temporary workers decreased the employment volatility of incumbent
workers in Germany between 1999 and 2012.
27

20

Table 9: Temporary Workers and Wages: Manufacturing
(1)
(2)
(3)
(4)
Excluding Including
Ratio of
Volatility
part-time temporary temporary of wages
workers
workers
workersBoth-0.049***-0.0140.153***
0.054***(0.015)(0.017)(0.028)(0.016)
Exports only
-0.083***-0.050***0.075***
0.030*(0.016)(0.018)(0.029)(0.017)
Imports only
0.042**
0.033*
0.137***
0.045**(0.018)(0.019)(0.036)(0.019)
Export intensity-0.178***-0.0900.024
0.231***(0.063)(0.060)(0.089)(0.063)
Import intensity
0.225***-0.028-0.295***0.153**(0.062)(0.061)(0.109)(0.067)
MNEs
0.019
0.036**-0.0200.028*(0.014)(0.015)(0.026)(0.014)Intrafirm both-0.0050.034
0.164***-0.000(0.019)(0.022)(0.034)(0.020)Intrafirm exports-0.0000.019
0.108***-0.013
only(0.020)(0.023)(0.036)(0.021)
Intrafirm imports
0.027-0.002
0.069
0.002
only
(0.024)(0.026)(0.047)(0.024)Intrafirm export
0.508***
0.496***
0.488**
0.075
intensity(0.152)(0.116)(0.190)(0.133)Intrafirm import-0.0660.203**
0.147-0.126intensity(0.106)(0.093)(0.159)(0.110)N
15,974
13,137
13,394
14,936
R2
0.060
0.081
0.167
0.027
Characteristics
Firm
Yes
Yes
Yes
Yes
Industry
Yes
Yes
Yes
Yes
For notes and sources, see those of Table 4.

21

Table 10: Temporary Workers and Wages: Wholesale Trade
(1)
(2)
(3)
(4)
Excluding Including
Ratio of
Volatility
part-time temporary temporary of wages
workers
workers
workersBoth
0.024-0.0070.320***
0.009(0.022)(0.025)(0.049)(0.021)
Exports only
-0.0130.035
0.219***
0.026(0.030)(0.035)(0.067)(0.029)
Imports only
0.103***
0.062**
0.146***
0.020(0.021)(0.026)(0.053)(0.021)
Export intensity-0.024
0.011
0.093
0.033
(0.130)(0.123)(0.253)(0.114)
Import intensity
0.241***
0.292***
0.003
0.268***(0.056)(0.065)(0.127)(0.055)
MNEs
0.018
0.039
0.008
0.048**(0.022)(0.026)(0.051)(0.022)Intrafirm both
0.046
0.047
0.295***-0.007(0.028)(0.035)(0.065)(0.028)Intrafirm exports
0.060*
0.092**
0.205**
0.046
only(0.034)(0.042)(0.082)(0.037)Intrafirm imports
0.031
0.006
0.229***
0.022
only(0.030)(0.040)(0.076)(0.030)Intrafirm export
0.305
0.050
-0.8430.387
intensity(0.290)(0.295)(0.528)(0.295)Intrafirm import-0.269***-0.279***0.609***-0.475***intensity(0.084)(0.100)(0.173)(0.091)
N
7,456
5,812
5,924
6,966
R2
0.045
0.043
0.102
0.034
Characteristics
Firm
Yes
Yes
Yes
Yes
For notes and sources, see those of Table 4.

22

workers. If the magnitude of the adjustment is the same between temporary and permanent workers, the estimated coefficients will be insignificant.
Column (3) indicates the results. Significantly positive coefficients are confirmedfor Both, Exports only, Imports only, Intrafirm both, and Intrafirm exports only commonly
in both manufacturing and wholesale trade. The results suggest that employment adjustments by trading firms are more likely to occur through temporary workers.
In this connection, one could also be interested in wage volatility along with employment volatility. When foreign shocks hit firms, firms could adjust through employment and/or wages. Thus, firms with lower employment volatility may have higher
wage volatility. To address this issue, we use wages rather than employment to compute the volatility and estimate the same regression equation as the baseline model. We
define wages as the total wage bill divided by the number of permanent workers.
Column (4) presents the results. We highlight two main results. First, in manufacturing, the coefficient for Exports only is significantly positive. Noting that Exports
only shows consistently negative and significant coefficients in Tables 4–6, one possible
interpretation is that firms that engage in Exports only absorb foreign shocks throughwage adjustments while reducing employment adjustments. Second, for wholesaletrade, the signs of the coefficients are generally the same as those in the baseline model
although their significance levels change slightly. One notable difference is that the coefficient for MNEs is now significantly positive. Combined with the insignificant relationship between MNEs and the employment volatility of regular workers, this positivecoefficient might imply that MNEs in wholesale trade absorb foreign shocks throughchanges in the wages of regular workers rather than through changes in employment.

5.2

Intrafirm trade and contractibility

In Table 4, we found that the relationship between exports and employment volatility
depended on the intrafirm export intensity for manufacturing. We interpret this resultthat the transmission of foreign demand shocks appears through intrafirm exports for
manufacturing. This result complements and corroborates the result of Bems et al.
(2010), who show that the international fragmentation of the production process canamplify the impact of demand shocks.
In this context, it may be interesting to ask how our results are related to productcontractibility or relationship-specificity because some studies found a positive relationship between intrafirm trade and product contractibility.28 For example, Bernard
et al. (2010a) focused on the costs associated with writing contracts for specialized inputs. Using detailed firm-product-level trade data, they found that high product contractibility was associated with less intrafirm trade.
However, such detailed firm-product trade data are not available in Japan because,
as previously mentioned, the information on firm-level trade cannot be disaggregatedat the country and/or product level. Nonetheless, we can at least examine the relationship between contractibility, intrafirm trade intensity, and employment volatility
at the industry level. For contractibility, we use the index employed by Antràs (2015).
This index measures the buyer’s contractibility and takes the value between 0 (lowcontractibility) and 1 (high contractibility).29 Given limited data availability, we focus
only on manufacturing.30 We expect that the higher the contractibility, the lower the28

High relationship-specificity means low contractibility.
For more detail about the contractibility index, see Appendix.
30
For wholesale trade, we found that employment volatility decreases as intrafirm import intensity
increases. Because the relationship between intrafirm imports and employment volatility for wholesaletrade itself is entirely new in the literature, a more detailed investigation is needed for further discussion.
29

23

.5

Figure 1: Contractibility and Intrafirm Export Ratio: Manufacturing Industries

Ratio of intrafirm exports to total exports
.1
.2
.3
.4

311

305
239293322
231
303
304
240
302
301
161
170
289
309
149
321
340
291

152
299
292
281169 142
143
261
262
319 251

220
272
151
193

182

252
204

202
201

141
209

131 259

129
219
205

122

181
211

271
121

123

0

132

.1

.2

.3

.4
.5
.6
Buyer contractibility

.7

.8

.9

Notes: Each figure indicates the three-digit industry code. The solid line indicates the fitted
value: ŷ = 0.335(0.036) − 0.265(0.072)x, where ŷ is the ratio of intrafirm exports to total exports (industry average) and x is the contractibility index, and figures in parentheses are heteroskedasticity robust standard errors. N = 53 and R2 = 0.249.
Source: The intrafirm export ratio is the authors’ calculation based on the BSJBSA. Contractibility is obtained from Antràs (2015).

intrafirm export intensity and employment volatility because the high contractibility
implies low contractual frictions in international trade.
Figures 1 and 2 describe the relationship between contractibility, intrafirm export
intensity, and employment volatility. As expected, we find negative correlations between the contractibility index and intrafirm export intensity (−0.50) and between
contractibility index and employment volatility (−0.25). We also estimate the baseline
equation (i.e., equation (4)), splitting the sample between industries with a high and a
low contractibility. Table 11 presents the regression results. Columns (1) and (2) indicate the results for industries with contractibility index above and below median, respectively. Columns (3) and (4) present the results based on mean rather than median.31
Table 11 indicates that the significantly positive coefficient of Intrafirm export intensity
is confirmed only for firms with below median and below mean. The results imply
that the correlation between the intrafirm export intensity and employment volatility
tends to be low in industries with high contractibility (i.e., low relationship-specificity).
While only indicative, this result suggests that contractibility plays an important rolein intrafirm trade and employment volatility.
31
Table 11 includes firm- and industry-level control variables but their coefficients are not reported. The
full results are provided in Table A7 in the Appendix.

24

.11

Figure 2: Contractibility and Employment Volatility: Manufacturing Industries

322

.1

305

Employment volatility
.07
.08
.09

303

311

291

304
251

302
309
161
169
131
321
151
293
220
142
152
340
301
272 252319
259
149170 143292
239
240 281289 262
193
299

261

121
123

271
182
209

.06

132

219

205
204

141
122

129

211
201
181
202

.05

231

.1

.2

.3

.4
.5
.6
Buyer contractibility

.7

.8

.9

Notes: Each figure indicates the three-digit industry code. The solid line indicates the fitted
value: ŷ = 0.087(0.004) − 0.017(0.009)x, where ŷ is employment volatility (industry average)
and x is the contractibility index, and figures in parentheses are heteroskedasticity robust standard errors. N = 53 and R2 = 0.064.
Source: The intrafirm export ratio is the authors’ calculation based on the BSJBSA. Contractibility is obtained from Antràs (2015).

25

Table 11: Regression Results, by Contractibility
(1)
(2)
(3)
(4)
Above
Below
Above
Below
median median
mean
mean
Both
0.012
-0.0200.008-0.017(0.022)(0.018)(0.024)(0.017)
Exports only
-0.029-0.051**-0.001-0.065***(0.022)(0.020)(0.025)(0.019)
Imports only
0.062***-0.006
0.063***-0.001(0.022)(0.022)(0.023)(0.020)
Export intensity-0.012-0.0670.057-0.074(0.153)(0.063)(0.154)(0.063)
Import intensity
0.084
0.242***
0.139
0.205***(0.085)(0.077)(0.088)(0.075)
MNEs
0.065***
0.025
0.068***
0.024(0.020)(0.016)(0.022)(0.016)
Intrafirm both-0.069**0.040*-0.0460.026(0.029)(0.022)(0.031)(0.021)
Intrafirm exports
0.003
0.022
0.006
0.023
only
(0.030)(0.023)(0.032)(0.022)Intrafirm imports
0.006-0.0020.012-0.005only(0.032)(0.028)(0.034)(0.027)Intrafirm export
0.480
0.421***
0.340
0.450***
intensity(0.365)(0.144)(0.371)(0.144)Intrafirm import
0.156-0.0890.113-0.051intensity(0.155)(0.120)(0.158)(0.118)
N
7,028
8,950
6,237
9,741
R2
0.058
0.043
0.068
0.038
Characteristics
Firm
Yes
Yes
Yes
Yes
Industry
Yes
Yes
Yes
Yes
Sector (2-digit) window-fixed effect
Yes
No
No
No
For notes and sources, see those of Table 4.

26

6Concluding Remarks

In light of the increasing concerns over the relationship between globalization andlabor market outcomes, this paper examines the relationship between international
trade and employment volatility and that between FDI and employment volatility using large-scale, firm-level data from Japan. The major contributions of this paper are
threefold. First, we distinguish among multinational firms, exporters, importers, anddomestic firms. This enables us to examine the heterogeneous relationship between themode of foreign exposure and employment volatility. Second, we expand the industry
coverage of the analysis, covering not only manufacturing, but also wholesale trade
firms. Third, we consider the difference between intrafirm trade and interfirm trade.
This difference allows us to examine the mechanism through which foreign shocks
transmit to domestic employment.
Our major findings are summarized as follows. First, in manufacturing, the relationship between exports and employment volatility varies depending on the share of
intrafirm exports to total sales. One possible interpretation is that the transmission offoreign demand shocks to domestic employment is via intrafirm exports. In wholesale trade, exports and employment generally have no significant relationship. Unlike
manufacturing, foreign demand shocks may not be significantly related to domestic
employment.
Second, in both manufacturing and wholesale trade, employment volatility tends
to become higher as the share of imports to total purchases increases. In wholesaletrade, however, intrafirm imports tend to offset such shocks. This possibly implies thatthe transmission of foreign supply shocks to domestic employment appears throughinterfirm imports for wholesale trade.
Third, MNEs exhibit higher employment volatility in manufacturing. Therefore,
multinational activities could invoke higher employment volatility. In wholesale trade,however, MNEs do not necessarily exhibit higher employment volatility. Note thatfirms that engage in intrafirm trade are a subset of MNEs. In wholesale trade, MNEs
may successfully mitigate the transmission of foreign supply shocks through intrafirm
trade. Fourth, employment adjustments by trading firms are more likely to occur
through temporary workers. Finally, contractibility may play an important role in intrafirm trade and employment volatility.
For manufacturing, our results are similar to those of Kurz and Senses (2016), whofound that, on average, firms that exported were less volatile. The explanation of theirresults becomes much richer if we consider intrafirm trade and if we extend the analysis
to wholesale trade. Our results together suggest that the transmission of foreign supply
and demand shocks could be through not only manufacturing, but also wholesale trade
firms. Further, a higher share of intrafirm trade could magnify the foreign demand
shocks in manufacturing and could mitigate the foreign supply shocks in wholesaletrade. In identifying the potential risks from foreign demand and supply shocks, policymakers must be aware of the heterogeneity in manufacturing and wholesale trade
and the possible transmission channels through intrafirm trade.
In conclusion, several possible future research topics exist that are worth mentioning. First, further investigation of FDI is an important extension. For example, employment volatility could vary between firms with production plants abroad and firms
without such plants if the substitution of domestic and foreign workers causes higher
employment volatility for MNEs. Second, it is also important to investigate the differences between regular and nonregular workers in more detail. As discussed in Section
3.2, the availability of information on temporary workers is limited. Moreover, somefirms employ neither part-time nor temporary workers (Table A2 in the Appendix),27

which prevents us from computing their employment volatility. However, nonregular
workers are those that likely bear the brunt of the demand and supply shocks. Theseissues will be explored in the next stage of our research.

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Appendix A

Contractibility Index

In Section 5.2, we use the contractibility index employed by Antràs (2015). This index
measures the buyer’s contractibility and takes a value between 0 (low contractibility)and 1 (high contractibility). This index is constructed as follows.
Let industry i purchases inputs from industry j. The buyer’s contractibility forindustry i (BSi ) is defined as follows:XBSi =sik (1 − Sk ),(A-1)
kwhere sik is the ratio of the sales of industry i to industry k to the total sales of industryi; and Sk is the contract intensity developed by Nunn (2007). This contract intensity isdefined as:X
vij Ij ,
(A-2)
Si =
j

where vij is the share of inputs from industry j in industry i to total inputs in industry i;
and Ij is the indicator variable that takes unity if industry j is an industry that produces
differentiated goods. Whether the industry produces differentiated or homogeneous
goods is based on the classification by Rauch (1999).
The index BSi provided by Antràs (2015) is based on the industry classification of
the 2002 U.S. input–output table compiled by Ministry of Economy, Trade and Industryof Japan. We convert BSi to Japanese industry classification by using the 2005 JapanU.S. input–output table to apply this contractibility index to Japanese data.

Appendix B

Variable Construction Procedure for TFP

The variables used in production function estimation are constructed as follows: We
use real value added as an output measure. It is defined as the difference between real
sales and real intermediate input. Output and input deflators are obtained from the JIP
database. We estimate capital stock with the perpetual inventory method. For initial
capital stock, we use fixed tangible asset deflated by the investment goods price index,
which is also obtained from the JIP database. Then, capital stock in the following years
is constructed by using deflated net increase in fixed tangible asset and depreciation
ratio. A deflator for net increase in fixed asset is investment goods price index. Bothinvestment goods price index and depreciation ratio come from the JIP database. Labor
input is defined as total employment.

30

Table A1. Number of Firms, by Industry and Year

Manufacturing
Food products andbeverages
TextilesPulp, paper and paper
products
ChemicalsPetroleum and coalproducts
Non-metallic mineralproducts
Basic metal
Fabricated metal
products
Machinery
Electrical machineryTransport equipment
Precision instruments
Other manufacturing
Wholesale trade
Total

1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
11,301 11,602 11,921 12,336 12,203 11,803 11,939 11,729 11,346 11,794 11,634 11,516 11,698 11,788 11,549 10,967 10,865 10,575
1,326

1,343

1,392

1,440

1,464

1,408

1,440

1,433

1,388

1,451

1,424

1,429

1,454

1,469

1,472

1,382

1,387

1,356

281

301

316

327

322

300

293

285

262

268

262

244

243

242

229

211

207

207

371

382

383

403

387

382

376

371

370

373

355

354

356

355

351

345

339

326

834

826

840

846

850

833

832

821

804

838

818

797

806

814

798

778

763

767

661

675

700

717

710

683

686

673

658

698

688

693

700

708

694

664

653

634

512

522

537

552

530

507

488

463

439

448

441

429

428

413

416

383

376

371

639

647

656

685

668

666

660

655

635

663

676

669

687

696

698

666

665

641

824

839

873

908

894

862

897

869

843

870

863

859

870

900

872

832

813

790

1,360 1,415 1,434 1,487 1,466 1,413 1,450 1,441 1,400 1,469 1,474 1,459 1,497 1,512 1,491 1,436 1,410 1,389
1,598 1,656 1,695 1,764 1,747 1,723 1,725 1,704 1,654 1,712 1,679 1,671 1,686 1,656 1,614 1,518 1,496 1,426
995 1,026 1,053 1,078 1,080 1,040 1,058 1,038 1,021 1,074 1,040 1,045 1,076 1,097 1,065 1,025 1,027 1,010
132
140
143
152
149
150
151
152
156
159
154
154
158
159
158
141
148
143
1,768 1,830 1,899 1,977 1,936 1,836 1,883 1,824 1,716 1,771 1,760 1,713 1,737 1,767 1,691 1,586 1,581 1,515
5,389 5,605 5,698 5,860 5,746 5,428 5,459 5,305 5,148 5,240 5,189 5,042 5,151 5,171 5,001 4,812 4,788 4,652
16,690 17,207 17,619 18,196 17,949 17,231 17,398 17,034 16,494 17,034 16,823 16,558 16,849 16,959 16,550 15,779 15,653 15,227

Note: Industry classification is based on sector (two-digit) level.
Source: Authors' calculation based on the BSJBSA.

Table A2. Summary Statistics
All industries# of firms Mean
23,556-2.61823,5560.335
23,5560.086
23,5560.102
23,5560.032
23,5560.050
23,5560.180
23,5560.058
23,5560.050
23,5560.009
23,5560.018
23,5560.289
23,5565.075
23,55626.48023,5561.354
23,55641.740

Manufacturing# of firms Mean
15,978-2.632
15,9780.347
15,9780.102
15,9780.092
15,9780.038
15,9780.042
15,9780.195
15,9780.067
15,9780.045
15,9780.011
15,9780.016
15,9780.309
15,9785.136
15,97827.14015,978
1.128
15,97841.870

S.D.
0.523
0.472
0.280
0.303
0.093
0.134
0.384
0.234
0.218
0.040
0.080
0.453
0.851
9.553
0.987
17.690p25-2.970
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
4.419
19.5300.619
30.000p75-2.252
1.000
0.000
0.000
0.012
0.023
0.000
0.000
0.000
0.000
0.000
1.000
5.519
30.4602.015
53.350

0.338

0.231

0.952

15,97823,556
0.007
0.023
0.000
R&D-sales ratio
23,556
0.081
0.096
0.014
Import penetration
23,556
0.703
0.214
0.550
Industry skill share
23,55612.300 1.333 11.250
Industry employment size
23,556
1.913
0.648
1.458
Industry capital-labor ratio
18,949
0.043
0.065
0.001
Share of temporary workers
23,430
0.094
0.132
0.010
Share of part-time workers
Notes: For the definition of variables, see the main text.
Source: Authors' calculation based on the BSJBSA and the JIP database.

0.005
0.105
0.936
13.980
2.149
0.058
0.120

15,97815,97815,97815,97815,97813,137
15,974

Employment volatility
BothExports only
Imports only
Export intensity
Import intensity
Intrafirm both
Intrafirm exports only
Intrafirm imports only
Intrafirm export intensity
Intrafirm import intensity
MNEs
Employment
Employment^2
Number of establishments
Age
Share of nonproduction workers

23,5560.528

Wholesale trade
N
Mean
7,578
-2.588
7,578
0.310
7,578
0.051
7,578
0.125
7,578
0.021
7,578
0.066
7,578
0.150
7,578
0.039
7,578
0.060
7,578
0.005
7,578
0.022
7,578
0.247
7,578
4.949
7,578
25.100
7,578
1.831
7,578
41.490

S.D.
0.505
0.476
0.303
0.289
0.100
0.108
0.396
0.251
0.208
0.045
0.063
0.462
0.876
9.932
0.938
17.670p25-2.968
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
4.457
19.8600.363
30.500p75-2.276
1.000
0.000
0.000
0.018
0.025
0.000
0.000
0.000
0.000
0.000
1.000
5.596
31.3101.719
53.500

0.342

0.217

0.178

0.462

7,578

0.010
0.113
0.589
11.510
2.133
0.050
0.096

0.024
0.101
0.165
0.806
0.684
0.070
0.133

0.000
0.050
0.511
10.960
1.591
0.002
0.013

0.009
0.136
0.666
11.920
2.587
0.069
0.120

7,578
7,578
7,578
7,578
7,578
5,812
7,456

S.D.
0.558
0.462
0.220
0.330
0.076
0.175
0.357
0.194
0.237
0.029
0.108
0.432
0.780
8.537
0.915
17.740p25-2.974
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
4.354
18.9601.275
29.500p75-2.191
1.000
0.000
0.000
0.003
0.021
0.000
0.000
0.000
0.000
0.000
0.000
5.342
28.5302.398
53.110

0.920

0.170

0.952

1.000

0.002
0.014
0.943
13.980
1.449
0.028
0.091

0.018
0.002
0.022
0.012
0.056
0.058
0.130

0.000
0.012
0.926
13.980
1.430
0.000
0.007

0.000
0.015
0.950
13.980
1.474
0.035
0.120

Table A3. Regression Results: Manufacturing, All Coefficients(0)
Kurz and
Senses(2016)
specification

(1)

(2)

(3)

Baseline

6-yearwindow

ActualgrowthAlternative
Adds TFP
trade /
shocks
MNE status

Adds
outputgrowth0.001(0.011)-0.042***(0.014)0.030**(0.014)0.115**(0.048)0.182***(0.040)-0.014(0.014)-0.047***(0.015)0.027*(0.015)-0.045(0.059)0.136**(0.057)0.038***(0.013)0.001(0.018)0.010(0.018)0.004(0.021)
0.486***(0.134)0.026(0.094)-0.003(0.015)-0.020(0.015)0.043***(0.016)-0.018(0.057)0.019(0.052)0.043***(0.014)0.020(0.020)0.001(0.020)-0.027(0.025)0.297***(0.114)0.196**(0.086)-0.002(0.001)-0.003***(0.001)0.001(0.001)-0.002(0.005)0.014***(0.005)0.003***(0.001)-0.000(0.001)-0.000(0.001)-0.001(0.002)0.045***(0.013)-0.006(0.008)-0.124***(0.016)-0.095***(0.016)-0.056***(0.020)0.112*(0.061)0.294***(0.060)-0.008(0.014)
0.096***(0.018)0.042***(0.015)0.058***(0.018)0.367***(0.135)-0.111(0.097)-0.019(0.014)-0.054***(0.015)0.022(0.016)-0.129**(0.061)0.110*(0.060)0.024*(0.013)0.000(0.018)0.017(0.019)-0.003(0.022)
0.533***(0.146)0.050(0.100)-0.026*(0.014)-0.057***(0.014)0.018(0.015)-0.257***(0.060)0.168***(0.056)
0.034***(0.013)-0.004(0.017)0.009(0.018)0.001(0.021)
0.692***(0.130)0.005(0.092)0.208***(0.046)-0.020***(0.004)0.006(0.006)-0.003***(0.000)-0.117***(0.023)-0.594(0.411)0.213***(0.046)-0.021***(0.004)0.004(0.006)-0.003***(0.000)-0.117***(0.023)-0.640(0.424)-0.053(0.047)-0.001(0.004)0.003(0.006)-0.003***(0.000)-0.077***(0.022)-1.113***(0.259)0.020***(0.004)-0.002***(0.000)0.000(0.001)-0.000***(0.000)-0.010***(0.002)-0.036(0.036)0.221***(0.046)-0.021***(0.004)0.011*(0.006)-0.003***(0.000)-0.110***(0.023)-0.511(0.388)0.172***(0.047)-0.016***(0.004)0.010*(0.006)-0.003***(0.000)-0.138***(0.023)-1.650***(0.256)
0.503***(0.025)0.215***(0.045)-0.020***(0.004)0.022***(0.006)-0.003***(0.000)-0.064***(0.022)-1.571***(0.244)
1.537***(0.066)0.305***(0.042)-0.145***(0.029)0.040***(0.005)-0.033***(0.006)-3.330***(0.138)0.308***(0.042)-0.142***(0.029)0.038***(0.005)-0.034***(0.006)-3.310***(0.139)0.175***(0.055)-0.256***(0.074)0.012(0.008)-0.039***(0.011)-2.248***(0.182)0.024***(0.003)-0.012***(0.002)0.003***(0.000)-0.003***(0.000)0.019*(0.011)

0.322***(0.042)-0.139***(0.029)0.040***(0.005)-0.032***(0.006)-3.367***(0.138)0.317***(0.043)-0.102***(0.030)0.042***(0.005)-0.042***(0.006)-3.416***(0.144)0.256***(0.041)-0.162***(0.029)0.033***(0.005)-0.039***(0.006)-3.504***(0.138)

Number of observations
15,978R-squared
0.041
For notes and sources, see Table 4.

15,9780.043

31,1740.048

15,9780.045

15,9780.046

14,2130.073

15,496
0.082

BothExports only
Imports only
Export intensity
Import intensity
MNEs
Intrafirm both
Intrafirm exports only
Intrafirm imports only
Intrafirm export intensity
Intrafirm import intensity
Firm characteristics
Employment
Employment^2
Number of establishments
Age
Share of nonproduction
workers
R&D-sales ratio
Volatility of productivity /outputIndustry characteristics
Import penetration
Industry skill share
Industry size
Industry capital-labor ratio
Constant

(4)

(5)

(6)

Table A3. Regression Results: Manufacturing, All Coefficients (Continued)(1)
Before
2009

(2)
Adds
number of
years

(3)
Over 60
workers(4)(5)
ExcludingThree-digit
part-time
fixed effectworkers

(6)
(7)
(8)
Including
Ratio of
Volatility of
temporary temporary
wages
workers
workers-0.008(0.015)-0.042***(0.016)
0.043**(0.017)-0.098(0.069)0.219***(0.064)0.050***(0.014)-0.018(0.020)-0.002(0.021)-0.031(0.025)0.595***(0.149)-0.029(0.107)-0.002(0.014)-0.040***(0.015)0.032**(0.015)-0.059(0.059)0.107*(0.056)
0.037***(0.013)0.001(0.017)0.008(0.018)0.004(0.021)0.498***(0.133)0.031(0.093)-0.013(0.015)-0.043***(0.017)0.019(0.018)-0.051(0.063)0.150**(0.066)0.044***(0.014)-0.003(0.019)-0.003(0.020)0.004(0.023)0.483***(0.138)0.052(0.102)-0.003(0.014)-0.036**(0.015)0.021(0.015)-0.017(0.060)0.142**(0.058)0.046***(0.013)0.001(0.017)0.019(0.018)0.001(0.021)0.360***(0.132)-0.037(0.094)-0.049***(0.015)-0.083***(0.016)0.042**(0.018)-0.178***(0.063)0.225***(0.062)0.019(0.014)-0.005(0.019)-0.000(0.020)0.027(0.024)0.508***(0.152)-0.066(0.106)-0.014(0.017)-0.050***(0.018)0.033*(0.019)-0.090(0.060)-0.028(0.061)0.036**(0.015)0.034(0.022)0.019(0.023)-0.002(0.026)
0.496***(0.116)0.203**(0.093)0.153***(0.028)0.075***(0.029)0.137***(0.036)0.024(0.089)-0.295***(0.109)-0.020(0.026)0.164***(0.034)
0.108***(0.036)0.069(0.047)0.488**(0.190)0.147(0.159)0.054***(0.016)0.030*(0.017)0.045**(0.019)0.231***(0.063)0.153**(0.067)0.028*(0.014)-0.000(0.020)-0.013(0.021)
0.002(0.024)0.075(0.133)-0.126(0.110)0.163***(0.050)-0.017***(0.004)0.008(0.007)-0.003***(0.000)-0.100***(0.025)-1.207***(0.289)0.278***(0.046)-0.026***(0.004)0.004(0.006)-0.003***(0.000)-0.123***(0.023)-0.597(0.406)-0.010***(0.001)0.173***(0.054)-0.016***(0.005)0.000(0.007)-0.004***(0.000)-0.099***(0.025)-1.373***(0.275)0.166***(0.046)-0.018***(0.004)0.012*(0.006)-0.003***(0.000)-0.104***(0.025)-0.595(0.428)0.284***(0.049)-0.028***(0.004)0.020***(0.007)-0.003***(0.000)-0.182***(0.024)-0.778**(0.346)

0.468***(0.052)-0.034***(0.005)-0.085***(0.007)-0.005***(0.000)0.099***(0.026)-0.893**(0.396)1.275***(0.064)-0.084***(0.005)-0.245***(0.012)-0.005***(0.000)0.203***(0.044)-0.911***(0.345)0.167***(0.050)-0.020***(0.004)0.043***(0.007)-0.001***(0.000)0.106***(0.025)0.654***(0.189)0.356***(0.048)-0.204***(0.044)0.032***(0.006)-0.034***(0.006)-3.108***(0.153)0.304***(0.042)-0.140***(0.029)0.038***(0.005)-0.030***(0.006)-3.411***(0.139)0.316***(0.047)-0.157***(0.034)0.041***(0.006)-0.042***(0.006)-3.250***(0.169)-0.410(0.294)-0.059(0.052)-0.066(0.135)-0.025(0.092)-2.130*(1.227)0.537***(0.045)-0.019(0.031)
0.069***(0.006)-0.070***(0.006)-3.716***(0.151)

0.239*** -0.264***0.025(0.044)(0.084)(0.047)-0.287*** 0.339*** 0.141***(0.046)(0.086)(0.032)0.070*** 0.202***
0.007(0.006)(0.010)(0.006)-0.0000.108*** -0.035***(0.007)(0.013)(0.007)-4.372*** -10.600*** -2.500***(0.160)(0.230)(0.156)

Number of observations
14,493R-squared
0.041
For notes and sources, see Table 4.

15,9780.052

12,8860.050

15,9780.073

15,9740.060

13,1370.081

BothExports only
Imports only
Export intensity
Import intensity
MNEs
Intrafirm both
Intrafirm exports only
Intrafirm imports only
Intrafirm export intensity
Intrafirm import intensity
Firm characteristics
Employment
Employment^2
Number of establishments
Age
Share of nonproduction
workers
R&D-sales ratio
Number of survival years
Industry characteristics
Import penetration
Industry skill share
Industry size
Industry capital-labor ratio
Constant

13,3940.167

14,9360.027

Table A4. Regression Results: Wholesale Trade, All Coefficients(0)
Kurz and
Senses(2016)
specification
Both
Exports only
Imports only
Export intensity
Import intensity

(1)

(2)

(3)

Baseline

6-yearwindow

ActualgrowthAlternative
Adds TFP
trade /
shocks
MNE status

Adds
outputgrowth

0.057***(0.017)-0.017(0.029)0.111***(0.020)0.178*(0.096)0.179***(0.040)0.010(0.021)-0.032(0.030)0.091***(0.021)
0.049(0.122)0.324***(0.057)0.019(0.022)
0.056*(0.028)0.057*(0.035)0.052*(0.030)0.304(0.288)-0.379***(0.089)-0.020(0.023)-0.040(0.031)0.116***(0.022)-0.125(0.119)0.245***(0.062)0.051**(0.024)0.078**(0.033)0.074*(0.039)0.024(0.037)0.247(0.299)-0.280***(0.095)0.000(0.002)-0.003(0.003)0.006***(0.002)0.008(0.012)0.030***(0.006)0.001(0.002)0.004(0.003)0.003(0.003)0.003(0.003)0.029(0.029)-0.035***(0.009)-0.096***(0.024)-0.116***(0.037)0.018(0.024)0.234*(0.124)0.394***(0.060)0.001(0.025)0.095***(0.031)0.090***(0.030)0.127***(0.027)0.285(0.289)-0.461***(0.089)-0.007(0.023)-0.045(0.031)0.083***(0.022)0.070(0.126)
0.356***(0.062)-0.002(0.023)0.049(0.030)0.048(0.036)0.078**(0.031)
0.288(0.323)-0.454***(0.096)-0.008(0.021)-0.052*(0.030)0.090***(0.021)-0.203(0.131)
0.341***(0.057)0.006(0.022)0.042(0.029)0.056(0.035)
0.061**(0.030)0.655**(0.292)-0.477***(0.086)

0.591***(0.086)-0.048***(0.008)-0.024***(0.009)-0.004***(0.000)-0.094***(0.036)-0.142(0.252)0.589***(0.086)-0.048***(0.008)-0.025***(0.009)-0.004***(0.000)-0.097***(0.036)-0.166(0.253)0.311***(0.088)-0.028***(0.008)-0.027***(0.009)-0.004***(0.000)-0.161***(0.038)-0.112(0.367)0.055***(0.007)-0.004***(0.001)-0.002**(0.001)-0.000***(0.000)-0.006*(0.003)-0.009(0.028)0.591***(0.086)-0.048***(0.008)-0.023**(0.009)-0.004***(0.000)-0.105***(0.036)-0.098(0.269)-4.043***(0.230)-4.026***(0.230)-3.246***(0.239)-0.050***(0.019)-4.024***(0.230)0.535***(0.091)-0.043***(0.008)-0.024**(0.010)-0.004***(0.000)-0.062(0.039)-2.625**(1.287)0.737***(0.056)-4.115***(0.245)0.638***(0.094)-0.051***(0.009)-0.031***(0.009)-0.003***(0.000)-0.083**(0.037)-1.837*(1.085)
1.861***(0.136)-4.399***(0.250)

7,578
0.048

13,856
0.035

7,578
0.050

7,578
0.049

6,454
0.076

7,279
0.074

MNEs
Intrafirm both
Intrafirm exports only
Intrafirm imports only
Intrafirm export intensity
Intrafirm import intensity
Firm characteristics
Employment
Employment^2
Number of establishments
Age
Share of nonproduction
workers
R&D-sales ratio
Volatility of productivity /outputConstant

Number of observations
7,578
R-squared
0.045
For notes and sources, see Table 4.

(4)

(5)

(6)

Table A4. Regression Results: Wholesale Trade, All Coefficients (Continued)(1)
Before2009
BothExports only
Imports only
Export intensity
Import intensity
MNEs
Intrafirm both
Intrafirm exports only
Intrafirm imports only
Intrafirm export intensity
Intrafirm import intensity
Firm characteristics
Employment
Employment^2
Number of establishments
Age
Share of nonproduction
workers
R&D-sales ratio
Number of survival years
Constant(2)
Adds
number of
years

(3)

(4)
(5)
(6)
(7)
(8)
(9)
Three-digit "Pure"
Excluding Including Ratio ofOver 60
Volatility
fixedwholesale part-time temporary temporary
workers
of wages
workers
effecttrade
workers
workers

0.031
0.021
0.007
(0.023)(0.021)(0.025)-0.021-0.038-0.055*(0.033)(0.030)(0.037)0.114*** 0.088*** 0.096***(0.023)(0.021)(0.024)-0.0170.022-0.157(0.136)(0.121)(0.139)0.341*** 0.305*** 0.325***(0.068)(0.056)(0.068)
0.010
0.019
0.007
(0.025)(0.022)(0.024)0.033
0.056**
0.071**(0.033)(0.028)(0.032)0.039
0.058*
0.050(0.040)(0.035)(0.039)0.020
0.059** 0.093***(0.034)(0.030)(0.035)0.539*
0.332
0.569*(0.316)(0.287)(0.314)-0.393*** -0.389*** -0.428***(0.106)(0.089)(0.102)0.640***(0.101)-0.053***(0.009)-0.026***(0.010)-0.004***(0.000)-0.093**(0.040)-0.176(0.246)0.689***(0.086)-0.057***(0.008)-0.025***(0.009)-0.004***(0.000)-0.104***(0.036)-0.212(0.249)-0.012***(0.001)-4.169*** -4.198***(0.269)(0.231)

Number of observations
6,877
R-squared
0.044
For notes and sources, see Table 4.

7,578
0.058

0.017
0.027
0.024
-0.007(0.021)(0.029)(0.022)(0.025)-0.021-0.026-0.0130.035(0.029)(0.039)(0.030)(0.035)0.065*** 0.107*** 0.103*** 0.062**(0.021)(0.028)(0.021)(0.026)0.191
0.154-0.0240.011(0.121)(0.169)(0.130)(0.123)0.258*** 0.346*** 0.241*** 0.292***(0.057)(0.072)(0.056)(0.065)0.015
0.049*
0.018
0.039(0.022)(0.029)(0.022)(0.026)0.079***
0.019
0.046
0.047
(0.028)(0.038)(0.028)(0.035)0.073**
0.030
0.060*
0.092**(0.034)(0.034)(0.046)(0.042)
0.036
0.045
0.031
0.006
(0.030)(0.042)(0.030)(0.040)0.242-0.1730.305
0.050(0.286)(0.587)(0.290)(0.295)-0.246*** -0.485*** -0.269*** -0.279***(0.089)(0.114)(0.084)(0.100)0.320***
0.009(0.049)(0.021)0.219***
0.026(0.067)(0.029)0.146***
0.020(0.053)(0.021)0.093
0.033(0.253)(0.114)0.003
0.268***(0.127)(0.055)0.008
0.048**(0.051)(0.022)0.295***-0.007(0.065)(0.028)0.205**
0.046(0.082)(0.037)0.229***
0.022(0.076)(0.030)0.387-0.843(0.528)(0.295)0.609*** -0.475***(0.173)(0.091)0.559*** 0.567*** 0.601*** 0.730*** 0.415*** 0.891*** 0.320***(0.115)(0.085)(0.114)(0.102)(0.096)(0.140)(0.099)-0.043*** -0.049*** -0.050*** -0.063*** -0.031*** -0.058*** -0.028***(0.010)(0.008)(0.011)(0.010)(0.009)(0.012)(0.009)-0.036***0.010-0.026** -0.040*** -0.029*** -0.042** 0.035***(0.010)(0.010)(0.011)(0.009)(0.011)(0.021)(0.009)-0.004*** -0.005*** -0.004*** -0.004*** -0.004*** -0.007*** -0.002***(0.000)(0.000)(0.000)(0.000)(0.000)(0.001)(0.000)-0.069-0.080**-0.194*** -0.147*** -0.325*** -0.317***(0.043)(0.037)(0.037)(0.044)(0.094)(0.038)-0.3010.030
0.551**-0.2090.019
1.260
0.943***(0.283)(0.246)(0.266)(0.237)(0.596)(1.627)(0.181)-4.047*** -3.942*** -4.161*** -4.215*** -3.570*** -7.129*** -2.726***(0.320)(0.233)(0.301)(0.267)(0.259)(0.400)(0.263)
5,666
0.046

7,578
0.071

4,773
0.049

7,456
0.045

5,812
0.043

5,924
0.102

6,966
0.034

Table A5. Year-to-Year Transition Probability Matrix
Manufacturing (%)
year tExports only
Imports only Non-trader
Total
year t-1
Both
Both
89.2
5.6
2.7
2.6
77.1
0.5
9.8
Exports only
12.5
Imports only
9.5
0.9
76.2
13.4
Non-trader
1.1
2.2
1.9
94.8
year tTotal
year t-1
Non-MNEs
MNEs
2.2
100.0
Non-MNEs
97.8
MNEs
4.2
95.8
100.0
Wholesale trade (%)
year tImports only Non-trader
Total
year t-1
BothExports only
3.2
4.0
3.1
Both
89.7
Exports only
14.5
72.9
1.0
11.7
10.1
Imports only
8.0
0.3
81.6
Non-trader
1.0
1.2
1.9
95.9
year tNon-MNEs
year t-1
MNEs
Total
Non-MNEs
98.3
1.7
100.0
MNEs
5.0
95.0
100.0
Source: Authors' calculation based on the BSJBSA.

100.0
100.0
100.0
100.0

100.0
100.0
100.0
100.0

Table A6. Number of Firms, by Three-digit Industry

SectorManufacturing
Food products andbeverages

TextilesPulp, paper andpaper products
ChemicalsPetroleum and coalproducts
Non-metallic
mineral products
Basic metal

Fabricated metal
products
Machinery

Threedigit
code

Industry

121 Livestock Foodstuff
122 Fisheries food
123 Flour and grain mill products
129 Other food and related products
131 Soft drinks, liquors, tea and tobacco
132 Feed and organic fertilizer
141 Spinning mills
142 Textiles and knit fabric
143 Dyeing organization
149 Other textile industry
181 Pulp and paper
182 Paper Processed
201 Chemical fertilizer / inorganic chemical industrial product
202 Organic chemical industry product
204 Oil and fat products, soaps, synthetic detergents
205 Pharmaceutical
209 Other chemical industry product211 Petroleum refining industry
219 Other petroleum products / coal products
251 Glass and the same product
252 Cement and the same product259 Other ceramic industry / stone products
261 Pig iron / crude steel / steel material
262 Forged castings and other steel products
271 Nonferrous metal refining and refining industry
272 Non-ferrous metal processed goods
281 Construction and building metal products
289 Other metal products
291 Metal processing machinery
292 Special industry machinery
293 Machine tool for office and service
299 Other machinery and parts

Source: Authors' calculation based on the BSJBSA.

Numberof firms
15,978
313
248
47
1,075
192
53
60
108
103
120
120
374
124
214
152
254
314
28
28
130
277
269
217
278
63
327
377
808
300
590
177
889

ThreeIndustry
digit
code
301 Industrial electrical machinery and equipment
Electrical machinery
302 Consumer electrical machinery and equipment
303 Communication machinery and equipment
304 Electronic equipment
305 Electronic parts / device
309 Other electrical machinery and equipment
Transport
311 Automobile and accessories
equipment
319 Other transportation equipment
Precision
321 Medical equipment / medical supplies
instruments
322 Optical machinery and equipment / lens
Other
151 Textiles / knitwear garment
manufacturing
152 Personal items and other textile
161 Sawing and plywood
169 Other wood product
170 Furniture and accessories
191 Newspaper industry
192 Publishing industry
193 Printing industries
220 Plastic product231 Tire and tube
239 Other rubber products
240 Leather, same product / fur
340 Other miscellaneous manufacturing
Wholesale trade
491 Textile
492 Apparel and accessories
501 Agricultural and livestock products / marine products
502 Food and beverage
511 Building materials
512 Chemical product513 Mineral and metal material
514 Recycled resource
520 General machinery and equipment
531 Furniture and Fittings
532 Medicine and cosmetics
539 Other miscellaneous wholesale trade
SectorNumberof firms
497
134
322
227
884
253
1,120
285
113
87
280
104
170
22
200
96
130
738
879
7
164
51
586
7,578
121
514
634
679
648
441
640
71
2,418
209
316
887

Table A7. Regression Results, by Contractibility: Manufacturing, All Coefficients
(1)

(2)
Above
median(3)
Belowmedian

(4)
Above
mean

(5)
Below
mean

-0.014(0.014)-0.047***(0.015)0.027*(0.015)-0.045(0.059)0.136**(0.057)0.038***(0.013)0.001(0.018)0.010(0.018)0.004(0.021)
0.486***(0.134)0.026(0.094)0.012(0.022)-0.029(0.022)0.062***(0.022)-0.012(0.153)0.084(0.085)0.065***(0.020)-0.069**(0.029)0.003(0.030)0.006(0.032)0.480(0.365)0.156(0.155)-0.020(0.018)-0.051**(0.020)-0.006(0.022)-0.067(0.063)0.242***(0.077)0.025(0.016)0.040*(0.022)0.022(0.023)-0.002(0.028)0.421***(0.144)-0.089(0.120)0.008(0.024)-0.001(0.025)0.063***(0.023)0.057(0.154)0.139(0.088)0.068***(0.022)-0.046(0.031)
0.006(0.032)0.012(0.034)0.340(0.371)
0.113(0.158)-0.017(0.017)-0.065***(0.019)-0.001(0.020)-0.074(0.063)0.205***(0.075)0.024(0.016)0.026(0.021)
0.023(0.022)-0.005(0.027)0.450***(0.144)-0.051(0.118)0.213***(0.046)-0.021***(0.004)0.004(0.006)-0.003***(0.000)-0.117***(0.023)-0.640(0.424)0.251***(0.073)-0.024***(0.007)0.020**(0.010)-0.002***(0.000)-0.131***(0.034)-2.772***(0.446)0.148**(0.059)-0.015***(0.005)-0.006(0.008)-0.004***(0.000)-0.116***(0.030)0.185(0.264)0.260***(0.079)-0.025***(0.007)0.018*(0.010)-0.003***(0.000)-0.152***(0.036)-2.487***(0.450)0.156***(0.056)-0.016***(0.005)-0.001(0.008)-0.004***(0.000)-0.104***(0.029)0.142(0.272)0.308***(0.042)-0.142***(0.029)0.038***(0.005)-0.034***(0.006)-3.310***(0.139)0.235***(0.061)-0.115***(0.043)0.043***(0.010)-0.045***(0.009)-3.493***(0.232)0.282***(0.060)-0.149***(0.039)0.021***(0.008)0.005(0.010)-2.970***(0.183)0.291***(0.065)-0.171***(0.046)0.049***(0.010)-0.059***(0.010)-3.525***(0.245)0.265***(0.054)-0.113***(0.037)0.021***(0.007)0.006(0.008)-3.029***(0.173)

7,028
0.058

8,950
0.043

6,237
0.068

9,741
0.038

Baseline
Both
Exports only
Imports only
Export intensity
Import intensity
MNEs
Intrafirm both
Intrafirm exports only
Intrafirm imports only
Intrafirm export intensity
Intrafirm import intensity
Firm characteristics
Employment
Employment^2
Number of establishments
Age
Share of nonproduction
workers
R&D-sales ratio
Industry characteristics
Import penetration
Industry skill share
Industry size
Industry capital-labor ratio
Constant

Number of observations
15,978R-squared
0.043
For notes and sources, see Table 4.