DPRIETI Discussion Paper Series 19-E-086

Heterogeneous Impact of Import Competition on Firm Organization:Evidence from Japanese firm-level data

MATSUURA, ToshiyukiKeio University

The Research Institute of Economy, Trade and Industryhttps://www.rieti.go.jp/en/

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RIETI Discussion Paper Series 19-E-086

October 2019

Heterogeneous Impact of Import Competition on Firm Organization: Evidence from Japanese

firm-level data1

Matsuura, Toshiyuki

Keio University

Abstract

This paper empirically investigates the effect of import competition on within-firm

employment reorganization using Japanese firm-level data sets. We conduct a firm-level

examination of whether the import competition against low wage countries leads to the

shift from manufacturing activity to non-manufacturing activity, such as headquarter

services or R&D. Moreover, we explore the heterogeneity of impacts of import

competition according to firm size and export status. We find that competition from

Chinese imports induces manufacturing firms to increase their share of service workers,

especially those workers that engage in wholesale & retail, and in other service activities.

Keyword: Import competition, Firm reorganization, Servicification

JEL classification: F61, L25, D22

The RIETI Discussion Papers Series aims at widely disseminating research results in the form of

professional papers, with the goal of stimulating lively discussion. The views expressed in the papers

are solely those of the author(s), and neither represent those of the organization(s) to which the author(s)

belong(s) nor the Research Institute of Economy, Trade and Industry.

1This study is conducted as a part of the Project “A Study of the Effects of Trade Policy: A microdata

analysis of Japan from the 1990s to 2010s” undertaken at the Research Institute of Economy, Trade and

Industry (RIETI). I thank Tadashi Ito, Eiichi Tomiura, Masayuki Morikawa, Jung Hur, Jota Ishikawa, Taiji

Furusawa and other seminar participants at JSIE annual conference, Niigata Prefecture University,

University of Hawaii, Kyoto University, Western Economic Association International. This study utilizes

the data from the questionnaire information based on “the Basic Survey of Japanese Business Structure and

Activities” (Ministry of Economy, Trade and Industry, METI), “Survey of Oversea Business Activities”

(METI), “Census of Manufacture” (METI) and “Economic Census for Business Activities” (Ministry of

Internal Affairs and Communications and Ministry of Economy, Trade and Industry). I also utilize the

Kikatsu-Kaiji converter and the Establishment ID Converter for the Census of Manufacture, which are

provided by RIETI.

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1. Introduction

Employment in the manufacturing sector has been declining in many high-income

countries. For example, according to the OECD’s Employment Outlook, as of 2015, the shares

of manufacturing employment in the U.S., UK, and Germany were 10.3%, 9.6%, and 19.3%,

respectively. One of the factors affecting the decline in manufacturing employment is

competition created by imports from emerging market countries such as China. For example,

Autor et al. (2013) demonstrate that 55% of the decline in U.S. manufacturing employment

from 2000 to 2007 can be explained by the increase in imports from China. The increase in

Chinese imports may also have caused a decline in U.S. wages and contributed to higher

unemployment, increasing transfer payments through various federal and state programs.

Japan has also experienced deindustrialization of its workforce. Figure 1 presents the

share of manufacturing employment in Japan from 1980 to 2015, which was 23.1% in 1980,

and was flat until 1992. This is partially due to strong domestic demand associated with the

asset price bubble and boom in late 1980 and early 1990s. However, employment in the

manufacturing sector started to decrease after that, showing a downward trend until 2005.

Thanks to strong export demand in the early 2000s, the share of manufacturing employment

increased slightly in 2006, just before the Great Financial Crisis of 2008. It turned downward

again, reaching 15.3% in 2015.

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== Figure 1 ==

While many studies investigate deindustrialization by focusing on the shift in

employment from the manufacturing sector to the services sector at industry-level, this study

sheds light on changes from manufacturing activities to service-related activities within a firm.

Recently, some manufacturing firms have relocated or outsourced parts of their manufacturing

processes to low wage countries, concentrating on R&D and product design in their home

country. Other firms have shifted activities from manufacturing to services by providing user-

friendly maintenance, technical support, and consulting services by monitoring their products

via the internet or by using the global positioning system.2 This phenomenon is often called

the servitization of manufacturing firms. The primary objective of this study is to explore the

factors affecting this servitization of manufacturing firms, mainly focusing on the competitive

pressure created by the surge of imports from China.

We also examine the heterogeneity of the impact of imports on the servitization of firms.

Since it involves a non-trivial fixed cost, it is not easy for small and medium enterprises to

invest in or outsource their production activity abroad. Providing high-quality after-sales

service requires additional investment in those service activities. Therefore, not all firms are

2 For example, Komatsu Ltd., a construction instrument manufacturer, monitors their products using GPS

to provide high-quality maintenance services. Their system is called “KOMTRAX.” Rolls-Royce Holdings

provide similar service, which is known as “Power by the Hour” for their aircraft engine.

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able to shift from manufacturing to service.

Furthermore, we analyze whether exporters respond to competitive pressures differently

from non-exporters. Many recent studies have investigated the structure of value-added trade

and have shown that the value of the indirect trade of services embedded in traded goods has

been growing substantially. For example, Francois et al. (2015) examine the trends in services

embedded in traded goods on a value-added basis using the world input-output tables and find

that exports by high-income countries are more service-intensive. This implies that exporters

may be more aggressively shifting their activities from manufacturing to services.

This study employs a unique firm-level panel data set covering the period from 1997 to

2014 from the Basic Survey of Japanese Business Structure and Activities (BSJBSA), collected

and compiled by Japan’s Ministry of Economy, Trade and Industry (METI). This survey covers

both manufacturing and major service industries and contains information about the

composition of the workforce by type of activities (i.e., administrative services in headquarters,

manufacturing, wholesale, and retail activities, R&D and other services), allowing us to

quantify the servitization of firms over time. To identify the shock of competition from imports,

we matched 6-digit-level plant-product data from the Census of Manufacture (COM) collected

by METI and construct a shock variable by aggregating product-level import penetration ratios

with a firm’s sales share weight.

The major findings in this paper can be summarized as follows. First, competition from

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Chinese imports induces manufacturing firms to increase their share of non-manufacturing

workers, especially workers that engage in wholesale, retail, or other services. Second, the

impact of imports on the servitization of Japanese firms is heterogeneous regarding firm size

and export status. We find that larger firms and exporting firms have actively shifted away from

manufacturing toward services in response to competition from imports. Third, the number of

Japanese manufacturing firms that made a complete switch to service was limited. However,

since the firms making this switch are relatively large, the cumulative contribution to the

decline in manufacturing’s share of employment was sizable, at 18.0%.

This study relies on two groups of earlier studies. The first group addresses the impact

on employment of competition pressure created by imports from emerging market countries

such as China. A series of studies by David Autor and his coauthors (Autor, 2013; Autor, 2014;

Acemoglu et al. 2016; Autor, 2016) reveals that the surge of imports from China has a

significant impact on labor markets in the U.S. They demonstrate that the effect of Chinese

imports is concentrated in a specific region, the so-called “Rust Belt.” They attempt to identify

the Chinese supply shock of trade to the U.S. by utilizing the Bartik instrument; changes in

imports from China to other high-income countries are used as instruments.

Several studies of European countries present similar evidence. For example,

Malgouyers (2016) finds that France experienced a decline in manufacturing employment due

to imports from China. Furthermore, it polarized the local employment in the manufacturing

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sector. Balsvik et al. (2015) and Dooso et al. (2014) report the same pattern for the impact of

Chinese imports on local employment in Norway and in Spain, respectively.

In contrast, the effect of imports from China on local employment in Germany and/or

Japan may differ from that in the US. For example, Dauth et al. (2014) investigate the effect of

competition from imports on Germany’s local labor market, comparing imports from Eastern

Europe with those from China. They demonstrated that the negative effect of Eastern European

imports is more prominent than the effect of Chinese imports since increases in capital goods

exported to China mitigated the negative impact of Chinese imports. Taniguchi (2018)

examines the impact of rising Chinese import on the Japanese local labor market following the

methodology in Autor et al. (2013) and find that the effect of imports from China is not negative,

and in fact had a positive effect, especially when focusing on the import of intermediate goods.

While most previous studies use industry-level data, firm or plant-level evidence is

somewhat limited. One exception is Iacovone et al. (2013) who explore the determinants of

plant closure and product churning using plant-product level data from Mexico. They find that

Chinese competition has played a significant role in creative destruction in the Mexican

manufacturing sector. This study goes a step further by investigating the impact of Chinese

imports on within-firm reallocation, focusing on the share of non-manufacturing workers.

The second group of studies that form the basis for this research examine the

characteristics of manufacturing firms that shift their primary activities from manufacturing to

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services. Bernard et al. (2017) demonstrate that the decline in manufacturing can be attributed

to firms’ switching from manufacturing to the service industry, as well as firms exiting and an

overall contraction in employment using Danish employer-employee matched data. The

number of wokers for switching out firms amounts to 8.6% of total Danish manufacturing

employment in 2007. They also find that switching firms are highly productive and have higher

import intensity, growth in sales and value-added.

Crozet and Milet (2017) uses a French firm-level panel dataset and examine the effect of

servitization of manufacturing firms on performance. They focus on firms that sell both

products and services and find that servitization increases profitability, sales, and employment.

Chun et al. (2018) examine the share of service workers in Korean manufacturing firms using

firm-plant matched data and investigate how foreign direct investment (FDI) affects the

servitization of firms. They demonstrate that MNEs, especially those that invest in emerging

Asian countries, tend to increase the share of service workers.3 Furthermore, among service

workers, FDI significantly increases R&D worker share.

Recent studies have examined the characteristics of so-called factory-less goods-

producing (FGPs) firms. FGPs mainly engage in R&D and product design but outsource the

production process to other manufacturing firms instead of owning production facilities and

3 Hawakawa et al. (2013) use a Japanese firm-level data set and also examine the firm-level impact of FDI

on employment, comparing the impact on total employment and on manufacturing employment, implicitly

exploring the impact on servitization. Their results are broadly consistent with Chun et al. (2018).

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engaging in production activities themselves. For example, Bernard and Fort (2015) use the

U.S. Census of Wholesale Trade and examine the characteristics of FGPs. Morikawa (2016)

explores the characteristics of Japanese FGPs using a firm-level survey that covers broader

categories of the service industries. Both find that FGPs are larger, more productive and pay a

higher wage.

The rest of this paper is organized as follows. The next section presents an overview of

the data and explains various data issues. Section 3 discusses the empirical framework.

Estimation results are reported in section 4, and section 5 concludes.

2. Analytical Framework and Data overview

2.1 Methodology

To examine the impact of competition from imports on servitization, we estimate the following

equation:

Δ𝑦𝑖𝑡 = 𝛽0 + 𝛽1𝑍𝑖𝑡−1 + 𝛽2𝛥𝐼𝑀𝑃𝑖𝑡 + ε𝑖𝑡.

The dependent variable, Δ𝑦𝑖𝑡 is the change in the percentage of workers by type of activity

for firm i in year t. For types of activities, we focus on manufacturing, R&D and headquarters

services, wholesale & retail and other services. Following Chun et al. (2018), changes in the

share of workers is defined as the weighted value;

Δ𝑦𝑖𝑡 =𝐿𝑖𝑡𝑆

𝐿𝑖𝑡

𝐿𝑖𝑡

𝐿𝑚𝑡−

𝐿𝑖𝑡−𝑠𝑆

𝐿𝑖𝑡−𝑠

𝐿𝑖𝑡−𝑠

𝐿𝑚𝑡−𝑠,

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where 𝐿𝑖𝑡 , 𝐿𝑖𝑡𝑆 and 𝐿𝑡 are the total number of employees for firm i in year t, the number of

workers in activity S for firm i in year t and the number of workers for the entire

manufacturing sector, respectively. This specification is used to capture the economic

significance of servitization in the manufacturing sector as a whole. The equation is estimated

using weights of the number of employee in t-s. 𝑍𝑖𝑡−𝑠 is a vector of firm-level characteristics

in year t-s and ε𝑖𝑡 is the error term. Since corporate restructuring may occur over a number

of years, we take one, three, and five-year differences.

𝛥𝐼𝑀𝑃𝑖𝑡 is the import competition measure, calculated as the changes in the Chinese

import ratio weighted by firm-product level shipment values:

𝛥𝐼𝑀𝑃𝑖𝑡 = ∑ 𝑤𝑖𝑗𝑡−𝑠

𝛥𝑀𝑗𝑡𝐶𝐻

𝑋𝑗𝑡−𝑠𝑖𝑗

where w is the share of a firm-product shipment value for firm i and product j in year t-s,

𝑀𝑗𝑡𝐶𝐻 is the amount imported from China for product i in year t and 𝑋𝑗𝑡−𝑠 is the

corresponding domestic demand. We use the sum of domestic production and total imports

for product j in year t. Since 𝛥𝐼𝑀𝑃𝑖𝑡 might be affected by a potential demand shock in

Japan, we use the identification strategy proposed by Autor et al. (2013). Specifically, we use

as an instrumental variable the changes in the import ratio from China to seven high-income

trading partners of China, except Japan.4

4 As high income countries, we use the same country set with Autor et al. (2013) other than Japan,

including Australia, Denmark, Finland, Germany, New Zealand, Spain, and Switzerland.

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𝛥𝐼𝑀𝑃𝑖𝑡𝑂𝑇𝐻 = ∑ 𝑤𝑖𝑗𝑡−𝑠

𝛥𝑀𝑗𝑡𝑂𝑇𝐻

𝑋𝑗𝑡−𝑠𝑖𝑗

The identification strategy behind this specification is that import demand in other high-

income countries is correlated to the Chinese supply shock, but import demand shocks are not

correlated across high-income countries.

To examine the heterogeneity of the impact of competition from imports, we divide

firms into quartiles using the distribution of firm size in t-s based on number of employees.

To explore the difference in exposure to export markets, we also split our sample based on

exporting status in t-s.

2.2 Data source and data construction procedure

In this study, we combine two data sets. The first one consists of firm-level data

acquired from the Basic Survey of Japanese Business Structure and Activities (BSJBSA)

compiled by Japan’s METI. This survey began in 1991 and has been conducted annually since

1994, covering Mining, Manufacturing, Wholesale, and Retail, Electricity, Gas and Water

suppliers, Information and Communication and certain other service industries. The BSJBSA

provides a statistical overview of Japanese corporations and insights into the diversification

and globalization of corporate activities and R&D strategies for Japanese firms. Variables such

as sales, costs, debt, assets, profits, employment, trade, R&D, and so on are available. The

number of employees is decomposed according to activities such as headquarter service,

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manufacturing, wholesale, retail, R&D and other activities.5 As discussed in the introduction,

we use this firm-level data from 1997 to 2014.

One strength of this survey is its coverage and reliability. The survey is mandatory for

all firms with more than 50 employees and capital of more than 30 million yen in target

industries. One disadvantage in using this survey is that a number of service industries, such as

Finance, Insurance, Transportation, Education, and Medical services are not covered, and firms

with fewer than 50 employees or with capital of less than 30 million yen are also excluded. We

also note that there is no information on the kind of products a company exports or imports and

the destination or source country for exports and imports, since BSJBSA data cannot be

matched against custom trade data.

The second dataset is the “Census of Manufacture” (METI), or COM.6 This dataset

covers all manufacturing establishments located in Japan, and provides plant-level information

on the manufacturer’s location, number of employees, the value of its tangible assets, and the

value of its shipments identified per product at a six digit-level identifier. We aggregated plant-

product level data from COM at the firm level and then matched it to the BSJBSA. Since there

is no official matching table between COM and BSJBSA, we matched the two datasets by

5 “Headquarter service” includes management, strategy, administration, international, information

technology, and R&D. A sales department is included in “Wholesale and retail” activities. 6 The data for year in 2011 was collected as “Economic Census for Business Activities” (Ministry of

Internal Affairs and Communications and Ministry of Economy, trade and Industry) in place of the COM.

We complement the data of the year 2011 with Economic Census . for Business Activities.

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referring to the firm name, phone number, zip code, and address.

The BSJBSA assigns an industry classification to each firm based on the primary source

of its sales. Therefore, firms that engage in both manufacturing and wholesale trade are

classified as wholesale trading firms if their primary sales come from wholesale trade activities.

In this study, since our focus is on all firms that engage in manufacturing activities, we select

those firms that have non-zero manufacturing sales at the beginning of the period and reassign

their industry classifications based on the source of their manufacturing sales. Using the

original industry classifications from the BSJBSA in 1997, there were 14,075 manufacturing

firms out of 26,270 total firms. As a result of our re-classification, since some firms that were

classified as non-manufacturing also engaged in manufacturing activities, we identified 14,703

manufacturing firms in the year 1997.

Regarding the data used to compute the import ratio, we obtain HS 9-digit-level import

data from Japan’s trade statistics (Ministry of Finance). HS 9-digit import data over the period

1997-2014 are reconciled using the concordance table from Aoyagi and Ito (2019). The

concorded HS 9-digit import data are then matched with the 6-digit COM product code using

the concordance table developed in Baek et al. (2019).7 Data on exports from China to other

high-income countries is obtained from the CEPII BACI database.

7 The concordance table between the COM six-digit commodity data and HS nine-digit trade data was

provided by Dr. Youngming Baek.

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As for the variables for firm characteristics, we include the logged number of employees

(Size), a dummy variable for multi-plant firms (Mplant), the firm’s age (Firm age), the lagged

capital-labor ratio (K-L ratio), the R&D to sales ratio (R&D intensity) and a dummy variable

for multi-product firms (Mproduct). We also control for the export and import status at the firm-

level (Exporter dummy and Importer dummy). As suggested by Chun et al. (2018), outward

FDI may affect the servitization of firms. As a proxy for the size of overseas production, we

include the log of foreign sales (ln(Foreign sales)), using the total sales of overseas

manufacturing subsidiaries.8

2.3 Data overview

Table 1 presents the shares of manufacturing and service workers. From column (3) to

column (7), we compare the percentage of employees who are manufacturing workers by firm

size. We find that although the percentage of manufacturing workers does not change vary by

much, it becomes lower as firm size increases. For example, for firms with fewer than 100

employees, the average share was 66.7% in 2007, while for firms with 3000 or more employees

the average share was 57.1%.

Furthermore, the absolute value of the changes in the share of manufacturing workers is

8 Sales of foreign manufacturing subsidiaries are obtained from the Survey of Oversea Business Activities

(SOBA) by METI. We match it with the firm-level data from BSJBSA. We take log for the sum of forign

subsidiary sales plus one.

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larger for large firms. While the shares for smaller firms, those with fewer than 100 employees,

and those with more than 100-999 employees, do not change over our sample period, the

percentage decreased by 2.8 percentage points and by 6.6 percentage points for larger firms,

those with 1000-2999 employees, and 3000 or more employees, respectively. This suggests

there is heterogeneity in the degree of servitization with respect to firm size.

Third, the trends in non-manufacturing worker share differ by service activity. In panel (b) of

Table 1, we divide the percentage of non-manufacturing workers according to types of

activities: R&D, Headquarter service, wholesale and retail, and other service activities. R&D

workers in this table include headquarter workers that engage in R&D activities, R&D workers,

as well as those employees that work for an R&D facility. Since the category of headquarter

worker in column (2) contains those employees that engage in R&D activity in the firm’s

headquarters, we calculate the share of headquarter worker excluding R&D workers in column

(3). Activities that increase the percentage of these workers include R&D, wholesale and retail

and other service activities. In contrast, the share of headquarter workers has declined by 2.3

percentage points when excluding R&D workers.

== Table 1 ==

Table 2 shows the share of workers according to types of activity, by industry. We find

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that the level and trend of the servitization of Japanese firms have substantial heterogeneity

across sectors. The share of manufacturing workers as of 2014, was fairly high for textile

(67.3%) Primary metal (70.8%) and Transport equipment (71.6%) firms, while Chemical firms

had the lowest percentage of manufacturing workers among manufacturing industries at

(49.4%). Regarding changes in the share of an industry’s manufacturing workers, the share of

manufacturing workers in the textile, metal products, and electric machinery industries

decreased by 5.7% points, 5.4% points, and 7.9% points, respectively. We also analyzed the

percentage of non-manufacturing workers based on four different activities. For the textile,

metal products, and electric machinery industries, metal products increased the share of the

worker in wholesale & retail activities (+5.1% points), textiles and electrical machinery

increased the share of worker in other service activities by 4.7% points, and 5.1% points,

respectively.

== Table 2 ==

3. Empirical results

3.1 Main results

In this section, we show our estimation results. Table 3, in columns (1) through (3),

shows the results for changes in manufacturing worker shares. In each column, we take one-,

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three- and five-year differences, respectively. The coefficients for Chinese imports are all

negative and significant. Moreover, as we take the difference over a longer period, the absolute

value of the import coefficient increases, implying that it takes time for organizational reforms

to take place. Looking at the impact of firm characteristics on changes in the share of

manufacturing workers, firms that are larger, have multiple plants, import intermediate goods

and have larger foreign subsidiary sales tend to retain their manufacturing activities.

In columns (4) through (9), we estimate the model using the growth rate in the number

of total workers and of manufacturing workers as dependent variables. As in the case of the

share of manufacturing workers, we take one-, three-, and five-year differences. The

coefficients for import competition are all negative. However, while the coefficients for the

growth rate in the number manufacturing worker are significant the ones for the growth in the

total number of employees are not statistically significant. This result may imply that the

negative impact on manufacturing workers may be offset by increases in service workers.

Regarding the size of the coefficients for the import ratio in predicting the growth in the number

of manufacturing workers, similar to the share of manufacturing workers it becomes larger as

we take differences over longer periods. Based on these results, the following analysis focus

on the 5-year differences.

== Table 3 ==

Table 4 decomposes non-manufacturing activities into five sub-sections. Two results are

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noteworthy. First, across the five sub-sections, competition from Chinese imports has a

significant positive effect on the percentage of wholesale & retail, and other service workers.9

Second, the coefficients for the log of foreign sales are negative for headquarter workers

excluding R&D (HQ excl. R&D) but are positive for R&D and other service sectors. The

results of the positive association between the size of foreign production and the share of R&D

workers is consistent with the findings in Chun et al. (2018). These results may imply that

offshoring and competition from imports have different impacts on the shift from

manufacturing activities to service activities. That is, as firms engaging in FDI reallocate

resources between their home country and the country in which they are investing and

concentrate on R&D at home, competition from imports accelerates the shift of their business

activities from manufacturing to service activities within the firm.

== Table 4 ==

Next, to explore the heterogeneous impact of competition from imports on servitization

with respect to size and export status, we split our sample according to four groups of firm size

and export status in t-5. Results are reported in Table 5. Columns (1) through (4) examine the

differences in the effects of competition from imports based on firm size in t-5. While

competition from imports has no significant impact on the smallest firms (Bin 1), it is

9 The types of activities included in the BSJBSA questionnaire varies by year. Among activities outside

headquarter service, only “manufacturing” and “wholesale & retail” are available throughout our sample

period, which prevents us from decomposing “other service” activities.

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significant for larger firms. Moreover, the coefficients of Chinese imports become larger as

firm size increases, suggesting that servitization caused by globalization is more pronounced

for larger firms. In columns (5) and (6), we divide our sample into exporters and non-exporters.

The effects of competition from imports are significant both for exporters and non-exporters.

However, the coefficients for exporting firms are larger than for non-exporters, suggesting that

exporting firms are more likely to shift their activities from manufacturing toward services.

== Table 5 ==

Next, we conduct several robustness checks. First, inspired by Dauth et al. (2014), we

include the share of exports from Japan to China as an additional control variable. The results

are presented in Table A3. While the coefficients for the shares of exports to China are

significantly positive for the shares of manufacturing workers and the growth rate for the

number of total and manufacturing employees, we confirm that the major results are unchanged.

Second, to control for increases in the number of temporary workers (tmp worker ratio), we

include the share of temporary workers as additional independent variables. In Japan, because

of the deregulation regarding the use of temporary worker in the manufacturing sector in 2004,

the number of temporary workers has substantially increased. Since the number of temporary

workers has been available in the BSJBSA data only since 2000, the estimation results are

restricted the period from 2001 to 2014, as presented in column (4) of Table A3. We confirm

that the temporary worker ratio does not have a significant impact on the results.

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3.2 Extension: Switching firms

One may be interested in how many Japanese manufacturing firms switch entirely from

manufacturing to the service industry, becoming “factory-less goods producers” and to what

extent this contributes to the deindustrialization of employment. As an extension of the analysis,

we calculate the contribution of these “switch out” firms and examine what types of firms

become “switch out” firms. Panel (a) of Table 6 presents the number of “switch out” firms and

panel (b) presents their contribution to the decline of manufacturing employment. In this

exercise, we focus on three sub-periods, 1997-2002, 2002-2007 and 2007-2014, and restrict

our samples to firms that are observable both at the beginning and the end of the sub-periods

in our survey. The number of “switch out” firms is not large; for the periods 1997-2002, 2002-

2007 and 2007-2014, the totals are 522, 598, and 186, respectively. However, the ratio of the

number of workers for switching firms to that of non-switching firms is not negligible. It ranges

from 2.0% to 6.8%. As we introduced in section 1, according to Bernard et al. (2017), the

number of workers for “switching out” firms in Denmark accounts for 8.6% of total

manufacturing employment. Although the period Bernard et al. (2017) focus on is slightly

different from ours, the ratio of the number of workers for Japanese “switch out” firms relative

to total employment in manufacturing firms are comparable to their estimates. We also

calculate the cumulative contribution to the decline of manufacturing employment, namely, the

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ratio of the sum of the number of manufacturing workers for “switch out” to total decline in

manufacturing workers. It amounts to 18.0% ((62,590+108,931+21,250)/(3,337,871-

2,264,874)) for the entire period from 1997 to 2014. This large cumulative contribution may

be because switch out firms are relatively large.

== Table 6 ==

Next, we estimate an IV probit model to examine what kind of firms are likely to be

“switch out” firms. The estimation result is reported in Table 7. Looking at firm characteristics,

R&D intensive, exporting, multi-product firms that own multiple plants and have large foreign

production operations are more likely to retain production facilities and continue to engage in

manufacturing activities. In contrast, the import dummy is positive and significant, implying

importers may replace their own production activities with foreign outsourcing and become

factory-less goods producers. The size of the firm is also positive and significant. This result is

consistent with the fact that regarding the impact of competition from imports, we find that it

has a significant positive impact, implying increased competition from imports induces firms

to switch from manufacturing to services. These results do not change regardless of the

presence of the share of export from Japan to China (∆𝐸𝑋𝑅).

== Table 7 ==

4. Conclusion

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The impact of rising imports from low wage countries such as China has attracted the

attention of policymakers as well as academic researchers. Recent studies such as Autor et al.

(2013) emphasize that imports from China to the U.S. and to European countries have a

negative impact on local employment. In contrast with previous studies, this paper empirically

investigates the effect of competition from imports on within-firm employment reorganizations

instead of industry-level employment by using Japanese firm-level data set. We examine

whether competition from imports from low wage countries leads to a shift from manufacturing

activity to non-manufacturing activity, such as headquarter services or R&D, at the individual

firm level. We also explore the heterogeneity of the impact of imports according to firm size

and the firm’s export status. We find that competition from Chinese imports induces

manufacturing firms to increase their share of service workers, especially those workers that

engage in wholesale & retail, and in other service activities.

Furthermore, we find the impact of competition from imports on the servitization of

Japanese firms is heterogeneous with respect to firm size and exporting status. We find that

larger firms and exporters have actively shifted their activities from manufacturing to services

in response to competition from imports. Among Japanese manufacturing firms that moved

their activities toward services, the number of firms that completely switched to being service

firms is not large. However, since the size of firms that do switch are relatively large, the

cumulative contribution of “switch out” firms to the decline in the share of manufacturing

22

employment over the period studied amounts to 18.0%.

Although this study provides new evidence on the impact of competition from imports,

it offers various avenues for future research. First, there may be a complementary relationship

between certain manufacturing activities and specific service activities. Exploring which kind

of manufacturing activities are most compatible with the servitization of firms might be

interesting research topic. Second, how the servitization of manufacturing firms affects the

geographical distribution of manufacturing facilities and service establishments is another issue

worthy of study. For example, are service activities conducted by manufacturing firms operated

in a city or close to an existing production site? This issue might be important for policymakers

concerned about the hollowing out of local industries.

23

References

Acemoglu, D., Autor, D., Dorn, D., and Hanson, D., 2016, Import Competition and the Great

U.S. Employment Sag of the 2000s, Journal of Labour Economics, 34(S1), S144-S198.

Aoyagi, T. and Ito, T., 2019, Did the Least Developed Countries Benefit from Duty-free Quota-

free Access to the Japanese Market?, Japan and the World Economy, 49: 32-39.

Autor, D., Dorn, D., and Hanson, G, 2013, The China Syndrome: Local Labor Market Effect

of Import Competition in the United States, American Economic Review, 103(6), 2121-

2168.

Autor, D., Dorn, D., Hanson, G., and Song, J., 2014, Trade Adjustment; Worker Level Evidence,

Quarterly Journal of Economics, 129(4), pp.1799-1860.

Autor, D., Dorn, D., Hanson, G., and Majlesi, K., 2016, Importing Political Polarization? The

Electoral Consequences of Rising Trade Exposure, NBER working paper, No.22637.

Baek, Y., Hayakawa, K., Tsubota, K., Urata, S., and Yamanouchi, K., 2019, Tariff Pass-through

in Wholesaling: Evidence from Firm-level Data in Japan, RIETI Discussion Paper.

Balsvik, R., Jensen, S., and Salvanes, K G., 2015, Made in China, Sold in Norway: Local Labor

Market Effects of an Import Shock, Journal of Public Economics, 127, 137-144.

Bernard A., and Fort, T., 2015, Factoryless Goods Producing Firms, The American Economic

Review, 105(5), 518-523.

Bernard, A., Smeets V., and Warzynski, F., 2017, Rethinking Deindustrialization, Economic

Policy, 5-38.

Chun, H., Hur, J., and Son, N-S., 2018, Servicification of Manufacturing: Evidence from

Korean Multinational Firms, mimeo.

Crozet, M., Milet, E., 2017, Should Everybody be in services? The Effect of Servitization on

Manufacturing Firm Performance, Journal of Economics & Management Strategy, 26(3),

820-841.

24

Dauth, W., Findeisen, S., and Suedekum, J., 2014, The rise of the east and the far east: German

labor markets and trade integration, Journal of the European Economic Association, 12(6),

1643-1675.

Donoso, V., Martin, V., and Minondo A., 2015, Do Differences in the Exposure to Chinese

Impacts Lead to Differences in Local Labour Market Outcomes? An Analysis for Spanish

Provinces, Regional Studies, 49(10), 1746-1764.

Francois, J., Manchin, M., and Tomberger, P., 2015, Services Linkages and the Value Added

Content of Trade, The World Economy, 38(11), 1631-1649.

Hayakawa, K., Matsuura, T., Motohashi, K., and Obashi, A., 2013, Two-Dimensional Analysis

of the Impact of Outward FDI on Performance at Home: Evidence from Japanese

Manufacturing Firms, Japan and the World Economy, 27, 25-33.

Iacovone, L., Rauch F., and Winters L A., 2013, Trade as an Engine of Creative Destruction:

Mexican Experience with Chinese Competition, Journal of International Economics, 89,

379-392.

Malgouyers, C., 2016, The Impact of Chinese Import Competition on Employment and the

Wage Distribution: Evidence from French Local Labor Markets, Journal of Regional

Science, 57(3), 411-441.

Morikawa, M., 2016, Factoryless Goods Produces in Japan, Japan and the World Economy, 40,

9-15.

Taniguchi, M., 2019, The Effect of an Increase in Imports from China on Regional Labor

Markets in Japan, Journal of the Japanese and international economies, 54, 1-18.

25

Table 1 The share of manufacturing and non-manufacturing worker

Panel (a) The number of employees and the share of manufacturing workers

Panel (b) The share of non-manufacturing worker

Source: Author’s calculation based on BSJBSA.

(1) (2) (3) (4) (5)

Share of manufacturing worker

50-99 100-999 1000-2999 3000-

1997 65.2% 66.7% 65.5% 57.4% 57.1%

2002 65.1% 67.7% 65.0% 52.9% 51.4%

2007 66.2% 67.3% 66.8% 59.0% 54.7%

2014 64.6% 67.3% 64.7% 54.6% 50.5%

AverageFirm size in terms of # of emp

(1) (2) (3) (4) (5)

1997 3.8% 15.4% 12.4% 14.2% 4.4%

2002 4.2% 13.8% 10.6% 15.0% 5.1%

2007 4.3% 13.6% 10.2% 13.0% 6.3%

2014 4.2% 13.3% 10.1% 14.2% 6.9%

Wholesale

&retailOther services

HQ (excl.

R&D worker)R&D HQ

26

Table 2 The share of manufacturing and non-manufacturing worker by industries.

Source: Author’s calculation based on BSJBSA.

a) 1997 b) 2014 b)-a) 1997 2014 b)-a) 1997 2014 b)-a)

Food 62.8% 59.4% -3.4% 2.4% 2.8% 0.4% 12.5% 11.7% -0.8%

Textile 73.0% 67.3% -5.7% 2.9% 3.2% 0.3% 13.8% 11.9% -1.9%

Pulp and Paper 71.7% 66.9% -4.8% 1.3% 1.3% 0.1% 13.1% 11.3% -1.8%

Chemical 52.6% 49.4% -3.2% 10.2% 11.0% 0.8% 18.3% 16.9% -1.4%

Coal and petroriu 67.5% 64.6% -2.8% 2.7% 3.0% 0.3% 12.6% 11.1% -1.5%

Non-metal minearal products 63.9% 60.4% -3.5% 2.3% 2.4% 0.2% 13.6% 11.9% -1.7%

Primary metal 73.5% 70.8% -2.7% 1.9% 1.7% -0.2% 13.8% 10.9% -2.9%

Metal products 67.4% 62.0% -5.4% 2.5% 2.5% 0.0% 15.0% 12.5% -2.5%

Machinery 63.5% 62.4% -1.1% 5.0% 5.5% 0.5% 17.3% 15.3% -2.0%

Electric Machinery 71.9% 63.9% -7.9% 4.9% 6.4% 1.5% 15.0% 15.3% 0.3%

Transport equipment 73.8% 71.6% -2.2% 3.9% 4.4% 0.5% 16.4% 13.9% -2.5%

Precision instrument 63.4% 62.8% -0.6% 6.9% 8.4% 1.5% 17.0% 16.7% -0.3%

Other manufacturing 63.7% 59.6% -4.1% 2.3% 2.6% 0.2% 14.7% 12.9% -1.7%

a) 1997 b) 2014 b)-a) 1997 2014 b)-a)

Food 19.5% 21.1% 1.5% 5.1% 7.8% 2.7%

Textile 9.8% 12.7% 2.9% 3.4% 8.1% 4.7%

Pulp and Paper 11.9% 15.9% 3.9% 3.3% 5.9% 2.6%

Chemical 20.8% 21.9% 1.0% 8.2% 11.8% 3.6%

Coal and petroriu 14.3% 15.6% 1.3% 5.6% 8.7% 3.0%

Non-metal minearal products 14.7% 18.1% 3.4% 7.9% 9.6% 1.8%

Primary metal 9.0% 10.5% 1.5% 3.7% 7.9% 4.2%

Metal products 14.0% 19.1% 5.1% 3.6% 6.5% 2.8%

Machinery 14.0% 14.9% 1.0% 5.2% 7.3% 2.1%

Electric Machinery 9.0% 11.6% 2.6% 4.2% 9.2% 5.1%

Transport equipment 4.8% 5.7% 0.9% 5.0% 8.7% 3.8%

Precision instrument 14.4% 12.2% -2.2% 5.2% 8.3% 3.1%

Other manufacturing 16.9% 19.9% 3.0% 4.7% 7.5% 2.9%

HQ

Wholesale &Retail Other service

Manufacturing R&D

27

Table 3 Estimation results: Manufacturing worker share, the growth rate of employment

Note: Robust standard errors in parentheses clustered at two-digit industry classification. Year

dummies are included. "***", "**" and "*" indicates the statistical significance at 1%, 5%

and 10%, respectively.

VARIABLES 1 year lag 3 year lag 5 year lag 1 year lag 3 year lag 5 year lag 1 year lag 3 year lag 5 year lag

-0.0858* -0.472** -0.900*** -0.245 -0.195 -0.262 -0.272* -0.431** -0.817***

(0.0512) (0.199) (0.339) (0.193) (0.124) (0.175) (0.164) (0.203) (0.278)

Size -0.0107*** -0.0375*** -0.0627*** -0.00572***-0.0167*** -0.0251*** -0.00339** -0.0162*** -0.0266***

(0.00384) (0.0113) (0.0192) (0.000848) (0.00309) (0.00491) (0.00148) (0.00469) (0.00782)

Mplant -0.00745***-0.0113*** -0.0105* -0.00335***-0.0115*** -0.0194*** -0.0366*** -0.0673*** -0.0840***

(0.00103) (0.00394) (0.00589) (0.000992) (0.00251) (0.00369) (0.00195) (0.00423) (0.00523)

Firm Age -1.97e-05 -6.72e-05 -0.000119 -9.99e-05**-0.000274**-0.000406** -7.45e-05* -0.000209**-0.000316**

(1.55e-05) (5.74e-05) (0.000115) (4.75e-05) (0.000122) (0.000189) (3.94e-05) (0.000101) (0.000154)

K-L ratio 0.000837 0.00271 0.00246 0.00630*** 0.0156*** 0.0234*** 0.00734*** 0.0198*** 0.0306***

(0.000684) (0.00209) (0.00388) (0.000721) (0.00200) (0.00328) (0.000832) (0.00241) (0.00442)

R&D intensity -0.124* -0.497* -0.903 0.0318 0.223*** 0.420*** -0.0604 -0.0535 0.0387

(0.0725) (0.294) (0.564) (0.0375) (0.0680) (0.0736) (0.0471) (0.104) (0.162)

Mproduct 0.000374 0.00129 0.00464 -0.00305** -0.00730** -0.0109* -0.00147 -0.00572 -0.0109

(0.000568) (0.00216) (0.00430) (0.00122) (0.00335) (0.00587) (0.00187) (0.00547) (0.00949)

d_export 0.00358 0.0138* 0.0174 0.00131 0.00268 0.00498 0.00325 0.00950 0.00677

(0.00228) (0.00834) (0.0110) (0.00152) (0.00445) (0.00591) (0.00229) (0.00671) (0.00954)

d_import 0.00487* 0.0219* 0.0404* -0.00176 -0.00638 -0.0115* -0.00632***-0.0171*** -0.0195***

(0.00281) (0.0124) (0.0240) (0.00142) (0.00441) (0.00605) (0.00201) (0.00524) (0.00702)

lfsales -0.00263** -0.0112*** -0.0190*** 0.000523*** 0.00122* 0.00173* -0.000180 -0.000541 -0.000638

(0.00109) (0.00278) (0.00476) (0.000195) (0.000624) (0.000922) (0.000396) (0.00109) (0.00148)

First stage

0.2576*** 0.396*** 0.3257*** 0.2576*** 0.3959*** 0.326*** 0.2583*** 0.3988*** 0.333***

(0.0594) (0.0816) (0.0518) (0.0594) (0.0816) (0.0519) (0.0595) (0.0843) (0.0554)

Frist stage F test 18.8 23.54 39.47 18.8 23.54 39.450961 18.87 22.37 36.168196

Observations 152,763 120,342 101,091 152,763 120,342 101,091 151,552 117,918 98,130

∆𝐼𝑀𝑅

∆ 𝐸 ) ∆ 𝑀 𝐸 ∆𝑀 𝑤 share

∆𝐼𝑀𝑅 𝑡

28

Table 4 Estimation results: Non-manufacturing activities

Note: Robust standard errors in parentheses clustered at two-digit industry classification. Year

dummies are included. "***", "**" and "*" indicates the statistical significance at 1%, 5%

and 10%, respectively.

(1) (2) (3) (4) (5) (6)

VARIABLES

First stageHQ

HQ (excl.

R&D)R&D

Wholesale

&retail

Other

services

-0.0798 -0.0723 0.269 0.334*** 0.370***

(0.0781) (0.0679) (0.292) (0.127) (0.118)

Size -0.000362 -0.00330 0.0177*** -8.86e-05 0.0484***

(0.00438) (0.00363) (0.00676) (0.00699) (0.0140)

Mplant 0.00228 0.00208* -0.00390 0.0146*** -0.00218

(0.00193) (0.00123) (0.00464) (0.00297) (0.00521)

Firm Age -4.35e-05 -4.36e-05 -7.74e-06 1.74e-05 0.000153

(4.04e-05) (3.12e-05) (2.51e-05) (4.39e-05) (0.000114)

K-L ratio -0.00254* -0.00188* -0.00148 0.000433 0.000471

(0.00154) (0.00107) (0.00126) (0.00169) (0.00292)

R&D intensity -0.0661 -0.119** 0.404 0.201* 0.417

(0.0862) (0.0593) (0.291) (0.116) (0.373)

Mproduct 0.00259 0.00253* -0.00292 0.00201 -0.00627*

(0.00183) (0.00145) (0.00192) (0.00312) (0.00332)

Export dummy 0.00424 0.00489* -0.00174 -0.00530 -0.0152*

(0.00305) (0.00258) (0.00550) (0.00532) (0.00815)

Import dummy -0.000612 0.00361 -0.0253* 0.00444 -0.0231

(0.00742) (0.00451) (0.0153) (0.00466) (0.0160)

ln(Foreign sales) -0.00143 -0.00299*** 0.00827*** -0.000824 0.0145***

(0.00203) (0.00101) (0.00267) (0.00131) (0.00419)

0.326***

(0.0519)

Frist stage F test 39.451

Observations 101,091 101,091 101,091 101,091 101,091 101,091

∆𝐼𝑀𝑅

∆𝐼𝑀𝑅

∆𝐼𝑀𝑅 𝑡

29

Table 5 Heterogeneous impact of import competition

Note: Robust standard errors in parentheses clustered at two-digit industry classification. Year

dummies are included. "***", "**" and "*" indicates the statistical significance at 1%, 5%

and 10%, respectively.

(1) (2) (3) (4) (5) (6)

Bin 1 (<25%) Bin 2 (25-50%) Bin 3 (50-75%) Bin 4 (>75%)

-0.00971 -0.0965*** -0.139** -2.518** -0.272** -1.840**

(0.0137) (0.0284) (0.0612) (1.197) (0.109) (0.793)

Size -0.00494*** -0.00404 -0.00813** -0.236*** -0.0283*** -0.110***

(0.00175) (0.00249) (0.00316) (0.0673) (0.0107) (0.0269)

Mplant -0.00397*** -0.00578*** -0.00920*** -0.0427** -0.00944*** -0.00676

(0.000773) (0.000834) (0.00145) (0.0190) (0.00295) (0.0140)

Firm Age 1.22e-05 9.29e-06 8.46e-06 -0.00116 4.15e-05 -0.00119

(1.37e-05) (6.33e-06) (9.00e-06) (0.00117) (3.33e-05) (0.000792)

K-L ratio 0.000648** 0.00149*** 0.00354*** 0.0125 -0.00118 0.0213**

(0.000308) (0.000447) (0.000783) (0.0205) (0.00197) (0.00918)

R&D intensity 0.0117*** 0.0280* 0.0114 -1.347 -0.354 -0.971*

(0.00423) (0.0145) (0.0296) (0.972) (0.277) (0.573)

Mproduct 8.43e-05 0.00108 0.00167 0.0266 -0.00190 0.0258**

(0.000490) (0.000663) (0.00131) (0.0193) (0.00292) (0.0126)

Export dummy 0.000387 0.000547 0.00500*** -0.00189

(0.000598) (0.000599) (0.00168) (0.0338)

Import dummy -2.84e-05 -0.000363 -0.00331** 0.0882 0.00949 0.0710*

(0.000635) (0.000743) (0.00146) (0.0600) (0.00690) (0.0389)

ln(Foreign sales) 0.000665*** -0.000208 -0.00104** -0.0127*** -0.0111 -0.0150***

(0.000254) (0.000254) (0.000420) (0.00352) (0.00981) (0.00257)

First stage

0.306*** 0.309** 0.303*** 0.378** 0.387*** 0.253***

(0.0749) (0.113) (0.0479) (0.161) (0.0747) (0.0646)

First stage F test 16.68 7.39 40.06 5.50 26.84 15.41

Observations 25,474 25,216 25,163 25,238 66,883 34,208

Initial size (# of emp)Non-Exporters Exporters

∆𝐼𝑀𝑅

∆𝐼𝑀𝑅 𝑡

30

Table 6 The characteristics of switch-out firms

Panel (a) The number of switching firms

(1) # of firms in the beginning (2) Switchers (3) Switcher ratio

1997-2002 11,034 522 4.7%

2002-2007 8,487 598 7.0%

2007-2014 7,280 186 2.6%

Panel (b) # of workers for switchiers and the contribution of switchers in terms of employment

(1) # of

workers for

non-switchers

(2) # of

workers for

switchers

(3) ratio of # of

worker for

switchers ((2)/(1))

(4) # of total MFG

worker

1997 5,452,406 3,337,871 62,590

2002 4,876,356 246,465 5.1% 2,462,692 108,931 1997-2002 7.2%

2007 4,217,095 284,906 6.8% 2,261,151 21,250 1997-2007 15.9%

2014 3,929,064 79,194 2.0% 2,264,874 1997-2014 18.0%

(6) Cumulative

contribution of

switchers to the decline

(5) # of MFG worker for

switchers in the

beginning

31

Table 7 IV Probit model estimation for switching behavior

Dependent variable: Dummy variable for switchers

Note: Robust standard errors in parentheses clustered at two-digit industry classification.

"***", "**" and "*" indicates the statistical significance at 1%, 5% and 10%, respectively.

Two-digit industry dummies and year dummies are included.

(1) (2)

2.061*** 2.177***

(0.312) (0.414)

-0.277

(0.646)

Size 0.114*** 0.114***

(0.00851) (0.00851)

Mplant -0.240*** -0.241***

(0.0156) (0.0157)

Firm Age 0.000154 0.000152

(0.000234) (0.000234)

K-L ratio -0.0351*** -0.0352***

(0.00777) (0.00778)

R&D intensity -1.267*** -1.265***

(0.394) (0.394)

Mproduct -0.114*** -0.114***

(0.0156) (0.0156)

Export dummy -0.0382* -0.0373*

(0.0214) (0.0215)

Import dummy 0.0876*** 0.0872***

(0.0204) (0.0204)

ln(Foreign sales) -0.0123*** -0.0123***

(0.00374) (0.00374)

Observations 125,643 125,643

∆𝐼𝑀𝑅

∆𝐸𝑋𝑅

32

Figure 1 The share of manufacturing employment in total employment

Source: System of National Account (Cabinet office of Japanese government)

23.1%

15.3%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

1980 1985 1990 1995 2000 2005 2010 2015

33

Appendix Table A1 Basic Statistics

Mean S.D. p1 p99-0.033 0.622 -0.983 0.506

0.000 0.273 -0.312 0.362

-0.002 0.230 -0.244 0.282

0.007 0.349 -0.187 0.258

0.003 0.352 -0.434 0.484

0.024 0.582 -0.411 0.754

-0.026 0.276 -0.817 0.739

-0.045 0.442 -1.406 1.142

Size 5.335 1.044 3.989 8.756

Mplant 0.548 0.498 0.000 1.000

Firm Age 45.564 31.510 5.000 91.000

K-L ratio 2.018 0.944 -0.911 4.158

R&D intensity 0.011 0.026 0.000 0.107

Mproduct 0.563 0.496 0.000 1.000

Export dummy 0.338 0.473 0.000 1.000

Import dummy 0.292 0.455 0.000 1.000

ln(Foreign sales) 0.810 2.577 0.000 11.432

0.008 0.039 -0.022 0.107

∆ : MFG∆ : HQ

∆ : R&D

∆ : Wholesale & Retail

∆ : Other service

∆ : HQ (excl. R&D)

∆ 𝐸 )

∆ 𝑀 𝐸

∆𝐼𝑀𝑅

34

Appendix Table A2 Correlation Matrix

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]

[1] 1.000

[2] 0.083 1.000

[3] 0.164 0.859 1.000

[4] -0.470 -0.173 -0.383 1.000

[5] -0.220 -0.203 -0.187 0.019 1.000

[6] -0.740 -0.225 -0.247 0.029 -0.221 1.000

[7] 0.022 -0.045 -0.041 -0.016 0.002 0.002 1.000

[8] 0.196 -0.060 -0.039 -0.070 -0.091 -0.105 0.624 1.000

[9] Size -0.128 -0.013 -0.035 0.078 -0.025 0.116 -0.083 -0.090 1.000

[10] Mplant -0.036 0.002 -0.003 0.015 0.011 0.025 -0.045 -0.081 0.269 1.000

[11] Firm Age -0.023 -0.005 -0.009 0.011 -0.002 0.023 -0.039 -0.025 0.131 0.095 1.000

[12] K-L ratio -0.023 -0.010 -0.014 0.012 -0.005 0.025 0.064 0.044 0.166 0.106 0.124 1.000

[13] R&D intensity -0.076 -0.010 -0.022 0.051 0.007 0.056 0.017 -0.021 0.294 0.054 0.056 0.100 1.000

[14] Mproduct -0.028 0.000 -0.005 0.014 0.000 0.024 -0.039 -0.037 0.214 0.107 0.087 0.097 0.083 1.000

[15] Export dummy -0.038 -0.002 -0.006 0.022 -0.007 0.034 -0.011 -0.027 0.313 0.107 0.093 0.118 0.290 0.139 1.000

[16] Import dummy -0.020 -0.006 -0.006 0.003 -0.004 0.024 -0.016 -0.034 0.277 0.083 0.072 0.098 0.227 0.119 0.572 1.000

[17] ln(Foreign sales) -0.119 -0.016 -0.039 0.080 -0.015 0.102 -0.019 -0.039 0.504 0.147 0.126 0.166 0.253 0.144 0.336 0.301 1.000

[18] -0.018 -0.002 0.002 0.009 0.001 0.012 -0.009 -0.018 0.006 -0.009 -0.006 -0.034 0.018 0.032 0.015 0.018 -0.002 1.000

∆ : MFG

∆ : HQ

∆ : R&D

∆ : Wholesale & Retail

∆ : Other service

∆ : HQ (excl. R&D)

∆ 𝐸 )

∆ 𝑀 𝐸

∆𝐼𝑀𝑅

35

Appendix Table A3 Robustness checks

Note: Robust standard errors in parentheses clustered at two-digit industry classification. Year

dummies are included. "***", "**" and "*" indicates the statistical significance at 1%, 5% and

10%, respectively.

(1) (2) (3) (4)

VARIABLES

MFG

share

MFG

share

-0.921** -0.290 -0.863*** -0.698***

(0.368) (0.183) (0.306) (0.186)

0.282* 0.368*** 0.621***

(0.162) (0.0870) (0.134)

Size -0.0624*** -0.0248*** -0.0261*** -0.0353**

(0.0193) (0.00505) (0.00807) (0.0144)

Mplant -0.0106* -0.0194*** -0.0840*** -0.0171***

(0.00589) (0.00368) (0.00530) (0.00602)

Firm Age -0.000115 -0.000401**-0.000307** -0.000148

(0.000115) (0.000187) (0.000152) (0.000169)

K-L ratio 0.00273 0.0237*** 0.0313*** 0.00455

(0.00395) (0.00322) (0.00445) (0.00359)

R&D intensity -0.906 0.416*** 0.0319 -0.720**

(0.568) (0.0753) (0.168) (0.355)

Mproduct 0.00405 -0.0117** -0.0122 0.00346

(0.00414) (0.00579) (0.00936) (0.00490)

Export dummy 0.0156 0.00262 0.00275 0.00941

(0.0115) (0.00600) (0.00968) (0.00936)

Import dummy 0.0408* -0.0110* -0.0185*** 0.0220

(0.0242) (0.00602) (0.00693) (0.0191)

ln(Foreign sales) -0.0190*** 0.00178* -0.000544 -0.00907***

(0.00473) (0.000933) (0.00150) (0.00317)

tmp worker ratio 0.00429

(0.0113)

First stage

0.320*** 0.320*** 0.327*** 0.312**

(0.0535) (0.0535) (0.0572) (0.111)

0.0504* 0.0504* 0.0477

(0.0286) (0.0286) (0.0292)

First stage

-0.0317 -0.0317 -0.0345

(0.0774) (0.0774) (0.0828)

0.516*** 0.516*** 0.513***

(0.0758) (0.0758) (0.0762)

First stage F test 10.86

Kleibergen-Paap rk

Wald F statistic33.25 33.25 32.04

Observations 101,091 101,091 98,130 42,740

∆ 𝐸 )∆ 𝑀 𝐸

∆𝐼𝑀𝑅

∆𝐸𝑋𝑅

∆𝐼𝑀𝑅 𝑡

∆𝐸𝑋𝑅 𝑡

∆𝐼𝑀𝑅 𝑡

∆𝐸𝑋𝑅 𝑡

∆𝐼𝑀𝑅

∆𝐸𝑋𝑅