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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
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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