KEO Discussion Paper No.158

Import competition and product churning: Evidence
from Japanese plant-product-level data
Flora Bellone and Toshiyuki Matsuura
January 19, 2021

Import competition and product churning: Evidence from Japanese plant-productlevel data1
Flora Bellone
Universite Cote d’Azur, CNRS, GREDEG
Toshiyuki Matsuura
Keio University
Abstract

This study examines how Japanese firms change their product portfolio in response to thesurge of imports from China. Using the comprehensive Japanese plant-product level panel
data set, we examine the impact of import competition on their product churning behavior.
We found that the import competition pressures do affect the product churning for multiproduct plants. Besides, the negative effects of import competition are mitigated by increased
export opportunities. This effect is more pronounced especially the year before 2007.
However, its impact on the plant-level reallocation is somewhat limited. Negative effects onplant-level sales and exit behavior are observed only for a single product-plant.

Keyword: Import competition, Product Portfolio, Local Product Relatedness
JEL classification: F61, L25, D22

1This research was supported by a grant-in-aid from Zengin Foundation for Studies on Economics and Finance.
This study utilizes the data of the questionnaire information based on “Census of Manufacture” (Ministry of
Economy, Trade and Industry, METI) and “Economic Census for Business Activities” (Ministry of Internal Affairs
and Communications), which is arranged in the project “A Study of Free Trade Agreements” undertaken at ResearchInstitute of Economy, Trade and Industry (RIETI). We also utilize the plant-id converter for the Census of
Manufacture, which is provided by RIETI.

1

1. Introduction
Recently, the surge of imports from China has attracted policymakers’ attention in many
developed countries. For example, a series of studies by David Autor and his colleagues revealthat rising Chinese import to the US has a substantial negative impact on the local labor market.
(Autor et al. 2013, Acemoglu et al. 2016). For example, Autor et al. (2013) demonstrate that 55
percent of the decline in US manufacturing employment from 2000 to 2007 can be explained byrising exposure to imports from China. Furthermore, the exposure to Chinese imports induces adecline in wages, a higher unemployment ratio, and increases in transfer payments throughmultiple federal and state programs. Some studies present similar evidence for Europe. For
example, Malgouyers (2017) finds that France also experienced a decline in manufacturing
employment due to import competition from China. Furthermore, it polarized the local
employment in the manufacturing sector. Similar patterns of the impact of Chinese imports on
local employment are found by Balsvik et al. (2015) for Norway, and by Donoso et al. (2014) for
Spain.
Turning to Japan, we see the increases in the import share from China and the decline in
manufacturing worker share. According to Japan Industry Productivity (JIP) Database 2015,
during the period from 1990 to 2012, while the share of import from China to total domestic
demand increased from 1.9% to 5.3%, the share of manufacturing in total employment has
decreased from 23% to 16%. The straightforward question is how does rising imports from China
affect Japanese manufacturing industries. In particular, is it more significant because of thegeographical proximity of Japan to China?
Existing studies regarding the effect of imports from China on employment in Japan present
somewhat different results compared with the ones in the U.S. or Europe. For example, Tomiura(2003) investigates the impact of the increase in imports from low wage countries using Japanesedetailed industry-level data and finds a negative impact on employment in some industries, but hereports that the economic impact is not so large. A more recent study by Taniguchi (2019) examines
the impact of rising Chinese imports on the Japanese local labor market at the prefecture-level
following the methodology used in Autor et al. (2013) and finds that the impact of imports from2

China is not negative; rather she found a positive effect in case of intermediate goods import.
Although the labor market effect of rising Chinese import is not significant, fierce competition
from low wage countries forces some firms to switch their products or give up producing existingproducts. This paper focuses on the effect of China import competition on plant level or plantproduct level sales and survival decision using comprehensive firm-plant-product level data inJapan. To precisely identify import competition pressure, we matched six digit-level plant-productlevel data with international trade data. And then we examine what kind of firm/plants are more
likely to reorganize their product portfolio and their production facilities. In this paper, we also
shed light on regional characteristics, such as local wage and local comparative advantages.
Our work is related to two strands of the literature. The first one is the impact of importcompetition from low-wage countries such as China. As we mentioned above, many papers
examine the impact of Chinese imports on the labor market, mainly in developed countries. The
second is the impact of trade liberalization on within-firm reallocation for multi-product firms.
Bernard et al. (2010) use the US plant-product level data set and shows that product switching
behavior is comparable to the entry and exit of the plant. Mayer et al. (2014) examine the impactof trade on within-firm reallocation and focus on the key role of “core products”. They find that in
response to import competition, firms tend to drop “non-core products” retaining “core products”
only. The paper which is more closely related to ours is Iacovone et al. (2013). They utilize the
Mexican plant-product level data set and examine the impact of Chinese imports on within-firm
reallocation at the plant-product level.
The rest of the paper is organized as follows. In section 2, we present an empirical model andexplain our data source, data, and variable construction procedures. After presenting somedescriptive statistics in section 3, the estimation results are shown in section 4. After confirming
the robustness of the results in section 5, we conclude this paper in section 6.

2. Estimation strategies
2.1 Empirical Model
Following Iacovone et al. (2013), we estimate the following equation;3

ywhere y

𝛽𝑋

𝛽 𝐼𝑀𝑃

𝛽 𝐼𝑀𝑃

∗𝑋

𝛽𝑍

𝜇

𝜆

𝜀

is a plant or plant-product-level outcome variable. Specifically, we use product exit,

plant exit, or log of plant or plant-product sales. IMP is the import competition measure, which is
proxied by Chinese import share in total import for sector k. X is plant characteristics. We include
logged number of employees, logged number of products, and within plant sales share of focal
products (Share). In the literature on multi-product firms and international trade (e.g., Bernard etal. 2011, Mayer et al. 2014), a product with the largest sales within a firm is regarded as the mostprofitable product or “core product.” To control within-plant heterogeneity across products, we
include the interaction term of Share with import competition measure, IMP. Finally, we also
include regional characteristics, Z. We include the average wage by prefecture and local productrelatedness (LPR) measure.
For the plant-product-level impact, we separately estimate the multi-product plant and singleproduct plant. As pointed out by Iacovone et al. (2013), the results for multi-product plants can beinterpreted as within plant reallocation. In contrast, samples for the single-product plant are thesubset of plant-level data, and the results for these plants are considered as the impact on small
plants.

2.2 Data and Variable Construction Procedure
Our primary data sources are the longitudinal data sets of the Census of Manufacture (COM)2.
The COM is an establishment-level data set compiled by the Ministry of Economy, Trade, andIndustry (METI). The COM data covers all plants with more than four or more employees locatedin Japan and includes information on plant characteristics, such as their location, number of
employees, amount of tangible assets, the value of shipments, and four-digit-level sector

2

The survey in year 2011 was conducted as “Economic Census for Business Activities”(Ministry of Internal Affairs and Communications) in place of the COM. We complement thedata of the year 2011 from Economic Census.
4classification.3 The COM also provides the plant-product-level shipment data at six-digit-levelcommodity classification. In this study, we use the plant-product-level data set over the period
from 1997 to 2014.
To identify plant exit or plant-product exit, we use a unique identification number given toeach plant. Exiting plants are defined as the plants that are observed at time t - 1 but not at t. A
limitation of the data is that it is not possible to distinguish firm closure from exclusion from thecensus due to a fall in employment below four employees. We find that some plants stop reportingto the census but re-start to report a few years later. We construct exit dummy variables referringto the final reporting year of each plant.
Our import competition measure is the share of Chinese import in total import at productlevel. To compute the share of Chinese imports, we use trade data at HS 9-digit of Japanesecustoms. Since HS 9-digit classification is often revised, we use the concordance table of HS 9digit code constructed by Aoyagi and Ito (2019). Then, the concordant HS 9-digit import data are,matched with the product code of the Census of Manufacture at 6-digit. The concordance tablebetween the COM six-digit commodity data and HS nine-digit trade data is constructed in Baek etal. (2019).4 In our matched data set, 1125 products are included5.
One may be concerned that the coefficient of the Chinese import share suffers from theendogeneity bias if the demand shock in Japan affects both imports from China to Japan anddomestic product sales. We follow the identification proposed by Autor et al. (2013) and use theshares of Chinese imports to the U.S. and Europe as instrument variables. We believe the supply
shock in China, such as an improvement of market access from China to developed countries andproductivity enhancement of Chinese firms, do affect both imports from China to Japan and thatto Europe and the U.S. However, the demand shock in Japan may not have any effect on the3

Unfortunately, the information on the international trade activates at plant-level is quite
limited. It provides the share of export revenue in total shipment from 2001. However, there is no
information for import side.
4
The concordance table is provided by Dr. Youngming Baek.
5 In original data set in the COM, we have 2378 products as of the year 2014. However, not all“products” in the COM are tradable goods (e.g., revenue from modification and repair fee or
piecework) or cannot be matched with trade statistics (e.g., miscellaneous office paper products).
5Chinese imports to Europe and the U.S. Therefore, we expect these variables will work as valid
instruments.
In addition to the China import intensity, we take into account three international trademeasures. The first is the share of exports from Japan to China. According to Dauth et al. (2014),
who investigates the impact of Chinese imports on German local labor markets, negative impactsof import competition are mitigated by export demand from China, and it contributes to retaining
manufacturing sectors in Germany. Since Japan’s exports to China is as large as its imports from
China, increased export opportunities may mitigate the negative impact of import competitionpressures as in Germany. To control this effect, we include the share of exports to China in totalexports as an additional variable. The second is the measure of imported inputs from China. China
imports may have a positive effect if firms become able to access cheaper and a larger variety ofimported inputs. To take into account this effect, we use Japanese Input-Output tables and calculate
Chinese import share weighted with input-output coefficients.6 This measure is frequently usedin previous studies, such as Acemoglu et al. (2016) and Iacovone et al. (2013).
The third is the import competition or export opportunity measures from/to other developing
countries. Japan’s imports from low-wage countries are not only from China but also otherdeveloping countries such as Southeast Asian countries. To take into account the increase inimports and exports from/to other developing countries, we calculate the import and export share
from/to ASEAN and include additional variables. Since these additional export and importvariables are considered as endogenous variables, we estimate the model with the instrument
variables which are constructed in the same manner with the share of Chinese imports.
Regarding variables for plant, plant-product, and regional characteristics, we include
the number of employees at plant-level, within-plant product share, the average wage by
region, and the local comparative advantage7. The wage is calculated using plant-level data6

We used the input coefficient in the year of 1995 which is included in Linked Input-Output (IO) Table for
1995-2000-2005. To link the sector classification of Linked IO Table with the COM commodity classification,
we use the concordance table between 2005 Basic IO Table and the COM commodity classification, which isprovided in the Appendix of 2005 Basic IO Table. Following Acemoglu et al. (2016), we choose 1995 IO tablebecause the input-output linkages in 1995 might be less likely to be affected by the import from China.
7 One may be interested in how productivity measure such as total factor productivity (TFP)
6

in the COM by prefecture. As for the measure of local comparative advantages, we use theproduct relatedness measure, which is developed by Hidalgo et al. (2007). This measureindicates the similarities or complementarities among products based on underlying
capabilities, which can be production factors, skills, knowledge base, institutions, businessnetworks, etc. Hausmann and Hidalgo (2011) show that product relatedness plays animportant role in product diversification and product upgrading at the country level. Morerecent studies relate the product-level product relatedness measure with regional
comparative advantages and examine its impact on growth path at the regional level or at thefirm or plant-levels (Boschma et al. 2012, Poncet and Starosta Valdemar, 2015, Hazir et al.
2019).8

3. Data Overview
Table 1 presents the breakdown of plants concerning their association to single or multi-plantfirms, and the number of products. Two things are noteworthy. First, there is a decline in the total
number of plants from 1997 to 2014, from 218,000 to 138,000 plants. Around 60% of the decline
in the total number of plants is accounted for by the decline in the number of single-product plantsthat are single-plant firms (column (2)). Second, the number of multi-products plants, especiallyof those belonging to multi-plant firms, is relatively stable (column (6)). As a result, the share ofthese plants has slightly increased from 6% to 7%.

== Table 1 ==Corresponding plant characteristics, such as the average number of employees per plant andthe average number of products per plant are shown in Table 2. Plants owned by multi-plant firms
are larger than those that are single-plant firms. In particular, the average size of single-productaffects product churning. However, the information on the amount of tangible asset is restricted
to plants with 30 or more employees, which prevent us from calculating TFP.
8
The details of variable definition of LPR is explained in Appendix A.
7

plants that are single plant firms (column (1)) is one-tenth of the size of multi-product plants owned
by multi-product firms. Another noticeable thing is that the average size of single-plants isincreasing over time. This fact is probably due to the increase in the plant shutdown of small firms.
Looking at the number of products for multi-product firms owned by multi-plant firms, it isdecreasing while the one for plants owned by single-plant firms is slightly increasing, implyingthat larger firms are more actively reorganizing their product portfolio.

== Table 2 ==Table 3 presents the transition matrix that indicates the probabilities of plants startingwith n products at the beginning of the period (1997 or 2007) ending with m products at theend of the period (2007 or 2014) — the sum of probabilities equals one along the rows.
Plants producing five products or more are categorized as 5+.9 We also calculate the shareof plants that have reduced or increased their number of products during the observation
window as compared to the number of products in the initial period. Off-diagonal elementsin the lower right are the shares of plants that have reduced the number of products, andthose in the upper left have increased the number of products.
We split our samples into two groups according to the degree of the competition; one
includes the plants facing the above-median import competition, the other covers thosefacing the below-median competition. Import competition is measured by the changes in
Chinese import share in total import at the four digit-level. We examine whether plants
facing severe competition have a higher probability of reducing their products or not. Cells
highlighted in red indicate the probability is higher than the one in an alternative competition
status in the same period.
Three things are noteworthy. First, regardless of periods and competition status, the9The analysis presented in Table 3 underestimates transitions because plants with an initial portfolio including
five or more products are grouped together. Expansions in their portfolio are not accounted for, whereas
contractions are accounted for only when the resulting portfolio contains less than five products.
8

probabilities of plants increasing their number of products are at most 12%, implying thatthe majority of the plants in our samples have reduced the number of products. Second,looking at the transition matrix, in both periods, cells highlighted in red concentrate in thelower right, indicating plants facing higher competition pressure tend to reduce the numberof products. The share of plants reducing their products in the high competition sector ishigher than that in the low competition sector in most cases. Third, comparing the share ofplants reducing their products between two periods, while the shares in the period from 1997
to 2007 are around 50-70%, those in the period from 2007 to 2014 are 40-57%. It implies
changes in portfolio size is more significant for the first period than for the second one.

== Table 3 ==4. Main Results
We start to examine the impact of Chinese imports at the plant-product and plant-level. The
results are presented in Table 4. While Column (1)-(4) presents the product churning for multiproduct plants, Column (5)-(8) and Column (9)-(12) are the results at plant-product-level forsingle-product plants and those at plant-level, respectively. As we mentioned, since product level
estimations for single-product firms boil down to the plant level, the results in Column (5)-(8) are
interpreted as a plant-level impact for single-product plants.10
The coefficients of import competition are negative for sales and positive for product exit orplant exit, regardless of estimation method, OLS or IV. Looking closely at the coefficients of IMP
in IV estimation, IMP significantly affects the plant-product sales and plant-product exit rate.
(column (2), (4)). Plant-level impact emerges only for single product firms (plant-product-level
impact for single product firms, column (6), (8)). We do not find any significant impact on plantlevel estimation (column (10), (12)). As for regional characteristics, for product or plant sales(product or plant exit), we expect a positive (negative) impact for LPR and a negative (positive)
Note that the import share is measured at six-digit level for plan-product-level estimation
while it is calculated at four-digit level for plant-level estimation.

10

9

impact for the regional wage. However, regarding regional wage, we do not find any systematiceffect. For LPR, there is a significant negative impact in the case of plant survival, implying thatplants located in the region with local comparative advantage are less likely to exit compared toplants located in other regions.

== Table 4 ==5. Robustness checks
We conduct several robustness checks.11 First, to consider export demand shock, we include
the share of exports from Japan to China as an additional control variable. However, even if we
consider the export opportunities, the major findings related to import competition measure do notchange; international trade shock does affect plant-product churning or plant-level sale andsurvival only for the single-product plant. Second, we also control the measure of imported inputs
from China. However, we do not find any significant impact. In panel (b), the shares of import andexport from/to ASEAN are included as additional controls. Again, there seems to be no systematiceffect on plant-product or plant-level sales and survival. Finally, we spilled our samples into two
periods; the period from 1997 to 2007 and the period from 2008 to 2014 to explore whether the
impact of import competition changes over time or not. In the former period, we find similar results
to those in our baseline estimates; Import and export from/to China significantly affect plantproduct churning or plant-exit for single-product plants. However, in the later period, we do findalmost no significant impact on product churning and plant exit behavior. These results may imply
that the firm’s restructuring in response to the increases in imports has been completed by the midor the end of the 2000s.

6. Concluding remarks
This study examines how Japanese firms change their product portfolio in response to
the surge of imports from China. Using the comprehensive Japanese plant-product level

11

For more details, see the earlier version of this paper; Bellone et al. (2019).
10panel data set, we examine the impact of import competition on firms’ product churning
behavior. We found that the import competition pressures do affect the product churning
for multi-product plants. Besides, the negative effects of import competition are mitigatedby increased export opportunities. This effect is more pronounced, especially the year
before 2007. However, its impact on the plant-level reallocation is somewhat limited.
Negative effects on plant-level sales and exit behavior are observed only for a single
product-plant.
We also explore the role of local factors such as local wage and local comparativeadvantages, measured by local product relatedness (LPR) indicator. While there is no
systematic result regarding local wage, LPR has a significant impact on plant-exit, implyingthat plants located in the regions with local comparative advantages are less likely to exitfrom the market.

11

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Employment Sag of the 2000s, Journal of Labor Economics, 34(S1), 141-198.
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 Effects ofImport Competition in the United States, American Economic Review, 103(6) 2121-2168.
Baek, Y., Hayakawa, K., Tsubota, K., Urata, S., and Yamanouchi, K., 2019, Tariff Pass-Through in
Wholesaling: Evidence from Frim-level Data in Japan, RIETI Discussion Paper, forthcoming.
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., Redding, S.J., Stephen, J., Schott, P K., 2010, Multi-Product Firms and ProductSwitching, American Economic Review, 100(1), 70-97.
Bernard, A., Redding, S.J., Stephen, J., Schott, P K., 2011, "Multiproduct Firms and Trade
Liberalization," The Quarterly Journal of Economics, Oxford University Press, vol. 126(3),pages 1271-1318.
Bellonem F., Hazir, Matusura, T., Import competition and product churning: Evidence fromJapanese plant-product-level data, RIETI Discussion Paper, 19-E074.
Boschma, R., Minondo, A., and Navarro, M. (2012). Related Variety and Regional Growth in Spain.
Papers in Regional Science, 91(2).
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.
Hausmann, R., Hidalgo C., 2011, The Network Structure of Economic Output, Journal of Economic
Growth, 16, 309-342.
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Hazir C. S., F. Bellone and C. Gaglio, 2019, Local Product Space and Firm Level Churning in
Exported Products, Industrial and Corpora Change, forthcoming.

Hidalgo, C. A., Klinger, B., Barabasi, A, L., Hausmann, R., 2007, The Product Space Conditions
the Development of Nations, Science, 317, 482-487.
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Mexican Experience with Chinese Competition, Journal of International Economics, 89, 379392.
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Distribution: Evidence from French Local Labor Markets, Journal of Regional Science, 57(3),411-441.
Mayer, T., Melitz, M., and Ottaviano, G., 2014, Market Size, Competition and the Product Mix ofExporters, American Economic Review, 104(2), 495-536.
Poncet, S. and Starosta de Waldemar, F. (2015). Product relatedness and firm exports in china. The
World Bank Economic Review, 29(3):579–605
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in Japan, Journal of the Japanese and International Economics, 51, 1-18.
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Journal of the Japanese and International Economies, 17(2), 118-133.

13

Appendix A Variable definition of LPR
In this appendix, we explain how we construct the product relatedness measure. The productrelatedness measure we use is so-called the output-based approach utilizing international trade dataat the product-level. First, following Ballassa (1965), we define RCA (Revealed Comparative
Advantage) index for product j in country k as follows;
1 𝑖𝑓

1
,

𝑅𝐶𝐴
0 𝑜𝑡ℎ𝑒𝑤𝑖𝑠𝑒

where a is the value of product k exported by country j. Then, we calculate the conditional
probability P that country has RCA in product k given that it has RCA in product m
𝑃 𝑘|𝑚# 𝑜𝑓 𝑐𝑜𝑢𝑛𝑟𝑖𝑒𝑠 𝑤𝑖𝑡ℎ 𝑅𝐶𝐴 𝑖𝑛 𝑏𝑜𝑡ℎ 𝑘 𝑎𝑛𝑑 𝑚# 𝑜𝑓 𝑐𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠 𝑤𝑖𝑡ℎ 𝑅𝐶𝐴 𝑖𝑛 𝑚

Based on this conditional probability, Hidalgo et al. (2007) define the relatedness indicators as
follows;𝜙min 𝑃 𝑘|𝑚 , 𝑃 𝑚|𝑘 .

Our relatedness indicator for selected products is presented in Table A1. The pair of Computer andTV&Radio is 0.264, while the one for the pair of Bread and TV&Radio is only 0.05 Next, to link
the regional data set, we calculate the RCA index using product-level data by region;
1 𝑖𝑓

1

𝑅𝐶𝐴

,
0 𝑜𝑡ℎ𝑒𝑤𝑖𝑠𝑒

where s is the sales of product k in region l. Using the conditional probability calculated withinternational trade data, local product relatedness is defined as follows;
𝐿𝑃𝑅

Σ

,

,

𝑅𝐶𝐴

.

For the calculation of the international trade based RCA index, we use the BACI data set preparedby CEPII. And region-product-level RCA index is calculated with the COM product-level data
aggregated at the prefecture-level.

14

Table 1. The number of plants by Single-Multiple Plant status and Single-Multiple Product Status
Single-plant firm
(1) Total
1997
186,448
2002
161,352
2007
146,573
2014
113,968

Multi-plant firm

(2) Single product (3) Multi-product(86%)
121,730 (56%)
64,718 (30%)(85%)
106,542 (56%)
54,810 (29%)(84%)
96,348 (55%)
50,225 (29%)(83%)
73,242 (53%)
40,726 (30%)(4) Total
31,53928,11827,16723,735(5) Single product (6) Multi-product(14%)
19,549 (9%)
11,990 (6%)(15%)
17,344 (9%)
10,774 (6%)(16%)
17,007 (10%)
10,160 (6%)(17%)
14,730 (11%)
9,005 (7%)

Source: A matched database of Census of Manufacturer with International trade data
Note: Figures in parentheses are the share of focal plants to the total number of plants in a given year.

Table 2. The average number of employees and the average number of products for multi-product plant# of employees
Single-plant firm

# of products

Multi-plant firm

(1) Single (2) Multi(3)Total
product
product
1997
17.25
26.35
20.41

(4) Single (5) Multi(6)Total
product
product
61.95
195.63112.77(8) Single (9) Multiplant firm plant firm
1.92
1.77
2.68

(7) Total

2002

17.75

26.39

20.68

64.41

181.30109.20

1.90

1.76

2.63

2007

20.22

29.04

23.24

77.78

193.37121.01

1.92

1.80

2.49

2014

22.32

31.45

25.58

81.29

190.16

122.60

2.01

1.89

2.52

Source: A matched database of Census of Manufacturer with International trade data

15

Total
217,987
189,470
173,740
137,703

Table 3. Transition matrix of the number of products
1997-2007
High competition
0
1
51.05
2
47.56
3
45.01
4
41.97
5+
36.97
49.39
Total
Low competition

1
43.01
19.07
10.93
7.29
4.38
34.19

2
4.35
26.02
14.95
9.75
6.43
9.52

3
1.13
5.44
21.43
14.82
10.48
4.08

4 5+
0.35
1.44
5.96
20.78
15.96
1.91

0
52.34
47.83
47.18
47.1
36.64
50.49

1
41.9
15.44
7.65
3.34
2.71
31.54

2
4.44
29.22
12.7
6.2
4.26
9.7

3
0.97
5.72
24
12.53
7.79
4.45

4 5+
0.26
1.46
6.56
24.54
16.58
2.53

0.09
0.33
1.91
6.3
32.02
1.29

0
1
40.29
2
35.28
3
31.74
4
27.56
5+
22.3
37.95
Total
Low competition

1
53.84
18.83
9.04
5.64
4.71
41.5

2
4.39
37.65
16.33
8.12
4.42
11.44

3
1.08
5.96
32.48
17.03
7.87
5.02

4 5+
0.27
1.73
7.74
29.76
14.35
2.39

Total
0.12
100
0.55
100
2.67
100
11.89
100
46.36
100
1.7
100

0
39.78
35.47
33.41
32.89
26.23
37.85

1
54.01
15.95
7.05
4.08
2.61
39.56

2
4.81
40.24
14.75
5.79
3.75
12.43

3
0.97
6.31
34.74
14.07
6.71
5.52

4 5+
0.28
1.49
7.5
32.44
14.34
2.82

Total
0.14
100
0.53
100
2.53
100
10.73
100
46.35
100
1.82
100

1
2
3
4
5+
Total

0.11
0.47
1.71
5.39
25.78
0.92

100
100
100
100
100
100

100
100
100
100
100
100

Share of plants that Share of plants that
reduce # of products increases # of products
51.05
5.94
66.63
7.35
70.89
7.67
73.83
5.39
74.22

Share of plants that Share of plants that
reduce # of products increases # of products
52.34
5.76
63.27
7.51
67.53
8.47
69.17
6.3
67.98

2007-2014
High competition

1
2
3
4
5+
Total

Share of plants that Share of plants that
reduce # of products increases # of products
40.29
5.86
54.11
8.24
57.11
10.41
58.35
11.89
53.65

Share of plants that Share of plants that
reduce # of products increases # of products
39.78
6.2
51.42
8.33
55.21
10.03
56.83
10.73
53.64

16

Table 4. Baseline results

VARIABLES
log (# of products)t-1
log (employees)t-1
Sharet-1
Local Product Relatednesslog (regional wage)t-1
IMPt-1

Plant-Product FE
Plant FE
Year FE
Observations
Kleibergen-Paap F
Hansen J test p-value
R-squared

(1)
(2)
(3)
(4)
Plant-product-level for multi-product plant
OLS
IV
OLS
IV
log(sales)log(sales) Product-exit Product-exit
0.0880***(0.0102)0.503***(0.0126)1.512***(0.0338)-0.101(0.0886)
0.114**(0.0567)-0.101**(0.0506)

0.0849***(0.0104)
0.508***(0.0150)
1.511***(0.0334)-0.142(0.0916)-0.0224(0.0826)-1.210***(0.304)-0.0166*** -0.0162***(0.00142)(0.00143)-0.0921*** -0.0925***(0.00489)(0.00505)-0.00144-0.00124(0.00134)(0.00135)
0.003120.00662(0.00853)(0.00893)-0.0874*** -0.0738***(0.0167)(0.0177)0.004960.112*(0.0107)(0.0656)

Yes
No
Yes
1,534,330

Yes
No
Yes
1,534,313
2614.738
0.537
0.944

Yes
No
Yes
1,697,514

0.945

0.684

Yes
No
Yes
1,697,497
2966.341
0.8294
0.683

(5)
(6)
(7)
(8)
Plant-product-level for single-product plant
OLS
IV
OLS
IV
log(sales)log(sales) Product-exit Product-exit

OLS
log(sales)

(10)
(11)
Plant-levelIV
OLS
log(sales)
Plant-exit

0.560***(0.00992)

0.557***(0.0105)-0.126***(0.00435)-0.125***(0.00450)

0.0507***(0.00487)
0.639***(0.0174)

0.0509*** -0.00837*** -0.00838***(0.00461) (0.000986) (0.000968)
0.641*** -0.0188*** -0.0190***(0.0167)(0.00252)(0.00264)-0.0305(0.0552)0.210***(0.0510)-0.0720(0.0498)-0.0568(0.0680)0.115*(0.0639)-0.979***(0.263)0.00185(0.0158)-0.136***(0.0193)-0.000903(0.0153)

0.00644(0.0180)-0.118***(0.0221)0.167***(0.0563)-0.178(0.110)0.129*(0.0745)-0.650*(0.364)-0.168(0.110)0.162**(0.0672)-0.0161(0.0757)-0.0330*** -0.0338***(0.00736)(0.00729)
0.0563*** 0.0535***(0.0115)(0.0114)0.0102-0.0439(0.0103)(0.0353)

Yes
No
Yes
1,730,743

Yes
No
Yes
1,730,738
2986.066
0.2359
0.952

Yes
No
Yes
1,944,412

Yes
No
Yes
1,944,408
3575.3
0.503
0.592

No
Yes
Yes

No
Yes
Yes
2,890,514
15000
0.1163
0.947

No
Yes
Yes
2,998,423

0.953

0.593

(9)

2,890,514

0.947

(12)
IV
Plant-exit

No
Yes
Yes

2,998,423
15000
0.9038

0.273

0.273

Note: Plant-product fixed effects are included. Robust standard errors in parentheses, which are clustered on the four-digit product level for plant-product estimationand two-digit industry level for plant-level estimation. ***, ** and * indicate the statistical significance at 1%, 5% and 10%.

17

Table A1. Relatedness indicator for selected products
971 Bread
1164 Shirt
1711 Oil refinery
3014 Radio&TV
3031 Computer

971

1164

1711

3014

0.146
0.150
0.056
0.096

0.105
0.129
0.076

0.096
0.100

0.264

18