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CENTRE FOR THE
STUDY OF LIVING
STANDARDS

Does Import Competition Reduce Domestic
Innovation? Evidence from the 'China Shock' andFirm-Level Data on Canadian Manufacturing

Myeongwan Kim

CSLS Research Report 2019-03

Prepared for Innovation, Science, Economic Development Canada
August 2019

Does Import Competition Reduce Domestic Innovation? Evidence from the'China Shock' and Firm-Level Data on Canadian Manufacturing
Abstract
A key economic issue in Canada is the declining Business Enterprise Research and Development
in manufacturing since the early 2000s. Accompanying this, the total factor productivity (TFP) growth inmanufacturing slowed after 2000. However, there has not been a definitive explanation for these trends.
To deepen our understanding of this phenomenon, we focus on the increasing Chinese import share in the
total domestic absorption in Canadian manufacturing since the early 2000s, which appears to be driven bypositive supply shocks within Chinese manufacturing. Based on a firm-level database covering allincorporated firms in Canadian manufacturing, we find that rising Chinese import competition led to
declines in R&D expenditure and TFP growth within firms but reallocated employment towards moreproductive firms and induced less productive firms to exit. The negative within-effects were pronounced
for firms that were initially smaller, less profitable, and less productive. These firms also experienced
declines in their profit margins due to rising Chinese import competition while larger and betterperforming firms did not. Our estimates imply that rising Chinese import competition can explain about 7
per cent of the total decline of $1.36 billion (2007 CAD) in R&D expenditure in Canadian manufacturing
between 2005 and 2010. Although it led to declines in TFP within firms, the positive reallocation effects
more than offset the negative within-effect. Had there been no increase in Chinese import competition
between 2005 and 2010, TFP in Canadian manufacturing would have declined by 1.26 per cent per year
instead of the actual 1.09 per cent per year over this period.

IIDoes Import Competition Reduce Domestic Innovation? Evidence from the'China Shock' and Firm-Level Data on Canadian Manufacturing
Table of Contents
Abstract ............................................................................................................................................ ITable of Contents ............................................................................................................................ II
List of Tables and Charts .............................................................................................................. IV
Executive Summary ........................................................................................................................ V
I. Introduction ..................................................................................................................................1
II. Chinese Imports, R&D Expenditure, and TFP Growth in Canadian Manufacturing .................5
1. Chinese Imports........................................................................................................................5
2. R&D Expenditure in Canadian Manufacturing ........................................................................6
3. TFP in Canadian Manufacturing ..............................................................................................8
III. Data ............................................................................................................................................91. T2-LEAP-SRED.....................................................................................................................10
1-1. Research and Development (R&D) ............................................................................10
1-2. Output and Inputs of Production ................................................................................11
2. Trade Data ..............................................................................................................................12
IV. Empirical Models and Identification Strategy .........................................................................12
1. Technical Changes within Firms ............................................................................................12
2. Technical Changes between Firms: Reallocation of Employment and Survival ...................13
3. Identification Strategy ............................................................................................................14
4. Descriptive Statistics ..............................................................................................................14
V. Regression Result: R&D Equation ...........................................................................................16
1. Baseline Results .....................................................................................................................16
2. Explaining the negative effect ................................................................................................18
VI. Regression Result: TFP, Employment, and Survival Equation ...............................................22
1. Within-Effect: TFP Equation .............................................................................................22
2. Between-Effect: Employment and Survival Equation .......................................................26
VII. Quantifying the Role of China ...............................................................................................28
1. R&D Expenditure ...................................................................................................................28
2. TFP .........................................................................................................................................29
III

VIII. Conclusion ............................................................................................................................33
References ......................................................................................................................................34
Appendix I: Constructing Firm-Level R&D Capital Stock ...........................................................40
Appendix II: Firm heterogeneity in the China shock effect on R&D expenditure ........................43
Appendix III: Estimating Firm-Level TFP ....................................................................................46
Appendix IV: Additional Tables ....................................................................................................49

IV

Does Import Competition Reduce Domestic Innovation? Evidence from the'China Shock' and Firm-Level Data on Canadian Manufacturing
List of Tables
Table 1: R&D Equation, Manufacturing, 2000-2012 ....................................................................10
Table 2: R&D and Profit Equation, Manufacturing, 2SLS, 2000-2012 ........................................10
Table 3: TFP and Profit Equation, TFP Sample, Manufacturing, 2SLS, 2000-2012 ....................10
Table 3-1: TFP, R&D Sample, Manufacturing, 2SLS, 2000-2012 ...............................................10
Table 4: Employment and Survival Equation, Manufacturing, 2SLS ...........................................10
Table 5: Change in R&D Expenditure due to China, Manufacturing, Millions of 2007 CAD,
2000-2012 ......................................................................................................................................10
Table 6: Change in TFP due to China, Manufacturing, 2005-2010...............................................10
Table A1: R&D Equation, Manufacturing, 2SLS, 2005-2010 ......................................................10
Table A2: Summary Statistics, R&D Sample, by Initial Employment Size, Manufacturing,
2000-2012 ......................................................................................................................................10
Table A3: Summary Statistics, TFP Sample, by Initial Employment Size, Manufacturing,
2000-2012 ......................................................................................................................................10
Table A4: R&D Equation, by Initial Condition, 2SLS, Manufacturing, 2005-2010 .....................10
Table A5: TFP Equation, by Initial Condition, 2SLS, Manufacturing, 2005-2010.......................10

List of Charts
Chart 1: Import Penetration Ratio in Canada, Low-wage Countries: China and Mexico,
Manufacturing, 1992-2015 ..............................................................................................................2
Chart 2: Real R&D Investment in Canadian Manufacturing, Millions of 2007 CAD, 1994-2012 .2
Chart 3: R&D Investment in Canadian Manufacturing ...................................................................2
Chart 4: TFP Index (2012=100) and Chinese Import Penetration Ratio in Canadian
Manufacturing, 1992-2015 ..............................................................................................................2
Chart 5: Changes (long-differences) in the Chinese Import Penetration Ratio, Canada and theEight Other Advanced Economies (and), 2000-2012, Percentage Point .........................2V

Does Import Competition Reduce Domestic Innovation? Evidence from the'China Shock' and Firm-Level Data on Canadian Manufacturing
Executive Summary
R&D is a key input to innovation and hence, a key determinant of productivity growth offirms. A key economic issue in Canada is the declining Business Enterprise Research andDevelopment (BERD) since the early 2000s. Especially, in manufacturing, BERD started to
decline after 2000 both in absolute and relative terms. The real BERD in manufacturing fell at
3.3 per cent per year over the 2000-2012 period and the manufacturing share of the total BERD
in Canada fell from 68 per cent to 43 per cent between 2000 and 2012. Accompanying this, totalfactor productivity (TFP) growth in manufacturing slowed after 2000 and TFP declined between
2005 and 2009.
To deepen our understanding of this phenomenon, we examine the increasing Chinese
import share in total domestic absorption (i.e. the import penetration ratio) in Canadianmanufacturing since the early 2000s, which appears to be driven by positive supply shocks
within Chinese manufacturing. After China joined WTO in 2001, the Chinese import penetration
ratio in Canadian manufacturing rose rapidly over time. Increasing competitive pressure on
domestic firms has implications for their decision to innovate and hence, their productivity
growth. Some firms may choose to increase their innovative effort (e.g. performing R&D) toremain competitive in the market while others may not be able to do so as their profit margins
are hurt. Also, increasing competition could induce less productive firm to exit and reallocate
resources towards more productive firms. Hence, rising Chinese import competition may have
distributional effects on innovation and productivity performance in Canadian manufacturing.
The overall effect on R&D investment and TFP in Canadian manufacturing is a priori
ambiguous
To examine these issues, we rely on firm-level data covering all incorporated firms
including those that performed R&D in Canadian manufacturing for the 2000-2012 period.
Using this comprehensive database, we assess the impact of rising Chinese import competition
on R&D and productivity growth within manufacturing firms by initial conditions of firms (e.g.
initial size, profitability or mark-up, and productivity level).
Exploiting the ‘natural experiment’ nature of the episode of rising Chinese exports to theworld market since the early 2000s, we capture the common component of rising Chinese
imports in Canada and other advanced economies, which presumably reflects rising Chinese
exporting capacity driven by China’s rising competitiveness in their manufacturing and lower
trade costs due to factors internal to China. We rely on this common component to estimate its
impact on R&D expenditure and TFP growth in Canadian manufacturing.
We find that rising Chinese import competition reduced R&D and TFP growth withinfirms. However, the negative within-effects on R&D and TFP growth were most pronounced
within initially smaller, less profitable, and less productive firms. It also appears that they scaledVIback their R&D investment but did not resort to other productivity-enhancing activities (e.g.
better management practices or inventory controls) to improve their TFP. In contrast, R&D
investment within initially larger, more profitable, and more productive firms was not affectedby rising Chinese import competition. We find evidence that, in response to rising Chinese
import competition, some larger and better-performing firms engaged in productivity-enhancing
activities other than R&D, and hence their TFP improved.
Importantly, the smaller and poorly-performing firms also experienced declining profit
margins due to rising Chinese import competition. We did not find evidence that profit margins
of larger and better-performing firms were affected by rising Chinese competition. Firms tend tofinance innovation using internal cash flows as external financing would be costly due touncertainty in its outcome. Thus, the declines in R&D and TFP within those firms may be
explained by the shrinking room to finance R&D and other innovations.
We estimate that China can explain about 7 per cent of the total decline of $1.36 billion(2007 CAD) in R&D expenditure in Canadian manufacturing between 2005 and 2010. The
impact is relatively small as most of the declines occurred within small firms who accounted fora small share of the total R&D expenditure in manufacturing.
However, rising Chinese import competition reallocated employment towards moreproductive firms and induced less productive firms to exit the domestic market. We estimate thatthe negative effect of rising Chinese competition on TFP growth within firms is potentially small
as it was most pronounced within small firms who tend to be less productive initially. It is foundthat the positive effect of the reallocation of resources more than offset the negative effect on
TFP growth within firms. Our estimates imply that had there been no increase in the share ofChinese imports in the total domestic absorption in manufacturing between 2005 and 2010, theaggregate TFP level in manufacturing would have declined by 1.26 per cent per year instead of
the actual 1.09 per cent per year.

VIIDoes Import Competition Reduce Domestic Innovation? Evidence from the'China Shock' and Firm-Level Data on Canadian Manufacturing 1
I. Introduction
A key economic issue in Canada is the declining Business Enterprise Research andDevelopment (BERD) – a key input to innovation – since the early 2000s. Especially, in
manufacturing, BERD started to decline after 2000 both in levels and as a share of sales.
Accompanying this, the total factor productivity (TFP) growth in manufacturing slowed after
2000. However, there has not been a definitive explanation for these trends.
Since the early 2000s, advanced economies including Canada had experienced a rapid
increase in imports from China. A large literature has documented that increasing trade with lowwage countries such as China have an impact on domestic innovation although the evidence ismixed for the direction of the impact.2 The implication of increasing trade is multi-dimensional:
rising competition in the markets for final goods and intermediate goods; expanding export
opportunities and access to imported intermediates. In this paper, we assess whether declining
R&D and productivity performance in Canadian manufacturing can be linked to rising Chinese
import competition in final product markets. As the competition in the domestic product market
rises, surviving firms are likely to adjust their innovative effort as their rents after innovation
relative to rents before innovation are affected. Less productive firms would exit and resources
could be allocated towards more productive surviving firms.
There are some close antecedents to our study but their empirical evidence is mixed.
Bloom, Draca, and Van Reenen (2016) use firm-level data from European countries to estimate
the effect of increasing Chinese import competition on four indicators for technical change:
patents, information technology intensity, R&D investment, and TFP growth. They findempirical evidence that increasing Chinese import competition had led to an increase in all fourmeasures of technical change within firms and also reallocated employment towards more
technologically-advanced firms.3 Autor, Dorn, Hanson, Pisano, and Shu (2017) find conflicting

1This report was written by CSLS economist Myeongwan Kim for Innovation, Science, Economic Development
Canada. The Centre for the Study of Living Standards would like to thank Innovation, Science, Economic
Development Canada for financial support for this research. An earlier version of the report was presented at the
CSLS session on "the Impact of China on the Canadian Economy" at the annual meeting of the Canadian Economic
Association held in Banff, Canada, June 1, 2019. The author thanks the participants at the CEA session and Alex
Murray (Finance Canada) for the discussion. The author also thanks Andrew Sharpe (CSLS), Bert Waslander, andJianmin Tang (ISED) for useful comments. The analyses in this report are based on data from the Canadian Centre
for Data Development and Economic Research at Statistics Canada. The opinions expressed in this report do not
represent the views of the agency. More information on CDER is available at http://www.statcan.gc.ca/cdercdre/index-eng.htm.
Email: kimyhtg@gmail.com.
2
See Shu and Steinwender (2018) for a comprehensive review of the studies on the impact of trade liberalization on
innovation and productivity.
3
They also find that technologically-advanced firms are more likely to survive for a given increase in Chinese
import competition than low-tech firms.

1evidence using firm-level data for the United States. They find that, in response to increasing
Chinese competition, firms scaled back their patent activity and R&D investment.4
Gong and Xu (2017) also studies the effect of rising Chinese import competition on R&D
expenditure of the U.S. firms but focuses on the reallocation effect. They find that rising Chinesecompetition reallocated R&D expenditure towards more productive and profitable firms but findno evidence of any impact on R&D at the aggregate level. Using survey data5, Keung, Li, andYang (2016) find that Canadian manufacturing firms scaled back their innovate effort but
process innovation more than product innovation in response to rising Chinese importcompetition between 1999 and 2005.6
Those conflicting empirical results are in line with the overall ambiguity in theoretical
implications for the effect of rising competition on domestic innovation. In the literature, there
are multiple theories underlying the relationship between competition and innovation. First,'trapped inputs' for production implies that increased Chinese import competition fosters
innovation as it reduces the relative profitability of low-tech products. Firms cannot get rid of
their 'trapped' inputs easily, having more incentives to allocate them to inventing new products or
technologies (e.g. Bloom, Romer, Terry and Van Reenen, 2010). Also, an increasing market size
for Canadian firms due to expanding trade opportunity with China may encourage innovation as
firms can spread the fixed costs of innovation over the larger market (e.g. Krugman, 1980;
Acemoglu, 2008).
In contrast, in a standard oligopoly model, increased competition in product markets islikely to reduce incentives to innovate as profits decline (e.g. Dasgupta and Stiglitz, 1980). If wetake into account differential degrees of competition faced by firms, the relationship between
innovation and competition exhibits an inverted U-shape (e.g. Aghion, Bloom, Blundell, Griffith,and Howitt, 2005). That is, innovation is low when firms produce very similar products (close toperfect competition) – no room to capture rents; and when firms produce very differentiatedproducts (close to oligopoly or monopoly) – hard for laggards to overtake leaders. At theintermediate level of competition, rents after innovation may exceed rents before innovation,
resulting in relatively high innovation in such market segments. Similarly, Schmidt (1997) showsthat increasing competition increases managerial effort to increase profit (so likely to increase
innovation) but when competition becomes too intense, managerial effort may decline eventually.
Domestic innovation could decline as domestic firms are deterred from entering export
markets. Innovation is a key factor that drives the level of competitiveness required to enter orremain in export markets. For example, Baldwin, Dar-Brodeur, and Yan (2016) find that
Canadian manufacturing firms that entered export markets are more likely to have invested in
R&D before entry and to invest more in R&D after entry. As the competitiveness of Chinese
manufacturing in the world market increased significantly since the early 2000s, Canadianmanufacturers might have been deterred from entry to or driven out of the world export market

4

They do not assess the effect on TFP growth and the reallocation effect.
The Workplace and Employee Survey (WES) by Statistics Canada.
6
They also find that more differentiated firms along product space or geographic dimensions are more likely to exit
but perform better if they survive.
5

2

by Chinese manufacturers, especially in clothing, textile, and electronics manufacturing.7 As aresult, Canadian firms could have had less incentive to perform R&D as it becomes morechallenging to enter or remain in export markets.8 Coupled with increasing Chinese competitionin the domestic market, increasing competition in export markets may further decrease
innovative activities such as R&D within Canadian manufacturing firms.
As emphasized in Melitz (2003) and Bloom et al. (2016), it is also important to consider
an economy-wide innovation or technical change that occurs through the reallocation of
resources towards more productive firms in response to an increase in Chinese importcompetition (i.e. between-firm effects). In theory, if we maintain the menu of products fixed in
the economy, then increasing trade with low-wage countries like China would results inshrinking low-tech firms and growing high-tech firms (where Canada has comparativeadvantages). The opposite would occur in China.
Our study adds to the literature in two ways. First, to our knowledge, there is noempirical study that uses Canadian firm-level data to explore the impact of rising Chinese importcompetition on R&D which is a representative indicator of innovation activities or on the overall
productivity performance in Canadian manufacturing. In this paper, we carry out acomprehensive assessment of trade-induced change in R&D and TFP within manufacturingfirms. Especially, our data capture a broad R&D expenditure of firms covering in-house R&D,
R&D contracted out, and the use of R&D performed by third-parties on a non-exclusive basis.
We also explore whether technical changes occurring between firms are important in Canada byanalyzing the effect on employment and survival of manufacturing firms, focusing on the
differential effects stemming from different initial technology levels of firms.
Second, most empirical studies focus on very large firms (e.g. public firms in Compustat)or firms with patents among those large firms. Large firms, especially publically traded firms orfirms with successful innovation outcomes (i.e. patents) could have different initial conditions
and hence, their response could be quite different than the majority of smaller firms in
manufacturing or firms that perform R&D whose outcome does not necessarily get patented. In
our study, we use administrative firm-level data covering all incorporated firms in Canadianmanufacturing. Also, the database is linked to the tax data covering all firms that claimed R&D
expenditure credits in Canada. Using this comprehensive database, we explore potential
heterogeneity in firm-level responses to rising import competition, providing a better
understanding of trade-induced change in innovation and productivity. In particular, we assess
the impact of rising import competition on R&D and TFP within firms by initial conditions offirms (e.g. initial size, profitability or mark-up, and productivity level).
We find that increasing Chinese import competition reduced R&D and TFP growthwithin firms but reallocated employment towards more productive firms and drove lessproductive firms out of the domestic market. The negative within-effects on R&D and TFP

7

These are the sectors with the highest exposure to Chinese import competition in Canada. See Murray (2017) andKim (2018a, b).
8This would be the case if Canadian manufacturing firms find no room to capture rents after innovation in export
markets.

3

growth were pronounced within initially smaller, less profitable, and less productive firms.9 It
appears that they scaled back their R&D investment but did not resort to other productivityenhancing activities (e.g. better management practices or inventory controls) when Chinese
import competition rose in their product markets. Even if they did, they were not successful.
R&D investment within initially larger, more profitable, and more productive firms was notaffected by rising Chinese import competition. We find evidence that some larger and betterperforming firms engaged in productivity-enhancing activities other than R&D when the Chinesepresence increased in their product market, and hence their TFP improved. Very large firms(employees>500) do not appear to have adjusted their innovative effort or other effort to enhancetheir productivity.
We find that the smaller and poorly-performing firms experienced declining profit
margins due to rising import competition while larger or their better-performing counterparts did
not. A larger reduction in R&D and TFP may be explained by the shrinking room to financeR&D and other innovations. Firms tend to finance innovation using internal cash flows asexternal financing would be costly in this case. Or in a different perspective, these firms arelikely to face greater product market competition with technology gaps initially. So, a further
increase in competition may have made additional innovation unprofitable. In other words, thebasic Schumpeterian effect dominates for these firms, reducing more the post-innovation rents
than the pre-innovation rents.
Using the estimated marginal effects of rising Chinese import competition on relevant
firm-level outcomes, we estimate that China can explain about 7 per cent of the total decline of$1.36 billion (2007 CAD) in R&D expenditure in Canadian manufacturing between 2005 and2010. Our productivity decomposition exercise indicates that had there been no increase in theshare of Chinese imports in the total domestic absorption in manufacturing between 2005 and2010, the aggregate TFP level in manufacturing would have declined by 1.26 per cent per year
instead of the actual 1.09 per cent per year. This implies that the positive between- and exiteffects more than offset the negative within-effects.
Our study can be related to the previous CSLS research report on the effect of Chinese
import competition on manufacturing employment by skill level. In Kim (2018a), the author
finds increasing Chinese import competition in Canadian manufacturing had a negative
employment effect on low-skilled occupations while it had no effect on high-skilled occupations.
This can be linked to our findings that Chinese import competition drove out low-tech firms;
reallocated employment towards more technologically advanced firms; and increased technicalprogress within larger and more productive firms in manufacturing, which in turn, decreased therelative demand for low-skilled occupations.
The remainder of the report is organized as follows. The following section provides an
overview of the trends in Chinese import competition, R&D expenditure, and productivity
performance in Canadian manufacturing. Data sources are discussed in section III. Section IV
introduces our empirical models and identification strategy, which is followed by the results in
9

For R&D, we also find the declines were larger within foreign-controlled firms and firms receiving regular tax
credits for R&D expenditure, compared to domestically-controlled firms and firms receiving enhanced tax credits,
respectively.

4

section V and VI. In section VII, we contextualize our empirical findings by quantifying the roleof rising Chinese import competition in driving the actual change in R&D investment and TFP
growth in Canadian manufacturing. Section VIII concludes.

II. Chinese Imports, R&D Expenditure, and TFP Growth in Canadian
Manufacturing
We provide an overview of the trends in Chinese import competition, R&D expenditure,
and TFP in Canadian manufacturing. Note that, in this section, we mainly use the data from theAnnual Survey of Research and Development in Canadian Industries (RDCI) and the Canadian
Productivity Account (CPA) publicly available from Statistics Canada since our firm-level data
on R&D and on the variables required to estimate firm-level TFP only cover the 2000-2012
period, preventing us from comparing the pre- and post-take-off in Chinese imports in Canada.10
However, when possible, we discuss some key observations found in the T2-LEAP-SRED data
for the post-2000 period. In general, we find that the aggregate-level and firm-level data show
consistent stories for R&D expenditure and productivity performance in manufacturing for the
post-2000 period.

1. Chinese Imports
Imports from low-wage countries have implications for technical change in developed
economies. A number of studies examine the impact of trade with low-wage countries oninnovation across manufacturing sectors within advanced economies (e.g. Bartel et al., 2007;
Freeman and Kleiner, 2005; Bugamelli et al., 2008). During the 2000s, two of the top ten
importers to Canada were low-wage countries: China and Mexico. However, import penetrationfrom China had been much more important in terms of both absolute levels and changes (seeChart 1). The import penetration ratio for Mexico remained below 4.0 per cent for most of the
1990s and 2000s. It increased from 2.0 per cent in 2000 to 4.0 per cent in 2015. The importpenetration ratio for China surpassed that for Mexico during the early 2000s when China joined
WTO, reaching 8.9 per cent in 2015. It grew by 6.9 percentage points between 2000 and 2015.
Moreover, increasing import competition from China is observed not only in Canada but
also in other advanced economies around the world. Kim (2019a) shows that increasing Chinese
import competition is observed in the United State and in other advanced economies such as
Australia, Denmark, Finland, Germany, Japan, New Zealand, Spain and Switzerland. Thisimplies that rising Chinese import competition is not driven by demand shocks within Canada orindividual advanced economies but likely to have been driven by rising exporting capacity of theChinese manufacturing industry.

10

RDCI is a more comprehensive source of information for R&D spending than the tax data which covers only R&D
performed or purchased in Canada by incorporated firms (i.e. T2-LEAP-SRED). The CPA data are based on
establishment-level data.

5

Chart 1: Import Penetration Ratio in Canada, Low-wage Countries: China and
Mexico, Manufacturing, 1992-2015
NAFTA

China's accession to WTO

8.9%

4.0%
China in 2000 = 2.0%
Mexico in 2000=
2.0%

1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015

10%
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%

China

MexicoNote: The import penetration ratio is defined as the ratio of imports to domestic absorption (total industry shipment
less exports plus imports). Mexico and China were low-wage countries among the top ten exporters to Canada
during the 2000s. The top ten exporters to Canada were: United States, China, Mexico, Germany, Japan, South
Korea, United Kingdom, Italy (including Vatican City State), France (including Monaco, French Antilles), andTaiwan.
Source: Authors' calculation based on trade data base maintained by Innovation, Science, and Economic
Development Canada and Statistics Canada Table 16-10-0047-01.

2. R&D Expenditure in Canadian Manufacturing
According to the official data publicly available at Statistics Canada, between 1994 and2000, the real BERD expenditure in Canadian manufacturing increased rapidly (see Chart 2).11
However, it started to decline when Chinese imports surged in the early 2000s. Real R&D
expenditure in manufacturing increased by 5.7 per cent per year on average during 1994-1999
but decreased by -0.58 per cent during 2000-2006. After 2006, real R&D expenditure fell year
over year at a faster rate, -4.19 per cent over 2006-2012 period. We observe similar trends in the
T2-LEAP-SRED database which covers only domestically performed R&D. The average annual
growth rate in real R&D expenditure was -0.2 per cent in manufacturing for the 2000-2012
period.12 The average annual growth rate was 4.1 per cent for the 2000-2005 period but fell to 3.2 per cent for the 2005-2012 period.13

11

We use the GDP deflator for R&D expenditure. In our analysis using T2-LEAP-SRED, we use industry-specificdeflator for R&D.
12
The average annual growth was 1.3 per cent in non-manufacturing during the same period.
13
A similar pattern is found in non-manufacturing: 5.9 per cent for the 2000/2001-2004/2005 period and -1.9 percent for the 2005/2006-2011/2012 period.

6

Chart 2: Real R&D Investment in Canadian Manufacturing, Millions of 2007 CAD,
1994-2012$14,000

8%$12,000

7%$10,000

6%
5%$8,0004%$6,000

3%

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

0%
1997

$0
1996

1%
1995

2%

$2,0001994$4,000Real BERD expenditure (left axis)
Chinese import penetration ratio (right axis)

Source: Author’s calculations based on Statistics Canada Table 27-10-0002-01, 27-01-0273-01, 16-100047-01, and Trade Data Online maintained by Innovation, Science, and Economic Development Canada.

BERD expenditure fell in relative terms as well. Manufacturing accounted for more thanhalf of the total BERD expenditure in Canada.14 However, the average share of manufacturing in
the total BERD expenditure in Canada was lower in the post-2000 period than in the pre-2000
period. The manufacturing share was 64.5 per cent during the 1994-1999 period but it declined
to 52.4 per cent during the 2000-2013 period. Over the 2000-2013 period, there was a gradualdecline in the manufacturing share, falling from 68.4 per cent in 2000 to 42.0 per cent in 2013.
We find a similar pattern in the T2-LEAP-SRED data for the post-2000 period. Panel A in Chart
3 shows the share of manufacturing in the total real R&D expenditure in Canada based on the
T2-LEAP-SRED database. Consistent with the RDCI data, we observe that the manufacturing
share declined from 42.8 per cent in 2000 to 39.2 per cent in 2012.15 Over the same period, theChinese import penetration ratio increased by 5.50 percentage points.
The R&D intensity in manufacturing also declined after 2000. Panel B in Chart 3 showsthat BERD expenditure as a share of the total sales in manufacturing increased from 1.61 percent to 1.92 per cent between 1994 and 2000. When the Chinese import penetration ratio started
to take off in the early 2000s, the R&D expenditure ratio started to decline, dropping to 1.23 percent by 2013. Again, we find a similar pattern in R&D intensity for manufacturing in the T2LEAP-SRED data. The R&D intensity in both manufacturing and non-manufacturing gradually
fell over the 2000-2012 period. The average R&D intensity in manufacturing was 3.2 per cent14

According to the RDCI data, the average share of manufacturing in total BERD expenditure is 56.0 per cent overthe 1994-2013 period.
15
The numbers are different from the shares measured based on the RDCI data. R&D expenditure in the T2-LEAPSRED covers only incorporated firms and includes R&D performed domestically while RDCI includes
unincorporated firms and R&D outsourced to non-residents.

7

during the 2000-2006 period. The average intensity then fell to 1.9 per cent over the 2006-2012
period. On the other hand, the average R&D intensity for non-manufacturing did not fall as much:
2.1 per cent during the 2000-2006 period and 1.4 per cent during the 2006-2012 period.
Chart 3: R&D in Canadian manufacturing

44%

8%

43%

7%

42%

6%

41%

5%

40%

4%

39%

3%

38%
37%

2%

36%

1%

35%

Chinese import penetration ratio

Manufacgturing share in total

Panel A: The Manufacturing Share in Total Real R&D Expenditure in Canada, Incorporated
Firms, T2-LEAP-SRED, 2000-2012

0%
2000

2003

2006

2009

2012

Share of manufacturing (left axis)
Chinese import penetration ratio in manufacturing (right axis)

Panel B: R&D Intensity in Canadian Manufacturing, RDCI data, 1994-2012
8%
7%
2.0%

6%
5%

1.5%

4%
1.0%

3%
2%

0.5%

1%
0.0%

Chinese import penetration ratio

BERD as a share of total sales

2.5%

0%
1994

1997

2000

2003

2006

2009

2012

BERD expenditure as a share of total sales (left axis)
Chinese import penetration ratio (right axis)

Note: Import penetration ratio is defined as the ratio of imports to domestic absorption (total industry shipment lessexports plus imports).
Source: Panel A – Author’s calculations based on the T2-LEAP-SRED database; Panel B- Author’s calculations
based on Statistics Canada Table 27-10-0002-01, 16-10-0047-01. Import penetration ratio is calculated based on
Trade Data Online maintained by Innovation, Science, and Economic Development Canada and Statistics Canada
Table 16-10-0047-01.

8

3. TFP in Canadian Manufacturing
TFP growth in manufacturing also slowed after 2000. Chart 4 shows time series for TFP
index for Canadian manufacturing. TFP grew at a faster rate during the 1990s than during the
2000s. For instance, the annualized growth in TFP was 3.13 per cent over the 1992-2000 period.
However, TFP declined at an annual rate of 1.09 per cent over the 2000-2009 period. After a
rapid increase during the 1990s, TFP started to level off from the early 2000s. Then, it declined
between 2006 and 2009. However, TFP started to recover after 2009 recovering to the levels
observed in the early 2000s.
Again, due to the limited information in our T2-LEAP-SRED data, we examine TFP only
for the 2000-2012 period.16 We estimate TFP at the firm-level and aggregate using firms' sharesin the total output in manufacturing. The TFP level in manufacturing based on T2-LEAP-SRED
exhibits a pattern similar to that found in the CPA data. For the 2000-2006 period, TFP isrelatively stable. Then, TFP fall between 2006 and 2009. TFP recovers after 2009.

110

10
9
8
7
6
5
4
3
2
1
0

TFP index (2012=100)

105
100
95
90
85
80
75
70
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
TFP index (left axis)

Chinese import penetration ratio (%)

Chart 4: TFP Index (2012=100) and Chinese Import Penetration Ratio in Canadian
Manufacturing, 1992-2015

Chinese import penetration ratio (right axis)

Source: Authors' calculation based Statistics Canada Table 36-10-0208-01 and on trade data base maintained by
Innovation, Science, and Economic Development Canada and Statistics Canada Table 16-10-0047-01.

III. Data
Our sample period is restricted to 2000-2012. Data on R&D investment and somevariables used to estimate firm-level TFP are not available for the 1990-1999 period. Ideally, we
would like our sample period to cover some years in the 1990s so that we can assess pre-China
shock estimates to support the plausibility of a causal interpretation of the effect of China ontechnical change. Nevertheless, we think this would not be a serious issue in our empiricalassessment of the impact of the China shock since most of the increase in Chinese importcompetition in Canada occurred during the 2000s (see Chart 1).
16

We do not have complete information for output in the data covering the 1990s preventing us from estimating a
production function to retrieve TFP estimates based on our preferred estimation strategy.

9

Our sample for R&D analysis consists of firms that performed or purchased R&D at least
once between 2000 and 2012. For the analysis on TFP, the sample consists of firms that havenon-missing values for all the variables required to estimate firm-level TFP (see Appendix III).
Hence, we have a larger number of firms in the TFP sample than in the R&D sample.

1. T2-LEAP-SRED
The main data source for our analysis is corporate income tax (T2) files linked to
Statistics Canada’s Longitudinal Employment Analysis Program (LEAP) data file. In order tohave information on R&D expenditure for each firm, the database has been linked to the Canada
Revenue Agency (CRA) form T661 filed by firms to claim their tax credits for expenditure on
scientific research and experimental development (SRED).17
The T2-LEAP-SRED file follows the statistical enterprise concept. The data include alllegal entities controlled by the same corporation as the basis for its panel structure. Hence, it ispossible that a firm consists of more than one legal entity filing a corporate income tax returnwith CRA. Also, the data are adjusted to remove spurious entries and exits resulting frommergers and acquisitions and legal restructurings. For example, when two firms merge, the newentity is synthetically created and assumed to have existed since the birth of the oldest of the two
firms.
Statistics Canada produces new version of T2-LEAP over time as new data become
available, reflecting new organizational structures among other things. The underlying
organizational structure in a given T2-LEAP is represented by a vintage. We rely on the 2016
vintage for our T2-LEAP-SRED database that covers the period 2000-2012. However, certain
variables not originally available in the T2-LEAP-SRED database are constructed based on the2019 vintage and merged with our main data.18
1-1. Research and Development (R&D)
In CRA form T661, firms report expenditure on scientific research and experimental
development. Specifically, they report expenditure on wages and salaries, materials costs,
equipment leasing, equipment purchase, contracts and “third-party payments” for R&D.19
However, firms do not report investment in structures used to perform R&D. Following Kim andLester (2019), adjustments are made to obtain a comprehensive measure of R&D expenditure.
Our measure includes R&D performed in-house for internal use; R&D performed under contractby other Canadian firms that the firm can exploit on an exclusive basis; and R&D performed by
17

The T2-LEAP-SRED database was developed by John Lester, Ryan MacDonald, Javad Sadeghzadeh and Weimen
Wang at the Centre for Data Development and Economic Research at Statistics Canada.
18
Note that the organizational structure is defined at the BN level, a lower level of aggregation than the LBRID
level which is our unit of analysis.
19
Contracts and third-party payments are distinguished by the degree of control over the performance of the R&D
and the right to use the R&D. In a contract, the payer has full control and exclusive use of the R&D. In a third-party
payment, the performer has full control over the performance of the R&D and the payer has non-exclusive rights to
use the outcomes of the R&D.

10

third parties in Canada that the firm can exploit on a non-exclusive basis. However, our data do
not capture spending on R&D outsourced to non-residents.
We deflate nominal values of R&D spending by industry-specific implicit deflatorsdeveloped by Statistics Canada. These deflators are constructed based on input costs for R&D
such as wages and salaries for R&D personnel and the cost of intermediate materials. The
deflator for wages and salaries is based on hourly compensation in occupations likely to perform
R&D. The deflator for materials is a weighted average of the KLEMS price indices for thematerials used in performing R&D.
In our analysis on the effect of rising Chinese import competition on the employment andsurvival of manufacturing firms, we rely on R&D capital stock to measure the initial technology
level of a given firm.20 We follow the methodology developed in Kim and Lester (2019) toestimate initial R&D capital stock at the firm level. The methodology is described in Appendix I.
1-2. Output and Inputs of Production
In T2-LEAP-SRED, we also have information on firm-level output and conventional
factors of production to estimate TFP. These variables are constructed based on information fromfinancial statements submitted by firms with their income tax return (i.e. the General Index of
Financial Information or GIFI). The files include information on items such as the value of sales,tangible investment, depreciation expenses, and the capital stock.
We adopt value-added as our measure of output21 and deflate it using industry-specificimplicit deflators from the KLEMS database. Value added is calculated as the sum of payroll
from the LEAP file and capital income constructed based on information available in the GIFI
file. Ideally, we would calculate the two components from the same data source. However, dataon employment levels (i.e. labour input) is only available from the LEAP file. Favouringconsistency between employment levels and labour income, we choose the LEAP file as thesource for labour income.
Capital income is calculated as operating revenue minus operating expenses. We makethree adjustments for operating expenses. First, we adjust them to exclude user costs (i.e.
depreciation and interests) and taxes. Second, we exclude expenses associated with R&D not
capitalized by firms to avoid the “expensing bias” – understatement of capital income by
excluding the items in a balance sheet (see Hall, Mairesse, and Mohnen, 2010). Third, following
Moussaly and Wang (2014), we make adjustments to attribute income generated by leased
tangible capital to the firm using the capital not the owner of the capital.22
20

We also use TFP level (relative to industry average) as a measure of the technology level of a firm.
It is also common to use sales as a measure of (gross) output in the literature. Hall, Mairesse, and Mohnen (2010)
state that although theory suggests gross output is the preferred measure, in empirical works, value-added appears tobe the better choice. For example, differences in the degree of vertical integration among firms lead to variations in
the materials-output ratio. It is difficult to reflect them in an empirical model .
22
In the GIFI files, payments for capital lease are recorded as income by the owner of the capital while the leased
capital is recorded in the balance sheet of the user. Hence, we remove capital lease income from the owner’s file andtreat capital lease payments as capital income of the user. Note that for an operating lease, the owner takes
21

11

We rely on balance sheet items from the GIFI files to calculate the net stock of tangible
capital for firms.23 Firms report the book value of tangible capital in use and the accumulated
depreciation expenses. Deflating net capital stock is challenging since book values of the capitalstock and accumulated depreciation expenses consists of different historical dollars. Due to thelack of completely satisfactory way of deflating, we resort to the industry-specific implicit
deflator for capital stock from the industry productivity database.
Since firm-level data on hours worked are not readily available, we instead use an
estimate of the number of employees developed for the LEAP data. We rely on average labour
unit (ALU) which is constructed by taking the ratio of total payroll to average annual earnings ofa typical worker in the firm’s 4-digit NAICS industry, province and firm size class.

2. Trade data
We use the Trade Data Online by Innovation, Science, and Economics Development
Canada and the UN Comtrade database to construct our measure of Chinese import competition
for Canada and other advanced economies required for our identification strategy. The UN
Comtrade database follows the Harmonized Item Description and Coding System (HS). We
therefore implement a mapping between HS and NAICS following the algorithm developed by
Pierce and Schott (2012).24 Refer to Murray (2017) and Kim (2018a, b) for more detailed
description of the data.

IV. Empirical Models and Identification Strategy
1. Technical changes within firms
Our empirical models assess the effect of Chinese import competition on technicalchange within firms in manufacturing. To do so, we analyze two indicators of technical change:
R&D expenditure and TFP growth. Following Bloom et al. (2016), we estimate the following
two equations:(1)(2)
where denotes firms and denotes sectors in manufacturing.
differences (e.g. 5-year long differences) for a given variable.

represents the operator for longincludes all other controls forresponsibilities for repair and maintenance. Hence, both ownership and rental incomes are attributed to the owner.
We make adjustments accordingly.
23
Tangible capital includes assets with a physical form such as buildings, land, and machinery and equipment.
24
Note that there is no mapping between HS and NAICS 3328 (Coating, engraving, cold and heat treating and allied
activities). As a result, we drop all firms in this sector. This is not likely to pose a serious problem as the value ofChinese imports in this sector in Canada is very close to zero, if not missing, for several years in our sample period.

12non-trade related factors specific to firms and to sectors in manufacturing.
represents period
fixed effects.
is a measure of Chinese import penetration which is constructed as follows:(3)
where the numerator
The denominatorinitial period .

denotes the change in import in sector from China over period .
represents the domestic absorption in sector in the

2.Technical changes between firms: reallocation of employment and survival
To assess between-firm effects of Chinese import competition, we estimate the following
equation:(4)
where is a measure of employment.
sector in the initial period .

is a measure of technology level for firm in

Increasing import competition from China could affect the probability of survival. Thus,
we estimate the effect of trade on survival of firms in our data as follows:
(5)
where
if firm in sector survives over period and zero otherwise. Equation (5) isestimated on a cohort of firms that exist in the sample in a given base period.25 We follow thosefirms over period to assess their value for.
The main parameter of interest is in equation 4 and 5 as it reflects whether the size ofthe effect of Chinese import competition on employment growth or survival varies with theinitial level of technology. If low-tech firms are affected more negatively by China then we
expect.26 In other words,
indicates that employment tends to shift towards high-techfirms and low-tech firms tend to exit in response to increasing Chinese import competition.

3. Identification strategy
To identify shocks exogenously driven by rising Chinese exporting capacity, we exploit
the fact that growth in Chinese exports to developed economies like Canada since the early
2000s were mostly driven by factors internal to China (e.g. urbanization, opening to foreign
25

Unlike the firm-level data used in Bloom et al. (2016) and Autor et al. (2017), T2-LEAP-SRED is adjusted formergers and acquisitions and legal restructuring. Therefore, we do not have to worry about whether a disappearanceof firms is a true exit.
26
We expect <0 in both Equation (4) and (5).

13

investment, rising competitiveness in manufacturing, and accession to the WTO) rather than bypositive demand shocks within developed economies.
We capture the common within-industry factors of rising Chinese exporting capacity,which stemmed from rising Chinese comparative advantage in manufacturing and lower trade
costs due to factors internal to China. Thus, following Autor, Dorn, and Hanson (2013), we
instrument for changes in the Chinese share of the domestic absorption in Canada using thechanges in Chinese imports in the following eight advanced economies: Australia, Denmark,Finland, Germany, Japan, New Zealand, Spain and Switzerland.27
The first-stage regression is given by the following:
(6)
where
countries.

represents changes in the Chinese import penetration ratio in the eight comparison

The above strategy has the following key identifying assumptions: 1. industry-specificshocks are uncorrelated across Canada and the eight countries; and 2. there are no strong
increasing returns to scale in Chinese manufacturing such that Canadian shocks increase
efficiency within relevant Chinese manufacturing industries and lead them to export more to theeight other economies. The former may be a concern in our analysis. We are particularly
concerned with the possibility of correlated shocks related to the innovation in the use of ICT
technologies, which were observed in most of the advanced economies around the world,increasing demand for ICT-related goods from China. We provide some robustness checks on
this issue in section V. The second is not of serious concern since Canada is a small open
economy. Shocks within 4-digit NAICS in Canada are not likely to have a substantial impact on
the efficiency within relevant Chinese industries.

27

We exclude the United States because its economy is highly integrated with Canada and is likely to haveexperienced similar demand shocks.

14

Chart 5: Changes (long-differences) in the Chinese Import Penetration Ratio, Canada and the
Other Advanced Economies (and), 2000-2012, Percentage Point

Note: The comparison countries include: Australia, Denmark, Finland, Germany, Japan, New Zealand, Spain and
Switzerland.
Source: Author's calculation based on Statistics Canada Table 16-10-0047-01 and the United Nations Comtrade
database after mapping CPI products to HS commodities following the algorithm developed by Pierce and Schott(2012).

It appears that the change in trade exposure to China in the eight advanced economies has
good predictive power for the change in Canada (see Chart 5). Approximately 52.5 per cent of
the variation in the import penetration ratio in Canada is presumably driven by exogenous supply
shocks for the 2000-2012 period. In our regression analysis, we use rolling 5-year sub-periods
covering the 2000-2012 period. R-squared varies across the 5-year sub-periods but is greater than
80 per cent in most cases.

4. Descriptive Statistics
We report summary statistics for some key variables used in our analysis by initial
employment size in Table A2 and A3 in Appendix IV. Firms in the R&D sample (Table A2)
tended to be larger in terms of employment and had a larger increase in their productivity overtime, compared to the firms in the TFP sample (Table A3) which is a larger sample including the
R&D-performing firms. Both in the R&D and the TFP sample, we find that the initial level ofprofitability (a proxy for the initial competition level) and the initial productivity level werelower for smaller firms than for larger firms. In other words, firms with initially smaller
employment are more likely to operate in more competitive markets with technology gaps in theinitial period.
Both in theoretical and empirical works, it is suggested that the initial level ofprofitability (or product market competition) and productivity of a firm have important
implications for the firm’s innovative effort in response to an increase in competition.28 It is
28

See, for example, Aghion, Bloom, Blundell, Griffith, and Howitt (2005) and Autor et al. (2017). They suggest
firms that are facing more competitive product markets with technology gaps in the initial period are more likely to
reduce innovation in response to an increase in product market competition.

15

observed that, on average, smaller firms spent a smaller amount on R&D and experienced alarger increase in the Chinese import competition; a larger decrease in profitability; and a smaller
increase in their productivity level. In the table, we report only the mean values for the variables
due to confidentiality requirements but the median values follow similar patterns.

V. Regression results: R&D equation
1. Baseline results
In Table 1, we report the regression results for estimating our R&D equation. We use
rolling long-differenced samples of a 5-year period (i.e. 2000-2005; 2001-2006; 2002-2007; andso on) to maximize the number of observations in our sample.29 Using first-differenced samples(i.e. change from one year to another) may lead to attenuation bias. It may not capture any
meaningful adjustment in the innovation effort in response to increasing Chinese importcompetition, which is likely to occur over the medium- or long-term.

In column 1 and 2, we estimate the same R&D equation but using different estimation
methods. The OLS estimate in column 1 indicates that there is no significant effect of the China
shock on the R&D expenditure growth within manufacturing firms. However, using
exogenously-driven variation in ∆IP (i.e. 2SLS), we find a negative and statistically significant
effect on R&D in column 2. Our estimate indicates that, on average, R&D expenditure growthwithin firms falls by 1.027 percentage points in response to a one-percentage-point increase in
the Chinese import share in the total domestic absorption.
Table 1: R&D Equation, 2SLS, Manufacturing, 2000-2012
1

2

3

4

5

No. firm x period-0.461(0.285)
118,427-1.027***(0.395)
116,683-0.857*(0.448)116,683-2.181**(1.051)
109,871-0.805**(0.398)
101,485

No. firms

17,314

17,066

17,066

16,867

15,529Estimation method

OLS

2SLS

2SLS

2SLS

2SLS

∆IP

Note: The dependent variable is ∆ln(R&D expenditure). All columns include period fixed effects. Standard
errors are in parenthesis. ∆ denotes a 5-year difference. The number of observations is smaller for thecolumns based on our IV approach than column 1 since there is no HS-NAICS mapping for NAICS 3328.
The number of observations for column 4 (alternative instrument) is smaller than the columns based on our
preferred instrument since some industries have missing values for imports from China in some years in theearly 1990s – see the main text for detail. *** < 0.01; ** <0 .05; * < 0.10.

However, there could be unobserved industry-specific shocks that are correlated withboth R&D investment and the Chinese import penetration ratio despite our effort to identify
exogenous shocks. Hence, as our first robustness check, in column 3, we report the results forcontrolling for industry trends in our sample. Here, we include 3-digit NAICS industry dummies,
29

Similar results are found when we use different lengths of rolling sub-periods (e.g. 3-year; 6-year). We also tried
using one or two long-differenced sub-periods (e.g. 2000-2007 or 2000-2005 and 2005-2010). Again, we found thatthe coefficients for ∆IP remain very similar to the ones reported in Table 1.

16which is equivalent to including 3-digit NAICS industry trends in the equation in levels.30 This isa quite tough test on the robustness of our estimate since we are essentially switching off some
important variation used to identify the parameter of interest. We continue to obtain a negativeand statistically significant coefficient on ∆IP although the coefficient is slightly smaller due
potentially to attenuation bias.
In column 4, we use an alternative instrument as proposed in Bloom et al. (2016), which
is similar in nature to the instrument used in Card (2001).31 Using the same argument for ourbaseline IV strategy (i.e. the rapid increase in Chinese imports is driven by positive supply
factors internal to China), we argue that Chinese manufacturing sectors with comparativeadvantages are likely to have exported more strongly than other sectors even before the take-off
in Chinese exports in the early 2000s. In other words, we can argue that sectors that already hadvery strong exports in the initial period (e.g. during the late 1990s) are likely to be the sectors inwhich China had a comparative advantage. Moreover, these sectors tend to have strong export
growth over the following years as well.32 Therefore, we use the Chinese import penetration ratio
measured in the initial period as instrument for its subsequent increases. Specifically, we use() as an instrument for.
is the initial Chinese importpenetration ratio (e.g. the ratio in 1999 for the 2000-2005 period).
is the exogenous
growth in Chinese imports in Canadian manufacturing at time .
We find that the alternative instrument has strong predictive power for the Chinese
import penetration ratio for Canada. The alternative instrument can explain roughly 62 per centto 86 per cent of the change in Chinese import competition in Canada in each of the 5-year subperiods covering the 2000-2012 period. Using this alternative instrument, we continue to obtain anegative and statistically significant coefficient for ∆IP although the magnitude of the coefficientis roughly twice as large as that reported in column (2).
In column 5, we include different firm-level controls to account for potential confounding
factors. Some observations have missing values for some of the control variables we consider.
Hence, the number of observations is slightly smaller than in the other columns. Here, we
include five different control variables. First, we include R&D intensity (R&D stock divided byvalue added) and tangible capital-to-value added ratio, both measured in the initial period (e.g.
the 2000 value for the 2000-2005 sub-period). Second, we include the log of wage per worker
averaged over our sample period.33 Similar to Bernard et al. (2006) and Bloom et al. (2016), we
use these variables as proxies for the initial technology level. Third, we include dummies forforeign-controlled firms and for the enhanced SR&ED tax credit recipients.34 The coefficient for30

We also allow the time trends to differ by 3-digit NAICS industry.
Card (2001) uses past shares of immigrants in the total population in a given region to instrument for the shares ofimmigrants in subsequent periods.
32
In our data, these sectors tend to be in textile, clothing, fabric, electronics and computer-related manufacturing.
They exhibit a high degree of the Chinese import competition during the 1990s and large increases in thecompetition measure over the following years.
33
Wage is defined as payroll divided by average labour unit. Since the average labour unit is defined as the total
payroll divided by average annual wage of a typical worker in the firm’s 4-digit NAICS industry, province and firm
size class, wage would be defined not at the firm-level but at the 4-digit NAICS industry x province x firm size classlevel.
34
We find that the control variables are jointly significant.
31

17∆IP remains negative and statistically significant with these control variables. We also tried
defining all the control variables for the initial technology level at the 4-digit NAICS industrylevel and found similar results.
As discussed in section IV, the role of ICT-related industries could potentially be
important in driving the total impact (through 2SLS) reported above. Thus, we estimate the R&D
equation: 1. on the sample excluding firms in NAICS 3341 or NAICS 3342 (computer orcommunication equipment manufacturing); 2. by including dummies for NAICS 3341 and
NAICS 3342; 3. by interacting ∆IP with the indicator for NAICS 3341 or NAICS 3342.
However, we find that the coefficient on ∆IP hardly changes in the first two tests. For the third
specification, we find that the coefficients for the two groups (i.e. firms not in NAICS 3341 or
3342 and firms in one of the two industries) remain very close to the aggregate estimate reported
in column (2) in Table 1. The coefficient for non-ICT-related firms is slightly greater than thatfor ICT-related firms but we find no statistical evidence that they are different.
In Appendix II, we provide further analysis on R&D by exploring potential firm
heterogeneity. We examine the differential response in R&D expenditure between domesticallycontrolled and foreign-controlled firms; between enhanced R&D tax credit recipients and regular
recipients; and between start-ups and established firms. In summary, we find that foreigncontrolled firms, regular tax credit recipients, and established firms scaled down R&D
investment to a greater extent for a given increase in Chinese import competition. More detailed
discussion of the results and our reasons for analyzing firms in these dimensions are provided inAppendix II.

2. Explaining the negative effectOur results indicate that manufacturing firms in Canada have reduced their R&D
investment over the 2000-2012 period in response to increasing competition from China. This isconsistent with the finding in Autor et al. (2017) who find that R&D expenditure growth fell in
response to an increase in Chinese import competition in the United States. However, our results
contradict those in Bloom et al. (2016) who find a positive effect of increasing Chinese imports
on R&D investment growth for firms in European countries. Such contradiction is theoreticallypossible as discussed in Section I.
It is possible that increasing Chinese imports leads to declining R&D expenditure bycreating competitive pressure on firms, reducing their expected rents after R&D relative to rents
before R&D. However, with differential degrees of competition initially faced by firms, theimpact of increasing competition on R&D investment may not be uniform across all firms. Firms
with significant market power (e.g. larger firms) may be less responsive in adjusting their R&D
effort. Their rents would not be significantly affected by rising competition as it would be
difficult for laggards (i.e. Chinese manufacturers) to overtake leaders (i.e. large Canadianmanufacturers) or Chinese manufacturers may operate in different product markets. On the other
hand, for firms already facing a high degree of competition with technology gaps (e.g. smallerfirms), increasing competition may substantially hurt their profit margins, implying less room tofinance innovation and/or to capture rents after R&D. As a result, if such firms survived, theymight have been more responsive in reducing their expenditure on R&D as competition rises.

18

To empirically examine this idea, we estimate the R&D equation in which we interact∆IP with the size indicator to examine whether the coefficient differs by size group (in terms of
the initial employment size).35 Small firms tend to be not only less profitable (or operating in
more competitive markets) but also less productive in the initial period (see Table A2 in
Appendix IV). In column 1, we find that the coefficient for medium-sized and large firms are
smaller in magnitude than that for small firms indicating they made smaller adjustment in R&D
investment in response to a given increase in import competition. However, the evidence for
their statistical significance is weak and one may conclude that the aggregate estimate of the
China effect on R&D investment is mainly driven by small firms.36 That is, there is statisticalevidence that only small firms' R&D was negatively affected by increasing Chinese importcompetition.
Table 2: R&D and Profit Equation, 2SLS, Manufacturing, 2000-2012∆IP∆IP x initially small∆IP x initially medium-sized∆IP x initially large
No. observations (firm x period)∆ln(R&D investment)
1-1.299***(0.468)-1.056(0.950)-0.991(0.836)116,683∆Profitability
2-0.057***(0.018)103,8163-0.068***(0.022)-0.014(0.031)-0.013(0.025)
103,816

No. firms
17,066
15,956
15,956
Note: Period fixed effects are included in all columns. Standard errors are in parenthesis. ∆ denotes a 5-year
difference. Initial employment size is measured as the average labour unit (ALU) observed in the initial year (e.g.
ALU in 2000 for the 2000-2005 sub-period). We define the three size groups based on the average labour unit
(ALU) observed in the initial period: small (ALU 100); medium (100 ALU 500); and large (500 ).
Profitability is defined as profit/revenue where we use net income or loss before tax as a proxy for profit. *** <0.01; ** <0 .05; * < 0.10.

It is likely that small firms’ profit margins were negatively affected (or competitive
pressure faced by them is increased) by rising import competition, leading them to reduce R&D
expenditure. We estimate the impact of increasing Chinese imports on the profitability of firms
performing R&D in manufacturing. Given the data availability in the T2-LEAP-SRED database,
we define profitability as profit divided by sales where we use net income or loss before tax as
profit. In column 2 in Table 2, our 2SLS estimate indicates that increasing Chinese importcompetition indeed reduced the profitability of firms. A further analysis by size in column 3
35

We define the three size groups based on the average labour unit (ALU) observed in the initial period: small(ALU 100); medium (100 ALU 500); and large (500 ). We use the conventional definition for small, medium
and large firms. These cut-offs are based on the number of employees which is not equivalent to the average labour
unit. Thus, based on the distribution of ALU in T2-LEAP we translated these numbers to ALU and carried out all
the analyses. The qualitative results remained the same.
36
We also estimated the equation based on two size groups by aggregating medium-sized and large firms. However,
we found the same qualitative results.

19indicates that only small firms were negatively affected by increasing import competition from
China. We find no evidence that the profitability of larger firms is affected by Chinese imports.
This implies that rising Chinese import competition translated into increased competitive
pressure (i.e. decreasing mark-ups) mostly for small firms.37
We carry out some robustness checks. A key relationship to show is that a higher initiallevel of competition faced by firms or a lower initial level of productivity is associated with alarger negative effect of rising Chinese competition on their R&D expenditure.
First, we divide the observations into three groups by their profitability level observed in
the initial year: high, mid, and low. We assign firms with zero or negative initial profitability to
the low group and firms in the top five per cent of the distribution for the initial profitability to
the high group. The rest of the firms are assigned to the mid group.38 The low group accounts forroughly 25 per cent of the observations. Then, we estimate the R&D equation allowing for adifferent effect of ∆IP for each group. The regression results are reported in Table A4 in
Appendix IV. We find that the coefficient for the low group is negative and statisticallysignificant while those for the mid and high groups are not statistically different from zero.39 In
column 2, we also tried separating the sample into two group using the mean initial profitability
as a cut-off (i.e. firms below and above the mean within period x 4-digit NAICS industry). We
found that the coefficient for the group with profitability below the mean was negative andstatistically significant while the other group was statistically insignificant.
As a second test, we carry out a similar exercise by separating the observations by the
initial TFP level (measured as relative to the industry average). We separate them into twogroups: firms below and above the mean initial TFP level. We find that the firms that were
initially less productive scaled back their R&D investment while their better-performing
counterparts did not respond – see column 4 in Table A4.40 This is consistent with Melitz (2003)
and Aghion, Bloom, Blundell, Griffith, and Howitt (2005). The former suggests more productive
firms are better positioned to benefit from increasing trade and the latter suggests increasing
competition reduces the incentives to innovate for firms operating in highly competitive markets
with technology gaps. Autor et al. (2017) also find that initially less profitable or less productive
firms scaled back innovative effort by more than their better-performing counterparts.

37

These results (both R&D expenditure and profit margins by size) are not driven by very small firm or “microfirms” in our sample. We tried dropping increasingly larger “micro-firms” (e.g. firms with ALU<2, ALU<3, ALU<4and so on) but the significance level and the sign of the coefficients remained the same. Also, the magnitude of thecoefficients did not change significantly. We also tried including industry fixed effects since small firms tend to be
in industries with large increases in Chinese import penetration ratio during our sample period (see Table A2 in
Appendix IV). With the industry fixed effects, we still found that only small firms experienced declines in their
R&D expenditure and profit margins due to rising Chinese import competition.
38
We tried different cut-off values to assign firms into three groups but the qualitative results did not change.
39
We carried out the same analysis for the profitability equation and found similar results. As another check, we
also regressed ∆ln(R&D investment) on ∆profitability and found that increasing (decreasing) profitability is indeed
associated with increasing (decreasing) R&D investment.
40
We also tried separating the firms using different criteria (e.g. high - top 5%; low - bottom 25%; mid - the rest,
which are reported in column 3 in Table A4 in Appendix IV) but the qualitative results that initially less productive
firms scaled back their R&D investment in response to rising Chinese import competition did not change.

20Moreover, based only on small firms, we estimated the R&D equation including theinteraction term between ∆IP and the initial level of profitability and the initial level of TFP(measured as relative to the industry average), respectively. We found a negative coefficient for∆IP and a positive coefficient for the interaction term. In other words, for a given increase in the
Chinese competition, firms make a larger downward adjustment in their level of R&D
expenditure over time if they initially faced a higher level of product market competition or ifthey were initially less productive.
In summary, smaller firms or firms initially operating in more competitive markets withtechnology gaps appear to be the ones most directly affected by increasing Chinese importcompetition. Declining profit margins indicates less room to finance R&D41 and/or lower postinnovation rents implying it is optimal for them to scale down their expenditure on R&D if theysurvive. This mechanism is consistent with the theoretical model introduced in Aghion, Bloom,
Blundell, Griffith, and Howitt (2005). The authors show that if competition is initially high, anincrease in competition would most likely to reduce the incentives for the incumbents toinnovate.42 We conclude that the negative effect found at the aggregate level is mainly driven by
the negative effect on small firms' ability and incentives to invest in R&D.
In contrast, profit margins of larger firms or firms initially operating in less competitivemarkets with relatively high levels of technology were not affected much by increasing Chinesecompetition, and hence, they made less or no adjustment in their R&D investment. These larger
firms may have been mostly neck-and-neck competing incumbents (i.e. firms competing witheach other with similar levels of technology) within their product markets but faced not muchcompetition having less or no incentives to innovate.43 Or if there were laggards they would
quickly catch up the leaders but once they have caught up they would have been slow to innovate
further given low competition. At equilibrium, there would be a larger fraction of neck-and-neckcompeting incumbents with not much innovation. Such initial state (or the competitiveness of themarket for these firms) were not affected much when Chinese manufacturers entered theCanadian market so that the incumbent firms maintained the status quo which we have observed
41

For example, Hall (1992) finds a positive elasticity of R&D investment with respect to cash flow controlling forother factors and that debt is not a preferred form of financing R&D. Her findings imply that R&D is financed out of
free cash flow. Using cash flow is likely to be the main avenue to finance R&D since external financing may be
costly due to asymmetric information between lenders and borrowers, which induces borrowers to finance risky
innovation using debt (e.g. Myers and Majluf, 1984) or the existence of moral hazard, which leads lenders not tofinance innovation through debt or some agency issues that arise when the interests of shareholders and managers
are not aligned leading the managers to pay a premium for external financing (e.g. Jensen and Meckling, 1976).
42
A larger decrease in pre-innovation rents relative to the decrease in post-innovation rents, the so-called “escapingcompetition” effect (i.e. having incentives innovate to “escape” competition to gain market power) may not be atwork for firms with initially low profitability with technology gaps and in direct competition with Chinese
manufactures. Compared to them, emerging Chinese manufacturers were much more cost-effective so that the
expected net reward for innovation would have been quite low (potentially lower than pre-innovation rents). It
would have required these domestic firms to achieve a significant leap in the technology level to gain some market
power over these Chinese manufacturers but such leap may have implied large investment which these small firms
would have found unprofitable or could not afford with the declining profitability. In other words, increasing
competition would have affected more the post-innovation rents for these firms (i.e. the basic Schumpeterian effectdominates).
43
This would be true if the incumbents can collude but not so true if they are in Bertrand competition earning zeroprofit. The former is more likely to be the case for larger firms (e.g. oligopoly). Theoretically, the incentives tocollude is negatively related to the degree of competition in the market.

21

in our data: no effect on their profitability (proxy for mark-up or competitiveness) and hence, notmuch adjustment in their innovative effort (e.g. R&D) in response to rising Chinesecompetition.44
Some large firms may have undergone industry-switching or reorganization such thatthey shift away from the physical production of goods towards “neuro-manufacturing” where
they focus more on the design, engineering, and marketing of their goods or towards producing
related professional services.45 This may have spurred additional R&D investment within theselarge firms but this would be observed in our data only if their primary industry code did notchange to non-manufacturing due to the shift of their economic activity.

Our results imply that firms accounting for a large share of the total R&D expenditure are
not likely to reduce R&D in response to increasing Chinese import competition. A small numberof medium-sized and large firms account for a disproportionately large share in the total R&D
expenditure in Canadian manufacturing. These larger firms account for 14 per cent of the total
observations but about 77 per cent of the total R&D expenditure in manufacturing.46 Hence, thecumulative partial impact of the China shock on the aggregate R&D expenditure in
manufacturing may be limited.

VI. Regression results: TFP, Employment, and Survival Equation
In order to assess the impact of China on the technical change in Canadian manufacturing
in a broader perspective, we estimate its impact on TFP within Canadian manufacturing firms
and on the employment and the survival of firms to assess potential reallocation effects. Using
the estimates from these analyses, we carry out a TFP decomposition in section VII to estimate
the share of the aggregate TFP change induced by increasing Chinese import competition.

1. Within-effect: TFP Equation
For estimation of firm-level TFP, we experimented with OLS and GMM for a dynamic
panel data model introduced in Blundell and Bond (1998) (i.e. system-GMM). The choice of theestimation strategy depends on the availability of data and how the data satisfy the underlying
assumptions for the chosen estimation strategy. We estimate a firm-level Cobb-Douglas
production function by three-digit NAICS industry in manufacturing and use the estimated44This is more likely to be the case for large firms or firms operating in a concentrated market. Medium-sized firms
or firms facing the intermediate level of product market competition may have been affected by rising Chinesecompetition having more incentives to innovate. In the following section, these firms appear to have resorted toother types of productivity-enhancing activities while not scaling back their R&D in response to rising importcompetition.
45
Some U.S. examples are Apple and IBM. Apple outsource its production to China and focus more on productdevelopment and producing related services in the United States. IBM sold their ThinkPad business line to Lenovo
which produces ThinkPad laptops within China. IBM now produces professional services related to data
management and system design.
46
Medium firms (large firms) account for 11 (3) per cent of the total observations but 23 (62) per cent of the total
R&D expenditure in manufacturing on average over the 2000-2012 period.

22coefficients for inputs to retrieve estimates of firm-level TFP.47 In this section, we report theresults based on OLS as we could not find a completely satisfactory specification for GMM. We
carried out all our analyses using firm-level TFP estimated with system-GMM and found that theresults were very similar.48 In Appendix III, we discuss in more detail different estimation
strategies including semi-parametric approaches and our reasons for adopting OLS to estimate
TFP in our study.
Table 3 reports the results from estimating the TFP equation as described in section IV.
Note that, since our estimates are based on a TFP equation defined at the firm-level, we can only
capture the within-effect of China on TFP. That is, the coefficient reflects only the TFP change
within firms rather than the aggregate TFP change between firms or through inducing firms withdifferent TFP levels to exit or enter markets. As with R&D, we find that increasing Chinese
import competition reduces the TFP growth within manufacturing firms as indicated by thenegative coefficient in column 1. The negative effect is robust to including industry fixed effects
or including the firm-level controls introduced in column 5 in Table 1 or using the alternative
instrument.
Table 3: TFP and Profit Equation, TFP Sample, 2SLS, Manufacturing, 2000-2012∆ln(TFP)∆Profitability

1

2

3

4

-

-0.012***(0.004)--∆IP x initially small-0.137***(0.023)-∆IP x initially medium-∆IP x initially large-∆IP

No. observations (firm x period)-0.164***(0.024)0.140*(0.081)
0.063(0.111)
241,054

241,054

223,886-0.015***(0.004)0.018(0.018)0.003(0.012)
223,886

No. firms
43,331
43,331
41,984
41,984
Note: Period fixed effects are included in all columns. Standard errors are in parenthesis. ∆ denotes a 5-year
difference. Initial employment size is measured as the average labour unit (ALU) observed in the initial year (e.g.
ALU in 2000 for the 2000-2005 sub-period). We define the three size groups based on the average labour unit
(ALU) observed in the initial period: small (ALU 100); medium (100 ALU 500); and large (500 ).
Profitability is defined as profit/revenue where we use net income or loss before tax as a proxy for profit. *** <0.01; ** <0 .05; * < 0.10.

In most empirical studies, increasing competition is found to induce productivity gains
within surviving firms. In response to increasing competition, firms would engage in innovationor other productivity-enhancing activities to remain in the market. Otherwise, they would choose
to exit. However, the exact mechanism through which increased competition induces
productivity gain within firms is not well understood. Some empirical studies examine what47

We also estimated a production function by two- and four-digit NAICS industry and carried out all the analyses.
We found that the qualitative results remained the same.
48
Again, we tried estimating production function using system-GMM by two- and four-digit NAICS industry andfound the results did not change significantly.

23

changes within surviving firms to enhance their productivity.49 However, as the theories
discussed in section I imply, the empirical evidence on the effect of increasing competition on
innovation is mixed. Some find positive effects (e.g. Bloom et al., 2016; Gorodnichenko et al.,2010; Iacovone, 2012a; Coelli et al., 2016) while others find either no or negative effects (e.g.
Hashmi, 2013; Autor et al., 2016; Hombert and Matray, 2015; Gong and Xu, 2015; Brandt et al.,2012; Gilbert, 2006; Arora et al., 2015; Keung, Li, and Yang, 2016). The latter implies a
potential negative effect on TFP growth within firms. Nevertheless, the studies that find negative
effects on innovation (e.g. patents and R&D) do not study the effect on the aggregate
productivity growth.
The negative within-effect reported in column 1 can be related to our finding based on
the R&D sample: in response to rising import competition, initially smaller and poorlyperforming firms experienced declining profit margins and scaled back their R&D effort overtime. If similar firms that survived in manufacturing scaled back their overall innovative effort
and/or did not resort to additional productivity-enhancing activities (e.g. better management),
then their TFP growth would decline.
As is the case for the R&D sample, we find a negative effect of increasing Chinese
import competition on profitability for the TFP sample (column 3 and 4). Importantly, we findthat only small firms experienced a negative effect on their profitability while larger firms did
not. We estimate the TFP equation by interacting ∆IP with the size indicator (defined based on
ALU observed in the initial period) in column 2 in Table 3. Again, only small firms experienced
a negative effect on TFP growth.50 Medium-sized firms actually experienced productivity gain
due to increasing Chinese import competition while we find no evidence that large firms' TFP
growth was affected.
Medium-sized firms may have focused on other productivity-enhancing activities (e.g.
better management practices or inventory controls) while not scaling back their R&D investment(if they have invested in R&D) to remain competitive in the market.51 In contrast, large firms'
effort to enhance productivity, if any, did not seem to have much effect on its TFP growth. Largefirms are more likely to have monopolistic power having fewer incentives to engage in additionalinnovative activities in response to increasing market competition. We also estimate the TFP
equation while splitting profitability and productivity level into two categories, below and above
the mean, and find that the negative effect on TFP growth was mainly on less profitable and lessproductive firms – the results are reported in Table A5 in Appendix IV.

49

See Holmes and Schmitz (2010) for a review of this literature.
As is the case in our R&D analysis by size, these results are not driven by very small firm or “micro-firms”. We
carried out the same sensitivity test as described in footnote 37. Also, small firms tend to be in industries with large
increases in the Chinese import penetration ratio (see Table A3 in Appendix IV). We tried including industry fixed
effects and still found that only small firms’ TFP growth and profit margins were negatively affected.
51
As suggested in Holmes, Levine, and Schmitz (2008), falling opportunity cost of investment in a productivityenhancing factor (e.g. better management) may have driven the firms’ behaviour. When firms innovate, they arelikely to forego or delay some output for some period of time (often called switchover disruptions). When
competition rises, the market price is likely to decline implying smaller profit lost when investment is made andhence, lower opportunity cost of investment. A similar argument is the “trapped-factors model of innovation”
suggested in Bloom, Romer, and Terry, and Van Reenen (2015), which is discussed in section I.
50

24

Similar patterns are observed in the R&D sample. In column 1 and 2 in Table 3-1, we
report the results from estimating the TFP equation based on the firms that performed orpurchased R&D at least once during the 2000-2012 period. We still find a negative effect ofincreasing Chinese competition on TFP growth within small firms only.52 A key channel throughwhich increasing Chinese import competition reduces TFP growth within these firms is likely to
be its impact on R&D expenditure within firms.
R&D is a key input to innovation and hence, productivity gain. There is a large literatureon estimating a firm-level production function or a TFP equation where R&D (owned internally)
is a key explanatory variable.53 The consensus in the literature is that R&D and productivity are
positively related. Also, Doraszelski and Jaumandreu (2013) develop a model of endogenous
productivity change and assess the effect of R&D investment on the productivity at the firm level.
They find that R&D investment is a key factor in explaining the differences in productivity
across firms and the evolution of firm-level productivity over time.
Table 3-1: TFP Equation, R&D Sample, 2SLS, Manufacturing, 2000-2012
Baseline

Controlling for ∆ln(R&D expenditure)

1

2

3

4

-0.127***(0.034)--0.087(0.146)-∆IP x initially small--∆IP x initially medium-∆IP x initially large--0.177***(0.040)0.057(0.082)0.115(0.143)85,3420.020(0.155)0.005(0.406)-0.634(0.124)25,064∆IP

No. observations (firm x period)

85,34225,064

No. firms
14,111
14,1119,430
9,430
Note: Period fixed effects are included in all columns. Standard errors are in parenthesis. ∆ denotes a 5-year
difference. Initial employment size is measured as the average labour unit (ALU) observed in the initial year (e.g.
ALU in 2000 for the 2000-2005 sub-period). We define the three size groups based on the average labour unit
(ALU) observed in the initial period: small (ALU 100); medium (100 ALU 500); and large (500 ).
Profitability is defined as profit/revenue where we use net income or loss before tax as a proxy for profit. In
column 3 and 4, ∆ln(R&D expenditure) and its lags ( -1 to -5) are included. Given our sample period, it isfeasible to control for lags up to -7 but including further lags does not change the qualitative results. ∆ln(R&D
expenditure) and its lags ( -1 to -5) are jointly significant. *** < 0.01; ** <0 .05; * < 0.10.

The above idea is further supported by the results from estimating the TFP equation (onthe R&D sample) in which we control for R&D expenditure growth. The results are reported incolumn 3 and 4 in Table 3-1. When we control for R&D expenditure growth and its lags,
increasing Chinese import competition has no statistically significant effect on TFP growthwithin firms (column 3). These findings suggest that the negative within-effect found in column

52

Again, this is not driven by very small firms in our sample. See footnote 50.
For example, see Hall, Mairesse, and Mohnen (2010), Lychagin et al. (2016), Bloom et al. (2013), and Kim andLester (2019). Kim and Lester (2019) use the same database as this study (i.e. T2-LEAP-SRED).
5325

1 is mainly driven by declining R&D expenditure induced by rising competition.54 Further
analysis by size in column 4 reveals that the China effect on small firms' TFP growth becomesinsignificant when we control for R&D investment, indicating that small firms are not likely tohave changed their effort in other productivity-enhancing activities in response to increasing
Chinese competition.

2. Between-effect: Employment and Survival Equation
The effect of increasing Chinese imports on the overall TFP in Canadian manufacturing
cannot be fully assessed with the within-effect. The aggregate TFP change also includes thechange driven by the reallocation of resources. That is, increasing Chinese import competition
may have reallocated employment or market share towards more productive firms or induced
less productive firms to exit while inducing more productive firms to enter the market. These
reallocation effects could be significant in driving the aggregate change in the TFP level inmanufacturing.
In this section, we report the results from estimating the effect of increasing Chinese
import competition on the employment growth and the survival of firms in manufacturing,particularly focusing on differential effects stemming from different initial technology levels offirms. We would like to study whether more technologically advanced firms are less likely to
reduce employment and more likely to survive in response to increasing Chinese competition in
the domestic market.
In Panel A of Table 4, we estimate the effect of China on the log change in employment
but allow the effect to differ by the initial technology level by interacting ∆IP with the initialtechnology level. We use two proxies for the technology level: R&D capital stock and the TFPlevel.
First, we find that increasing Chinese import competition is associated with a lower
employment growth. We also find that firms that were already technologically advanced are
more likely to grow (in terms of employment) as indicated by the positive and statisticallysignificant coefficient on the initial technology level. More importantly, the negative effect ofincreasing Chinese import competition on the employment growth is smaller the higher the
initial technology level. This is shown as the positive and statistically significant coefficients on
the interaction terms (column 2 and 4). These results indicate that high tech firms may have been"shielded" from the negative effect of increasing Chinese imports on employment. As the shareof low tech firms in the total employment declines, it is possible that increasing Chinesecompetition had some positive effect on the aggregate technology level in manufacturing.

54

We indeed find that R&D expenditure growth is positively related to TFP growth within firms. The coefficientson R&D expenditure growth and its lags are positive and statistically significant.

26

Table 4: Employment and Survival equation, 2SLS, Manufacturing
TECH variable:

R&D stock

TFP

Panel A: Employment equation
Dependent: ∆ln(employment)∆IP

Initial TECH

0.026***(0.004)2-1.253***(0.567)0.105***(0.053)0.022***(0.004)

No. observations (firm x period)

38,15338,153

165,825

165,825

No. firms

9,628

9,628

37,04237,042∆IP x Initial TECH

1
-0.136*(0.068)-3-0.289***(0.045)0.327***(0.008)4-0.294***(0.046)
0.381***(0.094)0.310***(0.008)Panel B: Survival equation
Dependent: S∆IP∆IP x Initial TECH
Initial TECH

1
-0.027***(0.012)-2-0.165(0.125)0.013(0.011)-0.004*(0.006)-0.003*(0.002)

3-0.033***(0.011)
0.048***(0.005)

4-0.032***(0.011)
0.068***(0.021)
0.043***(0.004)

No. firms
6,937
6,937
32,861
32,861
Note: Period fixed effects are included in all columns. Standard errors are in parentheses. ∆ denotes a 5-year
difference. The dependent variable in Panel A is the log change in employment. The dependent variable in Panel B
is S which equals one if a given firm survived the entire 2002-2007 period and zero otherwise. We use the average
R&D stock and TFP level (measured as relative to the 3-digit NAICS industry average) observed in the initial andthe two years prior to the initial year to mitigate potential measurement errors (e.g. average over the 2000-2002
period for the 2002-2007 period). Hence, the sample period is 2002-2012 for Panel A and 2002-2007 for Panel B.
R&D stock is divided by employment. Similar results are found when we use R&D stock divided by sales. Bothtechnology variables enter the equation in logs. *** < 0.01; ** <0 .05; * < 0.10.

Next, we estimate the survival equation. For the survival analysis, we focus on a cohort
of firms that was alive in a given initial year. We model the probability of their survival
following these firms for five years. Our sample period in this analysis is 2002-2007 since we
use as a measure of initial technology level the average R&D stock or the average TFP level(measured as relative to the 3-digit NAICS industry average) measured based on the initial andthe two years prior to the initial year to mitigate potential measurement errors. We continue tobase our analysis on a 5-year sub-period to be consistent with the other analyses in the paper.
The results reported in Panel B of Table 4 indicate that firms in industries with larger
increases in the Chinese import penetration ratios are less likely to survive (or more likely toexit). A one-percentage-point increase in the penetration ratio is associated with a decrease in thesurvival probability of 0.027 to 0.033 percentage points according to column 1, 3, and 4 in Panel
B.55 Again, we find that more technologically advanced firms (as proxied by the average TFPlevel) are "shielded" from the negative effect of increasing Chinese imports as indicated by the55

Note that the mean exit rate of this cohort is 6.1 per cent for the 2002-2007 period. Using the mean ∆IP and initialtechnology level, our estimates in column 4, for example, represent roughly 18.5 per cent increase (or 1.23
percentage-point-increase) in the mean exit rate.

27

positive and statistically significant coefficient on the interaction terms in column 4.56 Our
regression results imply that increasing Chinese import competition in Canada may havepositively contributed to the aggregate TFP in manufacturing by inducing less productive firms
to exit.57
These findings are consistent with Bernard et al. (2006) and Bloom et al. (2016) where
the authors show similar results for the U.S. and the European manufacturing firms andestablishments. Also, Hombert and Matray (2017) find that more R&D-intensive firms
experienced much less negative effects on their employment when Chinese import competitionrose in the United States. Our findings in this section are broadly consistent with theirs.

VII. Quantifying the Role of China
Using the results from our regressions, we contextualize the findings to provide a broad
picture of the role of China in driving the technical change in Canada. We would like to ask: fora given period, how much of the total change in real R&D investment and the TFP level in
Canadian manufacturing can be explained by increasing Chinese import competition?

1. R&D Expenditure
We calculate the predicted change in the aggregate R&D expenditure driven byincreasing imports from China as follows:
(7)
where
is the marginal response of R&D expenditure growth with respect to an increase in
Chinese import penetration ratio andis the exogenously-driven change in the Chinese
import penetration ratio for manufacturing sector over period . We estimatebydiscounting the actual
by the R-squared from the first stage regression.
is theactual level of R&D expenditure for firm in sector at the start of period .
According to our sample from the T2-LEAP-SRED database covering incorporated
firms in manufacturing, real expenditure on domestically performed R&D increased by 21.5 percent between 2000 and 2005 but declined by 19.3 per cent between 2005 and 2010. Real R&D
expenditure in manufacturing increased annually between 2000 and 2005 except for the 20012002 period (declined by 1.72 per cent) while it declined continuously year over year for the
2005-2010 period.

56

When R&D stock is used to proxy the initial technology level (column 2), we find the coefficient on theinteraction term is insignificant but positive.
57
Our sample and the estimates imply that increasing Chinese imports decreased the exit rate of high tech firms andincreased the exit rate of low tech firms. For example, based on the estimates from column 4 and the mean ∆IP andinitial TFP level observed for the 2002-2007 period, the firms in the top 30 per cent of the initial TFP level
distribution have a predicted mean exit rate of 2.4 per cent which is lower than their actual mean of 3.9 per centwhile the firms in the bottom 30 per cent have a predicted mean exit rate much higher than their actual mean rate
(13.2 per cent vs.9.4 per cent).

28

Table 5 reports the change in real R&D expenditure induced by increasing Chinese
import competition for the 2000-2005 and 2005-2010 period, respectively. The estimates are
based on the size-specific coefficients from column 1 in Table 2.58 First, we estimate that
increasing Chinese import competition led to a decline of $98 million CAD in R&D expenditure
between 2000 and 2005. For this period, R&D expenditure in manufacturing increased by $1,220
million CAD. Had there been no increase in Chinese import competition, R&D expenditure
would have increased by about 8 per cent more (i.e. $1,318 million instead of $1,220 million).
This counterfactual translates into a growth rate of 23.2 per cent between 2000 and 2005, a 1.9
percentage-point-increase from the actual growth rate of 21.5 per cent.
Between 2005 and 2010, R&D expenditure in manufacturing fell by $1,360 million CAD.
Our estimates imply that China can explain about 6.5 per cent of the total decline for this period.
Or the decline in R&D expenditure in manufacturing would have been 6.5 per cent lower had
there been no change in Chinese import competition in Canada. We estimate that $89 million
CAD of the total decline of $1,360 million CAD was driven by increasing Chinese importcompetition. This implies that if the Chinese import penetration ratio did not change between
2005 and 2010, R&D expenditure in manufacturing would have fallen by 18.0 per cent instead of
19.3 per cent.
Table 5: Change in R&D Expenditure due to China, Manufacturing, Millions of 2007
CAD, 2000-2010
2000-2005
5,670
1,220
21.5%
-98
1,318
23.2%
2005-2010
7,060-1,360-19.3%-89-1,271-18.0%(1) R&D expenditure in 2000
(2) Actual change(3) Actual % change(4) Induced change in R&D due to China(5) Counterfactual change in R&D (2 - 4)(6) Counterfactual % change (5/1)(1) R&D expenditure in 2005
(2) Actual change(3) Actual % change(4) Induced change in R&D due to China(5) Counterfactual change in R&D (2 - 4)(6) Counterfactual % change (5/1)

Note: Estimates are computed based on the coefficients reported in column 1 in Table 2. R&D
expenditure includes only domestically performed R&D in manufacturing.

58

We carried out the same calculations assuming only small firms adjusted their R&D in response to rising Chinese
import competition. We found that the role of China in explaining the total change in R&D falls roughly by a half.
We also tried using the aggregate coefficient from column 2 in Table 1. With this, in an increase in the role of China
by roughly 80 per cent. The former is potentially a lower bound since we assume all medium-sized and large firms
did not adjust their R&D. The latter is potentially an upper bound since we impose the larger aggregate coefficient(driven mainly by small firms) on all firms in our sample.

29

2. TFPTo assess the role of increasing Chinese imports in driving the aggregate TFP in
Canadian manufacturing, we carry out a standard productivity decomposition using the estimatesfrom Equation (1), (2), (4), and (5) following a similar decomposition methodology introduced in
Baily, Hulten, and Campbell (1992), Foster, Haltiwanger, and Krizan (2000), and Bloom et al.
(2016):
(8)

∈

0

0

0+ ∈

where denotes the aggregate TFP level at a given point in time .
represents the aggregatechange in TFP between time 0 and .
denotes the employment share of firm at time (i.e.
firm employment divided by total employment in manufacturing).59
is the average TFP of all
firms in sector at time . is the total number of firms in manufacturing.
The first term in Equation (8) is the within-firm effect which is the change in TFP level
holding employment shares constant. The second term is the between effect, the change in TFPlevel due to shifting employment from less productive firms to more productive firms holding
the initial productivity level constant. The third term is the cross effect which is simply thecorrelation between the change in TFP level and the change in employment share within firms.
The second last term is the exit effect which represents the change in TFP level due to firm exits.
The last term represents the entry effect. The contribution of entrants and exitors depends onentering or exiting firms' relative to the average of the incumbents.
We explicitly model each term in Equation 8 (except for the entry effect). Following
Bloom et al. (2016), we can re-write Equation 8 in terms of our estimates from Equation 2, 4,
and 5. Using our estimates from TFP, employment, and survival equations, we have:
(9)

0

∈

0

0

0

where
is the coefficient from Equation 2.
is the predicted share of employment
for incumbent firms andis the predicted share of employment in exiting firms as defined in
the following.

wheretime 0.

andare the coefficients from Equation 4.

59

is the employment level in firm at

Output shares could be used as weights in TFP decomposition. We adopt labour shares as weights in our
decomposition given our econometrics framework based on Bloom et al. (2016).

30

whereandare the coefficients from Equation 5.

Finally, we can compute the magnitude of each component in Equation 9 by computing
the ratio

whereis the actual change in the aggregate TFP level in manufacturing overthe period 0 - .
We cannot directly quantify the entry effect at the firm-level as it is not possible toobserve their lagged technology levels (i.e. technology level of a given firm before entry).60
Therefore, we implicitly assess the magnitude of the entry effect by estimating an industry-levelversion of Equation 2 and compare its coefficient (i.e.
) with the corresponding firm-level61
coefficients.
According to the CPA data covering the 1990s, TFP in Canadian manufacturing grew
rapidly during the 1990s before it stabilized from the early 2000s. The aggregate TFP level did
not change much between 2000 and 2005. However, the TFP level started to decline
substantially after 2005 until it recovered shortly after the recession following the financial crisis
in 2007. Between 2005 and 2010, the TFP level in Canadian manufacturing fell by 5.90 per centaccording to the CPA data or 5.43 per cent according to our estimates from the T2-LEAP-SRED
database.
Table 6 reports the results from our productivity decomposition focusing on the period
2005-2010.62 Note that the magnitudes are presented as a share of the actual decline in theaggregate TFP level in manufacturing between 2005 and 2010. Hence, a negative (positive) sign
implies that increasing Chinese import competition has positively (negatively) affected the totalchange in the TFP level.

60This is a well-known empirical issue in decomposing productivity using firm-level data. Note that it is not
appropriate to use the technology level at the time they enter as it is likely to be endogenous. This is similar in nature
to a problem often found in empirical labour economics where researchers cannot observe the reservation wage andwage offers of a given worker prior to employment. What we observe in data is the final wage offer accepted by aworker since the offer was greater than his or her personal reservation wage. The wage would probably be correlatedwith unobserved characteristics of that worker which may have affected his or her decision for entry.
61
The rationale is that, if Chinese import competition had discouraged entry of innovative firms, then our firm-level
results would overestimate the effect of China on productivity performance in manufacturing. Note that industrylevel coefficients reflect the combination of within, between, and entry effects.
62
We also analyzed the 2000-2005 period and the qualitative results were the same -- as is the case for the 20052010 period, the sum of the between- and exit-effect was positive and more than offset the negative within-effect.
However, the impact of China was not economically significant. The magnitude of the role of Chinese imports forthis period was much lower than that estimated based on the 2005-2010 period. Also, the actual TFP growth
between 2000 and 2005 was very small (e.g. 0.42 per cent based on the CPA data or 0.02 per cent based on the T2LEAP-SRED database). Our estimates from the decomposition exercise imply that increasing Chinese importcompetition explains less than 2 per cent of the total increase in the TFP level in manufacturing for this period.

31

We find that the TFP level declined within manufacturing firms between 2005 and 2010,
negatively affecting the aggregate TFP level in manufacturing.63 We estimate that the withineffect driven by increasing Chinese import competition can explain roughly 5 per cent of thetotal decline in the TFP level in Canadian manufacturing. However, as hinted by our estimatesfrom the employment equation, there were substantial gains in the aggregate TFP level throughthe reallocation of resources. In response to increasing Chinese import competition, employment
shifted from less productive firms to more productive firms, positively contributing to theaggregate TFP change in manufacturing. Also, less productive firms exited the market although
the overall impact on TFP is relatively small. The extent of decline in the TFP level would havebeen greater by roughly 21 per cent (17.1 plus 3.9) had there been no reallocation effect driven
by increasing Chinese import competition. Such effects were large enough to more than offset
the negative within-effect, resulting in a net positive effect of China on the aggregate TFPchange between 2005 and 2010. That is, had there been no increase in Chinese importcompetition in Canada, the per cent change in the aggregate TFP level would have been -6.31 percent instead of -5.43 per cent between 2005 and 2010 (or -1.26 per cent per year instead of -1.09
per cent per year).64 As is often the case in other empirical studies, the cross-effect is negligible.
Table 6: Change in TFP due to China, Manufacturing, 2005-2010
Within (-)
Between (+)
Exit (+)
Cross (+)
Total (+)

As a % of the decline in the TFP level between 2005 and 2010
5.4%
-17.1%-3.9%
0.0%-15.6%As discussed, our firm-level estimates do not include the entry effect. To assess the entry
effect indirectly, we estimate the TFP equation at the 4-digit NAICS industry level. Since theindustry-level measure reflects the behaviour of both entrants and incumbents, its regression
estimate would include not only the within- and between-effect but also the entry effect. Since
the between effect is estimated to be in the opposite direction and larger in the absolute terms
than the within-effect, we expect the estimate to be either less negative than the firm-level
coefficient or positive. If the former is the case, then the entry effect is negative and substantial
enough to offset the positive between-effect. If the latter, the entry effect is either negative but
not offsetting the net effect of between- and within-effect or is positive.
Our industry-level regression has a coefficient of roughly 0.359 which indicates that
increasing Chinese import competition has a net positive effect on TFP in manufacturing. Using
the shares reported in Table 6, one can compare the industry-level estimate with the firm-level
estimate (-0.137) and presume that the entry effect is positive (i.e. increasing Chinese
63

For the within-effect, we use the coefficient reported in column 1 in Table 3. If we use the size-specificcoefficients reported in column 2, the within effect becomes positive (a positive contribution to the overall TFPchange) but the absolute per cent level is nearly zero, increasing the total impact by about 6 percentage points.
Hence, one may conclude that the within effect is either negative but relatively small or zero.
64
Or -6.82 per cent instead of -5.90 per cent (or -1.36 per cent per year instead of -1.18 per cent per year) based on
the CPA data.

32

competition induced technologically-advanced firms to enter the market) but its magnitude ispotentially small.65 A back-of-the-envelope calculation shows that the entry effect adds about 2.5
percentage points to the total magnitude of the effect of increasing Chinese import competition(i.e. 18.1 per cent instead of 15.6 per cent). A key implication is that the total effect reported inTable 6 could be an underestimate.
Our estimates imply that the reallocation of resources appears to be the main channel
through which rising import competition raised the overall productivity performance in Canadianmanufacturing. The within effect is negative but potentially small since the effect was
pronounced in small firms which tend to be less productive in the initial period.
Our results are consistent with other empirical studies on assessing the impact of trade ortrade liberalization on the aggregate productivity performance in the domestic economy in thatthe productivity gain from the reallocation effect (i.e. between effect) is larger than the within
effect. For example, Pavcnik (2002) estimates the effect of trade liberalization on productivity at
the plant-level in Chile and finds that the between-effect accounts for 12.7 per cent of the total
increase in the aggregate productivity while the within-effect accounts for 6.6 per cent. However,
our finding that the within-effect is negative (or potentially zero – see footnote 63) is notconsistent with Pavcnik (2002) and some other empirical studies. For example, Bloom et al.
(2016) finds a positive within-effect induced by increasing Chinese import competition in
European countries and its magnitude is as large as their estimated (positive) between-effect.
However, de Loecker (2011) finds that the productivity gains within firms from tradeliberalization (as measured by the abolishment of quota protection for the Belgian textile
manufacturers) is positive but the effect is relatively small or sometimes negligible. He finds that
trade liberalization led to productivity gains mostly through inducing less productive firms toexit the market.

VIII. Conclusion
Utilizing as a natural experiment the rapid increase Chinese import share in the total
domestic absorption in Canadian manufacturing, we find that rising Chinese import competition
led to declines in R&D expenditure and TFP within firms. Especially, the declines in R&D and
TFP were pronounced in smaller, less profitable, and less productive firms. We find evidence
that the profit margins of these smaller and poorly-performing firms declined due to rising
import competition. Since firms tend to finance innovation using internal cash flows rather thancostly external financing, a larger reduction in R&D and TFP for these firms can be explained by
the shrinking room to finance R&D and other innovations. Or in a different perspective, thesefirms already operated in highly competitive product markets and an increase in competition may
have reduced more the post-innovation rents than the pre-innovation rents (i.e. the basic
65

To do so, first notice that, according to Table 6, the ratio of the total within and between-effect to the betweeneffect is 0.565 and our estimated firm-level coefficient is -0.137, which presumably represents only the within-effect.
Hence, the implied coefficient representing the sum of the within- and between-effect is about 0.297 which can becompared against our estimated coefficient at the industry level (0.359) representing the within-, between-, andentry-effect. One can carry out a similar back-of-the-envelope calculation to infer the implied share of the entry
effect discussed in the following sentence in the main text. These calculations are rough but show a key implicationof our industry-level estimate on the entry effect.

33

Schumpeterian effect dominates for these firms). It also appears that these firms may not haveresorted to additional productivity-enhancing activities other than R&D (e.g. better management
practices or inventory controls) to remain competitive in the market, resulting in lower TFP
growth. We find evidence that, in response to rising import competition, some larger firms didengage in productivity-enhancing activities other than R&D to remain competitive in the market
and hence, their TFP improved.
We estimate that rising Chinese import competition accounts for about 7 per cent of thetotal decline in the real R&D expenditure in Canadian manufacturing between 2005 and 2010.
Rising import competition reallocated employment towards more productive firms and drove
less productive firms out of the domestic market. We find that the reallocation of resources plays
an important role. We estimate that, had there been no increase in Chinese import competition
between 2005 and 2010, the actual decline in the TFP level in Canadian manufacturing would
have been 16 per cent greater for this period. In other words, the aggregate TFP level inmanufacturing would have declined by 1.26 per cent per year instead of 1.09 per cent per year.
This implies that the positive between- and exit-effects more than offset the negative withineffects on TFP. The negative within-effect is potentially small as the effect was pronounced
mostly in smaller firms which tend to be less productive in the initial period.
Our findings imply rising Chinese import competition had evident distributional effects
on firms’ innovation and their outcomes (i.e. TFP, employment, survival). Given these effects, afurther discussion on appropriate policy responses to trade liberalization would be desirable.

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39

Appendix I: Constructing Firm-Level R&D Capital StockIn our analysis, we need estimates for R&D capital stock at the firm-level (see equation 4
and 5 in section IV). To construct a time series for R&D capital stock for each firm, we first needto estimate their initial R&D capital stock (i.e. R&D capital stock in 2000). This is a difficult
task since the perpetual inventory method which is the usual approach in the literature is not best
suited to firm-level data. Therefore, researchers need to make a series of adjustments in themethodology to avoid undesirable properties. For example, it may result in negative capital stock
if firm-level equilibrium growth rates are estimated to be negative and larger in the absolutevalue than the assumed depreciation rate. If their absolute values are smaller but very close to thedepreciation rate, then the R&D stock would be extremely large.
To estimate the initial R&D stock, we start with equation (1A)66:

where andare the equilibrium level and growth rate of R&D investment for firm .
depreciation rate for R&D stock – we assume 15 per cent for all firms.

is theTo better estimate firm-level R&D stock, we follow the algorithm developed by Kim andLester (2019). They make a series of adjustments to the usual perpetual inventory method toprevent negative and implausibly large values for R&D capital stock.67
First, we use a modified version of equation (1A) for firms born between 1985 and 1999
since they would have a shorter period of R&D investment than firms born before 1984 – T2LEAP covers incorporated firms born in 1984 or after.68 Equation (1A) is appropriate for firmsthat perform R&D consistently and have existed for long enough so that their earlier investment
is fully depreciated by 2000.69 We also make adjustments for intermittent R&D performers andfirms that stop performing R&D before 2012. The average annual growth rates for these firms
are more likely to take a negative or a positive but extreme value, compared to continuous
performers, which would lead their initial R&D stock to take a negative or a very large positive
value (see equation 1A).70
66

Equation 1A is derived based on the assumption that the growth rate of the R&D capital stock can beapproximated by the growth rate of R&D investment.
67
If we apply the standard perpetual inventory method to our data without any adjustment, we are left with a nonnegligible number of observations with negative or implausibly large values for R&D capital stock.
68
We use a standard formula for the future values of a time series growing at a constant rate ( ) for periods. Thatis,69

For example, the value of R&D performed in 1984 would have been depreciated by roughly 93 per cent by 2000
with our assumed depreciation rate.
70
For non-continuous R&D performers, we calculate the average annual growth rate excluding increases in R&Dfrom zero and/or decreases to zero. For all firms, we set a floor for the average annual growth rate of -8.3%, whichis the 2000-12 average annual growth rate based on the change in R&D investment from 2000 to 2012 (i.e. 100%/12) for a firm that performs R&D in 2000 but not in 2012.
the 2000-12 average annual growth rate based on the change in R&D investment from 2000 to 2012 (i.e. -100%/12).

40

For , we use firm-level sample averages in R&D investment.
is estimated based on
the James-Stein estimator (JSE) which, in theory, results in lower mean squared errors compared
to MLE (i.e. using sample averages). The estimator relies on “shrinking” the mean values ofgrowth to a point chosen based on prior notions of the true equilibrium growth rate.71
For, we implement JSE based on the following equation:
(2A)

where
represents our estimate of the equilibrium growth rate in R&D investment for firm ;is the shrinkage point; is the average annual growth rate from 2000 to 2012 for firm ; andis the shrinkage factor72:(3A)
where n is the number of firms and.

is the standard deviation in annual growth rates for firm :

(4A)
where

is the number of years firm has a non-missing annual growth rate in R&D investment.

Equation 3A and 4A imply that would differ across firms, varying inversely with thestandard deviation of annual firm-level growth rates. A large standard deviation indicates a highdegree of uncertainty in our measurement. Therefore, we attribute any large measurement more
to random deviations than to a genuinely large equilibrium rate.73
We tried various shrinkage points and evaluated their performance by comparing theresulting estimate of the aggregate R&D stock in 2000 with that based on official surveys of
R&D spending at Statistics Canada (i.e. RDCI). This experiment led us to use as the shrinkage
point the average growth rate in 4-digit NAICS industry-level R&D investment.74 The estimateof the aggregate 2000 R&D stock was very close to our benchmark 2000 estimate based on the71

See Stein (1956), James and Stein (1961), and Efron and Morrison (1973, 1977).
Note that the shrinkage factor can take a negative value for some firms. Hence, we use the positive-part JamesStein estimator which is a JSE-dominating estimator (i.e. it performs better than the standard JSE based on somedecision rule such as mean squared errors) – see Baranchik (1964). This modification involves simply limiting theshrinkage factor to be non-negative.
73
See Efron and Morris (1977) for a similar treatment applied to their analysis on the distribution of the disease
toxoplasmosis in El Salvador at the city-level.
74
It may not be correct to specify a single shrinkage point for all firms. It is possible that equilibrium rates areheterogeneous across industries. Therefore, we establish our prior notion that the equilibrium growth rate in R&D
investment can be related to the industry in which a given firm operates. Specifically, we define the shrinkage point
and the shrinkage factor for each firm using statistics defined based on the firm's 4-digit NAICS industry (i.e.
andwhere denotes 4-digit NAICS industry). We tried different NAICS levels but the qualitative results were the
same.
72

41

aggregate RDCI data.75 Using other shrinkage points such as the average of all firm-levelaverage growth rates following Efron and Morris (1977) resulted in the aggregate R&D stock in2000 far below the estimate based on the RDCI data. We also experimented with various
maximum likelihood methods (i.e. using sample means) but their performance wasunsatisfactory.
We refer the readers to Kim and Lester (2019) for more detailed description and analysisof the methodology.

75

Our benchmark 2000 R&D stock is adjusted for the fact that our estimate based on the T2-LEAP-SRED database
does not include R&D performed by firms that drop out prior to 2012.

42

Appendix II: Firm heterogeneity in the China shock effect on R&D
expenditure
In this section, we explore whether firms with certain characteristics face differential
effects of increasing Chinese import competition. First, we examine whether the effect differs by
the country of control. Specifically, we analyze whether domestically-controlled firms responddifferently than foreign-controlled firms. Second, in Canada, small (in terms of profit andtangible capital stock) and domestically-controlled firms receive enhanced SR&ED tax credits(i.e. higher subsidy rates). We examine whether different subsidy rates lead to different firmlevel responses to increasing Chinese import competition. Third, we study whether there is any
difference between start-ups (defined as firms that are less five years old) and established firms.
Table A1: R&D Equation, 2SLS, Manufacturing, 2000-2012∆IP x domestically controlled∆IP x foreign controlled∆IP x enhanced recipients1-1.084**(0.459)-1.808***(0.620)
-

2
-

3
-

-

-

∆IP x regular recipient-∆IP x start-up--0.987**(0.478)-1.982***(0.616)-∆IP x established firm--

112,821112,821

No. observations (firm x period)

3.353**(1.433)-1.528***(0.419)112,821No. firms
16,664
16,664
16,664
Note: Period fixed effects are included in all columns. Standard errors are in parenthesis. We include fixed effects
for size groups, foreign controlled, enhanced tax credit recipients, and start-ups.
*** < 0.01; ** <0 .05; * < 0.10.

In column 1 in Table A1, we interact ∆IP with the indicator variable for the foreign anddomestically-controlled firms. Our results indicate the effect of increasing Chinese importcompetition on the R&D investment growth is greater for foreign-controlled firms than fordomestically-controlled firms although we cannot reject the null hypothesis that they are thesame in magnitude. Since most of our observations are domestically controlled (95.4 per cent of
the total observations), the aggregate estimate (column 2 in Table 1) is close to that fordomestically controlled firms. However, we note that the large marginal impact on foreigncontrolled firms is potentially important in explaining the total decline in R&D expenditure in
manufacturing. Despite the small number of foreign-controlled observations in our sample (4.6
per cent of the total observations), on average over the 2000-2012 period, they account for 24.3
per cent of the total R&D expenditure in manufacturing.

43

In response to increasing foreign competition in the domestic market, firms may choose
to reduce their R&D performed domestically especially if their R&D interacts with the
production process (e.g. production design and process innovation).76 It may cost less forforeign-controlled firms to adjust their domestic R&D activity by relocating their research effort
abroad (if they operate production facilities in other countries) or make use of R&D performed
abroad if they own any research lab in countries other than Canada.77 Hence, we observe a larger
marginal adjustment in domestic R&D investment for foreign-controlled firms in response to anincrease in Chinese import competition.
Second, it would be interesting to examine whether R&D investment within firms
eligible for higher subsidy rates is differentially affected by rising Chinese import competition.
In Canada, small firms are eligible for enhanced credits for their R&D expenditure.78 For
example, combined federal and provincial tax-based subsidy rate for the enhanced credit
recipients was higher than that for regular recipients by roughly 19 percentage points on averageover the 2010-2012 period (see Kim and Lester, 2019). A high subsidy rate (hence, a lower
required rate of return on R&D) for smaller firms may “shield” them from rising Chinesecompetition, alleviating the negative effect on innovation. If so, such innovation policy targetingsmall firms would be complementary to trade liberalization since we find in the main text that
only small firms scaled back their R&D in response to rising import competition.
According to column 2, both the enhanced and regular recipients scale back their R&D
expenditure in response to an increase in the Chinese import competition in Canada. However,
the marginal response of the regular recipients is larger than that for the enhanced credit
recipients. Although we cannot reject the null hypothesis that the two coefficient are the samewith a p-value of 0.202, this implies that a higher subsidy rate may play a role in alleviating thenegative impact of Chinese imports on R&D expenditure.
The response of start-ups is potentially important for technical change in the overall
economy as there is some empirical evidence that R&D performed by start-ups tends to generatemuch greater technology spillovers to the rest of the economy than that by established firms.79
Hence, the response of start-ups is potentially important in assessing the overall effect of Chinese
imports on the aggregate technical change in Canadian manufacturing.
In column 3, we report the results from estimating the R&D equation allowing separate
coefficients on ∆IP for start-ups and established firms. We find evidence that start-ups increased
their expenditure on R&D in response to an increase in the Chinese import competition. Startups are likely to be in the product development stage. So, innovation tends to be the main
activity for start-ups especially venture-capital-funded ones. Rising imports from China may
mean expanding future opportunities for them in the product market rather than competition. Or
76

See for example, Pisano and Shih, 2012, Fuchs and Kirchain (2010), and Branstetter, Chen, Glennon, Yang, andZolas (2017).
77
Note that our data include only domestically performed R&D.
78
Eligibility for the enhanced credit depends on the amount of R&D expenditure, profits and assets. The eligibility
criteria changed over time. For example, in 2012, the maximum amount of R&D expenditure eligible for theenhanced credit was $3 million CAD, which reduced to zero as taxable income increased from $500,000 to$800,000 and as assets increased from $10 million to $50 million.
79
See, for example, Kortum and Lerner (2000) and Schnizer and Watzinger (2014).

44

rising Chinese competition in within 4-digit NAICS industries could be correlated withexpanding opportunities in the domestic market for start-ups and new entrants from abroad.
Hence, the incentives for start-ups to complete their product development and/or to improve theirproduct development may have increased, leading them to scale up their R&D expenditure. Also,
they could have learned from competitive Chinese manufacturers entering the domestic market
about new production processes or new materials. This may have spurred additional R&D
complementary to their existing R&D.

45

Appendix III: Estimating TFP at the Firm Level
We first assume output is a function of inputs employed and the firm's productivity. Then,
we measure TFP for this firm as the residuals from the functional relationship between output
and input. In this paper, we adopt a Cobb-Douglas production function to define such
relationship. The exact functional form depends on the estimation methodology we employ. The
baseline form is given as follows:
(5A)
where
is output measured as real value added for firm at time .
andare real tangible
capital stock and labour input (measured as average labour unit) employed by firm at time .
is the Hicksian neutral efficiency level of firm at time , which is unobservable to theeconometrician.
Taking natural logs leads to a linear production function:(6A)
where lower-case letters represent natural logarithms and we define.

We assume thatcan be decomposed into the mean efficiency level across firms ( )and firm- and time-specific deviation from the mean observable to firms ( ) and unobservableto firms ( ). We denote the log of firm-level productivity (or TFP) as.
Following Olley and Pakes (1996), we identify the productivity term byassuming thatisa state variable that affects firms' production decision.
is an i.i.d. component, reflecting
unpredictable deviations from the mean due to external factors (e.g. unexpected delays in thedelivery of intermediates) or measurement error.
Hence, we estimate

as follows:(7A)
where we retrieve firm-level TFP in levels by exponentiating.

There are several empirical issues in estimating a firm-level production function. First,
and input choices are likely to be positively correlated (simultaneity bias), resulting in biased
input coefficients which would bias our TFP estimates.80 Also, in practice, we deflate nominal
values for output and input by industry-level price indices in the absence of firm-level priceinformation. However, if input choice is correlated with firm-level price variation in the presence
80

Endogeneity arises since firms could have prior knowledge ofwhen they make decisions on inputs. A positive(negative) shock to productivity would lead firms to increase (decrease) the amount of variable input employed –upward bias in the input coefficient. The direction of bias for fixed or quasi-fixed inputs (e.g. tangible capital) isdifficult to assess. Levinsohn and Petrin (2003) suggest that the coefficient for tangible capital is likely to be biased
downward if there is a positive correlation between labour and tangible capital.

46

of imperfect competition, then using industry-level price deflators will result in biased input
coefficients and hence, biased TFP estimates. Lastly, there is a number of firms that produce
more than one type of product (i.e. multi-product firms). Then, estimating a production function
at the firm-level would lead to biased TFP estimates since we are assuming identical production
technology and identical final demand (since we use the common price deflator for output)
across products of a given firm.
We cannot resolve all the issues summarized above due to the limitation on empiricalstrategies and on our data. Various estimation strategies have been developed and explored in theliterature (e.g. OLS, FE, GMM, semi-parametric approaches as suggested by Olley and Pakes,
1996 and Levinsohn and Petrin, 2003, and more recently developed methods that build on theexisting ones such as Wooldridge, 2009 and de Loecker, 2011). However, not all methodologies
are ideal in our empirical analysis due mostly to the nature of our firm-level data and some data
restrictions.
A semi-parametric approach suggested by Olley and Pakes (1996) requires us to have asufficiently large sample of firms with positive tangible investment. Moreover, it requires theassumption that firm-level investment decision responds completely to TFP shocks in a timely
manner. However, this may not be a valid assumption due to time to build, costly adjustment, or
if firms adjust investment only to a more permanent change in TFP. Also, investment may
respond to unobservable factors unrelated to productivity shocks making the assumption ofmonotonicity condition to break down.81 In our data, nearly half of the total observations have
either missing or zero tangible investment. Therefore, we choose not to pursue this approach.
Following Levinsohn and Petrin (2003), one may argue that using intermediate inputs as
a proxy could be a good alternative as data for investment are not readily available. However, theassumption that firms can costlessly adjust intermediate inputs immediately to new informationon their TFP may still not be defensible. Moreover, it requires intermediate inputs to be highly
correlated with TFP. In the T2-LEAP database, there is no separate information on intermediate
input, requiring us to compute it by subtracting wages and gross profit from sales (or sales minus
value added). However, as is the case in Leung, Meh, and Terajima (2008)82, we find that theintermediate input variable computed as sales minus value added is highly volatile. It may not becorrelated with TFP to the extent required to implement the Levinsohn-Petrin method. Also, due
to some data errors, a non-negligible number of observations in our data have a negative(sales<value added) or missing value (missing value for sales) for the intermediate input. Hence,
we do not pursue the Levinsohn-Petrin approach.
Collinearity between labour and non-parametric terms (investment in the O-P approachor intermediate inputs in the L-P approach) may cause the labour coefficient to be unidentified.
In other words, we need variations in labour input, which is independent of investment orintermediate inputs in order to identify the labour coefficient. A similar argument can be made81

Also, the assumption in the O-P approach that productivity dynamics is approximated by a first-order Markov
process may not be valid since productivity evolves over time responding to many observable and unobservablefactors. Moreover, Power (1998) suggests that productivity does not appear to be closely related to investment andthat fixed effects (i.e. firm heterogeneity) appear to play a larger role in explaining firms' productivity evolution.
82
The authors estimate firm-level TFP using the T2-LEAP database.

47

by proposing that if labour is also costly to adjust (so not likely to be a completely variable input),then labour becomes another state variable which depends on capital and productivity. So it
would no longer be identified in the semi-parametric estimation (see Bond and Söderbom, 2005).
This may be the case in reality due to the presence of costs associated with hiring, firing, andtraining. Bond and Söderbom (2005) suggest that if all inputs are costly to adjust then, theproduction function can be estimated consistently based on IV techniques while semi-parametricapproaches results in inconsistent estimates.
Our preferred estimator depends on how our data satisfy the assumptions underlying theestimation strategy. Thus, we rely on OLS and GMM to estimate TFP. We have all the necessary
information in our data to implement these two methods. To implement GMM, we rely on a
dynamic version of equation 5A and use all suitably lagged values to instrument for potentiallyendogenous variables (e.g. Arellano and Bond, 1991).83 Also, as pointed out in Blundell andBond (1998), in a first-differenced equation, we have weak instrument problems if the output andinput series are persistent over time within firms.84 Hence, we impose additional moment
conditions in levels (i.e. system-GMM).
We estimate a firm-level Cobb-Douglas production function by three-digit NACIS
industry in manufacturing.85 We found that the samples in roughly half of the three-digit NAICS
industries did not pass some important diagnostic tests (i.e. the over-identification test and theArellano and Bond test on serial correlation on error terms) or exhibit the "too-many-instrument"
problem as indicated by a close-to-unity p-value in the over-identification test.86 We also foundthat the common factor restriction is often rejected in these samples. However, in most of theindustry groups, the estimated output elasiticities with respect to labour and tangible capital stock
were in line with the findings in the literature and with our prior knowledge. Also, the differencein-Hansen test indicated that the additional moment conditions in levels were valid. However,
without a completely satisfactory specification for all the industry groups, we choose OLS as our
main estimator for firm-level TFP despite its weakness.

83

In the dynamic specification, we impose a common factor restriction and the associated coefficients on the factorsof input are computed by a Chamberlain-type minimum distance procedure (Chamberlain, 1984; Wooldridge, 2002).
If common factor restrictions are rejected, the long-run solution of the model are computed as nonlinear
combinations of the estimated coefficients. Then, we use the Delta method to compute the standard errors of thecalculated coefficients.
84
We find this is indeed the case in the T2-LEAP-SRED data. We estimated an AR (1) on value added, labour, andtangible capital stock using OLS, FE, first-differenced GMM, and system-GMM. We found that all the variables
exhibit substantial persistence regardless of the estimation strategy as indicated by a statistically significant
coefficient on the lagged dependent variable, which is close to unity. OLS estimates tend to be the largest (closest tounity) due potentially to simultaneity bias. On the other hand, FE estimates tend to be the smallest due to dynamic
panel bias (i.e. Nickell bias –see Nickell, 1981). We find that system-GMM estimates are in between the two,
indicating that our GMM strategy potentially mitigates the bias to some extent.
85
We estimate an unweighted regression equation (giving the same weight to all observations) since we would liketo focus on firm-level differences in TFP. A small number of large firms tend to dominate industry output. Hence, if
we weight the observations by, for example, their output shares, the estimates in the production function would be
more relevant for large firms in our data. This would be useful if we were interested in TFP at the industry level.
86
We tried collapsing the instrument as in Roodman (2009) or restricting the lag order of the instruments to ashorter window but the results were not satisfactory.

48

Appendix IV: Additional Tables
Table A2: Summary Statistics, R&D Sample, by Initial Employment Size, Manufacturing, 2000-2012
Employment

R&D
expenditure

Initialprofitability

InitialProductivity

∆IP

∆IPE

∆ln(TFP)∆Profitability

Mean

85

430

-0.009-0.051

0.049

0.077

0.073

-4.957

Std. Dev.

494

6,606

x

0.685

0.102

0.143

0.788

X

Mean

24

129

-0.016-0.068

0.050

0.080

0.057

-5.225

Std. Dev.

32

474

x

0.675

0.099

0.141

0.772

X

Mean

182

600

0.035

0.004

0.045

0.065

0.136

-3.520Std. Dev.

134

2,025

x

0.693

0.109

0.140

0.805

X

Mean

1,694

9,668

0.055

0.252

0.030

0.042

0.257

-2.440Std. Dev.

2,543

39,500

x

0.844

0.160

0.204

1.070

X

Total

Small

MediumLarge

Note: The number of observations is 116,683 (small: 100,894; medium-sized: 12,740; large: 3,049). ∆ denotes a 5-year difference.
All the initial values are the values observed in the initial year of a given sub-period. Employment is measured as the average labour
unit. We define the three size groups based on the average labour unit (ALU) observed in the initial period (e.g. ALU in 2000 for the
2000-2005 sub-period): small (ALU<100); medium (100≤ALU<500); and large (500≤). R&D expenditure is in thousand 2007
constant CAD. Productivity is measured as log of deviation from the industry average. Profitability is defined as net income or loss
before tax divided by sales. x indicates that the statistics is suppressed due to confidentiality requirements.

Table A3: Summary Statistics, TFP Sample, by Initial Employment Size, Manufacturing, 2000-2012
Employment

Initial profitability

Initial Productivity

∆IP

∆IPE

∆ln(TFP)∆Profitability

Mean

41

0.048

-0.010

0.051

0.073

0.057

-0.695

Std. Dev.

291

x

0.519

0.093

0.133

0.506

x

Mean

16

0.048

-0.067

0.051

0.074

0.052

-0.742

Std. Dev.

23

x

0.509

0.091

0.132

0.499

x

Mean

183

0.050

0.003

0.045

0.062

0.116

-0.017

Std. Dev.

127

x

0.598

0.102

0.130

0.576

x

Mean

1,578

0.074

0.249

0.029

0.041

0.155

0.013

Std. Dev.

2,366

x

0.765

0.169

0.207

0.672

x

Total

Small

MediumLarge

Note: The number of observations is 241,054 (small: 225,697; medium-sized: 12,886; large: 2,471). ∆ denotes a 5-year difference.
All the initial value is the value observed in the initial year of a given sub-period. Employment is measured as the average labour
unit. We define the three size groups based on the average labour unit (ALU) observed in the initial period (e.g. ALU in 2000 for the
2000-2005 sub-period): small (ALU<100); medium (100≤ALU<500); and large (500≤). Initial productivity is measured as log of
deviation from the industry average. Profitability is defined as net income or loss before tax divided by sales. x indicates that thestatistics is suppressed due to confidentiality requirements.

49

Table A4: R&D Equation, by Initial Condition, 2SLS, Manufacturing, 2000-2012
Initial profitability∆IP x below mean

1
-3.440***(0.745)0.094(0.456)0.520(0.886)-∆IP x above mean-

∆IP x initially low∆IP x initially mid∆IP x initially highNo. observations (firm x period)

Initial productivity
2--2.459***(0.734)-0.239(0.422)
102,523

102,5233-3.971***(1.078)0.559(0.538)-0.562(0.696)
113,5374-1.323***(0.450)0.044(0.768)113,537

No. firms
15,667
15,667
17,26017,260Note: Period fixed effects are included in all columns. Standard errors are in parenthesis. ∆ denotes a 5-year difference.
Profitability is defined as profit/revenue where we use net income or loss before tax as a proxy for profit. Productivity ismeasured as relative to the industry average. For column 1, we assign firms with zero or negative initial profitability to thelow group and firms in the top five per cent of the distribution for the initial profitability to the high group. The rest of thefirms are assigned to the mid group. For column 3, firms in the top 5 per cent and the bottom 25 per cent of the
distribution for the initial TFP (relative to the industry average) are assigned to the high and low group, respectively. The
rest are in the mid group. We tried different cut-off values but the qualitative results did not change. *** < 0.01; **<0 .05; * < 0.10.

Table A5: TFP Equation, by Initial Condition, 2SLS, Manufacturing, 2000-2012∆IP x below mean
∆IP x above mean
No. observations (firm x period)

Initial profitability-0.140***(0.023)0.364(1.048)
238,619

Initial productivity-0.135***(0.023)-0.090(0.091)
241,054

No. firms
43,02443,331
Note: Period fixed effects are included in all columns. Standard errors are in parenthesis. ∆ denotes a
5-year difference. Profitability is defined as profit/revenue where we use net income or loss before
tax as a proxy for profit. Productivity is measured as relative to the industry average. Productivity ismeasured as relative to the industry average. *** < 0.01; ** <0 .05; * < 0.10.

50