October 2022

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CENTRE FOR
THE STUDY
OF LIVING
STANDARDS

Firm-level Productivity Research in
Canada: Current State and Future
Directions

Andrew Sharpe
CSLS Research Report 2022-06
October 2022
Ottawa, CanadaFirm-level Productivity Research in Canada: Current Stateand Future Directions
Table of Contents
I. Executive Summary ............................................................................................................................. 1
I. Introduction ......................................................................................................................................... 3
1. Background and Motivation ............................................................................................................. 3
2. General Objectives of the Project ..................................................................................................... 4
3. Structure of the Report ..................................................................................................................... 4
II. Firm-level Data Availability in Canada and Internationally ................................................................. 5
1. Centre for Data Development and Economic Research at Statistics Canada ...................................... 5
2. Center for Economic Studies at U.S. Census Bureau ........................................................................ 6
3. OECD.............................................................................................................................................. 8
4. Eurostat ......................................................................................................................................... 11
5. Competitiveness Research Network ............................................................................................... 12
III. Statistics Canada Industry-level Productivity Data ........................................................................... 13
IV. Statistics Canada Firm-level Data on Business Dynamics ................................................................ 14
V. Disadvantages of Firm-level Data for Productivity Analysis .............................................................. 16
1. Access Issues ................................................................................................................................. 18
2. Data Issues .................................................................................................................................... 19
VI. Advantages of Firm-level Data for Productivity Analysis ................................................................. 22
1. Firm Dynamics .............................................................................................................................. 22
2. Productivity Decomposition ........................................................................................................... 23
3. Dispersion of Productivity ............................................................................................................. 23
4. CSLS Research on Firm-level Productivity Decomposition and Dispersion .................................... 25
5. Productivity Research Questions that Firm-level Data Can Elucidate.............................................. 24
VII. An Agenda for Firm-level Productivity Research in Canada ........................................................... 26
1. Canada's Productivity Performance ................................................................................................ 27
2. The Role of Frontier Firms in the Slowdown .................................................................................. 28
3. Changes in the Pace of Technical Change ...................................................................................... 30
4. Falling Business R&D ................................................................................................................... 30
5. Secular Stagnation ......................................................................................................................... 30
VIII. Conclusion.................................................................................................................................... 31
References............................................................................................................................................. 32
Appendix 1............................................................................................................................................ 39
Appendix 2............................................................................................................................................ 43IIFirm-level Productivity Research in Canada: Current Stateand Future Directions
Executive Summary
A key activity of the Productivity Partnership is to promote research using the micro-level
databases developed by Statistics Canada. The evidentiary base from applying micro-level data
may be used in direct analyses of productivity. The Centre for the Study of Living Standards iscontracted by ISED to produce a report outlining an agenda for firm-level productivity researchin Canada. This report has been developed in response to this request.
First, the report reviews the opportunities and challenges in the use of firm-level data for the
analysis of productivity trends and determinants. The difficulties of using firm-level data can beseparated into two categories: access issues and data issues. To access, at least one person
involved in the application must be a permanent resident, must pay a fee (generally around
10,000), must go through security clearance (may take up to 3 months). In addition, there is amajor learning curve in understanding the nature of the data and data preparation, including
cleaning and treatment of outliers, can be labor-intensive. The data issues include but not limited
to lack of firm-level data on human capital, long lags in data availability, changes in the firmlandscape through mergers and acquisitions, lack of firm-level deflators and lack of controls forcapacity utilization. However, there are efforts by Statistics Canada to reduce the significance ofdata issues and to make firm-level data more accessible.
The report identifies three areas of productivity research where firm-level data are needed: firm
dynamics and productivity growth: decomposition of productivity growth into firm-specific andreallocation effects, dispersion of firm productivity. The concepts related to dynamics,
decomposition, and dispersion can overlap considerably so clear-cut differentiation of research
into these three areas is not always possible. CSLS research project on estimating productivity
decompositions and dispersion for firms in Newfoundland and Labrador is included as an
example.
As the firm-level data is difficult to work with for the reasons mentioned above, industry level
data can be a cheaper and less cumbersome alternative to understand productivity. Although theanalysis on the industry level is not able to exploit heterogeneity in firm-level characteristics,
Statistics Canada provides an excellent industry-level data that can be used at no cost in the
productivity research.
Second, the report identifies the research questions related to productivity that firm-level data
can shed light on. These questions include 1) contributions to overall productivity growth in theindustry by firm size group; 2) contributions to overall productivity growth in the industry byfirm age group; 3) the contributions to overall productivity growth in the industry by firm
dynamism (gazelles (fast growing firms) versus lifestyle firms): 4) explanations for firm
heterogeneity in productivity dispersion and its persistence and policies to improve overall1performance; 5) contribution of market structure, prices, product quality and variable mark-ups
to measured firm-level productivity performance; 6) the link between firms that perform R&D or
patent and productivity
Third, the report reviews firm-level data availability in Canada and internationally. In Canada,
The Centre for Data Development and Economic Research (CDER) at Statistics Canada provides
access to various micro-level data that includes: 1) Canadian Employer-Employee Dynamics
Database; 2) Capital and Investment Program; 3) Longitudinal Employment Analysis Program(LEAP); 4) T2-LEAP; 5) National Accounts Longitudinal Microdata File; 6) Survey of
Financing of Small and Medium Enterprises; 7) Survey of Innovation and Business Strategies;
and 8) Workplace Employee Survey. Among them, T2-LEAP is most commonly used, and it isconstructed by linking corporate tax file (T2) to LEAP database. In addition, Statistics Canada
provides firm-level data on business dynamics that includes entry, exit rates for 17 two-digit
industries for Canada and for all ten provinces. Internationally, micro-level data can be found at:
1) Centre for Economic Studies at U.S. Census Bureau; 2) MultiProd; 3) OECD-Orbis Database;4) MMD at Eurostat; 5) CompNet at European Central Bank
Fourth, the report reviews the literature on productivity research based on firm-level data in
Canada and internationally. The CSLS has organized the productivity related studies into eightthemes: global value chains, trade, offshoring, the post-2000 productivity slowdown, firm size,
sector-specific studies, ICT and innovation, and multinationals. The size of the term has been
positively associated with firm size (Baldwin, Rispoli, Leung (2014)), therefore a shift in
distribution of employment over firm size groups can explain aggregate productivity. As eachsector has different characteristics, their productivity also evolves differently over time. For
example, Baldwin, Leung, Rispoli (2011) find that unincorporated sector has lower productivity
growth compared to the corporate sector in Canada. Factors such as offshoring, tariff reduction
and participation to global value chain can also increase firm productivity. There are different
explanations on why labour productivity and total productivity growth slowed in Canada after
2000. One explanation is from Gu (2018), and they find that roughly one quarter of the decline in
TFP in the Canadian business sector was due to an increase in the use of produced capitalrequired to extract natural resources in the oil and gas and mining sector and a decline in thecapital utilization in manufacturing. For ICT and innovation, the adoption of advanced
technologies lead to higher productivity growth as demonstrated by Baldwin and Sabourin (2001
and 2002) on manufacturing plants.
Finally, the report identifies four areas of productivity research where use of firm-level
data may have the potential to advance our understanding of the slowdown, namely 1) relative
performance of frontier firm; 2) changes in the pace of technical progress; 3) secular stagnationor changing demand conditions; 4) falling business sector R&D and investment.

2

Firm-level Productivity Research in Canada: Current Stateand Future Directions1
I. Introduction
Background and Motivation
In 2011, the Economic Research and Policy Analysis (ERPA) Branch of Industry Canada
produced for discussion purposes a document entitled “Research Frame: Excellence inMicroeconomic Policy and Business Performance.” The motivation for this document was
Canada’s poor productivity performance and the need to better understand it and to put forward
policies to improve the performance. The objective of the document was to develop a mediumterm research program aimed at better understanding productivity performance and determinants
ranging from economy-wide to firm-level studies. 2A key focus of the frame or plan was toextend productivity-related micro-econometric analysis of Canadian firms using a newly
expanded set of micro-level databases developed collaboratively with Statistics Canada.
Industry Canada indicated that it was looking to partner with academic colleagues inbuilding and interpreting this research program. This led to the development of the Productivity
Network, started in 2012 under the leadership of Mike Veall at McMaster University through a
SSHRC Partnership Development grant. The network was then successful in receiving
significant funding under the SSHRC Partnership Program.
A key activity of the Productivity Partnership is to promote research using the microlevel databases developed by Statistics Canada. It does this by providing assistance and financial
support to researchers including students.3 Almost all of the research projects have focused on
applying firm-level microdata and as a consequence that evidentiary base is much better
understood.

1

This report was written by CSLS Executive Director Andrew Sharpe, with contributions from CSLS economists
Atakan Bakiskan and Daniel Kim. The CSLS thanks ISED for financial support and Larry Shute, Mike Veall,
Danny Leung, and Jiang Beryl Li for comments. An earlier version was presented at the Productivity Partnership
Conference at the Bank of Canada on April 30, 2019, and at the CSLS-Statistics Canada Session “Firm-level
Perspectives on Canada’s Productivity Performance” at the annual meeting of the Canadian Economics Association.
2
The 2011 ERPA document identified five thematic areas for research: productivity and living standards; businessstrategy, growth ambitions and performance; business context; productivity performance and micro-economic policy
research; and micro-econometrics. Under each theme, the document listed a number of potential research directions.
From the perspective of this report on directions for firm-level productivity research, the most relevant research
directions in the document were in the micro-econometrics section, followed by the productivity performance andmicro-economic policy research.
3
A list of the projects funded is found at https://productivitypartnership.ca/research.

3

Government officials and members of the Productivity Partnership leadership team feelthat it is a good time to take stock of how that evidence may be used in further direct analyses of
productivity and seek ways to stimulate interest in such research. One suggestion has been theelaboration of an agenda for firm-level productivity research in Canada, and that the Centre for
the Study of Living Standards be contracted by ISED to produce a report outlining this agenda.
This report has been developed in response to this request.
General Objectives of the ProjectThis report on the current state of and directions for firm-level productivity research in
Canada has five general objectives.
1) To review the opportunities and challenges in the use of firm-level data for the
analysis of productivity trends and determinants.
2) To identify the research questions related to productivity that firm-level data can shedlight on.
3) To review firm-level data availability in Canada and internationally.
4) To review the literature on productivity research based on firm-level data in Canada
and internationally.
5) To put forward a research agenda based on firm-level data that can contribute to thebetter understanding of productivity developments in Canada and can point to public
policy measures and private section actions that can improve productivity
performance.
Structure of the Report
The report consists of seven main sections (excluding the introduction). The first section
provides an overview of the availability of firm-level data in Canada and internationally. The
second section briefly discusses the availability of very detailed industry-level productivity data
for Canada and the provinces, the first data stop for a productivity researcher. The third section
reviews the firm-level data on business dynamics that Statistics Canada already makes publicallyavailable. Again, productivity researchers should be aware of these data before attempting
projects using confidential firm-level data. The fourth part of the report discusses disadvantages
related to firm-level data, both in terms of difficulties of access and weaknesses in the data. The
fifth section examines the advantages of using firm-level data for three areas of productivity
research, namely business dynamics, decomposition of productivity growth into firm-specificand reallocation effects, and firm dispersion of productivity performance. This section also
include discussion of CSLS research in these areas and suggests specific research questions thatcan be addressed with firm-level data. The sixth section identifies the slower productivity growthsince 2000 as the key issue for applied or empirical productivity researchers in Canada andoutlines areas for research, namely frontier firms, the pace of technical progress, reasons for
4

falling business sector R&D and implications of secular stagnation. The seventh and final section
concludes.

II. Firm-level Data Availability in Canada and InternationallyThis section provides an overview of the availability of firm-level data in Canada throughthe Centre for Data Development and Economic Research (CDER) at Statistics Canada, andhighlights firm-level data availability in the United States through the Center for Economic
Studies at the Census Bureau and in three international organizations, the OECD, Eurostat, andthe Competitiveness Research Network.
Centre for Data Development and Economic Research (CDER) at Statistics Canada
In Canada, the Centre for Data Development and Economic Research (CDER) at
Statistics Canada provides access to various micro-level data for researchers undertaking
analytical studies. CDER develops and manages business micro-level data both at theestablishment- and the firm-level. The available establishment-level data at CDER are theAnnual Survey of Manufacturers (ASM) and the ASM-Import Registry linked database. The
databases available at CDER include eight separate firm-level databases namely: 1) Canadian
Employer-Employee Dynamics Database; 2) Capital and Investment Program; 3) Longitudinal
Employment Analysis Program (LEAP); 4) T2-LEAP; 5) National Accounts Longitudinal
Microdata File; 6) Survey of Financing of Small and Medium Enterprises; 7) Survey ofInnovation and Business Strategies; and 8) Workplace Employee Survey.
One of the most widely used database is T2-LEAP which is constructed by linking thecorporate tax file (T2) to the LEAP database. The database includes all incorporated firms in
Canada covering the 1984-2012.4The T2-LEAP database uses a statistical enterprise concept.
That is, a firm defined in the data includes all entities controlled by the same corporation. Hence,a firm may consist of more than one legal entity filing a tax return with the Canadian Revue
Agency (CRA).
T2-LEAP is a useful source of data as it contains almost all necessary information to
conduct productivity research at the firm-level. That is, the data contain firm-level informationon output (sales and value added), labour unit (average labour unit), and tangible capital stock.5
These are key variables needed to estimate firm-level labour productivity and total factor
productivity (TFP).6 Value added is constructed as the sum of labour income and capital income
based on the LEAP file and the General Index of Financial Information (GIFI), respectively.
Tangible capital stock is available from the GIFI balance sheet items reported by individual firms.
The average labour unit is developed based on the LEAP file. It is defined as the ratio of total

4

The database is soon expected to be augmented to cover the 2013-2015 period.
Tangible capital stock includes assets with a physical form such as buildings, land and machinery and equipment.
6
Industry-specific deflators are available in the data (at the 3-digit NAICS). They are based on the KLEMS database.
5

5

payroll to average annual earnings of an average worker in the firm’s 4-digit industry, provinceand firm size class.7
Apart from these variables, the T2-LEAP contains firm-level information such as R&D
expenditure8 and country of control. Also, each firm is assigned a 4-digit NAICS code in a given
year allowing researchers to do analysis by industry.9 Some additional information can be madeavailable if such information exist in T2 or LEAP (e.g., computer-related expenses which areavailable from Schedule 125 in T2). This allows researchers to explore heterogeneity inproductivity performance across firms focusing on different dimensions.10
Also, the T2-LEAP database is adjusted to eliminate spurious entries and exits due tomergers and acquisition and legal restructuring. For instance, when two firms are merged, then
the new firm is created and assumed to have existed from the organic birth of the oldest of thetwo firms. This eliminates some empirical issues often faced by researchers when they assess
between-effects in a productivity decomposition exercise, but at the cost of creating a time series
that reflects the corporate organizational structure of a benchmark year.
The database is suitable for various estimation strategies for total factor productivity(TFP). First, researchers often rely on semi-parametric approaches to estimate TFP to correct for
certain empirical issues (e.g., simultaneity bias). Most notable are the methodologies developed
by Olley and Pakes (1996) and Levinsohn and Petrin (2003). To account for simultaneity bias,
they rely on proxies such as tangible investment (Olley-Pakes) and intermediate inputs(Levinsohn-Petrin). In T2-LEAP, information on firm-level tangible investment is available fromT2 files.11 There is no separate information on intermediate inputs in the T2-LEAP database, but
one can compute it based on the available data (i.e., sales minus value added).12 With asufficiently long time series, the database appears to be suitable for Generalized Method ofMoments (GMM) estimation using lagged values as instruments of dynamic production
functions.
In order to obtain access to data at CDER, researchers must submit a research proposal
and cover all costs -- CDER operates on a cost-recovery basis. Once the proposal is approved,
researchers must go through the security screening process and take the Statistics Canada Oath of
office in Section 6(1) of the Statistics Act. All data are accessible at Statistics Canada's head
office in Ottawa, Ontario13.

7

The number of hours worked is not available in the database.
But only for the 2000-2012 period.
9
To be precise, there actually are more than one industry codes assigned to individual firms: the most dominant,second dominant and the third dominant industry -- dominant in terms of payroll (wages and salary). In most cases,
researchers rely on the most dominant industry to classify a given firm.
10
However, one issue in the T2-LEAP database is that it is difficult to allocate firm-level information (e.g. sales,
payroll, capital income) across provinces for firms that operate in more than one province. This becomes animportant issue when we conduct productivity analysis by province.
11
This is from line 203 in Schedule 8 in the CRA form T2.
12
However, the intermediate input retrieved this way may not be suitable for the Levinsohn and Petrin approach if it
is highly volatile, causing some key assumption made for the estimation to break down.
13
There were 2 pilot projects launched in 2018, making business microdata available in the Research Data
Centres. Additionally, there are efforts to have business microdata available in the VDL.
8

6

Centre for Economic Studies at U.S. Census Bureau
The Center for Economic Studies (CES) at the U.S. Census Bureau provides researcherswith rich establishment and firm-level micro-data. CES has a mandate to produce, maintain, andconduct research using longitudinal datasets. These datasets are constructed primarily based on
information from respondents to Census Bureau censuses and surveys, which have data on
individuals, households, and businesses. Traditionally, CES has focused on business data mostly
from the manufacturing sector. See the appendix for a detailed discussion of this data source.
OECD
Multifactor Productivity Project (MultiProd)
The Directorate for Science, Technology, and Innovation (STI) at the OECD has
developed a harmonized framework that provides non-confidential micro-aggregate statistics thatare comparable across countries.14 Such micro-aggregated statistics are constructed based on
existing official confidential data at the firm level, which are in turn based on official surveys
and administrative sources such as Business Registers. The whole process of collecting,processing, and constructing relevant productivity statistics is called the Multifactor Productivity(MultiProd) project. The resulting collection of micro-aggregated statistics is called theMultiProd database. See the appendix for a detailed discussion of this data source.
OECD-Orbis Database
There is another firm-level data source called the OECD-Orbis database. The database isdeveloped jointly by six OECD Directorates (started in 2008).15 The database contains several
productivity measures (variants of labour productivity and MFP) as well as other firm-level
information.16 The most recent version of the database covers 24 OECD countries over the 19972014 period for the non-farm and non-financial business sector (roughly 55 million firms). The
countries included are Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France,
Germany, Great Britain, Greece, Hungary, Ireland, Italy, Japan, Korea, Netherlands, Norway,
Poland, Portugal, Spain, Sweden, Slovenia, the Slovak Republic and the United States. Canada is
not included. See the appendix for a detailed discussion of this data source.

14

STI at OECD distributes the algorithm (DynEmp in Stata) that implements a distributed micro data analysis tostatistical agencies in the countries participating in the MultiProd project. The algorithm is run on confidential firmlevel data and the resulting aggregated statistics are compiled in the MultiProd database, implying that OECD has noaccess to confidential data in those countries. See Berlingieri et al. (2017b) for more detail.
15
They are: Centre for Entrepreneurship, SMEs and Local Development (CFE), Directorate for Employment,
Labour and Social Affairs (ELS), Environment Directorate (ENV), Statistics Directorate (STD), Directorate for
Science, technology and Industry (STI) and Directorate for Trade and Agriculture (TAD). Note that the database isaccessible to OECD researchers only.
16
We have in the database not only the variables that underlie the productivity measures such as sales, net income,the number of employees, tangible, and intangible fixed assets but also more detailed information such as current
and non-current liabilities, interest payment, and country of control (more than 200 variables). The OECD-Orbis
database are also linked with other OECD databases such as the PATSTAT database, which have information on
patent for individual firms.

7Eurostat
The only firm-level data available at Eurostat is the Micro-Moments Dataset (MMD).17
The MMD is a product of the Eurostat-funded projects. The main purpose of the database is toderive measures of the impact of ICT and innovation on business performance and productivity.
The MMD relates ICT and innovation variables to various indicators of economic performanceand characteristics of firms across industry groups and countries. Therefore, it is possible to linkthe data on ICT and innovation to other aggregate economic data on productivity such as EU
KLEMS.18 See the appendix for a detailed discussion of this data source.

Competitiveness Research Network
The Competitiveness Research Network (CompNet) is a research network founded by theEuropean System of Central Banks in 2012 to promote and inform the debate on competitivenessand productivity. Its activities involve not only research but also producing, updating, andmaintaining a firm-level database for a number of EU countries. The European Central Bank
(ECB) is one of the main contributors to the CompNet through not only research but also data
production. The ECB maintains the CompNet Competitiveness Dataset. The database contains
firm-level information covering 18 European countries19 and is released regularly based on its
latest vintage. The most recent version was released in 2018 (i.e., the 6th Vintage CompNet
database). The database is constructed based on information available from national central
banks and national statistical institutes. See the appendix for a detailed discussion of this data
source.
.

III. Statistics Canada Industry-Level Productivity Data
Statistics Canada now provides a wealth of detailed labour productivity estimates at theindustry level based on establishment data.20 Annual estimates for the 1997-2017 period areprovided for Canada and all 10 provinces for nominal output (value added), real output (value
added), deflators, hours worked, labour compensation, unit labour costs or number of jobs, andlabour productivity (real value added/hours worked) at the all business sector industries, goodproducing industries, service producing industries 17 industries at the two-digit NAICS level,
100 industries at the three digit NAICS level and 300 industries at the four-digit NAICS level.
Even some five-digit industries in mining and quarrying are included.
This is a massive database that provides a 20-year time series of key labour productivityvariables at a very detailed level both regionally and by industry (at the four-digit NAICS level
17

Refer to https://ec.europa.eu/eurostat/web/microdata for all other micro-data available at Eurostat.
The aggregate version of the MMD is publically available at https://ec.europa.eu/eurostat/web/digital-economyand-society/methodology.
19
They are Belgium, Croatia, Czech Republic, Denmark, Finland, France, Germany, Hungary, Italy, Lithuania,
Netherlands, Poland, Romania, Slovakia, Slovenia, Spain, and Sweden.
20
Statistics Canada. Table 36-10-0480-01 Labour productivity and related measures by business sector industry
and by non-commercial activity consistent with the industry accounts
18

8

for each variable there are around 88,000 industry-province cells (400 industries *11
jurisdictions (provinces and Canada) for each of 20 years of the time series). To illustrate thebreadth of the database, the table in Appendix 1 provides estimates of labour productivity (realvalue added per hour worked) for all available industries for Canada for 1997, 2007 and 2017and growth rates between periods.
A major advantage of this database is the complete absence of data gaps, no matter how
small the jurisdiction, or the industry in the jurisdiction or the number of firms operating in theindustry. There appear to be no confidentiality restrictions, even when there is only one firm or
establishment in an industry. Statistics Canada is able to provide these data as they are notreleasing the actual information reported to Statistics Canada by the establishment or firm.
Rather they are releasing data that has been manipulated and altered by Statistics Canada.
Let us give an example of the usefulness and insights that can be provided by theStatistics Canada industry productivity database. There is only one petroleum refinery in New
Brunswick (the largest in Canada) in Saint John owned and operated by Irving. It would be
impossible for outside researchers, even with access to firm-level data through CDER, to obtaindata on the productivity performance of the Irving refinery and the importance of the refinery for
the New Brunswick economy. Yet such information can be obtained in 10 minutes from the open
access Statistics Canada website.
Table 1-3 in Appendix 2 shows the output, employment and labour productivity in the
petroleum refining industry (NAICS 32411) in New Brunswick (the Irving refinery) over the
1997-2017 period. In 2017, real GDP expressed in 2012 chained dollars, was $630.8 million,
down from $796.6 million in 1997. Employment in the industry was 965 in 2017, up from only
240 in 1997. Labour productivity in the petroleum refinery industry in New Brunswick was$326.2 per hour (2012 chained dollars) in 2017, down massively from S1,565 per hour in 1997.
It is interesting to observe that the output of this industry, which consists of only one
refinery, accounted for 3.2 per cent of the real GDP in New Brunswick in 2017, down from 5.3
per cent in 1997 and 6.0 per cent of total real GDP of petroleum refining in Canada, down from
11.2 per cent in 1997. Employment on the NB petroleum refining industry accounted for 0.37
per of the provinces employment in 2017, up from only 0.10 per cent in 1997. These divergent
trends in output and employment shares had major implications for the relative labour
productivity of the New Brunswick petroleum refining industry. In 1997, the labour productivity
level in the industry in New Brunswick was 46 times the provincial average, By 2017, it was
only 7.7 times the average. Equally, in 1997 the relative labour productivity level of thepetroleum refining industry in New Brunswick was 189.0 per cent of the national average for theindustry, By 2017, it had fallen to only 47.5 per cent of the national average.
The point of this exercise is to illustrate that very granular productivity analysis can be
conducted using excellent data that Statistics Canada makes readily accessible at no cost to the
public. Such data sources should be full exploited before one embarking on research projects
aimed at developing productivity estimates from firm-level data at the detailed
industry/provincial level. Analysis at the industry level is however, constrained by not being able9

to exploit the heterogeneity that exists in firm-level characteristics and output and efficiency
performance.

IV. Statistics Canada Firm-level Data on Business Dynamics
Firm dynamics, defined as the turnover of firms in the economy through exit and entry,
can affect productivity in a number of ways. On the one hand, exiting firms may have low
productivity levels so their departure may boost average productivity levels and productivity
growth in an industry or in the economy as a whole through a reallocation effect. The entry of
new firms may be positive for productivity performance as such firms may be innovative andproductive, introducing new products, equipment, and production processes. A declining entry
rate may be an indication that the economy is lacking in economic dynamism.
Before looking at firm-level data issues related to firm dynamics, it is important to review
what information on firm dynamics is already available and easily accessible in Canada.
Statistics Canada (Table 33-10-0087-01) makes available data on the number of incumbent,
exiting, and entering firms for 17 two-digit industries for Canada and for all ten provinces and an
aggregate for the territories. Estimates are available for the 2000-2016 period.
Chart 1: Entry Rates and Exit Rates of Private Sector Employer Businesses, Canada, 20022016 (per cent)Panel A: Entry Rates
16
15

14
13
12
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 201610

Panel B: Exit Rates
13
12.5
12
11.5
11
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Panel C: Net Entry (Entry – Exit) Rates
4
3
2
1

0
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Note: The entry rate for a year is defined as the number of entrants as a per cent of the average of the number of active employer businesses in thecurrent year and the previous year. The exit rate for a year is defined as the number of exits as a per cent of the average of the number of active
employer businesses in the current year and the previous year. The net entry rate for a year is defined as the number of net entry (the number ofentrants – the number of exits) as a per cent of the average of the number of active employer businesses in the current year and the previous year.
Source: Statistics Canada Table 33-10-0164-01 (formerly CANSIM 527-0001).

Chart A1 in the Appendix provides the time series for the 2000-2016 period of the numberof firms (active employer business in the private sector economy) in Canada, broken down by
incumbents, new firms or entrants, and exiting or exitor firms. In 2016, there was 1,111 thousand
active employers in Canada, up from 907 thousand in 2001, a 22.5 per cent increase comparable
to the rate of growth of the working age population (20.2 per cent). The number of firms
consisted of 976 thousand incumbents and 135 thousand new entrants.
Chart 1 provides the exit and entry rates for firms in the private sector over the 20002016 period in Canada. One observes a significant decline in the entry rate, from around 15 percent in the 2004-2007 period to 12 per cent in 2016.21 Equally, the exit rate has also fallen,
although less, from around 11.5 per cent on average of 12.5 per cent in 2002-2009 to 11.5 per21

Macdonald (2014) shows that the fall in the entry rate pre-dates 2001. He finds that the entry rates was 25 per centin 1983/84. This means that 10 percentage points of the 13 percentage point fall in the entry rate between
1983/1984 and 2016 took place before 2001. From this perspective it is hard to explain the post-2000 slowdown in
productivity growth by an increased rate of decline in business dynamics.

11

cent in 2011-2015, with an uptick to 12 per cent in 2018. The net entry rate, defined as the entry
rate minus the exit rate, has fallen from around 3 per cent in 2004-2007 to around 1 per cent in2013-2015, before dropping to almost zero (0.2 per cent) in 2016.
Chart A2 in the Appendix provides estimates of the entry, exit, and net entry rates by
province for all private sector firms in 2016. If entry rates are indicative of business dynamism,
then the two most dynamic jurisdictions are the territories and Prince Edward Island, and theleast dynamic province is Quebec. It is unclear if this finding is consistent with other metrics ofbusiness dynamism. The highest exits rates were also in the territories, and Prince Edward Island(and Alberta) and the lowest exit rate was in Quebec. In terms of net entry Prince Edward Island
had the highest rate at 1.65 per cent while Alberta had the lowest (-2.86 per cent), followed by
the territories (-2.04 per cent).
Chart A3 in the Appendix provides entry, exit, and net entry rates for Canada for the 17
two-digit industries in 2016. One observes large differences in entry and exit rates acrossindustries. Transportation and warehousing had the highest entry rate at 13.2 per cent while
whole trade had the lowest at 5.7 per cent. Again, it is unclear if these inter-industry differencesin entry rates are consistent with other measures of business dynamism at the industry level.
Mining quarrying and oil and gas extraction had the highest exit rate at 17.4 per cent. This rate
implies that nearly one in six firms in this industry exited in 2016, which appears to be a highrate of turnover. Manufacturing had the lowest exit rate at 7.6 per cent.
Chart 2 provides a measure of firm dynamism or entrepreneurialism by province/territory
by dividing the number of firms by the working age population (15 and over). On this metric
Alberta in 2016 appears to be the most entrepreneurial province with 5.22 businesses for every
100 Albertans of working age and Quebec the least entrepreneurial with 3.16 business for 100
Quebeckers of working age. Between 2001 and 2016 the province that experienced the largest
increase in per capita businesses was Ontario (3.22 to 3.61) while the province experiencing thelargest fall was PEI (6.04 to 4.93). The national average edged up slightly from 3.60 in 2001 to
3.67 in 2016. This stability in the number of active employers at the national level on a percapita basis may suggest that there have been no major changes in business dynamism in thiscountry.
This brief review of trends in firm dynamics in Canada has had two purposes. First, tomake readers aware of the wealth of readily accessible data on firm entry and exit that Statistics
Canada already provides, admittedly in aggregated form. Second, to give readers a picture ofoverall trends in firm dynamics in Canada to both inform and motivate further research in the
area.

12

Chart 2: Number of Active Employer Businesses in the Private Sector as a Per Cent of Working Age Population (15 andOver), Canada and the Provinces and Territories, 2001 and 2016

7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00

6.04
5.21
5.22
5.00
4.93 4.64
4.24
4.05
4.48
4.34 4.17 3.95

2001

3.67 3.61 3.60
4.07
3.52
3.84
3.60 3.22
3.55
3.50
3.34
3.16

2016

Source: Statistics Canada Table 33-10-0164-01 (formerly CANSIM 527-0001) and Table 33-10-0087-01 (formerly CANSIM 527-0007).

V. Review of Firm-Level Productivity Research in Canada
Statistics Canada have been producing firm-level data in Canada for several decades, so a
significant body of research has been conducted. The vast majority of this research however has
been done at Statistics Canada because of the confidentiality issues. With the creation of CDER
and the establishment of the Productivity Partnership, this situation is changing and more
researchers from outside Statistics Canada are accessing firm-level data.
At the request of the Productivity Partnership, CDER has provided a list of over 800
studies that the staff of the Economic Analysis Division (which included CDER) of Statistic
Canada has conducted in the last 50 years. Out of this list the CSLS has identified around 100
studies that use firm-level data. All firm-level studies identify in the CDER list are included in
the references to this report.
The CSLS has organized the productivity related studies into eight themes: global value
chains, trade, offshoring, the post-2000 productivity slowdown, firm size, sector-specific studies,
ICT and innovation, and multinationals. This section highlights key finding from what areconsidered the most important studies.
Global Value Chains
Participating in a global value chain can enhance productivity performance as it leads to afiner division of labour and specialization across countries. Using establishment-level data for
Canadian manufacturing (i.e., the Annual Survey of Manufacturers with the Import Register)Baldwin (2014) finds that becoming part of a global value chain enhances firms' productivity in
Canadian manufacturing but the magnitude and timing of the enhancements vary by industry,
internationalization process, and import-source/export-destination country.

13

TradeEmpirical research in productivity has focused on the role of trade liberalization andexport market conditions on the productivity performance of manufacturing firms in Canada. For
example, using establishment-level data, Baldwin (2012) finds that a tariff reduction leads to anincrease in the probability that more efficient plants enter export markets and the productivity ofentrants and those remained in export markets improved and was higher than the comparable
plants that did not enter or exit export markets. However, macroeconomic events such as
currency appreciation offsets the productivity growth advantages associated with the entrance toexport markets.
Offshoring
Offshoring can also lead to better productivity performance of firms. For example,
Couture, Sydor, and Tang (2015) find that offshoring firms in Canadian manufacturing tend tohave higher productivity and that the gain in productivity is positively related to offshoring
intensity (the share of inputs that are imported). They also find that offshoring facilitates
resource reallocation within industries, positively affecting the aggregate productivity
performance.
Post-2000 Productivity Slowdown
Both labour productivity and total factor productivity growth slowed in Canada after
2000. To explain this slowdown, Gu (2018) examines the role of intangible capital, natural
capital, public infrastructure, and capacity utilization. The author finds that roughly one quarterof the decline in TFP in the Canadian business sector was due to an increase in the use of
produced capital required to extract natural resources in the oil and gas and mining sector and a
decline in the capital utilization in manufacturing. However, the slowdown is not associated withintangible capital and public infrastructure.
Another line of research is to examine the slowdown in productivity growth inmanufacturing post 2000 in relation to the depreciating Canadian dollars and the slowdown inexport growth in this period. Baldwin, Gu, and Yan (2013) find that most of the slowdown inlabour productivity growth in manufacturing post 2000 can be explained by exporters.
Firm size
Researchers are interested in whether there is any relationship between firm size andproductivity performance and if so, whether a shift in the distribution of employment or hours
worked over firm size groups can explain the aggregate productivity performance or whether
differences in such distribution among countries can explain cross-country gaps in productivity.
For example, Baldwin, Rispoli, and Leung (2014) find that there is a positive relationshipbetween firm size and productivity performance both in Canada and the United States and thatthe decreasing relative importance of small firms and increasing small firm's productivity
compared to large firms can account for most of the gap in productivity levels between Canada
14and the United States in the early 2000s. Baldwin, Leung, and Rispoli (2013) also find that apositive relationship between firm size and labour productivity levels but increasing theemployment share of large firms have a limited impact on the aggregate labour productivity level.
Sector-specific studies
Many firm-level studies have focused on a specific industry or on a specific businesssector of the economy (e.g., unincorporated sector). For example, Baldwin (2008) examines firm
turnover and productivity growth in the Canadian retail trade sector as there is considerably more
firm turnover in the sector compared to others. He finds that, unlike manufacturing, all of theaggregate productivity in the retail trade sector is driven by firm turnover and the reallocation of
resources from less to more productive firms.
Chan, Gu, and Tang (2011) examine the labour productivity slowdown in the Canadianelectronic and electrical product manufacturing following the bursting of the tech bubble in 2000.
They find that the decline in labour productivity growth within continuing plants is a primaryfactor behind the decline while the reallocation effect is relatively small.
Gu and Lafrance (2014) study the dynamics of the rapid labour productivity growth in the
Canadian broadcasting and telecommunications industry since the mid-1980s and find that the
productivity growth within firms driven by technical change and scale economies was the main
contributor to the aggregate productivity growth for the industry.
Gu (2014) constructs a direct measure of output for the hospital sector in Canada basedon the number of inpatient and outpatient cases and their cost shares. Based on this measure, theauthor estimates the labour productivity of the sector to have increased 2.6 per cent per year overthe 2002-2010 period.
Baldwin, Leung, and Rispoli (2011) examine the difference in the labour productivity
growth between unincorporated and corporate businesses. They find that unincorporated sector
has lower productivity growth compared to the corporate sector in Canada andincorporated/corporate sector in the United States. This accounted for the entire productivity gap
between Canada and the United States in 1998 but its role declined over time.

ICT and innovation
The adoption of advanced technologies or innovative efforts leads to higher productivity
growth. For example, Baldwin and Sabourin (2001 and 2002) find that manufacturing plants that
adopted advanced technologies such as information and communications technology has muchgreater productivity growth throughout the following decade. Also, Baldwin, Gu, and Macdonald(2012) develop a measure of intangible investment (e.g., software and R&D) and find that
including in the National Accounts as investment leads to 0.2 percentage-point increase in thelabour productivity.

15

Multinationals and Foreign Controls
Studies on multinationals in Canada using microdata have focused on the manufacturingsector. The main theme of these papers is the restructuring of the foreign ownership of theCanadian manufacturing sector. For example, Baldwin and Li (2017) find that almost all threedigit NAICS sub-sectors of the manufacturing sector experienced a decline in foreign control
after 2000. In addition, Gu and Li (2017) identify that foreign-controlled enterprises were more
important than domestically-controlled enterprises in the overall labour productivity growth from2001 to 2010, but their contributions declined after 2006 because of an increase in the exits oflarge and productive foreign-controlled firms. Other related findings include the higher
possibility of foreign plants being taken over than domestic plants (Baldwin and Wang, 2011)
and the role of foreign direct investment in increasing productivity of domestically controlledplants (Lileeva, 2009).

VI. Challenges in Working with Firm-level Data for Productivity Analysis
This section discusses the challenges researchers face in working with firm-level data for
productivity analysis in the Canadian context, focusing on both access and data issues
CSLS staff have been working with CDER officials for the last year and one half on threefirm-level research projects.22 Through this work we have obtained first-hand experience in bothaccessing the data including the procedures involved, and the characteristics of the data. Before
outlining some of the challenges involved, it is important to stress that Statistics Canada staff
have always been welcoming and cooperative, and most willing to explain protocols and provide
advice on data issues.

Access issues
Unlike aggregate and industry level productivity estimates which are readily accessible
on-line at no cost from Statistics Canada, even at the four-digit NAICS provincial level, firmlevel data are much more difficult and costly to access. A firm-level research project at CDER by
an outside researcher is a major undertaking with significant costs in time and money.
Researchers should be aware of these costs before proceeding. There are summarized below.
•

First, a project proposal must be submitted and approved by Statistics Canada. At
least one person involved in the application must be a Canadian citizen or
permanent resident, and no person involved in the application for on-site access tothe data can own shares in a Canadian corporation, a barrier for older researchers.

22

One project, supported by the Productivity Partnership, is on the private and external rate of return on R&D capitalby firm size. The second project, supported by ISED, is on the impact of increased trade with China on businesssector R&D in Canada. The third project, funded by Collaborative Applied Research in Economics (CARE) at
Memorial University, is on productivity trends in Newfoundland and Labrador.

16

•Second, a fee, generally around $10,000, for accessing the firm-level data for a
project must be paid.

•

Third, for access to some, but not all, datasets (the university-based Research
Data Centres have some micro-data sets, including T2-LEAP), researchers
generally must go on a regular basis to the CDER site at Statistics Canada, a costfor researchers outside Ottawa.

•

Fourth, a security clearance, which can take up to three months, is required andcan delay the start of a project.

•

Fifth, internal vetting of results by Statistics Canada officials must be factored
into timelines for the completion of a project.

•

Sixth, data preparation, including cleaning and treatment of outliers, can belabour-intensive.
Seventh, there are significant limitations on the firm level results that can be
publically reported. Researchers may not actually see the firm-level data for aparticular firm or even aggregated data for a number of firms, and may have very
limited in the results they can publically report.

••

Eighth, there is a major learning curve in understanding the nature of the data andthe implications of changes in definitions or benchmarks (changes in ownership
structures are less of a problem for establishment data which is not affected bychanges in ownership). For example, in one CSLS project the movement to amore recent benchmark year for firm ownership structures resulted in majorchanges in the results.

17

Figure 1. Aggregate Labour productivity in Canada,
1991 to 2015, 1991=100
170
150

130
110
90
70
50
1991

1994

1997

2000

2003

T2-LEAP

2006

2009

2012

2015

KLEMS

Source: Gu (2018b)
Figure 2. Annual average growth in aggregate labour
productivity in Canada (% per year)
3.50

3.00

2.88

2.96

2.50
2.00
1.50
0.78

1.00
0.50

0.18

0.00
1991-2000

2000-2014
T2-LEAP

KLEMS

Source: Gu (2018b)
Data Issues
Lack of consistency between aggregated firm-level productivity estimates and Canadian
Productivity Account estimates
As a general rule, monitoring and analysis of aggregate and industry level productivity
trends should not use aggregations of firm-level data. In principle, such aggregation should beconsistent with aggregations of the establishment data used to produce the national accounts andthe Canadian Productivity Accounts (KLEMS data). In practice, this is not always the case. For
example, Gu (2018) found that while firm-level data and national accounts data showed very
similar growth rates for aggregate labour productivity in the 1991-2000 period (2.88 per cent per
year for T2-LEAP and 2.96 per cent for KLEMS), in the 2000-2014 period the KLEMS-based
18

productivity growth rate was much higher at 0.78 per cent per year versus only 0.18 per cent for
productivity estimates based on T2-LEAP data (Figures 1 and 2). It is likely that the KLEMS
figure is more reliable. If the T2-LEAP numbers are correct, Canada’s productivity problem ismuch greater than realized. It is very unusual for a major developed economy that functions
relatively efficiently to have nearly zero labour productivity growth for 14 years.
Lack of firm-level data on human capital
The literature has identified quality of management as a key determinant of productivity
growth, as the work of Nicholas Bloom. There are different metrics for quality of management.
The most widely used is the formal education credentials (e. BA in business or MBA) of
managers. Unfortunately, firm-level data do not generally provide information on thecharacteristics of the managers of a firm (e.g., education, age, gender, Indigenous identity,
immigrants status, visible minority status). However, to address the issue of lack of firm-level
data on human capital, linkage of Census to CEEDD is being evaluated. An integrated firmhousehold survey such as the Workplace-Employee Survey (WES) can in principle provide suchdata, but the WES was discontinued in 2006. It may be possible to link other data sources whichcontain personal characteristics of senior managers with firm-level databases such as T2-LEAP,
but this is likely a substantial undertaking. The bottom line is that the lack of information on the
T2-LEAP on the characteristics of managers at the firm level is a major limitation to using firmlevel data for productivity analysis. It can be noted that both the Labour Force Survey and thecensus provide information on the educational attainment of senior managers at a detailed
industry level.
Long lags in data availability
The relevance of firm-level data is reduced by the long lags in making the data available
to researchers. For example, the T2-LEAP data file is currently officially only available to 2012.
This delay reflects the significant amount of work needed to make the data accessible toresearchers and relative priorities at Statistics Canada, given the competing demands forresources. The lack of timeliness reduces the impact that firm-level data can have on economic
policy debate.
Industry allocation of firm output
For accurate productivity measures at the industry level, it is important that inputs andoutput be correctly allocated to the appropriate industry of use and production. This is arelatively straightforward procedure for a plant or establishment as output for such a production
unit is generally confined to one industry. The problem is much more severe for firm-level data,
especially for firms that operate in different industries. Rules for the industry allocation for firmlevel totals such as profits must be developed. Often the dominant industry or dominantindustries are the only industries to which allocations of firm sales or revenues are made. Thismeans that output from non-dominant industries controlled by the firm is excluded. For thisreason, productivity analysis at the industry level based on firm data is less reliable than analysis
based on establishment data.

19

Provincial allocation of national totals
The T2-LEAP database is for Canada, T2-LEAP databases for the provinces andterritories have not been constructed but there is work in progress with the SNA. A key problem
in such construction is the provincial/territorial allocation of national totals. For firms that
operate in more than one province or territory, certain variables such as profits are only reported
at the national level. Since profits are part of value added, and value added is needed for
productivity calculation, a rule or formula must be developed for their provincial/territorial
allocation from the national total. CSLS researchers are currently working with Statistics Canada
on this issue in the context of a CSLS-CARE project on productivity in Newfoundland andLabrador.
Changes in the firm landscape through mergers and acquisitions
Because of mergers and acquisitions, the firm landscape in Canada is constantly evolving.
These changes have important implications for time series on specific firms. For example, if a
firm is taken over and no longer exists as a corporate entity, but continues to produce, its output
will be included in that of the takeover firm, creating a discontinuity for the time series for thatfirm. For that reason, a certain vintage or benchmark year for ownership structures is created andthat benchmark is then applied to all years in the series. In theory, a change in a benchmark year
should have no effect on totals. In practice, this is not always the case. Ownership changes
appear to be less a problem for establishments. If plants or establishments continue to operate
when bought out, they report for the same entity so there is no discontinuity.
Non-comprehensive nature of firm-level data
A problem with firm-level data sets is that are not comprehensive in nature, covering subsets of the overall population. For example, the T2-LEAP data set includes for incorporated
business only, excluding unincorporated businesses. On the other hand, CEEDD contains nonemployers and unincorporated firms. This means that the employment and output totals found in
the T2-LEAP will be less than in the national accounts where all agents producing economic
output, including unincorporated businesses, are included. This can have implications for
productivity performance when average productivity levels differ between incorporated andunincorporated firms. This is a particularly important problem in industries whereunincorporated business are important. Productivity measures at the total economy and industry
level should be comprehensive. This means that national accounts-based productivity measures
are superior to productivity measures constructed from firm-level data from a noncomprehensive dataset.
Lack of Firm-level Deflators
Productivity growth rates (as opposed to productivity levels) at the firm level require realor constant price estimates of output. Such estimates are calculated from data on nominal gross
output or value added and output prices. Unfortunately, information on output prices are notnormally collected in firm surveys. This means that it is impossible to calculate real output at the20

firm level based on the prices received by that firm. The standard solution is to use industry-level
deflators. However, the consequences of this procedure are unclear and merit study.
Lack of Controls for Capacity Utilization
All data collected in firm surveys are affected by the stage of the business cycle during
which the survey is conducted. This reality means that there may be a cyclical component in avariable, making comparisons over time of the variable risky unless comparisons are made at thesame point n the business cycle. Various techniques have been developed to control for capacity
utilization and produce cyclically adjusted estimates of variables. Currently, however there is noconsensus on a satisfactory method for doing this.

VII. Advantages of Firm-level Data for Productivity Analysis
This section of the report will highlight the advantages to using firm-level data for
productivity analysis that can offset the costs highlighted in the previous section.23 The obvious
strength of using firm-level data is that since economic decisions are made at the firm level,
these data on the individual firm characteristics, on the inputs used in production as well as theinformation on firm output can led to a greater understanding of why these decisions are made,the outcomes of the decisions, and how public policies can influence these decisions.
Despite the limitations of firm-level data for productivity analysis outlined above, accessto firm-level data is needed for certain productivity research questions. Working at CDER may
be the only avenue for completing such research. This section identifies three areas ofproductivity research where firm-level data are needed: firm dynamics and productivity growth:decomposition of productivity growth into firm-specific and reallocation effects, dispersion offirm productivity. The concepts related to dynamics, decomposition, and dispersion can overlap
considerably so clear cut differentiation of research into these three areas is not always possible.
Many studies combine the areas of research. The section also outlines a CSLS research projecton estimating productivity decompositions and dispersion for firms in Newfoundland andLabrador.

23

Drummond, Ryan and Veall (2013:87) make the case that the use of firm-level data in productivity analysis bystating:“In particular, the availability and potential “linkability” of data sets including firm-level tax data and new
firm-level national accounts micro-data could permit analyses involving the relationships between a wide
variety of financial variables and variables gathered in other surveys. This would provide insight into the
dynamics of firm creation and destruction, the growth decision by firms (perhaps, e.g. affected by special
tax and regulatory provisions for small firms in Canada or access to different forms of capital), managerial
decisions on innovation and business strategies and much else.”

21

Firm dynamics
As already noted in this report, data on firm dynamics (exit, entrants and incumbent firms)
are publically available on a provincial /territorial basis for 17 two-digit NAICS industries. But
the characteristics of the firms in each of the three different categories of firms are not readily
available and need to be calculated from firm-level data. These characteristics include firm size,
sales/revenues, intermediate inputs, value added. payroll, profits, hours worked, labour
productivity, capital input, total factor productivity, foreign ownership, and innovation indicatorssuch as R&D spending. Researchers may be able to show, for example, that exiting firm havesignificantly lower productivity growth rates and levels than continuing firms and that the poor
productivity performance contributed to the exit.
Productivity Decomposition
The overall productivity growth rate in an industry, or at the level of the total economy, isa function of both the firm-specific productivity growth rates and changes in the relative size,
measured in terms of hours or value added, of that firm, known as reallocation effects.24 For
example, if the most productive firm in an industry expands rapidly, increasing its share of
output and employment in the industry, this firm can make a significant contribution to the
productivity growth in the industry even though its actual productivity growth rate may be zero.
There is a significant literature on methodologies to decompose productivity growth at
the industry level into firm-specific productivity contribution to the industry’s productivity
growth and reallocation effects. Firm-specific contributions to productivity growth can in turn be
broken down into contributions firm by productivity level quartile or decile of the firms,
including frontier firms, by size of firm (small, medium, large), by age of firm (e.g start-ups,
established firms), by firm growth rates (gazelles versus lifestyle firms). Different decomposition
methodologies give different results based on the underlying properties of the decomposition
formula.
In addition to firm decomposition of productivity growth, productivity levels can be
decomposed into relative contribution of specific firms and groups of firms based on theirrelative productivity level and importance. For example, a firm accounting for 2 per cent of
employment on the industry, but with a productivity level only one half the industry average
would only be responsible for 1 per cent of the industry level. Decompositions of productivity
levels do not have a reallocation effect as there is no changes in output and employment shares
over time.
Decomposition analysis can be very useful for pin pointing the sources of productivity
growth in a sector, whether through reallocation effects of output to high productivity, low costfirms or specific contributions from a particularly important high tech firm, (e,g. Amazon or
24

Analogously, the overall productivity growth rare in an economy is a function of both the industry-specificproductivity growth rates and the relative size, measured in terms of hour/employment or value added, of thatindustry. On the various formulas for decomposing aggregate productivity growth, see Sharpe (2010a) Sharpe(2010b), Tang and Wang (2004) and Reinsdorf (2015).

22

Apple) or from a particular growth of firms such a start-ups or gazelles. Such information canpotentially be used for the development of public policies aimed at boosting productivity.
Dispersion of productivity
It has long been well known that firms within an industry can vary greatly in their levelof productivity, and that these differences can persist over time, both for labour productivity andtotal factor productivity. Economists are often surprised by these productivity differences orheterogeneity in production, wondering how firms in the bottom part of the productivity
distribution can survive (Syverson, 2011). Firm-level data allows researchers to obtain
information on the characteristics of individual firms and groups of firms (quartiles or deciles) by
their productivity level. Firm characteristics available from micro-data may include size, age,
R&D spending, capital stock and investment, indicators of innovation such as intensity ofbroadband use, and exports. Analysis of the differences in characteristics between low
productivity and high productivity firms can lead to the identification of the reasons for the better
performance of the latter and this knowledge may be useful for the development of programs andpolicies to improve the performance of the former. For example, it is well known that small firms
have on average lower productivity levels than large firms.
The causation may also run from productivity to firm behaviour and outcomes such as
propensity to growth or exit. Studies have found that more productive plants are more likely togrow and less likely to exit than non-productive plants (Foster et al, 2016 and Foster et al, 2017).
In addition to dispersion in productivity levels, firms also exhibit dispersion in
productivity growth rates, so the discussion above also applies to the firm distribution of
productivity growth.
The literature on productivity dispersion is large. In a recent survey, Bartelsman andWolf (2018) provide an excellent technical discussion of the productivity dispersion issue. Key
issues highlighted include the choice of gross output or value added for the output measure in the
productivity ratio (dispersion is much less for value added); the distinction between factors that
operate within a firm to affect productivity versus factors beyond the control of a firm (e.g.
externalities such as knowledge spillovers); approaches to measuring productivity or efficiency
dispersion; methods to deal with the lack of information on plant-level prices; the endogeneity ofa firm’s factor input decision in response to firm productivity; the production function versus
stochastic frontier estimation of the dispersion productivity distribution; measures of dispersion
e.g. standard deviations or inter-quartile range; and the relationship between productivity
dispersion and resource misallocation. Detailed discussion of these issues is beyond the scope of
this paper.
Bartelsman and Wolf put forward a research agenda for the productivity dispersion issue,
including the following topics:•

greater linkage of business surveys on production with information on
employee skills and education and managerial quality;23•

Better information on product markets to disentangle price, quality andmarkups thereby improving productivity measures;•

Research to isolate the sources driving heterogeneous productivity at the plantor firm level from market forces that select firms and allocate resources andmarket shares;25•

The role of adjustment frictions on measured productivity dispersion.

Industry Level Data vs. Firm Level Data
The Statistics Canada database provides time series of key labour productivity variables such ashours worked for all jobs and real value added both regionally and by industry. The database isaccessible at no cost with no confidentiality restriction, and it spans the 1997-2021 period withcomplete absence of data gaps regardless of the number of firms operating in the industry. One
disadvantage of the use of such industry-level dataset is that it does not capture the heterogeneityin firms, therefore any analysis made by this dataset will not be able to capture the firm-level
effects of an outside macroeconomic shock. However, in the presence of such shock, all firms
react in similar ways given that their objective is profit maximization regardless of theheterogeneity. Factors that affect aggregate labour productivity are at national level, thus one
should not lose sight that Statistics Canada is providing industry level data with no discontinuity,
a problem present in firm-level data due to Mergers and Acquisitions. Human capital is believed
to be an indispensable determinant of productivity and it is a variable that is absent in firm-level
data. The analysis done at national level can proxy human capital by the adult literacy rates,
school enrolment ratios etc., but this type of proxy would not be feasible in firm-level data.
However, it is important to note that the effect of firm size, age group of employees, firm
dynamism on productivity can only be captured by firm-level data.
CSLS research on firm-level productivity decomposition and dispersion
The CSLS, working closely with CRED officials, is currently undertaking a study on
firm-level productivity in Newfoundland and Labrador, with a particular emphasis on
decomposition and dispersion firm heterogeneity, both in terms of levels and growth rates.
To understand whether aggregate labour productivity level dispersion comes from a firmlevel dispersion or aggregate shocks that affect the whole sector, we follow Berlingieri et al.
(2017a) and decompose the variance of the total economy labour productivity level at time 𝑡 (𝑉𝑡 )into two components: (1) within-industry component (𝑉𝐹,𝑡 ) and (2) cross-industry component
25

For discussion of the different approaches to sources of firm- level productvity differences, see Comin andMulsani (2009) and Acemoglu et al (2013)

24

(𝑉𝑋,𝑡 ). 𝑉𝐹,𝑡 shows how much a firm’s productivity level different from the aggregated average
whereas 𝑉𝑋,𝑡 captures how much sectors vary from other sectors. The arithmetic sum of thesetwo components is the variance of the total economy labour productivity level. Formulae foreach component is as follows.
𝑉𝐹,𝑡 = ∑𝑗

𝐿𝑗,𝑡 2
𝐿𝑗,𝑡
𝐿𝑖,𝑡
2
2
̅̅̅̅
̅
(𝑃𝑖,𝑡 − ̅̅̅̅𝛿𝑗,𝑡 , 𝑉𝑋𝑡 = ∑(𝑃
𝑃𝑗,𝑡 ) , 𝑉𝑡 = 𝑉𝑋,𝑡 + 𝑉𝐹,𝑡𝑗,𝑡 − 𝑃𝑡 ) , 𝛿𝑗,𝑡 = ∑
𝐿𝑡
𝐿𝑡
𝐿𝑗,𝑡
𝑗

𝑖∈𝑗

where 𝑗 refers to industry 𝑗, 𝑖 denotes firm 𝑖 in industry 𝑗, 𝐿𝑗,𝑡 is the employment of industry 𝑗 attime 𝑡, 𝐿𝑖,𝑡 is the employment of firm 𝑖, 𝐿𝑡 is the economy-wide employment, ̅̅̅̅𝑃𝑗,𝑡 is the averagelabour productivity and 𝑃̅𝑡 is the average economy-wide labour productivity. If 𝑉𝐹,𝑡 is larger than
𝑉𝑋,𝑡 , factors internal to the sector has a greater influence on the dispersion. Following Berlingieri
et al. (2017a), we can quantify how much of the dispersion in aggregate labour productivity
comes from the microeconomic dispersion by looking at the ratio 𝑉𝐹,𝑡 /𝑉𝑡 . We also calculate theaverage number of years a frontier firm stays on the frontier to understand the dynamics offrontier firms.
The labour productivity level at the industry and the aggregate level is also decomposed
to identify the contributions from factors internal to the firm and the reallocation of labour input(Olley and Pakes, 1996). We decompose aggregate labour productivity level in the economy into
contributions from within firms and from the reallocation of labour input among firms.
The study also analyzes the firm heterogeneity of labour productivity growth within andacross industries, starting with an analysis of the distribution of labour productivity growth rates
within an industry over time. Labour productivity growth is decomposed at the firm level using
the CSLS method from de Avillex (2012) and the dynamic Olley-Pakes method developed byMelitz and Polanec (2015) to identify how reallocation of labour input among firms affects
labour productivity growth.
Although the CSLS method was designed for decomposing labour productivity growth at
the industry level, we explore the use of the CSLS method for firm-level data. We will use the
CSLS method to decompose labour productivity growth (Δ𝑃𝑡 ) at the aggregate and the industry
level into contributions from each firm (𝑖) at time 𝑡 (𝑐𝑖,𝑡 ):ci,t = Δ𝑃𝑖,𝑡 Δℓ𝑖,𝑡 + (𝑃𝑖,𝑡−1 − 𝑃𝑡−1 )Δℓ𝑖,𝑡 + (Δ𝑃𝑖,𝑡 − Δ𝑃𝑡 )Δℓ𝑖,𝑡where 𝑃𝑖,𝑡 is the labour productivity level of firm 𝑖 at time 𝑡 and ℓ𝑖,𝑡 is the labour share of firm 𝑖in the market at time 𝑡. The arithmetic sum of all 𝑐𝑖,𝑡 ’s will be the change in aggregate labour
productivity level between time 𝑡 − 1 and 𝑡 (i.e.Δ𝑃𝑡 = ∑𝑖 𝑐𝑖,𝑡 ).
25

Moreover, to account for the effect of firm entry and exit, we use a dynamic version ofequation (1) developed by Melitz and Polanec (2015):
̅̅̅̅̅
𝑃𝑖𝑡 − 𝑃𝑖𝑡−1 = Δ𝑃
𝑖𝑡,𝐶 + Δ𝐶𝑜𝑣𝑖∈𝐶 (𝑠𝑓𝑡 , 𝑃𝑓𝑡 ) + 𝑠𝑖∈𝑁,𝑡 (𝑃𝑁,𝑡 − 𝑃𝐶,𝑡 ) + 𝑠𝑖∈𝑋,𝑡−1 (𝑃𝐶,𝑡−1 − 𝑃𝑋,𝑡−1 ),where 𝑃𝑖,𝑡 is the firm-level productivity level in firm 𝑖 at time 𝑡 defined, 𝑝𝑖𝑡 is the unweighted
average of firm-level productivity for the firms in industry 𝑖, 𝐶 denotes continuer firms (thosewith employment in both 𝑡 − 1 and 𝑡), 𝑁, 𝑡 denotes entrants from 𝑡 − 1 to 𝑡, 𝑋, 𝑡 − 1 denotes
firms that exist from 𝑡 − 1 to 𝑡.
Productivity Research Questions that Firm-level Data Can Elucidate
Productivity growth at the industry level is driven by both factors external to the industry
that affect all firms in an industry such as competitive intensity and government policies liketaxes and R&D subsidies, and the characteristics of firms, such as firm size and the educationlevel of managers,26 and more importantly, the distribution of those characteristics among thefirms in the industry.27 These firm characteristics and their distribution can directly affect the
productivity performance of an industry. In addition, the impact of policies on productivity in anindustry can be conditioned and influenced by the characteristics of the firms in that industry andthe distribution of the characteristics. Firm-level data can thus shed light on productivity
performance. This information includes the distribution or dispersion of firms by size (assets,
output, employment), the distribution of firms by age, the share of firms by incumbents, newentrants, and exiting firms, the relative performance of frontier and non-frontier forms, thedistribution or dispersion of firms by productivity levels and productivity growth.28
Productivity research questions that micro-data can elucidate include:•

contributions to overall productivity growth in the industry by firm size group;•

contributions to overall productivity growth in the industry by firm age group;•

contributions to overall productivity growth in the industry by type of firm(incumbent, new entrants, exiting firm);

26

Other firm characteristics, for which data may or may not be available, include human capital intensity, IT capitalintensity, process innovation, product innovation, organization innovation, new product turnover, broadband
intensity, and supply chain integration.
27
Strictly speaking, researchers do not need firm-level data to obtain the average characteristics of firms in anindustry as Statistics Canada generally releases this information. On the other hand, firm-level data are needed for
calculation of the distribution of these characteristics among the universe of firms in an industry and these data are
not readily available from Statistics Canada.
28
For an excellent discussion of measuring productivity dispersion, see Bartelsman and Wolff (2018).

26

•

contributions to overall productivity growth by industry by exporters(incumbents and new entrants);•

the contributions to overall productivity growth in the industry by firm dynamism(gazelles (fast growing firms) versus lifestyle firms):•

explanations for firm heterogeneity in productivity dispersion and its persistence
and policies to improve overall performance;•

contribution of market structure, prices, product quality and variable mark-ups tomeasured firm-level productivity performance;•

the link between firms that perform R&D or patent and productivity.

•

the importance of resources reallocation effects, including misallocation, amongfirms for industry productivity growth;•

firm survival rates and links to productivity; and•relative productivity levels and growth rates for frontier and non-frontier firms
and reasons for these differences.

It is of course true that a number of the issues enumerated above them have been widely
researched (for example, firm size and productivity; the contribution of entrants, exits andincumbents to productivity growth). What is important from a policy development perspective is
to explain the productivity variation among firms. Policy makers want to know if specificinvestments or industry policies are currently playing a role playing a role in this variation andthe persistence of this variation.

VIII. An Agenda for Firm-Level Productivity Research in Canada 29
Canada’s productivity performance
Since 2000 productivity growth in Canada, and in most other advanced countries, hasbeen slow from both an historical and international perspective. In Canada, output per hour in thebusiness sector rose around 0.9 per cent per year over the 2000-2016 period, down from 1.7 per
29

For an earlier attempt at developing a framework for a research agenda to unbundle weak productivity growth in
Canada, see Sharpe (2010), particularly the section on the diagnostic phase and the section on identification ofknowledge gaps and research strategies and methodologies. When this report was written, access to firm-level data
by researchers outside Statistics Canada was much more restricted than it is today.

27

cent in the 1981-2000 period and 3.5 per cent in 1961-1973 period (Sharpe and Tsang, 2018).
Canada ranked 24th out of 33 OECD countries for aggregate productivity growth over the 20002016 period. Contributions by industry and province to the national slowdown can becalculated,30 but the more fundamental drivers of the slowdown are still poorly understood.
From a public policy perspective the two key issues for productivity researchers are tobetter account for slower productivity growth31 and to identify policies that can boost
productivity growth. Many hope (e.g. Drummond, 2011) that better access to and use of firmlevel data can shed light on both these issues. This section of the report identifies and discusses
four areas of productivity research where use of firm-level data may have the potential toadvance our understanding of the slowdown, namely•

relative performance of frontier firm;•changes in the pace of technical progress;•

secular stagnation or changing demand conditions; and•falling business sector R&D and investment.

The Role of Frontier Firms in the Slowdown
The post-2000 productivity slowdown is not just confined to Canada but is a global
phenomenon. The OECD, a major centre for productivity research, has put the explanation,
based on data from the OECD-Orbis firm database for 24 OECD countries, that the slowdownreflects a growing productivity growth gap between frontier forms, defined at the firms in the top
5 per cent of the productivity level distribution within each two-digit industry and the remaining
95 per cent of firms, the laggards (Andrews et al. 2016a and 2016b). There has been no
productivity slowdown at the top, only among non-frontier firms. They attribute this situation toa decline in market dynamism and growth-enhancing reallocation, as manifested by fewer exits
by weak firms and a decline in entry of new firms. This situation is in turn linked to a slowdown
in the pace of structural reform in product markets. They believe that a failure of policy to
30

Sharpe and Tsang (2018) show that manufacturing made a disproportional contribution to the slowdown
accounting for 0.62 points of the 0.69 points slowdown in business sector labour productivity growth between the
1981-2000 and 2000-2016 periods.
31
The issue of explaining slower productivity growth does not appear to be a priority for the academic economics
community. In 2018, the CSLS and Productivity Partnership issued a call for papers on this topic for a series ofsessions at the 2018 annual meeting of the Canadian Economics Association. Only one submission from universitybased researchers was received. Officials from federal government economic departments and agencies such as
ISED, Statistics Canada, and the Bank of Canada did submit proposals. This apparent lack of interest in explaining
slower productivity growth in the academic community is also seen in the very limited number of funding requests
on this topic made to the Productivity Partnership. This situation is also likely associated with the trend away frompolicy work among academic economists in Canada, as shown by Simpson and Emery in a recent Canadian Public
Policy article.

28encourage the diffusion of best practices has also contributed to the growing productivity gap
between frontier and non-frontier firms.
Figure 3. Average labour productivity growth of frontier and nonfrontier firms (% per year), 1990 to 2000 and 2000 to 2015
4.00
3.50

3.43

3.00
2.50

2.44

2.00
1.50

1.05

1.00
0.51

0.50
0.00
1991-2000

2000-2015
Frontier firms

Non-frontier firms

Source: Gu (2018b)
Unfortunately, this OECD analysis of the productivity slowdown does not include
Canada since Canada is not included in the OECD-Orbis productivity database. Consequently, an
obvious area for research is to see whether trends in firm-level data in Canada are consistent with
the OECD story, with the productivity of frontier forms advancing faster than that of non-frontierfirms.
Preliminary data from Statistics Canada provide only mixed support for the OECD
hypothesis in Canada (Figure 3). It is true that labour productivity growth for frontier firms since2000 in Canada has exceeded that of non-frontier firms: 1.1 per cent per year versus 0.5 per cent,a gap of 0.6 points. The productivity of the average frontier firm is moving away from that of theaverage non-frontier firm. But in the 1990s labour productivity growth among frontier firms waseven faster than among non-frontier firms: 3.4 per cent per year versus 2.4 per cent, a gap of 1.0
points. Both frontier and non-frontier firms experienced slower productivity growth after 2000
and the slowdown for the former was nearly four times that of the latter (2.3 points versus 0.6
points).
The individual firms in the top 5 per cent of the productivity level distribution change
every year as firms enter and leave this group based on their productivity growth rate andproductivity level in the previous year. If high productivity growth firms tend to have above
average productivity levels, then it may be a statistical artifact that the top 5 per cent in the
productivity level distribution will have a higher productivity growth rate then non-frontier firms.
29

For individual firms, there is reversion to the mean. But this is not necessarily the case for the topgroup of firms in the distribution as this group is always being replenished by high productivity
growth firms that enter this group.
In any case, the role of frontier firms in Canada’s productivity performance needs more
attention from researchers. Topics include work on the comparability of the T2-LEAP and
OECD-Orbis databases, trends in the performance of frontier and non-frontier firms by industry
and province, and the average duration of a firm’s stay in the frontier firm category.
However, a caveat exists for frontier firm research. Care is needed in discussion of therole of frontier firms in productivity. By definition, frontier firms are those firms with highest
productivity in each year. This means that the composition of frontier firms is different eachyear. Some argue that it may not makes sense to estimate productivity growth of frontier firms
over time because a firm can be frontier firm this year and a non-frontier firm next year. It may
be unclear that one can say that the productivity dispersion is a diffusion problem associated withtechnology or best practices.
Changes in the pace of technical change
In the long run the pace of technological change is the key driver on productivity growth.
This suggests that the post-2000 slowdown in productivity growth reflects a slower rate ofunderlying technological change, a view put forward by a number of researchers of whom
Robert J, Gordon (2014 and 2016) is the most prominent. In the Canadian context, Alexopoulos
and Cohen (2018) have shown that the growth in the number of technical books held in libraries
has fallen off, an indicator of slower technical progress.
Firm-level data may be able to shed light on this crucial issue of the pace of technologicalchange. In theory, TFP is a measure of technological change, so the recorded slower TFP growthdoes support the view that underlying pace of technical advance has fallen off. But TFP is more aresidual or measure of our ignorance than a true measure of technological change since so manyfactors affect TFP. These factors include measurement issues, resource depletion, imperfectcompetition, capacity utilization, economies of scale, among others. Techniques using firm-level
data have been developed in recent years to control for many of these factors and estimate ameasure of TFP than is closer to true underlying disembodied technological change. An
important aspect of this is to isolate the price and markups over cost from the firm-level output
measures to obtain better estimates of the technological change element,
Falling Business R&D
A key stylized fact of the Canadian economy in recent years has been the fall in businesssector research and development spending, both in nominal terms and as a share of GDP. This is
an important development because R&D is a key component of technological change and hence
productivity growth. The reasons for this downward trend in R&D are still poorly understood.
30

Micro-data sets such as the T2-LEAP and R&D can be linked to shed light on thecharacteristics of R&D performers, including their productivity performance (Kim and Lester,2019). Analysis of such data sets may shed light on the reasons for falling R&D. Is it because
fewer firms are performing R&D, or average R&D spending per performing firm is down, or
because there are fewer large R&D performers such as Nortel, Blackberry, or for all threereasons? The CSLS is currently undertaking a project that links R&D spending at the firm levelto trade with China.
Secular Stagnation
Economic growth in developed economies including Canada has been weak since theGreat Recession of 2008-2009. Some economists, of which Larry Summers is the mostprominent, see the situation as one of secular stagnation, reflecting factors such as weak
investment, slower world trade, government austerity at least in in Europe, and growing income
inequality. These weak demand conditions in turn result in mediocre productivity growth, as has
been the case in Canada since 2000 (Rao and Li, 2013 and Baldwin, Gu and Yan, 2013) and in
the United States since 2004.
Firm-level data may be able to shed light on the secular stagnation hypothesis through the
Verdoorn law literature, which positively relates the rate of growth of productivity to the rate of
growth of output through learning by doing, economies of scale, and reductions in xinefficiencies, and at least in the short-to-medium term, the lags in the adjustment of inputs tochanges in demand and output due to labour hoarding and overhead labour. Firm data may beable to shed light on how firms change their inputs and output in response to changes in theexternal economic environment.

IX. Conclusion
This report reviews the current states of firm-level productivity data in Canada, including
discussion of the challenges and advantages of firm-level data, and puts forward a research
agenda to shed light on the reasons for slower productivity growth in Canada through the
exploitation of firm-level data. This conclusion provided a number of points or recommendations
for researchers contemplating undertaking a research project involving firm-level data accessedat CDER.
Given the availability of excellent and easily accessible productivity estimates at a
detailed industry level for Canada and the provinces, researchers should gain familiarity with allof Statistics Canada’s publically available productivity data related or potentially related to theirarea of research before embargoing on a firm-level productivity project.
Statistics Canada should make publically available more aggregated firm-level data. It ismuch more efficient or cost effective for Statistics Canada than for individual researchers to31

produce these data. Examples of such data include the characteristics of incumbent, exiting andentering firms and the productivity level quartiles by firm size by industry.
The Centre for Economic Statistics (CES) at the US Census Bureau is a world leader in
the area of firm-level research on productivity issues. The CES produces a wealth of working
papers, many up top researchers, on the productivity issue from a firm perspective. Recent
examples include Goldschlag and Perlman (2017), Alon et al, (2018), Pugsley et al (2018)
Cunningham et al (2018), Ersahir (2018), Foster et al (2018, Aghion et al (2018) and Hsieh andKlenow (2018). Canadian researchers should follow closely this working paper series to keep
abreast of methodological developments in the use of firm-level data and to identify research
topics that may be relevant for Canada.
From the perspective of Canadian public policy, the most relevant productivity issue isexplaining slower productivity growth and identifying policies to improve this performance.
Priority research topics include the productivity of frontier and non-frontier firms, lagging
business sector R&D, the pace of technological change, and the relationship between demand
growth and productivity. Academic researchers should be encouraged to work on these topics.

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Appendix on International Sources of Firm-level Data

46

Centre for Economic Studies at U.S. Census Bureau
The Center for Economic Studies (CES) at the U.S. Census Bureau provides researcherswith rich establishment- and firm-level micro-data. CES has a mandate to produce, maintain, andconduct research using longitudinal datasets. These datasets are constructed primarily based on
information from respondents to Census Bureau censuses and surveys, which have data on
individuals, households, and businesses. Traditionally, CES has focused on business data mostly
from the manufacturing sector.
The very first longitudinal database produced by CES was the Longitudinal Research
Database (LRD). The database contains establishment-level information from the Censuses andAnnual Surveys of Manufacturing. See Bartelsman and Doms (2000) and Caves (1998) for
reviews of research projects using the LRD. Especially, the former provides an overview of theexisting firm-level productivity research using the CES micro data.
CES has improved upon the LRD by creating the Longitudinal Business Database (LBD)
which, unlike the LRD covering only manufacturing, covers all the non-farm private economy
and some public sector activities. The LBD is constructed based on a variety of sources such as
the Business Register, the Standard Statistical Establishment List, and Economic Censuses, andsurveys. The LBD covers all legally operating entities operating in the United States. and its
territories for the 1976-2013 period (all employer establishments with a minimum of one
employee).32 For the industry coverage, the database covers all industries except for privatehouseholds. The database has establishment-level information such as age, industry andgeographical classification, payroll, and employment.33
The database has an attractive feature. Any limited detail on the LBD can be mitigated by
the ease with which it can be linked to other sources of establishment- and firm-level
information. By linking other sources of data to the LBD, it is possible to enhance the scope anddepth of information for the LBD establishments. Thus, the database has large and statistically
representative samples and can potentially represent the tremendous heterogeneity of the U.S.
business population in terms of size and performance.
The database is available on a cost-recovery basis to qualified researchers for approved
projects in secure Federal Statistical Research Data Centers. 34 The eligibility criteria for access issimilar to the one at the Canadian Centre for Data Development and Economic Research(CDER) at Statistics Canada. Projects must provide benefits to Census Bureau programs,
demonstrate scientific merit, require non-public data, be feasible given the data, and pose no riskof disclosure.
32

In 2013, the data covers roughly 8.5 million establishments. For the 1973-1999, roughly 23 million establishments
are covered. The exact numbers of annual observations over the entire sample period do not appear to be available tothe public.
33
For more detailed description of the database, refer to Jarmin and Miranda (2002) available athttps://www.census.gov/ces/pdf/CES-WP-02-17.pdf.
34
There are other micro-data available at CES. For the list of available data, refer tohttps://www.census.gov/ces/dataproducts/economicdata.html.

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The CES discussion paper series includes over 500 papers using CES micro data
covering a wide range of topics (including firm-level productivity) and is available athttps://ideas.repec.org/s/cen/wpaper.html.
There are other databases produced by CES based on the LBD such as the Business
Dynamics Statistics (BDS) and Synthetic Longitudinal Business Database (SynLBD). The
aggregated version of BDS is publically available at the total economy, 2-digit SIC-, and statelevel. The BDS contains information on the number of firms and establishments, employment,
gross entry/exit, and job creation/destruction.35
The SynLBD is an experimental synthetic data product and unless validated, the CES
does not guarantee that the results based on the data reflect phenomena in the underlying
confidential data. The SynLBD is accessible through the Virtual RDC at Cornell University.
Researchers have to submit an application package for access which is determined based solely
on whether the data necessary to conduct the proposed analysis are included in the SynLBD data
file. Similar to the LBD, the database contains establishment-level information on employment,
payroll, birth and death years, and industrial classification. The synthetic data are created byfitting models on confidential information in the LBD and simulating replacement values fromthese models. The database is also tested for any disclosure risk. This appears to be similar to asynthetic version of the real T2-LEAP database created by CDER with the collaboration of the
Productivity Partnership.
OECD
Multifactor Productivity Project (Multiprod)
The Directorate for Science, Technology and Innovation (STI) at the OECDhas
developed a harmonized framework that provides non-confidential micro-aggregate statistics thatare comparable across countries.36 Such micro-aggregated statistics are constructed based on
existing official confidential data at the firm level, which are in turn based on official surveys
and administrative sources such as Business Registers. The whole process of collecting,processing, and constructing relevant productivity statistics is called the Multifactor Productivity(MultiProd) project. The resulting collection of micro-aggregated statistics is called theMultiProd database. OECD officials have indicated that they are willing to share access to thedata with other researchers.
A key feature of the MultiProd project is that its framework builds harmonized microaggregated statistics, implying that productivity performance is measured in exactly the same
way across countries. The comparability of results across countries is a huge advantage of thedatabase. There are 18 countries included in the MutiProd database: Australia, Austria, Belgium,

35

Refer to https://www.census.gov/ces/dataproducts/bds/definitions.html for the definitions of these variables.
STI at OECD distributes the algorithm (DynEmp in Stata) that implements a distributed micro data analysis tostatistical agencies in the countries participating in the MultiProd project. The algorithm is run on confidential firmlevel data and the resulting aggregated statistics are compiled in the MultiProd database, implying that OECD has noaccess to confidential data in those countries. See Berlingieri et al. (2017b) for more detail.
36

48

Canada37, Chile, Denmark, Finland, France, Hungary, Italy, Indonesia, Japan, Luxembourg, theNetherlands, Norway, New Zealand, Portugal and Sweden.
The firm-level data collected in MultiProd are processed by running a standardized
algorithm (written in Stata). The algorithm produces a collection of statistics based on the firmlevel longitudinal information on output, input, labour costs, sector of activity, age of firm, andownership. The algorithm relies on two main sources of data: Administrative data or production
surveys and business registers. To improve harmonization and representativeness, the algorithm
reweights production surveys to construct statistics representative of the whole population in agiven country. Weights are built based on information from business registers which tend tocover the whole population of firms. MultiProd is the first project to implement suchreweighting.
The resulting statistics of the algorithm is a set of statistics at different levels of
aggregation covering the period of 1994-2012 depending on data availability for each country.
All the firm-level information is first aggregated to the 2-digit industry level separately for eachyear by country. However, some statistics are further refined to allow for aggregation in various
dimensions: by productivity quantile; by sales quantile, by size (based on employees) group, by
age group, by ownership status (independent firms vs. affiliated of a business group, andnationality of the group), and by demographic group (entrants, exitors, and incumbents).
The main variables are levels and growth rates of value added, employment, investment,labour productivity, MFP, and other firm-level characteristics (e.g. age, ownership) at different
percentiles of the firm productivity distribution. Measures of within-industry productivity
dispersion and the covariance with size also are produced following Olley and Pakes (1996).
Also, structural measures of misallocation based on Hsieh and Klenow (2009) and Petrin andSivadasan (2013) are constructed for analyses on the role of allocation and selection in
productivity. Moreover, MultiProd provides results from distributed regressions to establish
some stylized facts by country for the relationship between productivity, firm characteristics (e.g.
size, age, ownership) and structural characteristics (e.g. concentration and misallocation).
There are two types of labour productivity measure available in the MutiProd database:the gross output or total revenue based labour productivity and value added based labour
productivity. There are also two types for MFP: index number approach and production function
approach (i.e. first estimate a production function and then obtain MFP as a residual).
The index number measure is based on the functional form that characterizes therelationship between output to a weighted sum of inputs. There are two types of index number
approaches: Solow residuals and the Superlative-index measure. In the MultiProd database, theSolow residual-based MFP is provided.38 There are various approaches developed in the37

The underlying data source for Canada in the MultiProd database is Statistics Canada's T2-LEAP longitudinal
database.
38
In the Solow residual approach, inputs are weighted using external, industry-specific factor shares. In theMultiProd database, MFP is constructed using cross-country industry-specific labour and intermediate shares.

49

literature in estimating a firm-level production function. These different approaches aredeveloped to correct for well-known empirical issues such as simultaneity bias. In the MultiProd
database, MFP is measured based on a (relatively) recent technique developed in Wooldridge(2009).39
As noted above, there are various dimensions in which researchers can analyze thestatistics in the MultiProd database. For example, researchers can study the role of particularindustries or groups of firms in explaining aggregate outcomes in productivity growth anddispersion (e.g. small vs. large; old vs. young; lagging firms vs. frontier firms). For example,
Berlingieri et al. (2017a) use the MutiProd database to investigate the role of productivity
heterogeneity across 18 countries over the period 2001-2012.
For example, the analysis in the size dimension can shed light on the so-called “granular”
hypothesis. The hypothesis states that aggregate fluctuations are driven by microeconomic
shocks and not economy-wide shocks. Those microeconomic shocks may not cancel on average
if sectors are dominated by a small number of large firms. The MultiProd database offer
empirical evidence for this hypothesis by allowing researchers to analyze how much aggregate
economic activity is concentrated in a small number of large firms, and how much of theobserved variation in productivity is stemmed from microeconomic variations.
OECD-Orbis Database
There is another firm-level data source called the OECD-Orbis database. The database isdeveloped jointly by six OECD Directorates (started in 2008).40 The database contains several
productivity measures (variants of labour productivity and MFP) as well as other firm-level
information.41 The most recent version of the database covers 24 OECD countries over the 19972014 period for the non-farm and non-financial business sector (roughly 55 million firms). The
countries included are: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France,
Germany, Great Britain, Greece, Hungary, Ireland, Italy, Japan, Korea, Netherlands, Norway,
Poland, Portugal, Spain, Sweden, Slovenia, the Slovak Republic and the United States. Canada is
not included.
The OECD-Orbis database are constructed based on annual balance sheet and income
statements made available by Bureau van Dijk, an electronic publishing firm. These information
is in turn based on various sources such as credit rating agencies, national banks, and financial
39

Wooldridge (2009) developed an algorithm that builds upon the method introduced in Levinsohn and Petrin (2003).
The algorithm overcomes the well-known identification problem in the Levinsohn-Petrin method. See Wooldridge(2009) for detail.
40
They are: Centre for Entrepreneurship, SMEs and Local Development (CFE), Directorate for Employment,
Labour and Social Affairs (ELS), Environment Directorate (ENV), Statistics Directorate (STD), Directorate for
Science, technology and Industry (STI) and Directorate for Trade and Agriculture (TAD). Note that the database isaccessible to OECD researchers only.
41
The database contains not only the variables that underlie the productivity measures such as sales, net income, thenumber of employees, tangible and intangible fixed assets but also more detailed information such as current andnon-current liabilities, interest payment, and country of control (more than 200 variables). The OECD-Orbis
database are also linked with other OECD databases such as the PATSTAT database, which have information on
patent for individual firms.

50

information providers (e.g. Thomson Reuters) -- see Appendix E in Andrews, Criscuolo, and Gal(2016) for the full list of data sources for the 24 countries included in the database.
Although the OECD-Orbis is the largest cross-country firm-level database for economic
research, a series of adjustments are required before the data can be used for economic analysis.
This is due to the fact that the underlying data are collected for use in the private sector with the
purpose of financial benchmarking, which is not necessarily aligned with that of economic
research. In general, the adjustments involve: 1) ensuring comparability of variables acrosscountries and over time; 2) constructing new variables required in productivity analyses (e.g.
value added and capital stock); and 3) procedures to resolve representative issues since thedatabase is a subsample of the total firm population within countries, focusing more on large
firms (and hence, potentially more productive firms). 42
See Ribeiro, Menghinello and Backer (2010) for more detailed discussion of the OECDOrbis database, especially for its disadvantages and advantages. As Orbis is a privately
constructed database, with the OECD purchasing access for its researchers, non-OECD
researchers do not have access without paying a significant fee,Eurostat
The only firm-level data available at Eurostat is the Micro-Moments Dataset (MMD).43
The MMD is a product of the Eurostat-funded projects. The main purpose of the database is toderive measures of the impact of ICT and innovation on business performance and productivity.
The MMD relates ICT and innovation variables to various indicators of economic performanceand characteristics of firms across industry groups and countries. Therefore, it is possible to linkthe data on ICT and innovation to other aggregate economic data on productivity such as EU
KLEMS.44
The database is produced based on four sources: 1) the Survey of ICT Usage and eCommerce in Enterprises; 2) the Community Innovation Survey; 3) the Business Register; and 4)
Structural Business Survey. The database has firm-level information with a longitudinal
structure, which are available for firms in Austria, Denmark, Finland, Germany, Ireland, Italy,
the Netherlands, Norway, Slovenia, Sweden, and the UK. In general, the data cover the period
2010-2013 but the sample period varies from one country to another as it is subject to theavailability of the Community Innovation Survey and the Survey on ICT Usage and e-Commerce
in Enterprise.45

42

See Gal (2013), Ribeiro, Menghinello and Backer (2010), Kalemli-Ozcan, Sorensen, Villegas-Sanchez,
Volosovych and Yesiltas (2015), and Andrews, Criscuolo, and Gal (2016) for more detail regarding the adjustments.
43
Refer to https://ec.europa.eu/eurostat/web/microdata for all other micro-data available at Eurostat.
44
The aggregate version of the MMD is publically available at https://ec.europa.eu/eurostat/web/digital-economyand-society/methodology.
45
For more details of the database, refer to the document prepared by Eurostat available athttps://ec.europa.eu/eurostat/documents/203647/6867168/MMD+description/f690281e-10e7-4407-9f323907746eaee3.

51

The MMD contains basic firm-level information such as employment, exit, entry, age,industry classifications, value added, gross output, payroll, and intermediates. These variables
are linked to ICT- and innovation-related information such as education level of employees,
share of employees with formal education in ICT and related fields and whether a firm hasinternet, broadband connection, website, or mobile access to internet, and whether a firm
sells/orders using computer networks. The database also has information on R&D expenditure
and R&D funding from the government or EU.46 Bartelsman, Hagsten, and Polder (2013)provide a good overview on the MMD database and technical possibilities for cross-countryfirm-level analysis on the ICT impact on productivity. They also present the findings from theirown analysis using the MMD database.
Researcher must submit a formal application package in order to gain access to the MMD
and the database can only be used for scientific purposes. Only authorized researchers can use
the MMD for an agreed research project through the Safe Centre at Eurostat. To qualify foraccess, researchers must be from recognized entities. To become a recognized entity, anorganization must have research as one of its main activities; provide evidence of publication ofresearch results; be independent and autonomous in formulating scientific conclusions; and haveadequate data security safeguards.
Competitiveness Research Network
The Competitiveness Research Network (CompNet) is a research network founded by theEuropean System of Central Banks in 2012 to promote and inform the debate on competitivenessand productivity. Its activities involve not only research but also producing, updating, andmaintaining a firm-level database for a number of EU countries.
The European Central Bank (ECB) is one of the main contributors to the CompNet
through not only research but also data production. The ECB maintains the CompNet
Competitiveness Dataset. The database contains firm-level information covering 18 European
countries47 and is released regularly based on its latest vintage. The most recent version was
released in 2018 (i.e. the 6th Vintage CompNet database). The database is constructed based on
information available from national central banks and national statistical institutes.
The most recent version of the data covers the period 1999-2016 for most of the countries
in the sample. The database covers non-financial corporations with at least one employee48
across NACE (Nomenclature statistique des Activités économiques dans la Communauté
Européenne) industries B-J and L-N. Various firm-level information is available such as
employment, value added, revenue, payroll, and other variables that are necessary to assess firm-46

For the complete variable list, refer tohttps://ec.europa.eu/eurostat/documents/203647/6867168/Variables_breakdowns_coverage/568118b1-bc04-496084b2-6c77899a0ea5.
47
They are Belgium, Croatia, Czech Republic, Denmark, Finland, France, Germany, Hungary, Italy, Lithuania,
Netherlands, Poland, Romania, Slovakia, Slovenia, Spain, and Sweden.
48
However, in some countries (e.g. Poland) firms are legally obliged to submit their balance sheet information only
if certain thresholds are met (e.g. only firms with more than 10 employees).

52

level labour productivity and TFP.49

The CompNet Competitive database consists of four different types of datasets: 1)
unconditional files (including all available variables); 2) productivity decomposition; 3) labour
market imperfections; and 4) misallocation. The unconditional files contain all productivity,
finance, labour, and competition indicators available in the CompNet database. They areavailable by country, industry (at the 2-digit NACE level), and size class dimension. The
productivity decomposition files contain the estimates of various allocative and dynamic
efficiency measures. The labour market imperfections files contain the labour share weighted
Dobbelaere-Mairesse (2013) indicator. Lastly, the misallocation files contain the within-sector
dispersion of 46 productivity and competition variables such as labour productivity, capital
productivity, and De Loecker and Warzynski markups (see De Loecker and Warzynski, 2012).
To gain access to the database, researchers must submit an application package to the
CompNet.50 If accepted, and the data are delivered through internal drives protected by password
access and/or personal access privileges. As any other confidential micro data, researchers canuse the database only for scientific purposes.

49

For the complete list of variables, refer to Table 16 in CompNet (2018).
The online application submission is available at https://www.iwh-halle.de/en/research/data-andanalysis/research-data-centre/compnet-database/request-form/.
50

53

Appendix 1: Trends in Firm Dynamics in Canada, 2001-2016
Chart A1: Number of Active Employer Businesses, Incumbents, Entrants and Exits in the
Private Sector, Canada, 2001-2016
Panel A: Number of Active Employer Businesses in the Private Sector (Entrants + Incumbents) andNumber of Private Sector Incumbents
1,200,000
1,100,000
1,000,000

900,000
800,000
700,000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Number of Active Employer Businesses in the Private Sector
Number of Private Sector Incumbents

Panel B: Number of Private Sector Entrants and Number of Private Sector Exits
160,000
150,000
140,000
130,000
120,000
110,000
100,000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Number of Private Sector Entrants

Number of Private Sector Exits

Note: According to footnote 1 of Statistics Canada Table 33-10-0164-01, employer businesses include both incorporated and unincorporated
businesses that issue one or more statements of remuneration (T4 slip(s)) to their employee(s) for tax purposes. The number of active
employer businesses in the private sector and the number of incumbents are stock variables while the number of private sector entrants and thenumber of private sector exits are flow variables.
Source: Statistics Canada Table 33-10-0164-01 (formerly CANSIM 527-0001).

54

Chart A2: Entry Rates of Private Sector Employer Businesses by Province and Territories
in Canada, 2016 (per cent)Panel A: Private Sector Entry Rates
25
20
15
10
5
0

19.1

16.0

14.0

13.3

13.2

13.0

12.5

12.1

12.0

11.7

10.3

12.0

11.6

11.5

10.0

-2.02

-2.04

Panel B: Private Sector Exit Rates
25
20
15
10
5
0

21.2

2
1
0
-1
-2
-3
-4

1.65

15.9

14.4

14.1

14.1

12.9

12.4

Panel C: Net Entry (Entry – Exit) Rates
1.33

1.18

1.01

0.30

0.07
-0.35

-0.92

-2.86

Note: The entry rate for a year is defined as the number of entrants as a per cent of the average of the number of active employer businesses in thecurrent year and the previous year. The exit rate for a year is defined as the number of exits as a per cent of the average of the number of active
employer businesses in the current year and the previous year. The net entry rate for a year is defined as the number of net entry (the number ofentrants – the number of exits) as a per cent of the average of the number of active employer businesses in the current year and the previous year.
Source: Statistics Canada Table 33-10-0087-01 (formerly CANSIM 527-0007).

55

Chart A3: Entry Rates and Exit Rates by Industry, Canada, 2016
Panel A: Entry Rate
Unclassified businesses
Transportation and warehousing [48-49]
Finance and insurance [52]Real estate and rental and leasing [53]Professional, scientific and technical services [54]Private sector
Information and cultural industries [51]Construction [23]Accommodation and food services [72]ASWMRS [56]Agriculture, forestry, fishing and hunting [11]Arts, entertainment and recreation [71]Retail trade [44-45]
Utilities [22]Mining, quarrying, and oil and gas extraction [21]Other services (except public administration) [81]Manufacturing [31-33]
Management of companies and enterprises [55]
Wholesale trade [41]

36.6
13.2
12.5
12.3
12.3
12.2
12.2
11.8
11.2
10.5
9.6
9.6
8.6
8.5
8
7.5
6.6
5.9
5.7
0

5

10

15

20

25

30

35

40

Panel B:Exit Rate
Unclassified businesses
Mining, quarrying, and oil and gas extraction [21]Management of companies and enterprises [55]Real estate and rental and leasing [53]Finance and insurance [52]Professional, scientific and technical services [54]Information and cultural industries [51]Private sector
Construction [23]Transportation and warehousing [48-49]
ASWMRS [56]Utilities [22]Accommodation and food services [72]Other services (except public administration) [81]Agriculture, forestry, fishing and hunting [11]Retail trade [44-45]
Arts, entertainment and recreation [71]
Wholesale trade [41]Manufacturing [31-33]

22.6
17.4
15.7
13.3
13.2
13.1
12.3
11.9
11.8
11.5
11.5
11.0
10.9
9.8
9.4
9.4
9.1
8.0
7.6
0

5

56

10

15

20

25

Panel C: Net Entry (Entry – Exit) Rates
Unclassified businesses
Transportation and warehousing [48-49]
Arts, entertainment and recreation [71]Accommodation and food services [72]Private sector
Agriculture, forestry, fishing and hunting [11]Construction [23]Information and cultural industries [51]
Retail trade [44-45]
Finance and insurance [52]Professional, scientific and technical services [54]Real estate and rental and leasing [53]Manufacturing [31-33]
ASWMRS [56]Wholesale trade [41]Other services (except public administration) [81]Utilities [22]Management of companies and enterprises [55]Mining, quarrying, and oil and gas extraction [21]

13.9
1.7
0.5
0.3
0.3
0.2

0.0
-0.1
-0.7
-0.7
-0.8
-0.9
-1.0
-1.0
-2.2
-2.3
-2.4
-8.3
-9.4
-15

-10

-5

0

5

10

15

20

Note: The entry rate for a year is defined as the number of entrants as a per cent of the average of the number of active employer businesses in
the current year and the previous year. The exit rate for a year is defined as the number of exits as a per cent of the average of the number ofactive employer businesses in the current year and the previous year. The net entry rate for a year is defined as the number o f net entry (thenumber of entrants – the number of exits) as a per cent of the average of the number of active employer businesses in the current year and theprevious year.
ASWMRS stands for Administrative and support, waste management and remediation services.
Source: Statistics Canada Table 33-10-0164-01 (formerly CANSIM 527-0001).

57

Appendix 2: Petroleum refining industry (NAICS 32411) in New Brunswick (theIrving refinery)
Table A1: Real value added in petroleum refineries in national total and in New Brunswick,
1997-2017(1) National total Pet. Ref.

(2) Pet. Ref. in NB(3) Total business sectors in NB(2) / (1)(2) / (3)1997

7,115,768

796,606

14,954,007

11.2%

5.3%

1998

8,356,322

785,482

15,602,289

9.4%

5.0%

1999

9,464,386

326,466

16,899,811

3.4%

1.9%

2000

9,890,974

390,594

17,383,959

3.9%

2.2%

2001

10,990,477

526,029

17,681,840

4.8%

3.0%

2002

10,714,301

464,942

18,608,167

4.3%

2.5%

2003

11,116,155

641,187

18,969,456

5.8%

3.4%

2004

11,531,788

680,304

19,566,220

5.9%

3.5%

2005

10,982,551

783,991

19,746,217

7.1%

4.0%

2006

10,556,345

737,968

19,975,795

7.0%

3.7%

2007

10,992,684

669,109

19,918,425

6.1%

3.4%

2008

10,494,460

669,744

19,872,028

6.4%

3.4%

2009

10,839,443

717,229

19,134,584

6.6%

3.7%

2010

10,185,275

695,537

19,494,413

6.8%

3.6%

2011

9,516,276

673,870

19,478,958

7.1%

3.5%

2012

9,774,279

643,341

19,062,813

6.6%

3.4%

2013

9,861,805

653,940

18,910,996

6.6%

3.5%

2014

10,168,849

551,179

18,884,487

5.4%

2.9%

2015

10,148,698

600,345

19,151,024

5.9%

3.1%

2016

10,061,701

585,450

19,440,262

5.8%

3.0%

2017

10,575,685

630,834

19,916,968

6.0%

3.2%

Average annual growth rate
1997-2017
2.00%-1.16%
Note: Value added is in 2012 chained thousand dollar.
Source: Statistics Canada Table 36-10-0480-01.

Change between 1997 and 2017
1.44%

58

-5.2 % pt.

-2.2% pt.

Table A2: Employment in petroleum refineries in national total and in New Brunswick,
1997-2017(1) National total Pet. Ref.

(2) Pet. Ref. in NB(3) Total business sectors in NB(2) / (1)(2) / (3)

1997

4,265

240

233,915

5.6%

0.1%

1998

3,475

175

238,365

5.0%

0.1%

1999

4,465

115

245,490

2.6%

0.0%

2000

4,300

235

250,600

5.5%

0.1%

2001

5,125

330

245,480

6.4%

0.1%

2002

4,540

230

256,545

5.1%

0.1%

2003

5,685

270

254,320

4.7%

0.1%

2004

6,790

375

261,110

5.5%

0.1%

2005

7,385

660

258,970

8.9%

0.3%

2006

7,910

815

261,775

10.3%

0.3%

2007

10,155875

267,760

8.6%

0.3%

2008

10,080985

268,465

9.8%

0.4%

2009

10,3401,375

265,410

13.3%

0.5%

2010

11,275

1,365

261,635

12.1%

0.5%

2011

11,4301,200

259,760

10.5%

0.5%

2012

10,795990

258,665

9.2%

0.4%

2013

8,235

1,195

258,560

14.5%

0.5%

2014

7,890

990

258,550

12.5%

0.4%

2015

8,095

1,095

254,930

13.5%

0.4%

2016

7,840

930

256,080

11.9%

0.4%

2017

7,760

965

259,460

12.4%

0.4%

Average annual growth rate
1997-2017

3.04%

7.21%

Change between 1997 and 2017
0.52%

Source: Statistics Canada Table 36-10-0480-01.

59

6.8% pt.

0.3% pt.

Table A3: Labour productivity in petroleum refineries in national total and in New
Brunswick, 1997-2017(1) National total Pet. Ref.

(2) Pet. Ref. in
NB(3) Total business sectors in NB(2) / (1)(2) / (3)

1997

828.1

1,565.00

33.7

189.0%

4643.9%1998

1,129.20

2,146.10

34.6

190.1%

6202.6%

1999

1,048.00

1,300.70

36.4

124.1%

3573.4%2000

1,146.50

782.8

36.7

68.3%

2133.0%2001

1,048.60

766.8

38.4

73.1%

1996.9%2002

1,156.30

970.7

38.9

83.9%

2495.4%

2003

959.4

1,157.40

39.9

120.6%

2900.8%

2004

834.5

876.7

40.3

105.1%

2175.4%

2005

737.8

573.9

41.7

77.8%

1376.3%

2006

643.8

458.1

41.3

71.2%

1109.2%

2007

529.3

370.3

40.6

70.0%

912.1%

2008

504.3

338.9

40.3

67.2%

840.9%

2009

517.6

249.3

39.7

48.2%

628.0%

2010

445.4

254.6

40.7

57.2%

625.6%

2011

401.7

286.6

41.7

71.3%

687.3%

2012

443.7

316.3

40.2

71.3%

786.8%

2013

590.7

274.1

40.5

46.4%

676.8%

2014

632.2

261.8

40.9

41.4%

640.1%

2015

613.4

251.2

41.6

41.0%

603.8%

2016

645.9

318.7

41.9

49.3%

760.6%

2017

687.5

326.2

42.4

47.4%

769.3%

Average annual growth rate
1997-2017-0.93%-7.54%
Note: Labour productivity is in 2012 chained thousand dollar per hour worked.
Source: Statistics Canada Table 36-10-0480-01.

60

Change between 1997 and 2017
1.15%

-141.5% pt.

-3874.6% pt.