Regulatory Accumulation, Business Dynamism and Economic Growth in Canada
Acknowledgements
The
author would like to thank John R. Baldwin and Danny Leung for extensive
discussions during this study. The author would also like to thank Mark Brown,
Joseph Kokou, Amélie Lafrance-Cooke, Beryl Li, Marcel Marette, Marie-Laure
Riel, Jianmin Tang and Weimin Wang for helpful comments.
Abstract
Despite their good intent, regulations and their accumulation over time impose real costs to businesses and may have a negative impact on economic growth and competitiveness. Accurately measuring these costs and benefits is important for understanding if regulations are achieving their desired results. This paper uses a new, modelled, measure of regulatory burden developed by KPMG and Transport Canada to inform about the possible overall impact of the changing number of regulations faced by firms on Canadian economic activity.
Measuring regulatory burden is complex, and there is not a consensus on the best approach. The novel Transport Canada – KPMG measure is based on counting the number of regulatory provisions in Federal legislation and is one of several aggregate measures of regulatory burden available. It shows that regulatory requirements in Canada rose 2.1% per year from 2006 to 2021. A measure from the US based Mercatus Center that is not as broadly defined showed an increase in the number of provisions rising 1.1% per year over the same period while the OECD measure of product market regulation (PMR) that tracks the stringency, rather than the number, of regulations declined.
Using the newly developed Transport Canada – KPMG measure, regression estimates show that regulatory accumulation from 2006 to 2021 is associated with a decline in gross domestic product (GDP) growth by 1.7 percentage points and reduced employment growth by 1.3 percentage points in the business sector. A smaller decline on labour productivity of 0.4 percentage points was also estimated. The business sector investment growth was lowered by an estimated 9.0% (with the effect being bigger for small firms than for large firms) for the period 2006 to 2021 and that regulatory accumulation is associated with lower business entry and exit rates.
Understanding economy wide costs and benefits from regulations is challenging. The results of the study provide a first indication for Canada of the estimated impacts of the changing number of regulations over time on businesses. While the results of the study point to potentially important costs for the economy, it is not meant to reflect a full economic assessment of the benefits of regulations nor economic impacts associated with not introducing regulations.
1 Introduction
Regulations are intended to correct market failures; ensure
the good functioning of markets; and protect the public interest, such as
safety, health and the environment. Despite their good intent, regulations and
their accumulation over time impose real costs to businesses and may have a
negative impact on economic growth and competitiveness.
This paper examines the effect of regulatory accumulation on aggregate economic growth. This represents one aspect of the costs and benefits associated with regulation. Regulations also have social, health and environmental effects that are not captured in their effects on economic performance. Those non-economic effects must be included when examining the costs and benefits and the overall impact of regulations.
Aggregate economic growth arises from growth occurring at individual firms and firm turnover through firm entry and firm exit. Therefore, the paper will examine the effect of regulatory accumulation on those two main drivers of aggregate economic growth separately: firm growth and business dynamism. Because an increase in regulatory requirements may disproportionately burden small businesses compared with large businesses, the paper will also examine whether the economic effect of regulations differs between small and large firms.
The accumulation of an increasingly complex set of
regulatory constraints is the most striking characteristic in the history of
regulations (Dawson and Seater, 2013; Coffey, McLaughlin and Peretto, 2020).
Dawson and Seater (2013) constructed a measure of regulatory accumulation using
the page counts of regulatory text and found that the measure rose by 3.5% per
year from 1949 to 2005 in the United States. A more sophisticated measure of
regulatory accumulation based on the number of restrictive provisions was
constructed by the Mercatus Center located at George
Mason University (Al-Ubaydli and McLaughlin, 2015; McLaughlin and Sherouse,
2019). According to that measure, the total number of restrictive provisions
rose 1.9% per year in the United States from 1970 to 2021.
A comprehensive examination of the effect of introducing new
regulations on economic performance must consider two factors. First, a
regulation is examined on its own for its effect on economic performance.
Second, the introduction of new regulations adds to the stock of regulations
already in place and increases the overall burden of regulations. A single
regulation may appear to have a net beneficial impact on economies when
examined on its own—such as a pro-competitive regulation and a reduction in
entry barriers—but may still have a net negative effect on economic growth when
it is added to other regulations (Dawson and Seater, 2013; Coffey, McLaughlin
and Peretto, 2020).
Many of the previous studies focused on the effect of
regulations on economic growth on its own and developed regulation measures
that target specific economic activities, such as pro-competitive measures
(reduction in entry barriers, privatization of public-owned enterprises, and
reduction in trade barriers and environmental regulation) (Cette, Lopez and Mairesse,
2014; Gu and Lafrance, 2008; Parker and Kirkpatrick, 2012, for a review of the
literature). These measures are then used to examine the effects of regulation
on firm and industry performance and economic growth. The Organisation for
Economic Co-operation and Development (OECD) measure of product market
competition is one of the most notable such initiatives (Conway and Nicoletti,
2006).
The OECD indicator of product market regulation (PMR)
measures the restrictiveness of regulations to market competition or the extent
to which regulations create barriers to entrepreneurship and restrict
competition in domestic markets where technology and demand conditions make
competition viable. The indicator of PMR was developed for several
non-manufacturing sectors, initially including energy, transport and
communication, which were then extended to include retail distribution and
professional services in 30 OECD countries (Conway and Nicoletti, 2006).
The other notable measure of regulation is the World Bank’s
Ease of Doing Business Index, which is also used in empirical studies to
examine the relationship between regulations and economic performance.
This paper differs from the previous studies that focused on specific areas of regulations. Rather, it centres on the accumulation of regulations over time and examines the effect of regulatory accumulation on economic performance and economic growth. The work became feasible after an experimental measure on the accumulation of regulatory provisions was developed for Canada by KPMG, in collaboration with Transport Canada (Transport Canada and KPMG, 2021). The measure by KPMG and Transport Canada (which will hereafter be referred to as the KPMG measure) is conceptually similar to the one (called RegData) developed by the Mercatus Center for the United States, which was further expanded to include Canada and several other countries (Al-Ubaydli and McLaughlin, 2015; McLaughlin and Sherouse, 2019). This paper uses the KPMG measure.
To the author’s knowledge, this is the first paper to
examine the effect of regulatory accumulation on firm performance and business
dynamism, as well as the differential effect of regulatory accumulation between
small and large firms. The previous studies focused on the aggregate effect of
regulatory accumulation on industry-level or economy-level performance (Dawson
and Seater, 2013; Coffey, McLaughlin and Peretto, 2020).
The rest of the paper is organized
as follows. Section 2 presents the novel KPMG measure of regulatory accumulation
for Canada and compares it with the regulation measure from the Mercatus Center
(RegData) and the OECD PMR measure for Canada. Section 3 shows the empirical
results on the relationship between regulation and firm performance and firm
dynamics. Section 4 concludes.
2 Measuring regulation: A
comparison of various measures
This section presents a brief discussion of the newly developed KPMG measure
that will be used for this study. A more detailed discussion of that measure
was prepared by Transport Canada and KPMG (2021), and it summarizes the main
difference between the KPMG measure and other measures that focus on specific
areas of regulations. As the main interest in the present paper is the effect
of regulation on economic performance and industry competitiveness, the main
comparison will be with the OECD PMR measure, which focuses on the
restrictiveness or “tightness” of regulations for competition. This comparison
will help to better understand the aspects of regulations that the KPMG measure
captures.
The measure will also be compared with RegData from the
Mercatus Center, which also developed a measure of regulatory accumulation for
Canada. Conceptually, both measures are similar and represent a count of
regulatory provisions. But there are some distinctions in the implementation,
discussed below, that give rise to differences between the two measures.
KPMG created a regulation measure using a similar approach
to the Mercatus Center RegData—by quantifying the overall burden of regulation.
It derived a measure of regulation burden by counting the number of regulatory
requirements and then using an artificial intelligence routine to assign them
to particular industries.
Regulations are scanned to be
placed into 1 of 10 categories: prohibitive provisions, restrictive provisions,
permissive permissions, operational requirements, administrative requirements,
ministerial compliance costs, ministerial enforcement costs, ministerial
administrative costs, no requirements or unclassified. The first five
categories (prohibitive provisions, restrictive provisions, permissive
permissions, operational requirements and administrative requirements) impose
burdens on industry participants. The next three categories (ministerial
compliance costs, ministerial enforcement costs and ministerial administrative
costs) impose burdens on the regulators. The final two (no requirements and
unclassified) are residual categories.
It should be mentioned that the collaboration between KPMG
and Transport Canada is much more than the development of a measure of
regulatory accumulation used to examine the effect of regulations on economic
performance in this paper. The main purpose of that collaboration is to create
an inventory of regulations or a regulatory platform that can be accessed by
businesses for their own economic activities, such as starting a new business.
The OECD PMR measure focuses on regulations that restrict
competition in product markets. Regulations covered in the OECD PMR measure
vary by industry. They include barriers to entry (available for all
industries), public ownership (all industries except road freight), vertical
integration (only for gas, electricity and railways), market structure (only
for gas, telecommunications and railways) and price controls (only for road
freight).
The OECD time series measure of PMR was developed for seven
non-manufacturing industries: gas, electricity, post, telecommunications,
passenger air transport, railways and road freight. These are the network or
infrastructure industries that supply inputs to other downstream industries,
and the performance of the network industries affects the overall performance
of the entire economy. By contrast, the KPMG measure in this paper includes
every regulation issued by the federal government and covers all industries. It
counts the number of regulatory provisions over time to measure the overall
burden of regulatory accumulation on industries, including prohibitions,
restrictions, permissions and administrative reporting. It also measures
enforcement costs.
The OECD measure essentially captures the tightness or
restrictiveness of regulations and the extent that regulations influence market
competition. Consider the airline industry in Canada, for instance. Various
regulations on safety and customer relations have been imposed on airlines. The
number of such regulations has increased over the years. But the industry has
gone from one that was protected against entry to one where entry by domestic
carriers is now allowed, and therefore the intensity of the entry barriers has
declined. Therefore, the OECD measure of PMR shows regulations for air
transportation becoming less restrictive over time and more friendly to
competition. By contrast, the KPMG regulatory accumulation measure shows that
regulations increased over time as new regulations are added to existing ones.
The KPMG and OECD measures serve different purposes in
empirical studies on regulation and economic performance. To examine the
economic effect of regulations related to competition, the OECD measure on PMR
and other measures that focus on specific types of regulations, such as the
World Bank’s Ease of Doing Business Index, are preferred. If, instead, the goal
is to examine the cumulative effect and overall burden of regulations, then the
regulation measure from KPMG is preferred.
These two regulation measures (from the OECD and KPMG) are
useful for evaluating regulatory impact on economic performance and for
designing regulations. When considering the introduction of new regulations, it
is necessary to examine both aspects—one in isolation and the other in relation
to other regulations—for the overall cumulative burden of regulations.
As of now, the KPMG measure includes only regulations at the
federal level. Regulations at the provincial and municipal levels will be
included in the future.
2.1 Trend in regulatory
requirements
Chart 1 presents the number of regulatory requirements,
along with their two main components: industrial regulatory requirements and
ministerial regulatory requirements. The number of total regulatory
requirements increased by 2.1% per year from 2006 to 2021. The number of total
regulatory requirements in 2021 was about 37.0% higher than that in 2006. Most
regulatory requirements were imposed on industries (95.2% in 2019), while a
very small share of the regulatory burden was placed on departments and other
government organizations responsible for administering these regulations (4.8%
in 2019).
Data table for Chart 1
| Year | Total | Industrial | Ministerial |
|---|---|---|---|
| number in thousands | |||
| Source: Author’s tabulation from the KPMG and Transport Canada database on regulatory requirements. | |||
| 2006 | 234.2 | 223.6 | 10.5 |
| 2007 | 238.2 | 227.4 | 10.8 |
| 2008 | 241.0 | 229.9 | 11.1 |
| 2009 | 239.7 | 228.5 | 11.2 |
| 2010 | 247.6 | 236.4 | 11.2 |
| 2011 | 252.3 | 240.8 | 11.5 |
| 2012 | 260.9 | 249.3 | 11.7 |
| 2013 | 269.2 | 256.8 | 12.4 |
| 2014 | 271.3 | 258.8 | 12.6 |
| 2015 | 283.9 | 271.0 | 12.9 |
| 2016 | 289.8 | 276.7 | 13.1 |
| 2017 | 291.3 | 278.1 | 13.2 |
| 2018 | 305.3 | 291.5 | 13.8 |
| 2019 | 299.5 | 285.0 | 14.5 |
| 2020 | 307.3 | 292.5 | 14.8 |
| 2021 | 320.9 | 305.5 | 15.3 |
For the empirical analysis on the effect of regulations on
firm performance, industrial regulatory requirements will be used. They impose
real compliance and administrative costs on firms. By contrast, ministerial
regulatory requirements impose costs on the ministers who administer these
regulations. From 2006 to 2021, the number of industrial regulatory
requirements increased by 2.1% per year, while the number of ministerial
regulatory requirements rose by 2.5% per year.
Table 1 presents annual growth in the number of total,
industrial and ministerial regulatory requirements from 2006 to 2021 by major
sector of the Canadian economy. The largest increase in the number of
industrial regulatory requirements was in the e-communications sector, followed
by media, financial services and electricity. For the agriculture and forestry
sector and the fisheries sector, the number of regulatory requirements declined
over this period.
| Sector | Total | Industrial requirements | Ministerial requirements | Share of industrial requirements |
|---|---|---|---|---|
| Source: Author’s tabulation from the KPMG and Transport Canada database on regulatory requirements. | ||||
| E-communications | 5.54 | 5.53 | 6.74 | 98.20 |
| Media | 5.01 | 4.86 | 0.49 | 96.98 |
| Financial services | 2.80 | 2.86 | 1.92 | 93.94 |
| Electricity | 2.58 | 2.54 | 3.64 | 97.01 |
| Transport | 2.42 | 2.40 | 2.99 | 96.50 |
| Distribution | 2.30 | 2.24 | 3.33 | 95.31 |
| Manufacturing | 2.27 | 2.24 | 2.85 | 94.97 |
| Construction | 1.67 | 1.65 | 2.67 | 97.03 |
| Mining and quarrying (including oil extraction) | 1.37 | 1.34 | 2.14 | 96.94 |
| Business services | 1.13 | 1.11 | 1.52 | 95.88 |
| Hotels and restaurants | 0.67 | 0.68 | 0.00 | 98.16 |
| Fisheries | -0.51 | -0.57 | 1.53 | 97.19 |
| Agriculture and forestry | -0.88 | -0.75 | -2.56 | 92.90 |
| Grand total | 2.10 | 2.08 | 2.50 | 95.47 |
2.2 A comparison of alternative
regulation measures
The concept of regulatory requirements from KPMG is similar
to that of RegData developed by the Mercatus Center. As shown in Chart 2, both
measures show similar increasing trends in regulatory requirements. The KPMG
measure rose at a faster rate than that of RegData. The KPMG measure increased
2.1% per year for Canada from 2006 to 2021, while the RegData measure rose 1.1%
per year for Canada over the same period. The level of regulatory requirements
also differs between the KPMG measure and the RegData measure. The number of
requirements in the KPMG measure is higher than that in the RegData measure, as
RegData has about 30% of the restrictions for Canada found in the KPMG measure.
This suggests that the concept of restrictions and regulatory requirements is
broader for KPMG than for RegData.
Data table for Chart 2
| Year | KPMG | RegData Canada |
|---|---|---|
| number of regulatory requirements (2006=1) | ||
| Source: Author’s tabulation from the KPMG and Transport Canada database on regulatory requirements and the Mercatus Center RegData Canada. | ||
| 2006 | 1.00 | 1.00 |
| 2007 | 1.02 | 1.01 |
| 2008 | 1.03 | 1.02 |
| 2009 | 1.02 | 1.03 |
| 2010 | 1.06 | 1.03 |
| 2011 | 1.08 | 1.04 |
| 2012 | 1.11 | 1.06 |
| 2013 | 1.15 | 1.08 |
| 2014 | 1.16 | 1.10 |
| 2015 | 1.21 | 1.12 |
| 2016 | 1.24 | 1.14 |
| 2017 | 1.24 | 1.13 |
| 2018 | 1.30 | 1.17 |
| 2019 | 1.28 | 1.16 |
| 2020 | 1.31 | 1.17 |
| 2021 | 1.37 | 1.18 |
Chart 3 presents the OECD measure of PMR and the KPMG
measure of regulatory accumulation for the network sectors (energy, transport
and telecommunication). The OECD measure shows a long-term decline in the
restrictiveness of product market competition in the network sectors since
1975, while the KPMG measure shows a trend in regulatory accumulation in these
sectors over the last 20 years. While regulatory requirements increased in the
network sectors after 2006 according to the KPMG measure, the regulations in
these sectors became less restrictive as a result of deregulation in certain
industries, such as the telecommunication and air transport sectors.
Data table for Chart 3
| Year | KPMG | OECD network sector PMR |
|---|---|---|
| index of regulations (2006=1) | ||
|
||
| 1975 | .. not available for a specific reference period | 3.08 |
| 1976 | .. not available for a specific reference period | 3.08 |
| 1977 | .. not available for a specific reference period | 3.08 |
| 1978 | .. not available for a specific reference period | 3.08 |
| 1979 | .. not available for a specific reference period | 3.08 |
| 1980 | .. not available for a specific reference period | 3.08 |
| 1981 | .. not available for a specific reference period | 3.08 |
| 1982 | .. not available for a specific reference period | 3.08 |
| 1983 | .. not available for a specific reference period | 3.08 |
| 1984 | .. not available for a specific reference period | 3.08 |
| 1985 | .. not available for a specific reference period | 2.97 |
| 1986 | .. not available for a specific reference period | 2.92 |
| 1987 | .. not available for a specific reference period | 2.75 |
| 1988 | .. not available for a specific reference period | 2.25 |
| 1989 | .. not available for a specific reference period | 2.07 |
| 1990 | .. not available for a specific reference period | 2.07 |
| 1991 | .. not available for a specific reference period | 1.98 |
| 1992 | .. not available for a specific reference period | 1.97 |
| 1993 | .. not available for a specific reference period | 1.97 |
| 1994 | .. not available for a specific reference period | 1.96 |
| 1995 | .. not available for a specific reference period | 1.70 |
| 1996 | .. not available for a specific reference period | 1.41 |
| 1997 | .. not available for a specific reference period | 1.29 |
| 1998 | .. not available for a specific reference period | 1.24 |
| 1999 | .. not available for a specific reference period | 1.07 |
| 2000 | .. not available for a specific reference period | 1.02 |
| 2001 | .. not available for a specific reference period | 1.02 |
| 2002 | .. not available for a specific reference period | 0.91 |
| 2003 | .. not available for a specific reference period | 0.91 |
| 2004 | .. not available for a specific reference period | 0.91 |
| 2005 | .. not available for a specific reference period | 1.00 |
| 2006 | 1.00 | 1.00 |
| 2007 | 1.02 | 1.00 |
| 2008 | 1.03 | 1.00 |
| 2009 | 1.02 | 1.00 |
| 2010 | 1.06 | 1.00 |
| 2011 | 1.08 | 1.00 |
| 2012 | 1.11 | 1.00 |
| 2013 | 1.15 | 0.99 |
| 2014 | 1.16 | 0.99 |
| 2015 | 1.21 | 0.99 |
| 2016 | 1.24 | 0.99 |
| 2017 | 1.24 | 0.96 |
| 2018 | 1.30 | 0.87 |
The KPMG measure as a count of regulatory requirements gives
equal weight to regulations that may be pro-competition and those that may be
less so. The OECD measure shows that competition-related regulations became
friendlier toward competition. By contrast, the KPMG measure demonstrates that
the number of regulatory requirements increased in the network sectors as new
regulations were added to existing ones.
3 Measuring the economic impact
of regulation
Growth at the industry and total economy levels arises from
growth at the firm level and from firm turnover (the entry and exit of firms).
To examine the economic impact of regulations, this paper will focus on the
source of economic growth at the firm level and, in particular, examine the
effect of regulations on firm growth and firm turnover.
This section will first present a theoretical framework and
regression model that will be used to examine the effect of regulatory
accumulation on firm performance and firm dynamics, from the entry and exit of
firms. It will then discuss the firm-level data used for the empirical analysis
and present regression results.
3.1 The effect of regulatory
accumulation on firm performance and firm turnover
This subsection presents a theoretical framework about how
regulatory accumulation affects firm performance and firm turnover. The
discussion is mostly informal, but a more rigorous and formal presentation of
the models can be found in the work of Coffey, McLaughlin and Peretto (2020)
and the Swedish Agency for Growth Analysis (2010).
Regulatory accumulation imposes costs and burdens on firms.
Perhaps the most direct and obvious cost of regulation is compliance costs—the
costs that businesses must incur to fulfill regulatory obligations. The costs
may include filling out paperwork, navigating the set of rules for starting up
a new business, and purchasing new equipment to meet mandated standards such as
safety and environmental standards.
The direct compliance costs of regulation are expected to
further affect firm investment, firm growth and firm dynamics, and ultimately
aggregate output and productivity growth. These indirect costs for economies
are found to be much higher than the direct compliance costs (Swedish Agency
for Growth Analysis, 2010).
The first component of the indirect costs of regulatory
accumulation is its effect on firm investment and firm growth. The compliance
costs of regulation increase the costs of production, reduce the demand for
products and therefore lower returns to business investment, leading to less
firm investment and innovation activity. As investment is a major contributor
to firm growth in output and productivity, a decline in investment activities
resulting from regulatory accumulation leads to a decline in output growth and
productivity growth.
The effect on firm employment is ambiguous and is a result
of two main factors. On the one hand, the increase in compliance costs of
regulatory accumulation leads to the hiring of additional staff for regulatory
compliance. On the other hand, the reduced production from regulatory
accumulation leads to a decline in employment. The overall impact of the
regulatory burden is a net effect of these two offsetting factors. If the
decline in employment from reduced sales is larger than the increase in employment
from compliance, overall employment will decline. If the increase from
compliance is larger than the decline from reduced sales, overall employment
will increase.
Second, regulatory accumulation is also expected to have a
negative effect on firm turnover and business dynamism. Regulation is expected
to reduce business start-ups as additional costs from regulatory compliance
reduce the value of potential entry, and firms will be less likely to enter an
industry. The decline in business entry from regulatory accumulation is
expected to further lead to an overall decline in business dynamism, the
process of creative destruction and firm exit.
As firm entry and exit and creative destruction are major
sources of innovation and economic growth (Schumpeter, 1942), the decline in
business dynamism from regulatory accumulation will reduce innovation
activities and economic growth. Empirical studies for Canada and other
countries show that a significant portion of productivity growth is from entry
and exit. For example, the contribution of entry and
exit to labour productivity growth was found to be about 20% over a 10-year
period in the manufacturing sector (Baldwin and Gu,
2006; Bartelsman et al., 2009). The contribution of entry and exit to
labour productivity growth is even larger in service sectors where entry and
exit are more frequent, such as the retail sector (Baldwin and Gu, 2011).
The increase in regulatory requirements may
disproportionately burden small businesses compared with large businesses.
Larger firms have more capacity to comply with additional regulatory
requirements, because they are likely to have lawyers and dedicated staff on
payroll or to contract out regulatory compliance. By contrast, small businesses
may have limited resources and expertise for regulatory compliance. The costs
of regulatory compliance can be seen as fixed costs that lead to economies of
scale. As a result of economies of scale for regulatory compliance, large firms
will have lower unit costs related to compliance, because they can spread fixed
compliance costs over larger production output than small firms. For example,
Tu (2020) found that in Canada, the direct compliance costs related to the
costs for filling forms to meet regulatory requirements, as a share of total
revenue, are negatively related to firm size.
Conversely, large businesses are more complex and often
involve more lines of business than their smaller counterparts. Large
businesses must navigate a more complex set of regulations compared with small
businesses. Therefore, economies of scale in regulatory compliance may be
limited for large firms. Regulatory accumulation may have a greater effect on
firm growth among large firms, especially when indirect costs of regulatory
accumulation are included.
The discussions above give rise to four sets of equations on
the economic effect of regulations that will be estimated in this paper.
Regression analysis will be used to estimate these equations. Essentially, the
performance of firms with different exposure to cumulative regulation burdens
will be compared to get an estimate of the effect of regulation on firm
performance after controlling for other factors that may affect performance.
First, the regression equation for firm growth in output,
employment and productivity will be estimated:
where
is firm growth in
employment, value-added output and labour productivity over a period in year
for firm
that is assigned industry ;
is
the number of regulatory requirements in industry
in year ;
is the dummy variable for small
firms, defined as firms with fewer than 100 employees;
is
the cyclical variable that controls for cyclical changes in the dependent
variable over time;
is the set of firm fixed
effects; and
is the error term for the regression.Note
The cyclical variable is measured by capacity utilization in all industries.
The set of firm fixed effects is included to account for
firm-specific factors that affect growth, such as business management skills,
technical prowess, specific human capital and other firm-specific intangibles
that affect growth.
In the empirical estimation, firm growth, , in year
is defined as the
growth of output, employment and labour productivity over three years for firm
from year
to year . The empirical specification in
equation (1) is based on the hypothesis that regulatory requirements in a year
will have an effect on firm growth in the future. For robustness, firm growth
over a period of one or two years is used in the regression. The results are
similar.
To include firm growth for entry and exit over these three
years, a generalized form of firm growth is used:
For this definition, firm growth for entry will be 2, while
firm growth for exit will be -2. For a robustness check, firm growth calculated
among continuers will also be used as the dependent variable in the regression.
The main interest of this paper is the coefficient estimate , which is expected to be negative for
growth in output and labour productivity. For employment growth, it could be
either positive or negative.
The second set of regressions relates to firm entry and exit
rates:
where
is entry or exit
rates for industry
in year ,
is
the number of regulatory requirements in industry
in year , is the set of year dummies, is the set of industry dummies and is the error term for the regression.
The entry rate for year
is calculated as the share
of new firms from year
to year
in the number of firms
in year . The exit rate is calculated as the share of firms that exited
from year to in the number of firms in year . These represent annual entry and exit rates. Entry and exit rates over a longer
period, such as three years, will also be estimated and be regressed upon to
test the robustness of the results. The empirical specification in equation (3)
is based on the hypothesis that regulatory requirements in year
will
have an effect on firm dynamics in the future.
The entry and exit rates are also calculated using the share
of total revenue to account for the fact that average entry and exits are
smaller than average incumbents.
The main interest is the coefficient estimate , which is expected to be negative for
both entry and exit rates. That is, regulatory accumulation has a negative
effect on firm entry and exit or business dynamism.
The third set of regressions being estimated is used to
examine the difference in the effect of regulatory accumulation between small
firms and large firms. For that purpose, the interaction term of regulatory
accumulation and firm size is included in regression (1):
If regulatory accumulation disproportionally affects small
firms, the coefficient estimate
will be negative.
The fourth set of regressions is for investment in tangible
assets such as machinery and equipment (M&E) and non-residential
construction:
The dependent variable
is either the incidence
of investment or investment intensity defined as real investment per unit of
employment in logarithm. The incidence of investment is a binary variable that
is 1 if the investment in tangible assets is positive and 0 if there is no
investment in tangible assets.
is the growth in
real gross output for a firm in a previous period from year
to .
When equations for the incidence of investment are estimated, the sample of
firms includes all those with or without positive investment. When equations
for investment intensity are estimated, the sample of firms used for estimation
is restricted to those with positive investment. All regressions control for
firm fixed effects, .
Ordinary least squares is used to estimate all four
equations, including incidence of firm investment. For the regressions at the
firm level (equations [1], [4] and [5]), cluster robust standard errors are
reported to take into account the possible correlation of firms belonging to
the same industry at the North American Industry Classification System (NAICS)
four-digit level of industry aggregation (Moulton, 1990).
3.2 Empirical results
The main data used for estimating firm performance are the National
Accounts Longitudinal Microdata File (NALMF), which is available for the years
after 2000. The NALMF was developed by combining several data sources,
including administrative tax records (T2 Corporation Income Tax Return and T4
Statement of Remuneration Paid).
The database covers both incorporated and unincorporated firms. The file
provides information from the income statement, balance sheets for each
incorporated firm that files a T2 and T4 employment data for those firms. The
firms in the file are assigned NAICS four-digit codes, which are linked to KPMG
regulation data available at the NAICS four-digit level of industry
aggregation.
Output and investment from the database are nominal values
that are deflated by output and investment deflators at the industry level to
derive output and investment at constant prices.
The period of the study is from 2006 to 2019, as regulation
measures from KPMG cover only a period starting in 2006. The
data from 2020 to 2021 during the COVID-19 pandemic are excluded, because the
effect of regulations may be compounded by the effects of the pandemic. The
sample of firms is further restricted to all incorporated firms in the business
sector that have at least one employee.
The main variable of interest is the number of industrial
regulatory requirements. Regulatory requirements borne by industries impose
burdens and costs on them, which affect firm performance. Ministerial
requirements for departments and other government organizations, which account
for a small fraction (5%) of the total number of regulatory requirements,
represent costs for these organizations that administer the regulations.
Therefore, ministerial requirements will not be included in the regression when
examining the effects of regulation on business performance.
Table 2 presents summary statistics of the main variables
used for regression. Mean value-added growth in a three-year period is 0.011,
or 1.1%. Mean employment growth in a three-year period is 0.9%, while mean
labour productivity growth in a three-year period is 0.1%. The mean entry and
exit rates in a year as a share of firm counts are both 0.09, or 9%. Mean entry
and exit rates as a share of revenue are 3% for entry rates and 4% for exit
rates, which are lower than their share in firm counts, as entrants and exiters
tend to be smaller than incumbents. The incidence of investment is 0.41. That
is, about 41% of firms in the sample invested in tangible assets. Mean
investment per worker in log among firms with positive investment is 6.31,
which represents $550 per worker in 2012 prices.
| Variables | Number of observations | Mean | Standard deviation |
|---|---|---|---|
| Notes: The number of observations represents firm-year pairs for all variables, except for entry and exit rates, for which the number of observations represents industry-year pairs. The growth in output, employment and labour productivity in a year is calculated for the next three years, and, therefore, the growth variable is not available for all firm-year pairs.
Source: Author’s tabulation from the National Accounts longitudinal microdata file. |
|||
| Output growth | 4,225,264 | 0.011 | 0.573 |
| Employment growth | 4,767,204 | 0.009 | 0.524 |
| Labour productivity growth | 4,225,264 | 0.001 | 0.484 |
| Entry rate as share of firm counts | 2,418 | 0.088 | 0.045 |
| Exit rate as share of firm counts | 2,416 | 0.086 | 0.036 |
| Entry rate as share of revenue | 2,418 | 0.032 | 0.064 |
| Exit rate as share of revenue | 2,416 | 0.038 | 0.068 |
| Regulation in log | 7,018,037 | 6.724 | 1.643 |
| Incidence of investment | 7,672,440 | 0.412 | 0.492 |
| Investment per worker in log | 2,415,917 | 6.331 | 2.165 |
| Cyclical indicator | 7,672,440 | 81.020 | 2.527 |
3.2.1 Effect of regulatory
accumulation on firm performance
Table 3 presents regression results for firm growth in
output, employment and labour productivity. Growth is expressed as three-year
cumulative growth in these variables. Firm fixed effects are included in all
these regressions, and cluster robust standard errors are reported in
parentheses.
| Variables | Output growth | Employment growth | Labour productivity growth |
|---|---|---|---|
|
Note: All regressions control for firm fixed effects. Source: Author’s estimation from the National Accounts longitudinal microdata file. |
|||
| Regulation | -0.131 Table 3 Note ** | -0.110 Table 3 Note *** | -0.019 Table 3 Note * |
| Small firms | 0.406 Table 3 Note *** | 0.472 Table 3 Note *** | -0.055 Table 3 Note *** |
| Cyclical indicator | 0.001 | 0.006 Table 3 Note *** | -0.004 Table 3 Note *** |
| Constant | 0.380 | -0.212 | 0.543 Table 3 Note *** |
| R-squared | 0.367 | 0.334 | 0.276 |
Regulatory requirements are negatively correlated with
growth in output, employment and labour productivity. All these coefficients
are statistically significant at the 5% level.
The coefficient estimate on the novel KPMG regulatory burden measure suggests that a 1%
increase in regulatory accumulation reduces output growth by 0.131 percentage
points in three-year cumulative growth, or 0.044 percentage points in annual
growth. Meanwhile, a 1% rise in regulatory accumulation decreases employment
growth by 0.110 percentage points in three-year cumulative growth, or 0.036
percentage points in annual growth. The effect of regulatory accumulation on
growth in firm labour productivity is smaller. A 1% increase in regulatory
accumulation reduces labour productivity growth by 0.019 percentage points in
three-year cumulative growth, or 0.006 percentage points in annual growth.
To estimate the effect of regulatory accumulation on GDP
growth for all firms in the sample from 2006 to 2021, the estimated coefficient
derived from the sample of firms over the 2006-to-2019 period is multiplied by
the growth in industrial regulatory requirements in a NAICS four-digit industry
from 2006 to 2021 to derive the effect on value-added growth in that industry.
These industry effects are then aggregated across industries to derive the
effect of regulatory accumulation on aggregate value-added growth using value
added as weight. As the firms in the data include all those in the business
sector, the aggregate effect estimated represents the effect of regulations in
the business sector.
This procedure for calculating the effect of regulatory
accumulation on value-added growth in the business sector is the same as an
alternative procedure. In this alternative procedure, industrial regulatory
requirements at the NAICS four-digit level are aggregated to derive an
aggregate index of industrial regulatory requirements using value added as
weight. The growth in these value-added weighted industrial regulatory
requirements is then multiplied by the estimated coefficient on regulatory
requirements to derive the effect of regulatory requirements on aggregate
value-added growth in the business sector. This alternative procedure will be
used to estimate the effect of regulations on GDP growth.
Log growth in this aggregate weighted index of industry
regulatory requirements is 38.8% for the 2006-to-2021 period. Growth in this
aggregate weighted index is higher than growth in a simple sum of industrial
regulatory requirements, whose log growth is 31.2%. This is because growth in
industrial regulatory requirements is higher for large industries with
relatively high value added.
This 38.8% log growth in weighted regulatory requirements is estimated to have
reduced real GDP growth by a cumulative 1.7 percentage points,Note
or 0.1 percentage points per year, over the 2006‑to-2021 period.
The effect of regulatory accumulation on employment growth
can be estimated similarly. Aggregate employment-weighted industrial regulatory
requirements increased by 0.35 log points. The effect of the accumulation of
industrial regulatory requirements on employment growth from 2006 to 2021 is
estimated to be 1.3 percentage points over that period, or 0.1 percentage
points per year.
The effect of regulatory requirements on growth in labour
productivity is much smaller. Regulatory requirements reduced labour
productivity growth by a cumulative 0.4 percentage points for the 2006-to-2021
period.
The coefficient on the dummy variable for small firms is
positive and statistically significant at the 1% level for employment and
output growth. It is negative and statistically significant at the 1% level for
labour productivity growth. That is, small firms have higher growth in value
added and employment than large firms, but lower growth in labour productivity.
Small firms in Canada are scaling up and catching up to large firms in
employment and output. However, small firms are not improving their relative
labour productivity compared with large firms.
The coefficient estimate on the dummy variable for small
firms suggests that three-year growth in value added is 0.41 log points higher
for small firms than large firms, which is about 14% per year in annual growth
in value added among small firms.Note
Three-year growth in employment is 0.47 log points higher
among small firms compared with large firms, while annual employment growth is
16% higher per year among small firms.
Small firms tend to have lower labour productivity growth;
three-year growth in labour productivity among small firms is about 6% lower
than that among large firms.
The cyclical variable is positively related to employment
and output growth and negatively related to labour productivity growth. This is
consistent with the view that employment and output are procyclical, while
labour productivity is countercyclical (Fernald and Wang, 2016).
Table 3 shows the regression results when the dependent
variable for firm growth is estimated over a three-year period. As a robustness
check, the regression equation is estimated with firm growth over a two-year
period. These results are presented in Table A.1 in the appendix. The estimated
effects of regulations on growth in output, employment and labour productivity
are slightly larger when calculated over a two-year period. For example, a 1%
increase in regulatory accumulation reduces output growth by 0.121 percentage
points in two-year cumulative growth, or 0.061 percentage points in annual
growth. By contrast, when the three-year growth in firm output is used for
regression, a 1% increase in regulatory accumulation is estimated to reduce
output growth by 0.044 percentage points per year.
As another robustness check, the regression equation is
estimated with firm growth over a three-year period among continuers. The
results are presented in Table A.2 in the appendix. The estimated effects of
regulations on growth in output, employment and labour productivity are very
similar to those reported in Table 3, where a generalized form of growth is
calculated for all firms, including entrants, exiters and continuers.
To sum up, changes in the novel KPMG measure of regulatory accumulation from 2006 to 2021 are estimated to have reduced output growth by 1.7 percentage points and employment growth by 1.3 percentage points. The effect on labour productivity growth is small; regulatory accumulation reduced labour productivity growth by 0.4 percentage points.
3.2.2 Effect of regulatory
accumulation on business dynamism
Table 4 presents the regression results for entry and exit
rates.
| Variables | Entry rate as share of firm counts | Exit rate as share of firm counts | Entry rate as share of revenue | Exit rate as share of revenue |
|---|---|---|---|---|
|
Note: All regressions control for industry fixed effects at the North American Industry Classification System four-digit level of aggregation. Source: Author’s estimation from the National Accounts longitudinal microdata file. |
||||
| Regulation | -0.018 Table 4 Note *** | -0.007 Table 4 Note ** | -0.105 Table 4 Note *** | -0.017 Table 4 Note ** |
| Cyclical indicator | 0.001 Table 4 Note ** | 0.001 Table 4 Note ** | 0.001 Table 4 Note ** | 0.002 Table 4 Note *** |
| Constant | 0.150 Table 4 Note *** | 0.080 Table 4 Note *** | 0.002 | 0.001 |
| R-squared | 0.732 | 0.606 | 0.237 | 0.212 |
The coefficient estimates on regulatory requirements are
negative and statistically significant at the 5% or 1% level. This suggests
that the accumulation of regulatory requirements reduces business dynamism. A
1% increase in regulatory requirements is related to a 0.02 percentage point
decline in the entry rate, measured as the share of entrants in the number of
firms or in total revenue. For the exit rate, a 1% increase in regulatory
requirements is associated with a 0.01 percentage point decline in the share of
exits in the number of firms or a 0.02 percentage point decrease in the share
of exits in total revenue.
To estimate the overall impact of regulatory accumulation on
the entry and exit rates, firm counts are the correct weights to aggregate
industrial regulatory requirements. The aggregate index of regulatory
requirements weighted by firm counts across industries rose by 0.33 log points
over the 2006-to-2021 period. The increase in regulatory requirements over this
period reduced the entry rate in firm counts by 0.01 (= 0.33*0.02), or 1
percentage point. Meanwhile, the increase in regulatory requirements during
this period reduced the exit rate by 0.003 (= 0.33*0.01), or 0.3
percentage points.
The annual entry and exit rates in the sample are about 8%
(Table 2). This estimated reduction in the entry and exit rates from the
accumulation of regulations over the period represents about a 10% reduction in
the entry rate and a 5% reduction in the exit rate. If the total number of
regulatory requirements had remained at the 2006 level, the entry rate would
have been 1 percentage point, or about 10%, higher in 2021, and the exit rate
would have been 0.5 percentage points, or about 5%, higher.
The coefficient estimates on the cyclical variable are
positive and statistically significant for the entry and exit rates. In the
regressions, the entry and exit rates in a year are defined in a future period
from the current year to the next year. These forward-looking entry and exit
rates are found to be procyclical. This differs from the well-documented
evidence that the current-year entry rate is procyclical and the exit rate is
countercyclical when the entry and exit rates are defined for the current year.Note
3.2.3 Difference in the effect of
regulatory requirements on firm performance by firm size
To examine whether regulatory requirements have a
disproportionally large effect on small firms, the interaction term of
regulatory requirements and the dummy variable for small firms is included in
the regression for firm performance. The results are presented in Table 5. The
regression in Table 5 includes firm fixed effects.
In the regressions with the interaction of regulations and
the small-firm indicator, the coefficient estimate on regulations is the effect
of regulations for large firms. The coefficients on regulations are negative
for all regressions, suggesting that regulatory accumulation has a negative
effect on output growth, employment growth and labour productivity growth for
large firms.
The coefficient estimates on the interaction term of small
firms and regulatory requirements measure the difference in the effect of
regulations between small and large firms. The estimated effect of regulations
on small firms is the sum of the coefficients on regulations and their
interaction with small firms. The sums of these two coefficients measuring the
effect of regulations on small firms are all negative for output growth,
employment growth and labour productivity growth. This suggests that regulatory
accumulation also reduced output growth, employment growth and labour
productivity growth for small firms.
The estimated coefficients on the interaction term of
regulations and the small firm indicator are positive and statistically
significant for all regressions. Therefore, the negative effect of regulations
on firm growth in output, employment and labour productivity is smaller for
small firms compared with large firms.
The coefficient estimate on regulations suggests that, for
large firms, a 1% increase in regulatory accumulation reduces output growth by
0.052 percentage points per year and employment growth by 0.043 percentage
points per year. For small firms, a 1% increase in regulatory accumulation
reduces output growth by 0.043 percentage points per year and employment growth
by 0.036 percentage points per year. The effect of
regulations on output and employment growth was about 20% lower for small firms
compared with large firms.
Once again, the negative effect of regulations on labour
productivity growth was lower for small firms. The negative effect of
regulations on labour productivity growth was about 25% lower for small firms
than for large firms.Note
Overall, regulation accumulation reduced firm growth in
output, employment and labour productivity for small and large firms. The
effect was lower for small firms compared with large firms.
3.2.4 Effect of regulatory
accumulation on investment
Table 6 presents the regression results for investment in
tangible assets, including M&E and construction. The first two columns are
for the regressions on the incidence of investment in tangible assets, while
the last two columns are for regression results for investment per unit of
employment in logarithm. All regressions control for firm fixed effects. The
growth in real output in the past year is also included to examine the effect
of demand growth on investment, which is expected to be positive.
Regulatory accumulation is negatively related to both the
incidence of investment and investment intensity. A 1% increase in regulatory
accumulation reduces the incidence of investment by 0.06 percentage points.Note
The aggregate index of regulatory requirements weighted by firm counts across
industries rose by 0.33 log points for the 2006‑to‑2021 period. The
increase in regulatory requirements over this period reduced the investment
incidence by 0.02, or 2 percentage points. The average incidence of investment
was about 41% in the sample (Table 2). The effect on investment incidence is
small.
Regulatory accumulation also reduces investment intensity,
as indicated by the negative coefficient on regulations in the last two
columns. A 1% increase in regulations reduces investment intensity by 0.269%
(column 3). The aggregate index of regulatory requirements weighted by
employment for firms with positive investment across industries rose by 0.33
log points from 2006 to 2021. The increase in regulatory requirements over this
period reduced investment intensity by 0.09 log points, or about 9.0%. If the
total number of regulatory requirements had remained at the 2006 level,
business sector investment would have been 9.0% higher in 2021.
The negative effect of regulatory accumulation on investment
incidence is similar for large and small firms, because the estimated
coefficient on the interaction term of regulations and the small firm indicator
is not statistically significant. Regulatory accumulation has a bigger negative
effect on investment intensity for small firms than for large firms, as the
coefficient on the interaction term of regulations and small firms is negative
and statistically significant. For large firms, a 1% increase in regulatory
accumulation reduces investment intensity by 0.23 log
points. For small firms, the effect of a 1% increase in regulatory
accumulation is to reduce investment intensity by 0.27 log points. That is, the
negative effect on investment intensity is about 20% higher for small firms
than for large firms.
The other noteworthy finding is that small firms have a
lower incidence of investment but higher investment intensity among those with
positive investments. Growth in demand is positively related to investment
intensity but not investment incidence.
In sum, the effect of regulations on investment is on
intensive margins, not on extensive margins. Regulatory accumulation reduced
business investment by 9% in 2021 through its effect on intensive margins.
4 Conclusions
In Canada, the newly developed Transport Canada – KPMG measure for regulatory burden rose by 2.1% per year, or by a total of 37%, from 2006 to 2021. This increase in the number of regulatory requirements was found to have a negative effect on growth in firm output, employment and labour productivity.
The estimates show that regulatory accumulation over the 2006-to-2021 period reduced gross domestic product (GDP) growth by 1.7 percentage points in the business sector. It also reduced employment growth in this sector by a cumulative 1.3 percentage points. The effect on labour productivity growth was small, at 0.4 percentage points.
The results point to costs associated with increasing numbers of regulatory provisions. However, understanding economy wide costs and benefits from regulations is challenging. The results of the study provide a first indication for Canada of the estimated impacts of the changing number of regulations over time on businesses. While the results of the study point to potentially important costs for the economy, it is not meant to reflect a full economic assessment of the benefits of regulations and costs associated with not introducing regulations.
The estimated effect of regulatory accumulation on GDP is
much smaller than the effect estimated by Coffey et al. (2020). They found that
had regulation been held constant at levels observed in 1980, GDP would have
been nearly 25% higher by 2012 in the United States. The estimates in the
present paper are derived from a large sample of firms and control for
individual firm-level effects such as skills, technical innovation and
organizational innovation that affect growth. By contrast, Coffey et al. (2020)
derived their estimates from aggregate industry data and did not include a
large number of firm-level fixed effects on firm growth, as in this paper.
Regulatory accumulation reduced growth in output,
employment, and labour productivity for small and large firms, but the effect
was lower for small firms. The negative effect of regulations was about 20%
lower among small firms than among large firms for output growth and employment
growth, while it was about 25% lower for labour productivity growth. Large
businesses must navigate a detailed set of regulations compared with small
businesses, because large businesses are more complex and often involve more lines
of businesses. As a result, regulatory accumulation reduced firm growth more
for large firms than for small firms.
Regulatory accumulation over the 2006-to-2021 period was
also found to reduce business sector investment by 9.0%. If the total number of
regulatory requirements had remained at the 2006 level, business sector
investment would have been 9.0% higher in 2021. This negative effect is bigger
for small firms than for large firms. The effect of regulations on investment
is on intensive margins rather than extensive margins.
Finally, regulatory accumulation reduced business start-ups
and business dynamism. If the total number of regulatory requirements had
remained at the 2006 level, the entry rate would have been 1 percentage point,
or about 10%, higher in 2021, and the exit rate would have been 0.5 percentage
points, or about 5%, higher.
Developing a regulatory accumulation measure and studying
its effect on economic performance should be seen to complement existing
economic impact studies on specific regulations, such as pro-competition
regulations and environmental regulations. To evaluate the impact of
implementing regulations, it is necessary to focus on both the specific effect
of a regulation and the overall effect of regulatory accumulation on economic
performance.
Appendix
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