Competitive Advantage Analysis
North Central Enterprise Region
Sask Trends Monitor
Sask Trends Monitor is preparing statistical profiles for the thirteen southern enterprise regions
in the province. The main elements of the profiles are:
– demographic data (population, age, ethnicity, Aboriginal identity, immigration, family
structures etc) from the census and other sources;
– socioeconomic data (education, employment, income, housing, etc.) about the residents
from the census and other sources;
– a competitive advantage analysis; and
– other available economic data (contribution to the economy, business counts, major
capital projects, e.g.).
This material is a short version of the Competitive Advantage Analysis (CAA) that will be
included in those reports.
It is provided in advance for the Enterprise Region until the full profiles are ready.
The methodology used in a competitive advantage analysis (CAA) was developed by economist
Dr. Emanuel Carvalho from the University of Waterloo. It is based on work he did for the
Ontario Ministry of Municipal Affairs.
December 2009 2
Competitive Advantage Analysis: What it is
The CAA is a particular kind of statistical analysis that looks at economic trends in a
region or community by sector (industry group).
The CAA is not a forecast of future opportunities. It is, instead, an examination of
past trends with a view to identifying industry sectors that have under-performed or
over-performed relative to other industries and other regions. While this may help
inform future growth, it is not a blueprint for it.
The CAA is only one of the tools that can be used to identify the economic
strengths and weaknesses in a geographic region.
The CAA compares a region’s economic performance by industry group over a
period of time compared with those same industries in another economy over the
same period. So there are five choices to make before embarking on the
December 2009 3
The Five Choices
Choosing the Regions: Because the enterprise regions have custom boundaries, the only
source of economic data that can be used for the CAA is the census.
Choosing the Economic Measure: Employment was used as the economic measure because
it is the only economic measure available from the census. This has both advantages and
Employment has the advantage of being easy to understand – a job is a readily
identifiable measure of economic success for an individual if not an economy.
R l ti l detailed data from Statistics Canada’s decennial census provides employment
Relatively d t il d d t f St ti ti C d ’ d i l id l t
by industry group at the sub-provincial level.
Changes in productivity can affect changes in employment; a firm or industry may be
more successful by every other measure except employment.
Changes in hours of work, multiple job holdings, and commuting can cause problems in
g , p j g , g p
the employment data.
Choosing the Time Frame: A short (2001 to 2006) period was chosen over longer options
because the industry group definitions used by Stats Canada changed between 1996 and 2001.
Choosing the Comparison Economy: The provincial economy was used as the reference
Choosing the Industry Groups: The most detailed possible industry breakdown available
translates into sixteen industry groupings.
December 2009 4
Choosing the Time Frame
Employment in Saskatchewan (off Reserve only) By using the 2001 and 2006
census, we are effectively
measuring what happened to
520 employment in the province
during that period
This was the period in which the
provincial economy was
prospering but not booming.
– This is after Y2K and the
“dot.com” bust and before
the commodity price boom.
– Consumer confidence was
high particularly late in the
– The population was declining
because of out-migration to
440 – The price of crude oil was
rising starting the period at
430 comparison period US$26/bbl and ending at
420 – Agriculture was in some
difficulty from low crop
prices and BSE
1992 1994 1996 1998 2000 2002 2004 2006 2008
December 2009 5
Choosing the Industry Groups
Experienced Labour Force in 2006, Saskatchewan There are 16 industry
groups used in the
analysis, the most that
Arts, entertainment, recreation 9,395 can be easily and
Information and culture 11,975 accurately derived from
Wholesale trade 19,100
Mining, oil and gas extraction, utilities 24,095 The public sector
Transportation and warehousing
p g ,
care, etc )
care etc.) is included for
Finance, insurance, real estate, rental 25,275 completeness, not
because there is any
Personal and household services 25,700 expectation that it is a
The experienced labour
Public administration 33,315 force is measured for
the industry of the
Business services 34,110 person’s main job or, if
not working, their most
Accommodation and f d services
A d ti d food i 34 580
recent job in the past 18
Education services 40,315 months.
Retail trade 56,730
Health care and social assistance 58,405
Agriculture, forestry, fishing and hunting 60,210
December 2009 6
Components of the Competitive
p pp y
There are four components to the CAA that are applied to each industry
sector. All are interesting and useful in their own right.
• Location Quotients – a measure of industry concentration
• Shift/Share Analysis – a measure of growth adjusted for “normal” patterns
• Leading/Lagging Analysis – measures relative growth rates
• Industry Classification – a summary of the three measures
North Central Enterprise Region
Shellbrook Prince Albert
Bi h Hill
Melfort Star City
December 2009 8
Experienced Labour Force, North Central Enterprise Region, 2006
Experienced Labour Force, by Industry, 2006, The public sector,
North Central Enterprise Region
broadly defined to
Health care and social assistance 4,225
include health and
Retail trade 4 205
4,205 education as well as
Agriculture, forestry, fishing and hunting 3,675
in the North Central
Public administration 3,580
Education services 2,869 Enterprise Region.
Accommodation and food services 2,314
Personal and household services 1,669
Business services ,
Transportation and warehousing 1,391
Finance, insurance, real estate, rental 1,205
Mining, oil and gas extraction, utilities 993
Wholesale trade 958
Arts, entertainment, recreation 706
Information and culture 514
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500
December 2009 9
Location Quotients, North Central Enterprise Region The location quotient measures the
size of the industry in the region
relative to the province as a whole.
Public administration Industries with a location quotient
in excess of 1.25 are classified as
Construction high ; 0 75
“high”; those less than 0.75 are
classified as “low”.
Arts, entertainment, recreation
Retail trade This doesn’t mean the industries
are small or large, just that they
Health care and social services are relatively smaller or larger than
in the province as a whole.
Accommodation and food services Industries with a ‘high” location
quotient tend to export
Personal and household services goods/services from the region.
Goods and services probably need
Agriculture, forestry, fishing and hunting
g , y, g g to be imported for those with a
“low” location quotient.
Transportation and warehousing
Wholesale trade Only one sector – public
administration – had a “high”
Manufacturing 2001 location quotient in 2006. Three
had a “low” location quotient
Finance, insurance, real estate, rental
including the resource/utility group
Business services Another nine groups are classified
as “medium” and three are near
Information and culture the border between low and
Mining, oil and gas extraction, utilities
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00
low medium high
December 2009 10
Shift/Share Analysis, 2001 to 2006
Regional Effects, 2001 to 2006, North Central Enterprise Region The shift/share analysis breaks
down employment change into
Mining, oil and gas extraction, utilities – the provincial effect;
– the industry effect; and
Finance insurance real estate rental
Finance, insurance, estate,
– the regional effect.
The regional effect is the most
interesting. It measures what part
of the employment increase or
decrease is attributable to factors
Transportation and warehousing unique to the region. These are
changes that cannot be explained
Information and culture by the performance of the sector at
the provincial level.
Personal and household services
Retail trade The North Central ER has a large
positive regional effect in the
Agriculture, forestry, fishing and hunting resource/utility group. The region
was doing particularly well in this
Construction sector relative to the province as a
Accommodation and food services There is a large negative regional
effect in three industry groups,
Health care and social services
health care, manufacturing, and the
Arts, entertainment, recreation
-400 -300 -200 -100 0 100 200 300 400
December 2009 11
Leading/Lagging Analysis, 2001 to 2006
The leading/lagging analysis compares, for each industry group:
– employment growth in the region relative to growth provincially in the same group; and
– employment growth provincially relative to total employment growth.
The graph on the next page maps the industry groups doing poorly and well on the horizontal axis with
their regional performance on the vertical axis.
Industry groups in the upper right quadrant are leading growth both in the region and provincially.
They are classified as “drivers”; examples for the North Central ER include the mining/oil and gas
sector and business services.
Those in the lower left are lagging in both cases and are classified as vulnerable or challenging. The
arts/entertainment group and manufacturing are in this quadrant.
Those in the upper left quadrant are typically in a state of evolution and the finance/insurance industry
group is the only one in the North Central region that is clearly in this quadrant.
Those in the lower right are classified as either modest, yielding, or promising, depending on their size.
The health care and social service group, for example, is classified as “yielding” because it is large.
y y groups
Many industry g p in the North Central ER are near the border so no real definitive statements can
be made about them.
December 2009 12
Leading/Lagging Analysis, 2001 to 2006, North Central ER
50% & gas/utilities
sector 20% information
relative & culture
finance, insurance, real estate administration services
accommodation & food
-10% wholesale retail trade personal &
trade manufacturing household services
& processing health care
-20% & social services
-30% arts, entertainment, recreation
5% 0% 5% 10% 15% 20% 25%
lagging provincial sector relative growth leading
December 2009 13
Industry Classification Scheme
The Competitive Advantage Analysis is future oriented in the sense that it classifies the industries in
the region into categories using forward-looking terminology. Some terms used, for example, are:
– Emerging strength
– High priority retention target
– Prospects limited by external trends
Besides the data quality issues and the fact that we are using relatively old employment data as a
measure of economic success, there are some problems with the classification scheme.
– The methodology implicitly assumes that employment growth at the provincial level is a measure
of success. If the industry group is not growing provincially but is doing so in the region, for
example, then it is assumed that the sector has limited prospects.
– There is a bias toward large sectors; relatively small industry groups can be classified as an
emerging st ength but are ha ing “ eak base”
eme ging strength b t most a e dismissed as having a “weak base”. The concept of a “niche
market” doesn’t enter into the equations.
In spite of the limitations, the classification does seem to generate the needed debate about which
p p y , , p
sectors have limited prospects and which do not. The analysis, to be useful, has to be tempered with
a realistic assessment by the region’s economic players.
The graph on the following page shows the classifications for the North Central Enterprise Region.
Industry groups that are on the borders of the four quadrants will be classified as between the two
December 2009 14
Industry Classification: North Central Enterprise Region
regional sector relative growth
current and emerging strengths
Note: The three sectors
dominated by the public
sector (public administration,
health care, education services)
are excluded from this graph. Mining/
Oil & Gas/
Finance, insurance, Transportation
real estate Retail
Personal & services
0% Agriculture household
cco oda o
F t services
trade Information & culture
prospects limited (unless small)
-20% -15% -10% -5% 0% 5% 10% 15% 20% 25% 30%
15 December 2009
Industry Classification, North Central ER, 2001 to 2006
Industry Carvalho Classification
Agriculture, forestry, fishing and hunting Prospects limited by external trends and declining competitiveness
Mining, oil and gas extraction, utilities Emerging strength
Construction High priority retention target
Manufacturing Prospects limited by weak base and declining competitiveness
Wholesale trade Prospects limited by external trends and declining competitiveness
Retail trade High priority retention target
T t ti d h
Transportation and warehousing i C t t
Information and culture Emerging strength
Finance, insurance, real estate, rental Prospects limited by external trends and small base
Education services Current strength
Health care and social services High priority retention target
Business services Emerging strength
Accommodation and food services Prospects limited by external trends and declining competitiveness
Arts, entertainment, recreation Prospects limited by external trends and declining competitiveness
Personal and household services g priority retention target
High p y g
Public administration Current strength
December 2009 16