Or how places, regions,
and nations earn a living.
ECONOMIC BASE THEORY - COMPARATIVE COST
The basis of economic base theory (aka export base
theory) is the market hypothesis:
"An area's growth rate depends on the export demand
for goods and services in which the area has a delivered
cost advantage."
In other words, an area (city, region, nation) must trade
to survive, and it must trade in those goods that it can
produce relatively cheaper than other places.
This is called comparative cost advantage.
And it is the heart of spatial market economics.
ECONOMIC BASE THEORY - COMPARATIVE COST
This is a very important economic concept to
understand about spatial markets.
Comparative cost advantage does not mean that a
nation/region has to produce goods more cheaply in
absolute terms than other nations/regions.
It means that you can produce goods more cheaply in
relative terms than your competitors can.
In fact, and here’s the subtle part:
you will trade even if you are better at everything
than your competitor, and they can trade even if they
are worse at everything than you!
ECONOMIC BASE THEORY - COMPARATIVE COST
It all has to do with the opportunity cost of producing
(or in this case not producing) different goods.
That is, even if you are better than everyone else at
producing two items, you may be much better at
producing one of those things.
This means that you would be better served by
producing more of the thing you can produce more
profitably, than by producing both things.
By analogy, there are neurosurgeons who may be
excellent typists. But it would not make much economic
sense for a neurosurgeon to work at being a typist.
ECONOMIC BASE THEORY - COMPARATIVE COST
Units of input needed to
produce 100 cars
Units of input needed to
produce 1,000 computers
Canada
2 units of input
(50 cars for each unit of input)
3 units of input
(333 computers for each unit of
input)
Italy
4 units of input
(25 cars for each unit of input)
4 units of input
(250 computers for each unit of
input)
Canada produces 2 times as
Canada produces about 1.3
Diff per
many cars as Italy per unit of times more computers as Italy
unit
input.
per unit of input.
Canada produces both items more cheaply per unit than Italy in
absolute terms but Canada is better relatively at producing cars than
computers – 2 times versus 1.3 times.
Thus, if Canada used all of its inputs for cars, it would get more cars than
computers, than if it used all of its inputs for computers.
ECONOMIC BASE THEORY - COMPARATIVE COST
Cost to
produce
100 cars
Cost to
Cars “lost” because Computers "lost”
produce
you produce
because you
1,000
computers
produce cars
computers
2 units
Canada
(50 each)
3 units
(333 each)
150
(3 units*50)
666
(2 units*333)
4 units
(25 each)
4 units
(250 each)
100
(4 units*25)
1,000
(4 units*250)
Italy
So if Canada and Italy choose not to produce computers and
redirect their resources to producing cars, Canada can produce
more extra cars that Italy.
And if Canada and Italy choose not to produce cars but redirect
their resources to producing computers, then Italy can produce
more extra computers that Canada.
ECONOMIC BASE THEORY - COMPARATIVE COST
As can be seen from the example,
even though Canada is more
efficient than Italy at producing
both products (computers and
cars), Canada is much better at
producing cars.
It will, therefore, produce cars and
leave computers to Italy.
In Theory at least 
ECONOMIC BASE THEORY - COMPARATIVE COST
Economic Base Theory (EBT) postulates two parts to an
economy:
The basic sector (aka the export or traded sector):
Activities in this sector earn export income for the area.
E.G. a factory.
The non-basic sector (aka the local or non-traded sector):
Activities in this sector do not earn export income for an
area - they are the support activities for the basic sector.
E.G. a retail store.
However, as we shall see, factories and stores can be both.
ECONOMIC BASE THEORY - BASIC NON-BASIC ACTIVITIES
The EBT relationship:
Non-basic activities support the basic activities, while
basic activities in turn support the economic health of
the area.
Elements:
Basic economic activity (income earning).
Non-basic economic activity (supports basic activity).
Household multiplier (supports basic and non-basic).
Linkages (connections between the elements).
ECONOMIC BASE THEORY - BASIC NON-BASIC ACTIVITIES
Basic economic activity (income earning) includes:
Exports: goods, export services, tourism, extra-local retail.
Investments: housing, business infrastructure e.g. offices
and factories, urban infrastructure e.g. roads, etc.
Government Expenditures: government spending, current
operations, transfer payments, etc.
Basic activities are considered "exogenous" in the EBT
model - i.e. independently introduced and/or stimulated
from outside the region/nation.
ECONOMIC BASE THEORY - BASIC NON-BASIC ACTIVITIES
Non-basic economic activity (not income earning)
includes:
Local oriented activities such as retail, services, local
public sector such as schools, police.
They support basic activities.
Basic activities are considered “endogenous" in the EBT
model - i.e. effects are generated by supply/demand
relationships within the region/nation.
Basic question to help determine if basic or non-basic:
Would this activity survive on its own?
ECONOMIC BASE THEORY - BASIC NON-BASIC ACTIVITIES
Basic economy
driven by
external
dollars.
$$$$$$
Regional Economy
Local
Economy
$$$$$$
Local economy
driven by the
basic economy.
EXPORT
INCOME
Growing basic
activities generate
jobs and export
income
Growing area
attracts more basic
activities.
Area infrastructure
grows as
prosperity grows.
EXPORT DEMAND
Non-basic activities
required to support
basic sector.
Jobs and local
income generated.
Household multiplier 
ECONOMIC BASE THEORY - BASIC NON-BASIC ACTIVITIES
EXPORT INCOME
DECLINES
Declining basic
activities decrease
jobs and export
income
Declining area
discourages more
basic activities.
Area infrastructure
stagnates as
prosperity declines.
EXPORT DEMAND
Non-basic activities
required to support
basic sector decline.
Jobs and local
income are lost.
Household multiplier 
ECONOMIC BASE THEORY - BASIC NON-BASIC ACTIVITIES
Two of the major problems in conceptualising
and operationalizing EBT are:
Defining the spatial areas involved – that is,
what does “outside” the region and hence
exports mean?
Determining which activities or proportion of
an activity are basic and which are non-basic;
that is, what activities are export?
ECONOMIC BASE THEORY - DEFINING AREAS
Basic and non-basic proportions can be measured in terms of
employment, production, sales etc. Employment is usually used.
Economic
activity #1
20%
Consumer #2
100%
80% Area A
Consumer #1
80%
Economic activity #1 is non-basic to Area A.
Economic activity #1 is basic & non-basic to Area A.
Economic 20%
Economic activity #1 is non-basic to national economy. activity #2
Economic activity #2 is basic to Nation B.
Nation B
Economic activity #2 is basic
&
non
basic
to
Nation
B.
ECONOMIC BASE THEORY - DEFINING AREAS
Primary Sector: resource extraction activities, such
as agriculture, mining, lumber.
Secondary Sector: manufacturing or goods
producing activities.
Tertiary Sector: services activities such as retail,
wholesale, tourism, health care, banking, etc.
Quaternary sector: involves activities of the socalled intellectual/information sector, such as
government, education, culture, information,
scientific research.
ECONOMIC BASE THEORY - DEFINING ACTIVITIES
Simple model: Primary and secondary activities are
basic and all others are non-basic.
Strength:
Easily understood and easy to apply.
Weakness:
Completely unrealistic, especially in today’s globalized
service based economy.
Realistic model: That all activities have some
proportion of their output as both basic and non-basic.
Strength:
Much more realistic.
Weakness:
Much more difficult to measure and apply.
ECONOMIC BASE THEORY - DEFINING ACTIVITIES
Forestry
Electronics
Electronics
store
Schools
Farm
Auto maker
Auto
Dealer
Grocery
store
University
ECONOMIC BASE THEORY - DEFINING ACTIVITIES
Resources sector
Manufacturing
sector
Services sector
LOCAL
EXPORTS
IMPORTS
HOUSEHOLD
Quaternary sector
Forestry
Electronics
Electronics
store
Schools
Farm
Auto maker
Auto
Dealer
Grocery
store
University
ECONOMIC BASE THEORY - DEFINING ACTIVITIES
Resources sector
Manufacturing
sector
Services sector
LOCAL
EXPORTS
IMPORTS
HOUSEHOLD
Quaternary sector
EBT is linked to the overall socio-economic structure of
communities in terms of local and non-local inputs and
outputs, and their relative strengths. So…
When a factory that exports goods opens (or closes) not
only will the factory jobs be gained (or lost), but non-basic
support jobs that depended on the factory jobs will also be
gained (or lost).
On the other hand, if a non-basic activity (such as a retail
store) closes down, only the jobs from that store will be lost.
This relationship is conceptualised in the basic/non-basic
ratio and the multiplier concept.
ECONOMIC BASE THEORY - RATIO AND MULTIPLIER
Number of non-basic jobs
Number of basic jobs
For example, if 60% of the employment in a place
is non-basic, and 40% is therefore basic, the NB:B
ratio is 60/40, or 1.5, meaning that there are 1.5
non-basic jobs for every basic job.
Thus, we can say that NB = f(B), which leads us
to the very important concept of the economic
multiplier.
ECONOMIC BASE THEORY - RATIO AND MULTIPLIER
Postulates that changes in basic activity in an economy ripples
through or multiplies its effect due to the relationship
between basic and non-basic activities.
The relationship NB = f(B) generates the previously discussed
NB:B ratio, with the total impact or multiplier being:
M± = 1 + (NB/B)
Using the previous numbers (NB = 60%, B = 40%):
M± = 1 + (60/40) = 2.5
Thus, if a new factory opens in an area, the total impact on
the employment structure of the area will be 2.5 jobs: 1 new
basic job and 1.5 non-basic jobs.
ECONOMIC BASE THEORY - RATIO AND MULTIPLIER
This model can be developed even further to incorporate all the
iterations that must be accounted for when a new basic job is
created:
TE = DE + IE + ME + FE + HD
2.5
where,
TE: total induced employment.
DE: Direct induced employment (basic export jobs, all B).
IE: Indirect induced employment (spin-off industries NB + some B).
ME: Indirect municipal employment (services & NB jobs, mostly NB).
FE: Indirect final demand (created by iteration, all NB).*
HD: Indirect household demand (created by families of workers, all
NB).
*Iteration is the process by which non-basic jobs create other nonbasic jobs, etc.
ECONOMIC BASE THEORY - RATIO AND MULTIPLIER
May all seem pretty easy and it is – it’s just arithmetic after
all. But the devil is in the details.
To calculate an economic base, even assuming the f in
NB+(f)B, and the relationships are all constant over time as
space, you would need:
Accurate and timely employment data for economic activities.
Some type of categorization for those activities, at a small
enough sectoral scale to be useful (resource, manufacturing,
service sectors is not).
Are the data available at a small enough spatial scale for your
community.
Do we have all this? And if so where would one find all this?
ECONOMIC BASE THEORY - RATIO AND MULTIPLIER
It assumes that a relationship exists between B and NB jobs, which
is probably O.K., but is this relationship constant:
Over time? Do we get more efficient at providing non-basic jobs;
I.E. the relationship between the basic and non-basic sectors is
curvilinear and not linear.
Over city size? Are larger cities more efficient at providing support
for new basic jobs, given scale economies?
Over magnitude of B? That is, does every one of the new B jobs
generate in the same ratio as the first?
Backwards?? That is, does the negative multiplier work in the same
ratio as the positive multiplier?
What proportion of the NB jobs are actually B? People who use
malls in a place do not all come from that place.
The two problems mentioned earlier with respect to boundaries
and exactly what activities are B and NB.
ECONOMIC BASE THEORY - ISSUES
Despite the issues, EBT has much utility in
conceptualizing, describing and explaining why
spatial economies work the way that they do
and we will be returning to it on many
occasions throughout this course.
We will look at some of the empirical ways in
which EBT can be measured in the methods
and data lecture.
ECONOMIC BASE THEORY - ISSUES
Read Word docs on what to do, process flowchart,
proposal example table, and the PDFs of good and poor
proposals.
This is a research proposal and that is what you will be
graded on and not the topic you choose.
Do not pick a huge topic that is undoable because:
You can’t get the data.
You need too much data.
It’s too complex for this level.
There really isn’t any cause and effect to find.
Any topic in economic geography will do – that means
find something within the lecture areas dealt with in this
course.
I have uploaded the lecture slides on data classification and
methods, in case you need the material. We won’t do this stuff in
class due to time constraints.
Getting data depends on the scale at which you choose to work so
the rule of thumb is:
The more detail you want, the harder it becomes to get the data –
for example:
• Global data are easy to find on any topic – city data are not.
• Some variables such as employment are common - other
variables such as capital investment are not.
• Data on big economic activities such as rubber manufacturing
are easy to find – data on the condom industry are not.
• The further back in time you go, the harder the data will be to
find and convert to constant dollars.
• Annual data are easy to find – quarterly are not.
So – keep it simple and larger scale.
This project requires you to formulate a research question and
then answer it.
To do that you need:
A context – some area you are interested in that can generate…
???A question – one that can be answered with data.???
A way of answering it – have expectations that state…
This variable for this place over this time period should do this
thing if my research question is correct.
Some examples:
You have a few detailed examples already done in the handouts for the
assignment. Read through them and understand what they are trying to do.
CONTEXT
RESEARCH QUESTION
POTENTIAL PROBLEMS
Globalisation should have led
to more trade than production
as a proportion of GDP.
Has global trade value grown
faster than global
manufacturing value as
proportions of GDP?
Can you get global values on
trade, manufacturing and
GDP?
Climate change has
increased/decreased yield of
maple sap leading to
increases/decreases in maple
syrup production in
Canada/Quebec. (You find out
which it is).
Has global production of
Very detailed data required
maple syrup, and temperature for maple syrup production?
increased/decreased over the
Are temperatures actually
past 100 years?
increasing/decreasing at small
regional scales such as
Quebec?
Globalisation should have led
to increasing national incomes
and demographic
development.
Lots here. Has income
increased at all and has it led
to any or all of:
Declining birth/death/ fertility
rates. Changed demographic
transition
Not many, all data are
available for regional groups
of nations from UN, World
Bank, IMF, Population
Reference Bureau, etc.
You have a few detailed examples already done in the handouts for the
assignment. Read through them and understand what they are trying to do.
EXPECTATIONS
DATA SOURCES
METHODS
Trade as a % of GDP should
have increased faster than
manufacturing (industry) since
1960.
World and regional (e.g. high,
middle, low income nations)
data from World Bank
Indicators or IMF.
Three graphs one each for
regions, with trade and
industry proportions plotted.
As climate has warmed, maple
For syrup production don’t
syrup production has
know, but maybe Stats Canada
increased (say). Could ask “has or Maple Syrup Association.
temperature affected
For temperature, Environment
production?”
Canada, Weather Network
maybe.
GDP per capita in constant
dollars should have increased
and led to decreases/changes
in whatever demographic
variable(s)/model you like.
World Bank, U.N., IMF, PRB.
Plot production in gallons
against temp over time, say
last 50 years.
Plot graphs looking for
required direct or inverse
relationships and/or do
correlations if you know how.
Absolute and Relative Change
This idea is easy to look for and to overlook.
Economic change is rarely a +/- <> = game in an
absolute sense.
That is, the data we use and the comparisons we
make rarely present themselves as absolute
increases and decline, positives, negatives, and
equalities.
Most times you are dealing with relative and not
absolute change.
Absolute and Relative Change
Consider the following scenario for employment change in
two regions.
It is clear that one is growing and one is declining in absolute
terms.
Region
A
Percent
Employment
Change
Region B is
declining in
absolute terms.
Region
B
Time
Absolute and Relative Change
Now consider this scenario, where it is much less clear what is
happening.
Both regions are growing, but region A has higher growth relative to
Region B. From an economic point of view, Region B can be said to be
declining in relation to region A.
Region
A
Percent
Employment
Change
Region
B
Region B is declining
in relative terms.
Time
Absolute and Relative Change
In this scenario it is region A that is declining relative to Region B.
From an economic point of view, Region A can be said to be declining
in relation to region B, even though both are growing.
Region
A
Percent
Employment
Change
Now region A is
declining in relative
terms.
Time
Region
B
Absolute and Relative Change
In this scenario it is region A that is ‘growing’ relative to Region B.
From an economic point of view, Region A can be said to be growing
in relation to region B, even though both are declining.
Region
A
Percent
Employment
Change
Now region A is increasing
in relative terms.
Region
B
Time
Absolute and Relative Change
In this scenario region B shows an ‘inflection point’ where its profile
changes from relative decline to relative growth in relation to region A.
Region
A
Percent
Employment
Change
Region
B
Region B is declining
then increasing in
relative terms.
Inflection point
Time
Absolute and Relative Change
The point of these simple examples is to illustrate that the
idea of relative and absolute change need to be considered
when analysing spatial economic data.
The same can be said for the idea of rates of change versus
levels of change.
A rate of change is a mathematical measure of change in a
variable’s value over time.
A level of change is a mathematical measure of a variable’s
value attained at a given point in time.
While this may seem pedantic, it is important for the same
reasons that absolute and relative change is.
Rates Versus Levels of Change
In this scenario region B has attained the same level of change (in this
case growth) as region A, but has done so much faster. Subsequently,
region B may suffer from problems associated with such a rapid pace
of growth (e.g. supply issues, labour shortages, pollution) that region
A did not suffer. This is typical of rapidly developing economies.
Region A’s Periodicity
Region B’s Periodicity
Level of change
Percent
Employment
Change
Region
A
Region
B
Time
Speed of Decision making:
The complexities of making decisions are further
complicated by the increasing speed at which such
decisions have to be made and reacted to.
Before 1970, financial and corporate decisions rarely
occurred in much shorter intervals than quarterly and
most about annually.
After 1970 corporate decisions have to be made almost
weekly, and some pertinent data, such as the exchange
rates that govern the price of exports are being made
several times a day.
Currently, virtually all financial decision making takes
place several times a day.
Speed of Decision Making:
Which rate of change is more sustainable?
Which will have the lower impact?
Which will be controllable?
CHANGE
Which is us?
TIME
This is a test.
Assume that you are asked to cut the price of a
$100 dress by 25%, then raise the price by 25%.
How much would the dress now cost?
If you said $100, don’t go shopping for dresses.
If you said $93.75 you can probably make some
money off the other person.
This is what it looks like:
$100 – 25% = $75
$75 + 25% = $93.75
Moral: even calculating percentages can be tricky.
…Or How to Pick Your Truth
Consider the following:
The city increases the your property tax rate
from 3% to 5%. So by how much did it
increase?
The answer is: by how much would you like it
to have increased?
This is not a trick question, but there is a trick
to picking which truth makes your case sound
better – and the “other” person’s sound
worse.
A Tale of Two Percent
If you are the city:
“We only increased the property tax by 2%.”
(The absolute difference between 5% and 7%).
If you are the tax payer association:
“The government increased our property tax by
67%!”
(The percentage change between 5 and 7).
If you are a homeowner, which would you think is
the most accurate interpretation because they are
both correct?
Have you heard this one?
Boss walks into your office and says:
“I’m giving everyone in the company a 10% pay
raise!”
Your response is:
a. “Wow! Thanks! That’s an extra $8,000 a year!
I’m so grateful!”
b. “What? You’re kidding right? Thanks a lot. I get
$8K and you get $80K.”
Or this one?
Toronto growth rate, 2006-2011 = 8.5%
Ho-hum.
Stouffville growth rate, 2006-2011 = 100.5%
Holy Cow!
Stouffville’s growth rate is almost 12 times that of
Toronto!
Stouffville growth rate, 2006-2011 = 12,475 people.
Ho-hum.
Toronto growth rate, 2006-2011 = 400,433 people.
Holy Cow!
Toronto’s growth rate is over 32 times that of
Stouffville!
Growth or Not And By How Much?
The answer is not 12%.
First there is the effect of inflation/deflation.
Second, there is the effect of population change.
Third, there are also issues about comparability of:
the variables/units:
apples or oranges?
litres or kilos?
dollars or yen?
differences or magnitudes?
time scales?
Fourth, there are components to a growth value.
Start with a Simple Question
Should you measure prosperity in
income or wages?
Huh?
Has income increased? Yes, by steadily larger increments
for wealthier people. All the trend lines are going up.
Highest
Lowest
Let’s try again.
Has income increased? Yes for sure. Growth
rates have all gone up, much more for more
for rich people for sure.
The Question Once Again Then…
… But this time we’ll ask the question slightly
differently:
Has income increased if its measured with wages?
The subtlety here is that income measures what the
household brings in regardless of the number of hours
needed to do so.
Wages measures how many hours you need to work to
earn that income.
So your income can go up but if the number of hours
needed to earn it goes up as well then how much better
off are you?
U.S. Male Hourly Wage 1979-2009
Wages have not increased for lower income groups while
they have quite considerably for upper income groups.
On Being Subtle and Sophisticated
Statistics is about extracting information from data.
But you have to be able to get the right information.
Even simple data such as percentage change can hide
nuances in the information it is apparently giving you.
When you calculate a 40% change between two periods
of time, you cannot ever take it for granted that you
actually had a 40% growth rate.
Is the magnitude really 40%?
What is hidden inside the value?
Can a large positive growth rate actually be a small
negative one (strange but true)?
An Illustration –The Canadian Economy
Look at the numbers - Two Questions:
Is consumer spending growing faster than GDP?
Are consumer spending and GDP really growing at all?
Growth rate: 40.9%
Date
2002
2003
2004
2005
2006
2007
2008
2009
2010
GDP
$1,152,905,000,000
$1,213,175,000,000
$1,290,906,000,000
$1,373,845,000,000
$1,450,405,000,000
$1,529,589,000,000
$1,603,418,000,000
$1,528,985,000,000
$1,624,608,000,000
The Answer?
Maybe, because
Consumer Spending
the values are
$655,722,000,000
increasing:
$686,552,000,000
GDP grew by
$719,917,000,000
@41% and CS by
$758,966,000,000
@43%.
$801,742,000,000
Growth rate: 43.5%
$851,603,000,000
$890,601,000,000
$898,215,000,000
$940,620,000,000
But looks can be
deceiving for
three reasons…
Growth or Not And By How Much?
REASON #1: UNITS
Are the variables and/or units of measurement
comparable?
This asks whether you are talking about:
•
•
•
•
•
Different things such as guns and butter.
Different magnitudes such as GDP$ or spending$.
Different measurement units such as annual or
quarterly, litres or kilos, totals or per capita.
Different base lines such as percentage points
difference or percentage change difference.
Different sized economies.
You fix it by indexing your data.
Growth or Not And By How Much?
REASON #2: IN/DEFLATION
Removing the effect of inflation/deflation.
Inflation and deflation is caused when the costs and therefore
subsequent prices of products increase or decrease, so any
“growth/decline” is not caused by more/less consumption but by
increased/decreased price.
You fix it by converting current dollars to constant dollars using
the consumer/producer price indices and purchasing power
parity.
A Note on Terms Used
Economists use the term “nominal” for values that have not been corrected for
inflation and “real” for those that have. However, most of the documents you
see use the terms “current” for non-corrected values and “constant” for
corrected values.
Growth or Not And By How Much?
REASON #3: POPULATION
Compensating for the effect of population change.
More people equals an increase in consumption and
production and not an increase in individual spending
and production.
Compensating for the effect of population size.
Bigger places/nations have more people (duh) so
comparing absolute values is mostly pointless.
You fix both by using per capita rates.
So, back to our example…
Look at the numbers - Two Questions:
Is consumer spending growing faster than GDP?
Are consumer spending and GDP really growing at all?
Date
2002
2003
2004
2005
2006
2007
2008
2009
2010
GDP
Consumer Spending
$1,152,905,000,000 $655,722,000,000
$1,213,175,000,000 $686,552,000,000
$1,290,906,000,000 $719,917,000,000
$1,373,845,000,000 $758,966,000,000
$1,450,405,000,000 $801,742,000,000
$1,529,589,000,000 $851,603,000,000
$1,603,418,000,000 $890,601,000,000
$1,528,985,000,000 $898,215,000,000
$1,624,608,000,000 $940,620,000,000
The Answer?
Maybe, because
the values are
increasing – GDP
grew by 40% and
CS by 43%.
But looks can be
deceiving for two
reasons…
Are the values comparable?
Date
2002
2003
2004
2005
2006
2007
2008
2009
2010
Current
Current
Dollar
Dollar Consumer
GDP
Spending
Index # Index #
2002=100 2002=100
100.00
100.00
105.23
104.70
111.97
109.79
119.16
115.75
125.80
122.27
132.67
129.87
139.08
135.82
132.62
136.98
140.91
143.45
No. The magnitudes of values are much
different – billions versus trillions. Usually
a problem when comparing different
countries, cities of different sizes.
How to fix it? Create a base 100 index
number:
1.Make an arbitrary year’s real data value
equal to 100.
2. Calculate every other year’s index
number in relation to this base year value
(% change).
Now both sets of data values are directly
comparable because they are relative.
Removing the effects of inflation and
population change/size.
2002
2003
2004
2005
2006
2007
2008
2009
2010
Consumer
Price Index
2002 = 100
100.0
102.8
104.7
107.0
109.1
111.5
114.1
114.4
116.5
Canada
Population
31,373,000
31,676,000
32,048,000
32,359,000
32,723,000
33,115,000
33,506,000
33,894,000
34,349,200
1. Collect the consumer
price index (CPI) values.
2. Collect population data.
3. Use CPI to convert
current to constant dollars
for both variables.
4. Use population values
to calculate per capita
spending and GDP thus…
Current to Constant Dollar Conversion
Current dollars are not corrected for inflation so part of the
change over time in values does not come from growth, so you
need to find or calculate constant dollar values.
To remove effect of inflation:
Current 2003 GDP = $1,213,175,000,000
CPI (2002=100) = 102.8
Formula:
Constant 2002$ = Current $/(CPI/100)
Calculated:
Current 2003 GDP $ = $1,213,175,000,000/(102.8/100) =
Constant 2003 GDP $ = $1,180,131,322,957.20
Removing the effect of inflation from GDP
Date
2002
2003
2004
2005
2006
2007
2008
2009
2010
GDP Current Dollars
$1,152,905,000,000.00
$1,213,175,000,000.00
$1,290,906,000,000.00
$1,373,845,000,000.00
$1,450,405,000,000.00
$1,529,589,000,000.00
$1,603,418,000,000.00
$1,528,985,000,000.00
$1,624,608,000,000.00
GDP Constant 2002
Base year
Dollars
values are
$1,152,905,000,000.00 always the
$1,180,131,322,957.20
same for
$1,232,957,020,057.31 current and
$1,283,967,289,719.63
constant
$1,329,427,131,072.41
dollars.
$1,371,828,699,551.57 That’s how
$1,405,274,320,771.25 you can tell
$1,336,525,349,650.35
the base
$1,394,513,304,721.03
year.
Note that when removing inflation the constant dollar values are
always lower than the current dollar values. When (rarely)
removing deflation they are always higher.
Removing the effect of inflation from consumer spending
Date
2002
2003
2004
2005
2006
2007
2008
2009
2010
Consumer Spending
Current Dollars
$655,722,000,000.00
$686,552,000,000.00
$719,917,000,000.00
$758,966,000,000.00
$801,742,000,000.00
$851,603,000,000.00
$890,601,000,000.00
$898,215,000,000.00
$940,620,000,000.00
Consumer Spending
Constant 2002 Dollars
$655,722,000,000.00
$667,852,140,077.82
$687,599,808,978.03
$709,314,018,691.59
$734,868,927,589.37
$763,769,506,726.46
$780,544,259,421.56
$785,152,972,027.97
$807,399,141,630.90
Base year
Values are
always the
same for
current and
constant
dollars.
That’s how
you can tell
the base
year.
Note that when removing inflation the constant dollar values are
always lower than the current dollar values. When (rarely)
removing deflation they are always higher.
Compensating for the effect of population change/size
CURRENT DOLLARS
2002
2003
2004
2005
2006
2007
2008
2009
2010
Per Capita GDP
Current Dollars
$36,748.32
$38,299.50
$40,280.39
$42,456.35
$44,323.72
$46,190.22
$47,854.65
$45,110.79
$47,296.82
CONSTANT DOLLARS
Per Capita
Consumer
Per Capita GDP
Spending
Constant 2002
Current Dollars
Dollars
$36,748.32
$20,900.84
$37,256.32
$21,674.20
$38,472.20
$22,463.71
$39,678.83
$23,454.56
$40,626.69
$24,500.87
$41,426.20
$25,716.53
$41,940.98
$26,580.34
$39,432.51
$26,500.71
$27,384.04
$40,598.13
Per Capita
Consumer
Spending
Constant 2002
Dollars
$20,900.84
$21,083.85
Base
$21,455.31
$21,920.15 Years
$22,457.26Same
$23,064.16
$23,295.66
$23,164.95
$23,505.62
Note that when correcting for inflation the constant dollar values
are always lower than the current dollar values. When (rarely)
correcting for deflation they are always higher.
Correcting The Canadian Economy in Summary
What You See
Data uncorrected for…
Population growth/difference…
Use population to correct.
Per capita values
Inflation…
Use CPI to correct.
in
constant dollars
What You Get
Data corrected for both…
So did the Canadian Economy ‘grow’ and if so
by how much?
Growth Correction
No correction
(current dollars)
Corrected for
inflation only
(constant dollars)
Corrected for
population change only
(per capita rates)
Corrected for both
(Constant per capita)
Difference
GDP Growth
Rate
Consumer
Spending
Growth Rate
40.9%
43.5%
21.0%
23.1%
28.7%
31.0%
10.5%
12.5%
-30.4%
-31.0%
An Illustration –The Canadian Economy
Two Questions
1. Is consumer spending growing faster than GDP?
2. Are consumer spending and GDP really growing at all?
Date
2002
2003
2004
2005
2006
2007
2008
2009
2010
GDP Current Dollars
$1,152,905,000,000.00
Raw Percent Change
$1,213,175,000,000.00
2002-10
$1,290,906,000,000.00
40.9%
$1,373,845,000,000.00
$1,450,405,000,000.00
Real Percent Change
$1,529,589,000,000.00
2002-10
$1,603,418,000,000.00
10.5%
$1,528,985,000,000.00
$1,624,608,000,000.00
Consumer Spending
Current Dollars
$655,722,000,000.00
Raw Percent Change
$686,552,000,000.00
2002-10
$719,917,000,000.00
43.5%
$758,966,000,000.00
$801,742,000,000.00
Real
Percent Change
$851,603,000,000.00
2002-10
$890,601,000,000.00
12.5%
$898,215,000,000.00
$940,620,000,000.00
Two Answers
1. Can’t tell
from these
raw data.
2. Maybe,
because the
numbers are
increasing.
But looks can
be deceiving.
Measuring Performance in an
Economy
Productivity
Capacity Utilization
Input-Output
Productivity Indices
Measures the efficiency with which economy’s labour and
capital are being used to produce output using three basic
measures:
Output Per Person Hour
Output/number of hours worked to produce it
Compensation Per Person Hour
Output/wages paid to produce it
Unit Labour Cost
Wages/Output
OR
Compensation Per Person Hour/Output Per Person Hour)
A Word on Unit Value
You see this a lot and it is a very useful concept.
Generally refers to how much you get for how much
you give.
E.Gs.
Unit revenue = Total revenues/number of units sold
Unit cost = Total cost/number of units produced
And from these…
Unit profit = unit revenues/unit costs
Unit Labour Cost most used in estimating productivity
Marginality is a unit value:
How much extra output you got for the last unit of
input.
Calculating Productivity Indices
VARIABLES USED TO CALCULATE
INDICES
Output in Value Added
Person Hours Worked
VALUES 2012
$1,558,077,367
30,541,021
Wages Paid
PRODUCTIVITY INDICES
Output Per Person Hour
$972,179,228
Compensation Per Person Hour
Unit Labour Cost
$31.83
$0.62
$51.00
Interpretation: In 2012 Canadian workers:
Produced $51.00 for every hour they worked,
Got paid about $ 31.83 for every hour they worked, and so…
Their labour accounted for .62 cents of each dollar of production.
Source: Statistics Canada. CANSIM Table383-0029
Canada, Productivity Indices 1981 to 2014
Index Number (2007 = 100)
115.0
105.0
95.0
85.0
75.0
65.0
55.0
But average hours are decreasing,
which means workers are being
replaced by capital.
Yet ULC are still increasing
meaning the labour that’s
left is expensive – as seen in
compensation per hour.
When Unit Labour Cost increases it means that
compensation is increasing faster than productivity.
45.0
Unit Labour Cost
Average Hours Worked
Year
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
35.0
Total Number of Jobs
Compensation Per Hour Worked
Statistics Canada. Table 383-0008 - Indexes of labour productivity, unit labour cost and related variables, seasonally adjusted, quarterly (index, 2007=100), CANSIM
(database). (accessed: )
Capacity Utilisation Rates
Compares what you do produce against what you could
have produced.
High CUR indicates that an industry is approaching
capacity and is an indicator of inflation.
Low CUR indicates that unit costs of production are
higher than they should be.
Simple to understand but difficult to measure.
Two basic methods - both try to set an ideal upper limit
and then compare actual production against it.
The two methods are:
The Wharton Trend Through Peaks
StatsCan Capital Output Ratio
The Wharton Trend Through Peaks Approach
This method attempts to set the ideal upper limit by setting a trend
line through the peaks of actual production for a given period of time,
and then measuring divergence from this ideal line:
Seasonally
Adjusted
Quarterly
Index of
Production
Actual Production Output
Capacity Utilisation rate
(expressed as a
percentage, actual of
trend)
1
2
3
4
1
2010
2011
Quarters
2
3
The Stats Canada Capital Output Ratio.
Potential output is estimated by relating capital's ability to
produce at its maximum, to the existing stock of capital.
That is, how much capital is available and how much output
should you expect from it, expressed as a percentage of what it
does produce.
What you should expect from a given unit of capital (i.e. its
ability to produce) is reflected in a number called the capitaloutput ratio.
The minimum capital-output ratio represents what you can
do in terms of productive capacity, and hence represents the
maximum potential output.
It’s asking “how little do I need and how much can I get.”
Calculation of the Stats Canada Capital Output Ratio.
1. Collect data on production output (Pt) and capital stocks
(Kt) for each quarter from Statistics Canada (StatsCan uses
the industry's proportion of Gross Domestic Product and
the Capital Stocks Series).
2. Calculate capital-output ratios for each quarter as per the
model below (Kt/Pt).
3. Select the minimum capital-output ratio for the series to
date as the ideal capital-output ratio (Ko/Po).
4. Calculate the potential output (CPt) that the period's capital
stock should produce by dividing the capital stock (Kt) by
the ideal capital-output ratio (Ko/Po).
5. To get the CUR for the period (CURt), express the actual
output (Pt) as a percent of the potential output (CPt).
The Stats Canada Capital Output Ratio.
The full model is:
CURt = (Pt/CPt) * 100
where,
Yes, It’s complicated.
CPt it
= Kt/(Ko/Po)
But StatsCan does
all for you – to a point.
where,
Kt: fixed capital stocks at time `t'.
Pt: actual output at time `t'.
Kt/Pt: capital-output ratio at time `t'.
Ko/Po: minimum capacity-output ratio.
CPt: estimated capacity output at time `t'.
CURt: capacity utilisation rate at time `t'.
Examples of Stats Canada Capacity Utilization Rates (CURt).
Source: CANSIM table:
028-0002.
Total non-farm
Construction
t
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
MEAN
Total Manufacturing
CUR percent
85.7
86.6
85.0
82.2
78.9
78.8
80.6
83.0
82.1
82.0
83.6
84.6
86.0
87.0
84.3
85.4
84.2
84.9
84.2
82.8
82.4
77.5
71.4
76.0
82.5
92.6
93.6
94.5
91.1
83.6
78.8
76.3
78.8
75.8
78.5
83.1
84.7
86.8
88.7
90.5
89.8
88.0
86.1
83.5
81.7
80.3
79.0
70.0
74.0
83.7
Mostly you want high ratios.
82.8
82.6
81.2
78.2
74.2
76.4
79.9
83.5
83.9
82.8
83.6
84.3
85.8
86.0
81.7
82.9
81.5
83.5
83.7
82.7
82.8
75.6
70.9
76.2
81.1
Capacity Utilisation Rates, Canada, 1987-2010
100.0
But even more mostly you want
them to stay high over time.
95.0
90.0
85.0
80.0
75.0
70.0
And, oh! Canada’s have not 
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
65.0
Total Non-farm
Construction
Manufacturing
Linear (Total Non-farm)
Linear (Construction)
Linear (Manufacturing)
http://www.statcan.gc.ca/pub/11-210-x/2010000/tablelist-listetableaux5-eng.htm
Input Output Analysis
This is a very complex method of analysing the
throughput of commodities in an economy.
It is designed to estimate how much of the output of a
given commodity is the input of every other commodity,
including itself.
There are various methods for generating results but
the end result is an input-output matrix that shows
proportionately where that commodity’s output is
going.
An I-O matrix allows for estimation of the impact on any
given economic activity from changes in any other
activity, and so is a very powerful economic tool.
ECONOMIC
ACTIVITIES
How Input
Output
Matrices Work
ECONOMIC ACTIVITIES
(e.g.
lumber)
2
(e.g.
paper)
3
…
…
2
(e.g.
lumber)
(e.g.
paper)
Lumber
Lumber
gets
gives to
from
lumber
lumber
Paper
Lumber
gets
gives to
from
paper
lumber
3
…
…
…
n
NOTE: Every cell value is both an input and
an output, depending how you read it.
Columns represent
what each activity Rows represent what each
receives from every activity supplies to every
other activity.
other activity. Therefore
Therefore columns rows represent the outputs
represent inputs to
from activities 1, 2, 3, n.
activities 1, 2, 3, n.
n
TOTAL
Column totals represent the total inputs to
activities 1, 2, 3, n.
TOTAL
Row totals represent the total
outputs from activities 1, 2, 3, n.
1
1
Examples of Input Output Analysis
SCOTTISH NATIONAL INPUT OUTPUT MATRIX
(All in £ millions)
Purchases by
product group from
1
2.1
2.2
3.1
3.2
4
5
product group
Sales by product
Coal
Forestry Forestry
Sea
Fish
Oil & gas
group to product Agriculture
extraction
planting harvesting fishing farming
extraction
group
etc
Agriculture
164.7
6.5
Forestry planting
0.2
76.4
Forestry harvesting
0.0
Sea fishing
0.0
Fish farming
0.0
10.7
Coal extraction etc
0.2
10.2
Oil & gas extraction
0.0
46.3
Agriculture sold £164.5 million worth of goods to agriculture.
Agriculture sold £6.5 million worth of goods to forestry planting.
Agriculture bought £164.5 million worth of goods from agriculture.
Forestry planting bought £6.5 million worth of goods from agriculture.
Finally:
Go look through the following
classifications, methods, data slide show
for ideas on analysis.
Going to look at:
• Economic activities classification systems.
• Data and sources.
• Methods of economic analysis.
Here we are talking about:
• The North American Industrial Classification
System or NAICS (and SIC).
• Statistics Canada and other more global sources.
• Methods (especially indices) & variables commonly
used.
Classification systems (E.G. NAICS (and SIC), StatsCan)
•
•
•
•
Attempt to group similar activities.
Attempt to separate dissimilar activities.
Be as comprehensive as possible.
Be as consistent as possible (sectorally, spatially, temporally).
Because classifications are by definition static and activities are
not, they always become out-dated and must be changed.
Different systems exist for:
• Economic activities.
• Occupations.
• Products.
• Commodities.
The NAICS and the SIC:
• SIC developed starting after WW2 with the GATT.
• NAICS evolved from this with the NAFTA.
• Both are structured hierarchical typologies that group
economic activities:
• into Divisions,
• divisions into Major Group,
• major groups into Industry Group,
• industry groups into Industry Classes.
• All groupings are based on similarity between hierarchical
levels; that is, like activities are grouped together into
increasingly larger classes.
• But as they get larger, they get more dissimilar!
SIC: Standard Industrial Classification System:
• Now obsolete but old datasets still have SIC “codes” and not
NAICS codes.
• Structure is similar to NAICS however, as we will see shortly.
• Uses divisions, major groups, industry groups, industry classes.
• Divisions have a letter code, and each of the others its own
digit or digits in a four digit number code.
NAICS: the North American Industrial Classification System:
• “New” system started in 1997 and updated 3 times since, with
2012 the latest.
• Developed to ensure consistent classes of activities between
NAFTA nations – but U.S. and Mexico’s are slightly different!
• Uses sectors, sub-sectors, industry group, industry, national
industry, each having its own digit in up to a six digit code.
Sectors
Primary
Primary
Tertiary
Tertiary
Secondary
Tertiary
Tertiary
Tertiary
Quaternary
Quaternary
NAICS 2012 CLASSIFICATION STRUCTURE – SECTORS
http://www.statcan.gc.ca/cgi-bin/imdb/p3VD.pl?Function=getVDPage1&db=imdb&dis=2&adm=8&TVD=118464
11
Agriculture, forestry, fishing and hunting
21
Mining, quarrying, and oil and gas extraction
22
Utilities
23
Construction
31- 33 Manufacturing
41
Wholesale trade
44-45 Retail trade
48-49 Transportation and warehousing
51
Information and cultural industries
52
Finance and insurance
53
Real estate and rental and leasing
54
Professional, scientific and technical services
55
Management of companies and enterprises
56
Administrative and support, waste management and remediation services
61
Educational services
62
Health care and social assistance
71
Arts, entertainment and recreation
72
Accommodation and food services
81
Other services (except public administration)
91
Public administration
Industry Group
31-33 Manufacturing
NAICS:
312 Beverage and Tobacco Product Manufacturing
The Manufacturing Sector‘s 21 Classes
31-33 Manufacturing
311 Food Manufacturing
313 Textile Mills
314 Textile Product Mills
311 Food Manufacturing
315 Clothing Manufacturing
312 Beverage and Tobacco Product Manufacturing
316 Leather and Allied Product Manufacturing
321 Wood Product Manufacturing
322 Paper Manufacturing
313 Textile Mills
323 Printing and Related Support Activities
314 Textile Product Mills
324 Petroleum and Coal Products Manufacturing
325 Chemical Manufacturing
315 Clothing Manufacturing
326 Plastics and Rubber Products Manufacturing
327 Non-Metallic Mineral Product Manufacturing
331 Primary Metal Manufacturing
316 Leather and Allied Product Manufacturing
332 Fabricated Metal Product Manufacturing
333 Machinery Manufacturing
334 Computer and Electronic Product Manufacturing
335 Electrical Equipment, Appliance and Component Manufacturing
336 Transportation Equipment Manufacturing
337 Furniture and Related Product Manufacturing
339 Miscellaneous Manufacturing
31-33 Manufacturing
311 Food Manufacturing
31-33 ManufacturingNAICS
312 Beverage and Tobacco Product Manufacturing
Code Hierarchy for
311 Food Manufacturing Textile Mills
313 Textile Mills
Six Digits
and TobaccotoProduct
Manufacturing
3131 Fibre, Yarn312
andBeverage
Thread Mills
313 Textile Mills
314 Textile Product Mills
315 Clothing Manufacturing
316 Leather and Allied Product Manufacturing
321 Wood Product Manufacturing
31311 Fibre, Yarn and Thread Mills
313 Textile Mills
323 Printing and Related313110
Support ActivitiesFibre, Yarn and Thread Mills
324 Petroleum and Coal Products Manufacturing
314 Textile Product Mills
3132 Fabric Mills
325 Chemical Manufacturing
31321 Broad-Woven Fabric Mills
326 Plastics and Rubber Products Manufacturing
315 Clothing Manufacturing
Broad-Woven Fabric Mills
327 Non-Metallic Mineral313210
Product Manufacturing
331 Primary Metal Manufacturing
31322 Narrow
Fabric Mills
and Schiffli
Machine
Embroidery
316 Leather
and Allied
Product
Manufacturing
332 Fabricated Metal Product Manufacturing
313220 Narrow Fabric Mills and Schiffli Machine Embroidery
333 Machinery Manufacturing
31323
Nonwoven Fabric Mills
334 Computer and Electronic
Product Manufacturing
335 Electrical Equipment,313230
Appliance and Component
Manufacturing Fabric Mills
Nonwoven
336 Transportation Equipment Manufacturing
31324 Knit Fabric Mills
337 Furniture and Related Product Manufacturing
313240 Knit Fabric Mills
339 Miscellaneous Manufacturing
3133 Textile and Fabric Finishing and Fabric Coating
322 Paper Manufacturing
Canadian statistical collection agency, greatly respected in the
world and considerably important if you are interested in actually
making objective, data based decisions (unfortunately politicians
often are not).
They produce the Census of Canada every five years, and about
350 other products ranging across all aspects of Canadian life.
You should be intimately familiar with SC data, much of it being
economic based.
Spatial scales range from the national aggregate down to DAs
(designated areas) – the smallest areas for which data is
collected.
Index Numbers
Index Numbers and Ratios
One of the most common types of quantitative tools,
seen in many types of geographic data.
Examples are Consumer Price Index, stock indices such
as TSX, NASDAQ, Location Quotients, Gini coefficients,
demographic analyses such as birth and death rates, etc.
Just about any data can be converted to indices or
ratios.
Allows for comparison of datasets and/or growth rates
between different datasets.
Index Numbers – Some Examples
Consumer Price Index:
Agency ‘purchases’ a ‘shopping cart’ of goods and services,
then uses the cost of them as a base for measuring
increases in prices of the same shopping cart at other times.
Stock Indices (TSX, NASDAQ, etc):
Same as CPI – a ‘portfolio’ of stocks are valued at one point
in time, then re-valued and compared at other points in
time.
Big Mac Index:
Developed by The Economist magazine, this tongue in
cheek but surprisingly accurate index compares the
Purchasing Power Parity of currencies in one nation to
another – I.E. are they over-valued.
Ceridian-UCLA Pulse of Commerce Index:
Monitors the purchase of diesel fuel by truckers using credit
and debit card swipes, as an indicator of the flow of raw
materials, finished and semi finished products.
How its done – stock indices
1. Pick a selection of stocks (the Dow has 30, the TSX
Composite has 100).
2. Add up their values, then either use that raw value or
‘weight’ by some measure or convert to a base 100
index.
3. Compare the resulting index with recalculations
across time (rarely done, hence TSX, Dow, Hang Seng
index numbers of 12,000+).
Stock indices can be based on value of the stocks or
market capitalization or growth etc.
They measure general trend of the markets or
specialized stocks such as the NASDAQ tech stocks.
What the Dow(s) looks like.
What the S&P(s) looks like.
What it looks like plotted
Note the “dot.com” spike around 2000 as
recorded by the NASDAQ. Also note that
S&P500 is more heavily weighted with tech
stocks.
The Big Mac Index
1. Divide the price of a Big Mac in one country by
the price in another (create the index number).
2. Compare that index number with the exchange
rate between theU.S.
two countries.
Argentina
3. If it is lower then the currency
of the first country
is undervalued compared
to the second; if it is
Australia
higher, then it is
overvalued.
Brazil
U.K. (underpriced currency)
Canada (overpriced currency)
Grey line actual.
Blue line MA3
(moving average
period 3.
Index created from
truckers’ purchases
of diesel fuel; used as
an indicator of
commerce and hence
health of the
economy.
Simple Index Numbers
Very simple to calculate by taking the base year
value, chosen arbitrarily, dividing it into each
subsequent year’s value, then multiplying by 100.
Date
2002
2003
2004
2005
GDP
$1,152,905,000,000
$1,213,175,000,000
$1,290,906,000,000
$1,373,845,000,000
Index
100.00
105.23
111.97
119.16
$1,213,175,000,000.00/$1,152,905,000,000 = 105.23
$1,290,906,000,000.00/$1,152,905,000,000 = 111.97
NOTE THAT THIS IS JUST A PERCENTAGE CHANGE VALUE
ADDED TO 100
The Consumer Price Index
• What is the CPI?
• Basket of goods and services are “bought” and their
cost is set to equal an index number of 100.
• The year the basket is “bought” is called the base
year, and it remains until a new base year is chosen.
• The same basket is “bought” the next year and its
value is given an index # equal to 100 plus the
percentage change from the previous year’s basket.
• This process is repeated each year (or other time
period) until a new base year is chosen.
• The CPI is listed in a table and the base year is noted
somewhere as, for example, 2002=100.
Therefore you do not calculate the CPI – you must
look it up from Stats Canada
Examples of Price Indices
• Data tables for Prices and price indexes – some SC examples:
•
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•
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•
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Table 25.a Consumer Price Index
Table 25.b Average retail food prices
Table 25.1 Consumer Price Index, 1991 to 2010
Table 25.2 Consumer Price Index, All-items, by province and territory, 2005
to 2010
Table 25.3 Consumer Price Index, food, 2004 to 2010
Table 25.4 New Housing Price Index, by province, 2004 to 2010
Table 25.5 Raw Materials Price Index, 2004 to 2010
Table 25.6 Farm Product Price Index, 2004 to 2010
Table 25.7 Industrial Product Price Index, 1991 to 2010
Table 25.8 Machinery and Equipment Price Index, domestic and imported,
by industry, 2005 to 2010
Table 25.9 Composite Leading Index, March 2005 to March 2011
Table 25.10 Inter-city indexes of retail price differentials, by selected goods
and services, 2005 and 2009
The CPI – Example Tables
Table 25.a Consumer Price Index
All-items
Food
Shelter
2000
2010
2002=100
95.4
116.5
93.3
123.1
95.6
123.3
Household operations, furnishings and equipment
96.7
108.8
Clothing and footwear
Transportation
Health and personal care
100.3
97.2
97.0
91.6
118.0
115.1
Recreation, education and reading
97.0
104.0
Alcoholic beverages and tobacco products
79.0
133.1
Core Consumer Price Index1
95.7
115.6
Note: Annual average indexes are obtained by averaging the indexes for the 12 months of the calendar year.
1. Bank of Canada definition.
Source: Statistics Canada, CANSIM table 326-0021.
Farm Product Price Indices – Example Tables
Canada
Total crops
Grains
Oilseeds
Specialty crops
Fruit
Table 25.6 Farm Product Price Index, 2004 to 2010
2004
2005
2006
2007
1997=100
99.4
96.8
97.4
108.6
100.6 88.3
92.7
117.5
94.1
76.5
84.3
133.3
95.2
74.5
72.2
97.5
102.5 85.2
80.2
120.6
108.7 117.4
124.6
124.4
2008
2009
2010
122.0
144.9
168.3
133.5
185.9
126.3
113.5
126.3
128.5
116.5
158.6
112.5
111.0
114.8
102.5
113.1
137.9
118.3
Vegetables (excluding potatoes)
116.8
113.1
118.2
114.3
119.3
125.3
124.1
Potatoes
119.4
125.9
148.6
135.0
150.7
183.2
175.9
Total livestock and animal products
98.3
103.9
101.3
101.5
103.5
103.6
109.2
Cattle and calves
87.6
103.2
102.7
99.4
99.0
97.7
103.0
Hogs
Poultry
Eggs
Dairy
89.7
97.9
105.6
119.9
83.0
96.4
97.3
128.0
72.3
93.2
98.7
130.3
68.3
102.2
100.8
137.2
67.3
115.0
107.9
139.9
67.5
116.6
103.4
142.4
80.4
111.8
109.0
143.3
The CPI and Purchasing Power Parity (PPP)
CPI only one part of cost of living – some places are
just more expensive to live in.
When values are corrected for the cost of living in
different places the resulting data is labeled as PPP.
This means Purchasing Power Parity – that is, dollar
values are corrected for the differences in the cost of
things like food, housing, taxes, gasoline, etc.
In this case the same basket of goods is valued in
each place, compared, then indexed, and the
resulting indexed values are used to inflate or deflate
prices, incomes, GDP, etc.
Inter City CPI PPP – Example Tables
Vancouver
Edmonton
Winnipeg
Toronto
Montréal
St. John's
Charlottetown &
Summerside
Table 25.10 Inter-city indexes of retail price differentials, by selected goods and services, 2005 and 2009
2005 2009 2005 2009 2005 2009 2005 2009 2005 2009 2005 2009 2005 2009
combined city average=100
All-items
94
97
93
95
110 107 92
Food
103 105 100
103
97
102
101
99
98 101 101 100 106 105
Food purchased from
stores
105 104 103
103
99
101
99
99
99 103 101 102 106 106
Meat, poultry and fish
101 103 108
102
103
99
97
99
93
96
99 103 106 108
101
94
101
91
100
96
106 107 101 106
Dairy products and eggs
95
96
105 102
99
93
94
97 102 102 101
Current
and
Constant
Dollar$
Current to Constant Dollar Conversion
Current dollars are not corrected for inflation so part of
the change over time in values does not come from
growth.
To remove effect of inflation:
Current 2003 GDP = $1,213,175,000,000
CPI (2002=100) = 102.8
Formula:
Constant 2002$ = Current $/(CPI/100)
Calculated:
Current 2003 GDP $ = $1,213,175,000,000/(102.8/100) =
Constant 2003 GDP $ = $1,180,131,322,957.20
RATIOS
Definition:
Basically, one number divided by another
number.
Many Types – simple to complex:
Per capita rates.
Many demographic stats such as birth and death
rates.
Location Quotients (a ratio of ratios).
Gini coefficients (complex – an index number
measuring inequalities).
Simple Ratios
The ‘Per’ Rates
Demographic Per Rates
Incorporates the effect of population size.
Gives comparable and understandable values.
Easy to calculate – divide variable of interest (e.g.
births) by the population and multiply by the base
value – ‘per 1,000’
Base values vary according to the expected
magnitude of the final ratio. For example, you
generally try to get a ratio between 1 and 100.
Birth, death, fertility, infant mortality rates are
usually expressed as a value per 1,000 whereas
disease incidence rates would be per 10,000 or
100,000.
Demographic Rates – Some Examples:
Birth rates:
BR = # (births/population)*1,000
381,382/34,108,800=0.0112
BR = (381,382/34,108,800)*1,000
BR = 11.2 births per 1,000 population
Infant mortality rates:
IM = (# deaths <= 1 year olds/population)*1,000
IM = (160,311/34,108,800)*1,000
IM = 4.7 infant deaths per 1,000 population
Morbidity (disease death) rates – all cancers:
MR = (# cancer deaths/population)*100,000
MR = (75,000/34,108,800)*100,000
MR = 2.2 cancer deaths per 100,000 population
Complex Ratios
Ratio of Ratios Approach
Components Approach
Ratio of Ratios Example
Location Quotients
(LQ)
Location Quotients
A method of estimating whether a region has a surplus,
deficit or the average employment in an industry. Given by:
LQ =
Employment in industry i in region j
Total employment in region j
Employment in industry I in the nation J
Total employment in the nation J
Sometimes given as:
LQ = (Eij/Ej)/(EIJ/EJ)
Where i and j represent “industry” and “area” respectively,
with lower case referring to the region in question and
upper case referring to, usually, the nation.
Location Quotients
You should be able to see that this is a ratio of
ratios thus:
Employment in industry i in region
Total employment in region j
Employment in industry I in the nation
Total employment in the nation J
Ratio of regional
employment in i to total
regional employment j
Ratio of national
employment in I to total
national employment J
The resulting ratio is interpreted as follows:
> 1.0:
surplus
national
average
production
In other
words,
is thetoactivity
basic,
non-basic,
or neither?
= 1.0:
national average
production
Thus
we equal
have atotechnique
that could
be used for
< 1.0: deficit
to national
average
production
measuring
economic
base.
Using Location Quotients for Immigration Analysis
Components Approach – An Example
Shift-Share Analysis
Shift Share Analysis
A fairly simple but powerful tool for analyzing
components of growth.
Disaggregates a single overall growth rate in an
industry into components of that growth rate, in
order to isolate:
- the region’s national growth component;
- the region’s regional growth component;
- the region’s industry growth component.
The reasons for doing this, by way of example…
Shift Share Analysis
If a region (say the GTA) shows a growth rate in
electronics manufacture as 12% for a given time
period, the questions are:
What portion- if any - of the 12% can be
attributed to the fact that the nation was
growing anyway?
What portion- if any - of the 12% can be
attributed to the fact that the GTA was growing
anyway?
What should the electronics industry have been
growing by, given the national growth rate in the
industry?
GTA Growth in the Auto Industry
6% growth due to
growth in the national
economy.
12% Total
Growth
3% growth due to
growth in the GTA
economy.
3% growth due to
growth in the auto
industry.
A single growth value hides complexity.
Shift Share Analysis
The Basic Model:
Gij = Nij + Mij + Dij
where,
Gij = total growth in industry `i' at place `j'.
Nij = national growth component in industry `i' at place `j'.
Mij = industry mix component in industry `i' at place `j'.
Dij = regional growth component in industry `i' at place `j'.
Shift Share Analysis - GTA Growth in the Auto Industry
Nij
Gij
12% Total
Growth
Mij
Dij
6% growth due to
growth in the national
economy.
3% growth due to
growth in the GTA
economy.
3% growth due to
growth in the auto
industry.
A single growth value hides complexity.
Shift Share Analysis
where,
Nij = Eo * r
Mij = Eo * (ri - r)
Dij = Eo * (rij - ri)
Note that you are removing
the effect of national growth
from industry growth and
industry growth from regional
industry growth.
where,
r, ri, rij, = (Et - Eo)/Eo
where,
Eo = employment in `i' at `j' at start of period
Et = employment in `i' at `j' at end of period
r = national growth rate, all employment
ri = national growth rate in `i'
rij = growth rate in `i' at `j'
And in case you were wondering what it all looks like
together…
Gij = Nij+Mij+Dij
Nij = [Eo*(Etr-Eo)/Eo]
Mij = [(Etri-Eori)-(Etr-Eo)/Eo)]
Dij = [(Etrij-Eoij)/Eoij)/(Etri-Eori)]
Gij = [[Eo*(Etr-Eo)/Eo]]+[[(Etri-Eori)-(Etr-Eo)/Eo)]]+[[(Etrij-Eoij)/Eoij)/(Etri-Eori)]]
Interpreting Shift Share Analysis
NATIONAL INDUSTRY MIX COMPONENT
Mij
Growth Industry Declining Industry
(+)
(-)
REGIONAL
GROWTH
RATE
COMPONENT
Dij
Growth
Region
(+)
Good. Your region Poor. Your region
is growing in a
is growing in a
growth industry. declining industry.
Decline
Region
(-)
Poor. Your region Good. Your region
is declining in a
is declining in a
growth industry. declining industry.
Summing up.
The next time you see a growth rate,
think about it:
Current of constant dollars?
Units?
Scale?
Good growth or bad growth?
Components of growth?
Coefficients Approach – An Example
The Gini Coefficient
The Gini Coefficient
The Gini coefficient is a widely used measure
of the degree of inequality in a distribution.
Gives a number between 0 and 1 where 0 is
complete equality and 1 is complete
inequality.
Sometimes numbers are multiplied by 100 to
give coefficients between 0 and 100.
It is used most generally in describing
income inequality in a population.
Interpreting the Gini Coefficient
A nation with a coefficient of 0 would mean
everyone had exactly the same income.
A nation with a Gini coefficient of 1 (or 100)
would mean that one person had all the
income.
Sweden = 23, Namibia = 70.7, USA = 45,
Canada = 31.
Global Gini is 40.
Global Gini Coefficient 2010