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7/29/2019 Myles Economic overview
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Understanding Retail Trade
Analysis
by
Al Myles, Economist and Extension
ProfessorDepartment of Agriculture
Economics
Mississippi State UniversityDecember 11, 2008
Presented at Oktibbeha County Leadership Forum
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Retail Trade Analysis
- Is a way to identify market trends within a localcommunity, including the degree of surplus or
leakage of dollars within specific retail sectors.
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PURPOSE
Gives an historical overview of a communitys or countys retail
trade sector
Provides a basis for comparison with similar size communities
and counties
Is useful for identifying opportunities in the retail sector
Similar to annual health physical at the doctors office. Tells you
whats right and wrong.
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Why Retail Trade?
Retail trade is one of the most important indicators of economicactivity in a community or county because local citizens spend a
large part of their incomes on goods and services.
The measures of retail trade and spending reflect consumers
preference for the retail mix in the area and show how well the
economy is doing overall.Since retail is one of the major economic forces in the country,
local officials often want to know how they compare with their
competitors.
.
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Purpose of Retail Promotions
Keeping Local Dollars at Home
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Indicators of Retail Activity
Sales Tax CollectionsMarket Capture
Gap Analysis (Potential sales-Actual Sales)
Pull factors
Sales leakage
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Introduction
-Defining a towns trade area is an important first stepin developing a strong retail sector.
-This is the foundation of retail market analysis. It helps
existing businesses to identify ways to expand their own
market.
-Increasing retail sales is one way an area can:
capture dollars
increase incomeimprove employment multipliers of its local
industries.
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Defining the Trade Area
-Whatever the reasons for existing retail sales, city and
county leaders can help local businesses to improve
these trends.
-To determine the potential for increasing retail sales,
one should establish the trade area.
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A trade area is the geographic region from which
a town draws the majority of its retail customers.
This can be done in several ways:
1. Conducting a traffic flow study,
2. Using a retail gravity model,
3. Using a zip code method, and
4. Using commuting data to define the trade area
boundaries.
Of these methods, COMMUTING and RETAILGRAVITY approaches present the least amount
of work to implement.
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Traffic Flow.
Is the random canvassing of parking lots at majorlocations in town at different times on different days
and over several weeks.
The locations might include
The downtown area,
Major shopping destinations such as
shopping malls and centers, Wal-Mart
Super Center, Home Depot, Krogers, and Other popular establishments in
town.
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One should combined the results of vehicle license
plates from the different locations to obtain a compositecount of vehicles from surrounding counties and
compare them to regional commuting data.
Results from a traffic study will usually reveal
the major towns and counties that comprise the local
trade area or market.
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To determine the major communities in the local
market one should:
1. Rank order the number of cars from various
counties in the region, and
2. Select the top five or six localities based on thehighest frequency and/or maximum percentage
(10% or more) of license plates in the area.
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Commuting
Commuting time to work by local residents is another
way of delineating a communitys retail trade area.
Converting commuting time to work into spatial
distances or miles and plotting these data on a map,
provide a visual picture of the geographic size of its
trade area.
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Figure 1. Trade Area: Major Commuting Counties
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Figure 1. Trade Area: Immediate Commuting Counties
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Reilys Law
Another easy way of defining the retail trade area is to
use a gravity model. In retail trade analysis, the most
popular method is Reilys Law of Retail
Gravitation.
Reilys law is a rule-of-thumb used to ESTIMATE the
distance customers will travel to PURCHASE goodsand SERVICES after comparing price, quality, and
style.
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Reillys Law
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The law assumes that people desire to shop in larger
towns, but their desire declines the farther the distanceand time they must travel to get there. Thus, LARGER
TOWNS DRAW CUSTOMERS FROM FARTHER
DISTANCES THAN SMALLER TOWNS.
The maximum distance a customer will travel to shop in
a smaller town can be calculated using the following
formula.
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Population and Travel Distances in Community As Trade Area
County Total Population Distance (FROM
Community A to
County Seat)
Trade Area Distance
Community A 22,000
Community B 1,543 27 5.65
Community C 23,799 23 11.73
Community D 2,145 27 6.42
Community E 7,169 33 11.99
Community F 8,489 17 6.51
Average 10,/8/ 25.4 8.46
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Figure 1. Picture of Communitys Trade Area
W E
Community B
Community E
N
S
Community F
6.51 miles
Community ACommunity D Community C
5.65 miles
11.99 miles
6.42 miles
11.27 miles
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Estimating Total Market Size
Once the physical boundaries of the trade area have
been identified, one should estimate the total market
size.
The total market consists of populations in the host
community plus population from surrounding towns in
the trade area.
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Additional customers can be derived using the
formula:
3.14 X (Average Retail Trade Miles)2 X Average County
Population DensityExample:
Community As population = 22,000
Average trade area retail miles = 8.46
Average trade area population density per square mile = 51.45
Number of new customers = (3.14 x ((8.46)^2) x 51.45) =11,563
Total retail customer base = 33,372 (22,000 + 11,563)
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In using this approach, there are a few caveats:
1. Areas with large populations and densities per square mile
can distort the actual situation in retail trade analysis.
2. Reilys Law is less accurate when involving larger towns.
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Trade Area Population Model
Answers the basic question: What is the probability that a consumer located
in communityi will shop in communityj, given the presence of competing
towns? The spatial interaction model takes into account such variables as
distance, attractiveness and competition in different sites.
The probability (Pij)1 that a consumer located in communityi will choose to
shop in communityj is calculated as:
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Where:
Aj is a measure of attractiveness of communityj, such as total retail sales,total personal income, or population of area.
Dij is the distance from i to j.
2 is an attractiveness parameter from empirical observation.
3
is the distance decay parameter estimated from empirical observations.Simply, it is a parameter that reflects the propensity to travel by
consumers.
n is the total number of communities including the host communityi .
The product derived from dividing by is known as the perceivedutility of communityjby a consumer located in communityi.
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Using Information About
Market Size
After defining the trade area, one can ESTIMATE the
local sales potential and COMPARE them to actual
sales in the area. The following formula can be used toestimate potential retail sales.
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POTENTIAL SALES
Potential sales for a given sector in a given county can be
estimated as
Where
-PSij is potential sales for commercial sector j in county i
-Pi is population for county i
-SSPCj is state sales per capita for commercial sector j
-PCIi is per capita income for county i-PCIs is per capita income for state s
PCIs
PCIiSSPCjPiPSij **
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By comparing POTENTIAL with ACTUAL retail
sales, one can determine whether the city has room for
retail growth.
One should compare retail sales over SEVERAL
YEARS to determine theLONG-TERMhealth of retail
sectors in the city.
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TRADE AREA ANALYSIS
Example:
Pristine County, USA
General Merchandise sector, 2005
Figures for trade area capture estimation:
-ARSij (2005 taxable retail sales for Automotive sector in Pristine
Co.) = $1,011,060-ARSsj (annual taxable retail sales for General merchandise sector forUSA) = $3,799,963,834
Pprstc (Pristine County population) = 4,896 people
Pu.s (USA population) = 2,412,301 people
Yprstc (Pristine Co. per capita income) = $26,363
Yu.s (USA per capita income) = $35,744
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TRADE AREA ANALYSISExample:
Potential Sales
The equation becomes:
The potential sales are considerably greater than the actual sales
of $1,011,060
281,688,5$
744,35$
363,26$
*301,421,2
834,963,799,3$
*)896,4(
PS
PS
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Potential Sales: Interpretation
Can compare estimates of potential sales for commercial sector j
in county i to realized sales of commercial sector j in county i
-Derive a value of captured or lost commercial sales for that
sector and county
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Determining Retail Power
Trade Area Capture (TAC)
Information about the trade area can help one to
estimate the ability of community merchants to capture
the retail business of people in the area.
Trade Area Capture (TAC)
is an estimate of the number of people who shop in the
local area during a certain period.
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Pull Factors
Knowledge of the trade area is the first step in retailmarket analysis.
Knowing the trade area, one can determine the size and
pulling power of local merchants in the market using aconcept call pull factors.
Pull factors are ratios that estimate the proportionof local sales that occurs in a town.
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The most common method of calculating pull factors is
as follows:
Pull Factor (PF) = Trade Area Capture
City Population
See slide 23
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PF Value Interpretation
> 1 Retailers drawing customers from outside trade
area
< 1 Retailers losing customers from outside tradearea
= 1 Retailers maintaining customers in trade area
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1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Clay 0.76 0.73 0.73 0.74 0.76 0.76 0.77 0.75 0.77 0.76 0.75 0.74 0.76 0.73 0.70 0.70 0.71 0.71 0.73 0.74 0.73 0.71 0.69 0.70 0.73 0.71 0.71
Lowndes 1.07 1.12 1.00 1.00 1.00 1.01 1.03 1.03 1.11 1.19 1.01 1.03 1.07 1.00 1.01 1.00 1.00 0.97 0.99 1.12 1.11 1.11 1.08 1.06 1.00 0.98 1.03
Oktibbeha 0.78 0.74 0.74 0.75 0.76 0.76 0.76 0.75 0.75 0.76 0.75 0.76 0.79 0.74 0.73 0.72 0.73 0.76 0.75 0.83 0.84 0.87 0.85 0.85 0.83 0.84 0.82
Mississippi 0.79 0.82 0.78 0.77 0.77 0.76 0.75 0.74 0.74 0.74 0.72 0.74 0.76 0.74 0.73 0.74 0.74 0.73 0.73 0.77 0.76 0.76 0.76 0.76 0.74 0.74 0.74
Pull factors for Selected Counties in Mississippi
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0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Clay
Lowndes
Oktibbeha
Mississippi
gure 1 Weighted Average Pull Factors for Mississippi Counties 2007
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PF>1.0 WhitePF>.8.6.41.0 WhitePF>.8.6.4
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Some questions to think about when interpreting pull factors:
1. How has the pull factor changed over time? If it has
increased, why do you think that is so? If it has declined,
what are some possible causes?
2. How does the local pull factor compare to other counties?
The state? Why do you think it is higher or lower?
3. What are some strategies your community can adopt to
increase the amount of money drawn in from outside the
county?
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What Is Happening Locally?
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Table 1. Oktibbeha County With and Without Federal Funds
Year With Without Median State Index Rank
1993 4.02 3.77 3.57 24
1994 3.95 3.69 3.56 27
1995 3.94 3.68 3.57 26
1996 3.88 3.63 3.57 281997 3.88 3.62 3.58 28
1998 3.90 3.65 3.56 28
1999 4.00 3.70 3.55 25
2000 4.06 3.74 3.56 26
2001 4.12 3.83 3.55 26
2002 4.18 3.87 3.55 24
2003 4.19 3.86 3.57 24
2004 4.16 3.86 3.52 23Average 4.02 3.75
Economic Strength Index
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Trade Area Capture
Current
Population2002
Projected
Population 2019
TAC to
Population
Ratio
County
Clay 21,751 21,979 22,840 98.96
Lowndes 98,344 61,586 65370 159.69
Oktibbeha 51,136 42,902 51200 119.19
Region
Total 173,153 126,467 139,410 136.92
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0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
Marke t Population
Figure 1. Trade Capture
Series1 21,751 98,344 51,136 173,153
Clay Lowndes Oktibbeha Region Total
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Figure 2. TAC and 2002 Population
0 50,000 100,000 150,000 200,000
Clay
Low ndes
Oktibbeha
Region Total
Series2 21,979 61,586 42,902 126,467Series1 21,751 98,344 51,136 173,153
Clay Low ndes Oktibbeha Region Total
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Figure 3. TAC, 2002 Population, and Projected 2019 Population
0
50,000
100,000
150,000
200,000
Series1 21,751 98,344 51,136 173,153
Series2 21,979 61,586 42,902 126,467
Series3 22,840 65370 51200 139,410
Clay Low ndes Oktibbeha Region Total
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98.96
159.69
119.19
136.92
-
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
Clay Lowndes Oktibbeha Region Total
Percent
Figure 4. Market Capture Above Population
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Figure 5. County Retail Sales
$-
$100,000,000
$200,000,000
$300,000,000
$400,000,000
$500,000,000
$600,000,000
Series1 $363 $375 $398 $408 $435 $426 $447 $455 $529
98 99 00 01 02 03 04 05 06
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Figure 6. Starkville Retail Sales
$-
$50,000,000
$100,000,000
$150,000,000
$200,000,000
$250,000,000
$300,000,000
$350,000,000
$400,000,000
Series1 $251, $272, $292, $300, $306, $302, $320, $328, $374,
98 99 00 01 02 03 04 05 06
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Figure 7. Oktibbeha County Per Capita Sales Ratio
$-
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
$8,000
$9,000
Series2 $5,967 $6,419 $6,799 $7,027 $7,203 $7,101 $7,447 $7,539 $8,499
98 99 00 01 02 03 04 05 06
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Summary
This presentation shows how a few simple techniques
can be used to determine the geographic size of a towns
trade area.
A trade area will often extend beyond its own
geographic borders.
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CONCLUSIONS
Trade area analysis shows how businesses can use existing datato learn more about their business power
Trade area analysis provides information about:
-The number of customers in a county
-A sectors pull factor in the region-Potential sales in an area
This information can all be used to create a plan or strategy for
business owners
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Shift-Share Results for Your
Area
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In economics, there is a technique called shift-share
analysis. Its purpose is to take the change in employment foran area and decompose it into the three sources that caused the
change.
National growth
Industrial growth
Competitive effect
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The industries are ordered according to how many people they employed in the latest year selected
( 2007) .
During the period 1990 to 2007, employment in Oktibbeha County grew by 2,869 jobs. In terms
of employment growth, the most important industry was Professional and Business Services (1,411
jobs). It is followed by Education and Health Services( 1,376 jobs), and leisure and Hospitality (
1,929 jobs).
Table 1 presents the employment changes for the time period selected in Oktibbeha County, MS.
During the period 1990 to 2007, employment in the county grew by 2,869 jobs.
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Table 1: Employment Changes in Your Area, 1990 to 2007.
Sector Employment,1990
Employment,
2007 Employment ChangePercent Growth,
1990 - 2007Education and Health
Services 1,868 3,244 1,376 73.7Trade, Transportation, and
Utilities 2,025 2,299 274 13.5
Leisure and Hospitality 1,207 2,136 929 77.0Professional and Business
Services 396 1,807 1,411 356.3
Manufacturing 2,111 1,582 -529 -25.1Public Administration 1,369 809 -560 -40.9Financial Activities 554 437 -117 -21.1Construction 330 410 80 24.2Other Services 249 234 -15 -6.0Information 119 162 43 36.1Natural Resources and
Mining 66 43 -23 -34.810,294 13,163 2,869
Table 1: Employment Changes in Oktibbeha County, 1990 to 2007.
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Table 2: Shift-Share Analysis for Your Area, 1990-2007.
SectorNational Growth
Component,
PercentNational Growth
Component,
JobsIndustrial Mix
Component,
PercentIndustrial Mix
Component,
Jobs
Competitive
Share
Component,
Percent
Competitive
Share
Component,
JobsProfessional
and Business
Services24.7 98 44.8 177 286.8 1,136
Education and
Health Services 24.7 461 23.2 434 25.8 481Leisure and
Hospitality 24.7 298 17.9 216 34.4 415
Information 24.7 29 -15.2 -18 26.6 32Natural
Resources and
Mining24.7 16 -20.3 -13 -39.3 -26
Trade,
Transportation,
and Utilities24.7 500 -8.7 -176 -2.5 -50
Manufacturing 24.7 521 -47.2 -997 -2.5 -53Construction 24.7 81 19.3 64 -19.7 -65Other Services 24.7 61 3.1 8 -33.8 -84Financial
Activities 24.7 137 -5.4 -30 -40.4 -224Public
Administration24.7 338 -10.0 -136 -55.6 -762
2,540 -471 800
Table 2: Shift-Share Analysis for Oktibbeha County, 1990-2007.
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1. The National Growth Component
The first source of change is the growth or contraction in the United States economy. This growth rate is listed in Table 2
as the national growth component.
Overall, the national growth component was responsible for a total of 2,540 jobs in Oktibbeha County.
An understandable goal of some local leaders is to make their economy more 'recession proof'. Economies
with more employment in government, military and education will experience less fluctuation because those
sectors are not directly related to the business cycle.
Also, economic sectors that are experiencing more growth will provide larger employment gains to a local
economy.
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2. The Industrial Mix Component
The industrial mix component measures how well an industry has grown, net the effects from the business cycle.
Table 2 lists these components for each sector.
If the county's employment were concentrated in these sectors with higher industrial mix components, then the area
could expect more employment growth. After adding up across all eleven sectors, it appears that the industrial mix
component was responsible for decreasing Oktibbeha Countys employment by -471 jobs.
Thus, the area has a concentration of employment in industries that are decreasing nation-wide, in terms of
employment. The majority of these jobs can be attributed to decreases (-997 jobs) in the Manufacturing sector.
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3. The Competitive Share
The third and final component of shift-share analysis is called the competitive share. It is the remaining employment change that is
left over after accounting for the national and industrial mix components.
If a sector's competitive share is positive, then the sector has a local advantage in promoting employment growth.
The top three sectors in competitive share were Professional and Business Services, Education and health Services, and leisure and
Hospitality. Across all sectors, the competitive share component equaled 800 jobs. This indicates the county is competitive in
securing additional employment.
A positive competitive share component indicates the county has a productive advantage. This advantage could be due to local
firms having superior technology, management, or market access, or the local labor force having higher productivity and/or lower
wages.
A negative competitive share component could be caused by local shortcomings in all these areas.
By examining the competitive share components for each industry, the development official can easily identify which local
industries have a positive competitive share component. This also indicates which industries have competitive advantages over
other counties and regions.
Local officials can then devise strategies to improve local conditions faced by particular industries selected for focus. These
strategies may include specialized training programs for workers and management, improved access to input and product markets
through transportation and telecommunications, or arranged financial alternatives for new machinery and equipment.
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Questions?
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THANK YOU!