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									Understanding Retail Trade
        Analysis
                 by
 Al Myles, Economist and Extension
             Professor
     Department of Agriculture
            Economics
    Mississippi State University
        December 11, 2008

      Presented at Oktibbeha County Leadership Forum
               Retail Trade Analysis
-
    Is a way to identify market trends within a local
    community, including the degree of surplus or
    leakage of dollars within specific retail sectors.
                         PURPOSE
•Gives an historical overview of a community’s or county’s 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 doctor’s office. Tells you
 what’s right and wrong.
               Why Retail Trade?
Retail trade is one of the most important indicators of economic
activity 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.
.
      Purpose of Retail Promotions
Keeping Local Dollars at Home
          Indicators of Retail Activity
Sales Tax Collections
Market Capture
Gap Analysis (Potential sales-Actual Sales)
Pull factors
Sales leakage
                   Introduction
-Defining a town’s trade area is an important first step
in 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 income
       improve employment multipliers of its local
       industries.
         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.
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 RETAIL
   GRAVITY approaches present the least amount
   of work to implement.
Traffic Flow….
Is the random canvassing of parking lots at major
locations 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.
One should combined the results of vehicle license
plates from the different locations to obtain a composite
count 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.
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 the
   highest frequency and/or maximum percentage
   (10% or more) of license plates in the area.
Commuting…

Commuting time to work by local residents is another
way of delineating a community’s 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.
Figure 1. Trade Area: Major Commuting Counties
Figure 1. Trade Area: Immediate Commuting Counties
Reily’s 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 “Reily’s Law of Retail
Gravitation.”

Reily’s law is a rule-of-thumb used to ESTIMATE the
distance customers will travel to PURCHASE goods
and SERVICES after comparing price, quality, and
style.
Reilly’s Law
The law assumes that people desire to shop in larger
towns, but their desire declines the farther the distance
and 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.
Population and Travel Distances in Community A’s Trade Area
County             Total Population    Distance (FROM         Trade Area Distance
                                       Community A to
                                       County Seat)
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
Figure 1. Picture of Community’s Trade Area
                                                           N

                                                               Community F
                                                                 6.51 miles




                             6.42 miles

    W          Community D                         Community A                          Community C   E
                                                                              11.27 miles




                    5.65 miles


 Community B                                 11.99 miles




                               Community E
                                                           S
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.
Additional customers can be derived using the
  formula:
3.14 X (Average Retail Trade Miles)2 X Average County
    Population Density
Example:
   Community A’s 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)
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.   Reily’s Law is less accurate when involving larger towns.
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:
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.
   α2 is an attractiveness parameter from empirical observation.
   Β3 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 perceived
utility of communityj by a consumer located in communityi.
         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 to
estimate potential retail sales.
                 POTENTIAL SALES
•Potential sales for a given sector in a given county can be
 estimated as                             PCIi
                  PSij  Pi * SSPCj *
                                         PCIs
•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
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 the LONG-TERM health of retail
sectors in the city.
             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 for
  USA) = $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
             TRADE AREA ANALYSIS
Example:
Potential Sales
•The equation becomes:
                            $3,799,963,834   $26,363 
            PS  (4,896) *                  *        
                            2,421,301   $35,744 
            PS  $5,688,281

•The potential sales are considerably greater than the actual sales
 of $1,011,060
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
        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.
Pull Factors…
Knowledge of the trade area is the first step in retail
market analysis.

Knowing the trade area, one can determine the size and
pulling power of local merchants in the market using a
concept call pull factors.

 Pull factors are ratios that estimate the proportion
         of local sales that occurs in a town.
The most common method of calculating pull factors is
as follows:

      Pull Factor (PF) = Trade Area Capture
                         City Population



                                       See slide 23
PF   Value                    Interpretation

>     1      Retailers drawing customers from outside trade
             area
<     1      Retailers losing customers from outside trade
             area

=     1      Retailers maintaining customers in trade area
                       Pull factors for Selected Counties in Mississippi

              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
       0.00
              0.20
                            0.60
                                   0.80
                                          1.00
                                                 1.20
                                                        1.40




                     0.40
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
igure 1. Weighted Average Pull Factors for Mississippi Counties, 2007




                                                                                                           Mississippi
                                                                                                           Total .74



                                                             PF>1.0     White
                                                           PF>.8<=1     Light Blue
                                                          PF>.6<=.79    Green
                                                          PF>.4<=.59    Yellow          PF>1.0    White

                                                                                      PF>.8<=1    Light Blue

                                                                                     PF>.6<=.79   Green

                                                                                     PF>.4<=.59   Yellow
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?
What Is Happening Locally?
Table 1. Oktibbeha County With and Without Federal Funds
                 Economic Strength Index



      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          28
      1997       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          23
Average          4.02                  3.75
                                                            TAC to
                          Current           Projected     Population
  Trade Area Capture   Population2002     Population 2019    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
                   Figure 1. Trade Capture



180,000

160,000

140,000

120,000

100,000

 80,000

 60,000

 40,000

 20,000

     0
          Clay        Low ndes     Oktibbeha   Region Total

Series1   21,751       98,344       51,136       173,153

                        Market Population
                      Figure 2. TAC and 2002 Population



Region Total




  Oktibbeha




  Low ndes




       Clay



               0            50,000              100,000            150,000             200,000

                   Clay              Low ndes         Oktibbeha         Region Total

     Series2       21,979             61,586              42,902             126,467
     Series1       21,751             98,344              51,136             173,153
     Figure 3. TAC, 2002 Population, and Projected 2019 Population


200,000


150,000

100,000


 50,000

     0
             Clay           Low ndes        Oktibbeha       Region Total

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
                   Figure 4. Market Capture Above Population

          180.00
                                   159.69
          160.00
                                                                  136.92
          140.00
                                                   119.19
          120.00
                    98.96
Percent




          100.00

           80.00

           60.00

           40.00

           20.00

              -
                    Clay          Lowndes        Oktibbeha     Region Total
               Figure 5. County Retail Sales

$600,000,000

$500,000,000

$400,000,000

$300,000,000

$200,000,000

$100,000,000

        $-
                 98   99   00   01   02   03   04   05   06
      Series1 $363 $375 $398 $408 $435 $426 $447 $455 $529
                Figure 6. Starkville Retail Sales

$400,000,000
$350,000,000
$300,000,000
$250,000,000
$200,000,000
$150,000,000
$100,000,000
 $50,000,000
        $-
               98    99    00    01    02    03     04   05    06
      Series1 $251, $272, $292, $300, $306, $302, $320, $328, $374,
         Figure 7. Oktibbeha County Per Capita Sales Ratio

$9,000

$8,000

$7,000

$6,000

$5,000

$4,000

$3,000

$2,000

$1,000

  $-
          98      99       00       01       02       03       04       05       06
Series2 $5,967   $6,419   $6,799   $7,027   $7,203   $7,101   $7,447   $7,539   $8,499
                    Summary
This presentation shows how a few simple techniques
can be used to determine the geographic size of a town’s
trade area.

A trade area will often extend beyond its own
geographic borders.
                     CONCLUSIONS
•Trade area analysis shows how businesses can use existing data
 to learn more about their business power
•Trade area analysis provides information about:
 -The number of customers in a county
 -A sector’s 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
Shift-Share Results for Your
            Area
In economics, there is a technique called shift-share
analysis. Its purpose is to take the change in employment for
an area and decompose it into the three sources that caused the
change.
    National growth
    Industrial growth
    Competitive effect
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.
  Table 1: Employment Changes in Oktibbeha County, 1990 to 2007.
                              Employment,            Employment,                                      Percent Growth,
Sector                                                                      Employment Change
                                 1990                   2007                                            1990 - 2007

Education and Health
                                             1,868                  3,244                  1,376                         73.7
Services


Trade, Transportation, and
                                             2,025                  2,299                   274                          13.5
Utilities


Leisure and Hospitality                      1,207                  2,136                   929                          77.0


Professional and Business
                                              396                   1,807                  1,411                        356.3
Services

Manufacturing
                             Table 1: Employment Changes in Your Area, 1990 to 2007.
                                         2,111              1,582                 -529                                  -25.1

Public Administration                        1,369                   809                    -560                        -40.9

Financial Activities                          554                    437                    -117                        -21.1

Construction                                  330                    410                        80                       24.2

Other Services                                249                    234                        -15                      -6.0

Information                                   119                    162                        43                       36.1

Natural Resources and
                                               66                     43                        -23                     -34.8
Mining

                                            10,294                 13,163                  2,869
  Table 2: Shift-Share Analysis for Oktibbeha County, 1990-2007.
                                                                                           Competitive     Competitive
                  National Growth National Growth Industrial Mix      Industrial Mix
                                                                                              Share          Share
Sector             Component,      Component,      Component,          Component,
                                                                                           Component,      Component,
                      Percent          Jobs          Percent              Jobs
                                                                                             Percent         Jobs
Professional
and Business                 24.7              98             44.8               177               286.8           1,136
Services
Education and
                             24.7             461             23.2               434                25.8            481
Health Services
Leisure and
                             24.7             298             17.9               216                34.4            415
Hospitality
Information                     Table 2: Shift-Share Analysis for Your Area, 1990-2007.
                             24.7               29            -15.2              -18                26.6             32
Natural
Resources and                24.7              16             -20.3               -13              -39.3             -26
Mining
Trade,
Transportation,              24.7             500              -8.7              -176               -2.5             -50
and Utilities
Manufacturing                24.7             521             -47.2              -997               -2.5             -53
Construction                 24.7              81             19.3                64               -19.7             -65
Other Services               24.7              61              3.1                     8           -33.8             -84
Financial
                             24.7             137              -5.4               -30              -40.4            -224
Activities
Public
                             24.7             338             -10.0              -136              -55.6            -762
Administration
                                            2,540                                -471                               800
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.
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 County’s 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.
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.
Questions?
THANK YOU!

								
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