Myles Economic overview

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!

Documents

Myles Economic overview