The Effect on the Labor Force

Running head: Effects on the Labor Force with the Introduction of a New Wal-Mart 1
The Effects on the Labor Force with the Introduction of a New Wal-Mart Supercenter:
An Alabama Case Study
Jeff Bridges
University of Alabama at Birmingham
Master of Public Administration
MPA 697
Dr. Akhlaque Haque, Ph.D.
November 7, 2011

The Effects on the Labor Force with the Introduction of a New Wal-Mart Supercenter:
An Alabama Case Study
From 2001 to 2011, Wal-Mart Stores, Inc. has tripled the number of their supercenter
store formats from 888 to 2,898 in the United States alone (Wal-Mart Stores, Inc., 2001; Wal-
Mart Stores, Inc. 2011). While Wal-Mart has enjoyed much success with their expansion of their
supercenter format, many other stakeholders have cried foul over the way Wal-Mart conducts
business. These Wal-Mart naysayers have proclaimed that when a new Wal-Mart comes into an
area employees are displaced, wages are diminished, and many local competitors are put out of
business. Since it is likely that Wal-Mart and other big box retailers will continue expanding the
supercenter format into new markets, it is important for local government administrators to take
notice of any negative effects that may come about from the store’s entrance in order to make
policy decisions for their community. With this background, my essay seeks to answer the
question: Does the introduction of a Wal-Mart Supercenter into a county effect county retail
wages, the number of retail employees, or the makeup of retail establishments in any positive or
negative manner?
This question leads me to the following research hypothesis: the introduction of a new
Wal-Mart Supercenter into a county for the first time will have a negative impact on the county’s
retail wages, the number of retail employees, and the makeup of retail employees.
Literature Review
There have been many studies that concentrate on the effects of big box retail and the
effect that they might have on a community. Due to its rapid growth and being the industry
leader, these studies typically focus on Wal-Mart Stores, Inc. Other common themes that are
apparent throughout these studies are the type of data being used. The data used typically focuses

on communities at the county level, likely due to this being the smallest municipality size with
consistent data regarding employment information. Researchers then try to isolate the positive or
negative consequences around the big box retailer and the employment data for these counties.
Some of the common areas that researchers seem to focus on when studying big box retailers are
the effects on: wages and benefits, employment, and on other establishments.
Retail Wages
One of the more common variables to be studied is the effect that big box retailers such
as Wal-Mart seemingly have on a county population’s benefits and wages. Researchers have
studied how big box retailers can affect retail wages in terms of a municipality is affected, the
effect on surrounding municipalities, the impact on public safety programs, and on how a higher
wage standard would impact the consumer (Ketchum & Hughes, 1997; Neumark, Zhang, &
Ciccarella, 2008; Boarnet & Crane, 1999; Dube, Lester, & Eidlin, 2007; Dube & Jacobs, 2004;
Jacobs, Graham-Spire, & Luce, 2011). The literature gives a broad view of many ways a big box
retailer can impact a community.
In 1997, Ketchum and Hughes studied the effect of a new Wal-Mart on county
employment and wages in Maine. In their study, they focused on the mean capita employment
and mean wages for the three sectors: retail, services, and manufacturing. This study separated
out twelve counties with a Wal-Mart between the time periods of 1990 and 1994 and used the
remaining four counties without Wal-Marts as a control group. In this study, the researchers
found that the all three sectors had statistically significant gains in terms of wages.
There are a few discrepancies in this study regarding the years studied and the makeup of
the counties studied. One of the twelve counties that were studied got its first Wal-Mart on
October 26, 1994. This means that the researchers are comparing approximately four years and

ten months worth of data about this county against the two months that the county had the Wal-
Mart. Another issue with this study is the difference in the test and control groups. The control
group studied had populations that were approximately 30% of the test group. Retail wages more
densely populated areas could be higher or lower that those typically in areas with lower
populations.
Dube, Lester, and Eidlin (2007) studied how a new Wal-Mart opening affected the
surrounding counties and states in terms of wages and benefits. This study took into account how
Wal-Mart expanded in an attempt to show why Wal-Mart chose a specific place to locate so the
results would be controlled for any preexisting economic conditions that could skew the data in
either a positive or negative manner. The study concluded that the average county level retail
wage is 0.5%-0.9% lower after the introduction of a new Wal-Mart. According to the study, this
means that when a new Wal-Mart store opens in a county, better paying jobs are replaced with
jobs that pay less.
Neumark, Zhang, and Ciccarella (2008) estimated the effect that a Wal-Mart store has on
county retail employment and earnings using a model that had controls for the location and
timing of when the Wal-Mart opened. By controlling for time and location, their model is
supposed to eliminate any discrepancies that may alter any data that may happen when a Wal-
Mart is opened. With their model, these researchers find that counties retail payrolls drop by
approximately 1.3% after a Wal-Mart enters the market.
Dube and Jacobs (2004) looked at how Wal-Mart’s wages and benefits could have an
effect on public safety programs in California. The purpose of their study was to not only see
how Wal-Mart’s wage and benefit policies affected public safety programs, but also how these
programs would be affected if other similar industries set their policies to match Wal-Mart’s.

They concluded by saying that the reliance of Wal-Mart employees on public assistance
programs cost taxpayers approximately $86 million annually and they if other large retailers
adopted the same wage and benefits standards, the total cost to the taxpayer could be as much as
$410 million annually.
Numbers like $86 million and $410 million seems like a huge number, but one has to
wonder how this would actually affect the average taxpayer. California is the largest state in
terms of population and a number like $410 million might not seem so much if you break it
down to the per person level. Using California’s budgetary information and employment data
from the Bureau of Labor and Statistics, it is easy to see the effect a $410 million dollar swing
can have on the California population’s personal income tax amounts to a +/-$13.83 impact on
the average employee.
Jacobs, Graham-Squire, and Luce (2011) study the effects on both Wal-Mart employees
and the consumer if Wal-Mart were forced to impose a higher wage standard. They found that
not only would Wal-Mart employees earn approximately $1670 to $6500 more annually, the
average impact passed on to the consumers would amount to $12.49. The $13.83 gathered from a
previous study (Dube and Jacobs, 2004) converted to 2011 dollars is approximately $10.87. This
means that without any living wage policies, the average employee would approximately pay
$10.87 more in taxes, but as a consumer would save approximately $12.49 from Wal-Mart not
having to institute any wage policies. Since these studies have similar researchers and come from
the same organization, it would be interesting to see if they might put some of their data together
to see if there is a best policy for the taxpayer, the consumer, and the big box retailer.
In a report to the Orange County Business Council, Boarnet and Crane (1999) studied the
impact on how big box grocers affect jobs, wages, and municipal wages. This report estimates

that the wages and benefits in the California grocery sector would be depressed between $500
million to $1.4 billion a year. In other words, if a huge rush of super center type stores opened
around the State of California, the average grocery store employee would see a drop in their
average annual pay by approximately $2000-$5600 a year. This statistic really only show what
could happen if a rapid increase of Wal-Mart super center style stores began to pop up
everywhere. With a more gradual increase of the big box stores, a more modest effect on
incomes could be shown.
Employment
Another common theme in the literature is the effect that a big box retailer has on
employment. Ketchum and Hughes (1997) studied the effects on employment in twelve Maine
counties after a Wal-Mart entry. This study focuses on the employment level in these counties
for the years between 1990 and 1994 and uses Maine’s other four counties as a control group.
This study concludes that the twelve Maine counties with Wal-Mart that were being studied did
not show any declines in retail employment during this time period.
In 2005, Emek Basker studies the effect of Wal-Mart on county retail and wholesale
employment by controlling for time-variant county characteristics using where the Wal-Mart’s
are located and their opening dates. She finds that a new Wal-Mart entry nets around 50 new
retail jobs per year for the county, but the wholesale sector loses around 20 jobs. This is another
study that report’s findings contrary to the hypothesis that a Wal-Mart entry leads to less retail
jobs.
In Drewinka and Johnson’s (2006) study on Wal-Mart’s effect on local labor markets,
researchers study the effects of local retail and non-retail employment after Wal-Mart’s entry. In
this study Drewinka and Johnson used controls for local trends that happened before Wal-Mart

entered the area. Their study concludes that Wal-Mart has a small positive impact on local area
retail employment, but there is a slight drop in employment at other retailers as a result of Wal-
Mart’s entry. According to the researchers, this could mean that when Wal-Mart enters, it can
replace other retail jobs with new jobs.
Contrary to the other studies regarding big box retailers and retail employment, Neumark,
Zhang, and Ciccarella (2008) studied the effects of retail employment accounting for the
geographic and time pattern of Wal-Mart’s expansion. The researchers’ results conflict with the
other studies on Wal-Mart’s impact on retail employment by reaching the conclusion that Wal-
Mart actually reduces employment in a county by approximately 150 employees. They conclude
that Wal-Mart actually reduces retail employment on the whole by about 2.7% a year.
Establishments
The last common variable mentioned in these studies was the effect the big box retailer
had on the establishments in a county. Researchers have looked at any effect on establishments
in a number of ways. Researchers have looked at how big box retail has affected the number of:
retail establishments, small retail establishments, and retail establishments in rural communities
(Drewinka and Johnson, 2006; Hicks, 2009; Stone, 1997). Another study was conducted to see
the importance of local firm ownership (Fleming and Goetz, 2010). The literature covers a broad
range of topics regarding establishments.
In Drewinka and Johnson’s (2006) study on local labor markets, they look to see if there
is any correlation to a new Wal-Mart entry into a county with the number of retail establishments
in a county. In this study, researchers control for the way Wal-Mart tends to expand into places
experiencing growth but have weak retail sectors. Their study found that a Wal-Mart entry into a
county has little to no effect on the number of retail establishments.

Hicks (2009) studied the effect on small businesses when a new Wal-Mart entered an
Iowan county. In this study, Hicks studied retail firms from the three smallest categories from in
the County Business Patterns data set. The data set includes firms with 1-4 employees, 5-9
employees, and 10-19 employees. He then tested to see the effect of what introducing a new
Wal-Mart had on the county and the effect that it had on the neighboring counties. His study
concluded that his model was unable to find any statistically meaningful impact on the number
of small businesses, but his model did find weak statistical evidence of a reduction of small
businesses in neighboring counties.
Stone (1997) studies the impact of the Wal-Mart phenomenon on rural communities. He
studies 34 Iowan towns with Wal-Marts for at least 10 years and compared them to 15 towns
with comparable populations. Stone concludes that the retail sectors in rural towns have
diminished over time and he attributes this to the increase in discount mass merchandise stores in
larger towns and cities. From his research, Stone concludes with several policy implications
regarding big box retail. He states that policies to completely keep the big boxes out of your
community can backfire because a neighboring community can still build a big box store and
lure business away from your community. He also notes that big box retailers can have negative
effects on local businesses, employment, and the tax base in the long term.
Fleming and Goetz (2010) conducted a study regarding the importance of local firm
ownership. In their study, researchers find that there is a positive relationship between the
density of locally owned firms and per capita income growth. This effect was only found for
smaller firms though. They found that large firms with more than 100 workers showed a negative
effect on the per capita income growth.

Data and Methodology
Data
There were two selection criterions for how the Alabama counties were chosen. The first
and most obvious criterion is that the county must be within the state of Alabama since this is a
study based on the state of Alabama. The second criterion is that the county must have had its
first Wal-Mart Supercenter open between the years of 2001 and 2005. These years were selected
because the U.S. Census Bureau’s County Business Pattern data set covers the years of 1998-
2009. By using the selected years, this study will be able to see what was happening on average
to these counties from two years leading up to the Wal-Mart entry and then what happened in the
entry years through the following five years. Table 1 list the counties studied for this project.
These counties were based off of a data set that list the opening dates of all Wal-Marts and Wal-
Mart Supercenters (Holmes, 2010).
Table 1 Alabama Counties Observed
Counties Selected
Year Wal-
Mart
Introduced
2001 2002 2003 2004 2005
Years
Studied
1998-2005 1999-2006 2000-2007 2001-2008 2002-2009
Counties Shelby Cullman Cherokee
Dale
Elmore
Etowah
St. Clair
Butler
Randolph
Franklin
Lawrence
Marengo
Average Annual Wage.
U.S. Census Bureau’s County Business Patterns data set under the NAICS code
description of Retail Trade was used to gather information about the average annual wage for the
selected counties (U.S. Census Bureau, 2009). Information on the number of employees and the
annual payroll in the retail sector are listed in this data set.

To determine how much the average employee made during a given year, we multiply the
annual payroll given to us by $1,000. Since this study is based off of time series, we need to
account for inflation for the different time series given. Using the implicit price deflators for
retail trade from the National Income and Products Accounts Table from the Bureau of
Economic Analysis, the annual payrolls were deflated to 1998 prices. Next, we can divide the
deflated annual payrolls by the number of retail employees to give us the average annual wage
for a retail sector employee. Finally, we take each county by their selected years studied and get
the average annual wage for all of the counties from three years prior to the year a Wal-Mart
Supercenter entered the county, to the following five years. The average annual wages for the
counties are listed below in Table 2.
Table 2 Average Annual Wages
Year Average
1 $ 14,516.96
2 $ 14,685.38
3 $ 14,472.94
4 $ 14,024.66
5 $ 13,308.85
6 $ 13,276.12
7 $ 13,456.49
8 $ 14,234.46
All Employees.
The County Business Patterns data set also includes information on the number of
employees employed in the retail sector and in total industries for each county. Employment data
was collected for the counties for three years prior to the Wal-Mart Supercenter entry to the
following five years. The total number of employees was also gathered for each county to use as
a weight for the number of employees in the retail sector over each year. The “all employees in
retail” weighted by “all employees in total industries” will be used to see if the percentage of

employees in the retail sector makes up in terms of the counties total employment in all
industries. The data collected for each county are displayed in Table 3, which is listed below.
Table 3 Employment Data
Year All Retail
Employees
All Industry
Employees
Makeup of County
Retail Employment
1 23816 171,274 13.91%
2 24504 172,993 14.16%
3 24709 176,634 13.99%
4 26410 181,457 14.55%
5 27647 190,549 14.51%
6 27830 193,930 14.35%
7 30350 196,068 15.48%
8 29696 199,734 14.87%
Number of Establishments.
The U.S. Census Bureau’s County Business Patterns gives us the number of
establishments in each county by listing the total number of establishments in each county and in
nine other different size categories. The size of an establishment is based off of the amount of
employees that an establishment employs. The categorical breakdown of the establishment size
and the data collected for the counties are listed below in Table 4.
Table 4 Alabama County Establishments
Number of Establishments by the Number of Establishment
Employees
Trend Total 1-4 5-9 10-19 20-49 50-99 100-249
1 229.50 119.25 46.50 30.58 12.17 5.33 2.67
2 206.92 108.75 44.67 26.17 8.83 5.08 2.67
3 211.92 110.08 49.08 26.08 11.00 5.42 2.58
4 210.50 108.92 47.67 27.08 11.58 4.92 2.50
5 210.83 104.25 50.25 27.75 11.58 5.00 1.75
6 213.83 108.17 48.75 30.42 12.83 4.58 1.58
7 218.00 106.08 51.92 29.08 13.08 5.08 1.92
8 218.75 108.42 49.83 33.75 11.83 5.25 1.75

The County Business Patterns data set includes establishment categories that have more
than 250 employees, but due to the infrequency or overall lack of retail establishments going
over the 100-249 numbers of employees, this study will not include establishments that have 250
or more employees.
Methodology
This study uses three different regression models to analyze if there are any apparent
trends seen in these counties due to a new Wal-Mart Supercenter’s entrance. The models will be
compared and the model with the best fit will be used to show the counties have trended. The
time period used in the trend analysis includes three years prior to the Wal-Mart Supercenter’s
entrance in order to gauge how the counties were trending before the supercenter entered the
county. The regression equations used are listed in below in Table 5.
Table 5 Regression Equations
Regression Equations
Linear ŷ = α + β1x1
Quadratic ŷ = α + β1x1 + β2x1
2
+
Cubic ŷ = α + β1x1 + β2x1
2
+ β3x1
3
The independent variables used in these equations are: the average annual wages, the
number of employees employed in the retail sector, the number of employees in the retail sector
weighted by the number of employees in all industry sectors, and in the number of retail
establishments. All income related data used in this study are deflated to 1998 using the National
Income Without Capital Consumption Adjustment by Industry implicit price deflator chart
provided by the U.S. Bureau of Economic Analysis. The analysis for the number of employees in
the twelve selected counties retail sectors is tested by itself and weighted in order to see if the
Wal-Mart Supercenter has an effect on the total number of employees in the retail sector and to
see how the retail sector employment levels have changed in relation to the employment levels in

all other industries. The number of retail establishments is taken “as is” from the County
Business Patterns to see if Wal-Mart Supercenters have had any effect on the total number of
establishments and to see if there is an effect on the number of different sized establishments.
Results
Average Annual Wage
In order to see the trend average annual retail wages for the county for before and after
the Wal-Mart Supercenter entered the county, we used three different regression equations. The
regression equations used where to determine if the annual average wages relationship has a
linear, quadratic, or cubic trend. The regression output is listed below in Table 6.
Table 6 Annual Wage Regression Results
Average Annual Wage Regression Results
Linear Quadratic Cubic
MAD 322.50 320.39 53.55
Adjusted R2 0.297 0.481 0.969
Intercept 14662.777*** 15,509.834*** 13,673.324***
x -147.954* -656.189* 1254.524***
x2 56.471 -444.396***
x3 37.101***
The cubic regression model performed the best in terms of the average annual wages. The
model shows that the average annual wages for the counties that were studied had peaked two
years before the Wal-Mart Supercenter arrived, with the average annual wage at approximately
$14,700. The average annual wages then reached their lowest point two years after Wal-Mart
Supercenter arrived, bottoming out at around $13,200. The model also tells us that the average
annual wages begin to trend upwards during the seventh and eighth year. If the model’s
prediction holds true, then the average annual retail wage in the ninth year should be
approximately $16,000. As you can see from the Figure 1, there is clearly a cubic relationship
between time and the annual average wages.

Figure 1 Average vs. Predicted Average Retail Wages
What the data implies is that if there is any negative impact on the annual average wage
from a new Wal-Mart Supercenter entering a county, then it is seen in the first two years of the
Wal-Mart Supercenters entrance. If this trend is correct, the county should actually see a new
high in retail wages five years after the Wal-Mart Supercenter enters the county.
Employment
Total Employment.
We tested the trends of employment in the retail sector with linear, quadratic and cubic
trend equations. We compared each model’s fit with their adjusted R2
and mean average
deviation of the residuals. Using the adjusted R2
to compare the models, the linear model
performed best. Using the mean absolute deviation of the residuals to compare the models, the
cubic model performed best. Table 7 below shows how each model performed as well as the
trend predictors for each regression equation.
$13,000
$13,500
$14,000
$14,500
$15,000
1 2 3 4 5 6 7 8
Actual vs. Predicted Average Retail
Wages
Actual Cubic Predicted

Table 7 Total Retail Employment Regression Results
Total Retail Employment
MAD 437.588 439.869 375.786
Adjusted R2 0.933 0.920 0.930
α 22531.50*** 22668.64*** 24372.14***
x 964.17*** 881.88 -890.45
x2 9.14 473.73
x3 -34.41
The linear regression equation shows that the total number of employees in the retail
sector is predicted to continually increase over time in these counties. This result is expected
because the data was not weighted against any other population. In order to see if or how a Wal-
Mart Supercenter has affected these counties in terms of retail employment, the average retail
employment has to be held constant in by some other means.
Average Percentage of All Employment is Retail.
In order to get a clear view of how a Wal-Mart Supercenter might have affected these
counties, we weighted the employment in the retail sector against the total employment in all of
the county industry sectors. This will show us if the employment level in the retail sector is
making up more or less of the counties total employment. The regression results are displayed
below in Table 8.
Table 8 Total Retail Employment Weighted by Total Industry Employment
Average Percentage of All Employment is Retail
MAD 0.0021 0.0021 0.0022
Adjusted R2 0.6093 0.5335 0.4347
α 13.71%*** 13.77%*** 14.05%***
x 0.17%** 0.13% -0.16%
x2 0.004% 0.08%
x3 -0.01%

As you can see from the mean absolute deviation and from the adjusted R2
, the linear
regression model performed the best. What this means is that as time moves forward,
employment in the retail sector is making up a higher percentage of total employment in these
counties. The linear regression model shows that the percentage of a county’s labor force
employed in the retail sector is positively correlated and is statistically significant at the 95%
confidence level.
The knowledge that employment in these counties job sector is rising is not a good
indicator alone as to being a good or bad for a county. According to Drewinka and Johnson’s
(2006) study on local labor markets, Wal-Mart tends to locate new stores in areas with weak
retail sectors. In order to get a better view of these counties retail sector employment, we can use
a location quotient on each individual county to gauge the employment levels for each individual
counties compares against national retail employment. The location quotients for where the
counties were at in terms of employment in the retail sector are listed below in Table 9.
Table 9 County Retail Employment Location Quotients
Counties Location Quotient
Shelby 0.96
Cullman 1.17
Cherokee 1.81
Dale 1.21
Elmore 1.24
Etowah 1.06
St. Clair 0.99
Butler 1.22
Randolph 1.10
Franklin 0.81
Lawrence 1.12
Marengo 1.12
As you can see from Table 9, only three counties had employment levels in the retail
sector lower than what the ratio of retail to all industry jobs nationally. In other words, nine of

the twelve counties had seemingly healthy retail sectors before Wal-Mart entered their counties.
If this is true, then these Wal-Mart Supercenters would essentially be moving in as direct
competition against other retailers in healthy markets for nine out of twelve of the new
supercenter entries. This also means that the other three counties would be getting a boast in their
retail sectors from the new supercenter entry. So the new supercenter entry could be a positive or
negative on the counties retail sector, depending on the county.
Number and Sizes of Establishments
Total Establishments.
From the regression results for the total number of establishments, you can see that the
model that fits the total establishment’s trend is the cubic model. Although there is little
statistical significance to this prediction equation, the trend line does give some useful
information. The regression results shown in Table 10 that the total number of retail
establishments increase after a Wal-Mart Supercenter first arrives in a county rather than
decrease.
Table 10 Total Number of Establishments Regression Results
Total Number of Establishments
MAD 5.391 3.228 2.331
Adjusted R2 -0.163 0.372 0.616
α 215.768*** 231.728*** 249.863***
x -0.164 -9.740* -28.608**
x2 1.064* 6.010*
x3 -0.366
1-4 Employee Establishments.
The mean absolute deviation and adjusted R2
show that the cubic regression equation
model is the best fit for the establishment sizes that fall into the “1-4” employee category. The

quadratic model outperforms the other two models for the “1-4” employee establishments.
According to the quadratic regression results, the number of establishments in the category of “1-
4” employees is decreasing at an increasing rate and is statistically significant at the 95%
confidence level. This means that the number of establishments in this category was actually
dropping before the supercenter entry then begins to show and increase a few years after the
supercenter enters the county. The regression results for establishments that fall into the ‘1-4”
employee categories are listed below in Table 11.
Table 11 "1-4" Employee Establishments Regression Results
1-4 Establishments
MAD 2.473 1.702 1.718
Adjusted R2 0.333 0.666 0.642
α 114.574*** 122.424*** 126.810***
x -1.186* -5.895** -10.458
x2 0.523** 1.719
x3 -0.089
According to the mean absolute deviation of the residuals, the cubic regression equation
is the best model fit for the category of “5-9” employees. Alternatively, the adjusted R2
says the
linear model is the best fit model for this category. The cubic regression shows that the number
of establishments in the “5-9” employee category increases over time until the seventh year then
begins to decrease. The linear model shows that the number of “5-9” employee establishments is
positively correlated with time. As you can see from the regression results in Table 12 on the
next page, the cubic model did not show any statistical significance, whereas the linear model
was found to be statistically significant at the 95% confidence level.

5-9 Establishments
MAD 1.218 1.218 1.079
Adjusted R2 0.550 0.494 0.429
α 45.307*** 44.220*** 46.512***
x 0.728** 1.380 -1.004
x2 -0.072 0.553
x3 -0.046
The cubic regression equation is the best model fit for the category of “10-19”
employees. The cubic model shows the relationship between “10-19” employees to time at a
decreasing rate until the year of the supercenter entry, and then the number of establishments
begins to increase at a decreasing rate. As listed in Table 13 below, the results for the “10-19”
employee establishments’ sizes were not statistically significant for the cubic model.
10-19 Establishments
MAD 1.814 0.998 0.834
Adjusted R2 0.196 0.69 0.697
α 26.162*** 31.692*** 34.786***
x 0.600 -2.717** -5.936
x2 0.369** 1.212
x3 -0.063
employees. The model shows the number of establishments decreasing until the year of the
supercenter entry, and then the number of establishments begins to increase at a decreasing rate.

The regression results show statistical significance at the 90% confidence level for this model.
The regression results are listed below in Table 14.
MAD 0.802 0.802 0.508
Adjusted R2 0.175 0.015 0.494
α 10.307*** 10.537*** 15.089***
x 0.291 0.152 -4.584*
x2 0.015 1.257*
x3 -0.092*
employees. The equation shows the number of establishments began increasing for the first
trending year, and then begins to decrease at an increasing rate beginning in the second year. The
regression results listed in Table 15 show that this model shows no statistical significance.
MAD 0.196 0.149 0.145
Adjusted R2 -0.038 0.128 0.250
α 5.244*** 5.661*** 5.036***
x -0.036 -0.286 0.365
x2 0.028 -0.143
x3 0.013
employees. The cubic equation shows the number of establishments begins to increase until the
second year, and then begin to decrease at an increasing rate. As seen in Table 16, the results for

the “100-249” establishment sizes are not statistically significant for the cubic regression
equation.
Table 16 "100-249" Employee Regression Results
MAD 0.195 0.192 0.128
Adjusted R2 0.710 0.669 0.773
α 2.923*** 3.079*** 2.256**
x -0.166*** -0.259 0.597
x2 0.010 -0.214
x3 0.017
Conclusion
Local government administrators should continue to analyze the effect that supercenter
store formats like Wal-Mart have on their communities. With these stores continually expanding
into markets, policymakers should know whether or not they should implement regulations to
protect their communities or possibly to relax regulation to encourage new supercenter stores to
enter their communities. A good administrator should always make an informed decision
regarding how a big box retailer would affect the health of their communities.
This study concludes that there were some statistically significant relationships observed
after the Wal-Mart Supercenter entered a county. The results that were observed did not
necessarily coincide with the hypothesis that a Wal-Mart Supercenter’s entrance into a county
would have a negative effect on county wages, employment, and establishments. Instead, there
was actually a positive effect seen after a super center would enter the county.
In terms of annual retail wages in the retail sector, this study finds that the selected
counties were experiencing a decline in retail wages prior to the big box entrance. If retail wages
were affected by the entrance of the new supercenter, any negative effect was short lived. If the

annual wages continue in the predicted pattern, the counties are expected to experience new
highs in terms of retail wages. In essence, there appears to be no long term effect on annual retail
wages by the entrance of a new Wal-Mart supercenter. Although there is a statistical significance
to the relationship between retail wages and time in these counties, the hypothesis that the
entrance of a Wal-Mart Supercenter will have a negative effect can be rejected. The annual retail
wages were already in decline prior to the supercenter entrance and began to make a noticeable
increase two to three years after the supercenter’s entrance.
County employment levels in retail were also shown to be positively correlated with time.
The total number paid retail employees by itself and the total number of paid retail employees
weighted by a county’s total paid employees were both correlated positively in their respective
linear models. Both models also showed statistical significance at the 95% confidence level. The
means that the total number of employees in the retail sector continued to grow even after the
supercenter entered the county. It also means that the percentage of the jobs in these counties
retail sector make up a higher proportion in these counties industry mix. The hypothesis that a
there would be a negative correlation on the number of retail employees over time for these
counties can therefore be rejected.
Finally, the number of retail establishments that showed any sort of statistical
significance were the establishments that fell into the “1-4” and “20-49” employee categories.
The establishments that fell into the “1-4” category showed that the total number of
establishment in county were in decline before the supercenter arrived and the number of
establishments began to increase two years after the supercenters entrance. The “20-49”
employee category showed a noticeable increase after the supercenter entrance, but began to
decline three years after the supercenters entrance. The hypothesis that a supercenter entrance

has a negative effect on all establishment sizes and categories other than those in the “20-49”
employee category can be rejected according to the regression patterns observed. The
establishments in the “20-49” category might need to be studied further to see if maybe there is a
delayed impact on the number of establishments correlated with a supercenters entrance into a
county.
Policy Implications
This research leaves but a few policy implications. If the prediction equation for retail
wages is giving an accurate picture of what happens when a supercenter comes to town, then
local administrators should try to encourage these supercenters to locate in their areas. Because
this research is limited to twelve counties in a specific geographic area with not overtly large
populations, these results might not be useful to every policymaker outside of Alabama. A good
policy maker should find areas that are similar to their own in terms of industry makeup and
population size and make their policy decision based on the trends that they may observe.
Local policy-makers should be aware of their industry makeup and wage data prior to
making policy decisions regarding a supercenter. If the county already has a saturated retail
sector with high paying retail jobs, a new Wal-Mart could hurt the local labor force. In this
instance, a county should consider a wage floor policy for big box retailers. If a county is lacking
in the retail sector and/or county retail wages fall below what the new big box would pay in
wages, the county should consider relaxing some of their policies to encourage a big box
entrance. All policy decisions regarding big box retail should be examined on a case by case
basis by policy-makers to determine what policy path should be followed.

Future Research
One limitation that this research has is the relatively small sample size studied. It would
be interesting to see how the models might perform with a larger sample size or even from the
whole population of counties with Wal-Mart Supercenters. The cubic regression model showed
an almost perfect relationship between time and annual retail wages for these selected counties
and this would be of some interest to see if this relationship carries over on a larger scale.
Another limitation in to research with studying a how a big box retailer might affect a
community is the size of the areas being researched. The smallest area that is generally studied
for any “big box effect” is a county, typically due to data constraints. With a more detailed data
set, it would be of some interest to see how the immediate areas, such as the surrounding block
groups, are possibly affected by the introduction of a big box retailer.

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