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 + β3x13

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.


References

Basker, E. (2005). Job creation or destruction? labor market effects of Wal-Mart expansion. The

Review of Economic and Statistics , 1-10.

Basker, E. (2007). The causes and consequences of Wal-Mart's growth. TheJournal of Economic

Perspectives , 1-26.

Bernhardt, A., Chaddha, A., & McGrath, S. M. (2005). What do we know about Wal-Mart? an

overview of facts and studies for new yorkers. Economic Policy Brief , 1-9.

Boarnet, M., & Crane, R. (1999). The impact of big box grocers on southern California: jobs,

wages, and municipal finances. Orange County Business Council , 2-118.

Chambers, S. (2005). Reviewing and revising Wal-Mart's benefits stratefy. Center for a

Changing Workforce , 2-27.

Drewianka, S., & Johnson, D. (2006). Wal-Mart and local labor markets, 1990-2004. Retrieved

October 27, 2011, from University of Wisconsin-Milwaukee:

https://pantherfile.uwm.edu/sdrewian/www/walmartandlocallabormarkets.pdf

Dube, A., & Jacobs, K. (2004). Hidden cost of Wal-Mart jobs: use of safety net programs by

Wal-Mart workers in California. UC Berkley Labor Center's Briefing Paper Series , 2-8.

Dube, A., Lester, T. W., & Eidlin, B. (2007). A downward push: the impact of Wal-Mart stores

on retail wages and benefits. UC Berkeley Center for Labor Research and Education , 1

8.


Dube, A., Lester, T. W., & Eidlin, B. (2007). Firm entry and wages: impact of Wal-Mart growth

on earnings throughout the retail sector. Institute for Research on Labor and

Employment, 1-37.

Hicks, M. J. (2009). Wal-Mart and small business: boon or bane? The Review of Regional

Studies, 39 (1), 73-83.

Flemming, D., & Goetz, S. J. (2010). Does local firm ownership matter? Economic Development

Quarterly, 2-15.

Jacobs, K., Graham-Spire, D., & Lace, L. S. (2011). Living wage policies and big-box retail:

how a higher wage standard would impact Walmart workers and shoppers. UC Berkley

Center for Labor Research and Education , 1-16.

Ketchum, B. A., & Hughes, J. W. (1997). Wal-Mart and Maine: the effect on employment and

wages. Maine Business Indicators , 1-7.

Neumark, D., Zhang, J., & Ciccarella, S. (2008). The effects of Wal-Mart on local labor markets.

Journal of Urban Economics , 63, 405–430.

Stone, K. E. (1997). Impact of the Wal-Mart phenomenon on rural communities. Increasing

Understanding of Public Problems and Policies , 2-21.

U.S. Census Bureau. (2009). Butler County, Alabama – 2001-2008. County Business Patterns

(NAICS). Retrieved from http://censtats.census.gov/cgi-bin/cbpnaic/cbpsect.pl

U.S. Census Bureau. (2009). Cherokee County, Alabama – 2000-2007. County Business Patterns

(NAICS). http://censtats.census.gov/cgi-bin/cbpnaic/cbpsect.pl

http://censtats.census.gov/cgi-bin/cbpnaic/cbpsect.pl



U.S. Census Bureau. (2009).Cullman County, Alabama – 1999-2006. County Business Patterns


U.S. Census Bureau. (2009). Dale County, Alabama – 2000-2007. County Business Patterns


U.S. Census Bureau. (2009). Elmore County, Alabama – 2000-2007. County Business Patterns


U.S. Census Bureau. (2009). Etowah County, Alabama – 2000-2007. County Business Patterns


U.S. Census Bureau. (2009). Franklin County, Alabama – 2002-2009. County Business Patterns


U.S. Census Bureau. (2009). Lawrence County, Alabama – 2002-2009. County Business

Patterns (NAICS). Retrieved from http://censtats.census.gov/cgi-bin/cbpnaic/cbpsect.pl

U.S. Census Bureau. (2009). Marengo County, Alabama – 2002-2009. County Business Patterns


U.S. Census Bureau. (2009). Randolph County, Alabama – 2001-2008. County Business Patterns


U.S. Census Bureau. (2009). St. Clair County, Alabama – 2000-2007. County Business Patterns


U.S. Census Bureau. (2009). Shelby County, Alabama – 1998-2005. County Business Patterns


U.S. Department of Commerce. Bureau of Economic Analysis. (2011, October 27). U.S. Bureau

of Economic Analysis (BEA). Retrieved November 2, 2011, from

http://www.bea.gov/national/nipaweb/TableView.asp?SelectedTable=181&ViewSeries










http://www.bea.gov/national/nipaweb/TableView.asp?SelectedTable=181&ViewSeries


NO&Java=no&Request3Place=N&3Place=N&FromView=YES&Freq=Year&FirstYea

=1998&LastYear=2009&3Place=N&Update=Update&JavaBox=no#Mid

Wal-Mart Stores, Inc. (2001) Wal-Mart 2001 Annual Report. Retrieved November 1, 2011 from

http://media.corporate-ir.net/media_files/irol/11/112761/ARs/2001_annualreport.pdf

Wal-Mart Stores, Inc. (2011) Wal-Mart 2011 Annual Report. Retrieved November 1, 2011 from

http://walmartstores.com/sites/annualreport/2011/

http://media.corporate-ir.net/media_files/irol/11/112761/ARs/2001_annualreport.pdf

http://walmartstores.com/sites/annualreport/2011/

Documents

The Effect on the Labor Force