RETAIL DISTRIBUTION CENTERS: 

HOW NEW BUSINESS PROCESSES 

IMPACT MINORITY LABOR MARKETS

U.S. EQUAL EMPLOYMENT OPPORTUNITY COMMISSION

2004
U.S. Equal Employment Opportunity Commission

RETAIL DISTRIBUTION CENTERS

-iFOREWORD

As part of the emphasis on proactive prevention in the Equal Employment Opportunity

Commission’s (EEOC’s) Five Point Plan, this report seeks to aid retailers and similar

employers in taking full advantage of America’s labor markets. Our nation’s retailers fulfill

an important role in our economy and according to the EEOC’s EEO-1 reports in 2002,

employ nearly 15 percent of all private sector employees. In this second in a series of reports

on this important industry, this report examines a unique sector of retailing: distribution

centers. In contrast to traditional warehouses, a modern distribution center is essentially an

operations center, managing the flow of information and goods between retailers and

suppliers through the use of standardized bar codes, high-speed conveyors, laser scanners,

and computerized databases.

These distribution centers represent not only a significant change in the way retail firms

operate, but unlike store fronts they are often not very visible to the general public. These

centers are commonly located away from central cities, either outside metropolitan areas

altogether or on the edge of such areas. In searching for inexpensive land, favorable leases

and low tax rates, retailers can easily lose sight of the value of a diverse workforce. This

report attempts to alert retailers and others to the potential impact that distribution center

location can have on the ability to develop and maintain a multi-cultural workforce. 

Readers, especially retailers, are encouraged to share their "best practices" in making

location decisions for distribution centers in a manner that encourages a diverse work force. 
U.S. Equal Employment Opportunity Commission

RETAIL DISTRIBUTION CENTERS

-iiEXECUTIVE SUMMARY

This report examines how a recent business innovation, the retail distribution center, impacts

the employment opportunities of minorities and women.

• In the retail industry, traditional warehouses designed to house inventory are being

replaced by much large and more technologically sophisticated distribution centers

that work towards maintaining a “just-in-time” inventory in stores and making the

goods “shelf ready”.

• Distribution centers often require greater space encouraging location away from the

central city to lower priced real estate in less populated areas.

• Using EEO-1 data to identify retail distribution centers, the study finds that as retail

distribution centers grow in size (measured by number of employees), they tend to be

located in less populated areas. As areas become less populated, the percentage of

women and minorities in the relevant job groups (operatives and laborers) declines.

• A comparison of the location of retail distribution centers/warehouses in 1982 to their

location in 2002 suggests that had the locations remained in the same counties as in

1982, the relevant labor markets would have had ten percent (based on EEO-1 data)

to 14 percent higher (based on 2000 Census data) minority representation as

operatives and laborers.

• EEOC action in a number of areas might be effective in addressing this new type of

business organization:

– Educate corporate officials on the workforce ramifications of distribution

center locations;

 – Collect equal employment opportunity (EEO) “best practices” that recognize

the characteristics of logistic and supply chain operations;

– Conduct outreach and education activities regarding EEO rights and

responsibilities for potentially under served communities when distribution

centers are located in less populated areas; and

– Provide training and technical assistance regarding statutory requirements to

employers in these less populated areas.
U.S. Equal Employment Opportunity Commission

RETAIL DISTRIBUTION CENTERS

-iiiTABLE OF CONTENTS

INTRODUCTION .................................................... Page 1

CHARACTERISTICS OF RETAIL DISTRIBUTION CENTERS ........... Page 8

COMPARISONS OF 1982 AND 2002 LOCATIONS ...................... Page 15

SPECIFIC AREAS .................................................. Page 21

PROGRAM IMPLICATIONS ......................................... Page 24

CONCLUSION ..................................................... Page 27

ADDITIONAL INFORMATION ....................................... Page 28

SELECTED REFERENCES ...................................... References i
U.S. Equal Employment Opportunity Commission

RETAIL DISTRIBUTION CENTERS

INTRODUCTION

Firms in a competitive business environment, such as retailing, develop mechanisms, such as

outsourcing, relocating, retraining, and technological innovations, to obtain an advantage

(Appelbaum, Bernhardt, and Murnane 2003, table 1.1). The development of new techniques

can inadvertently have major consequences for the gender and racial/ethnicity of their

workforce (e.g., Reskin, McBrier, and Kmec 1999; Tomaskovic-Devey 1993; Smith

1997:334-335; Stinchcombe 1990:261-265). This research uses data from the 2002 EEO-1

Survey of Private Employers and the Census 2000 EEO Special File to examine the gender

and racial composition of a recent innovation, the retail distribution center. Specifically, we

are primarily interested in the effects of retail distribution center location decisions on the

employment of women and minorities. 

BACKGROUND

The traditional retail warehouse was “a holding station for inventory ordered well in advance

of sale” (Abernathy et al. 1999:57). Most shipments were large, infrequent and laborintensive. 

The typical shipment between an apparel manufacturer and retail customers was large

and of low frequency—usually once a season. Once delivered, the retailer held the

products in central warehouses or as inventory in individual stores’ “back rooms.” 

When the desired time of display and sale arrived, workers stocked the product on the

selling floor and replenished from store or warehouse inventories as the selling period

progressed. Inventory control relied on painstaking, manual comparisons between

sales records (paper receipts) and physical counts of items on the floor, in the back

room, and in warehouses. (Abernathy et al. 1999:42)

Traditional warehouses require hundreds of people, usually working on a

single shift. In addition to loading and unloading trucks, a large number of

jobs were devoted to receiving and inspecting incoming packages and

stocking storage bins in the warehouse. A second group of workers was

involved in “picking and packing,” that is, assembling outgoing orders for

stores by going to storage areas and bins and picking the required items and

packing them for outbound shipment. Additional workers moved goods

within the warehouse to adjust to space limitations arising from unexpected

delays in shipping out orders, unexpected early arrival of goods, or holding

unsold inventory. Capital per worker reflected the relatively low level of
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1

 Starting in 2005, Wal-Mart will require major suppliers to use radio-frequency

identification (RFID) on cases and pallets. Unlike bar codes, RFID allows hands-free

scanning without a direct line of sight. “One of the biggest motivating factors for people

looking at RFID is the elimination of labor,” says Melling [a RFID specialist at Symbol

Technologies]. “They want the ability to know where everything is without having to pay a

warehouse guy to constantly go and scan it, which is too expensive and too time consuming”

(Murphy 2003:3). 

Page 2

technology in place in the warehouse (primarily equipment, such as forklifts,

to load, lift, and unload pallets and boxes). (Abernathy et al. 1999:63-64)

By contrast, a modern distribution center is essentially an operations center, managing the

flow of information and goods between retailers and suppliers through the use of

standardized bar codes, high-speed conveyors, laser scanners,1

 and computerized data-bases:

The most advanced set of practices are applied to incoming shipments

that can be ‘cross-docked.’ In this case, goods are unloaded at one bay

of the distribution center and moved to another bay by conveyer for

shipment in the same day . . . The cross-docking procedure begins

when trucks are unloaded (manually with some lifting equipment for

heavier items or pallets), packages are positioned so that the bar code

on the shipping container marker (SCM) can be scanned . . .The

package then moves onto a scale and its weight is checked against the

weight indicated on the label . . . At the same time, the information on

the SCM is matched against data in the distribution center’s database

on purchase orders . . . Shipments that pass weight and purchase order

verification move on to the main conveyer line, which has multiple

sublines or “spurs” that correspond either to docks where packages are

being consolidated directly for shipment to stores or to areas in the

center devoted to opening certain types of goods for price marking,

reconsolidation, or other manual processes . . . The various spurs of

the main conveyer system end up at truck loading bays–often located

on the opposite side of the distribution center–where storebound trucks

can be docked. . . The computer completes its file associated with the

particular shipment by indicating that the package has been loaded for

shipping. Financial payments to the vendor are then initiated, along

with a shipping manifest for the truck. The trucks are then sent out for

deliveries to stores, where they will be unloaded and products stocked

directly on the sales floor. (Abernathy et al. 1999:65-66)
U.S. Equal Employment Opportunity Commission

RETAIL DISTRIBUTION CENTERS

2

 Hale and Van Bodegraven (2002:3) state: “The trend driving the provision of

more in-DC [distribution center]-value-added services comes from both the sourcing end of

the business and from customers. On the sourcing end, supply chain initiatives that drive

inventory levels down by postponing value-added services, like packaging and labeling, are

resulting in inventory deployed at the DC that still needs to be labeled, packaged,

embroidered, printed, configured, loaded with software, populated, kitted, folded, re-boxed,

wrapped–whatever. From the other end, customers are negotiating for traditional store backroom activities like price and security labeling, assortment building and display-ready

product assembly done in the DC.” 

3

 James H. Coridan, a commercial real estate specialist, describes the Columbus,

Ohio distribution center market, “Building sizes and configurations have changed

dramatically in the last five to 10 years. Buildings that were built 15 to 20 years ago are now

functionally obsolete and sitting empty for prolonged periods. Building sizes are now

400,000 to 1,000,000 square feet, with 30-to 40-foot ceiling heights.” The State of

Distribution Markets, Summer 2003. Retrieved November 19, 2003

(http://www.sior.com/publications).

4

 Jedd (2001:3) states: “While the rush is on to get into major markets, general

urban sprawl is pushing DC’s and other sites to outlying areas. . . Porter [an Atlanta-based

logistic specialist] says. ‘The site size required to build a million-square-foot facility is 50 to

60 acres of useable land. You typically can’t locate those larger sites in the mainsteam

distribution markets.’” 

5

 “Walgreens is the first drug chain to invest in an automated storage retrieval

system that uses a robotic crane to transport product from the dock to a rack and vice versa. 

Lewis [senior vice president distribution and logistics for Walgreens] said that by eliminating

the need for a forklift driver, the ASRS system–used in facilities more than 100 feet

tall–reduces Walgreens productivity costs of an estimated 10 percent a year” (Parks, Liz. 

2003. “DCs back Walgreens’ Stores with ‘Brawn.’” Drug Store News March 23:2). 

Page 3

Increasingly retail distributions centers also handle returns and value-added processing such

as monogramming and final assembly (Bartholdi and Hackman forthcoming:17).

2

According to trade publications, modern retail distribution centers tend to be large (covering

one million square feet or more), recently constructed,3 located in rural or semi-rural areas,4

with substantial investments in material handling technology. As described by Foster

(2003), a new Walgreens distribution center includes approximately 700,000 square feet of

storage plus 14 miles of conveyors and a ten-story automated storage and automated retrieval

(AS/RS) system with 48,000 pallet locations.5 The total construction cost is over $100
U.S. Equal Employment Opportunity Commission

RETAIL DISTRIBUTION CENTERS

6

 Don Hampton, “Waxahachie Wins the ‘Super Bowl’” (Southern Business and

Development, Summer 2001) attributes this decision to a low cost of living, affordable land,

access to major highways linking Dallas, Houston, and Mexico City, and substantial

financial incentives including a seven year tax abatement of 60 percent. 

7

 2002 population data retrieved, December 16, 2003, from http://www.citydata.com. 

Page 4

million. The typical facility employs 300 to 400 people, most hired locally. Walgreens

actively seeks non-unionized areas with 30 minute commuting times, reliable electric power,

and low tax rates. Drug Store News (March 25, 2002) reports that Walgreens opened or plans

to open four new distribution centers between 2002 and 2004, located in Jupiter, Florida

(population 39,328), Waxahachie, Texas (population 21,426),6 Perrysburg, Ohio (population

16,945), and Moreno Valley, California (population 142,381).7

 

It might seem that retail distribution centers are classical instances of “white flight,” the

movement of businesses from inner-cities to outlying suburbs. However, the anecdotal

evidence in the trade press appears to be mixed. Some of the newer retail distribution centers

have been built in predominantly White cities and towns. Others appear to located in areas

with substantial minority populations, especially in California and the Southwest. For

example, at the four Walgreens sites, the minority population ranges from 5.7 percent in

Perrysburg, Ohio to 67.8 percent in Moreno Valley, California. Other recent examples,

obtained from public information, can be seen in Table 1.

Viewed as rational decision processes, decisions to locate or relocate a distribution center

involve a complex balancing of multiple economic and demographic considerations (Jedd

2001). For example, the Logistics QuotientTM, published by Expansion Management and

Logistics Today magazines, ranks the desirability of distribution locations by metropolitan

areas using such factors as labor costs/availability/skill levels, highway spending, road

congestion, fuel taxes, and interstate highway access. Using the Logistics QuotientTM data 

for 2003 (King and Keating 2003), Spearman rank correlations among the six labor,

highway, and taxation indices across 328 metropolitan areas are computed for this report. 

Our results show that the fifteen rank correlations range from -0.33 to 0.35 suggesting

substantial tradeoffs among the distribution indices. Comparing overall metropolitan

rankings to Frey’s analysis of the 2000 Census, about one half of the top twenty distribution 
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RETAIL DISTRIBUTION CENTERS

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TABLE 1

EXAMPLES OF RECENTLY OPENED

RETAIL DISTRIBUTION CENTERS

 

Retailer

 

Location

 Start

 Year

 Size 

Sq. Ft.

 Place 

Pop.

 Percent 

Minority

Family Dollar Morehead, KY 2000 907 K 5,914 6.3

Michaels Hazleton, PA 2002 692 K 23,329 6.8

American Eagle Ottawa, KS 2001 400 K 11,921 9.4

Target Midlothian, TX 2004 1.35 M 7,480 17.7

Dollar Tree Marietta, OK 2003 603 K 2,445 29.5

Wal-Mart Hopkinsville, KY 2002 1.20 M 30,089 34.7

Sports Authority McDonough, GA 1997 300 K 8,493 40.1

Kohl’s Corsicana, TX 2001 353 K 24,485 48.2

Lowes Perris, CA 2001 1.20 M 36,189 77.2

Best Buy Dinuba, CA 1999 635 K 16,844 79.4

locations are in areas (Nashville, Atlanta, Minneapolis, Houston, Macon, Louisville, DallasFort Worth, Indianapolis, Columbus, and Memphis) where the 1990-2000 growth in Black

population exceeded 20 percent (Frey 2001, tables 2 and 3). At least three of these areas,

Dallas-Fort Worth, Houston, and Atlanta, also show substantial 1990-2000 gains in Hispanic

population (Frey 2002, table 1).

RESEARCH QUESTION

Based on the literature suggesting that retail distribution centers are locating in less

populated areas, the hypothesis that these location decisions will negatively affect the

employment prospects of minority workers is explored. This is not to suggest that location

decisions are based on discriminatory motives. Rather it appears that these business

decisions focus on inexpensive land and location as the major consideration. 
U.S. Equal Employment Opportunity Commission

RETAIL DISTRIBUTION CENTERS

8

 EEOC obtains and maintains EEO-1 reports pursuant to its authority under section

709 of the Civil Rights Act of 1964, as amended, 42 U.S.C. §2000e-8. Paragraph (e) of that

section prohibits the EEOC and its employees from disclosing individual EEO-1 reports to

the public. Violation of that section is punishable by fine and imprisonment. Aggregated

data is available to the public.

9

 Details regarding the report characteristics can be found in the EEO-1 instruction

booklet at, http://www.eeoc.gov/stats/jobpat/e1instruct.html

10

 For a discussion of Food and Beverage Stores and the employment status of

minorities and women, see Sheryl Skaggs, “Producing Change or Bagging Opportunity? The

Effects of Discrimination Litigation on Women and Minorities in Supermarket

Page 6

DATA 

The Equal Employment Opportunity Commission (EEOC) as part of its mandate under Title

VII of the Civil Rights Act of 1964, as amended operates a data collection system that

collects data from nearly all employers in the United States with more than 100 employees. 

Private employers must file an Employer Information Report, EEO-1, with separate reports

for each facility with 50 or more employees. In 2002, more than 39,000 employers

submitted, individual establishment and headquarters reports for nearly 200,000 reporting

units with about 52 million employees (U.S. Equal Employment Opportunity Commission,

2002).8 The EEO-1 collects race/ethnic and gender data annually on nine major job

categories.9 In addition to the workforce data, information about each establishment’s North

American Industrial Classification System code, the establishment’s county, its metropolitan

area code and other data is included. The EEO-1 provides data that are more firm specific

that other possible data sources and provides useful data by job groups, race/ethnicity and

gender. For this research, EEO-1 data are supplemented with 2000 Census data from the

2000 U.S. Gazeteer (http://www.eeoc.gov/ stats/census/index.html) to obtain population data

within geographic areas and the EEO Special File ( http://www.eeoc.gov/stats/census/

index.html) to obtain supplemental workforce data.

IDENTIFICATION OF DISTRIBUTION CENTERS 

We identified distribution centers in several steps. First, we defined the retail industry using

North American Industrial Classification System (NAICS) codes for Retail Trade, (44 and

45). Motor Vehicle and Parts Dealers, Food and Beverage Stores and Non Store Retailers

were then excluded because they utilize different occupations.10 Second, we separated non
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Management.” Presented at the Annual Meeting of the American Sociological Association.

2002.

11

 Decision is based on the use of an exact binomial test and a 0.05 level of

probability.

Page 7

sales establishments from sales establishments. Those establishments whose use of sales

workers was significantly lower than their company’s overall proportion of sale workers

were identified as non sales establishments.11 This produced a total of 1,046 establishments. 

Third, we distinguished between distribution centers and other non sales establishments such

as fulfillment centers and call centers. Based on the trade literature, we selected easily

identified distribution centers. Operatives and laborers were the dominant job groups in

these examples. Therefore, as a tentative rule, we retrieved EEO-1 reports from all non

sales establishments where operatives and laborers made up more than 50 percent of an

establishment’s workforce or the unit name referred to a distribution center. To verify these

tentative selections, we searched publicly available sources such as retail web sites and

Security and Exchange Commission filings. Four hundred and sixty-four establishments

were confirmed in this manner. Fourth, establishments in the Warehousing industry (NAICS

code of 49311) were examined to determine if they contained any retail distribution centers. 

Of 709 such establishments, 168 had a headquarters number indicating that their parent firm

was a retailer. We then applied the criteria, described above, regarding percentage of

operatives and laborers and unit names. Finally, the relevant establishments from the retail

industry were combined with those from the warehousing industry to produce a final sample

size of 589 retail distribution centers. 
U.S. Equal Employment Opportunity Commission

RETAIL DISTRIBUTION CENTERS

12

 The “inner” and “outer” measure represents a factor composite score of two variables: 

(1) the relative county 2000 population density within the appropriate CSA or if not

available, the appropriate MSA; and (2) the relative distance, measured in Census ZIP Code

Tabulation Areas (ZCTAs) latitudes and longitudes, between the distribution center and the

central business district of the first principal city in the CSA or MSA title (where the central

business district was arbitrarily identified by postal addresses of federal courts, state courts,

or EEOC district offices). The Pearson correlation between the two variables is 0.55, and the

eigenvalue of the single principal component factor is 1.55. Each of these variables, taken

separately, has various strengths and weaknesses. The ranking of county population

Page 8

CHARACTERISTICS OF RETAIL DISTRIBUTION CENTERS

Retail Distribution Centers are described from a number of perspectives. To examine the

characteristics of these retail distribution centers, it is useful to first use a measure of

geographic location. 

LOCATION OF RETAIL DISTRIBUTION CENTERS

We used the rural-urban continuum index developed by the Economic Research Service

(ERS) of the U.S. Department of Agriculture (ERS 2003). The index, based on the 2000

Census and the 2003 OMB definitions of metropolitan status, classifies counties into nine

categories, divided into counties within and outside Metropolitan Statistical Areas (MSA). 

The three MSA-based categories are further subdivided into counties with one million or

more population (ERS code 1), counties with 250,000 to one million population (ERS code

2), and counties with fewer than 250,000 population (ERS code 3). The six non-MSA

categories range from counties with an urban population of 20,000 or more, adjacent to a

MSA (ERS code 4) to completely rural counties with an urban population of less than 2,500,

not adjacent to an MSA (ERS code 9).

Since many distribution centers are located within Combined Statistical Areas (CSA) and/or

MSA’s, we modified the rural-urban continuum index in several ways. The most rural

categories (ERS codes 5 through 9) were combined into a single group labeled NonMetropolitan Other. Given the geographic dispersion of the urban categories (ERS codes 1

and 2), locations within CSA’s and MSA’s were sub-classified using the Census 2000 place

definitions. The urban areas were bifurcated based into “inner” locations and “outer”

locations based on a composite measure that accounts for both population density and

geographic location.12 For example, among places with a population between 50,000 and
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RETAIL DISTRIBUTION CENTERS

densities is most appropriate for CSA’s or MSA’s with a large number of counties. The

business district measure is most appropriate for CSA’s or MSA’s centered on a single “hub”

surrounded by smaller cities and suburbs. Taken together, however, the composite scores,

sub-divided into equal-sized sub-groups within the place categories, provide a plausible

measure of distribution centers likely to be located on the fringes of CSA’s or MSA’s. 

Page 9

249,999, the measure distinguishes between Buena Park, California classified as “inner”

(30.1 miles from Los Angeles) and Rialto, California classified as “outer”(68.3 miles from

Los Angeles); among places 20,000 to 49,999 population, the distance measure distinguishes

between Niles, Illinois (16.3 miles from Chicago) and Valparaiso, Indiana (49.6 miles from

Chicago); and among places with less than 20,000 population, the distance measure

distinguishes between Secaucus, New Jersey (11.4 miles from New York) and Cranbury,

New Jersey (46.0 miles from New York).

Table 2 summarizes the twelve categories in the modified rural-urban continuum index. 

Approximately four-fifths (84.1 percent) of the retail distribution centers are located in

MSA’s: 61.5 percent in MSA’s with 1 million or more population, 17.4 percent in MSA’s

with 250,000 to 1 million population, and 5.1 percent in MSA’s with less than 250,000

population. Less than 10 percent of the retail distribution centers are located in the most

rural category. 

A striking feature of the modified rural-urban continuum index is the variation in county

population densities. The median county population density varies from 1,980 persons per

square mile in the largest urban locations to 55 persons per square mile in the smallest rural

locations. Within MSA’s with 1 million or more population, outer locations consistently

have smaller median population densities than inner locations. The difference between inner

and outer locations is 1,498 persons per square mile for places with 50,000 to 249,999

population, 1,729 persons per square mile for places with 20,000 to 49,999 population, and

1,131 persons per square mile for places with less than 20,000 population. If all of the low

density categories are combined, 61.2 percent of the retail distribution centers are located in

counties with population densities of less than 500 persons per square mile, and 21 percent of

the retail distribution centers are located in counties with population densities of less than

200 persons per square mile. 
U.S. Equal Employment Opportunity Commission

RETAIL DISTRIBUTION CENTERS

13

 Strictly speaking, the first EEO-1 submission represents the first year the facility

met the size and eligibility requirements for the EEO-1 survey. Facilities that changed

counties in the period 1990-2002 were assigned the location of the most recent address. It

should be noted that the 2002 EEO-1 Survey represents either new firms or continuing firms. 

It does not include former firms with no EEO-1 reports in 2002. 

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TABLE 2

MODIFIED RURAL-URBAN CONTINUUM 

FOR RETAIL DISTRIBUTION CENTERS

RURAL

URBAN

LOCATION

DIST.

CENTERS PLACE

POP.

COUNTY

POP. SQ 

MILE

FIRST

EEO-1

TOTAL

EMPLOYEES

# % MEDIAN

METCNTY 1M, PLACE 250K+ 95 16.1 650,100 1,980 1996 163

METCNTY 1M, PLACE 50-249K: INNER 33 5.6 128,358 1,957 1998 136

METCNTY 1M, PLACE 50-249K: OUTER 34 5.8 106,221 459 1997 213

METCNTY 1M, PLACE 20-49K: INNER 40 6.8 29,489 1,980 1996 110

METCNTY 1M, PLACE 20-49K: OUTER 39 6.6 30,004 251 1995 216

METCNTY 1M, PLACE < 20K: INNER 60 10.2 7,787 1,404 1999 163

METCNTY 1M, PLACE < 20K: OUTER 61 10.4 8,771 273 1998 275

METCNTY 250-999K, PLACE 50K+ 45 7.6 131,510 469 1994 191

METCNTY 250-999K, PLACE < 50K 58 9.8 10,897 358 1995 387

METCNTY < 250K 30 5.1 37,622 153 1997 411

NONMET, URBAN 20K+, NEAR METRO 46 7.8 17,982 111 1997 445

NONMET OTHER 48 8.1 8,525 55 1998 459

589 100

AGE OF RETAIL DISTRIBUTION CENTERS

The literature, summarized in the earlier Background section, suggests that facilities built in

secondary markets are more likely to be newer, larger and more technologically

sophisticated. We measured a retail distribution center’s age by the year of the facility’s first

EEO-1 report.13 The first reported year in our sample varies from before 1978 to 2002. 
U.S. Equal Employment Opportunity Commission

RETAIL DISTRIBUTION CENTERS

14

 It should be noted that the size of the workforce may not be directly related to

square footage or capital investments. Lower land costs can increase the room for more

employees, but they also can allow the introduction of labor-saving equipment that reduce

the number of employees.

Page 11

Most of these retail distribution centers are relatively new. Thirty two percent of the

distribution centers first filed an EEO-1 report in 2000 or later. Only 14.8 percent of the

establishments first reported in 1985 or earlier. The corresponding percentages for all

establishments in the 2002 EEO-1 statistical file are 22.5 and 22.2 percent respectively.

Table 2 reports the median of first reported year by the modified rural-urban continuum

index. There does not seem to be a strong relationship between the distribution center’s age

and geographic location. Distribution centers in the most rural category have more recent

median start years than distribution centers in the most urban category (1998 versus 1996

respectively). However, within MSA’s with 1 million or more population, the “inner” urban

areas have more recent median start years than the “outer” urban areas, and all of the “inner”

areas have more recent median start years than the place sub-categories in MSA’s with

250,000 to 999,999 population.

SIZE OF RETAIL DISTRIBUTION CENTERS

We do not have systematic information on plant size in square footage or the dollar value of

capital investments. The EEO-1 report, however, does provide information on the total

number of employees. Most of the retail distribution centers in our sample are moderately

large. The total number of employers per distribution center in our sample varies from 50

persons to 1,354 persons. (Keep in mind that centers with less than 50 employees are not

required to file individual EEO-1 reports.) The median firm size is 212 employees with an

upper quartile of 507 or more employees and a lower quartile of 98 or fewer employees.14

By contrast, the median firm size, for all establishments in the EEO-1 Survey, is 111

employees with an upper quartile of 201 or more employees and a lower quartile of 70 or

fewer employees.

Table 2 reports the median number of total employees by the modified rural-urban

continuum index. Distribution centers in the most rural category are about three times larger

than distribution centers in the most urban category (a median number of employees of 459

versus 163 respectively). Within MSA’s with 1 million or more population, the “outer”

locations all have larger median workforces than the “inner” locations, often by substantial

margins. The difference between outer and inner locations with respect to median total
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Page 12

employees can be expressed as ratio of 1.6 (213 versus 136) for places with 50,000 to

249,999 population, 2.0 (216 versus 110) for places with 20,000 to 49,999 population, and

1.7 (275 versus 163) for places with less than 20,000 population. In addition, there is a

steady increase in the median number of employees in the last five categories of the modified

rural-urban continuum index, from 191 employees in MSA’s with 250,000 to 999,999

population to 459 employees in the unclassified non-metropolitan category. Unlike the

results by age of distribution centers, there appears to be a systematic inverse relationship

between establishment size and geographic location. That is, as employment at retail

distribution centers increase, their location becomes less urban.

GENDER AND MINORITY COMPOSITION

Table 3 examines laborer and operative employees at retail distribution centers. 

Employment in these key distribution center jobs is just like total employment as it increases

as locations become less urban. The total number of distribution center laborers and

operatives in the most rural category (19,455 employees) is slightly larger than the total

number of distributive laborers and operatives in the most urban category (17,328). Within

MSA’s with 1 million or more population, the “outer” locations all have a larger number of

distribution center laborers and operatives than the “inner” locations. The difference

between outer and inner locations with respect to the number of laborers and operatives can

be expressed as ratio of 1.4 (7,436 versus 5,253) for places with 50,000 to 249,999

population, 1.6 (10,238 versus 6,335) for places with 20,000 to 49,999 population, and 1.7

(20,559 versus 11,958) for places with less than 20,000 population. In addition, within

MSA’s with 250,000 to 999,999 population, the number of distribution laborers and

operatives in places with a population under 50,000 is more than twice the number of

distribution laborers and operatives in places with a population of 50,000 or more (21,295

versus 9,076 employees respectively). 
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TABLE 3

MODIFIED RURAL-URBAN CONTINUUM INDEX

FOR RETAIL DISTRIBUTION CENTERS

EMPLOYMENT OF MINORITIES AND WOMEN

RURAL

URBAN

LOCATION

NUMBER PERCENT LABORER/OPERATIVES

DIST.

CENTERS

LABORER/

OPERATIVES TOTAL WOMENMINORITY BLACKHISPANIC

METCNTY 1M, PLACE 250K+ 95 17,328 11.1 35.6 59.8 31.5 23.5

METCNTY 1M, PLACE 50-249K: INNER 33 5,253 3.4 37.6 74.6 20.8 41.9

METCNTY 1M, PLACE 50-249K: OUTER 34 7,436 4.7 36.9 68.9 16.5 48.2

METCNTY 1M, PLACE 20-49K: INNER 40 6,335 4.0 40.4 45.1 18.2 18.6

METCNTY 1M, PLACE 20-49K: OUTER 39 10,238 6.5 36.0 52.4 17.2 27.9

METCNTY 1M, PLACE < 20K: INNER 60 11,958 7.6 44.3 47.7 22.3 20.4

METCNTY 1M, PLACE < 20K: OUTER 61 20,559 13.1 34.3 43.8 19.6 22.0

METCNTY 250-999K, PLACE 50K+ 45 9,076 5.8 31.7 57.0 23.3 28.4

METCNTY 250-999K, PLACE < 50K 58 21,295 13.6 34.5 35.6 13.8 17.9

METCNTY < 250K 30 9,350 6.0 43.0 33.9 19.8 10.7

NONMET, URBAN 20K+, NEAR METRO 46 18,287 11.7 28.4 24.8 15.6 8.0

NONMET OTHER 48 19,455 12.4 23.3 20.2 12.1 6.9

589 156,570 100

Table 3 also reports the percentage of females and minorities among laborer and operative

employees. The percentage of minorities appears to more strongly related to geographic

location than the percentage of females. As a general rule, both females and minorities are

more likely to be employed in more populated areas than less populated areas, but the

differences between the highest and lowest percentages are much larger for minorities than

females. The percentage of female laborers and operatives is 35.6 percent in the most urban

category and 23.3 percent in the most rural category, a difference of 12.3 percent. By
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15

 Notice that patterns for Black and Hispanic employees appear to be different. 

Within MSA’s with 1 million or more population, Black laborers and operatives are slightly

more likely to be found in inner areas than outer areas. Hispanic laborers and operatives are

slightly more likely to be found in outer areas than inner areas.

Page 14

contrast, the percentage of minority laborers and operatives is 59.8 percent in the most urban

category and 20.2 percent in the most rural category, a difference of 39.6 percent.15 

SUMMARY

As expected, population density is positively related to the percentage of minority laborers

and operatives (Spearman correlation coefficient of 0.345) and negatively related to the total

number of retail distribution center employees (Spearman correlation coefficient of -0.292). 

The higher the population density, the smaller the workforce and the greater the percentage

of minority laborers and operatives. On the the other hand, there is no relationship between

population density and the percentage of female laborers and operatives (Spearman

correlation coefficient of 0.0005). There is also no relationship between the age of the retail

distribution center (measured as first year of EEO-1 reporting) and the percentage of

minority laborers and operatives (Spearman correlation coefficient of -0.004). 
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COMPARISONS OF 1982 AND 2002 LOCATIONS

Given the relationship between the location of distribution centers and the employment of

minorities in key operative and laborer jobs, this section further examines this relationship. 

Specifically, we determine if the change in locations of retail distribution centers from 1982

to 2002 influences the minority composition of their labor markets. We identified

distribution centers in 1982 using the same process as before with the exception that we

translated NAICS codes to the Standard Industrial Classification (SIC) codes used in 1982. 

The process yields 420 establishments.

COUNTY CHANGES

There were 306 counties with distribution centers in 2002. The mean number of distribution

centers per county was 1.9, and the mean employment in distribution centers per county was

663. In 1982 there were 188 counties with distribution centers. The mean number of

distribution centers per county was 2.2, and the mean employment level was 321 employees. 

TABLE 4

TEN COUNTIES WITH THE LARGEST INCREASE IN

RETAIL DISTRIBUTION CENTERS

RANK COUNTY

INCREASED

UNITS

INCREASED

EMPLOYMENT

PERCENT 

MINORITY

OPERATIVES/

LABORERS

1 FRANKLIN, OH 16 5,755 27.9749

2 SAN BERNARDINO, CA 11 3,675 70.4940

3 RIVERSIDE, CA 8 2,094 72.7871

4 MIDDLESEX, NJ 8 2,479 64.4120

5 SAN JOAQUIN, CA 6 1,140 65.3296

6 YOLO, CA 5 2,503 63.1034

7 DU PAGE, IL 5 809 54.3082

8 HARFORD, MD 5 2,282 34.9419

9 RUTHERFORD, TN 5 632 30.8204

10 WINDHAM, CT 4 971 13.0236
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The total sample of counties with distribution centers in either time period was 381. The

mean change in the number of distribution centers per county is 0.44, and the mean change in

employment per county is 373. Both changes are significantly different from no change.

Table 4 lists those counties with the largest increase in the number of retail distribution

centers.

TABLE 5

TEN COUNTIES WITH THE LARGEST DECREASE IN

RETAIL DISTRIBUTION CENTERS

RANK COUNTY

INCREASED

UNITS

INCREASED

EMPLOYMENT

PERCENT 

MINORITY

OPERATIVES/

LABORERS

1 COOK, IL -13 -4,365 65.5001

2 LOS ANGELES, CA -10 863 82.4761

3 HUDSON, NJ -5 -140 71.1335

4 HARRIS,TX -5 401 72.2892

5 SAN FRANCISCO, CA -4 -980 65.4664

6 HENNEPIN, MN -4 -333 30.1419

7 OKLAHOMA, OK -4 -313 39.3978

8 SALT LAKE , UT -4 -228 32.4858

9 SAN METEO, CA -3 -315 76.0444

10 DENVER, CO -3 -165 51.0335

 

Table 5 lists those counties with the largest decrease in the number of retail distribution

centers. Note those counties associated with large cities. For example, Cook County

(Chicago), Los Angeles County (Los Angeles) and Harris County (Houston).

The other important factor for examining changes in retail distribution center locations is

employment. Table 6 lists those counties with the largest increase in distribution center

employment. These counties might be associated with non urban areas or smaller cities. For

example, Columbus is located in Franklin County, Ohio.
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TABLE 6

TEN COUNTIES WITH THE LARGEST INCREASE IN

RETAIL DISTRIBUTION CENTER EMPLOYMENT

RANK COUNTY

INCREASED

UNITS

INCREASED

EMPLOYMENT

PERCENT 

MINORITY

OPERATIVES/

LABORERS

1 FRANKLIN, OH 16 5,755 27.9749

2 SAN BERNARDINO, CA 11 3,675 70.4940

3 YOLO, CA 5 2,503 63.1034

4 MIDDLESEX, NJ 8 2,479 64.4120

5 HARFORD, MD 5 2,282 34.9419

6 RIVERSIDE, CA 8 2,094 72.7871

7 WAUKESHA, WI 3 1,785 24.6978

8 ONEIDA, NY 2 1,708 8.0416

9 CLAY, MO 1 1,704 23.7316

10 LUZERNE, PA 4 1,609 8.3228

Table 7 is the companion table showing those counties with large decreases in employment. 

Counties with large cities are prevalent although perhaps not as dominant as before. For

example, Cook County once again tops the list but also see San Francisco County (San

Francisco), New York County (New York) and Milwaukee County (Milwaukee).
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TABLE 7

TEN COUNTIES WITH THE LARGEST DECREASE IN

RETAIL DISTRIBUTION CENTER EMPLOYMENT

RANK COUNTY

INCREASED

UNITS

INCREASED

EMPLOYMENT

PERCENT 

MINORITY

OPERATIVES/

LABORERS

1 COOK , IL -13 -4,365 65.5001

2 SAN FRANCISCO, CA -4 -980 65.4664

3 ALLEN , IN -1 -801 22.5759

4 NORFOLK, MA -1 -702 28.7060

5 NEW YORK, NY -3 -685 68.8479

6 FAIRFIELD, CT -1 -627 55.5369

7 INGHAM, MI -1 -613 20.7415

8 KING, WA -2 -565 37.0434

9 MILWAUKEE, WI -3 -551 41.9348

10 BRISTOL , MA -2 -531 18.8348

EEO-1 BASED CALCULATIONS

We used 2002 EEO-1 data to analyze the distribution center counties in different ways. 

First, we examined “new entrants” by comparing counties with distribution centers in 2002

but not in 1982 (“new 2002 counties”), computing a simple percentages of minority

operatives and laborers currently employed in the two groups of counties. Differences in

these percentages suggests that the new 2002 counties with distribution centers have

substantially different labor markets. The percentage of minority operatives/laborers in the

1982 counties was 50.4 percent, and in 2002 it was 38.8 percent. The difference in minority

representation as operatives and laborers is 11.6 percent. The difference is largest for

Hispanics which is 8.0 percent lower for the new 2002 counties. Asian employment is 2.5

percent lower. African American (0.9 percent) and American Indian (0.1 percent) rates are

just slightly lower in the new 2002 counties. 

To account for changes in the locations of distribution centers, we also weighted all counties

by their proportion of distribution center employment in 1982 and in 2002. For example, in
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16

 Identified job titles are “Laborers and Freight, Stock and Material Movers, Hand

(962)”, “Packers and Packagers, Hand (964)”, and “Miscellaneous Material Moving

Workers, Including Conveyor Operators and Tenders; Shuttle Car Operators; and Tank Car,

Truck and Ship Loaders (975).” Data by job title is not available for all counties.

Page 19

Maricopa County Arizona, minorities make up 54.2 percent of operatives/laborers in 2002. 

In 1982, the proportion of all distribution center employees located in Maricopa County was

0.004229. So the weighted percentage of minority operatives/laborers in Maricopa County is

0.229 (54.2 X 0.004229) based on distribution center employment in 1982. In contrast, in

2002, the proportion of all distribution center employees located in Maricopa County was

0.0091. The weighted percentage of minority operatives/laborers in Maricopa County is

0.493 (54.2 X 0.0091) based on distribution center employment in 2002. (For a similar

application of weights, see Gastwirth and Haber 1976.) The increase from 0.229 to 0.493

reflects how the increased employment of distribution workers in Maricopa County by 2002

influences the total labor market for minority operatives/laborers. Again, only 2002

workforce data is used but it is weighted by the proportions from the relevant years. This

approach controls for the dramatic changes that occurred in minority employment over the

past two decades. Adding the weighted percentages across counties, the overall percentage

of minority operatives and laborers, weighted by the location of employees in 1982, is 50.4

percent. The overall percentage of minority operatives and laborers, weighted by the

location of employees in 2002, is 40.4 percent. EEO-1 based estimates suggest than if

distribution centers maintained the same location and proportion of employees in 2002 as

they had in 1982, one might expect minorities to make up 10 percent more of the relevant

labor market.

CENSUS-BASED CALCULATIONS

These computations are replicated using 2000 Census data from the EEO Special File. 

Given the present absence of available files with EEO-1 job groups, we relied on the job

group aggregation for State and Local Governments based on the EEO-4 report job

categories (EEO Special File, Table 5). The occupational titles likely to dominate the work

force in distribution centers are all located in the job group “Service/Maintenance.” 16 This is

roughly equivalent to combining operatives and laborers but adding service workers. The

percent of minorities in this job group is computed for each county. (In this instance it

represents all relevant employees except those classified as White, non Hispanic.) Once this

figure is derived, the weights described above are applied for each county and then the

results are added to provide a summary measure. For example in Maricopa County Arizona,

minorities make up 46.9 of the Census job category service/maintenance worker in 2000. 
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Recall that in 1982, the proportion of all distribution center employees located in Maricopa

County was 0.004229. So the weighted percentage of minority service/maintenance workers

in Maricopa County is 0.198 (46.9 X 0.004229) based on distribution center employment in

1982. In contrast, the proportion of all distribution center employees were located in

Maricopa County in 2002, was 0.0091. The weighted percentage of minority

operatives/laborers in Maricopa County is 0.427 (46.9 X 0.0091) based on distribution center

employment in 2002. Adding the weighted percentages across counties, the overall

percentage of minority service/maintenance workers, weighted by the location of employees

in 1982, is 48.6 percent. The overall percentage of minority service/maintenance workers,

weighted by the location of employees in 2002, is 34.5 percent. The census-based estimates

suggest than if distribution centers maintained the same location and proportion of

employees in 2002 as they had in 1982, one might expect minorities to make up 14 percent

more of the relevant labor market.
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SPECIFIC AREAS

The shift in the location of distribution centers (warehouses) from 1982 to 2002 may be more

complex than summary statistics suggest. It is useful to examine a few of the shifts in

greater detail in order to get a better sense of the regional dynamics.

 

LOS ANGELES, CALIFORNIA VS. RIVERSIDE/SAN BERNADINO, CALIFORNIA

When counties are ranked in terms of the largest gains in the number of distribution centers

from 1982 to 2002, San Bernadino and Riverside are ranked second and third highest. At the

same time, Los Angeles County is ranked second to last. The small increase in Los Angeles

 distribution center

employment (863

employees) pales in

comparison to the gains

made by Riverside (2,094 )

and San Bernadino (3,675). 

“A noteworthy trend in

Southern California’s

warehousing industry is the

shifting of firm location

eastward into San

Bernardino and Riverside

County” (Kirshner,

2002:12). Although, very

close in proximity, Los

Angeles County and San

Bernadino and Riverside

Counties are in separate

Metropolitan Statistical

Areas. Hispanic

employment as operatives

and laborers is similar among the three counties, Los Angeles (65 percent), Riverside (61.6

percent) and San Bernadino (57.4 percent). However, the percentage of African Americans

(10.1 percent) and Asians (6.9 percent) in Los Angeles County is higher than in Riverside

where African Americans make up 6.2 percent and Asians make up 4.3 percent of
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operatives/laborers and higher than in San Bernadino where African Americans are 9.2

percent and Asian are 3.5 percent. While Hispanics dominate all three labor markets, the rise

in distribution center employment in the two outlying counties could have a negative

influence on African Americans and Asians.

FRANKLIN, OHIO VS. COOK, ILLINOIS

Franklin County, Ohio, where Columbus is located, is the county with the largest increase

(5,755 employees) in distribution center employment between 1982 and 2002. Cook County,

Illinois, where Chicago is located, has the largest decrease (4,365). While these two counties

do not share the geographic proximity of Los Angeles, Riverside and San Bernadino, the

contrasts are interesting. If a retailer decides to locate a distribution center in Franklin rather

than Cook County, the proportion of Hispanic operatives/laborers is likely to be affected. 

While the percentage of these workers is 35.7 percent in Cook County, it is just 2.8 percent

in Franklin County. The proportion of African Americans (25.2 percent versus 22.1 percent)

and Asian (4.4 percent versus 2.6 percent) is also lower in Franklin County. 

WAUKESHA COUNTY VS. MILWAUKEE COUNTY, WISCONSIN

Two counties that are close geographically are Waukesha and Milwaukee in Wisconsin. 

Waukesha is basically a suburb of Milwaukee and they are in the same Metropolitan

Statistical Area. Milwaukee County ranked ninth among counties losing distribution center

employment (-551) while

Waukesha ranked seventh

among counties with

increased distribution

center employment (1785). 

The difference may reflect

a preference for a suburban

location for retail

distribution centers. 

Despite their proximity, the

labor markets are quite

different. Minorities make

up 41.9 percent of

operatives/laborers in

Milwaukee County but just
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24.7 percent in Waukesha County. This difference is comprised largely of African

Americans who make up 23.3 percent of operatives/laborers in Milwaukee County but just

9.7 percent in Waukesha County. 

SAN FRANCISCO, CALIFORNIA VS. YOLO, CALIFORNIA

San Francisco and Yolo counties are relatively close in northern California. Yolo is part of

the Great Central valley, specifically the Sacramento valley, and San Francisco is located on

the Pacific ocean. It seems likely that the eastward movement of distribution centers from a

Pacific port city to a nearby

valley location in Los

Angeles observed by

Kirshner (2002) also occurs

in San Francisco. While San

Francisco County was

ranked second among

counties losing distribution

center employment between

1982 and 2002, with a loss

of 980, Yolo county gained

2,503 such employees and is

ranked third behind Franklin

and San Bernadino among

counties with the largest gain

of these employees. San

Francisco and Yolo counties

are similar in the

employment of total

minority operatives/laborers. 

The percent of minorities in

these jobs is 36.9 percent in

Yolo County and 34.5 percent in San Francisco County. However, in Yolo County, African

Americans make up just 4.9 percent of these workers, while they comprise 14.2 percent of

these workers in San Francisco County. Similarly, Asians are 13.5 percent of

operatives/laborers in Yolo County but 21.7 percent in San Francisco County. In contrast,

Hispanic operatives/laborers are much better represented in Yolo County (42.8 percent) in

comparison to San Francisco County (28.5 percent).
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17

 Table 1. Job Patterns for Minorities and Women in Private Industry, 2001. p. 21.

Page 24

PROGRAM IMPLICATIONS

Retail distribution centers appear to be a growing segment of the retail industry with more

than 200,000 jobs at the 589 retail distribution centers identified for these analyses. They

have roughly the same level of EEO-1 employment reported for the railroad industry.17

Additionally, these low-wage jobs may be more valuable than other lower skilled jobs,

particularly within the retail industry. Waxman and Lambert’s study of the Chicago area

(2002) indicates that starting retail jobs typically pay more than the federal minimum wage

and that entry-level employees in distribution centers are among the highest paid in this

industry. They report that these jobs pay about $2.00 an hour more than beginning sales jobs

and $3.00 an hour more than starting cashiers (Waxman and Lambert: 11.) This is roughly

equivalent to Bureau of Labor Statistics (BLS) figures. For example BLS reports that in the

Warehousing and Storage industry Laborers and Freight, Stock and Material Movers had

median hourly earnings of $10.87 and Packers, and Packagers earned $9.47. In contrast,

cashiers in General Merchandise Stores (other than Department Stores) have a median hourly

pay rate of $7.27 and sales workers earn a median of $7.84 per hour. Further, Waxman and

Lambert suggest that jobs in distribution centers tend to be full-time (Waxman and Lambert:

12) while hours worked in the retail industry overall are declining. Harold Forman of the

United Food and Commercial Workers International Union notes that a major issue for retail

workers is declining work hours at the same time that stores are open longer. BLS statistics

for retail trade, excluding eating and drinking places, show that the average hours worked

was 37.4 hours per week in 1964 but was just 31.0 hours per week in 2002. Thus, reductions

in retail distribution center labor market opportunities for minorities can result in important

job losses and reduced economic well-being. Of course, these distribution centers are also

important to the communities where they are located. Often they become one of the largest

employers in the area. They may also introduce new technologies and skills that enhance the

local labor market.

While business decisions about the location of distribution centers can have a serious

influence on the labor market that will be used and thus the employment of minorities, there

is no clear indication that the selected locations are based on discriminatory motives. Trade

journals suggest that the motivating force in deciding on the location for these centers is the

availability of large parcels of inexpensive land and geographic proximity to major

transportation routes. (Certainly, the ability of local governments to provide incentives such

as tax relief and infrastructure improvements are important but related factors.) However,
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when decisions are made solely on those criteria, important workforce characteristics may

not be adequately addressed. In fact, a recent trade publication warns companies, “The

reasons behind a move should go beyond the factors of land cost, the length of a lease or a

comparison of local taxes” (Hess: 3). The direct involvement of human resource experts in

deciding on the location of these centers would assist retailers develop and maintain the

advantages of a diverse work force. In fact, Hess recommends that companies utilize a

relocation task force that includes human resources staff. 

A key role for the EEOC is the education of retailers concerning the potential impact that

distribution center location can have on their ability to develop and maintain a multi-cultural

workforce. It would be advantageous to provide information and suggest solutions that will

equip retailers to better understand how location decisions can influence their workforce. The

focus, of course, needs to be at the corporate level because location decisions are most likely

to be made there. 

It would also appear that a useful mechanism here would be the communication of “Best

Practices.” For example, areas with what were previously locations for manufacturing firms

may be converting to locations for distribution centers. Rockford, Illinois is one example

(Andrews, 2004). 

Rockford has become a case study of how an industrial area can respond to a

shifting economic landscape. This city has long been synonymous with

manufacturing . . . Confronted with the choice between adopting or dying off,

Rockford has tried to reinvent itself. “We are in a global economy, and we are

in the throes of a major transformation.” . . . That transformation mirrors a

strategic shift in the Rock River valley area. Manufacturing is either stagnant

or in decline, but transportation and logistics are booming (p. BU-7).

As the President of Rockford Powertrain explains, “We want to stay in Rockford . . . We just

don’t want to manufacture here” (p. BU-7). It is possible then that retailers may be able to

take advantage of an existing skilled and presumably more diverse workforce by locating

distribution centers in declining manufacturing areas rather than turn to more rural areas.

Corporate experiences with the issue of distribution center location warrant collection and

dissemination of “Best Practices” to retail colleagues, and the EEOC can facilitate this

process.

Another role here is enforcement. District Offices need to be cognizant of retail distribution

centers in their jurisdictions. The location of distribution centers away from central cities

where the EEOC has offices presents two obstacles to effective monitoring of compliance. 
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First, more rural locations make filing charges more difficult for employees. Outreach and

education activities in these communities appear to be appropriate. Second, EEOC officials

are likely to be less aware of these facilities and potential equal employment opportunity

problems because our staff might not be exposed to these employers during the normal

course of business or through the local media. This suggests that field staff with large

distribution centers in their jurisdictions might benefit from more actively monitoring of their

EEO-1 reports to make certain that hiring practices reflect local labor market characteristics. 

In fact, these employers are sometimes located in isolated “industrial parks” with other

employers or are otherwise isolated. McGranahan and Gale’s (2003) description of

manufacturing in rural areas might apply as well to retail distribution centers.

Manufacturing is a relatively invisible rural economic sector. Driving through

a rural county, agriculture often occupies much of the open country, while

stores occupy the town main streets and malls. But manufacturing, often

secluded on two back streets or outskirts, was directly responsible for over 1

in every 4 dollars earned in the rural private sector in 1997. Manufacturing is

an essential rural industry sector, particularly in low-education counties (p. 1).

These pockets of commerce may need attention, including training and technical assistance

efforts, to assure that they are aware of and complying with their equal employment

requirements.
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CONCLUSION

This paper examines the rural-urban distribution of retail distribution centers in 2002 and

finds that retail distribution centers in 2002 are more likely to have minority workers in

well-populated areas than less populated areas. This paper also examines changes in the

geographic location of retail distribution centers from 1982 to 2002 and finds that the

minority composition of local labor markets would have increased if the facilities had been

located in the same counties in 2002 as in 1982. Readers are cautioned that the analyses

focus on the demographic consequences of location or relocation decisions and not questions

of intentional discrimination or disparate recruitment. Future research on retail distribution

centers would benefit from (1) a more precise definition of fringe MSA or CSA locations

based on GIS mapping software (2) plant data regarding capital investments,

material-handling equipment, employee wages and skills especially among entry level

workers; and (3) observation studies of actual work patterns and (4) the employment of

minorities and women as officials and managers at these centers. 
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Prepared By:

Office of Research, Information and Planning

U.S. Equal Employment Opportunity Commission

Washington, D.C.

ADDITIONAL INFORMATION

For additional information, visit our web site at http://www.eeoc.gov. Click on STATISTICS

and JOB PATTERNS FOR MINORITIES AND WOMEN

(http://www.eeoc.gov/stats/jobpat/jobpat.html) for sample copies of the EEO-1 form, an

instruction booklet and aggregate statistics. 
U.S. Equal Employment Opportunity Commission

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References i

SELECTED REFERENCES

Abernathy, Frederick H., John T. Dunlop, Janice H. Hammond, and David Weil. 1999. A

Stitch in Time: Lean Retailing and the Transformation of Manufacturing–Lessons

from the Apparel and Textile Industries. New York, NY: Oxford University Press.

Andrews, Edmund L. “The Joyless Recovery.” New York Times. January 4, 2004. pp. BU-1

and BU 3-7.

Appelbaum, Eileen, Annette Bernhardt, and Richard J. Murnane. 2003. “Low-Wage

America: An Overview.” Pp.1-29 in Low Wage American: How Employers are

Reshaping Opportunity in the Workplace, edited by Eileen Appelbaum, Annette

Bernhardt, and Richard J. Murnane. New York: Russell Sage Foundation.

Bartholdi, John J. III and Steven T. Hackman. Forthcoming, draft manuscript revised March

23, 2003. Warehouse & Distribution Science, Release 0.30. Georgia Institute of

Technology. Retrieved December 12, 2003 (http://www.warehouse-science.com). 

Bureau of Labor Statistics. U.S. Department of Labor. “2002 National Industry – Specific

Occupational Employment and Wage Estimates.” www.bls.gov/oes .

Bureau of Labor Statistics. U.S. Department of Labor. “National Employment, Hours and

Earnings” www.bls.gov .

Economic Research Service, U.S. Department of Agriculture. 2003. “Measuring Rurality:

Rural-Urban Continuum Codes.” Retrieved December 9, 2003

http://www.ers.usda.gov./Briefing/Rurality/Rural/UrbCon).

Fennelly, Katherine and Helga Leitner. 2002. “How the Food Processing Industry is

Diversifying Rural Minnesota.” JSRI Research Report No. 59. The Julian Samora

Research Institute, Michigan State University, East Lansing, Michigan. 

Form, William, Robert L. Kaufman, Toby L. Parcel, and Michael Wallace. 1988. "The

Impact of Technology on Work Organization and Work Outcomes." pp. 303-328 in

Industries, Firms, and Jobs: Sociological and Economic Approaches, edited by

George Farkas and Paula England. New York: Plenum Press.
U.S. Equal Employment Opportunity Commission

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References ii

Forman, Harold, Director, Research Office, Field Services Department, United Food and

Commercial Workers International Union. personal interview. February 13, 2003.

Foster, Thomas A. 2003. “The Walgreens Prescription for DC Site Selection.” Global

Logistics & Supply-Chain Strategies November:1-4. Retrieved November 18, 2003

(http://www.supplychainbrain.com/archives).

Frey, William H. 2001. “Census 2000 Shows Large Black Return to the South, Reinforcing

the Region’s ‘White-Black’ Demographic Profile.” Population Studies Center

Report No. 01-473. Institute for Social Research, University of Michigan.

Frey, William H. 2002. “Metro Magnets for Minorities and Whites: Melting Pots, the New

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