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Retail Distribution Centers: How New Business Processes Impact Minority Labor Markets The U.S. Equal Employment Opportunity Commission Retail Distribution Centers: How New Business Processes Impact Minority Labor Markets Foreword 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.
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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. Executive 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. TABLE 1 EXAMPLES OF RECENTLY OPENED RETAIL DISTRIBUTION CENTERS Retailer Location StartYear Size Sq.
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. The first reported year in our sample varies from before 1978 to 2002. 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. 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 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). 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 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. 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). 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.
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. 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 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.' 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. 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. 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 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).
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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 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). 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. 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, 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. As the President of Rockford Powertrain explains, 'We want to stay in Rockford. We just don't want to manufacture here' (p. 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. 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. 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.
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. Additional Information For additional information, visit our web site. Click on STATISTICS and JOB PATTERNS FOR MINORITIES AND WOMEN for sample copies of the EEO-1 form, an instruction booklet and aggregate statistics. Prepared By: Office of Research, Information and Planning U.S.
Equal Employment Opportunity Commission Washington, D.C. Selected References Abernathy, Frederick H., John T. Dunlop, Janice H. Hammond, and David Weil. A Stitch in Time: Lean Retailing and the Transformation of Manufacturing–Lessons from the Apparel and Textile Industries. New York, NY: Oxford University Press.
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'Taking Stock in Opportunity in Retail: The Structure and Experience of Lower Level Jobs.' The Project on the Public Economy of Work, The Study of Work-Child Care Fit, The University of Chicago. Unpublished manuscript. Footnotes 1Starting 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).
2Hale 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 back-room activities like price and security labeling, assortment building and display-ready product assembly done in the DC.” 3James 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 ( 4Jedd (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.
“DCs back Walgreens’ Stores with ‘Brawn.’” Drug Store News March 23:2). 6Don 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. 72002 population data retrieved, December 16, 2003, from 8EEOC 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.
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. 9Details regarding the report characteristics can be found in the EEO-1 instruction booklet at, 10For 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 Management.” Presented at the Annual Meeting of the American Sociological Association. 11Decision is based on the use of an exact binomial test and a 0.05 level of probability. 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 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. 13Strictly 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. 14It 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.
15Notice 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.
16Identified 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. Job Patterns for Minorities and Women in Private Industry, 2001. This page was last modified on October 7, 2004.