HIGH END DEPARTMENT STORES, THEIR 

ACCESS TO AND USE OF DIVERSE LABOR

MARKETS: TECHNICAL REPORT

U.S. EQUAL EMPLOYMENT OPPORTUNITY COMMISSION

2004
U.S. Equal Employment Opportunity Commission

HIGH END DEPARTMENT STORES

-iEXECUTIVE SUMMARY

Our nation’s retailers fulfill an important role in our economy and according to the

Commission’s EEO-1 reports, employ nearly 15 percent of all private sector employees. 

This report is one in a series that examines the retail industry. The focus here is on retail

department stores, the largest employer of all retail subsectors except for food and beverage

stores. It is also a major employer of women who make up a large portion (75 percent) of

the retail salespersons in these stores. There is a wide range of stores within the broad

category of department stores. The most exclusive high end department stores are

considered as offering superior employment situations in terms of environment,

compensation and benefits. This study seeks to determine how people of color fare in these

stores. Two key questions surround these stores. Do high end department stores focus so

much on locations in affluent neighborhoods that they limit access that nonwhite workers

might have to employment there? Second, regardless of accessibility to diverse work forces,

do high end department stores maintain a diverse work force? Department stores in the ten

largest metropolitan areas are examined: New York, Los Angeles, Chicago, Philadelphia,

Dallas, Miami, Washington, Houston, Detroit and Boston. Major findings include:

• The labor market for African American and Hispanic sales workers relevant to high

end department stores is not significantly different than the labor markets for other

types of department stores.

• The labor market for Asians sales workers is somewhat better for high end

department stores than for other types of department stores.

• On average, high end department stores are more likely to have significant shortfalls

in the employment of African Americans, Hispanics and Asians as sales workers.

• Chain ownership has a significant effect on diversity of employment. 

• There appears to be some evidence that exclusive department stores have

substantially more disparities with Hispanic sales workers than other kinds of

race/ethnic groups. 

 
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-iiTABLE OF CONTENTS

EXECUTIVE SUMMARY ................................................. -iTABLE OF CONTENTS ................................................. -iiINTRODUCTION .................................................... Page 1

METHODOLOGY ................................................... Page 5

RESULTS .......................................................... Page 10

SUMMARY ........................................................ Page 22

ADDITIONAL INFORMATION ....................................... Page 24

SELECTED REFERENCES .................................. REFERENCES i

APPENDIX 1: MEASURING MARKET SEGMENTATION ........ APPENDIX 1 - i

APPENDIX 2: WORKFORCE ESTIMATES ..................... APPENDIX 2 - i
U.S. Equal Employment Opportunity Commission

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1 Census 2000 EEO Data Tool www.census.gov/cgi-bin/broker downloaded June 25,

2004.

INTRODUCTION

Retail department stores represent a significant portion of employment in the retail industry. 

General merchandise stores, comprised largely of department stores, account for 14 percent

of all retail employment. It is the largest employer of all retail subsectors except for food

and beverage stores (18.8 percent). It is also a major employer of women who make up a

large portion (75 percent) of the retail salespersons in general merchandise stores.1 

A question regarding equal employment opportunity among different department stores is,

how does the quality of work life vary by type of store and location? That is, one might

anticipate that higher-end department stores are more likely to offer commissions, higher

wages, greater benefits, greater flexibility in work hours and more valuable employee

discounts. Bernhardt (2000) is more specific about the advantages of working at high end

department stores.

Bloomingdale’s and Stern’s department stores, both owned by Federated

holding company, illustrate this split. Bloomingdale’s serves high-income

customers. Only 20 percent of its workers are part-time, turnover is low,

wages are above average for Federated stores, and a significant amount of

money is spent on recruiting polished workers with good “people skills” –

mostly young white women attending college. By contrast Stern’s is a massmarket operation. It focuses not on service but rather on centralizing and

streamlining operations with new technology and fewer people. Wages are

significantly lower, about 60 percent of the jobs are part-time, and turnover is

high. Employees tend to come from working-class backgrounds (p. 23).

Further, one might expect higher-end department stores to be located in the more affluent

suburban areas. Thus, if nonwhites are isolated from these areas due to residential choice

patterns and/or housing discrimination, their opportunities for employment here will be

reduced. A similar concern regarding the limitation of employment opportunities due to

residential characteristics, the “spatial mismatch hypothesis” has been studied since the

1960's (Kain, 1968). It had renewed interest in the 1990's especially in the context of the

Department of Housing and Urban Development’s (HUD) “Moving to Opportunity

Program” (Stoll 1999) and welfare reform (Ihlanfeldt and Sjoquist 1998). This “spatial

mismatch hypothesis” is based on “the idea that suburbanization of jobs and serious
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limitations on Black [and other nonwhite] residential choice have acted together to create a

surplus of workers relative to the number of available jobs in inner-city neighborhoods where

Blacks [and other nonwhites] are located” (Ihlanfeldt and Sjoquist, p. 849).

Ihlanfeldt and Sjoquist conduct an extensive review of the spatial mismatch hypothesis

research literature. They conclude that “the lack of geographic access to employment is an

important factor in explaining labor market outcomes . . . (p. 881).” This occurs not

necessarily because the commute is longer with respect to time and distance (Khattak et al.

2000). But because of a number of factors regarding access to employment opportunities, 

“. . . social distance, i.e., networks seems to matter more than spatial distance” (Pastor, 1996,

p. 138). (Also see Holtzer, Quigley and Raphael, 2003, pp. 416-417.) In addition to

documenting the existence of a spatial mismatch for nonwhites, Thompson (1997) focuses on

the problem for women of color. “The results also suggest that the relatively poorer job

access of Black and Hispanic women provides a partial explanation for their lower levels of

labor force participation in comparison with that of White women, more so for Black

women” (p. 144).

However, Arnott (1998) warns that if applying the spatial mismatch hypothesis to today’s

society, one must recognize significant changes.

. . . the spatial mismatch hypothesis seems to be predicated on the notion of a

downtown where the majority of Blacks live, surrounded by a homogeneous,

white suburban fringe. But the pattern of residential location by income and

race has become more complex in the years since the spatial mismatch

hypothesis was originally formulated and is becoming increasingly complex.

(p. 1173).

Fastenfest et al. (2004) document a number of changes in residential neighborhoods between

1990 and 2000. For example, the number of predominately White neighborhoods fell by 30

percent over the decade (p. 6). “Over the decade, Whites and Blacks became less likely, and

Hispanics and Asians became more likely, to live in neighborhoods in which their group

predominated” (p.8).

Further, Cooke (1996) points out that a suburban residence for African Americans does not

always have positive benefits. He examines probability of employment, accounting for

whether or not an individual lives in the suburbs. For Washington and New York suburbs he

finds that living in the suburbs has a negative influence on the probability of employment for

African Americans. Pastor and Adams (1996) also point out the disadvantage of using the

simple urban/suburban dichotomy. 
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Additionally, recent spatial mismatch research has been limited to examining larger

geographic areas using Public Use Microdata Sample (PUMS) and older Census data from

1990 (Stoll, 1999; Thompson, 1997; Cooke, 1996; and Pastor and Adams, 1996). These

studies often only examine one dimension of the labor market by examining the

characteristics of employees and ignore employer characteristics. Holzer, Lane and

Vilhuber (2003) point out the value of looking at both the supply and demand side of labor

markets.

Finally, a number of these studies are geared toward three policy alternatives (1) community

development, (2) “personal mobility programs” or transportation-oriented, and (3)

“residential mobility strategies” (Cooke, 1996). However, in completing their research other

scholars recognize the role that employment discrimination may play (Stoll, 1999, Cooke,

1996). One of these studies Stoll (1999) focuses on Washington DC, particularly the suburbs

and youth employment. He finds that employment opportunities are greater in the suburbs,

but even if African Americans “overcome the problem of place,” they still confront the

problem of race (p. 94). For example, in examining the suburb of Montgomery County,

Maryland, he estimates that if young African American men lived in the same areas as young

White men, their employment rate would increase by nine percentage points, but if they were

treated the same as young White males in that labor market, their employment would rise by

eight percentage points. He concludes that, nonwhite “residents will not realize the full

potential of employment benefits that a suburban residential location can provide unless

antidiscrimination enforcement measures in suburban labor markets are included . . . ” 

(p. 95).

RESEARCH QUESTIONS

The purpose of this research is to determine if high end department stores vary from that of

other department stores with respect to the pool of available nonwhite sales workers and

actual employment results. Given the dominance of women as retail salespersons in general

merchandise stores, as reported on the Census, this research question is particularly

important for women of color. Two research hypotheses flow from this question, 

Hypothesis 1: Access to nonwhite workers will vary by type of department store. 

Hypothesis 2: Employment of nonwhite sales workers will vary by type of department 

 store.
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In examining these questions, this study will attempt to provide certain advantages over prior

research. The geographic unit of analysis will be the zip code which appears to be a precise

unit available for race/ethnic data. Recent data from the Census and the 2002 EEO-1 report

will be utilized. The analyses will account for both employer location and work force

resident data so there is no need to rely on assumptions about “job rich suburbs.” The data

regarding employer location will not only be firm specific but establishment specific as well.

The research will recognize the role that employment discrimination can play by basing

analyses on statistics comparing nonwhite labor pools to nonwhite employment. The

analyses will examine nonwhite groups separately (African Americans, Hispanics and

Asians) to account for variation in residential patterns and employment. For example, “. . .

for Asians the most common neighborhood type is a mixed white-and-other neighborhood. 

This suggests a higher level of integration among Asians and whites than among Hispanics

and whites at the neighborhood level” (Fastenfest et al, 2004, p. 10).
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METHODOLOGY

A number of methodological hurdles were encountered in conducting the research. These

included classification of department stores, sample selection, development of employment

data relevant to both the general labor market as well as the employer, and determining each

store’s access to nonwhite, African American, Hispanic and Asian workers. We also had to 

apply a technique for adjusting labor market employment data to account for commuting

practices.

STORE CLASSIFICATION

To test the notion that higher end department stores have different labor market access and

employment patterns, we classified stores on the basis of relative price points. Appendix 1

details the process used to develop the classifications and lists the classifications by chain

and parent. As a result, of these analyses, each department store was assigned to one of four

price point categories. 

• Designer/Bridge (the most exclusive or high end stores)

• Better/Moderate

• Moderate/Popular

• Off-Price, Discount (the least exclusive stores)

An example of a Designer/Bridge or high end department store would be Saks Fifth Avenue. 

A Better/Moderate department store would be Macy’s. Sears Roebuck is an example of a

Moderate/Popular department store. A chain in the Off-Price, Discount category is K-Mart.

SAMPLE SELECTION

Two factors suggested that it would be useful to focus the analysis on selected areas rather

than the entire nation. First, the amount of data required to construct small labor market

areas is voluminous. Second, in order to draw useful comparisons it was necessary to have

an adequate number of high end department stores. These types of stores are not as widely

distributed throughout the country as other department stores. Therefore, the research

focuses on the ten metropolitan areas with the largest population in the 2000 Census. These
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2

 OMB Bulletin No. 03-04 2003. “Metropolitan Statistical Areas, Micropolitan

Statistical Areas, Combined Statistical Areas, New England City and Town Areas, Combined

New England City and Town Areas” Statistical and Science Policy Branch, Office of

Information and Regulatory Affairs, Office of Management and Budget.

3

 ZCTA’s are statistical areas developed by the Census Bureau for tabulating data

from the 2000 Census. They represent U.S. Postal Service ZIP Code areas. See

http://www.census.gov /geo/ZCTA/zcta.html for a technical discussion of these areas.

4

 Robert D. Clair, U.S. Bureau of the Census.

Page 6

metropolitan areas are based on the 2003 definitions.2 They are in order of population, New

York, Los Angeles, Chicago, Philadelphia, Dallas, Miami, Washington, Houston, Detroit and

Boston. Table A2 of Appendix 1 provides the detailed metropolitan area name and

population figures. 

WORK FORCE ESTIMATES

Once these broad market markets were selected, it was necessary to develop estimates of

nonwhite employment that would allow the construction of small labor market areas within

these larger metropolitan areas. Zip code or more precisely ZIP Code Tabulation Area

(ZCTA)3 was identified as the smallest geographic area that would allow aggregation to

formulate appropriate labor market areas. These areas appear to be smaller and presumably

more precise than the Public Use Microsample Data (PUMS) often used by scholars.4

 

In the case of individual-level data, neighborhood descriptors are generally

not available in most data sets because the individual’s neighborhood or

census tract is not identified for reasons of confidentiality. For example,

many of the recent studies employ data from the Public Use Microsample

Data (PUMS) that do not identify intrametropolitan residential areas smaller

than 100,000 people. (Ihlanfeldt and Sjoquist p.857).

To identify a labor market for potential sales workers, the present study utilizes ZCTA

Census data on wage earners. Census Table PCT73(A-I), “Sex by Work Experience in 1999

by Earnings in 1999 for the Population 16 Years and Over” from Summary File 3 was used

to obtain number of individuals in the workforce by nine race/ethnic groups. For each

race/ethnic and gender group the total number of individual reporting earnings (whether they

worked full time or not) was computed. These data files were enhanced by assigning a

county code to each zip code which in turn allowed the assignment of a metropolitan area
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5

 Private employers required to file are those with: (a) 100 or more employees, or (b)

50 or more employees and: (1) have a federal contract or first-tier subcontract worth $50,000

or more, or (2) act as depositories of federal funds in any amount, or (3) act as issuing and

paying agents for U.S. Savings Bonds and Notes. Single-establishment employers submit

only one EEO-1 report, while those employers whose business was conducted at more than

one location submit a company-wide consolidated report, a headquarters report, and

individual reports for each establishment with 50 or more employees. Employment figures

could be reported for any pay period in the third quarter (July through September). In 2002,

more than 39,000 employers submitted, as appropriate, individual establishment and

headquarters reports for more than 225,000 reporting establishments with about 52 million

employees. 

6

See “Section 5, Description of Job Categories” in the EEO-1 instruction booklet at

http://www.eeoc.gov/stats/jobpat/e1instruct.html

7 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 EEO-1 reports to the public. 

Violation of that section is punishable by fine and imprisonment. Aggregated data are

available to the public.

Page 7

code. The 2003 definitions were used. (Appendix 2 provides more details regarding the

process for developing the work force estimates.)

Firm specific employment information is also necessary. This information came from 

EEO-1 reports. These annual reports indicate the composition of employers’ workforces by

gender and by race/ethnic category.5

 The EEO-1 collects data on nine major job categories

that includes sales workers.6

 The present study focuses exclusively on the EEO-1 sales

worker data. In addition to the work force data provided by the employer, information about

each establishment is added to the database. This additional information includes the

establishment’s North American Industrial Classification System code, the establishment’s

zip code and its metropolitan area code. Firm level EEO-1 data are confidential.

7

ACCESSIBILITY

To analyze employment access for nonwhites, we utilized the proximity to job location

model developed by Gastwirth and Haber (1996:Formula 1). The Gastwirth-Haber model

has two components: a measure of employment probability that varies by residential area,

and Census data on the residential workforces. These components are combined to produce
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8

 Sung (1996) evaluates nine alternative measures of accessibility which he defines

as “the ease with which economic activities can be reached from a location” (p. 474). Our

study uses Sung’s Gaussian formulation with accessibility values ranging from 1.0 to 0.0. 

We defined accessibility values of 1.0 at zero miles, accessibility values of 0.05 at ten miles

and accessibility values less than 0.05 at distances beyond ten miles. Probabilities were

constructed by dividing the Gaussian accessibility values for each zip code by the sum of

Gaussian distance values for all zip codes in a given metropolitan area. To simplify the

exposition, we do not include the intermediate accessibility values in Table 1. We set the

0.05 accessibility value at ten miles because it was the median commuting distance in all but

two of the ten largest metropolitan areas. Those two, Detroit (12 miles) and Miami (9 miles)

were very close to that figure. Commuting distance by metropolitan area was extracted from

the American Housing Survey 2001 using Federated Electronic Research, Review, Extract,

and Tabulation Tool ( FERRETT) from the Department of Census.

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an estimated access probability that describes the likelihood of employing a nonwhite

employee at each job location. This estimated access probability can also be interpreted as

the expected proportion of nonwhites available for employment at each job location. We

will primarily use the proportion formulation in our analysis of the research hypotheses.

Table 1 illustrates these calculations for a single department store surrounded by five

residential neighborhoods. The neighborhoods are designated by zip code (Census ZCTA as

discussed above). We assume that the probability of employment at this store is inversely

related to distance: the greater the distance between the department store and the residential

area, the smaller the likelihood of employment. The columns on the left show the distances

in miles and the estimated employment probabilities. The employment probabilities follow

what is known as a Gaussian distribution that decreases slowly at closer distances and

decreases rapidly at further distances.8 For example, the zip code area zero miles from the

store has a relatively high employment probability (0.394), and the zip code area six miles

from the store has a relatively low employment probability (0.101). The middle columns

show two workforce characteristics for each residential area: the total number of workers

and the proportion of nonwhite workers. We first adjust the total number of workers to

estimate “fraction of a firm’s labor pool residing” in a particular area (Gastwirth and Haber,

1996:33). For example, the zip code area “xxx10" has 23,024 total workers. Multiplying

23,024 by the probability of employment (0.2980) yields a new adjusted figure of 6,861

workers. Dividing 6,861 by the total number of adjusted workers for the five residential

areas (13,976) indicates that about one-half (0.4909) of the workers are likely to reside in

the zip code area “xxx10.” We then multiply the adjusted fraction of workers (0.4909) by
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TABLE 1: GASTWIRTH-HABER PROXIMITY TO JOB LOCATION MODEL, 

HYPOTHETICAL DATA

ZIP

RESIDENTIAL

WAGE EARNING

WORKFORCE

EMPLOYMENT

ACCESS

NONWHITE

PROBABILITY 

OF

EMPLOYMENT

TOTAL

NUMBER

WORKERS

TOTAL WORKERS

ADJUSTED FOR

EMPLOYMENT

PROBABILITY

PROPORTION

NONWHITE

DISTANCE

IN MILES

EMPLOY

PROB

ADJUSTED

NUMBER

WORKERS

ADJUSTED

WORKER

FRACTION

xxx15 0.000 0.3939 8,679 3,418.68 0.2446 0.2224 0.0544

xxx10 2.003 0.2980 23,024 6,861.59 0.4909 0.5422 0.2662

xxx05 4.620 0.2026 7,388 1,497.13 0.1071 0.5776 0.0619

xxx52 6.610 0.1010 20,756 2,096.45 0.1500 0.3122 0.0468

xxx48 12.004 0.0044 23,215 102.86 0.0074 0.9169 0.0067

TOTAL 1 83,062 13,976.70 1 0.4360

the proportion of nonwhite workers (0.5422). Summing the multiplication results for all five

residential areas yields an estimated employment probability of 0.4360 or alternatively an

estimated proportion of 0.4360 nonwhites in the store’s labor pool. Based on this

distribution of nonwhite workers across the five residential areas, we would expect about two

employees out of five (or 43.6 percent) at this particular store to be a nonwhite worker. In

the analyses to follow, these calculations are repeated for each store and all zip codes in the

ten major metropolitan areas.
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9

 See APPENDIX1: MEASURING MARKET SEGMENTATION for the technical

explanation of how department stores are defined and included in the data base.

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RESULTS

Our research is guided by the hypothesis that more exclusive department stores tend to locate

in higher income neighborhoods, and as a result, their access to a nonwhite work force is

lower than other department stores. Reduced labor market access, in turn, is likely to lead to

a lower employment of nonwhite sales workers. The first hypothesis concerns the labor

market availability of nonwhite workers. The second hypothesis concerns the actual

utilization of nonwhite workers. We focus on the demographic relationship between store

locations and nonwhite labor markets rather than issues of motivation or intentional

discrimination.

H0

: Access to nonwhite workers does not vary by type of department store.

Labor market accessibility, in this study, is measured by the proportion of nonwhite wage

earners within a given geographic area. It is assumed that all wage earners are equally

available, qualified, and interested in seeking employment as a sales worker. As described

previously, the relevant local labor market for this study is defined as a weighted average of

all Census ZCTA (Census representation of five-digit zip codes) residential areas in a store’s

metropolitan area.9 The proportion of nonwhite wage earners is weighted by the total ZCTA

population and discounted by the distance between the store and the ZCTA area. More

populous ZCTA areas receive greater weight than smaller ZCTA areas, and nearby

neighborhoods greater weight than distant neighborhoods. Under this concept of labor

market access, the relevant proportion of nonwhites in the general population can vary

widely depending on a store’s location within a metropolitan area. Flagship stores in older

downtown areas, surrounded by a mixture of business elites and low-income nonwhites, may

have relatively high accessibility values while similar stores in the outer suburbs, located in

largely white neighborhoods, may have relatively low accessibility values. Overall

metropolitan area characteristics can also vary depending on store locations. Metropolitan

areas with large nonwhite populations may have relatively low accessibility values if a

majority of stores are located outside of nonwhite areas, and metropolitan areas with small

nonwhite populations may have relatively high nonwhite accessibility values if a majority of

stores are located close to nonwhite areas. 
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10

 All mean difference tests use the Dunnett two-sided multiple comparison

procedure with the designer/bridge category as the control group.

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Table 2 reports the mean proportion of department store accessibility to nonwhites and other

racial/ethnic groups for the four major retail price point categories. 

TABLE 2: MEAN ACCESSIBILITY PROPORTION BY STORE TYPE

MODEL PRICEPT4

STORES

(N) MEAN

STANDARD 

DEVIATION

PROBABILITY

( > |T|)

NONWHITE DESIGNER/BRIDGE 111 0.4014 0.1474 _

NONWHITE BETTER/MODERATE 215 0.3907 0.1771 0.8757

NONWHITE MODERATE/POPULAR 718 0.3738 0.2054 0.2851

NONWHITE OFF-PRICE, DISCOUNT 1347 0.3657 0.1959 0.1211

AFRICAN

AMERICAN

DESIGNER/BRIDGE 111 0.1278 0.0814 _

AFRICAN

AMERICAN

BETTER/MODERATE 215 0.1345 0.1016 0.8318

AFRICAN

AMERICAN

MODERATE/POPULAR 718 0.1202 0.1053 0.7185

AFRICAN

AMERICAN

OFF-PRICE, DISCOUNT 1347 0.1269 0.1107 0.9989

HISPANIC DESIGNER/BRIDGE 111 0.1804 0.1196 _

HISPANIC BETTER/MODERATE 215 0.1697 0.1313 0.7690

HISPANIC MODERATE/POPULAR 718 0.1782 0.1620 0.9941

HISPANIC OFF-PRICE, DISCOUNT 1347 0.1628 0.1415 0.3703

ASIAN DESIGNER/BRIDGE 111 0.0730 0.0387 _

ASIAN BETTER/MODERATE 215 0.0679 0.0465 0.5509

ASIAN MODERATE/POPULAR 718 0.0583 0.0465 0.0046

ASIAN OFF-PRICE, DISCOUNT 1347 0.0583 0.0468 0.0032

With the exception of minor differences for Asian populations, the current study is not able

to detect significant differences in accessibility between the designer/bridge category and the

other price point categories.10 The mean proportion of nonwhites varies from 0.366 (36.6

percent) to 0.401 (40.1 percent). The largest mean difference occurs between the
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designer/bridge category and the off-price, discount category, a difference of 0.036 (3.6

percent). None of the nonwhite mean difference tests are statistically significant. The mean

proportion of nonwhites accessible to the high end (designer/bridge) department stores were

compared to each of the different types of department stores. The differences were tested to

determine if they were statistically significant. This was then repeated for African

Americans, Hispanics and Asians. The nonwhite two-sided probabilities, using the

designer/bridge category as a reference group, vary from about seven chances out of eight (a

probability of 0.876) to about one chance out of eight (a probability of 0.121). The

accessibility proportions for African Americans and Hispanics show similar results. The

smallest mean difference probability is 0.719 for African Americans and 0.370 for Hispanics. 

There is some evidence that designer/bridge stores are located in closer proximity to Asian

wage earners than moderate/popular and off-price, discount stores (probability values of

0.005 and 0.003 respectively). On average, it appears the four types of price point categories

have similar labor markets in terms of the proportion of nonwhites, African Americans, and

Hispanics.

Labor market accessibility varies widely across the top ten metropolitan areas. Table 3

reports the mean proportion of department store accessibility to nonwhites by the top ten 

TABLE 3: MEAN ACCESSIBILITY PROPORTION BY METROPOLITAN AREA

MSA

ACCESS TO 

NONWHITE WORKERS

STORES

(N) MEAN

STANDARD

DEVIATION

PROBABILITY

LOS ANGELES 332 0.5789 0.1500 _

MIAMI 185 0.5447 0.2132 0.0942

HOUSTON 204 0.4425 0.1469 0.0000

WASHINGTON 174 0.4012 0.1740 0.0000

NEW YORK 365 0.3566 0.1656 0.0000

DALLAS 234 0.3495 0.1248 0.0000

CHICAGO 355 0.3187 0.1461 0.0000

PHILADELPHIA 211 0.2337 0.1111 0.0000

DETROIT 177 0.2075 0.1367 0.0000

BOSTON 154 0.1646 0.0880 0.0000

2391
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11 All of the regression equations are statistically significant, with overall

probabilities less than 0.0001, and the proportion of explained variation or R squared ranges

from 0.204 to 0.614.

Page 13

metropolitan areas. The mean proportion of nonwhites varies from 0.579 in the Los Angeles

metropolitan area to 0.165 in the Boston metropolitan area. Using the Los Angeles

metropolitan area as a reference group, the two-sided mean difference tests are highly

significant (probability values less than 0.0000) for all metropolitan areas except for the

Miami metropolitan area. The relatively low accessibility proportions for Philadelphia and

Detroit (0.234 and 0.208 respectively) are due, in part, to the concentration of stores in

suburban areas such as King of Prussia, Pennsylvania and Troy, Michigan.

Table 4 summarizes the overall results for the labor market accessibility hypothesis based on

linear multivariate regressions. There are four regression equations.11 Each equation uses a

different dependent variable, representing the four race/ethnic proportions for nonwhites,

African Americans, Hispanics, and Asians. All of the regression equations use the same

independent or explanatory factors: the four price point categories (PRICEPT4), the ten

metropolitan areas (MSA), and a control variable for the major chains within each price point

category (PP1C through PP4C). The latter variable indicates whether chains within a price

point category have different accessibility patterns.

TABLE 4: REGRESSION EQUATIONS EXPLAINING ACCESSIBILITY

MODEL

F

VALUE DF

PROB

F

R

SQUARE PRICEPT4 MSA PP1C PP2C PP3C PP4C

NONWHITE 36.20 58 <.0001 0.473757 0.2109 <.0001 0.6200 0.8042 <.0001 <.0001

BLACK 10.32 58 <.0001 0.204177 0.5882 <.0001 0.7161 0.6873 0.0018 <.0001

HISPANIC 63.90 58 <.0001 0.613794 0.7695 <.0001 0.6974 0.5268 <.0001 <.0001

ASIAN 50.34 58 <.0001 0.555977 0.0018 <.0001 0.9257 0.7373 0.0034 <.0001

The price point categories, capturing type of department store, controlled for metropolitan

area, are not significant explanatory factors except for Asian accessibility. That is, the

influence of type of department store on the store’s accessibility to nonwhite, African

American and Hispanic workers is not significantly different from zero influence. Chains in

the upper two price point categories, designer/bridge and better/moderate, appear to have no
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Page 14

significant influence on the accessibility of nonwhites measured collectively or by individual

group. However, chains in the lower two price point categories, moderate/popular and offprice, discount, show significant influence. Some chains appear to locate further from

various types of nonwhite workers, and other chains appear to locate closer to various types

of nonwhite workers. These differences in labor market accessibility appear to reflect, in

part, well-known preferences, among certain chains, for rural rather than urban locations. 

Taken as a whole, the present study suggests that access to most types of race/ethnic workers

tends to vary more by type of chain within price point categories rather than by the type of

department store as measured by price point categories. As expected, metropolitan area is a

major explanatory variable for all four of the race/ethnic regression as the employment of

these groups reflecting regional differences in the size of race/ethnic populations. These

results suggest that high end department stores are located in a manner that provides the

same accessibility to nonwhite workers as other types of department stores.

H0: Employment of nonwhite workers does not vary by type of department store.

For the purposes of this study, employment of nonwhite workers is measured by a shortfall

ratio. The shortfall ratio is defined as the difference between observed and expected number

of nonwhite sales workers at each store, divided by the total number of sales workers at each

store:

Nonwhite shortfalls per sales worker = (observed number of nonwhite sales workers -

expected number of nonwhite sales workers)/

total number of sales workers

The expected number of sales workers is computed by the formula,

Expected number of nonwhite sales workers =

(the proportion of nonwhites as defined by the measure of labor

market accessibility described above) x (total number of sales workers

per store)

The shortfall is computed as a ratio of total sales workers in order to standardize the index

for variations in store employment sizes. Negative values of the shortfall ratio indicate a

deficit of nonwhite sales workers, i.e., fewer sales workers than expected from a particular

race/ethnic group. Positive values of the shortfall ratio indicate a surplus of nonwhite sales

workers, i.e., more sales workers than expected from a particular race/ethnic group. Since

the expected number of nonwhite sales workers cannot exceed the total number of sales
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12 For these department stores, the ratios range from a negative value of -0.664. to a

positive value of 0.665. The shortfall ratio distribution is approximately symmetric with a

skewness measure close to zero (-0.126) and similar values for the mean and median (0.113

and 0.107 respectively).

Page 15

workers, the shortfall ratio per sales worker is always less than 1.0.12 The positive shortfall

mean value of 0.113 is significantly different from a neutral value of 0.0 (a two-sided t test

probability less than 0.0001) indicating that the stores in this segment of retail industry

employ more nonwhite workers, on average, than might be expected from their labor market

accessibility in the general population.

Table 5 reports the shortfall counts and percentages, by type of department store (price point

categories) for nonwhite sales workers. For display purposes, the shortfall distribution has

been divided into three equal-sized groups: low values (shortfall ratios from -0.664 to

0.028), medium values (shortfall ratios from 0.028 to 0.189) and high values 

TABLE 5: DEPARTMENT STORES BY SHORTFALL CATEGORY

MODEL PRICEPT4

COUNTS

LOW

COUNTS

MEDIUM

COUNTS

HIGH

TOTAL

STORES

NONWHITE DESIGNER/BRIDGE 90 17 4 111

NONWHITE BETTER/MODERATE 56 101 58 215

NONWHITE MODERATE/POPULAR 303 265 150 718

NONWHITE OFF-PRICE,DISCOUNT 348 414 585 1347

PERCENT

LOW

PERCENT

MEDIUM

PERCENT

HIGH

PERCENT

TOTAL

81.08 15.32 3.60 100.0

26.05 46.98 26.98 100.0

42.20 36.91 20.89 100.0

25.84 30.73 43.43 100.0
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13

 It should be noted that low values are not necessarily negative values of the

shortfall ratio. Low values simply indicate a relative rank ordering, i.e., shortfall ratios that

are in the lower one-third of the shortfall distribution. 

Page 16

(shortfall ratios from 0.189 to 0.665).13 As hypothesized, stores in the designer/bridge price

point category fall predominantly in the deficit direction. About four-fifths (81.1 percent) of

the designer/bridge stores are in the low group, 15.3 percent are in the medium group and 3.6

percent are in the high group. By contrast, stores in the better/moderate price point category

are distributed more symmetrically - - 26.1 percent in the low group, 47.0 percent in the

medium group, and 27.0 percent in the high group. Although the designer/bridge category

has a larger percentage of stores in the low group than the better/moderate category (81.1 and

26.1 percent respectively), the percentage of stores in the low group increases to 42.2 percent

in the moderate/popular price point category and then decreases again to 25.8 percent in the

off-price, discount category. Moderate/popular department stores are more likely to have

more of a deficiency of nonwhite sales workers than the less exclusive off-price, discount

stores. 

Table 6 reports the employment results by mean shortfall ratios rather than by shortfall

category. Mean values for high end department stores (designer/bridge) are compared to

those for the other department store types and tested for statistical significance. As expected,

the mean shortfall ratio in the designer/bridge category is significantly less than the mean

shortfall ratios in the other price point categories. The two-sided mean difference tests show

probabilities of less than 0.0001 for all four race/ethnic conditions (nonwhite, African

American, Hispanic, Asian). 

In addition, the relative ranking of the price point categories is the same in all four

race/ethnic groups. Ranked from low mean shortfall ratios to high mean shortfall ratios, the

designer/bridge category has the lowest mean shortfall ratio, followed by the

moderate/popular category. (Lower shortfall ratios mean a greater disparity between

expected and observed employment of nonwhites.) The highest mean shortfall ratios occur

in the better/moderate category and the off-price, discount category. Although the

designer/bridge category is always lower than the off-price, discount category, there does not

appear to be a uniform inverse relationship between price point categories and mean shortfall

ratio levels. The better/moderate category, by our measures, is more exclusive than the

moderate/popular category, but the better/moderate category also has higher mean shortfalls

ratios, indicating a greater employment of nonwhites, than the moderate/popular category.
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TABLE 6: MEAN EMPLOYMENT SHORTFALL RATIOS

MODEL PRICEPT4 N MEAN

STANDARD 

DEVIATION

PROBABILITY

( > |T|)

NONWHITE DESIGNER/BRIDGE 111 -0.0732 0.1284 _

NONWHITE BETTER/MODERATE 215 0.1105 0.1353 <.0001

NONWHITE MODERATE/POPULAR 718 0.0580 0.1689 <.0001

NONWHITE OFF-PRICE,DISCOUNT 1347 0.1588 0.1710 <.0001

AFRICAN

AMERICAN

DESIGNER/BRIDGE 111 0.2003 0.1583 _

AFRICAN

AMERICAN

BETTER/MODERATE 215 0.3667 0.1811 <.0001

AFRICAN

AMERICAN

MODERATE/POPULAR 718 0.3116 0.2354 <.0001

AFRICAN

AMERICAN

OFF-PRICE, DISCOUNT 1347 0.3976 0.2469 <.0001

HISPANIC DESIGNER/BRIDGE 111 0.1477 0.1332 _

HISPANIC BETTER/MODERATE 215 0.3315 0.1840 <.0001

HISPANIC MODERATE/POPULAR 718 0.2535 0.2196 <.0001

HISPANIC OFF-PRICE, DISCOUNT 1347 0.3617 0.2284 <.0001

ASIAN DESIGNER/BRIDGE 111 0.2552 0.1565 _

ASIAN BETTER/MODERATE 215 0.4333 0.2161 <.0001

ASIAN MODERATE/POPULAR 718 0.3734 0.2479 <.0001

ASIAN OFF-PRICE, DISCOUNT 1347 0.4661 0.2646 <.0001

Table 7 summarizes the overall results for the employment hypothesis based on linear

multivariate regressions. Analogous to the access results in Table 4, each equation uses the

same independent variables but different dependent variables. The independent or

explanatory variables comprise the four price point categories, the ten metropolitan areas,

and the major chains within each price point category. The dependent variables refer to
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14

 All of the employment regression equations are statistically significant, with

overall probabilities less than 0.0001, and the proportion of explained variation varies from

0.360 to 0.464. 

Page 18

shortfall ratios for the four race/ethnic groups (nonwhites, African Americans, Hispanics,

and Asians).14

TABLE 7: REGRESSION EQUATIONS EXPLAINING EMPLOYMENT 

SHORTFALL RATIO

MODEL F

VALUE DF PROBF

R

SQUARE PRICEPT4 MSA PP1C PP2C PP3C PP4C

NONWHITE 24.80 58 <.0001 0.381486 <.0001 <.0001 <.0001 0.0877 <.0001 <.0001

AFRICAN

AMERICAN

34.85 58 <.0001 0.464335 0.0002 <.0001 0.0001 0.3031 <.0001 <.0001

HISPANIC 22.60 58 <.0001 0.359793 <.0001 <.0001 0.0046 0.2907 <.0001 <.0001

ASIAN 23.84 58 <.0001 0.372275 0.0048 <.0001 0.0202 0.2841 <.0001 <.0001

Consistent with the discussion of mean shortfall ratios, the price point categories, controlled

for metropolitan area, show statistically significant differences in shortfall ratios across all

four race/ethnic groups. In addition, chains in the better/moderate category appear to have

no significant influence on employment shortfalls, whereas the chain probability differences

within the other price point categories indicate they have a significant influence. It is not

clear why the chains in the better/moderate price point category should have relatively

similar shortfall values. Some potential explanations include the impact of centralized

human resource practices, the prevalence of employee trade unions, or the competitive

pressures of operating in similar labor markets. Taken as a whole, the shortfall ratio index

suggests that the employment of nonwhite sales workers varies both by price point categories

as well as by selected chains within the upper and lower price point categories.

The employment analyses, presented so far, have been based on the shortfall ratio index

which covers the full range of values from negative shortfalls to positive shortfalls. The

shortfall index measures the direction of nonwhite employment (that is, whether a store

employs more nonwhites or fewer nonwhites than might be expected from its surrounding

labor pool), but it does not show which stores, if any, have employment deficits that might

have occurred by chance. We now examine the shortfalls that would be considered both
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Page 19

negative and statistically significant. Focusing on the firms with the lowest rates of nonwhite

employment has the added benefit that it allows us to measure how many stores might be

classified as potential candidates for some sort of remedial action. 

EXAMINATION OF DISPARITIES AT THE STORE LEVEL

A binomial test is used to compare each store’s employment of nonwhite sales workers to the

computed proportion of nonwhite workers in each store’s labor market. These results are

then summarized. Those firms with probability less than 0.025 and with a negative disparity

between expected and observed nonwhite sales workers are classified as having significant

disparities. The stores are subdivided into those in the designer/bridge category and all

others. A Fishers Exact test is used to determine if the expected number of designer/bridge

department stores with significant disparities is greater than expected. Indeed it is. We

would expect ten stores to have significant disparities, but 47 have such disparities

(probablity less than 0.00000). For African Americans, we would expect four

designer/bridge department stores to have significant disparities, 18 have such disparities

(probability less than 0.0000). Based on chance, 15 stores might be expected to have

significant disparities of Hispanics, instead 55 have such disparities (probability less than

0.0000). Unlike, the results for African Americans and Hispanics, while there are more high

end stores with significant disparities (13) for Asian sales workers than would be expected

(10) this difference was not statistically significant. Consistent with the results reported in

Table 7, chain appears to matter with some chains having a larger percentage of stores with

significant disparities than others.

Results are summarized for nonwhites, African Americans, Hispanics and Asians and are

also summarized by type of department store. Table 8 summarizes results for the relevant

race/ethnic groups. Here the percentage is computed within each race/ethnic group and refers

to the number of stores with significant disparities within a department store type divided by

the total number of stores with significant disparities. For example, 20.8 percent of all

department stores with a significant disparity in the employment of nonwhite sales workers

are in the designer/bridge category. (This is derived from 47 designer/bridge stores with

significant disparities for nonwhite sales workers out of 226 total stores with such

disparities.)
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TABLE 8: PERCENT OF STORES WITH SIGNIFICANT DISPARITIES BY 

RACE/ETHNIC GROUP

STORE

TYPE

NONWHITESAFRICAN

AMERICANS

HISPANICS ASIANS TOTAL

STORES

PERCENT

OF

STORES

DESIGNER/

BRIDGE (1)

20.8 20.45 16.37 5.94 111 4.64

BETTER/

MODERATE (2)

7.52 4.55 19.94 9.59 215 8.99

MODERATE/

POPULAR (3)

42.48 40.91 33.93 30.14 718 30.33

OFF-PRICE,

DISCOUNT (4)

29.2 34.09 29.76 54.34 1347 56.34

100 100 100 100

Although Designer/Bridge stores account for just 4.64 percent of all department stores, they

account of 20.8 percent of significant disparities for nonwhites, 20.45 percent for African

Americans, 16.37 percent for Hispanics. Only when computing disparities for Asians are the

percent of Designer/Bridge stores with significant disparities close to their overall percentage

of department stores. In contrast the percent of stores with disparities in the Off-Price

Discount category is far below their portion of all stores (56.34 percent) with the exception

of Asians.

Table 9 summarizes results by type of department store but can also be used to see how

results vary by different race/ethnic groups. Here the percentage refers to total number of

stores with disparities in a price point category divided by all stores in that price point

category. For example, 42.34 percent of all high end (designer/bridge) department stores

have a significant disparity in their employment of nonwhite sales workers. (This is derived

from 47 designer/bridge stores with significant disparities for nonwhite sales workers out of

111 designer/bridge stores.)
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Page 21

TABLE 9: PERCENT OF STORES WITH SIGNIFICANT DISPARITIES

BY STORE TYPE

STORE

TYPE

NONWHITES

AFRICAN

AMERICANS HISPANICS ASIANS

DESIGNER/BRIDGE (1) 42.34 16.22 49.55 11.71

BETTER/MODERATE (2) 7.91 1.86 31.16 9.77

MODERATE/POPULAR (3) 13.37 5.01 15.88 9.19

OFF-PRICE, DISCOUNT (4) 4.9 2.23 7.42 8.83

Looking at Designer/Bridge department stores, 42.34 percent of these stores have significant

disparities between the expected and actual number of nonwhites sales workers employed.

(This means that 57.66 percent of Designer/Bridge department stores do not have significant

disparities of nonwhite sales workers.) In contrast only 16.22 percent of these high end

department stores have significant disparities in the employment of African American sales

workers. The percentage for Hispanics is 49.55 and 11.71 for Asians. What is particularly

striking here is the percentage of stores, even across different types of stores, with significant

disparities for Hispanics. While the percentage of stores for African Americans and for

Asians is fairly similar, the percentage for Hispanics is always the highest and for the two

most exclusive store types is more than three times as high. The exception to these findings

is Off-Price, Discount Stores where Asians have the largest portion of stores with significant

disparities.

 
U.S. Equal Employment Opportunity Commission

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15

 There is also some evidence of chain disparities, especially among the less

exclusive department stores, that are probably related to preferences among selected chains

for rural rather than urban locations. 

Page 22

SUMMARY

We began our investigation with two hypotheses: the more exclusive the department store,

the lower the proportion of nonwhites in its labor pool, and the more exclusive the

department store, the lower its utilization of nonwhite sales workers. We measured

department store exclusivity in terms of four price point categories: designer/bridge,

better/moderate, moderate/popular, and off-price, discount. The results generally disconfirm

the first hypothesis. This study is unable to detect significant differences in labor market

accessibility between the designer/bridge category and the others types of department stores

for nonwhites, African Americans, and Hispanics. There is some evidence that more

exclusive department stores have slightly larger Asian labor pools than other types of

department stores.15 But, on average, the differences in potential labor markets are much less

than we expected even when controlled for metropolitan areas.

By contrast, the results generally support the second hypothesis. We measured employment

utilization in terms of a ratio of shortfall to sales workers (the difference between the actual

employment of nonwhite sales workers versus the expected employment of nonwhite sales

workers divided by the total number of sales workers). Negative ratios indicate fewer

nonwhite sales workers than expected. Positive ratios indicate more nonwhite sales workers

than expected. As hypothesized, the mean shortfall ratios in the designer/bridge category are

significantly less than the mean shortfall ratios in the other types of department stores

regardless of the race/ethnic group. The four types of department stores also show

statistically significant differences in average shortfall ratios controlled for metropolitan

areas. 

Three additional findings were somewhat surprising. First, there does not seem to be a

systematic inverse relationship between average shortfalls and levels of exclusivity. While

the designer/bridge category always has lower average shortfall values than the off-price,

discount category, the two middle categories seem to behave differently. The

moderate/popular category (ranked third from the top) tends to resemble the designer/bride

category, and the better/moderate category (ranked second from the top) tends to resemble

the off-price, discount category. Second, chain ownership has a significant effect on average

shortfall ratio values for all types of department stores except the better/moderate category. 

Within three types of departments stores, designer/bridge, moderate/popular, and off-price
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Page 23

discount, there appears to be a certain number of outlying chains, that is, chains with

substantially higher or lower average shortfall ratios than the other chains in the same price

point category. Why the chains in the better/moderate category should appear to be more

homogeneous than the chains in other price point categories is not clear. Some potential (but

speculative) explanations include the impact of centralized human resource practices, the

prevalence of employee trade unions, or the competitive pressures of operating in similar

labor markets. Third, focusing exclusively on department stores with statistically significant,

negative shortfalls, there appears to be some evidence that exclusive stores have

substantially more disparities with Hispanic sales workers than other kinds of race/ethnic

groups. It is not clear from this research whether these disparities might be tied to

qualifications unique to these exclusive stores but the differences between chains within this

type of store suggest that qualifications alone may not explain these results.
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Page 24

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

HIGH END DEPARTMENT STORES

REFERENCES i

SELECTED REFERENCES

Arnott, Richard. 1998. “Economic Theory and the Spatial Mismatch Hypothesis.” Urban

Studies 35(7) 1171-1185.

Bernhardt, Annette. September/October 2000. “The Wal-Mart Trap.” Dollars and Sense 23-

25.

Bureau of Labor Statistics. February 27, 2004. “Clothing, Accessory, and General

Merchandise Stores.” Career Guide to Industries http://stats.bls.gov/oco/cg/

downloaded June 25, 2004.

Cooke, Thomas J. 1996. “City-Suburb Differences in African American Male Labor Market

Achievement.” Professional Geographer 48(4) 458-467.

Fasenfest, David, Jason Booza and Kurt Metzger. April 2004. “Living Together: A New

Look at Racial and Ethnic Integration in Metropolitan Neighborhoods, 1990-2000.” 

The Living Cites Census Series The Bookings Institute, Center on Urban and

Metropolitan Policy.

Gastwirth, Joseph L. and Sheldon E. Haber. March 1976. “Defining the Labor Market for

Equal Employment Standards.” Monthly Labor Review 99(3) 32-36.

Gastwirth, Joseph L. 1988. Statistical Reasoning in Law and Public Policy, Volume 1

Statistical Concepts and Issues of Fairness 68-70.

Holzer, Harry J. and Keith R. Ihlanfeldt 1996. “Spatial Factors and the Employment of

Blacks at the Firm Level.” New England Economic Review (Federal Reserve Bank of

Boston) May/June Special Issue 65-82.

Holzer, Harry J., Julia Lane, and Lars Vilhuber 2003. “Escaping Low Earnings: The Role of

Employer Characteristics and Changes.” Institute for Research on Poverty

(http://www.ssc.wisc.edu/irp). Discussion Paper No. 1269-03.

Holzer, Harry J., Steven Raphael and Michael A. Stoll. 2003 “Employers in the Boom: How

Did the Hiring of Unskilled Workers Change during the 1990s?” Institute for

Research on Poverty (http://www.ssc.wisc.edu/irp). Discussion Paper No. 1267-03.
U.S. Equal Employment Opportunity Commission

HIGH END DEPARTMENT STORES

REFERENCES ii

Holzer, Harry J., John M. Quigley and Steven Raphael. 2003 “Public Transit and the Spatial

Distribution of Minority Employment: Evidence from a Natural Experiment.”

Journal of Policy Analysis and Management 22(3) 415-441.

Ihlanfeldt, Keith R. and David L. Sjoquist 1998. “The Spatial Mismatch Hypothesis: A

Review of Recent Studies and Their Implications for Welfare Reform.” Housing

Policy Debate 9(4) 849-892.

Kain, John F. 1968. “Housing Segregation, Negro Employment and Metropolitan

Decentralization.” Quarterly Journal of Economics 82 175-197.

Khattak, Asad J., Virginie Amerlynck and Roberto G. Quercia 2000. “Are Travel Times and

Distances to Work Greater for Residencts of Poor Urban Neighborhoods.”

Transportation Research Board, 79th Annual Meeting.

http://www.unc.edu?~khattak/wktt.html downloaded January 28, 2004.

Pastor, Manuel Jr. and Ara Robinson Adams. 1996. “Keeping Down with the Joneses:

Neighbors, Networks and Wages.” Review of Regional Studies 26 115-145.

Song, Shunfeng. November 1996. “Some Tests of Alternative Accessibility Measures: A

Population Density Approach.” Land Economics 72(4) 474-482.

Stoll, Michael A. 1999. “Spatial Mismatch, Discrimination, and Male Youth Employment in

the Washington DC Area: Implications for Residential Mobility Policies.” Journal of

Policy Analysis and Management 18(1) 77-98.

Thompson, Mark A. 1997. “The Impact of Spatial Mismatch on Female Labor Force

Participation.” Economic Development Quarterly 11(2) 123-141.
U.S. Equal Employment Opportunity Commission

HIGH END DEPARTMENT STORES

16

 The present report examines the distribution of nonwhite sales workers across

different types of retail stores. Our analysis of market segmentation presumes, but does not

demonstrate, a positive relationship between store prices and salesperson compensation. In

particular, this report does not examine store variations in employee hours, wages and

benefits, nor does it investigate the potential effects of such factors as union affiliation,

personnel practices and employee skills. Since opinions about fashion tend to be highly

subjective, we have attempted, as much as possible, to develop segmentation measures based

on publically available data sources. For historical background on changes in the fashion

industry, see Crane (2000).

17

 Hughes and Bernhardt (1999:4) suggest that “the trend toward market

segmentation has been found in diverse industries such as fast-foods and retail sales,

computers and high-tech, financial services, insurance, and telecommunications. The key

outcome is not just a divergence in job quality and wages, but rather a splitting of the paths

that previously connected jobs and constituted a career, often geographically. For example,

separate department stores now specialize in either high-income, middle-income or mass

markets; thus it is unlikely that a Wal-Mart customer service representative can make a

career advance to Bloomingdale’s.”

APPENDIX 1 - i

APPENDIX 1: MEASURING MARKET SEGMENTATION

In order to examine the relationship between department store type and relevant labor market

characteristics it was necessary to develop a typology to classify the department stores. 

Existing industry classification schemes such as the North American Industrial Classification

System (NAICS) and its predecessor, the Standard Industrial Classification system (SIC) do

not distinguish types of department stores.16 Our interest is not in fashion per se, but the

consequences of market segmentation for the retail workforce, especially nonwhite

workers.17 We assume, by and large, that exclusive stores and shops are relatively desirable

places to work. The combination of expensive merchandise, large markups, and generous

commissions are likely to produce reasonably high wages for sales workers. It is also likely

that these employers provide better working conditions in terms of fringe benefits, job

security, scheduling, training, employee discounts, and perhaps promotion than other

segments of the industry. Put simply, we assume that the types of apparel and accessory

stores on Fifth Avenue in New York, Michigan Avenue in Chicago, or Wilshire Boulevard in

Los Angeles have better jobs, for most sales workers, than other types of apparel and

accessory stores. The following discussion briefly introduces the concept of retail price
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18

 Approximate dollar values are taken from “Is It Really ‘Designer’?” Retrieved

February 5, 2004 (http://fashion.about.com/cs/stylebasics/a/pricepoints.htm). For alternative

definitions of the bridge category, see Greene (2002).

19

 Relative price is, of course, not the only form of market segmentation. Apparel

goods also differ by gender, age, physical size, and type of activity (e.g., sportswear). For an

interesting account of how Chico’s discovered the buying power of women in their 50's, see

Rozhon (2004).

20 The composite price index represents scores from a single factor, principal

components analysis based on twelve retail chains. The three types of merchandise have 

pearson correlation values of 0.91 (handbags and shoes), 0.98 (handbags and apparel) and

0.86 (shoes and apparel). 

APPENDIX 1 - ii

points and describes how we classified retail stores in the 2002 EEO-1 survey on the basis of

relative price points. 

Retail Price Points. Apparel and accessory manufacturers tend to classify brands by price

ranges known as “price points.” Although the prices and terminology are not always

consistent, the retail industry generally recognizes five broad categories: ready-to-wear

designer goods (more than $1,000), bridge goods ($500 to $999), better goods ($100 to

$499), moderate goods ($50 to $99) and popular or budget goods (less than $50).18 19 To

identify price points at selected chains, we surveyed the items listed on retail websites. We

selected three kinds of merchandise as follows,

1. Women’s apparel (including suits, dresses, jackets and skirts but excluding gowns,

outerwear, lingerie, and sweaters)

2. Women’s shoes (including flats, pumps, sandals, slides, and mules but excluding

boots and athletic shoes)

3. Women’s handbags (including satchels, shoulders, hobos, and totes but excluding

clutches and backpacks)

Within each category, we computed an average of the five most expensive items as a means

of estimating maximum price points. Prices were recorded at list prices unless all items on

the website had a sales price. We then combined the category averages together to form a

composite price score.20 The resulting composite scores appear to be intuitively plausible. 

Chains such Neiman Marcus and Saks Fifth Avenue have the highest price points, chains

such as Kohl’s and Sears Roebuck have the lowest price points, and chains such as Macy’s

and Dillard’s have middle-range price points. (See Table A1.)
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21

 For example, excluding Lord & Taylor, the websites for the May Company

display cosmetics and accessories, but they do not display women’s shoes or apparel. The

website for Barneys New York lists prices for selected blouses, sweaters, and handbags, but

it does not list prices for designer shoes and apparel. The website for Von Maur announces

special events at selected stores and mentions available brand names, but it does not feature

specific items for sale. 

22 We are indebted to Gertner and Stillman’s 2001 study of vertical integration in

the apparel industry for suggesting the importance of brand names as measurement devices. 

APPENDIX 1 - iii

Estimating price points from retailer websites has several drawbacks. Some websites offer

few, if any, selections, and some websites list items without prices.21 In addition, web-based

TABLE A1: LISTED MERCHANDISE

MERCHANDISE LISTED ON WEBSITES FOR SELECTED RETAIL CHAINS

RETAIL CHAIN

AVERAGE OF FIVE HIGHEST PRICES PRICE

HANDBAGS SCORE SHOES APPAREL

NEIMAN MARCUS GROUP INC $1,059.00 $922.00 $1,161.00 2.073

SAKS FIFTH AVENUE $1,194.00 $550.00 $1,549.00 2.009

NORDSTROM INC $410.00 $369.80 $439.20 0.244

LORD & TAYLOR $279.60 $166.80 $431.60 -0.139

MARSHALL FIELDS $200.60 $243.60 $386.80 -0.147

BLOOMINGDALES $204.00 $206.20 $285.60 -0.266

MACYS $160.00 $93.00 $306.00 -0.435

DILLARD'S INC $205.60 $130.80 $150.00 -0.461

BOSCOVS EAST $197.00 $54.80 $64.00 -0.629

J C PENNEY CO INC $92.00 $60.00 $121.20 -0.676

SEARS ROEBUCK AND CO $49.80 $61.00 $86.20 -0.739

KOHLS DEPARTMENT STORES INC $24.20 $29.60 $43.60 -0.834

merchandise may not reflect what is displayed in stores, and it is difficult to determine to

what extent price points vary across stores within the same chain. As an alternative, we

surveyed the store locations listed on designer websites.22 To make the project manageable, 
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HIGH END DEPARTMENT STORES

Strictly speaking, store locations on a designer’s website only mean that at least one order

was placed from a store at some point in the past, not that the goods are currently displayed

or actively sold. As Davis (1992:143) notes, “to the extent that genuine haute couture

apparel is actually stocked by ‘upscale’ department stores, like Neiman Marcus or Saks Fifth

Avenue, this, too, is done primarily to lend prestige to the store and not from any serious

expectation of actually selling such extraordinarily expensive ware for a profit.”

23

 Both Saks Fifth Avenue and Neiman Marcus sell bridge-to-designer price points 

(Moin 2003), and we could have sampled designers from either or both chains. We chose

Saks Fifth Avenue primarily because its website appears to list a larger number of designers. 

Moin (2002) reports that “Neiman’s average customer is 43 and earns approximately

$200,000 while UnCircle customers, who get gifts and other amenities based on purchase

frequency, earn $350,000 annually and have a net worth in excess of $5 million.”

24

 For example, Burberry, Carolina Herrera, Escada, and Gucci, list only their own

stores. Akris Punto, Andrew Marc, Nanette Lepore, and Natori list chain names without

locations. 

APPENDIX 1 - iv

TABLE A2: TEN LARGEST METROPOLITAN AREAS

MSA_TITLE POPULATION

New York-Newark-Edison, NY-NJ-PA Metropolitan Statistical Area 18,323,002

Los Angeles-Long Beach-Santa Ana, CA Metropolitan Statistical Area 12,365,627

Chicago-Naperville-Joliet, IL-IN-WI Metropolitan Statistical Area 9,098,316

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Metropolitan Statistical Area 5,687,147

Dallas-Fort Worth-Arlington, TX Metropolitan Statistical Area 5,161,544

Miami-Fort Lauderdale-Miami Beach, FL Metropolitan Statistical Area 5,007,564

Washington-Arlington-Alexandria, DC-VA-MD-WV Metropolitan Statistical Area 4,796,183

Houston-Baytown-Sugar Land, TX Metropolitan Statistical Area 4,715,407

Detroit-Warren-Livonia, MI Metropolitan Statistical Area 4,452,557

Boston-Cambridge-Quincy, MA-NH Metropolitan Statistical Area 4,391,344

we concentrated on designers of women’s apparel and accessories. Specifically, we

identified all the designers of women’s apparel, women’s shoes and women’s handbags from

the Saks Fifth Avenue website (www.saksfifthavenue.com) as of March 4, 2004.23 We then

searched the internet for the websites of the Saks Fifth Avenue designers. Many of the

designers did not have websites, some were not in English, some did not list store locations,

and some only listed their own stores or Saks Fifth Avenue.24 Among the designers who

listed multiple retail partners on their website, we selected all the stores with identifiable

addresses from the ten largest Metropolitan Statistical Areas listed in Table A2.
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25

 Among the top European luxury companies with a significant women’s fashion

component, Chanel, “a perennial favorite among fashion editors,” ranked number two in

2002 with estimated sales of $2 billion (Women’s Wear Daily, August 28, 2003, p. 10).

26

 At the new Dolce & Gabbana flagship, 825 Madison Avenue, New York City,

“Retail prices for the women’s line range from $300 to $975 for tops, $275 to $1,200 for

pants, and $450 to $2,500 for dresses. Average prices for accessories are $450 for shoes,

$550 for handbags, and $260 for scarves” (Women’s Wear Daily, March 12, 2002, p. 3).

27 “In five years, the designers [Richard Lambertson and John Truex] have . . .built 

a luxury accessories firm that comfortably sits next to established European houses like

Hermes, Pradea, Gucci, Bottega Veneta and Tod’s. . . Retail price points for the handbags

from about $395 for a day bag to $7,800 for an exotic, but the average price point is $795"

(Karimzadeh 2003). 

28

 “The brand’s higher-end line, Carmen Marc Valvo Couture, features product

between $1,500 and $3,000 retail and is currently a top performer” (Greene 2003).

APPENDIX 1 - v

This produced a sample of eighteen designers and/or brand names as listed in Table A3. 

TABLE A3: DESIGNER AND BRIDGE BRANDS

DESIGNER AND BRIDGE BRANDS

Chanel Fashions25 Dolce and Gabbana26

Lambertson Truex27 Carmen Marc Valvo Couture28

Anne Klein Isabella Fiore

Diane von Furstenberg Stuart Weitzman

Longchamp St. John

Kate Spade Ellen Tracy

Salvatore Ferragamo Dana Buchman

Lilly Pulitzer Donald J. Pliner

Elie Tahari James Perse

Some of these designers have higher price points than others, and some cover a wider range

of price points than others, but taken as a whole, they appear to be representative of the brand

names appearing in such publications as the New York Times Spring Fashion Magazine. To
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29

 Apparel and accessory manufacturers frequently cite specific price points and

market segments in their SEC filings. For example, the Jones Apparel Group, Inc. divides its

brand labels into better and moderate apparel (10-K Report for Fiscal Year Ending December

31, 2002, p. 5). Fossil, Inc. lists watch brands by suggested price ranges (10-K Report for

Fiscal Year Ending January 4, 2003, pp. 8-9). Perry Ellis International, Inc. states that “the

Perry Ellis brand appeals primarily to higher income, status conscious, professional 25-40

year old men” (10-K Report for Fiscal Year Ending January 31, 2003, p. 12). 

30 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 Management. 

Present at the Annual Meeting of the American Sociological Association, 2002.

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

32

 Limitations on the size of EEO-1 respondents, described in the main body of the

text, exclude most boutiques which tend to be small and locally owned.

APPENDIX 1 - vi

supplement this list, we surveyed SEC 10-K Reports and websites for major apparel

manufacturers searching, primarily, for “better” and “moderate” brand names.29 Apparel

manufacturers usually do not list store locations, but we did obtain explicit store addresses

for three additional brands (Kasper suits, Nine West clothing, and Jones New York suits and

dresses) designed for the “better” apparel market. 

 

The information on brand names was combined with the 2002 EEO-1 survey data as follows:

• The retail industry for the 2002 EEO-1 data was defined using North

American Industrial Classification System (NAICS) codes for Retail (44, 45). 

Motor Vehicle and Parts Dealers, Food and Beverage Stores, and Non Store

Retailers were then excluded because they utilize different occupations.30

• We separated sales establishments from non sales establishments based on the

proportion of sales workers. Establishments whose use of sales workers was

either greater than or not significantly different from their company’s overall

proportion of sales workers were designated as sales establishments.31

• We focused on the sales establishments classified as department stores

(NAICS 4521), clothing stores (NAICS 4481), and other merchandise

(NAICS 4529 and 4539). From this group, we excluded boutiques (such as

Burberry, Gucci, Escada),32 specialized clothing stores (such as Gap, Express,

Ann Taylor), discount outlets of high end department stores (such as

Nordstrom Racks, Saks Off Fifth, Neiman Marcus Last Call), discount buying

clubs (such as Sam’s Club, Costco, BJ’s), and miscellaneous coding errors

(such as grocery stores listed as department stores).
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33

 The trade press frequently distinguishes between stores with and without sales

promotions, but readers are reminded that we have no data showing higher levels of

employee compensation and/or employee qualifications in chains with promotional sales. 

APPENDIX 1 - vii

• Finally, we matched the store data on designers to the EEO-1 observations by

name of store and address and standardized establishment names to eliminate

spelling variations, store numbers, or geographic designations. 

The resulting store sample contains a total of 2,391 sales establishments representing

332,667 sales workers. 

We divided the store observations into two broad groups: stores carrying none of the brand

names in our sample (neither bridge nor best brands) and stores carrying at least one of the

brand names in our sample (either bridge or best brands). Depending on the type of analysis,

we created various sub-groups. The sub-groups with no sampled brand names were

classified on the basis of chain characteristics. The sub-groups with some sample brand

names were classified on the basis of individual stores. To preserve the confidentiality of the

EEO-1 respondents, each of the sub-groups represents at least five parent corporations and

none of parent corporations represent more than 50 percent of the sales workers in any given

sub-group. The different sub-groups can be described as follows:

• Within stores with no sampled brand names, we distinguished between chains 

generally known for promotional sales, i.e., advertised specials on selected

items on particular days, and chains generally known for reduced prices on

almost all items.33 The former, including Sears Roebuck, Gottschalks, and

Carson Pirie Scott, typically carry moderate and popularly priced lines such as
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HIGH END DEPARTMENT STORES

34

 Kellwood Company, the manufacturer of Sag Harbor, classifies Sag Harbor as a

moderately priced brand (www.kellwood.com/brands). The Sag Harbor “Where to Buy”

option (www.sag-harbor.com) lists the following retail partners, many of whom can be found

in our moderate/popular price point category: Bealls, Belk, Bon Marche, Bon Ton, Boscovs,

Burdines, Carsons, Elder-Beerman, Famous-Barr, Filene’s, Foley’s, Fred Meyer, Goodys,

Gottschalks, Hechts, JC Penny, Kaufmann’s, Kohls, L.S. Ayers, Meir & Frank, Meijer,

Mervyns, Military Exchanges, Peebles, Proffitt’s, Rich’s/Lazarus, Robinsons-May, Sears

USA, Strawbridge’s, Stage Stores, The Jones Store, and Younkers. 

35

 Retail Ventures, Inc., formerly Value City Department Stores, describes off-price

retailing as follows: “Off-price retailing, as distinguished from traditional full-price retailing

and discount or off-brand merchandising, is characterized by the purchase of primarily high

quality brand name merchandise, at prices below normal cost to most retailers. . . . We

regularly purchase overstocked or overproduced items from manufacturers and other

retailers, including end-of-season, out-of-season, and end-of-run merchandise and

manufacturers’ slight irregulars” (10-K Report for the Fiscal Year Ending February 1, 2003,

pp. 4-5). 

36

 It is difficult to identify discount merchants on the basis of brand labels since

many brands are chain-specific (e.g., Levi Strauss Signature jeans were initially developed

for Wal-Mart and have only recently been sold at Target). 

37

 The cluster analysis utilized the nonparametric SASTM Procedure MODECLUS

(method 1, smoothing parameter R=0.5). The cluster procedure weights each of the

designers equally. It would be possible to assign greater weight to some labels (e.g., Chanel)

and lesser weight to other labels (e.g., Lilly Pulitzer), but we do not believe that we could

make such judgments reliably. The analysis actually produced five separate designer

clusters, one large cluster with 105 observations and four smaller clusters with six

observations. The smaller clusters comprise Barneys New York, Bergdorf Goodman (owned

by Neiman Marcus), and two other Neiman Marcus stores (both of whom carry Dolce &

Gabbana). Consistent with their general reputation, stores in the smaller clusters appear to

specialize in high end designers not readily found elsewhere. For statistical purposes, the

four smaller clusters were combined with the larger designer cluster for a total of 111

observations. 

APPENDIX 1 - viii

Sag Harbor and Koret.34 The latter typically carry off-price35 and discount

merchandise.36

• Within stores with at least one designer (either bridge or better), we conducted

a statistical cluster analysis37 that produced two broad clusters: one centered

on the bridge designers and one centered on the better designers. Counting all

twenty-one designers together (both bridge and better), the designer cluster

has a median of eight designers per store and the better cluster has a median

of zero designers per store. The stores in the top quartile of the designer
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38

 For example, a prominent department store in the Washington, DC area has a

store directory showing the better women’s department on the main floor and the moderate

women’s department in the basement. 

APPENDIX 1 - ix

cluster include Saks Fifth Avenue on 611 Fifth Avenue, New York; Neiman

Marcus on 9700 Wilshire Boulevard, Los Angeles; and Bloomingdales on

1000 Third Avenue, New York.

The overall list of parent companies and individual chains, grouped by relative price points,

is summarized in Table A-1. Since many chains cover multiple price points,38 the categories

are labeled with slash marks indicating price point ranges (e.g., “better/moderate” signifies

the better to moderate price range). Relative frequencies within the price point categories are

suppressed to preserve confidentiality. It should be noted that the larger chains, such as

Dillard’s, Hechts, and Macys, have some stores in the better to moderate range and some

stores in the moderate to popular range. This reflects the common observation that stores in

the same chain often carry different lines in different neighborhoods, typically featuring

better lines in stores frequented by business and professional women. The results in Table

A-1 are generally consistent with our survey of merchandise prices on retail websites. The

main difference is the assignment of selected Marshall Fields and Bloomingdales stores to

the designer/bridge category and the assignment of selected Nordstrom stores to the

better/moderate category.

Summary. This report combines EEO-1 survey data and web-based data sources to classify

retailers on the basis of relative price points. Knowledgeable shoppers and industry

observers may disagree with specific classification decisions, but hopefully most readers will

regard the price point categories presented in this appendix as a reasonable, if imperfect,

attempt to measure market segmentation on the basis of publically available information (for

a comprehensive survey of market segmentation methods and data sources, see Wedel and

Kamakura, 2000). 
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APPENDIX 1 - x

APPENDIX 1: SELECTED REFERENCES

Crane, Diana. 2000. Fashion and its Social Agendas: Class, Gender, and Identity in

Clothing. Chicago: University of Chicago Press.

Davis, Fred. 1992. Fashion, Culture, and Identity. Chicago: University of Chicago Press. 

Gertner, Robert H. and Robert S. Stillman. 2001. “Vertical Integration and Internet

Strategies in the Apparel Industry.” Journal of Industrial Economics 49(4) 417-440.

Greene, Joshua. 2002. “Defining Bridge: It Depends Who’s Asked.” Women’s Wear Daily,

June 19, 2002, p. 8.

Greene, Joshua. 2003. “Improving the Career Outlook.” Women’s Wear Daily, December

2, 2003, p. 15.

Karimzadeh, Marc. 2003. “Life in the Luxury Fast Lane.” Women’s Wear Daily,

September 13, 2003, p. 42.

Hughes, Katherine and Annette Bernhardt. 1999. “Market Segmentation and the

Restructuring of Banking Jobs.” Institute on Education and the Economy (IEE)

Working Paper No. 9, Columbia University, Teachers College, New York City. 

Moin, David. 2002. “Saks Fifth Avenue’s Latest Strategy: An ‘Invitation to Luxury’.” 

Women’s Wear Daily, January 6, 2003, p. 1.

Moin, David. 2003. “Neiman Marcus’ Luxury Goods.” Women’s Wear Daily, October 25,

2002, p. S24.

Rozhon, Tracie. 2004. “A Big Middle-Age Demand for the Not so Revealing but Still

Stylish.” New York Times, April 7, 2004, pp. C.1, C.4.

Wedel, Michel and Wagner A. Kamakura. 2000. Market Segmentation: Conceptual and

Methodological Foundations. 2nd Edition. Dordrecht, Netherlands: Kluwer

Academic Publishers.
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APPENDIX 1 - xi

DESIGNER/BRIDGE

PARENT COMPANY CHAIN NAME 

BARNEYS INC BARNEYS INC

FEDERATED DEPARTMENT STORES INC BLOOMINGDALES

NEIMAN MARCUS GROUP INC NEIMAN MARCUS GROUP INC

NORDSTROM INC NORDSTROM INC

SAKS INCORPORATED SAKS FIFTH AVENUE

TARGET CORPORATION MARSHALL FIELDS

BETTER/MODERATE

PARENT COMPANY CHAIN NAME 

DILLARD'S INC DILLARD'S INC

FEDERATED DEPARTMENT STORES INC BURDINES

FEDERATED DEPARTMENT STORES INC MACYS

MAY DEPARTMENT STORES COMPANY FILENES

MAY DEPARTMENT STORES COMPANY FOLEYS

MAY DEPARTMENT STORES COMPANY HECHTS

MAY DEPARTMENT STORES COMPANY LORD & TAYLOR

MAY DEPARTMENT STORES COMPANY ROBINSONS-MAY

MAY DEPARTMENT STORES COMPANY STRAWBRIDGES

NORDSTROM INC NORDSTROM INC

SAKS INCORPORATED CARSON PIRIE SCOTT

SAKS INCORPORATED PARISIAN

TARGET CORPORATION MARSHALL FIELDS

VON MAUR INC VON MAUR INC
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APPENDIX 1 - xii

MODERATE/POPULAR

PARENT COMPANY CHAIN NAME 

BELK STORES SERVICES BELK STORES

BON TON DEPARTMENT STORES INC BON TON DEPARTMENT STORES INC

BOSCOVS EAST BOSCOVS EAST

DILLARD'S INC DILLARD'S INC

DUNLAP COMPANY DUNLAP

ELDER BEERMAN STORES ELDER BEERMAN STORES

FEDERATED DEPARTMENT STORES INC BURDINES

FEDERATED DEPARTMENT STORES INC LAZARUS

FEDERATED DEPARTMENT STORES INC MACYS

GOTTSCHALKS INC GOTTSCHALKS

HAROLD'S STORES INC HAROLD'S STORES INC

J C PENNEY CO INC J C PENNEY CO INC

KOHLS DEPARTMENT STORES INC KOHLS

MAY DEPARTMENT STORES COMPANY FILENES

MAY DEPARTMENT STORES COMPANY FOLEYS

MAY DEPARTMENT STORES COMPANY HECHTS

MAY DEPARTMENT STORES COMPANY L S AYRES

MAY DEPARTMENT STORES COMPANY LORD & TAYLOR

MAY DEPARTMENT STORES COMPANY ROBINSONS-MAY

MAY DEPARTMENT STORES COMPANY STRAWBRIDGES

NORDSTROM INC NORDSTROM INC

SAKS INCORPORATED CARSON PIRIE SCOTT

SAKS INCORPORATED YOUNKERS

SEARS ROEBUCK AND CO SEARS ROEBUCK AND CO

STAGE STORES INC BEALLS

STAGE STORES INC PALAIS

SWEZEY AND NEWINS INC SWEZEY AND NEWINS INC

TARGET CORPORATION MARSHALL FIELDS

TARGET CORPORATION MERVYNS
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APPENDIX 1 - xiii

OFF-PRICE AND DISCOUNT

PARENT COMPANY CHAIN NAME

AMES DEPT STORES INC AMES

BURLINGTON COAT FACTORY WAREHOUSE BURLINGTON COAT FACTORY 

CENTURY 21 CENTURY 21

CONWAY STORES INC CONWAY STORES INC

DAFFYS INC DAFFYS INC

KMART CORPORATION KMART

LOEHMANNS INC LOEHMANNS

ROSS STORES INC ROSS STORES INC

SHOPKO STORES INC SHOPKO STORES INC

STEIN MART INC STEIN MART INC

TARGET CORPORATION TARGET

TJX COMPANIES INC MARSHALLS

TJX COMPANIES INC T J MAXX

VALUE CITY STORES FILENES BASEMENT

VALUE CITY STORES VALUE CITY

WAL-MART STORES INC WAL-MART
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39

 The Census Bureau constructs a special zip code values ZCTA5 and ZCTA3 that

parallel the zip postal codes. It is these Census zip codes values that are used in these

analyses.

APPENDIX 2 - i

APPENDIX 2: WORKFORCE ESTIMATES

As an initial step to developing the necessary data for the retail research, Census Table

PCT73(A-I), “Sex by Work Experience in 1999 by Earnings in 1999 for the Population 16

Years and Over” from Summary File 3 was identified as being particularly useful. There are

separate tables for nine race/ethnic groups. These tables are particularly useful in providing

the number of wage earners in the workforce. This is in contrast to just using population

numbers. 

Census Data sets (segments 47 to 51 contain data for “Sex by Work Experience in 1999 by

Earnings in 1999 for the Population 16 Years and Over” by the various race/ethnic

categories. Data set 47 contains data for those classified as “White Alone.” The other

categories are: African American Alone (data set 48), American Indian, Alaskan Native,

Alone (data set 48), Asian Alone (data set 49), Native Hawaiian and Other Pacific Islander,

Alone (data set 49), Some Other Race, Alone (data set 50), Two or More Races (data set 50),

Hispanic or Latino (data set 51), and White Alone, Not Hispanic or Latino (segment 51). In

order to be able to calculate valid proportions all of the groups listed above were used except

“White Alone.” Additionally it was necessary to download a separate file (data set 29) with

the totals for computing percentages.

For each race/ethnic and gender group and for a total computation the following variables

were obtained.

• The total number in the population 16 years and over

• The total number that reported that they worked full- time year- round in 1999

• The total number that reported that they worked full- time year- round, with

earnings

• The total number that reported that they had not worked full- time year- round

• The total number that reported that they had not worked full- time yearround, with earnings

In order to utilize the Work Experience data files discussed above, it is necessary to enhance

the files in the following manner. 

• Only those observations with zip code values were retained.39

• For each observation with a five digit zip code value, a county code was

added. This was completed by using a data set provided by the Office of

Social and Economic Data Analysis at the University of Missouri
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APPENDIX 2 - ii

(downloaded from www.oseda.missouri.edu/jgb/zip.resources.html on April

12, 2004. Specifically, “correlation files” are available at

http://mcdc2.missouri.edu/cgi-bin/uexplore?/pub/data/corrlst). 

• This new county code was then used to generate a metropolitan area code and

title. The 2003 definitions were used.

• Totals and proportions for each of the race/ethnic groups were computed for

each five digit zip code. The work groupings maintained were full time, full

time with earnings, other than full time, other than full time with earnings. 

Totals and proportions were also computed for a calculated group that

combined full time with earnings and part time with earnings. 

• The results were then compared to those obtained from AMERICAN FACT

FINDER. The Census Bureau, web tool for retrieving data by individual zip

code. Two zip codes were tested and the raw counts (the only data provided

on the web site) matched the data set above.