The Geospatial Distribution of Employment: Examples from the Bureau of Labor Statistics Quarterly 

Census of Employment and Wages Program 

By 

Sheryl Konigsberg, David Talan, Richard Clayton 

Division of Administrative Statistics and Labor Turnover 

Office of Employment and Unemployment Statistics 

Bureau of Labor Statistics 

Introduction 

The advent of powerful computing capabilities and mapping software now allows more sophisticated analysis 

of new and existing problems through the visual display of information. The centerpoint of these new features 

is the ability to provide pinpoint locations for geographic features; defined by precise latitude and longitude 

coordinates, called geocodes. In any geocoding system involving businesses, the key is to have accurate 

physical location addresses. 

This article discusses the background of the Quarterly Census of Employment and Wages (QCEW) program, 

the definition of geocoding and its current and potential uses, examples of existing applications using labor 

market information, new ways of presenting these data, and early efforts, during a pilot project, to obtain and 

use geocodes for the Bureau of Labor Statistics (BLS) business establishment list. Finally, confidentiality, 

future uses, and how BLS plans to complete the geocoding project are profiled. 

Quarterly Census of Employment and Wages 

The Quarterly Census of Employment and Wages (QCEW) program, commonly known as the ES-202 program, 

is a product of the Unemployment Insurance (UI) system and is managed in a Federal/State cooperative 

environment. This program releases comprehensive tabulations of employment and wage information for 

workers covered by State UI laws and Federal workers covered by the Unemployment Compensation for 

Federal Employees (UCFE) program. BLS provides policies, standards and funding while states collect, edit, 

tabulate and publish the data. 

The QCEW program serves as a non-farm business census and constitutes the most complete set of monthly 

employment and quarterly wage information by six-digit North American Industry Classification System 

(NAICS) at the national, State, Consolidated Metropolitan Statistical Area (CMSA), Metropolitan Statistical 

Area (MSA), and county levels. 

Under the laws of each state, businesses are required to report the number of employees for all three months, 

total wages, taxable wages, contributions, and other related data on a quarterly basis. After these UI reports are 

collected and entered by the State, they are passed to the state QCEW unit for review, editing, and publication. 

These data are also used as the business register. 

In addition to the UI reports, BLS funds two other collections to support the needs of its users. The first is the 

Annual Refilling Survey (ARS) that, over a three-year period, contacts all businesses to update or complete 

industry information (NAICS codes), and addresses. This is the primary method for updating physical location 

addresses within the business register. The second is the quarterly Multiple Worksite Report (MWR) that 

collects data for each individual establishment of a multi-unit business. The combination of information from 

these three sources comprises the resulting QCEW Covered Employment and Wages program.2 Its 

comprehensiveness results in accurate data with substantial industry and geographic detail. 

Geocoding 

Geocoding is the process of adding geographic information such as longitude, latitude, county, and census tract 

to a file or database for use in a Geographic Information System (GIS). A GIS is an organized collection of 

computer hardware, software, geographic data, and personnel designed to capture, store, update, manipulate, 

analyze, and display spatial information. 

Geocoding may use either a point or polygon approach. In a point-based approach, record information is linked 

to longitude and latitude coordinates. This information allows for locations to be plotted on a map. In a 

polygon-based approach, record information is linked to the center of a polygon that represents a reference layer 

such as census block group, census tract, or county. With the information, a user can associate all types of data 

that may be collected. The QCEW microdata file contains a rich set of geographic information that can be 

geocoded and applied to answer questions about the labor market. 

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There are two methods for deriving a geocode. The first and most accurate method is to use the physical 

location address. Addresses are geocoded by BLS using commercial software that accesses U.S postal data 

files. This software estimates the location of each address record from an input file and standardizes it. These 

standardized addresses are then matched against a Geographic Base File (GBF), which contains directories of 

street segment records. The second method is to approximate the location using zip codes. If the geocoding 

software is unable to make a match against an address, it will attempt to geocode at the zip centroid. A centroid 

is the calculated center of an area. The zip centroid assigns all locations to the geographic center of the zip 

code. These matching processes assign geographic codes to address records, establishing their spatial location. 

Current and Potential Range of Uses 

Currently, the QCEW program provides economic data at the national, State, and county level. Data from the 

QCEW serve as an important input to many BLS programs as well as other Federal and State programs. These 

data are used as a benchmark for the Current Employment Statistics and Occupational Employment Statistics. 

The QCEW also is used by the Bureau of Economic Analysis for GDP and personal income estimates. 

Geocoded data are used extensively in government, business, and research for a wide range of applications 

including environmental resource analysis, land use planning, locational analysis, tax appraisal, utility and 

infrastructure planning, real estate analysis, marketing and demographic analysis, and habitat studies. 

At the most detailed levels or, geocoded business addresses are valuable to transportation planning where 

approximate locations or higher level county aggregations are inadequate. For this purpose, the side of the 

street, the location along the block, and the exact corner of an intersection are critical to optimal planning of bus 

lines and other public transportation. 

Geocoding data presents labor market information in a new dimension. Demands for more local data are 

providing an incentive to provide data for cities, towns, and even smaller areas. With the availability of 

geocoded data, BLS can potentially develop lower levels of aggregations, including cities, postal zip codes, 

census tract, census block, and natural boundaries such as floodplains. 

Data Presentation 

The conventional way of presenting economic data is two-dimensional, through tables and graphs. If tabular 

data is geocoded, it can be used to create a three-dimensional drawing. With the rise of the Internet and 

growing technology, GIS has made it possible to plot economic data to create abstractions and publish in the 

form of maps. This can be done by using geographic information, computers and geographic software to read 

the information and create spatial data visually. 

As an example, the Quarterly Census of Employment and Wages (QCEW) program produces an annual bulletin 

with tabular data aggregated by State and Industry. Table 1 is a section from the 2002 QCEW publication, 

Private industry by State and 6-digit NAICS industry: Establishments, employment, and wages, 2002 annual 

averages, information sector. 

The data in Table 1 is a standard way of presenting labor market information that has been in practice for many 

years. When looking at this table, it can be challenging to interpret what is being conveyed. 

Table 2 is another example of tabular data and demonstrates how geocoded data can be displayed at the subcounty level. In this figure, employment and wage data by industry are shown for the city of Cleveland, Ohio. 

With the traditional way of displaying data, the lowest level of aggregation by boundaries is by county. 

Without the latitude and longitude information, these data could not have been aggregated at this fine detail. 

While useful, these data can be used in even richer applications, which are illustrated in further examples of this 

article. 

In September 2003, Hurricane Isabel was a major hurricane in the Atlantic Ocean, which made landfall on the 

east coast of North Carolina. Table 3 displays industries in the floodplain areas of Brunswick and New 

Hanover counties. With the use of geocoding and QCEW data, the average number of employees and 

employers that potentially were affected by this recent phenomenon are shown. 

These examples illustrate the traditional method of displaying data in a tabular format. The following 

examples demonstrate the power of a GIS and how it conveys information quickly. A GIS also provides 

a strong visual that was previously unavailable. 

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In Map 1, the positions of the locations presented convey an immediate visual impression that is not 

seen in table presentations. Map 1 displays information about employment in the floodplain areas in 

North Carolina. Just by looking at the numbers in Table 2, it is difficult to determine the impact the 

hurricane had on business establishments and employment. It is very apparent in the map below; a 

picture gives a whole new dimension to the data. 

Map 2 shows the 2004 hurricanes that hit Florida- Hurricanes, Charley, Frances, Ivan, and Jeanne. Florida 

tracks the path of each hurricane with a 20 mile buffer to show the impact on employment within the affected 

areas. 

Map 3 shows the impact of the recent fires of October 2003 in San Diego, California. The State of 

California was able to use the QCEW data from second quarter 2003 to visually display employment 

and wages within the fire affected areas of Southern California and their relationship with the labor 

market. 

In Map 4, Minnesota is able to display employment around major highways by using a thermal density map. 

With this type of map, data can be displayed by showing areas with a high concentration of employment 

without displaying confidential information. 

It is apparent that map displays show how “a picture paints a thousand words.” Information is processed more 

quickly by the reader, therefore, allowing for more timely decisions and conclusions to be made about a 

particular set of information. 

Geocoding Pilot Project 

In March 2003, the QCEW program completed a geocoding pilot project with the following 15 States: 

California, Connecticut, the District of Columbia, Florida, Hawaii, Maine, Maryland, Minnesota, Missouri, 

North Carolina, Ohio, Oregon, South Carolina, Texas, and West Virginia. These States published data based on 

the geocodes derived from the QCEW data. This study was used to help refine programs and plans for 

implementing geocoding in all States. 

A great deal of time was spent working on address refinement for this project. The States’ primary resource for 

locating addresses was the Internet. They also used other sources such as telephone books and phone calls to 

employers to obtain addresses. These last two sources proved to be less reliable and more time consuming for 

most States. 

Obtaining government physical location addresses was a major obstacle for all States that participated in the 

pilot project. Governments tend to provide county-wide reports and finding a geocodeable address can be 

difficult. 

Lastly, non-disclosure is an issue. Many States were unsure if they could publish sub-county data and to what 

extent. Some questions that arose during the project were: 

• Does a dot on a map constitute confidential data based on address? 

• Does a dot on a map constitute confidential data based on employment? and 

• Does a dot on a map constitute confidential data based on industry? 

Some States came to the conclusion that they could publish this type of information while others could not since 

non-disclosure laws vary from State to State. 

Confidentiality 

The issue of confidentiality is limiting the full potential of the geocoded data, just as it does for the basic 

economic data. Under BLS policies, the name and address of a business, as well as the industry code, and 

other data are considered confidential. The confidentiality of this information is essential for the full faith in the 

Bureau’s data collection efforts. Geocoding and the use of GIS allow for the manipulation and display of 

spatial data. The QCEW data set contains individual establishment and employment listings. Uses of these 

data can be very important for research and planning. For that reason, it is very important to be able to display 

data while protecting confidentiality. In order to maintain confidential information, State agencies have been 

asking their legal departments for answers. How much information can be displayed? How far can you go? 

These answers are not always clear and are different in each State. One example of mapping data without 

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breaching confidentiality previously outlined in this paper is use of density thermal mapping, where no 

individual establishment or employment can be determined, but high or low levels of concentration are visible. 

Future Uses 

The work that has been done with geocoding for the past two years has provided insight on the techniques for 

improving the accuracy of QCEW physical location addresses. These techniques have involved extensive work 

researching and updating the Bureau’s existing business establishment data set. BLS will now be able to make 

data available for future research. 

By using geocoded data from the QCEW, new economic information can be created such as sub-county 

estimates. There also is the potential to standardize addresses and reduce mailing costs for sample users. 

Another use of geocoded QCEW data includes improving the Business Employment Dynamics (BED). BED is 

a set of statistics generated from the QCEW. These quarterly data series consist of gross job gains and gross 

job losses statistics from 1992 and subsequent years. These data help to provide a picture of the dynamic state 

of the labor market. Most data in these series are linked across time using a process that matches establishments 

by a unique number-the State Employment Security Agency identification numbers. Records that are not linked 

by this process are linked by various other means, one of which is a weighted match. The weighted match 

involves creating blocks to match data by mutiple criteria. With geocoded data, longitude and latitude 

information can be used in these blocks to create more accurate matches, thus allowing for better gross job 

gains and job losses data. 

Lastly, GIS technology and spatial data play an important role in emergency response and preparedness. Large 

scale emergencies that impact humans and land are unpredictable and hard to envision. Two types of hazards 

are natural disasters and human-induced disasters. Natural disasters include events such as, earthquakes, 

volcanoes, landslides, wildfires, floods, and hurricanes. Human-induced disasters include events such as manmade fires, toxic spills, war, and bioterrorism. Using GIS, a great deal of time in decision making and 

evaluating the impact of a disaster can be saved before and after it occurs. 

Getting the QCEW Fully Geocoded 

The QCEW database contains approximately 8.5 million establishments with about 130 million employment. 

For the first quarter of 2005, the QCEW data file had 82 percent of its records and 93 percent of employment 

geocoded. The remainder of the units are mostly new firms, small firms, or government units that do not 

provide QCEW data by worksite. Over 1 million new businesses are started annually, placing a huge burden on 

the UI and QCEW to obtain accurate industry, geographic, and address information. Thus, every new business 

will always be a challenge. A small number of large reporters also do not provide QCEW data by worksite. 

The largest group of these is Professional Employer Organizations (PEO’s). PEO’s are very large employers 

that take over the actual workers and responsibility of handling the administrative side of employment such as 

hiring, firing, establishing and maintaining employee files, processing payroll, filing payroll taxes, and other 

human resource support so a firm can focus on the day-to-day tasks involved in maintaining and growing their 

buisiness. While many PEO’s provide detailed reports covering each of their clients, many do not. These cause 

signifigant gaps in the ability to correctly allocate employment to the correct county and industry and to 

geocode the client locations. BLS continues to work with these firms to obtain the detail needed to support 

accurate data by county and industry. When obtained, these reports will also allow BLS to geocode the location 

and industry. 

Conclusion 

In summary, geographic data are an asset in data analysis, especially with the QCEW. Since BLS has 

implemented this aspect to the QCEW, the original two-dimensional data can be combined or used to create a 

three-dimensional way of looking at data. Using a geographic information system such as geocoding and 

mapping software, many datasets can be combined into one picture, thus saving time in reviewing data results 

and providing new insights which previously were unobserved. This article has provided a few examples of 

how the QCEW can benefit from the use of geocoded data. 

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Appendix: Tables and Maps 

Table 1 

Table 2 

City of Cleveland Geocoded data on 

Establishments, Average Employment and Total Wages Paid by Industrial Sector 

as Covered under the Ohio and Federal Unemployment Compensation Laws 

First Quarter 2002 (a) 

Industrial Sector 

Number of 

Establishments 

Average 

Monthly 

Employment 

Total Wages 

(in Thousands 

of Dollars) 

Total covered under Ohio UC Law (b) 

Private Sector 

9,365 

9,273 

279,396 

230,658 

$2,958,645 

2,441,135 

Agriculture, forestry, fishing and hunting 4 21 94 

Mining 7 231 3,295 

Utilities 14 1,042 15,761 

Construction 511 6,198 73,192 

Manufacturing 1,138 31,964 333,537 

Wholesale trade 678 12,229 159,430 

Retail trade 1,247 13,458 73,763 

Transportation and warehousing 243 4,116 34,241 

Information 163 7,151 99,972 

Finance and insurance 500 23,046 420,040 

Real estate and rental and leasing 311 2,696 20,157 

Professional and technical services 1,162 21,367 312,719 

Management of companies and enterprises 53 6,418 93,517 

Administrative and waste services 446 15,624 99,983 

Educational services 73 9,960 92,572 

Health care and social assistance 768 46,598 445,038 

Arts, entertainment, and recreation 108 5,150 56,995 

Accommodation and food services 898 14,112 47,337 

Other services, except public 

administration 949 9,276 59,492 

State & Local Government 92 48,738 517,510 

State Government 

Local Government 

21 

71 

3,940 

44,798 

46,608 

470,902 

Federal Government (c) 18 8,213 103,766 

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Table 3 

1st Quarter 

2003 

Brunswick and New Hanover Counties 

QCEW QCEW Units % Emp % Units % Emp 

In In 

Sector Units Avg Emp GeoCoded GeoCoded Floodplain Floodplain 

11 36 222 77.78% 94.14% 10.71% 2.39% 

21 Suppressed * * * * * 

22 Suppressed * * * * * 

23 1,287 8,680 78.09% 91.28% 11.84% 10.67% 

31-33 288 7,564 83.33% 98.19% 4.58% 11.88% 

42 420 3,006 80.71% 92.18% 8.26% 6.64% 

44-45 1,313 15,871 86.75% 97.92% 9.22% 6.15% 

48-49 217 3,474 77.42% 89.87% 11.31% 3.68% 

51 121 1,718 87.60% 94.82% 9.43% 1.60% 

52 406 2,907 82.51% 85.55% 5.37% 5.31% 

53 374 2,244 73.53% 79.55% 17.82% 29.19% 

54 841 5,639 79.07% 81.72% 11.13% 11.85% 

55 31 1,365 90.32% 96.56% 10.71% 0.68% 

56 545 4,888 75.23% 88.09% 8.78% 4.39% 

61 124 9,446 88.71% 99.84% 10.00% 17.17% 

62 655 15,537 85.34% 92.19% 5.01% 1.79% 

71 166 2,528 77.71% 89.60% 17.83% 9.58% 

72 698 11,766 83.67% 99.46% 20.21% 18.16% 

81 692 3,276 76.73% 87.97% 8.10% 7.11% 

92 116 5,655 26.72% 54.94% 29.03% 7.02% 

99 148 157 0.00% 0.00% 0.00% 0.00% 

Total 8,478 107,273 78.88% 91.56% 10.64% 10.36% 

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Map 1 

Map 2 

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Map 3 

Map 4 

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Notes 

Clayton, Richard, “Geocoding the Business Register at the Bureau of Labor Statistics”, 15th International 

Rountable on Business Survey Frames, Washington, DC, October 22-25, 2001. 

Establishments, employment, and wages, 2002 annual averages, Information“, Employment and Wages Annual 

Averages, 2002, U.S. Department of Labor; Bureau of Labor Statistics, November 2003, 3-7. 

“Table 10. Private industry by State and 6-digit NAICS industry: Establishments, employment, and wages, 2002 

annual averages, Information“, Employment and Wages Annual Averages, 2002, U.S. Department of 

Labor; Bureau of Labor Statistics, November 2003, 496. 

“Challenges for GIS in Emergency Preparedness and Response”, An Esri White Paper, Enivironmental System 

Research Institute, 2000. 

“GIS Aids Emergency Response”, Arcuser, July-September 2001. 

Page, Dan, “GIS Played a Critical Role in the Aftermath of September’s Terroist Attacks”, 

Government Technology, Jaunary 2003. 

“Understanding GIS: The ArcInfo Method”, Redlands, CA: Enivironmental System Research Institute, 1990, 1-

2. 

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