HIGHLIGHTS

The statistics about older workers in

Maryland in 2004 show this group’s proportion of the state’s labor force has

increased. Changes in the size and composition of age groups may affect government program and policy choices and the

options available to businesses. National

projections indicate that the population

65 and older will increase from about 1 in

8 people to 1 in 5 people by 2030, so

that older workers will likely compose an

increasingly larger proportion of each

state’s workforce.1 Whether, and in what

industries, the large wave of workers

born during the Baby Boom of 1946 to

1964 are currently working may influence

their labor force behavior beyond traditional retirement ages. That is important

information for firms planning for the

eventual loss of experienced workers and

the payout of pensions. In 2004, the Baby

Boom cohort was aged 40 to 58. 

This report uses data from the Local

Employment Dynamics (LED) program to

show the geographic distribution and the

economic dynamics among private sector

workers 55 and older (also including

some statistics on those aged 45 to 54).

It includes comparisons among the counties (and county equivalents) and

between metropolitan and nonmetropolitan areas of Maryland.2

U.S.Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

Issued June 2008

LED-OW04-MD

The Geographic Distribution and

Characteristics of Older Workers 

in Maryland: 2004

By 

Cynthia Taeuber

and 

Matthew R. Graham

Sponsored by the 

National Institute on Aging

National Institutes of Health

U.S. Department of Health 

and Human Services

Local Employment Dynamics

U S C E N S U S B U R E A U

Helping You Make Informed Decisions

1 U.S. Census Bureau, 2004. “U.S. Interim

Projections by Age, Sex, Race, and Hispanic Origin,”

<http://www.census.gov/ipc/www/usinterimproj

/natprojtab02a.xls>. 2 The metropolitan and nonmetropolitan county

classifications are based on Census 2000.

For definitions of specific metropolitan statistical

areas, see <http://www.census.gov/population/www

/estimates/metroarea.html>.

What’s in This Report?

HIGHLIGHTS

THE LOCAL EMPLOYMENT

DYNAMICS PROGRAM

SOURCES AND ACCURACY OF

THE ESTIMATES

CHARACTERISTICS AND

EMPLOYMENT DYNAMICS OF

OLDER WORKERS

Table 1— Percentage of Workers by Age

in Metropolitan Statistical

Areas and Nonmetropolitan

Area Workplaces in Maryland:

2004

Figure 1— Maryland Workforce by Age

Group: 1990 to 2004

Figure 2— Percentage of Workers 45 to

54 Years Old by County of

Workplace in Maryland: 2004

Figure 3— Percentage of Workers 55 to

64 Years Old by County of

Workplace in Maryland: 2004

Figure 4— Percentage of Workers 65

and Older by County of

Workplace in Maryland: 2004

Figure 5— Percentage Change in

Number of Workers 55 and

Older by County of Workplace

in Maryland: 2001 to 2004

ADDITIONAL RESOURCES 
2 The Geographic Distribution and Characteristics of Older Workers in Maryland: 2004 U.S. Census Bureau

Industries are classified according

to the North American Industry

Classification System (NAICS).

Because the Quarterly Workforce

Indicators (QWI) are updated every

3 months, the numbers in this

report may differ from the most

recent ones on the current LED Web

site, <http://lehd.did.census.gov>.

This report defines “older workers”

as those 55 and older. Information

is displayed for all workers by age

groups to facilitate comparisons

among workers and provide information about the potential characteristics of future older workers.

The characteristics and geographic

distribution throughout Maryland

of three groups of older workers

are shown: those who may be

receiving pension income but who

are working (65 and older) and two

preretirement groups of workers

(those aged 45 to 54 and aged 55

to 64), who may start collecting

pensions and social security over

the next two decades. 

With the LED information, state

planners can monitor changes in

the workforce and emerging

trends. Detailed statistics about

workers by age in counties and

metropolitan and nonmetropolitan

areas of Maryland are available on

the U.S. Census Bureau’s Web site,

<http://www.census.gov>.

Following are highlights from the

detailed statistics.

Age Composition of 

the Workforce

• Of the 24 counties in Maryland,

20.0 percent or more of the

total workforce in 1 county was

55 and older.

• Statewide, 14.6 percent of workers were 55 and older. The five

counties with the highest percentage of workers 55 and older

were:3

Percentage 

County of workforce

Kent . . . . . . . . . . . . . . . 20.6

Caroline . . . . . . . . . . . . . 18.1

Talbot . . . . . . . . . . . . . . 17.6

Dorchester . . . . . . . . . . . 17.4

Worcester . . . . . . . . . . . 16.4

• Statewide, 3.4 percent of workers were 65 and older. The five

counties with the highest percentage of workers 65 and older

were:4

Percentage 

County of workforce

Kent . . . . . . . . . . . . . . . 5.6

Worcester . . . . . . . . . . . 4.9

Caroline . . . . . . . . . . . . . 4.8

Talbot . . . . . . . . . . . . . . 4.7

Somerset . . . . . . . . . . . . 4.7

• Of the 24 counties in Maryland,

all 24 counties experienced an

increase from 2001 to 2004 in

the percentage of the county

workforce that was 55 and

older. The largest increase was

in Kent County.

• Of the total workforce employed

in metropolitan statistical areas,

about 14.5 percent was 55 and

older; in nonmetropolitan area

workplaces, the proportion was

16.5 percent.

Industry Sectors With the

Highest Proportions of Older

Workers in 20045

• Statewide, among industry sectors that employed 100 or more

workers 55 and older, Mining

(NAICS 21) had the highest proportion of workers in this age

group. This sector did not have

the highest percentage of workers 55 and older in any individual county.

• Statewide, the only industry sector with more than 1 in 5 workers 55 and older that employed

at least 100 or more workers

from that age group was:

Percentage 

Industry of workforce

Mining . . . . . . . . . . . . . . 20.6

• In metropolitan statistical areas

of the state, the industry sector

that employed the largest percentage of workers 55 and older

was Mining (NAICS 21), with 20.9

percent; the industry sector with

the highest proportion of workers 65 and older was Agriculture,

Forestry, Fishing, and Hunting

(NAICS 11), with 7.1 percent.

• In nonmetropolitan area workplaces in Maryland, the industry

sector that employed the largest

percentage of workers 55 and

older was Educational Services

(NAICS 61), with 25.5 percent.

Other Services (except Public

Administration) (NAICS 81) was

the industry sector with the

highest proportion of workers

3 Counties with low employment (fewer 65 and older, with 7.5 percent.

than 100 employees) in the 55-and-older age

group are not included in this list. 4 Counties with low employment (fewer

than 100 employees) in the 65-and-older age

group are not included in this list.

5 Sectors are groups of industries. 

For more information, see 

<http://www.census.gov/epcd/www

/naicsect.htm>.
U.S. Census Bureau The Geographic Distribution and Characteristics of Older Workers in Maryland: 2004 3

Industry Sectors Most Likely

to Employ Older Workers 

in 2004

• Of the workers in the state 55

and older, 15.2 percent were

employed in Health Care and

Social Assistance (NAICS 62), the

highest proportion for that age

group of any industry sector in

the state. This industry was

ranked number one in 4 of 

24 counties.

• Of the workers 55 and older in

the state’s metropolitan statistical areas, 15.1 percent were

employed in Health Care and

Social Assistance (NAICS 62), the

highest proportion for that age

group statewide among industrial sectors.

• Of the workers 55 and older in

the state’s nonmetropolitan area

workplaces, 17.1 percent were

employed in Health Care and

Social Assistance (NAICS 62), the

highest proportion for that age

group statewide among industrial sectors.

Quarterly Job Gains and

Losses in 2004

• On average, for workers 55 to

64 years old, 16,344 jobs were

created quarterly and 14,483

jobs were lost quarterly. For

workers 65 and older, the numbers were 5,857 and 6,263,

respectively.

• The county with the largest

share of job gains for workers

55 to 64 years old was

Montgomery County, with 17.5

percent. The largest share of job

losses for such workers was

also in Montgomery County,

with 18.2 percent.

• The county with the largest share

of job gains for workers 65 and

older was Montgomery County,

with 19.0 percent. The largest

share of job losses for such

workers was also in Montgomery

County, with 18.5 percent.

• The industry sector with the

largest gain in jobs for workers

55 to 64 years old was Retail

Trade (NAICS 44–45), with an

average of 2,354 jobs gained

per quarter at the state level.

The most jobs lost by that age

group were also in Retail Trade

(NAICS 44–45), with an average

of 1,982 jobs lost per quarter at

the state level.

• The industry sector with the

largest gain in jobs for workers

65 and older was Retail Trade

(NAICS 44–45), with 839 jobs

gained per quarter at the state

level. The most jobs lost by that

age group were also in Retail

Trade (NAICS 44–45), with 900

jobs lost per quarter at the 

state level.

Average Earnings of Older

Workers in 2004

• Statewide, on average, workers

55 and older earned $3,903 a

month.

• Of industry sectors employing

at least 100 workers 55 and

older, the highest paying was

Professional, Scientific, and

Technical Services (NAICS 54).

Workers in that sector earned,

on average, $6,381 per month.

The lowest paying was Arts,

Entertainment, and Recreation

(NAICS 71). Workers in this sector earned, on average, $1,858

per month. The following table

shows statewide average

monthly earnings in 2004 for

full-quarter, private-sector wage

and salary workers 55 and older

by NAICS sector.

Earnings 

Industry (dollars)

Professional, scientific, and 

technical services . . . . . . . . 6,381

Utilities . . . . . . . . . . . . . . . . . 6,247

Finance and insurance . . . . . 6,217

Management of companies 

and enterprises . . . . . . . . . . 5,426

Wholesale trade . . . . . . . . . . 4,781

Manufacturing . . . . . . . . . . . 4,762

Information . . . . . . . . . . . . . . 4,414

Construction . . . . . . . . . . . . . 4,327

Mining . . . . . . . . . . . . . . . . . 4,128

Educational services . . . . . . . 4,123

Real estate and rental 

and leasing . . . . . . . . . . . . . 3,893

Health care and 

social assistance . . . . . . . . . 3,614

Transportation and 

warehousing . . . . . . . . . . . . 3,014

Administrative and support 

and waste management and 

remediation services . . . . . 2,666

Other services (except 

public administration) . . . . 2,513

Retail trade . . . . . . . . . . . . . . 2,466

Agriculture, forestry, 

fishing, and hunting . . . . . . 2,418

Accommodation and 

food services . . . . . . . . . . . 1,911

Arts, entertainment, 

and recreation . . . . . . . . . . . . 1,858
4 The Geographic Distribution and Characteristics of Older Workers in Maryland: 2004 U.S. Census Bureau

Older Workers in Metropolitan

Statistical Areas and in

Nonmetropolitan Area

Workplaces in 2004

• In metropolitan statistical areas,

the five industry sectors with

the largest percentage of workers 55 and older were:

Percentage 

Industry of workers

Mining . . . . . . . . . . . . . . . 20.9

Agriculture, forestry, 

fishing, and hunting . . . 19.7

Educational services . . . . 19.5

Other services (except 

public administration) . . 19.1

Manufacturing . . . . . . . . . 18.3

• In nonmetropolitan area workplaces, the five industry sectors

with the largest percentage of

workers 55 and older were:

Percentage 

Industry of workers

Educational services . . . . . 25.5

Other services (except 

public administration) . . 22.5

Agriculture, forestry, 

fishing, and hunting . . . . 21.0

Real estate and rental 

and leasing . . . . . . . . . . 20.6

Management of companies 

and enterprises . . . . . . . 20.1

• In metropolitan statistical areas,

of industry sectors employing at

least 100 workers 55 and older,

the highest paying for workers

55 and older was Professional,

Scientific, and Technical Services

(NAICS 54), which paid, on average, $6,399 a month. The lowest paying was Arts,

Entertainment, and Recreation

(NAICS 71), which paid, on average, $1,883 a month.

• In nonmetropolitan area workplaces, of industry sectors

employing at least 100 workers

55 and older, the highest paying

for workers 55 and older was

Professional, Scientific, and

Technical Services (NAICS 54),

which paid, on average, $6,044 a

month. The lowest paying was

Arts, Entertainment, and

Recreation (NAICS 71), which paid,

on average, $1,562 a month.

THE LOCAL EMPLOYMENT

DYNAMICS PROGRAM

The LED Program is a partnership

between the Census Bureau and the

participating states. LED produces

QWI for each partner state, as well

as each partner state’s metropolitan

areas, combined nonmetropolitan

areas, counties, and Workforce

Investment Board areas. Quarterly

and annual averages are available at

<http://lehd.did.census.gov>.6

Overview

The QWI are measures of economic

characteristics and change selected

jointly by the Census Bureau and

its partner states. Each component

of the QWI provides a critical measure of an area’s economy and can

be used as a tool to better understand changes in the core performance of local economies. 

Listed in this report are figures and

data tables that show selected QWI

statistics on older workers.

Comprehensive summary data that

cover geographic areas and include

age and gender composition by

industry, total employment, net job

flows, job gains and job losses, separations, new hires, skill level (quarters of employment), and average

monthly earnings are available at

<http://lehd.did.census.gov>.

Nine months after a quarter ends,

the Census Bureau and its partners

update the workforce indicators for

that quarter. This provides current

and historical information about the

characteristics of America’s workers

and a tool to monitor economic

change.7 The statistics are comparable across time, making it possible

to identify emerging workforce

trends and turning points and to

compare geographic areas and

demographic groups working in

specific industries. Industries are

classified according to the NAICS.

The QWI come from a mixture of

data sources, the base of which is a

census of jobs. The LED database

includes all jobs a worker holds and

allows multiple definitions of

“employment” in order to respond

to a wide variety of questions about

the workforce (see “Sources and

Accuracy of the Estimates” in the

following section). The definition of

“employment” in this report, unless

stated otherwise, is “beginning of

quarter” employment—that is, the

total number of workers who were

employed by the same employer in

the reference quarter and the

previous quarter. 

QWI for partner states and

detailed information about

the LED program are available

without cost at

<http://lehd.did.census.gov>.

6 For more complete information on QWI,

see Abowd, John M., Bryce E. Stephens, Lars

Vilhuber, Fredrik Andersson, Kevin L.

McKinney, Marc Roemer, and Simon

Woodcock, 2005. The LEHD Infrastructure

Files and the Creation of the Quarterly

Workforce Indicators. LEHD Technical Paper,

TP-2006-01. U.S. Census Bureau, Washington,

DC. Available at <http://lehd.did.census.gov

/led/library/techpapers/tp-2006-01.pdf>.

7 Because the QWI are updated quarterly,

the numbers in this report may differ from

the most recent ones, which are shown on

the current LED Web site. For the latest list 

of partner states, see 

<http://lehd.did.census.gov/led/led

/statepartners.html>. Additional states are 

in the process of joining.
U.S. Census Bureau The Geographic Distribution and Characteristics of Older Workers in Maryland: 2004 5

As job-based statistics, the QWI are

not directly comparable with statistics from worker-based surveys,

such as the decennial and economic censuses, the American

Community Survey, or the Current

Population Survey.8 Neither are the

QWI exactly comparable with data

from establishment surveys, such

as those from the U.S. Bureau of

Labor Statistics’ Quarterly Census

of Employment and Wages (QCEW)

program, which capture employment data at establishments on the

12th of the month. 

Throughout this report, “earnings”

refer only to the earnings of workers who were employed for a full

quarter—that is, those who were

employed by the same employer in

the reference, previous, and subsequent quarters. This earnings

measure reflects the earnings of

“attached” employees, generally

workers who worked for the same

employer for the whole quarter.

The measures of earnings from the

QWI are not directly comparable

with measures of earnings from

the Bureau of Labor Statistics.

SOURCES AND ACCURACY

OF THE ESTIMATES

Because the QWI are job-based statistics, not the worker-based statistics familiar to many researchers,

the LED database allows multiple

definitions of “employment” and

can respond to a wide variety of

questions about the workforce.9

Sources

Enhanced unemployment insurance

(UI) wage records and the QCEW

are the basic data sources for the

QWI. These are administrative data

provided to the Census Bureau by

partner states. The QWI’s coverage,

timing of data collection, and concept definitions differ from those

in worker-based surveys, such as

the decennial and economic censuses, the American Community

Survey, and the Current Population

Survey. Also, QWI data are not

exactly comparable with Bureau of

Labor Statistics’ information due to

timing differences.

Administrative data from these

sources almost certainly contain

nonsampling errors. The extent of

the nonsampling errors is

unknown. Sources of nonsampling

errors include errors made in data

collection, such as recording and

coding errors, errors made in processing the data, errors made in

estimating values for missing data,

and errors from failing to represent

all units within a target population

(undercoverage).

The LED program undertakes a

process of continuous monitoring

to attempt to control the nonsampling errors in the integrated data

that underlie the LED database. In

particular, identifiers on both the

UI wage records and the QCEW

records are subjected to longitudinal editing every quarter. A set of

quality assurance tests is applied

to the integrated data. These tests

detect problems known to cause

nonsampling errors—primarily,

tests for missing records of various

types (based on estimates of the

number of expected records from

alternative sources), tests for

incomplete wage or earnings information, and tests for changes in

the structure of identifiers or entities. Problems detected by these

quality assurance tests are investigated and corrected before data

integration and production of the

QWI are allowed to continue.10

Industries are based upon the

NAICS.

Coverage

This report covers civilian noninstitutionalized workers in the private

sector only. While this report does

not include federal government

workers, the complete QWI database does include most state and

local government employees. The

QWI database covers about 98 percent of nonagricultural, private

wage, salaried employment. The

remaining 2 percent of the nonagricultural, private wage, salaried

workers are railroad workers and

workers for some nonprofit organizations. Self-employed workers

and independent contractors are

not in the covered universe.11

Definitions

The LED database includes all 

jobs held:

• In a quarter, regardless of the

length of time the job is held.

• At the beginning of a quarter—

the measure used in this report

(workers employed by the same

employer in the reference quarter and the previous quarter).

• At the end of a quarter.

• For a full quarter (total number

of workers who were employed

by the same employer in the reference, previous, and subsequent quarters). This measure is

10 Technical documentation is available at

<http://lehd.did.census.gov>. 11 See David W. Stevens. Employment That

Is Not Covered by State Unemployment.

LEHD Technical Paper, TP-2002-16. U.S.

Census Bureau, Washington, DC. Available at

<http://lehd.did.census.gov

/led/library/techpapers/tp-2002-16.pdf>.

8 Information about the decennial census

is available at <http://www.census.gov/main

/www/cen2000.html>. American

Community Survey information is available

at <http://www.census.gov/acs/www>.

Information about economic censuses is

available at <http://www.census.gov/econ

/census02/>. 9 For the QWI, a “job” is defined as an

employer-employee pair among administrative datasets. 
6 The Geographic Distribution and Characteristics of Older Workers in Maryland: 2004 U.S. Census Bureau

used in this report for average

earnings because it reflects the

earnings of employees in more

stable jobs.

The measure that is closest to the

QCEW definition of employment is

the second one, jobs held at the

beginning of a quarter. This second

measure has the additional advantage of capturing trends similar to

those shown by worker-based surveys, such as the decennial census.

Annual figures are simple averages

with each quarter weighted

equally. There is no differential

weighting of averages for seasonal

industries, for example.

Earnings are measured differently

among the various datasets.

According to the BLS Handbook of

Methods (1997), UI wage records

measure “gross wages and salaries,

bonuses, stock options, tips, and

other gratuities, and the value of

meals and lodging, where supplied.” They do not include

amounts paid for Old-Age,

Survivors, and Disability Insurance

(OASDI), health insurance, workers’

compensation, unemployment

insurance, private pensions, and

welfare funds. The LED database

does not include the number of

hours or weeks an employee

worked. Thus, low average earnings in a given year or quarter in

an industry sector may reflect relatively low hourly wages, many

part-time jobs, or both, as often

occurs in the retail trade sector. 

Some large companies have multiple work sites but may report all

their workers at the company’s

main address. This creates a problem for the correct geographic distribution of the workers. LED uses

an imputation process to allocate

workers to geographic areas in

order to maintain appropriate distributions within the QWI dataset.

Confidentiality of information

about individuals and firms 

is protected.

The Census Bureau and the state

partners are committed to protecting the confidentiality of the data

used to create the LED estimates.

One technical approach used to

conceal individual information

involves combining cell suppression

methodology and statistical noise,

thereby controlling key measures to

county employment levels as

reported by the Bureau of Labor

Statistics. In other words, the

Census Bureau uses statistical techniques in which the actual statistics

are not shown if the numbers in a

cell are small. In addition, the statistics that are shown are “fuzzy,”

meaning close to the actual information but not exact.

Only Census Bureau employees

and individuals who have Special

Sworn Status are permitted to

work with the input data. Everyone

who has access to data protected

by Title 13 of the U.S. Code must

have an official security clearance

based on a background check,

including fingerprinting.12

Additionally, these individuals are

subject to a fine of up to

$250,000, up to 5 years in prison,

or both, if confidential information

is disclosed. The Census Bureau

and the state data custodians

review all products before release

to avoid disclosure of confidential

information. 

More detailed information about

the confidentiality protection system is available under the

“Confidentiality” menu at

<http://lehd.did.census.gov>.

12 The Census Bureau’s Data Protection

and Privacy Policy, including information 

on Title 13, is available at 

<http://www.census.gov/privacy>.
U.S. Census Bureau The Geographic Distribution and Characteristics of Older Workers in Maryland: 2004 7

CHARACTERISTICS AND EMPLOYMENT DYNAMICS OF OLDER WORKERS

Table 1.

Percentage of Workers by Age in Metropolitan Statistical Areas and Nonmetropolitan

Area Workplaces in Maryland: 2004

Area of workplace 45 to 54 years 55 to 64 years 65 to 99 years 55 to 99 years

Maryland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 11.2 3.4 14.6

Baltimore-Towson, MD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.5 11.4 3.4 14.7

Cumberland, MD-WV (MD part) . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 12.7 3.4 16.1

Hagerstown-Martinsburg, MD-WV (MD part) . . . . . . . . . . . . . . 22.4 12.2 3.3 15.6

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

(MD Part) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.8 10.6 3.8 14.4

Salisbury, MD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.6 11.8 3.9 15.7

Washington-Arlington-Alexandria, DC-VA-MD-WV

(MD Part) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.9 10.7 3.3 14.0

All metropolitan areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 11.2 3.3 14.5

All nonmetropolitan area workplaces. . . . . . . . . . . . . . . . . . . . . 21.4 12.3 4.2 16.5

Note: Discrepancies may occur due to rounding.

Source: U.S. Census Bureau and the state of Maryland, Local Employment Dynamics program, 2006. See <http://lehd.did.census.gov>.

Beginning-of-quarter

employment

Total number of workers

employed by the same

employer in the reference

quarter and the previous

quarter.

Figure 1.

Maryland Workforce by Age Group: 1990 to 2004

Note: Universe is all jobs identified by the LED program.

Source: U.S. Census Bureau and the state of Maryland, Local Employment 

Dynamics program, 2006. See <http://lehd.did.census.gov>.

Percent of beginning-of-quarter employment

65+

14–44

45–54

55–64 

1990 1992 1994 1996 1998

0

10

20

30

40

50

60

70

80

90

100

Q23412341234123412341234123412341234123412341234123412341234

2000 2002 2004
8 The Geographic Distribution and Characteristics of Older Workers in Maryland: 2004 U.S. Census Bureau VIRGINIA WEST VIRGINIA PENNSYLVANIA DELAWARE

DC

GARRETT CECIL

FREDERICK

KENT

BALTIMORE

CHARLES

CARROLL HARFORD

DORCHESTER

ALLEGANY

WORCESTER

TALBOT

WICOMICO

MONTGOMERY

WASHINGTON

CAROLINE

SOMERSET

QUEEN

ANNE'S

HOWARD

ST.

MARY'S

PRINCE

GEORGE'S

ANNE

ARUNDEL

CALVERT

BALTIMORE

Spatial Data Source: U.S. Census Bureau, Census 2000.

Statistical Data Source: Longitudinal Employer-Household

Dynamics Program, U.S. Census Bureau, 2006.

0 25 50 75 100 Kilometers

0 25 50 75 100 Miles

Key values may not reflect

precise category breaks due

to rounding.

Percentage of Workers45 to 54 by County

Note: All boundaries and names 

are as of January 1, 2000.

22.4 to 24.021.0 to 22.3

20.2 to 20.918.3 to 20.1

STATECOUNTY

Figure 2. Percentage of Worker

s 45 to 54 Year

s Old by County of Workplace in Maryland: 2004
U.S. Census Bureau The Geographic Distribution and Characteristics of Older Workers in Maryland: 2004 9 VIRGINIA WEST VIRGINIA PENNSYLVANIA DELAWARE

DC

GARRETT CECIL

FREDERICK

KENT

BALTIMORE

CHARLES

CARROLL HARFORD

DORCHESTER

ALLEGANY

WORCESTER

TALBOT

WICOMICO

MONTGOMERY

WASHINGTON

CAROLINE

SOMERSET

QUEEN

ANNE'S

HOWARD

ST.

MARY'S

PRINCE

GEORGE'S

ANNE

ARUNDEL

CALVERT

BALTIMORE

Spatial Data Source: U.S. Census Bureau, Census 2000.

Statistical Data Source: Longitudinal Employer-Household

Dynamics Program, U.S. Census Bureau, 2006.

Percentage of Workers55 to 64 by County

Note: All boundaries and names 

are as of January 1, 2000.

12.3 to 1STATECOUNTY

5.0

11.6 to 12.2

10.6 to 11.59.7 to 10.5

0 25 50 75 100 Kilometers

0 25 50 75 100 Miles

Key values may not reflect

precise category breaks due

to rounding.

Figure 3. Percentage of Worker

s 55 to 64 Year

s Old by County of Workplace in Maryland: 2004
10 The Geographic Distribution and Characteristics of Older Workers in Maryland: 2004 U.S. Census Bureau VIRGINIA WEST VIRGINIA PENNSYLVANIA DELAWARE

DC

GARRETT CECIL

FREDERICK

KENT

BALTIMORE

CHARLES

CARROLL HARFORD

DORCHESTER

ALLEGANY

WORCESTER

TALBOT

WICOMICO

MONTGOMERY

WASHINGTON

CAROLINE

SOMERSET

QUEEN

ANNE'S

HOWARD

ST.

MARY'S

PRINCE

GEORGE'S

ANNE

ARUNDEL

CALVERT

BALTIMORE

Figure 4. Percentage of Worker

s 65 and Older by County of Workplace in Maryland: 2004

Spatial Data Source: U.S. Census Bureau, Census 2000.

Statistical Data Source: Longitudinal Employer-Household

Dynamics Program, U.S. Census Bureau, 2006.

Percentage of Workers65 and Older by County

Note: All boundaries and names 

are as of January 1, 2000.

4.0 to STATECOUNTY

5.7

3.6 to 3.9

3.3 to 3.5

2.7 to 3.2

0 25 50 75 100 Kilometers

0 25 50 75 100 Miles

Key values may not reflect

precise category breaks due

to rounding.
U.S. Census Bureau The Geographic Distribution and Characteristics of Older Workers in Maryland: 2004 11 VIRGINIA WEST VIRGINIA PENNSYLVANIA DELAWARE

DC

GARRETT CECIL

FREDERICK

KENT

BALTIMORE

CHARLES

CARROLL HARFORD

DORCHESTER

ALLEGANY

WORCESTER

TALBOT

WICOMICO

MONTGOMERY

WASHINGTON

CAROLINE

SOMERSET

QUEEN

ANNE'S

HOWARD

ST.

MARY'S

PRINCE

GEORGE'S

ANNE

ARUNDEL

CALVERT

BALTIMORE

Spatial Data Source: U.S. Census Bureau, Census 2000.

Statistical Data Source: Longitudinal Employer-Household

Dynamics Program, U.S. Census Bureau, 2006.

0 25 50 75 100 Kilometers

0 25 50 75 100 Miles

Key values may not reflect

precise category breaks due

to rounding.

Percentage Change in Number ofWorkers 55 and Older,

Fro

m 2001 to 2004, by County

Note: All boundaries and names 

are as of January 1, 2000.

23.8 to 33.216.STATECOUNTY

5 to 23.7

11.1 to 16.46.7 to 11.0

Figure 5. Percentage C

hange in Number of Worker

s 55 and Older by County of Workplace in Maryland: 2001 to 2004
12 The Geographic Distribution and Characteristics of Older Workers in Maryland: 2004 U.S. Census Bureau

ADDITIONAL RESOURCES

Other data tables with information

about older workers are available

for download from the LED Web site

in a comma-separated value (.csv)

format. Brief descriptions of the

available tables are given below.

See <http://lehd.did.census.gov>

for additional details.

Characteristics and

Employment Dynamics of

Older Workers

Age composition

A series of tables shows absolute

and relative shares of older workers

disaggregated into four standard

age ranges. The county aggregation

level and the metropolitan statistical

area and nonmetropolitan area

workplace aggregation levels are

presented for 2004.

Industry sectors with a high

proportion of older workers

Two tables contain data on the top

five industry sectors for older workers in 2004 at the county aggregation level and at the metropolitan

statistical area and nonmetropolitan

area workplace aggregation levels.

Most likely industry sectors of

employment for older workers

A table contains the top five industry sectors most likely to employ 

workers 55 and older. The aggregation level is the county of workplace for 2004.

Job gains and losses

A series of tables displays gains,

losses, and net changes in jobs for

older workers disaggregated into

four standard age ranges. The

aggregation level is the workplace

county for 2004.

Average monthly earnings of 

older workers

A series of tables displays average

monthly earnings for workers 55

and older across industry sectors

and aggregated at the county, metropolitan statistical area, and nonmetropolitan area workplace levels. An additional table presents

earnings across the four standard

age ranges. 

Appendix tables

These tables contain all remaining

datasets—aggregated by county,

metropolitan statistical area, and

nonmetropolitan area workplace

levels and organized by industry

and age. Notable data include:

employment totals for 2001 to

2004, quarterly job loss/gain composition for 2004, and average

monthly earnings and employment

by Workforce Investment Areas.

ACKNOWLEDGMENTS

Research for and production of this

report was supported under an

interagency agreement with the

Behavioral and Social Research

Program, National Institute on

Aging, Agreement No. Y1-AG-9415-

07, and under Grant No. R01-

AG018854.

Thanks to Heath Hayward for production of the state maps. Also,

thanks to Liliana Sousa, Corinne

Prost, and Matthew Armstrong for

assistance in the statistical analysis.

MORE INFORMATION

This report is one of a series of

reports on older workers in states

in the LED partnership. Additional

tables of data and other detailed

information can be found at 

the LED Web site, 

<http://lehd.did.census.gov>.

Other data tools and applications,

such as QWI Online and

OnTheMap, based upon LED partnership data, can also be found on

the LED Web site.

SUGGESTED CITATION

Taeuber, Cynthia and Matthew R.

Graham, 2008. The Geographic

Distribution and Characteristics of

Older Workers in Maryland: 2004.

LED Older Workers Profile, LEDOW04-MD. U.S. Census Bureau,

Washington, DC.