the geographic distribution and characteristics of … · the statistics about older workers in...

HIGHLIGHTS

The statistics about older workers inMinnesota in 2004 show this group’s pro-portion of the state’s labor force hasincreased. Changes in the size and com-position of age groups may affect govern-ment program and policy choices and theoptions available to businesses. Nationalprojections indicate that the population65 and older will increase from about 1 in8 people to 1 in 5 people by 2030, sothat older workers will likely compose anincreasingly larger proportion of eachstate’s workforce.1 Whether, and in whatindustries, the large wave of workersborn during the Baby Boom of 1946 to1964 are currently working may influencetheir labor force behavior beyond tradi-tional retirement ages. That is importantinformation for firms planning for theeventual loss of experienced workers andthe payout of pensions. In 2004, the BabyBoom cohort was aged 40 to 58.

This report uses data from the LocalEmployment Dynamics (LED) program toshow the geographic distribution and theeconomic dynamics among private sectorworkers 55 and older (also includingsome statistics on those aged 45 to 54).It includes comparisons among the coun-ties (and county equivalents) andbetween metropolitan and nonmetropoli-tan areas of Minnesota.2

U.S.Department of CommerceEconomics and Statistics Administration

U.S. CENSUS BUREAU

Issued October 2008

LED-OW04-MN

The Geographic Distribution andCharacteristics of Older Workers in Minnesota: 2004

By Cynthia Taeuberand Matthew R. Graham

Sponsored by the National Institute on AgingNational Institutes of HealthU.S. Department of Health and Human Services

Local Employment Dynamics

U S C E N S U S B U R E A UHelping You Make Informed Decisions

1 U.S. Census Bureau, 2004. “U.S. InterimProjections by Age, Sex, Race, and Hispanic Origin,”<http://www.census.gov/ipc/www/usinterimproj/natprojtab02a.xls>.

2 The metropolitan and nonmetropolitan countyclassifications are based on Census 2000.

For definitions of specific metropolitan statisticalareas, see <http://www.census.gov/population/www/estimates/metroarea.html>.

What’s in This Report?

HIGHLIGHTS

THE LOCAL EMPLOYMENTDYNAMICS PROGRAM

SOURCES AND ACCURACY OFTHE ESTIMATES

CHARACTERISTICS ANDEMPLOYMENT DYNAMICS OFOLDER WORKERS

Table 1— Percentage of Workers by Agein Metropolitan StatisticalAreas and NonmetropolitanArea Workplaces inMinnesota: 2004

Figure 1— Minnesota Workforce by AgeGroup: 1994 to 2004

Figure 2— Percentage of Workers 45 to54 Years Old by County ofWorkplace in Minnesota: 2004

Figure 3— Percentage of Workers 55 to64 Years Old by County ofWorkplace in Minnesota: 2004

Figure 4— Percentage of Workers 65 andOlder by County of Workplacein Minnesota: 2004

Figure 5— Percentage Change inNumber of Workers 55 andOlder by County ofWorkplace in Minnesota:2001 to 2004

ADDITIONAL RESOURCES

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

Industries are classified accordingto the North American IndustryClassification System (NAICS).Because the Quarterly WorkforceIndicators (QWI) are updated every3 months, the numbers in thisreport may differ from the mostrecent ones on the current LED Website, <http://lehd.did.census.gov>.

This report defines “older workers”as those 55 and older. Informationis displayed for all workers by agegroups to facilitate comparisonsamong workers and provide infor-mation about the potential charac-teristics of future older workers.The characteristics and geographicdistribution throughout Minnesotaof three groups of older workersare shown: those who may bereceiving pension income but whoare working (65 and older) and twopre-retirement groups of workers(those aged 45 to 54 and aged 55to 64), who may start collectingpensions and social security overthe next two decades.

With the LED information, stateplanners can monitor changes inthe workforce and emergingtrends. Detailed statistics aboutworkers by age in counties andmetropolitan and nonmetropolitanareas of Minnesota are available onthe U.S. Census Bureau’s Web site,<http://www.census.gov>.

Following are highlights from thedetailed statistics.

Age Composition of the Workforce

• Of the 87 counties in Minnesota,20.0 percent or more of thetotal workforce in 9 countieswas 55 and older.

• Statewide, 13.5 percent of work-ers were 55 and older. The fivecounties with the highest per-centage of workers 55 and olderwere:3

Percentage

County of workforce

Lincoln . . . . . . . . . . . . . 25.8

Norman . . . . . . . . . . . . . 23.4

Faribault . . . . . . . . . . . . 21.0

Sibley . . . . . . . . . . . . . . 21.0

Swift . . . . . . . . . . . . . . . 20.6

• Statewide, 3.0 percent of work-ers were 65 and older. The fivecounties with the highest per-centage of workers 65 and olderwere:4

Percentage

County of workforce

Sibley . . . . . . . . . . . . . . 10.3

Lincoln . . . . . . . . . . . . . 9.7

Norman . . . . . . . . . . . . . 9.7

Lac qui Parle . . . . . . . . . 7.5

Waseca . . . . . . . . . . . . . 6.7

• Of the 87 counties in Minnesota,83 counties experienced anincrease from 2001 to 2004 inthe percentage of the countyworkforce that was 55 andolder. The largest increase wasin Le Sueur County.

• Of the total workforce employedin metropolitan statistical areas,about 12.8 percent was 55 andolder; in nonmetropolitan areaworkplaces, the proportion was15.7 percent.

Industry Sectors With theHighest Proportions of OlderWorkers in 20045

• Statewide, among industry sec-tors that employed 100 or moreworkers 55 and older,Transportation and Warehousing(NAICS 48–49) had the highestproportion of workers in thisage group. This sector had thehighest percentage of workers55 and older in 16 counties as well.

• Statewide, no individual indus-try sectors employed more than20.0 percent of workers whowere 55 and older.

• In metropolitan statistical areasof the state, the industry sectorthat employed the largest per-centage of workers 55 and olderwas Mining (NAICS 21), with17.8 percent; the industry sec-tor with the highest proportionof workers 65 and older wasAgriculture, Forestry, Fishing,and Hunting (NAICS 11), with6.4 percent.

• In nonmetropolitan area work-places in Minnesota, the industrysector that employed the largestpercentage of workers 55 andolder was Transportation andWarehousing (NAICS 48–49), with24.2 percent. Real Estate andRental and Leasing (NAICS 53)was the industry sector with thehighest proportion of workers 65and older, with 8.9 percent.

3 Counties with low employment (fewerthan 100 employees) in the 55-and-older agegroup are not included in this list.

4 Counties with low employment (fewerthan 100 employees) in the 65-and-older agegroup 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 Minnesota: 2004 3

Industry Sectors Most Likelyto Employ Older Workers in 2004

• Of the workers in the state 55and older, 15.4 percent wereemployed in Health Care andSocial Assistance (NAICS 62), thehighest proportion for that agegroup of any industry sector inthe state. This industry wasranked number one in 30 of 87 counties.

• Of the workers 55 and older inthe state’s metropolitan statisti-cal areas, 14.7 percent wereemployed in Health Care andSocial Assistance (NAICS 62), thehighest proportion for that agegroup statewide among indus-trial sectors.

• Of the workers 55 and older inthe state’s nonmetropolitan areaworkplaces, 19.9 percent wereemployed in Retail Trade (NAICS44–45), the highest proportionfor that age group statewideamong industrial sectors.

Quarterly Job Gains andLosses in 2004

• On average, for workers 55 to64 years old, 13,330 jobs werecreated quarterly and 16,947jobs were lost quarterly. Forworkers 65 and older, the num-bers were 5,292 and 7,172,respectively.

• The county with the largestshare of job gains for workers55 to 64 years old wasHennepin County, with 31.1 per-cent. The largest share of joblosses for such workers wasalso in Hennepin County, with31.1 percent.

• The county with the largest shareof job gains for workers 65 andolder was Hennepin County, with27.8 percent. The largest shareof job losses for such workerswas also in Hennepin County,with 28.9 percent.

• The industry sector with thelargest gain in jobs for workers55 to 64 years old was RetailTrade (NAICS 44–45), with anaverage of 1,857 jobs gainedper quarter at the state level.The most jobs lost by that agegroup were in Manufacturing(NAICS 31–33), with an averageof 2,269 jobs lost per quarter atthe state level.

• The industry sector with thelargest gain in jobs for workers65 and older was Retail Trade(NAICS 44–45), with 802 jobsgained per quarter at the statelevel. The most jobs lost by thatage group were also in RetailTrade (NAICS 44–45), with1,145 jobs lost per quarter atthe state level.

Average Earnings of OlderWorkers in 2004

• Statewide, on average, workers55 and older earned $3,690 amonth.

• Of industry sectors employing atleast 100 workers 55 and older,the highest paying wasManagement of Companies andEnterprises (NAICS 55). Workersin that sector earned, on aver-age, $8,574 per month. The low-est paying was Accommodationand Food Services (NAICS 72).Workers in this sector earned, onaverage, $1,273 per month. Thefollowing table shows statewide

average monthly earnings in2004 for full-quarter, private-sector wage and salary workers55 and older by NAICS sector.

Earnings

Industry [dollars]

Management of companies

and enterprises . . . . . . . . . 8,574

Utilities . . . . . . . . . . . . . . . . 6,563

Finance and insurance . . . . . 5,994

Professional, scientific,

and technical services . . . . 5,463

Mining . . . . . . . . . . . . . . . . . 4,796

Wholesale trade . . . . . . . . . . 4,699

Manufacturing . . . . . . . . . . . 4,454

Construction . . . . . . . . . . . . 4,355

Information . . . . . . . . . . . . . 4,270

Health care and

social assistance . . . . . . . . 3,330

Educational services . . . . . . 3,259

Transportation and

warehousing . . . . . . . . . . . 3,196

Real estate and rental

and leasing . . . . . . . . . . . . 2,931

Administrative and support

and waste management

and remediation services . . 2,159

Agriculture, forestry,

fishing, and hunting . . . . . 2,157

Retail trade . . . . . . . . . . . . . 2,013

Other services (except

public administration) . . . . 1,884

Arts, entertainment,

and recreation . . . . . . . . . . 1,621

Accommodation and

food services . . . . . . . . . . 1,273


Older Workers in MetropolitanStatistical Areas and inNonmetropolitan AreaWorkplaces in 2004

• In metropolitan statistical areas,the five industry sectors withthe largest percentage of work-ers 55 and older were:

Percentage

Industry of workers

Mining . . . . . . . . . . . . . . 17.8

Transportation and

warehousing . . . . . . . . . 17.5

Educational services . . . . 17.4

Agriculture, forestry,

fishing, and hunting . . . 16.9

Utilities . . . . . . . . . . . . . . 16.1

• In nonmetropolitan area work-places, the five industry sectorswith the largest percentage ofworkers 55 and older were:

Percentage

Industry of workers

Transportation and

warehousing . . . . . . . . . 24.2

Real estate and rental

and leasing . . . . . . . . . . 24.0

Other services (except

public administration) . . 21.9

Educational services . . . . 20.0

Health care and

social assistance . . . . . . 18.2

• In metropolitan statistical areas,of industry sectors employing atleast 100 workers 55 and older,the highest paying for workers55 and older was Managementof Companies and Enterprises(NAICS 55), which paid, on aver-age, $8,827 a month. The low-est paying was Accommodationand Food Services (NAICS 72),

which paid, on average, $1,383a month.

• In nonmetropolitan area work-places, of industry sectorsemploying at least 100 workers55 and older, the highest payingfor workers 55 and older wasUtilities (NAICS 22), which paid,on average, $4,988 a month.The lowest paying was Arts,Entertainment, and Recreation(NAICS 71), which paid, on aver-age, $1,059 a month.

THE LOCAL EMPLOYMENTDYNAMICS PROGRAM

The LED program is a partnershipbetween the Census Bureau and theparticipating states. LED producesQWI for each partner state, as wellas each partner state’s metropolitanareas, combined nonmetropolitanareas, counties, and WorkforceInvestment Board areas. Quarterlyand annual averages are available at<http://lehd.did.census.gov>.6

Overview

The QWI are measures of economiccharacteristics and change selectedjointly by the Census Bureau andits partner states. Each componentof the QWI provides a critical meas-ure of an area’s economy and canbe used as a tool to better under-stand changes in the core perform-ance of local economies.

Listed in this report are figures anddata tables that show selected QWIstatistics on older workers.Comprehensive summary data thatcover geographic areas and includeage and gender composition byindustry, total employment, net jobflows, job gains and job losses, sep-arations, new hires, skill level (quar-ters of employment), and averagemonthly earnings are available at<http://lehd.did.census.gov>.

Nine months after a quarter ends,the Census Bureau and its partnersupdate the workforce indicators forthat quarter. This provides currentand historical information aboutthe characteristics of America’sworkers and a tool to monitor eco-nomic change.7 The statistics arecomparable across time, making itpossible to identify emergingworkforce trends and turningpoints and to compare geographicareas and demographic groupsworking in specific industries.Industries are classified accordingto the NAICS.

The QWI come from a mixture ofdata sources, the base of which is acensus of jobs. The LED databaseincludes all jobs a worker holds andallows multiple definitions of“employment” in order to respondto a wide variety of questions aboutthe workforce (see “Sources andAccuracy of the Estimates” in thefollowing section). The definition of“employment” in this report, unlessstated otherwise, is “beginning ofquarter” employment—that is, thetotal number of workers who wereemployed by the same employer inthe reference quarter and theprevious quarter.

QWI for partner states anddetailed information aboutthe LED program are availablewithout cost at<http://lehd.did.census.gov>.

6 For more complete information on QWI,see Abowd, John M., Bryce E. Stephens, LarsVilhuber, Fredrik Andersson, Kevin L.McKinney, Marc Roemer, and SimonWoodcock, 2005. The LEHD InfrastructureFiles and the Creation of the QuarterlyWorkforce 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 fromthe most recent ones, which are shown onthe 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.


As job-based statistics, the QWI arenot directly comparable with statis-tics from worker-based surveyssuch as the decennial and eco-nomic censuses, the AmericanCommunity Survey, or the CurrentPopulation Survey.8 Neither are theQWI exactly comparable with datafrom establishment surveys, suchas those from the U.S. Bureau ofLabor Statistics’ Quarterly Censusof Employment and Wages (QCEW)program, which capture employ-ment data at establishments on the12th of the month.

Throughout this report, “earnings”refer only to the earnings of work-ers who were employed for a fullquarter—that is, those who wereemployed by the same employer inthe reference, previous, and subse-quent quarters. This earningsmeasure reflects the earnings of“attached” employees, generallyworkers who worked for the sameemployer for the whole quarter.The measures of earnings from theQWI are not directly comparablewith measures of earnings fromthe Bureau of Labor Statistics.

SOURCES AND ACCURACYOF THE ESTIMATES

Because the QWI are job-based sta-tistics, not the worker-based statis-tics familiar to many researchers,the LED database allows multipledefinitions of “employment” andcan respond to a wide variety ofquestions about the workforce.9

Sources

Enhanced unemployment insurance(UI) wage records and the QCEWare the basic data sources for theQWI. These are administrative dataprovided to the Census Bureau bypartner states. The QWI’s coverage,timing of data collection, and con-cept definitions differ from thosein worker-based surveys, such asthe decennial and economic cen-suses, the American CommunitySurvey, and the Current PopulationSurvey. Also, QWI data are notexactly comparable with Bureau ofLabor Statistics information, due totiming differences.

Administrative data from thesesources almost certainly containnonsampling errors. The extent ofthe nonsampling errors isunknown. Sources of nonsamplingerrors include errors made in datacollection, such as recording andcoding errors, errors made in pro-cessing the data, errors made inestimating values for missing data,and errors from failing to representall units within a target population(undercoverage).

The LED program undertakes aprocess of continuous monitoringto attempt to control the nonsam-pling errors in the integrated datathat underlie the LED database. Inparticular, identifiers on both theUI wage records and the QCEWrecords are subjected to longitudi-nal editing every quarter. A set ofquality assurance tests is appliedto the integrated data. These testsdetect problems known to causenonsampling errors—primarily,tests for missing records of varioustypes (based on estimates of thenumber of expected records fromalternative sources), tests forincomplete wage or earnings infor-mation, and tests for changes inthe structure of identifiers or enti-ties. Problems detected by these

quality assurance tests are investi-gated and corrected before dataintegration and production of theQWI are allowed to continue.10

Industries are based upon theNAICS.

Coverage

This report covers civilian noninsti-tutionalized workers in the privatesector only. While this report doesnot include federal governmentworkers, the complete QWI data-base does include most state andlocal government employees. TheQWI database covers about 98 per-cent of nonagricultural, privatewage, salaried employment. Theremaining 2 percent of the nona-gricultural, private wage, salariedworkers are railroad workers andworkers for some nonprofit organi-zations. Self-employed workersand independent contractors arenot in the covered universe.11

Definitions

The LED database includes all jobs held:

• In a quarter, regardless of thelength of time the job is held.

• At the beginning of a quarter—the measure used in this report(workers employed by the sameemployer in the reference quar-ter and the previous quarter).

• At the end of a quarter.

• For a full quarter (total numberof workers who were employedby the same employer in the ref-erence, previous, and subse-quent quarters). This measure is

10 Technical documentation is available at<http://lehd.did.census.gov>.

11 See David W. Stevens. Employment ThatIs 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 censusis available at <http://www.census.gov/main/www/cen2000.html>. American CommunitySurvey information is available at<http://www.census.gov/acs/www>.Information about economic censuses isavailable at <http://www.census.gov/econ/census02/>.

9 For the QWI, a “job” is defined as anemployer-employee pair among administra-tive datasets.


used in this report for averageearnings because it reflects theearnings of employees in morestable jobs.

The measure that is closest to theQCEW definition of employment isthe second one, jobs held at thebeginning of a quarter. This secondmeasure has the additional advan-tage of capturing trends similar tothose shown by worker-based sur-veys, such as the decennial census.

Annual figures are simple averageswith each quarter weightedequally. There is no differentialweighting of averages for seasonalindustries, for example.

Earnings are measured differentlyamong the various datasets.According to the BLS Handbook ofMethods (1997), UI wage recordsmeasure “gross wages and salaries,bonuses, stock options, tips, andother gratuities, and the value ofmeals and lodging, where sup-plied.” They do not includeamounts paid for Old-Age,Survivors, and Disability Insurance(OASDI), health insurance, workers’compensation, unemploymentinsurance, private pensions, andwelfare funds. The LED databasedoes not include the number ofhours or weeks an employee

worked. Thus, low average earn-ings in a given year or quarter inan industry sector may reflect rela-tively low hourly wages, or manypart-time jobs, or both, as oftenoccurs in the retail trade sector.

Some large companies have multi-ple work sites but may report alltheir workers at the company’smain address. This creates a prob-lem for the correct geographic dis-tribution of the workers. LED usesan imputation process to allocateworkers to geographic areas inorder to maintain appropriate dis-tributions within the QWI dataset.

Confidentiality of informationabout individuals and firms is protected.

The Census Bureau and the statepartners are committed to protect-ing the confidentiality of the dataused to create the LED estimates.One technical approach used toconceal individual informationinvolves combining cell suppressionmethodology and statistical noise,thereby controlling key measures tocounty employment levels asreported by the Bureau of LaborStatistics. In other words, theCensus Bureau uses statistical tech-niques in which the actual statisticsare not shown if the numbers in a

cell are small. In addition, the statis-tics that are shown are “fuzzy,”meaning close to the actual infor-mation but not exact.

Only Census Bureau employeesand individuals who have SpecialSworn Status are permitted towork with the input data. Everyonewho has access to data protectedby Title 13 of the U.S. Code musthave an official security clearancebased on a background check,including fingerprinting.12

Additionally, these individuals aresubject to a fine of up to$250,000, up to 5 years in prison,or both, if confidential informationis disclosed. The Census Bureauand the state data custodiansreview all products before releaseto avoid disclosure of confidentialinformation.

More detailed information aboutthe confidentiality protection sys-tem is available under the“Confidentiality” menu at<http://lehd.did.census.gov>.

12 The Census Bureau’s Data Protectionand Privacy Policy, including information on Title 13, is available at <http://www.census.gov/privacy>.


CHARACTERISTICS AND EMPLOYMENT DYNAMICS OF OLDER WORKERS

Table 1.Percentage of Workers by Age in Metropolitan Statistical Areas and NonmetropolitanArea Workplaces in Minnesota: 2004

sraey99ot55sraey99ot56sraey46ot55sraey45ot54ecalpkrowfoaerA

Minnesota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.6 10.4 3.0 13.5Duluth, MN-WI (MN Part). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.7 10.5 2.2 12.7Fargo, ND-MN (MN part) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.9 10.4 3.6 14.0Grand Forks, ND-MN (MN part) . . . . . . . . . . . . . . . . . . . . . . . . . 22.4 11.2 4.4 15.6La Crosse, WI-MN (MN part) . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.8 12.7 6.5 19.2Minneapolis-St. Paul-Bloomington, MN-WI (MN part) . . . . . . 21.3 10.1 2.7 12.8Rochester, MN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.1 10.4 2.9 13.3St. Cloud, MN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.0 9.3 2.9 12.3

All metropolitan areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.5 10.1 2.7 12.8All nonmetropolitan area workplaces. . . . . . . . . . . . . . . . . . . . . 22.0 11.6 4.2 15.7

Note: Discrepancies may occur due to rounding.

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

Beginning-of-quarteremployment

Total number of workersemployed by the sameemployer in the referencequarter and the previousquarter.

Figure 1.Minnesota Workforce by Age Group: 1994 to 2004

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

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

Percent of beginning-of-quarter employment

65+

14–44

45–54

55–64

2000199819961994

0

10

20

30

40

50

60

70

80

90

100

4321432143214321432143214321432143214321Q4

2002 2004


ST.LOUIS

CASS

LAKE

ITASCA

POLK

PINE

BELTRAMI

AITKIN

COOK

KOOCHICHING

ROSEAU

CLAY BECKER

OTTERTAIL

MARSHALL

TODD

STEARNSPOPE

LYON

SWIFT

KITTSON

RICE

WILKIN

RENVILLE

MORRISON

HUBBARD

CROWWING

NORMAN

SIBLEY

NOBLES MOWERMARTIN

CARLTON

WRIGHT

FILLMOREROCK

MURRAY

REDWOOD

BROWN

MEEKER

GRANT

JACKSON

KAN-DIYOHI

WINONA

GOODHUE

DOUGLAS

FREEBORN

ISANTI

OLMSTED

DAKOTA

STEVENS

FARIBAULT

STEELE

MILLELACS

LIN-COLN

WAD-ENA

LAKEOFTHE

WOODS

HENNEPIN

ANOKA

DODGEBLUE

EARTH

TRA-VERSE

MCLEOD

CHIPPEWA

HOU-STON

WABASHA

SCOTT

WAS-ECA

LESUEUR

BENTON

NICOLLET

CARVER

MAH-NOMEN

PENNINGTON

REDLAKE

COTTON-WOOD

YELLOWMEDICINE

BIGSTONE SHERBURNE

WATON-WAN

CLEAR-WATER

KAN-ABEC

CHIS-AGO

LACQUI

PARLE

PIPE-STONE

WASHINGTONRAM-SEY

IOWA

WISCONSIN

NORTHDAKOTA

SOUTHDAKOTA

Spatial Data Source: U.S. Census Bureau, Census 2000.Statistical Data Source: Longitudinal Employer-HouseholdDynamics Program, U.S. Census Bureau, 2006.

Percentage of Workers45 to 54 by County

Note: All boundaries and names are as of January 1, 2000.

STATECOUNTY

23.9 to 27.1

22.2 to 23.8

21.0 to 22.1

18.2 to 20.9

0 25 50 75 100 Kilometers

0 25 50 75 100 Miles

Key values may not reflectprecise category breaks dueto rounding.

Figure 2. Percentage of Workers 45 to 54 Years Old by County of Workplace in Minnesota: 2004


ST.LOUIS

CASS

LAKE

ITASCA

POLK

PINE

BELTRAMI

AITKIN

COOK

KOOCHICHING

ROSEAU

CLAY BECKER

OTTERTAIL

MARSHALL

TODD

STEARNSPOPE

LYON

SWIFT

KITTSON

RICE

WILKIN

RENVILLE

MORRISON

HUBBARD

CROWWING

NORMAN

SIBLEY

NOBLES MOWERMARTIN

CARLTON

WRIGHT

FILLMOREROCK

MURRAY

REDWOOD

BROWN

MEEKER

GRANT

JACKSON

KAN-DIYOHI

WINONA

GOODHUE

DOUGLAS

FREEBORN

ISANTI

OLMSTED

DAKOTA

STEVENS

FARIBAULT

STEELE

MILLELACS

LIN-COLN

WAD-ENA

LAKEOFTHE

WOODS

HENNEPIN

ANOKA

DODGEBLUE

EARTH

TRA-VERSE

MCLEOD

CHIPPEWA

HOU-STON

WABASHA

SCOTT

WAS-ECA

LESUEUR

BENTON

NICOLLET

CARVER

MAH-NOMEN

PENNINGTON

REDLAKE

COTTON-WOOD

YELLOWMEDICINE

BIGSTONE SHERBURNE

WATON-WAN

CLEAR-WATER

KAN-ABEC

CHIS-AGO

LACQUI

PARLE

PIPE-STONE

WASHINGTONRAM-SEY

IOWA

WISCONSIN

NORTHDAKOTA

SOUTHDAKOTA




STATECOUNTY

12.9 to 16.1

11.9 to 12.8

10.7 to 11.8

8.9 to 10.6


0 25 50 75 100 Miles


Figure 3. Percentage of Workers 55 to 64 Years Old by County of Workplace in Minnesota: 2004


ST.LOUIS

CASS

LAKE

ITASCA

POLK

PINE

BELTRAMI

AITKIN

COOK

KOOCHICHING

ROSEAU

CLAY BECKER

OTTERTAIL

MARSHALL

TODD

STEARNSPOPE

LYON

SWIFT

KITTSON

RICE

WILKIN

RENVILLE

MORRISON

HUBBARD

CROWWING

NORMAN

SIBLEY

NOBLES MOWERMARTIN

CARLTON

WRIGHT

FILLMOREROCK

MURRAY

REDWOOD

BROWN

MEEKER

GRANT

JACKSON

KAN-DIYOHI

WINONA

GOODHUE

DOUGLAS

FREEBORN

ISANTI

OLMSTED

DAKOTA

STEVENS

FARIBAULT

STEELE

MILLELACS

LIN-COLN

WAD-ENA

LAKEOFTHE

WOODS

HENNEPIN

ANOKA

DODGEBLUE

EARTH

TRA-VERSE

MCLEOD

CHIPPEWA

HOU-STON

WABASHA

SCOTT

WAS-ECA

LESUEUR

BENTON

NICOLLET

CARVER

MAH-NOMEN

PENNINGTON

REDLAKE

COTTON-WOOD

YELLOWMEDICINE

BIGSTONE SHERBURNE

WATON-WAN

CLEAR-WATER

KAN-ABEC

CHIS-AGO

LACQUI

PARLE

PIPE-STONE

WASHINGTONRAM-SEY

IOWA

WISCONSIN

NORTHDAKOTA

SOUTHDAKOTA




STATECOUNTY

5.4 to 10.3

4.3 to 5.3

3.2 to 4.2

2.0 to 3.1


0 25 50 75 100 Miles


Figure 4. Percentage of Workers 65 and Older by County of Workplace in Minnesota: 2004


ST.LOUIS

CASS

LAKE

ITASCA

POLK

PINE

BELTRAMI

AITKIN

COOK

KOOCHICHING

ROSEAU

CLAY BECKER

OTTERTAIL

MARSHALL

TODD

STEARNSPOPE

LYON

SWIFT

KITTSON

RICE

WILKIN

RENVILLE

MORRISON

HUBBARD

CROWWING

NORMAN

SIBLEY

NOBLES MOWERMARTIN

CARLTON

WRIGHT

FILLMOREROCK

MURRAY

REDWOOD

BROWN

MEEKER

GRANT

JACKSON

KAN-DIYOHI

WINONA

GOODHUE

DOUGLAS

FREEBORN

ISANTI

OLMSTED

DAKOTA

STEVENS

FARIBAULT

STEELE

MILLELACS

LIN-COLN

WAD-ENA

LAKEOFTHE

WOODS

HENNEPIN

ANOKA

DODGEBLUE

EARTH

TRA-VERSE

MCLEOD

CHIPPEWA

HOU-STON

WABASHA

SCOTT

WAS-ECA

LESUEUR

BENTON

NICOLLET

CARVER

MAH-NOMEN

PENNINGTON

REDLAKE

COTTON-WOOD

YELLOWMEDICINE

BIGSTONE SHERBURNE

WATON-WAN

CLEAR-WATER

KAN-ABEC

CHIS-AGO

LACQUI

PARLE

PIPE-STONE

WASHINGTONRAM-SEY

IOWA

WISCONSIN

NORTHDAKOTA

SOUTHDAKOTA



0 25 50 75 100 Miles

Percentage Change in Number ofWorkers 55 and Older,From 2001 to 2004, by County


STATECOUNTY

20.7 to 36.5

13.8 to 20.6

5.6 to 13.7

-12.9 to 5.5


Figure 5.Percentage Change in Number of Workers 55 and Older by County of Workplace in Minnesota: 2001 to 2004


ADDITIONAL RESOURCES

Other data tables with informationabout older workers are availablefor download from the LED Web sitein a comma-separated value (.csv)format. Brief descriptions of theavailable tables are given below.See <http://lehd.did.census.gov>for additional details.

Characteristics andEmployment Dynamics ofOlder Workers

Age composition

A series of tables shows absoluteand relative shares of older workersdisaggregated into four standardage ranges. The county aggregationlevel and the metropolitan statisticalarea and nonmetropolitan areaworkplace aggregation levels arepresented for 2004.

Industry sectors with a highproportion of older workers

Two tables contain data on the topfive industry sectors for older work-ers in 2004 at the county aggrega-tion level and at the metropolitanstatistical area and nonmetropolitanarea workplace aggregation levels.

Most likely industry sectors ofemployment for older workers

A table contains the top five indus-try sectors most likely to employ

workers 55 and older. The aggre-gation level is the county of work-place for 2004.

Job gains and losses

A series of tables displays gains,losses, and net changes in jobs forolder workers disaggregated intofour standard age ranges. Theaggregation level is the workplacecounty for 2004.

Average monthly earnings of older workers

A series of tables displays averagemonthly earnings for workers 55and older across industry sectorsand aggregated at the county, met-ropolitan statistical area, and non-metropolitan area workplace lev-els. An additional table presentsearnings across the four standardage ranges.

Appendix tables

These tables contain all remainingdatasets—aggregated by county,metropolitan statistical area, andnonmetropolitan area workplacelevels and organized by industryand age. Notable data include:employment totals for 2001 to2004, quarterly job loss/gain com-position for 2004, and averagemonthly earnings and employmentby Workforce Investment Areas.

ACKNOWLEDGMENTS

Research for and production of thisreport was supported under aninteragency agreement with theBehavioral and Social ResearchProgram, National Institute onAging, Agreement No. Y1-AG-9415-07, and under Grant No. R01-AG018854.

Thanks to Heath Hayward for pro-duction of the state maps. Also,thanks to Liliana Sousa, CorinneProst, and Matthew Armstrong forassistance in the statistical analysis.

MORE INFORMATION

This report is one of a series ofreports on older workers in statesin the LED partnership. Additionaltables of data and other detailedinformation can be found at theLED Web site, <http://lehd.did.census.gov>. Other data tools andapplications, such as QWI Onlineand OnTheMap, based upon LEDpartnership data, can also befound on the LED Web site.

SUGGESTED CITATION

Taeuber, Cynthia and Matthew R.Graham, 2008. The GeographicDistribution and Characteristics ofOlder Workers in Minnesota: 2004.LED Older Workers Profile, LED-OW04-MN. U.S. Census Bureau,Washington, DC.

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