Monthly Labor Review • June 2011 3

Mass Layoffs and Employment

Dina Itkin 

and 

Laurie Salmon I

n recent years, mass layoffs have affected 

large numbers of workers.1

 Even during times of stable employment levels 

or economic expansion, mass layoffs occur 

because of cost-cutting initiatives, relocation of operations, changes in technology 

or consumer demand, or other reasons. Not 

surprisingly, some occupations are more affected by these layoffs than are others. By 

using a sample of establishments that had 

at least one extended mass layoff during the 

2000–2007 period, this article examines the 

types of jobs affected by layoffs. An examination of this period offers insight into the 

mass layoff effects on occupational employment before the start of the 2007–2009 recession.2

By combining data from two Bureau of 

Labor Statistics programs—Mass Layoff 

Statistics (MLS) and Occupational Employment Statistics (OES)—pre- and post-layoff 

employment snapshots were compared for 

each sampled establishment. The total employment of the 4,520 establishments in the 

sample was more than 2.5 million before 

layoffs and less than 2.2 million after layoffs—an overall decline in employment of 

approximately 350,000 jobs, or 14 percent. 

This study focuses on changes in occupational employment overall and by industry, 

geographic region, and reason for the layoff.

The pattern of changes shows that, in 

Dina Itkin is an economist in the Office of 

Occupational and Unemployment Statistics, 

Bureau of Labor Statistics, Boston, MA. Email: 

itkin.dina@bls.gov. Laurie 

Salmon is a supervisory 

economist in the Office 

of Occupational Employment Statistics, Bureau of 

Labor Statistics, Washington, DC. Email: salmon.

laurie@bls.gov.

How occupational employment is

affected by mass layoffs

An analysis of business establishment microdata—created by combining 

microdata from the Occupational Employment Statistics program and 

the Mass Layoff Statistics program—reveals that jobs lost between 2000 

and 2007 in establishments where extended mass layoffs occurred tended 

to be those which required less training and fewer analytical skills; jobs in 

occupations that were core to the specific industry generally were retained

general, occupations that were retained or 

whose employment expanded after layoffs 

were those that tended to require analytical 

skills and extensive technical training, such 

as computer, financial, and legal analysts. 

Establishments generally let go of workers 

in occupations that tended to require less 

training, such as clerical and personal care 

occupations, and in occupations that tended 

to require mainly nonanalytical skills, such 

as material moving and production occupations. This finding was evident in the most 

commonly reported reason for layoffs. 

This overall pattern was driven, in part, 

by the industries that experienced relatively 

large numbers of layoff events and by the 

occupations’ relative importance in their respective industries. Layoffs in the manufacturing and information technology industries during the study period contributed to 

the employment declines in production and 

computer occupations; however, these occupations’ relative importance to their respective industries seemed to lessen the impact 

of the layoff. 

A second finding was that establishments 

were more likely to retain employment in 

occupations that are core to their industry. 

For example, establishments in finance and 

insurance industries tended to increase employment in business and financial operations occupations, schools tended to increase 
Mass Layoffs and Employment

4 Monthly Labor Review • June 2011

Table 1. OES observations by number of years before and 

after extended mass layoff

Number of 

years

Observations before layoffObservations after 

layoff

Cumulative 

percentage 

of total, 

before

Cumulative 

percentage 

of total, 

after

0 715 416 15.8 9.2

1 1,342 1,081 45.5 33.1

2 1,303 1,237 74.3 60.5

3 754 857 91.0 79.4

4 232 442 96.2 89.2

5 101 230 98.4 94.3

6 55 170 99.6 98.1

7 17 84 100.0 99.9

8 1 3 100.0 100.0

Total 4,520 4,520 n/a n/a

employment of teachers, and hospitals tended to increase 

employment in healthcare occupations, despite layoffs in 

other occupations. Other industries saw declines in core 

occupations, but the declines were smaller than the declines in occupations with support functions.3

 For example, manufacturing industries saw employment declines in 

most occupational groups, but production workers were 

laid off at lower rates than were production workers in 

other industries.

This finding was most noticeable in geographic regions 

where mass layoffs occurred in industries that were dominant in their economies. For example, the Midwest, which 

had higher employment concentrations in manufacturing 

and wholesale and retail trade industries than did other 

regions, lost relatively fewer workers in occupations core 

to those sectors: production, sales, and transportation occupations. The other regions had larger percentage declines in employment in these occupations. Likewise, the 

West region, which had relatively high concentrations of 

employment in motion picture and sound recording industries, lost relatively little employment in arts, design, 

entertainment, and media occupations.

Methodology and data description

The first step was to identify establishments that had an 

extended mass layoff and also had reported occupational 

employment data to the OES survey both before and after 

the layoff event. To obtain the largest number of sample 

observations, establishments that had a layoff during the 

2000–2007 period were matched with OES data from 

1999 to 2008. 

MLS defines the universe of extended mass layoffs as 

private nonfarm establishments that had at least 50 initial 

claims for unemployment insurance benefits filed against 

them during a 5-week period, with at least 50 workers 

separated for more than 30 days.4

 Companies were identified by their State-specific unemployment insurance (UI) 

number. From 2000 to 2007, there were 45,027 extended 

mass layoff events. Excluding Puerto Rico, the full MLS 

dataset contained 44,623 events. The OES data set comes 

from a nationwide establishment survey of occupational 

employment and wages. Some 400,000 business establishments are surveyed every year, and 2,611,373 distinct 

establishments reported data between January 1999 and 

May 2008.5 

If multiple layoffs occurred within a company and county, only the first layoff event was used, in order to best 

capture the occupations most vulnerable to layoff. Of the 

44,623 layoff events, many were within both the same 

company and county. Counting only the first event, there 

were 24,537 unique company/county layoff events. MLS

records were matched to OES establishments by State, 

county, and UI account. There were 10,969 events that had 

at least one corresponding OES observation. An MLS record may match more than one of the company’s establishments in the OES survey if there are several establishments within a county. 

These “before” OES observations were linked to corresponding “after” observations by UI account number, 

State, county, and Reporting Unit Number (RUN, a number that identifies a particular physical establishment under a parent company or UI account). The sample included 

multiple matching pairs of OES observations, to maximize 

the chance of capturing the establishments that had layoffs. The sample included all branches within the county 

and UI account that had the same sets of physical establishments report both before and after the layoff. Hence, 

the study essentially examines the effect of company-level 

layoffs on staffing across branches within the county. 

The study found 4,520 usable sets of “before layoff ” and 

“after layoff ” OES observations, a total of 9,040 observations. The 4,520 observations that were in both the OES 

and MLS data represented slightly more than 10 percent 

of the layoff events during the 2000–2007 period. Because 

total OES unweighted employment in these establishments before layoffs was 2,517,133, and total unweighted 

employment after layoffs was 2,165,688, the net loss was 

351,445 jobs.6

 

Approximately 96 percent of the OES observations occurred within the 4 years preceding the layoff event, and 

nearly 90 percent of OES observations occurred within 

the 4 years following the layoff event. Table 1 shows the 

distribution of establishments by the number of years between the OES observation and the first layoff. The lag 
Monthly Labor Review • June 2011 5

between data capture and layoff may impose limitations 

on the research findings. By the time the establishment’s 

staffing was captured the second time, employment may 

have returned to its original levels, some workers may 

have been re-hired, or staffing could have been influenced 

by factors other than the layoff. There is also a chance that 

an establishment underwent layoffs before the study’s 

time window. 

Another limitation is that MLS captures layoffs at the 

company level within a county, whereas OES samples individual establishments within a company and county. Twothirds of the study units are known to be establishmentlevel matches because there exists only one establishment 

in the company and county. For the other one-third of 

study units, there is a chance that although MLS captured 

a layoff event in a company in a particular county, the 

matching OES units in the sample did not actually lay off 

any workers. The layoff might have occurred elsewhere in 

the company within the same county, but not necessarily 

in the physical units surveyed. 

A third limitation is that because this study requires OES

observations both before and after the layoff in order to 

detect staffing changes, firms that go out of business completely before OES is able to sample them again are not 

included. That is, the study includes only establishments 

that go out of business if the permanent closure occurred 

after the second OES observation. According to MLS statistics, there were 6,590 extended mass layoff events that 

resulted in permanent worksite closures from 2000 to 

2007. Because these closures were likely not random with 

respect to each individual characteristic (e.g., industry, occupation, and region), there is attrition bias in the sample 

selection. 

About half the establishments in the sample had fewer 

than 250 employees before layoffs. Table 2 shows the distribution of establishments by number of employees. After 

layoffs, there was an aggregate shift toward smaller establishments. The number of establishments that were either 

very small (1 to 9 employees), small (10 to 49 employees), 

or medium-sized (50 to 249 employees) increased, while 

the number of establishments with either a large (250 to 

999 employees) or very large (more than 1,000 employees) size decreased. 

Most establishments in the survey were in the West 

and Midwest regions (defined later in this article). There 

were also large numbers of establishments in the Southeast, Southwest, and New York-New Jersey regions. The 

Mountain-Plains, Mid-Atlantic, and New England regions had less representation in the study sample. Table 

2 also shows the distribution of establishments by region.

The industry sectors that had more than 100 establishments each were manufacturing; retail trade; construction; health care and social assistance; administrative and 

support and waste management and remediation services; 

information; transportation and warehousing; accommodation and food services; finance and insurance; and arts, 

entertainment, and recreation. Table 2 shows the distribution of establishments by sector.

The primary reasons for layoffs that most establishments 

in the sample reported were slack work/insufficient demand/nonseasonal slowdown, reorganization or restructuring, contract completion, financial difficulty, an extreme weather-related event, business-ownership change, 

and contract cancellation. Table 3 shows the distribution 

of establishments in the sample by primary layoff reason.

Finally, the study categorizes occupations into two 

general groups: “analytical” and “nonanalytical.” This categorization is based on the 2000 Standard Occupational 

Classification (SOC) system descriptions of tasks performed by each occupation and on Occupational Outlook 

Handbook descriptions, and are supported by O*NET.

7

The SOC definitions of detailed occupations in the analytical group often include words describing analysis. In 

addition, occupations in the analytical group are related 

to skills and abilities such as written expression, speaking, critical thinking, and deductive and inductive reasoning. At the major occupational group level, the analytical 

group includes occupations such as legal, healthcare, and 

business and financial operations. On the other hand, occupations in the nonanalytical group are related to skills 

and abilities such as troubleshooting; repairing; dynamic, 

explosive, static, and trunk strength; and stamina. The 

nonanalytical group includes occupations such as production; transportation and material moving; office and administrative support; sales, installation, maintenance, and 

repair; building and grounds cleaning and maintenance; 

and protective service. 

Employment changes by occupation

A comparison of employment before and after layoffs 

shows that the largest numbers of jobs lost were in occupations that involved clerical or nonanalytical labor; 

included among these jobs were those in the production, 

office and administrative support, and transportation and 

material moving occupational groups. Table 4 shows that, 

despite layoffs, seven of the occupational groups grew, 

including legal occupations; healthcare practitioners and 

technical; healthcare support; and food preparation and 

serving occupations. Employment in these occupations 
Mass Layoffs and Employment

6 Monthly Labor Review • June 2011

Table 2. Establishments by industry sector, establishment size, and geographic region, sorted by number of establishments in 

“before layoff” study sample

Category

Number of 

establishments in 

full OES dataset, 

1999–20081

Number of unique 

company/county 

layoff events in 

full MLS dataset, 

2000–2007

Number of 

establishments 

in "before layoff" 

study sample

Number of 

establishments 

in "after layoff" 

study sample

Total number of establishments 22,822,082 24,750 4,520 4,520

Industry group

Goods-producing industries 374,801 12,778 2,154 n/a

NAICS 31–33 Manufacturing 202,453 9,321 1,604 n/a

NAICS 23 Construction 152,417 3,219 504 n/a

NAICS 21 Mining 10,330 238 46 n/a

NAICS 11 Farming 9,601 0 0 n/a

Service-providing industries 1,597,846 11,972 2,366 n/a

NAICS 44–45 Retail trade 261,270 1,717 560 n/a

NAICS 62 Health care and social assistance 192,536 1,016 298 n/a

NAICS 56 Administrative and support and waste management 

 and remediation services 123,146 1,953 265 n/a

NAICS 51 Information 54,028 940 263 n/a

NAICS 48–49 Transportation and warehousing 79,839 1,381 222 n/a

NAICS 72 Accommodation and food service 129,017 1,235 200 n/a

NAICS 52 Finance and insurance 89,925 1,034 145 n/a

NAICS 71 Arts, entertainment, and recreation 45,099 348 101 n/a

NAICS 81 Other services 124,662 327 84 n/a

NAICS 42 Wholesale trade 134,951 689 82 n/a

NAICS 54 Professional, scientific, and technical services 155,903 853 68 n/a

NAICS 61 Educational services 74,382 117 36 n/a

NAICS 22 Utilities 9,298 70 15 n/a

NAICS 55 Management of companies and enterprises 17,427 92 14 n/a

NAICS 53 Real estate and rental and leasing 61,439 120 8 n/a

NAICS 99 Public administration 44,924 80 5 n/a

Establishment size3

Establishments with 50–249 employees 544,595 n/a 1,835 1,893

Establishments with 250–999 employees 110,750 n/a 1,701 1,474

Establishments with 1,000 or more employees 20,759 n/a 505 420

Establishments with 10–49 employees 1,106,893 n/a 400 547

Establishments with 1–9 employees 1,039,085 n/a 79 186

Geographic region

West 454,996 5,696 1,307 n/a

Midwest 539,060 5,680 1,211 n/a

Southeast 568,758 4,262 702 n/a

Southwest 353,174 2,264 492 n/a

New York-New Jersey 210,890 2,730 436 n/a

Mountain-Plains 213,473 1,078 251 n/a

Mid-Atlantic 283,567 1,849 91 n/a

New England 198,164 1,191 30 n/a

1

 Excluding Puerto Rico, Virgin Island, Guam

2

 There are 849,435 establishments that have only an SIC code and no 

NAICS code; these establishments are not included in the NAICS data and 

so the sum of establishments by industry does not equal the total shown. 

None of these 849,435 establishments were part of the MLS dataset. 3

 MLS data do not include establishment size. Although the data include 

the number of separations, they are not comparable because not all 

establishments lay off the same proportion of employees.

increased 18 percent, 11 percent, 8 percent, and 4 percent, respectively. The occupational groups that grew were 

service-providing occupations and, with the exception of 

food preparation and serving occupations, tended to include higher paying and higher skilled occupations. 

Even within the groups with the most job losses, detailed occupations that tended to have workers with more 

training and education were least likely to experience layoffs. For instance, the detailed office support occupations 

whose employment shrank the most tended to have more 

workers whose educational attainment was a high school 

diploma and short-term on-the-job training.8

 Detailed 

office support occupations with the greatest losses included customer service representatives; general office clerks; 
Monthly Labor Review • June 2011 7

Table 3. Establishments by primary reason for extended mass layoff, 2000–2007

Reason for layoff

Number of unique company/

county layoff events in full MLS

dataset, 2000–2007

Number of establishments in 

"before layoffs" study sample

Total number of establishments 24,750 4,520

Economic difficulties 16,700 3,078

Slack work/insufficient demand/non-seasonal business slowdown 4195 926

Reorganization or restructuring of company 3021 757

Contract completion 2424 346

Financial difficulty 1966 277

Bankruptcy 1003 39

Business-ownership change 940 116

Contract cancellation 726 115

Extreme weather-related event 543 130

Import competition 487 74

Domestic relocation 313 39

Overseas relocation 188 26

Product line discontinued 187 49

Cost control/cost cutting/increase profitability 157 52

Labor dispute/contract negotiations/strike 143 36

Plant or machine repair/maintenance 96 18

Material or supply shortage 63 16

Automation/technological advances 39 15

Model changeover 39 13

Excess inventory/saturated market 35 4

Non-natural disaster 32 8

Energy related 30 3

Hazardous work environment 23 7

Natural disaster (not weather related) 23 8

Governmental regulations/intervention 14 0

Domestic competition 13 4

Seasonal reasons 6,072 1,304

Seasonal 4551 967

Other seasonal 916 184

Vacation period/school related or otherwise 605 153

Other reasons 1,978 138

Data not provided: refusal 1061 60

Data not provided: does not know 917 78

shipping and traffic clerks; first-line supervisors; secretaries (except legal, medical, and executive); bill and account 

collectors; and data entry keyers. Table 5 shows the 20 

occupations with the largest declines in employment levels and table 6 shows those with the largest percent declines. For many of these occupations, most workers had 

an educational attainment level of high school diploma or 

equivalent.9

Among office support occupations that grew the most 

following layoffs were interviewers, medical secretaries, 

and payroll and timekeeping clerks. Table 7 shows the 

20 occupations with the largest increases in employment 

after layoffs, and table 8 shows those with the largest percent increases. More workers in these occupations had the 

educational attainment of either a bachelor’s degree, or 

some college or no degree. 

Within the production occupations group, detailed occupations with the most losses were team assemblers, miscellaneous metal and plastic workers, electrical and electronic 

equipment assemblers, slaughterers and meat packers, and 

first-line supervisors. For these types of assembly and fabrication jobs, a high school diploma was the most prevalent 

level of education, but experience and additional training 

were often needed for advanced assembly work.10 Employment declines due to productivity growth and strong foreign competition in manufacturing11 may have contributed 

to the job losses evident in the layoff study sample. In fact, 

a control group (to be explained in the next section) shows 

that employment in production occupations declined by 2 

percent among establishments from 2004 to 2008 regardless of layoff status, while employment in production occupations declined by 20 percent during a similar period in 

establishments with mass layoffs. 

Similarly, transportation occupations that lost the most 
Mass Layoffs and Employment

8 Monthly Labor Review • June 2011

Table 4. Employment before and after extended mass layoff, by occupational group, 1999–2008, sorted from largest loss to 

largest gain 

Occupational group Before layoffs After layoffs Change after 

layoffs

Percent change 

after layoffs

Production 560,997 441,624 –119,373 –21.3

Office and administrative support 379,743 307,211 –72,532 –19.1

Transportation and material moving 227,004 186,961 –40,043 –17.6

Sales and related 146,752 117,806 –28,946 –19.7

Management 116,128 94,388 –21,740 –18.7

Installation, maintenance, and repair 131,028 111,526 –19,502 –14.9

Architecture and engineering 125,699 106,762 –18,937 –15.1

Personal care and service 121,066 105,212 –15,854 –13.1

Construction and extraction 131,891 121,397 –10,494 –8.0

Arts, design, entertainment, sports, and media 34,291 28,389 –5,902 –17.2

Protective service 26,682 21,232 –5,450 –20.4

Computer and mathematical science 102,263 97,675 –4,588 –4.5

Building and grounds cleaning and maintenance 62,563 60,547 –2,016 –3.2

Life, physical, and social science 21,970 20,997 –973 –4.4

Community and social services 7,782 7,908 126 1.6

Business and financial operations 111,618 111,847 229 0.2

Legal 3,532 4,161 629 17.8

Education, training, and library 30,352 31,429 1,077 3.5

Healthcare support 19,209 20,800 1,591 8.3

Food preparation and serving related 81,386 84,849 3,463 4.3

Healthcare practitioners and technical 72,433 80,353 7,920 10.9

employment were those which involved predominantly 

nonanalytical skills and short-term on-the-job training: 

hand packers and packagers; freight, stock, and material 

hand movers; and industrial truck drivers. Conversely, 

employment increased in transportation occupations for 

jobs that required either more training, or certification 

or licensure: driver/sales workers,12 as well as excavating 

and loading machine and dragline operators (with moderate-term on-the-job training being the most significant 

source of training for the two kinds of operators). 

Although employment in the computer occupational 

group declined overall, employment grew in some of the 

most highly skilled occupations in the group: computer 

applications software engineers (which also saw the thirdhighest growth of all detailed occupations across groups) 

and computer and network systems analysts. Employment declined, however, among computer programmers 

and computer support specialists—occupations with job 

functions that, according to SOC definitions, involve less 

research and analysis.

Among business and financial operations occupations, 

employment increased in those which involved analysis 

and technical skills: management analysts, logisticians, 

accountants, financial analysts, and personal financial advisors. The most significant source of education for the 

five specified occupations was a bachelor’s degree, and 

several of the occupations had high proportions of workers at the highest educational attainment level (doctoral 

or professional degree). Conversely, most employment 

losses in this group were among occupations that generally required less academic preparation—training and 

development specialists, buyers, and cost estimators. The 

most significant source of education for buyers was longterm on-the-job training, and none of the aforementioned 

three occupations had high proportions of workers who 

had attained the highest educational level.13

The net number of jobs lost in each occupational group 

is a result of the rates at which companies laid off different types of workers, as well as the types of workers 

that tended to be employed in companies that had layoffs. To isolate these factors, the percent change is useful for assessing employment growth and decline relative 

to an occupation’s initial employment level. (See tables 6 

and 8.) The occupational groups that had the largest percent declines in employment were production, protective 

service, sales and related occupations, office and administrative support, and management. The groups with the 

highest percent growth in firms with mass layoffs were 

legal occupations and healthcare practitioner occupations. 

Healthcare practitioners; food preparation and serving; 

healthcare support; and education, training, and library 

occupations were the occupational groups with the highest levels of growth in employment. Some of the detailed 

occupations that grew were service-related occupations 

and included registered nurses, waiters and waitresses, cashiers, and interviewers. (See table 7.)
Monthly Labor Review • June 2011 9

Table 5. The 20 occupations1 with the largest decline in employment level after extended mass layoff, 1999–2008, sorted by 

size of decline

SOC Occupation Occupational group

Employment Establishments

Before 

layoffs

After 

layoffs Change Percent 

change

Number 

reporting 

before 

layoffs

Number 

reporting 

after 

layoffs

Percent 

change in 

number 

reporting 

51–2092 Team assemblers Production 85,058 63,870 –21,188 –24.9 496 507 2.2

41–2031 Retail salespersons Sales and related 45,972 34,116 –11,856 –25.8 491 508 3.5

43–4051 Customer service 

representatives

Office and administrative 

support 55,650 43,832 –11,818 –21.2 1394 1429 2.5

51–4199 Metal workers and plastic 

workers, all other Production 16,930 6,341 –10,589 –62.5 105 80 –23.8

51–2022 Electrical and electronic 

equipment assemblers Production 22,914 14,684 –8,230 –35.9 186 177 –4.8

51–3023 Slaughterers and meat 

packers Production 14,358 6,149 –8,209 –57.2 30 21 –30.0

53–7064 Packers and packagers, hand Transportation and 

material moving 23,890 15,736 –8,154 –34.1 596 491 –17.6

51–1011 First-line supervisors/managers of production and 

operating workers Production 30,617 22,657 –7,960 –26.0 1678 1578 –6.0

39–3091 Amusement and recreation 

attendants Personal care and service 13,936 6,508 –7,428 –53.3 77 90 16.9

43–9061 Office clerks, general Office and administrative 

support 29,746 22,661 –7,085 –23.8 2004 1921 –4.1

15–1021 Computer programmers Computer and mathematical science 13,857 7,183 –6,674 –48.2 697 528 –24.2

51–9061 Inspectors, testers, sorters, 

samplers, and weighers Production 31,934 25,396 –6,538 –20.5 1170 1148 –1.9

51–9199 Production workers, all other Production 25,968 19,498 –6,470 –24.9 375 309 –17.6

53–7062 Laborers and freight, stock, 

and material movers, hand

Transportation and 

material moving 52,270 45,992 –6,278 –12.0 1269 1244 –2.0

43–5071 Shipping, receiving, and 

traffic clerks

Office and administrative 

support 20,581 14,607 –5,974 –29.0 1678 1582 –5.7

41–9041 Telemarketers Sales and related 13,182 7,586 –5,596 –42.5 117 113 –3.4

49–9042 Maintenance and repair 

workers, general

Installation, maintenance, 

and repair 32,387 26,801 –5,586 –17.2 1831 1907 4.2

17–2199 Engineers, all other Architecture and 

engineering 18,085 12,579 –5,506 –30.4 405 311 –23.2

53–7051 Industrial truck and tractor 

operators

Transportation and 

material moving 24,398 18,943 –5,455 –22.4 919 858 –6.6

11–9199 Managers, all other Management 15,221 9,852 –5,369 –35.3 914 739 –19.1

1

 Excluded are any occupations with fewer than 10 reporting establishments before layoffs.

Comparison with a control group. A control group serves 

to compare staffing changes among establishments that 

experienced layoffs with occupational changes in the 

economy as a whole. The change in published OES estimated employment from May 2004 to May 2008 was used 

as the control group. The May 2004 estimates are based 

on employment staffing patterns from November 2001 to 

May 2004. Likewise, the May 2008 estimates are based 

on staffing patterns from November 2005 to May 2008. 

These periods cover a large portion of the study sample 

frame. The employment changes for establishments that 

had layoffs and for the economy as a whole are shown in 

chart 1. The distance and direction from the 45-degree 

line show the differences in behavior between establishments with layoffs and the economy as a whole. Legal occupations make up the only occupational group above the 

45-degree line; the group is the only one that grew more 

in establishments with layoffs than in the control group 

Quadrant I comprises occupational groups with employment growth in establishments that had layoffs (the 

study group) and in the economy as a whole (the control group). Groups that grew in employment in both the 

study and control subsets included healthcare and legal 

occupations; food preparation and serving; and education, 

training, and library occupations. 

Occupational groups whose employment shrank in the 

study subset but grew in the control subset are shown in 

quadrant II. These groups were the most vulnerable to 
Mass Layoffs and Employment

10  Monthly Labor Review • June 20

Table 6. The 20 occupations1 with the largest percent decline in employment after extended mass layoff, 1999–2008, sorted by 

size of decline

SOC Occupation Occupational group

Employment Establishments

Before 

layoffs

After 

layoffs Change Percent 

change

Number 

reporting 

before 

layoffs

Number 

reporting 

after 

layoffs

Percent 

change in 

number 

reporting 

53-7072 Pump operators, except wellhead

pumpers

Transportation and 

material moving 511 36 -475 -93.0 19 7 -63.2

33-2011 Fire fighters Protective service 2,850 240 -2,610 -91.6 16 11 -31.3

43-2099 Communications equipment operators, all otherOffice and administrative support 615 57 -558 -90.7 33 8 -75.8

15-2099 Mathematical scientists, all other Computer and

mathematical science 365 37 -328 -89.9 17 10 -41.2

51-6091 Extruding and forming machine

setters, operators, and tenders,

synthetic and glass fibers Production 3,933 427 -3,506 -89.1 17 15 -11.8

49-2095 Electrical and electronics repairers,

powerhouse, substation, and relay

Installation, maintenance, and repair 888 112 -776 -87.4 24 14 -41.7

51-8012 Power distributors and dispatchers

Production 276 36 -240 -87.0 15 5 -66.7

51-7099 Woodworkers, all other Production 621 91 -530 -85.3 24 12 -50.0

39-9021 Personal and home care aides Personal care and

service 2,140 323 -1,817 -84.9 26 14 -46.2

53-2012 Commercial pilots Transportation and 

material moving 1,438 273 -1,165 -81.0 30 32 6.7

51-2093 Timing device assemblers,

adjusters, and calibrators Production 431 106 -325 -75.4 13 10 -23.1

43-5111 Weighers, measurers, checkers, and

samplers, recordkeeping

Office and administrative support 5,560 1,407 -4,153 -74.7 325 251 -22.8

51-4194 Tool grinders, filers, and sharpeners Production 1,253 321 -932 -74.4 125 64 -48.8

17-3021 Aerospace engineering and operations techniciansArchitecture and

engineering 2,618 735 -1,883 -71.9 30 21 -30.0

51-2021 Coil winders, tapers, and finishers Production 1,490 432 -1,058 -71.0 35 23 -34.3

29-1199 Health diagnosing and treating 

practitioners, all other

Healthcare practitioners

and technical 1,689 503 -1,186 -70.2 43 22 -48.8

27-1027 Set and exhibit designers Arts, design, entertainment, sports, and

media 156 47 -109 -69.9 16 12 -25.0

51-7031 Model makers, wood Production 318 96 -222 -69.8 25 16 -36.0

49-9045 Refractory materials repairers,

except brickmasons

Installation, maintenance, and repair 81 25 -56 -69.1 10 6 -40.0

27-4014 Sound engineering technicians Arts, design, entertainment, sports, and

media 277 86 -191 -69.0 30 19 -36.7

1

Excluded are any occupations with fewer than 10 reporting establishments before layoff

layoff in struggling businesses despite overall growth elsewhere in the economy. The occupations with the greatest inverse relationship were arts, design, entertainment, 

sports, and media; sales; and protective service occupations. Quadrant III shows occupational groups whose 

employment shrank in both the control and study groups: 

production, transportation, and material moving; and 

management occupations.14

Finally, for comparison with another type of control, 

table 2 shows the distribution of the full OES dataset from 

1999 to May 2008 by region, industry sector, and establishment size. Table 3 shows the distribution of the full 

MLS dataset of unique company/county layoff events from 

2000 to 2007 by reason for layoff. A comparison shows 

that the sample is representative of the full data set, but 

there are some exceptions. For example, manufacturing 

had more representation in the study sample than in the 

control group, which might explain why we see such large 

employment changes in production occupations.

Regression analysis. Comparing the employment change 

in the study group with published estimates of employment change is useful in assessing whether the study’s 

results are reflected in the economy overall. Formal regression analysis achieves the same goal but also lets us 

empirically control for other factors, such as industry, 

geographic region, establishment size, and time between 

observations. 
Monthly Labor Review • June 2011 11

Table 7. The 20 occupations1 with the largest increase in employment level after extended mass layoff, 1999–2008, sorted by 

size of increase

SOC Occupation Occupational group

Employment Establishments

Before 

layoffs

After 

layoffs Change Percent 

change

Number 

reporting 

before 

layoffs

Number 

reporting 

after 

layoffs

Percent 

change in 

number 

reporting 

29–1111 Registered nurses Healthcare practitioners 

and technical 34,917 40,876 5,959 17.1 321 291 –9.3

51–2031 Engine and other machine 

assemblers Production 2,925 8,015 5,090 174.0 46 37 –19.6

15–1031 Computer software engineers, 

applications

Computer and mathematical science 16,704 21,679 4,975 29.8 393 447 13.7

51–2099 Assemblers and fabricators, all 

other Production 42,913 46,901 3,988 9.3 257 190 –26.1

35–3031 Waiters and waitresses Food preparation and 

serving related 18,934 22,356 3,422 18.1 211 200 –5.2

41–2011 Cashiers Sales and related 23,293 26,514 3,221 13.8 669 655 –2.1

43–4111 Interviewers, except eligibility and 

loan

Office and administrative support 4,294 7,057 2,763 64.3 79 90 13.9

13–1111 Management analysts Business and financial 

operations 8,645 11,408 2,763 32.0 461 568 23.2

35–3022 Counter attendants, cafeteria, food 

concession, and coffee shop

Food preparation and 

serving related 4,183 6,928 2,745 65.6 174 155 –10.9

17–2072 Electronics engineers, except 

computer

Architecture and engineering 6,205 8,779 2,574 41.5 205 224 9.3

51–9023 Mixing and blending machine 

setters, operators, and tenders Production 3,293 5,558 2,265 68.8 188 172 –8.5

13–1081 Logisticians Business and financial 

operations 1,857 4,098 2,241 120.7 219 342 56.2

41–3099 Sales representatives, services, all 

other Sales and related 6,002 7,879 1,877 31.3 214 458 114.0

31–1012 Nursing aides, orderlies, and 

attendants Healthcare support 9,278 11,134 1,856 20.0 78 82 5.1

49–2022 Telecommunications equipment 

installers and repairers, except 

line installers

Installation, maintenance, and repair 4,898 6,708 1,810 37.0 141 158 12.1

49–9041 Industrial machinery mechanics Installation, maintenance, and repair 10,244 11,929 1,685 16.4 484 558 15.3

19–1042 Medical scientists, except 

epidemiologists

Life, physical, and social 

science 1,522 3,203 1,681 110.4 37 36 –2.7

47–2081 Drywall and ceiling tile installers Construction and 

extraction 2,349 4,027 1,678 71.4 30 34 13.3

13–1079 Human resources, training, and labor relations specialists, all otherBusiness and financial 

operations 2,586 4,247 1,661 64.2 517 889 72.0

19–3021 Market research analysts Life, physical, and social 

science 4,769 6,405 1,636 34.3 387 477 23.3

1

 Excluded are any occupations with fewer than 10 reporting establishments before layoffs.

Two sets of OES observations were created to run a regression. The study group was the set of 4,520 establishments that had layoffs. The control group was the set of 

205,339 establishments that reported twice to the OES 

survey in the study period—once in the 1999–2000 period and then again between November 2005 and May 

2008—that was not in the study group. 

There were 206,377 establishments that reported to OES

in both sets. Approximately 1,000 establishments that had 

been in both the control group and the study group were 

deleted from the control group to prevent duplication, 

resulting in 205,339 remaining control observations. For 

each pair of matching establishments, the change in employment by occupational group was calculated. The other 

variables for this data set were region, goods-producing or 

service-providing industry groups, establishment size, and 

time between observations. The regression was based on a 

total of 209,859 records. 

The econometric model used was

∆employmentSOC major group = β0 + β1layoffi + 

β2goods + β3totalemp_firsti + Σj

δjI(geographic regionji) 

+ Σj

γjI(number of years between observationsji) + εi,
Mass Layoffs and Employment

12  Monthly Labor Review • June 20

Table 8. The 20 occupations1 with the largest percent increase in employment after extended mass layoff, 1999–2008, sorted 

by size of increase

SOC Occupation Occupational group

Employment Establishments

Before 

layoffs

After 

layoffs Change Percent 

change

Number 

reporting 

before 

layoffs

Number 

reporting 

after 

layoffs

Percent 

change in 

number 

reporting 

43-4061 Eligibility interviewers, 

government programs

Office and administrative 

support 98 412 314 320.4 15 15 0.0

19-1012 Food scientists and 

technologists

Life, physical, and social 

science 60 191 131 218.3 19 44 131.6

39-9041 Residential advisors Personal care and service 78 237 159 203.8 13 12 -7.7

51-9193 Cooling and freezing equipment operators and tenders Production 139 405 266 191.4 20 32 60.0

19-2012 Physicists Life, physical, and social 

science 179 509 330 184.4 14 16 14.3

51-2031 Engine and other machine 

assemblers Production 2,925 8,015 5,090 174.0 46 37 -19.6

29-1129 Therapists, all other Healthcare practitioners 

and technical 43 114 71 165.1 13 13 0.0

19-4011 Agricultural and food science

technicians

Life, physical, and social 

science 194 506 312 160.8 42 40 -4.8

35-2019 Cooks, all other Food preparation and 

serving related 67 174 107 159.7 11 24 118.2

47-3011 Helpers—brickmasons, blockmasons, stonemasons, and tile 

and marble setters

Construction and

extraction 159 395 236 148.4 12 14 16.7

39-6032 Transportation attendants, 

except flight attendants and 

baggage porters Personal care and service 933 2,239 1,306 140.0 29 31 6.9

11-9039 Education administrators, all

other Management 61 141 80 131.1 16 23 43.8

47-3014 Helpers—painters, paperhangers, plasterers, and stucco

masons

Construction and

extraction 131 300 169 129.0 12 19 58.3

29-9011 Occupational health and safety

specialists

Healthcare practitioners 

and technical 438 992 554 126.5 180 293 62.8

27-1025 Interior designers Arts, design, entertainment, sports, and media 175 394 219 125.1 37 67 81.1

29-9012 Occupational health and safety

technicians

Healthcare practitioners 

and technical 142 316 174 122.5 44 85 93.2

13-1081 Logisticians Business and financial

operations 1,857 4,098 2,241 120.7 219 342 56.2

19-1042 Medical scientists, except 

epidemiologists

Life, physical, and social 

science 1,522 3,203 1,681 110.4 37 36 -2.7

49-2098 Security and fire alarm systems

installers

Installation, maintenance, 

and repair 271 563 292 107.7 20 13 -35.0

11-3049 Human resources managers,

all other Management 909 1,851 942 103.6 331 637 92.4

1

Excluded are any occupations with fewer than 10 reporting establishments before layoff

where the dependent variable was the change in employment level for each major occupational group, calculated 

with the use of the first and second employment levels 

reported for each occupational group. Layoff was an indicator dummy variable for whether the establishment had 

a layoff; where layoffi = 1, a layoff occurred. Goods was a 

dummy variable for the combined goods-producing industries, as opposed to the service-providing industries. 

Totalemp_first was the total employment at the time of the 

first observation of the establishment. The geographic region dummy variables indicated geographic regions: West, 

Southwest, Southeast, Mountain-Plains, New York-New 

Jersey, Midwest, Mid-Atlantic, and New England (captured in the intercept). Finally, there were nine dummy 

variables representing the number of years between observations, ranging from 1 to 9; 9 years was captured in 

the intercept.

In each of the 21 regressions, the significance of the 

layoff coefficient indicates whether the establishments 

in the study group were statistically different from establishments in the control group after controlling for these 

other variables. For example, some of the employment 
Monthly Labor Review • June 2011 13

Legal

Healthcare 

practitioners and 

technical Healthcare support

Food preparation and 

serving related

Business and financial 

operations

Life, physical, and 

social science

Computer and mathematical science

Transportation and 

material moving 

Production

Sales and related

Protective 

service

Education, training, 

and library

Building and grounds 

cleaning and maintenance

Construction and extraction

Personal care and service

Arts, design, entertainment, 

sports, and media

Architecture and 

engineering

Office and 

administrative support

Community and 

social services

20

15

10

5

0

–5

–10

–15

–20

–25

20

15

10

5

0

–5

–10

–15

–20

–25

Chart 1. Change in employment after extended mass layoffs in study control groups

Quadrant II

Quadrant IV Quadrant I

Quadrant III

–5 0 5 10 15 20

Percent change for 

study group, 1999–2009

Percent change for 

study group, 1999–2009

Percent change for control group (total establishments), May 2004–May 2008

Management

Installation, 

maintenance, and repair

change in production occupations in the study group may 

have been affected by the manufacturing plants that were 

letting production workers go regardless of whether the 

plant had mass layoffs. Appendix table A-1 shows the 

output for these regressions.

Of the 21 layoff indicator coefficients, 14 were statistically different from zero at the 5-percent significance 

level. Appendix table A-1 shows the layoff variable coefficients and their p-values. Because the dependent variable 

is the change in employment level, the coefficients of the 

variables are interpreted as the additional change in the 

employment of an occupation because of layoff. 

Controlling for the preceding variables, the model indicated that an extended mass layoff was associated with 

a decline in employment for the following occupational 

groups: production; office and administrative support; 

sales and related; management; transportation and material moving; architecture and engineering; installation, 

maintenance, and repair; construction and extraction; 

personal care and service; and arts, design, entertainment, 

sports, and media. An extended mass layoff was associated 

with an additional employment decline of 20.8 percent in 

production occupations, on average. 

Conversely, with the same variables controlled for, an extended mass layoff was associated with growth in employment for the following occupational groups (presented in 

descending order of magnitude of growth): food preparation and serving related; building and grounds cleaning 

and maintenance; business and financial operations; and 

legal. 

These results are consistent with those set forth in the 

previous section, with some exceptions. The occupational 

groups with the largest declines in employment using regression also shrank relative to the control group in the 

first comparisons. That is, the occupations with the largest 

declines in the regression were generally in the lower left 

area of chart 1 (quadrant III and part of quadrant II); this 

is where declines were large in the group with layoffs relative to the economy as a whole. For the most part, occupational groups with differences that were not significant 

in the regression comparison were closest to the 45 degree 

line in the chart. 

The largest differences between outcomes were in building and grounds cleaning and maintenance occupations 

and food preparation and serving related occupations. In 

the initial comparison, building and grounds cleaning and 

maintenance occupations grew in the economy and shrank 

in the layoff group. The regression comparison indicates 

that building and grounds cleaning and maintenances occupations grew more in the layoff group relative to the 
Mass Layoffs and Employment

14 Monthly Labor Review • June 2011

control group. In both comparisons, food service occupations grew in the layoff group and in the control group. In 

the initial comparison to the economy as a whole, food 

service occupations grew less in the layoff group than in 

the control group; in the regression comparison to a control group, the food service occupations grew more in the 

layoff group than in the control group. 

Differences in the comparison may in part be due to the 

differences in the control groups. The first control group, 

where the control is the entire economy, captures growth 

as a result of new establishments and may include a better 

representation of smaller establishments. The second control group, which matches existing establishments at two 

points in time, does not capture any “births,” or new establishments, in the comparison period. Also, because the 

OES survey uses a probability-proportional-to-size sample, it is less likely that the matched set includes smaller 

establishments.

Seasonal versus “economic difficulties” reasons for layoffs. Another regression analysis was conducted to determine 

whether occupational changes differed significantly—after controlling for industry, region, time between observations, and establishment size—depending on whether the 

layoff was due to seasonal reasons. On the basis of the 

4,520 observations from the study sample, the primary 

layoff reasons were grouped into three broad categories: 

“economic difficulties,” “seasonal,” and “other.” The “economic difficulties” category included business demand, 

financial difficulty, reorganization or restructuring of the 

company, production, and domestic and overseas relocation reasons. The “other” category covered disaster/safety 

reasons and unidentified reasons. The “seasonal” category 

included seasonal, vacation period/school related or otherwise, and other seasonal reasons.15

The regression model used was 

∆employmentSOC major group = β0 + β1layoffi × economici

+ β2 layoffi × seasonali + β3goods + β4totalemp_beforei + 

Σj

δjI(geographic regionji) + ΣjγjI(number of years between 

observationsji) + εi,

where the dependent variable was the change in employment level for each SOC major occupational group, calculated with the use of employment levels reported for each 

occupational group in each establishment before and after 

layoff. Layoff was a dummy variable indicating whether 

the establishment had a layoff; where layoffi = 1, a layoff occurred. (In this data set, all observations had a layoffi value of 1.) Economic, seasonal, and other were dummy 

variables indicating the broad category of “reason for layoff.” Goods was a dummy variable for the combined goodsproducing industries, as opposed to the service-providing 

industries. Totalemp_before was the total employment at 

the time of the first observation of the establishment. The 

geographic region dummy variables indicated geographic 

region: New England (captured in the intercept), New 

York-New Jersey, Mid-Atlantic, Southeast, Midwest, 

Southwest, Mountain-Plains, and West. Finally, there 

were nine dummy variables representing the number of 

years between observations, ranging from 1 to 9; 9 years 

was captured in the intercept. 

Appendix table A-2 shows the output of these regressions. In none of the 21 regressions were the two reason 

variables (economic and seasonal) statistically different from 

each other at the 90-percent confidence level. This finding 

suggests that, after other variables were controlled for, the 

occupational changes did not differ significantly between 

seasonal and economic layoff reasons. The data show that, 

in the long term, establishments that had seasonal layoffs had staffing changes that were similar to establishments that had layoffs because of economic difficulties.16 

It should be noted that some establishments that report 

seasonal change as their primary reason for layoff might 

also be undergoing other staffing changes. Because OES

surveys take place at the same time each year, changes as a 

result of seasonal effects are mixed with other effects.

Occupations eliminated from establishments after layoffs. Another way to examine the effects of mass layoffs on jobs 

in a particular occupation is to look at the change in the 

number of establishments reporting employment in that 

occupation after the layoff. This approach allows an examination of whether and how often establishments choose 

to eliminate all workers in a certain occupation or, alternatively, choose to retain at least some employees in that 

occupation. 

The occupations whose employment count changed 

from positive to zero were those which performed functions that businesses shed completely or outsourced after 

layoffs. These occupations were predominantly auxiliary 

administrative and managerial. The group whose occupations were most likely to be eliminated from establishments after layoffs was office and administrative support. Switchboard operators, including answering service, 

topped the list, with 363 establishments eliminating the 

occupation completely; the number of establishments reporting them dropped from 781 to 418. Several human 

resources occupations had the same fate: employment, 
Monthly Labor Review • June 2011 15

recruitment, and placement specialists; training and development specialists; human resources assistants; and 

payroll and timekeeping clerks. Other supporting administrative occupations affected were computer operators, 

data entry keyers, file and procurement clerks, and janitors and cleaners. 

Conversely, establishments reporting occupations commonly found in many businesses such as general managers and administrative clerks (bookkeeping, general office, 

shipping, and payroll) tended to keep at least one of the 

employees in those occupations. The share of establishments completely eliminating these occupations was relatively low, ranging from 1 percent to 12 percent. Workers 

fulfilling these business functions apparently were considered essential for maintaining the basic operations of the 

company. 

Many of the occupations with the largest employment 

losses overall were essential, or core, to their business, so 

the occupations tended to be retained within the establishment, although at much lower levels of employment. 

In fact, the three occupations with the largest employment declines—team assemblers, retail salespersons, and 

customer service representatives—existed in more establishments after layoffs than before; the number of jobs 

in the occupation, however, was smaller after the layoffs. 

Similarly, although employment in sales occupations declined overall by almost 29,000 jobs, more establishments 

reported having employment in sales occupations after 

layoffs. This finding could be a result of shifts in staffing 

patterns after restructuring. The effect of layoffs on employment in core occupations is discussed further in the 

next section. 

Occupational changes by industry sector

This section examines occupational employment changes 

within and across industry groups. The first analysis shows 

that, within sectors, core occupations generally were retained. Looking across sectors, the second analysis uses 

regression to see how these changes differed between the 

goods-producing and service-providing industry groups.

Employment changes within sectors. Examining employment changes by industry sector provides insight into the 

effects of mass layoffs on the occupational structure of 

specific types of businesses. It allows the identification of 

core and support business functions in different industries 

and shows that the severity of mass layoffs in terms of job 

loss varied by occupation and the industry of the business 

experiencing a layoff. 

After layoffs, industry sectors that followed the pattern 

of reducing employment in occupations requiring less 

specialized skills and maintaining or increasing employment in analytical occupations included information; finance and insurance; professional, scientific, and technical 

services; and the durable goods portion of the manufacturing sector. Examples of occupations with reduced employment in these sectors were sales and office workers; 

examples of occupations with increased employment were 

various types of analysts and engineers. These industry 

sectors—and particularly the durable goods manufacturing portion of the manufacturing sector—experienced 

large numbers of layoff events during the study period. 

Establishments were more likely to retain employment 

in occupations that were core to their industry. (See table 

9.) For example, employment in business and financial 

operations occupations grew in finance and insurance establishments with layoffs. The same was observed among 

teachers in the education sector, as well as among healthcare workers in hospitals, extraction workers in mining, 

and computer and mathematical science occupations in 

the information sector. Other sectors saw smaller declines 

in core occupations than in occupations that have support 

functions. For example, in the manufacturing industries, 

most occupational groups saw decreases, but production 

workers had lower percent declines in manufacturing than 

in several other industries. The same pattern was observed 

in core occupations for other industries, including installation and maintenance occupations in the utilities sector, 

transportation and material moving occupations in the 

transportation sector, and personal care and service and 

food preparation occupations in the accommodation and 

food services sector.

 The overall pattern of reducing the number of jobs in 

occupations requiring less specialized skills and retaining 

jobs in analytical occupations was driven by the industry 

sectors with relatively large numbers of layoff events. These 

sectors included information (NAICS 51); finance and insurance (NAICS 52); professional, scientific, and technical 

services (NAICS 54); and the durable goods portion of the 

manufacturing sector (NAICS 33). Employment declined 

in these sectors for occupations requiring less specialized 

skills, such as sales and office workers, while increasing for 

various types of analysts and engineers. 

In the fourth quarter of 2007, manufacturing industries 

accounted for 24 percent of private nonfarm extended 

layoff events. This study reflects that distribution, with 

the three manufacturing components—food, wood, and 

durable goods—experiencing the largest net losses in 

employment compared to other industries. In durable 
Mass Layoffs and Employment

16 Monthly Labor Review • June 2011

Table 9. Percent change in employment after extended mass layoff, by (NAICS) industry and occupation, 1999–2008

Occupational group

Goods-producing industries group Information

Financial 

activities Professional and business services

Mining 

(21)

Construction 

(23)

Manufacturing

(31)

Manufacturing 

(32)

Manufacturing 

(33)

Information 

(51)

Finance 

and insurance 

(52)

Real estate and 

rental 

and 

leasing 

(53)

Professional, 

scientific, and 

technical 

services 

(54)

Management of 

companies and 

enterprises 

(55)

Administrative 

and support and 

waste management and remediation services 

(56)

Management 18.7 –3.4 –43.2 –11.5 –13.9 –51.0 2.4 –43.8 5.3 –59.2 –27.7

Business and financial 

operations 118.6 20.2 –29.2 –17.0 –1.7 6.6 2.1 2.5 59.8 –26.7 2.9

Computer and mathematical science 57.6 –.5 –43.4 .1 –10.6 13.8 11.6 –28.7 –16.1 3.3 –17.9

Architecture and 

engineering 2.2 –14.0 –48.5 –32.7 –13.8 –19.6 29.8 .0 –14.1 67.6 –48.9

Life, physical, and social 

science 50.9 26.3 8.2 –15.3 –23.6 9.5 80.0 (

1

) –2.1 (

1

) 8.0

Community and social 

services .0 .0 (

1

) (

1

) .0 (

1

) –54.5 .0 (

1

) (

1

) 218.0

Legal (

1

) 211.1 –47.6 85.0 25.5 13.4 9.7 (

1

) –14.0 2.6 –3.8

Education, training, and 

library .0 (

1

) (

1

) –100.0 –6.7 –71.9 94.1 .0 37.5 12.8 91.2

Arts, design, entertainment, sports, and media (

1

) –54.0 –19.1 108.2 .4 –5.9 1.5 –67.7 –4.2 –1.0 –41.5

Healthcare practitioners 

and technical –11.7 –20.6 12.6 91.0 –19.7 73.9 14.3 (

1

) –70.0 –17.5 8.6

Healthcare support .0 (

1

) (

1

) .0 –50.0 .0 –23.3 .0 (

1

) (

1

) –64.9

Protective service 6.7 –2.2 –55.0 –50.8 –27.0 –54.0 –7.9 –42.9 .0 –14.3 –14.5

Food preparation and 

serving related (

1

) –27.1 216.8 –90.9 –63.0 –75.0 138.1 –24.7 (

1

) –23.9 16.4

Building and grounds 

cleaning and maintenance 70.0 –18.4 –16.7 –38.6 –42.4 –60.2 –48.9 (

1

) 46.0 .6 –1.5

Personal care and service (

1

) –50.7 (

1

) (

1

) –50.0 –55.4 (

1

) .0 (

1

) –7.8 30.1

Sales and related 208.6 –31.6 –24.0 61.8 –16.5 –41.7 –35.2 –71.0 –5.9 –73.8 –36.9

Office and administrative 

support 6.1 4.1 –21.2 –16.5 –21.6 –34.3 –33.2 31.6 –21.7 –21.2 –20.6

Construction and 

extraction 25.5 –2.8 –42.6 –35.9 –29.2 –57.3 (

1

) (

1

) –71.9 (

1

) 21.4

Installation, maintenance, 

and repair 3.9 7.4 –13.0 –27.2 –22.6 8.1 –26.8 .0 –60.4 –22.3 12.1

Production –30.4 42.2 –23.4 –20.2 –22.8 –28.1 61.2 (

1

) –58.2 –34.9 11.0

Transportation and material moving –12.8 .0 –17.0 –30.9 –22.1 –47.9 31.4 .2 3.4 –65.9 8.2

See note at end of table.

goods manufacturing—which had the largest net employment loss of the three manufacturing components 

in the study—losses in employment levels were mostly in 

production occupations, such as team assemblers, electrical equipment assemblers, and weighers. Durable goods 

manufacturers hired workers in analytical occupations, 

such as electronics engineers, computer applications software engineers, and logisticians. 

Within durable goods manufacturing, transportation 

equipment manufacturing (NAICS 336) had the highest 

net employment loss. Table 10 shows the transportation 

equipment manufacturing occupations that shrank by 

more than 1,000 jobs in the study group. Most of the 

losses were in production occupations. Some occupations 

that grew were related to product design and engineering: 

computer software applications engineers, logisticians, 

commercial and industrial designers, and mechanical 

engineers.
Monthly Labor Review • June 2011 17

Management –21.9 –28.4 –34.4 –17.0 –25.1 –26.0 –6.9 –7.7 –7.6 –25.1 –11.3 –3.5

Business and financial 

operations –24.1 –22.7 1.4 8.6 –11.2 13.9 –2.3 6.4 48.2 9.9 25.4 –16.4

Computer and mathematical science –37.3 –26.5 –1.8 –9.6 13.2 31.9 1.6 4.0 57.9 –2.4 86.4 –30.1

Architecture and 

engineering –74.4 21.8 –43.0 –55.8 –45.6 78.4 35.0 –46.1 409.4 –54.8 –4.4 –69.7

Life, physical, and social 

science –94.5 –34.3 9.4 –40.4 –10.6 .0 84.5 –7.7 –8.8 –62.7 14.3 (

1

)

Community and social 

services .0 .0 .0 .0 (

1

) .0 –33.9 10.4 (

1

) (

1

) –29.3 .0

Legal 20.8 –31.5 270.0 81.3 –29.5 (

1

) 33.3 104.5 472.7 109.1 (

1

) (

1

)

Education, training, and 

library (

1

) (

1

) (

1

) (

1

) –46.0 .0 2.7 5.0 –23.2 –22.2 13.5 (

1

)

Arts, design, entertainment, sports, and media 161.4 –19.2 21.1 34.4 –9.9 –90.0 12.6 22.9 –48.6 9.4 126.6 –42.8

Healthcare practitioners 

and technical (

1

) –47.1 55.1 13.8 –33.8 31.3 –2.0 12.1 –10.6 93.6 21.1 (

1

)

Healthcare support .0 .0 –38.0 108.2 –19.4 (

1

) –5.0 11.4 –91.4 48.7 (

1

) .0

Protective service –91.9 –44.7 –68.1 –12.3 –10.2 –34.1 –11.0 9.7 –35.9 –1.8 –42.0 (

1

)

Food preparation and

serving related .0 –99.0 13.2 –30.4 24.4 (

1

) 15.4 .5 76.3 –6.5 23.5 (

1

)

Building and grounds 

cleaning and maintenance –81.4 35.9 –49.0 –22.2 13.0 –64.7 –2.2 8.7 52.4 –5.0 –16.4 –35.9

Personal care and service (

1

) .0 –43.1 –4.3 –9.7 (

1

) –59.2 –39.0 –24.9 –5.0 –.6 .0

Sales and related 90.8 –46.1 14.2 –21.6 –14.4 –19.0 7.6 23.6 –40.3 –13.9 –32.5 2400.0

Office and administrative 

support –40.5 –33.7 –10.7 –11.9 –21.3 42.9 2.1 6.7 –8.0 –5.2 –25.8 –45.3

Construction and 

extraction 85.5 50.3 –41.3 –80.4 32.3 (

1

) –23.9 –37.0 –39.1 19.9 –34.2 50.9

Installation, maintenance, 

and repair –18.4 12.5 –9.7 –16.4 –15.5 –79.1 26.6 31.4 –11.3 –6.3 –49.5 –28.6

Production –71.6 –9.0 –39.3 –10.0 8.3 20.3 –43.2 –10.5 43.8 –12.9 –22.7 –30.9

Transportation and material moving –12.5 –25.5 –16.4 –19.0 –15.0 12.7 348.3 –40.6 –21.4 –9.5 –65.8 –53.9

1 Percent change excluded because it is based on fewer than 5 establishments reporting occupations in the occupational group before layoffs.

Occupational group

Trade, transportation, and utilities Education and 

health care

Leisure and 

hospitality

Other 

services

Public 

administration

Utilities 

(22)

Wholesale 

trade 

(42)

Retail 

trade 

(44)

Retail 

trade 

(45)

Transportation 

(48)

Warehousing 

(49)

Educational 

services 

(61)

Health 

care and 

social 

assistance 

(62)

Arts, 

entertainment, and 

recreation 

(71)

Accommodation 

and food 

services 

(72)

Other 

services 

(81)

Public 

administration 

(99)

Table 9. Continued—Percent change in employment after extended mass layoff, by (NAICS) industry and occupation, 1999–2008

Establishments that had layoffs in the information sector reduced employment in occupations that require less 

specialized training: sales supervisors and representatives, 

customer service representatives, and stock clerks. After 

layoffs, they had higher employment in occupations involving technical skills: computer software engineers; 

telecommunications equipment repairers; management, 

computer systems, and network systems analysts; and accountants and auditors.

Similarly, most finance and insurance businesses which 

had layoffs shed jobs in occupations that tended to pay less 

and that did not include analysis as a primary job function: 

clerical workers, such as customer service representatives, 

telemarketers, brokerage clerks, and general office clerks. 

These establishments increased employment in analytical 

occupations, such as computer systems analysts, financial 

analysts, market research analysts, and management analysts. They also added technical positions that tended to 
Mass Layoffs and Employment

18 Monthly Labor Review • June 2011

Table 10. Transportation equipment manufacturing (NAICS 336) occupations whose employment declined by at least 1,000 

after extended mass layoff, 1999–2008

SOC Occupation

Employment Establishments

Before layoffs After layoffs Change

Number 

reporting before layoffs

Number 

reporting after 

layoffs

51–2092 Team assemblers 36,788 23,907 –12,881 104 93

51–4199 Metal workers and plastic workers, all other 13,208 5,068 –8,140 39 42

51–9199 Production workers, all other 10,973 6,496 –4,477 59 55

51–1011 First-line supervisors/managers of production and 

operating workers 8,683 5,681 –3,002 228 205

17–2011 Aerospace engineers 8,266 5,360 –2,906 24 18

51–2011 Aircraft structure, surfaces, rigging, and systems 

assemblers 6,801 4,228 –2,573 15 11

51–4121 Welders, cutters, solderers, and brazers 6,720 4,493 –2,227 126 109

51–4111 Tool and die makers 6,629 4,651 –1,978 126 121

53–7051 Industrial truck and tractor operators 5,090 3,195 –1,895 117 115

51–9061 Inspectors, testers, sorters, samplers, and weighers 9,020 7,357 –1,663 184 175

43–5061 Production, planning, and expediting clerks 3,060 1,469 –1,591 159 143

49–9042 Maintenance and repair workers, general 5,623 4,079 –1,544 166 163

53–7062 Laborers and freight, stock, and material movers, 

hand 4,831 3,371 –1,460 99 97

51–9122 Painters, transportation equipment 3,998 2,564 –1,434 61 56

13–1199 Business operations specialists, all other 6,966 5,698 –1,268 105 88

53–6051 Transportation inspectors 1,508 332 –1,176 20 7

51–4031 Cutting, punching, and press machine setters, 

operators, and tenders, metal and plastic 3,570 2,404 –1,166 74 59

be highly paid, such as computer software engineers, accountants and auditors, and personal financial advisors. 

The professional, scientific, and technical services sector also followed this pattern. These businesses reduced 

the employment of general office clerks, computer support specialists, customer service representatives, and data 

entry keyers. They added computer systems analysts, management analysts, and market research analysts, in addition to accountants and auditors.

Most establishments in the health care and social assistance sector tended to lay off administrative support occupations not directly related to healthcare, such as general 

office and billing clerks. They hired health care workers 

including registered nurses, nursing aides, and licensed 

practical nurses. The number of medical secretaries grew, 

but by less than the employment decline among other administrative support occupations.

Four sectors fared relatively well after layoffs and grew 

in total employment. Those with net gains in employment 

were health care and social assistance (NAICS 62); educational services (NAICS 61); mining (NAICS 21); and postal 

service/couriers/warehousing (NAICS 49). (See table 9.) 

Within health care and social assistance—which was the 

sector with the highest net gain in employment—hospitals and ambulatory health care services grew the most, 

increasing the number of jobs with functions related to 

health care and administration: office and administrative 

support, business and financial operations, management, 

and computer and mathematical science occupations. 

Production occupations experienced the largest losses; 

hospitals especially reduced the number of laundry and 

drycleaning jobs.

It is informative to examine the occupations that declined in employment after layoffs in sectors which nonetheless experienced net employment gains. Personal and 

home care aides lost the most employment overall in the 

health care sector. Mining establishments that underwent layoffs shed several occupations that required less 

specialized training and skills: general maintenance and 

repair workers, industrial truck and tractor operators, and 

machinery maintenance workers. They added operating 

engineers, industrial machinery mechanics, heavy and 

tractor-trailer truck drivers, and mobile heavy equipment 

mechanics.

Occupational changes in goods-producing versus service-providing establishments. A regression analysis was conducted to analyze the effect of layoffs by goods-producing and 

service-providing aggregations (simply termed “groups”) 

on occupational employment, controlling for region, time 

between observations, and establishment size. The variables of interest were the two interaction dummy variables 

for layoff × group. To see if the large employment decline 

in production occupations in goods-producing industries 
Monthly Labor Review • June 2011 19

was due to the overall decline in employment in production industries, the model also includes a non-interaction 

dummy variable for the group of goods-producing establishments. The regression was based on a total of 209,858 

observations—205,339 control observations and 4,520 

study observations. 

The model used was

∆employmentSOC major group = β0 + β1 layoffi × goodsproducing group + β2 layoffi × service-providing group 

+ β3totalemp_firsti + β4goods-producing group + 

Σj

δjI(geographic regionji) + ΣjγjI(number of years between 

observationsji) + εi,

where the dependent variable was the change in employment level for each occupational group between the first 

observation and the second. Layoff was a dummy variable 

indicating whether the establishment had a layoff; where 

layoffi = 1, a layoff occurred. Goods was a dummy variable 

for the goods-producing aggregation, as opposed to the 

service-providing aggregation. Layoff × goods and layoff × 

service were interaction dummy variables. Totalemp_first

was the total employment at the time of the first observation of the establishment. The geographic region dummy 

variables indicated geographic region: West, Southwest, 

Southeast, Mountain-Plains, New York-New Jersey, 

Midwest, Mid-Atlantic, and New England (captured in 

the intercept). Finally, there were nine dummy variables 

representing the number of years between observations, 

ranging from 1 to 9; 9 years was captured in the intercept. 

With region, time between observations, and establishment size controlled for, an extended mass layoff in the 

goods-producing aggregation was associated with a greater 

employment decline than in the service-providing aggregation for two occupational groups: architecture and 

engineering; and installation, maintenance and repair. 

For production occupations, layoffs were associated with 

employment decline in the goods-producing group, while 

layoffs in the service-providing group were actually associated with slight employment growth (significant only at 

the 11-percent level). The differences between the interaction term coefficients were statistically significant, and 

the coefficients themselves were statistically significant. 

Appendix table A-3 shows the regression parameter estimates and statistics for the goods-producing and serviceproviding interaction variables.

With region, time between observations, and establishment size controlled for, an extended mass layoff in the 

service-providing aggregation was associated with a greater employment decline than in the aggregation of goodsproducing sectors for management and for sales and related occupations. For sales and office and administrative 

occupations, the employment decline was substantially 

greater in the service-providing group. In protective service occupations and personal care and service, a layoff in 

the service-providing group was associated with employment decline, while a layoff in the goods-producing group 

was associated with employment growth. 

Conversely, in building and grounds cleaning and maintenance occupations, an extended mass layoff in the service-providing group was associated with greater employment growth than in the goods-producing group.

Finally, for three occupational groups, the individual 

goods-producing and service-providing group parameter 

estimates were significant, but the differences between the 

two were not. Employment in transportation and material 

moving occupations declined the same amount in both 

groups. The difference between them was not statistically 

significant. Similarly, employment in legal occupations 

and food preparation and serving-related occupations 

grew significantly in both the goods-producing and service-providing groups, but the difference between the two 

coefficients was not statistically significant. 

Occupational changes by reason for layoff

The MLS program asks employers for a primary reason for 

the layoff. Employers could report 30 reasons for extended mass layoffs over the study period. These reasons can 

be grouped into six broad categories: business demand, 

financial, organizational, production, disaster/safety, and 

seasonal. Business demand accounted for 34 percent of 

the events in the fourth quarter of 2007, the highest in the 

economic reasons category excluding seasonal and other 

reasons. Extended mass layoffs stemming from financial 

issues accounted for 7 percent of layoff events, the next 

highest in the economic reasons category. (See table 11.)17 

The pattern of employers retaining or adding workers 

in higher skilled analytical or technical occupations while 

letting go of workers in occupations that require nonanalytical or office and clerical skills was, in general, evident 

regardless of the reason for the layoff. It is apparent from 

table 11 that workers in production, material moving, installation and maintenance, and office and administrative 

support occupations were let go after almost all types of 

layoffs. The number of jobs in computer and mathematical science occupations, architecture and engineering occupations, and business and financial operations occupations either grew, or declined proportionally less than the 

number of jobs for lesser skilled workers, regardless of the 
Mass Layoffs and Employment

20 Monthly Labor Review • June 2011

Table 11. Percent change in employment in the study group after extended mass layoff, by primary reason for layoff and occupation, 1999–2008

Occupational 

group

 Business demand Disaster/safety Financial

Contract 

cancellation

Contract 

completion

Domestic 

competition

Excess 

inventory/saturated 

market

Import 

competition

Slack 

work/insufficient 

demand/

nonseasonal 

business 

slowdown

Hazardous 

work 

environment

Natural 

disaster 

(not 

weather 

related)

Nonnatural 

disaster

Extreme 

weatherrelated 

event

Bankruptcy

Cost control/cost 

cutting/

increase 

profitability

Financial difficulty

Management –21.4 1.3 –55.0 –24.3 –36.7 –22.0 9.3 –9.1 –32.9 13.1 –16.9 –39.5 –13.0

Business and 

financial operations 9.4 21.8 –78.6 –23.5 –10.6 –7.5 –21.4 –16.1 280.0 4.2 –27.0 .3 9.0

Computer and 

mathematical 

science –29.8 –10.1 (

1

) (

1

) –25.1 –12.4 5.0 77.8 (

1

) 5.9 –27.0 41.3 –20.3

Architecture and 

engineering –51.2 23.5 –43.8 –44.4 –25.2 –26.2 –2.0 170.4 (

1

) –7.2 –46.3 –76.1 –15.5

Life, physical, and 

social science –49.6 11.5 .0 .0 25.0 19.6 57.1 (

1

) .0 –40.2 218.4 –36.8 –2.1

Community and 

social services –69.4 92.2 .0 .0 .0 7.4 .0 .0 .0 107.4 (

1

) .0 10.8

Legal 46.7 8.7 .0 .0 (

1

) 10.8 .0 .0 .0 (

1

) –22.5 11.4 .3

Education, training, and library 32.6 –14.8 .0 .0 (

1

) –35.3 .0 .0 .0 281.3 (

1

) (

1

) 2.5

Arts, design, 

entertainment, 

sports, and 

media 14.2 –81.4 .0 .0 –51.3 –10.1 (

1

) (

1

) .0 111.8 –40.5 40.6 –12.0

Healthcare practitioners and 

technical –37.2 27.3 (

1

) (

1

) –50.0 –7.3 (

1

) (

1

) (

1

) –30.5 –7.0 32.5 23.3

Healthcare 

support (

1

) (

1

) .0 .0 .0 –42.5 .0 .0 .0 –47.8 –1.3 (

1

) 6.8

Protective service –16.7 –8.2 .0 .0 –44.4 –5.2 (

1

) (

1

) –29.4 –35.6 –23.2 50.3 30.4

Food preparation 

and serving 

related –72.7 –16.3 .0 .0 (

1

) 0.6 –100.0 –3.3 –23.9 –23.4 –15.6 7.9 21.2

Building and 

grounds 

cleaning and 

maintenance –6.9 –6.2 (

1

) (

1

) –57.2 –4.5 (

1

) –68.4 9.4 –40.4 –57.2 32.8 2.3

Personal care 

and service –31.4 –43.0 .0 .0 .0 –16.3 (

1

) (

1

) –68.4 –40.2 –9.1 50.3 –11.6

Sales and related –.5 –38.2 .0 .0 –58.3 –24.3 (

1

) –32.4 –16.1 –29.1 –29.3 –17.4 –21.8

Office and administrative support –34.9 1.0 –55.6 –2.9 –36.8 –15.6 –36.7 –51.2 22.7 –27.4 –17.7 –27.7 –11.2

Construction and 

extraction 19.8 1.2 –42.0 (

1

) –39.2 –13.8 –28.8 22.3 (

1

) 49.7 –56.6 88.2 3.7

Installation, 

maintenance, 

and repair –14.5 –22.2 –21.1 –27.7 –51.9 –14.3 –4.6 –9.1 –20.5 12.7 –27.0 –26.6 –14.0

Production –16.1 –9.1 –66.1 –7.5 –50.4 –17.8 –28.2 5.6 –41.3 13.5 –5.4 –36.7 –32.8

Transportation 

and material 

moving –12.1 14.8 –69.4 –72.7 –52.5 –7.5 –46.1 –41.0 75.6 –31.0 –12.2 2.6 –34.4

See notes at end of table.

reason for the layoff, although there were some exceptions. 

At the detailed occupation level, employment of customer 

service representatives, general office clerks, and bookkeeping clerks declined after most types of layoffs. 

This overall pattern was driven, in part, by layoffs due to 

a number of reasons: the reorganization or restructuring 

of a business, a change in ownership, financial difficulty, 

slack work, competition from imports, cost control or cost 
Monthly Labor Review • June 2011 21

Table 11. Continued—Percent change in employment after extended mass layoff, by primary reason for layoff and 

occupation, 1999–2008

Occupational 

group

 Organizational Production Other

Business–

ownership 

change

Reorganization or 

restructuring 

of company

Automation/

technological 

advances

Energy 

related

Governmental 

regulations/

intervention

Labor 

dispute/

contract 

negotiations/

strike

Material or 

supply 

shortage

Model 

changeover

Plant 

or machine 

repair/

maintenance

Product 

line 

discontinued

Domestic 

relocation

Overseas 

relocation

Management –37.3 –12.5 –45.0 –36.8 0.0 21.1 –19.0 –31.1 –13.0 –31.8 11.5 –60.1

Business and 

financial operations 25.1 –4.0 –.7 –18.6 .0 8.9 –20.7 –30.9 16.2 146.5 40.6 –16.0

Computer and 

mathematical 

science –17.1 9.3 57.7 (

1

) .0 20.8 30.0 –45.5 5.9 112.7 2.4 –61.1

Architecture and 

engineering –26.6 –12.7 111.6 (

1

) .0 48.5 –7.2 –47.1 –3.8 36.3 14.0 –57.7

Life, physical, and 

social science –47.4 –11.3 –75.5 (

1

) .0 –61.7 –41.7 –73.3 –37.3 –56.4 –77.7 –88.5

Community and 

social services –14.0 –17.1 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0

Legal 12.4 14.7 (

1

) .0 .0 .0 .0 .0 .0 (

1

) (

1

) –46.2

Education, training, and library –95.3 –31.4 .0 .0 .0 –45.5 .0 .0 .0 (

1

) (

1

) (

1

)

Arts, design, 

entertainment, 

sports, and 

media –55.7 46.1 –14.3 .0 .0 –1.8 (

1

) –100.0 (

1

) 33.0 111.5 54.2

Healthcare practitioners and 

technical –17.5 5.9 –15.8 (

1

) .0 39.0 (

1

) –57.1 (

1

) –16.3 –15.6 –84.6

Healthcare 

support –16.5 13.1 (

1

) .0 .0 48.7 .0 (

1

) .0 (

1

) (

1

) (

1

)

Protective service –57.9 –19.9 –21.3 (

1

) .0 –4.8 (

1

) (

1

) (

1

) 51.0 –95.7 –12.5

Food preparation 

and serving 

related –12.8 10.7 –19.7 .0 .0 3.4 .0 24.7 –24.7 (

1

) 25.9 –100.0

Building and 

grounds 

cleaning and 

maintenance –25.4 –6.3 –57.2 (

1

) .0 –34.6 (

1

) 14.3 355.6 –26.4 –85.7 –15.9

Personal care 

and service 9.9 –8.4 33.8 –3.1 .0 –87.5 .0 (

1

) .0 .0 5.7 .0

Sales and related –32.6 –23.3 –32.9 (

1

) .0 24.7 (

1

) –89.3 4.7 52.2 –2.2 –55.1

Office and administrative support –18.7 –23.4 –35.1 6.0 .0 –21.2 –61.1 –76.7 3.8 –6.1 –40.3 12.2

Construction and 

extraction –92.7 –28.6 –47.6 (

1

) .0 –70.4 2.1 –1.0 –70.0 –18.3 –93.8 –75.0

Installation, 

maintenance, 

and repair –26.7 –14.2 –40.1 –4.4 .0 5.3 –28.1 13.6 –7.7 –30.1 –59.8 –21.3

Production –31.4 –29.3 160.7 –28.9 .0 –26.4 –13.4 –8.6 1.9 –29.2 –68.8 –36.7

Transportation 

and material 

moving –43.5 –34.4 –57.3 –31.4 .0 –14.2 –25.2 176.3 –38.9 14.4 –56.9 –56.8

NOTE: Table does not show "data not provided (refusal);" "data not 

provided (does not know);" "seasonal;" "vacation period/school related 

or otherwise;" or "other seasonal."

1 Percent change excluded because it is based on fewer than 5 establishments reporting occupations in the occupational group before 

layoffs.

cutting, and the relocation of domestic work.18 These 

reasons accounted for a large number of layoffs and 

a large share of the employment losses among lesser 

skilled occupations and increased employment in 

analytical or technical occupations during the period 

studied. Layoffs that occurred after either the reloca-
Mass Layoffs and Employment

22 Monthly Labor Review • June 2011

tion of domestic work or the discontinuation of a product 

line followed the pattern closely. However, the types of 

jobs affected by layoffs often depended on the reason for 

the layoff. Patterns within each group are examined next. 

Organizational change. The largest cause of job loss from 

layoffs was organizational change, which includes the reorganization or restructuring of companies and changes 

in business ownership. Establishments that reorganized

or restructured (representing 10 percent of all layoffs, the 

third most commonly reported reason for a layoff event19) 

tended to eliminate jobs in production occupations—these 

jobs declined by more than 27,000—and in administrative 

support occupations, such as customer service representatives, general office clerks, and data entry keyers; employment in administrative support occupations declined by 

23,000. Businesses that reorganized or restructured also 

reduced jobs for occupations that involved less technical 

skill, such as retail salespersons, hand laborers, hand packers, team assemblers, and general maintenance workers. 

Employers cut back on jobs in some technical occupations 

and added jobs in others, resulting in a net gain in computer and mathematical occupations.

Reorganized and restructured establishments hired more 

workers in occupations that develop new software and 

applications—occupations such as computer systems and 

applications software engineers, computer systems analysts, computer hardware engineers, engineering managers, electronics engineers, telecommunications equipment 

installers, and logisticians—while reducing the number of 

other technical jobs—such as jobs for computer programmers, who primarily code programs for existing software. 

Some occupational groups, however, fared well after this 

type of layoff: arts, design, entertainment, sports, and media; education; legal; community and social service; and 

life, physical, and social sciences occupations. 

Establishments with layoffs resulting from businessownership changes, the seventh most commonly reported 

reason for a layoff event,20 followed the pattern of shedding workers in less technical occupations and hiring additional analytical workers. After production, office and 

administrative, and transportation and material moving 

occupations, sales workers accounted for most of the job 

loss. Following layoffs induced by business-ownership 

changes, establishments had fewer workers in occupations related to sales, marketing, and maintenance; these 

occupations included customer service representatives, 

general office clerks, marketing managers, market research analysts, sales managers, janitors and cleaners, and 

maintenance and repair workers. The production occupation that lost the most employment was textile cutting 

machine setters, operators, and tenders. As was seen in 

businesses that reorganized, the computer occupations 

that declined in employment among establishments with 

business-ownership changes required less technical skill 

than those which increased in employment. Other occupations that grew involved financial and accounting business functions: among these occupations were payroll and 

timekeeping clerks; management analysts; bookkeeping, 

accounting, and auditing clerks; financial managers; and 

accountants and auditors.

Business demand. The second-largest cause of job loss 

from layoffs was a decline in business demand. Compared 

with other reasons for layoffs, business demand factors 

resulted in relatively greater losses of technical workers 

and also resulted in large losses of lesser skilled workers. 

Specifically, layoffs due to slack work, insufficient demand, 

and nonseasonal business slowdown resulted in the largest employment declines among any of the 30 reasons for 

layoffs. Production occupations accounted for the most 

losses after this type of layoff. The production occupations that topped the list of losses were aircraft structure, 

surfaces, rigging, and systems assemblers; miscellaneous 

metal and plastic workers; team assemblers; slaughterers 

and meat packers; electrical and electronic equipment assemblers; and production first-line supervisors. 

Employment in computer and mathematical science 

occupations shrank after layoffs for at least four of the 

layoff reasons related to business demand, and business 

and financial occupations and architecture and engineering occupations lost employment from layoffs due to at 

least four of the reasons. 

Layoffs because of slack work resulted in employment 

declines in many occupational groups, about a third of 

which were production jobs. Most affected were metal 

and plastic workers, team assemblers, production supervisors, electrical equipment assemblers, and sewing machine 

operators. The occupations that grew were hand laborers, computer applications software engineers, customer 

service representatives, stock clerks, and market research 

analysts.

After layoffs due to excess inventory and domestic competition, overall employment levels shrank in every occupational group in which employment had been reported 

before the layoffs; cutbacks occurred in both core and 

noncore occupations regardless of business function. 

Financial. Financial-related reasons for layoffs include 

financial difficulty; bankruptcy; and measures to control 
Monthly Labor Review • June 2011 23

costs, cut costs, and increase profitability. As with layoffs 

related to business demand, financial-related layoffs resulted in job losses among skilled workers, in addition to 

losses among less skilled workers. Notable outcomes were 

a large decline in personal care and service occupations 

after bankruptcy, sales workers after cost control layoffs, 

and architecture and engineering occupations after financial difficulty. 

The largest employment declines in layoffs due to general financial difficulty were in production, transportation and material moving, and office and administrative 

support occupations. Production occupations with job 

losses included inspectors, testers, and weighers; team assemblers; and production supervisors. This type of layoff 

also resulted in the employment of fewer transportation 

workers; recordkeeping weighers, measurers, checkers, 

and samplers; flight attendants; and parking lot attendants. Among computer and engineering jobs lost were 

computer programmers and computer systems software 

engineers, and applications engineers. The same set of 

establishments eventually hired workers for computer 

science occupations that were less research intensive in 

nature: computer systems analysts; network support and 

data communications analysts; and network and computer systems administrators. They also hired many more 

registered nurses, cashiers, and accountants and auditors. 

Occupations that experienced employment cutbacks after bankruptcy were reservation and transportation ticket 

agents, stock clerks and order fillers, industrial truck and 

tractor operators, vehicle and equipment cleaners, and 

electronics engineers (except computer), among others.

Cost control and cost cutting resulted in large employment 

declines, in terms of both percentages and levels, among 

architecture and engineering occupations, but the change 

was concentrated in a few establishments. Establishments 

with this type of layoff had employment declines in several administrative support and sales occupations directly 

related to sales functions: customer service representatives; shipping, receiving, and traffic clerks; stock clerks 

and order fillers; and retail salespersons. These establishments also cut some production and maintenance workers 

who tended to be paid higher wages: supervisors of mechanics, installers, and repairers; transportation managers; 

and supervisors of production workers. 

Some administrative support occupations whose employment grew after cost-cutting layoffs were those related 

to internal staffing and support: payroll and timekeeping 

clerks, human resources workers, administrative support 

supervisors, general office clerks, and bookkeeping and accounting clerks. In addition, employers whose layoffs were 

a result of controlling or cutting costs hired workers for 

several laborer occupations that tended to be paid lower 

wages: hand laborers and freight and stock movers; and 

janitors and cleaners. 

Production. The kinds of jobs lost from production-related extended mass layoffs related to the specific reason 

cited for the layoff. Although production worker employment shrank overall, it grew in establishments whose layoffs had been due to automation or technological advances. Transportation and material moving occupations grew 

in establishments whose layoffs had been due to model 

changeover or product line discontinuations.

After product line discontinuation, employment changes 

in a few large establishments accounted for the large decreases in production worker employment. Occupations 

that shrank included slaughterers and meat packers, assemblers and fabricators, transportation equipment painters, synthetic and glass fiber machine setters, inspectors 

and weighers, welders, semiconductor processors, and 

engine assemblers. Production occupations that grew included production worker helpers, packaging and filling 

machine operators, bakers, coating and painting machine 

operators, and upholsterers. 

Layoffs due to plant or machine repair or maintenance 

tended to affect occupations directly related to the operation of machines and production systems, and more 

production workers were eventually added than dropped. 

Occupations whose employment decreased included inspectors and weighers, extruding and compacting machine 

operators, furnace operators and tenders, and chemical 

plant and system operators. Occupations with employment increases included metal and plastic drilling and 

boring machine tool operators; meat, poultry, and fish cutters and trimmers; cleaning and metal pickling equipment 

operators; and coating and spraying machine operators. 

Occupations whose employment declined after automation/technological advances provide insight into the types 

of jobs at risk as technology advances. Declines occurred 

among engine machine assemblers, machine feeders and 

offbearers, metal and plastic computer-controlled machine tool operators, tool and die makers, data entry keyers, and tool grinders. 

Disaster/safety. Disaster/safety concerns comprised a 

hazardous work environment, a natural (not weather related) disaster, a nonnatural disaster, and extreme weather-related events. Extreme weather-related events were 

responsible for most of the employment declines in this 

category. Jobs lost after layoffs that were due to extreme 
Mass Layoffs and Employment

24 Monthly Labor Review • June 2011

weather-related events affected primarily service workers 

providing transportation (transit and intercity bus drivers), security (security guards), food service (waiters and 

waitresses and restaurant cooks), housekeeping (maids, 

housekeeping cleaners, and janitors), and entertainment 

(gaming dealers, and tour guides and escorts); many of 

these occupations may be affected by tourism. Job gains 

were in construction occupations.

Domestic and overseas relocation. Although the sample 

size of establishments that laid off workers due to domestic and overseas relocation is smaller than the sample for 

other layoff reasons (MLS ended the two series with the 

2003 data), the study sample still had almost 300 units reporting under the former reason and more than 200 units 

under the latter—enough to study the outcomes of layoffs 

for these reasons. 

After layoffs due to overseas relocation, only two occupational groups grew in employment: office and administrative support; and arts, design, entertainment, sports, and 

media. Detailed occupations that shrank included various 

assemblers, machine operators, hand laborers, and industrial and electronic engineers and their managers. Despite 

the reductions among major occupational groups, the establishments hired workers in occupations related to sales, 

shipping, human resources, and computer network support (such as stock, billing, and shipping clerks; sales representatives; network administrators; and human resource 

specialists). 

After layoffs due to domestic relocation,

21 establishments 

reduced employment in two of the higher skilled groups: 

healthcare practitioner and technical occupations; and life, 

physical, and social science occupations. Establishments 

reduced employment in occupations involving nonanalytical skill, such as production; office and administrative 

support; transportation and material moving; installation, 

maintenance, and repair; protective service; building and 

grounds cleaning and maintenance; construction and extraction; and sales and related occupations. In contrast, 

the establishments with layoffs due to domestic relocations hired more analytical occupations: business and 

financial operations; architecture and engineering; management; arts, design, entertainment, sports, and media; 

computer and mathematical science; as well as personal 

care and service occupations. 

Occupational changes by geographic region

Without regression analysis. The effect of layoffs on occupational employment levels varied across the country 

because of differences in industry composition, local labor market conditions, and other economic factors. In the 

entire MLS data set (the universe of mass layoff events 

during the 2000–2007 period), the Midwest, West, and 

Southeast regions had the most worker separations, with 

losses of 2.5, 2.2, and 1.2 million jobs, respectively. The 

New York-New Jersey, Mid-Atlantic, and Southwest regions had between 500,000 and 900,000 separations. New 

England and the Mountain-Plains region had the fewest number of worker separations, each less than 500,000 

over the same period. 

Table 12 shows the percent change in employment after 

layoffs, by geographic region and occupational group. The 

States within each region are shown in figure 1.

Occupational groups that shrank in employment across 

most regions involved administration, personal service, 

and mainly nonanalytical skills: production, sales, office 

and administrative support, protective service, management, transportation, installation, construction, personal 

care, and building maintenance. Employment in occupation groups involving analytical skills grew in more regions than did other occupations. Business and financial 

operations, legal, computer and mathematical science, 

healthcare practitioner and technical, and community and 

social service occupations had lower percent declines in 

employment in most regions relative to declines among 

other occupational groups.

Within regions, the pattern of reducing employment 

in occupations involving clerical and nonanalytical skills 

while retaining jobs requiring analytical skills was most 

prevalent in the Mid-Atlantic, New England, New YorkNew Jersey, and Southwest regions. The first three regions 

have high concentrations of industries that experienced 

relatively large numbers of layoffs. Establishments undergoing layoffs in the Mid-Atlantic region shed team assemblers; data entry keyers; shipping, receiving, and order 

clerks; retail salespersons; and hand laborers. The MidAtlantic establishments added financial analysts, industrial machinery mechanics, and computer systems analysts. 

The primary finding in this regional analysis was that 

when entire industries retained or increased employment in occupations core to their business, the pattern 

also manifested itself throughout most of the geographic 

regions. In 6 of the 8 geographic areas—all except the 

Southeast and Southwest—employers either added workers or lost fewer workers in job functions core to the industries dominant in their economies. The industry distribution of each region’s employment was used to estimate 

which industries were dominant.22 

In the Midwest region, which had the most worker 
Monthly Labor Review • June 2011 25

Table 12. Percent change in employment after extended mass layoff, by geographic region and occupational group, 1999–2008

Occupational group New 

England

New 

York–New 

Jersey

Mid–

Atlantic Southeast Midwest Southwest Mountain–

Plains West

Management –38.5 24.7 –47.2 –26.2 –18.0 –34.2 –39.9 –15.5

Business and financial operations 70.4 20.1 –21.5 –12.6 –7.4 8.4 35.7 –3.3

Computer and mathematical science –25.6 35.9 31.7 –.5 –5.5 –13.3 –3.0 –16.4

Architecture and engineering –63.8 –16.0 –26.4 –9.0 –20.9 –4.9 –9.1 –17.5

Life, physical, and social science –69.5 17.8 261.4 –7.3 20.6 –50.4 –44.1 –31.1

Community and social services .0 3.7 67.2 16.8 –2.7 –6.8 –10.6 1.8

Legal 19.57 49.8 .9 7.5 56.4 –27.8 44.1 –2.5

Education, training, and library –35.6 21.0 –30.8 –33.3 4.4 4.1 –2.2 8.5

Arts, design, entertainment, sports, and media –78.1 26.7 27.7 –58.0 1.9 –22.0 –12.4 –8.7

Healthcare practitioners and technical (

1

) 5.2 –.8 –3.4 –9.9 79.6 –13.5 6.3

Healthcare support (

1

) 26.2 (

1

) 12.6 –24.0 78.5 –34.4 –11.3

Protective service (

1

) –40.4 –12.1 –32.6 –8.0 –20.5 –63.0 –6.0

Food preparation and serving related (

1

) .7 –42.6 67.0 –27.9 –1.3 –20.0 1.0

Building and grounds cleaning and maintenance –16.8 –7.6 –30.8 24.1 –3.9 –.8 –50.4 –2.6

Personal care and service (

1

) –20.3 –39.5 –30.9 –3.7 –3.1 –9.6 –4.2

Sales and related –64.0 –38.0 –23.9 –24.8 –11.9 –22.6 –19.7 –13.7

Office and administrative support –29.7 –21.9 –30.5 –21.4 –9.1 –16.2 –21.8 –23.8

Construction and extraction 3.1 –13.0 –16.7 –2.6 –14.0 –11.7 –15.1 –.1

Installation, maintenance, and repair –39.1 –11.7 –28.5 –25.8 –14.4 27.2 –15.9 –21.4

Production –52.8 –17.6 –28.1 –31.3 –18.3 –4.9 –24.3 –19.8

Transportation and material moving –3.0 –29.8 –26.9 –28.8 –12.3 –14.6 9.6 –16.7

1 Percent change excluded because it is based on fewer than 5 establishments reporting occupations in the occupational group before layoffs

separations, food preparation and serving-related occupations composed the group that experienced the largest percent decline in employment. The Midwest division23 had relatively high employment concentrations in 

manufacturing (15.7 percent) and wholesale and retail 

trade (15.2 percent) in 2004. After layoffs, Midwest region employment in the occupations core to these sectors—production, sales, and transportation and material 

moving occupations—showed relatively small losses. The 

other regions had larger percent declines in employment 

in these occupations.

Likewise, the Pacific division24 had relatively high concentrations of employment in the information sector (3.0 

percent)—particularly in motion picture and sound recording industries—and the study’s West region lost relatively little employment in the arts, design, entertainment, 

and media occupations, which are core to this industry. 

New York and New Jersey, which both have relatively 

high proportions of their employment in the information and financial activities sectors, tended to hire workers 

in the sectors’ core occupations.25 Employers in the New 

York-New Jersey region hired workers in analytical occupations such as computer applications engineers, management and computer systems analysts, accountants, 

and industrial engineers; they let go of retail salespersons, 

parking lot attendants, team assemblers, hand laborers, 

and telemarketers.

Employers in New England cut jobs of office and production worker supervisors, general maintenance workers, 

janitors and cleaners, and stock clerks. Meanwhile, they 

hired accountants and financial managers. New England 

had relatively high concentrations of employment in the 

education (9.8 percent), health care (14.4 percent), and 

finance and insurance (6 percent) sectors. After layoffs, 

businesses in the region added workers in occupations 

that are core to some of these industries; employment in 

business and financial operations occupations increased 

by 70 percent in the study group. 

Employers in the Mountain-Plains region also followed 

the pattern of shedding relatively few workers in occupations core to the industries that dominate the region’s 

economy. In 2004, among all Census regions, the Mountain division26 was the geographic area with the highest 

concentrations of employment in leisure and hospitality 

(11.2 percent). Perhaps, as a result, the region had lower 

percent declines in employment in food preparation, personal care, and sales occupations than did other regions.

As noted earlier, however, not every region followed the 

pattern of retaining or increasing jobs in occupations core 

to the region’s dominant industries, in part due to changing technology, consumer trends, or business practices. 

The combined South Atlantic and East South Central 

Census divisions—together approximating the study’s 

Southeast region27—had relatively high employment con-
Mass Layoffs and Employment

26 Monthly Labor Review • June 2011

centrations in the transportation and utilities industries 

but lost a comparatively large number of jobs in transportation and material moving occupations. Similarly, the 

Southwest region deviated from the pattern of regions retaining occupations core to dominant industries. Relative 

to other regions, the Census-defined West South Central 

division28 had a high proportion of employment in the 

mining sector (1.5 percent) and the telecommunications 

industry (1.2 percent). In May 2008, telecommunications 

businesses were one of the largest employers of telemarketers. After layoffs, however, the study’s Southwest region had fewer telemarketers. 

Regression analysis. A final regression analysis was conducted to analyze the effect of layoffs on occupational employment by geographic region, controlling for industry, 

time between observations, and establishment size. The 

variables of interest were the eight interaction terms for 

layoff × region. The model included non-interaction dummies for all regions except the Mountain-Plains region, so 

the interaction terms did not combine the impacts of being located in a particular region with having layoffs in 

that region. The regression was based on a total of 209,858 

observations: 205,339 control observations and 4,520 

study observations. 

The model used was

∆employmentSOC major group = β0 + β1 layoffi × West + 

β2 layoffi × Southwest + β3 layoffi × Southeast + β4 layoffi

× Mountain-Plains + β5 layoffi × New York-New Jersey 

+ β6 layoffi × Midwest + β7 layoffi × Mid-Atlantic + β8

layoffi × New England + β9goods + β10totalemp_firsti + 

Σj

δjI(geographic regionji) + ΣjγjI(number of years between 

observationsji) + εi,

where the dependent variable was the change in employment level for each occupational group between the first 

and second observations. Layoff was a dummy variable 

indicating whether the establishment had a layoff; where 

layoffi = 1, a layoff occurred. The geographic region dummy 

variables indicated geographic region: West, Southwest, 

Southeast, Mountain-Plains, New York-New Jersey, 

Midwest, Mid-Atlantic, and New England. Geographic 

region included non-interaction dummies for all regions 

except the Mountain-Plains region. Layoff × [region] was 

an interaction dummy variable. Goods was a dummy variable for the goods-producing aggregation, as opposed to 

the service-providing aggregation. Totalemp_first was the 

total employment in the establishment at the time of the 

first observation. Finally, there were nine dummy variables 

Figure 1. Definition of regions by state, as used in this study

New England—includes 

Connecticut, Maine, Massachusetts, New Hampshire, Rhode 

Island, and Vermont

New YorkNew Jersey—

includes New 

York and New 

Jersey

Mid-Atlantic—includes 

Pennsylvania, Delaware, 

District of Columbia, 

Maryland, Virginia, and 

West Virginia

Southeast—includes Alabama, 

Florida, Georgia, Kentucky, Mississippi, North Carolina, South 

Carolina, and Tennessee

Midwest—includes Illinois, 

Indiana, Iowa, Michigan, 

Minnesota, Nebraska, North 

Dakota, Ohio, South Dakota, 

and Wisconsin

Southwest—includes 

Arkansas, Louisiana, New 

Mexico, Oklahoma, and Texas

Mountain-Plains—includes Colorado, Kansas, 

Missouri, Montana, Utah, 

and Wyoming

West—

includes 

Alaska, 

Arizona, 

California, 

Hawaii, Idaho, 

Nevada, 

Oregon, and 

Washington
Monthly Labor Review • June 2011 27

representing the number of years between observations, 

ranging from 1 to 9; 9 years was captured in the intercept. 

In general, the Mid-Atlantic region saw the most 

substantial employment change for many occupational 

groups, compared with other regions. Appendix table A-4 

shows the regression output for the eight regional interaction variables. For seven occupational groups, an extended 

mass layoff was associated with a decline in additional 

employment in all geographic areas (where a change was 

statistically significant). Production employment shrank 

in every region, most noticeably in the Mid-Atlantic, followed by the Southwest, Mountain-Plains, New England, 

and Midwest regions. Production employment declined 

the least in New York-New Jersey. Employment in transportation and material moving occupations declined the 

most in the Mid-Atlantic, Southeast, and New York-New 

Jersey regions. It grew only in the Mountain-Plains. Employment in office and administrative support occupations declined the most in the Mid-Atlantic region and 

the least in the Mountain-Plains. 

Similarly, employment in sales and related occupations 

declined in all regions; the largest decline was in New 

York-New Jersey and the Southeast, and the smallest was 

in the Midwest. Construction and extraction employment 

shrank everywhere (where a change was statistically significant), especially in the Midwest. Finally, architecture 

and engineering employment declined substantially in 

New England and fell the least in the Southeast. 

For four occupational groups, an extended mass layoff 

was associated with a decline in additional employment in 

nearly all geographic areas (where statistically significant). 

Healthcare practitioners and technical employment grew 

only in the Southwest and actually declined in the Midwest and Southeast. Similarly, healthcare support employment grew only in the Southwest and declined in the 

West and Midwest. Management employment declined 

the most in the Mid-Atlantic and Mountain-Plains, and 

grew only in New York-New Jersey. Employment in the 

arts, design, entertainment, sports, and media occupational group declined the most in the Southeast and New 

England, and grew only in New York-New Jersey.

In contrast, for three occupational groups, an extended 

mass layoff was associated with an increase in additional 

employment in all geographic areas (where statistically 

significant). Employment in legal occupations grew the 

most in New York-New Jersey. The Southeast was the 

region with the most growth in building and grounds 

cleaning and maintenance employment, and also in food 

preparation and serving occupations employment.

Finally, the regression yielded mixed results for five occupational groups. Business and financial operations grew 

the most in New England and the Mountain-Plains, but 

declined in the Southeast and Midwest. Computer and 

mathematical science employment grew the most in the 

Mid-Atlantic and New York-New Jersey regions, but 

declined in the West and Southwest. Life, physical, and 

social science employment grew the most in the Mid-Atlantic, and declined the most in the Southwest. Personal 

care and service employment grew in the Midwest and 

declined the most in the Southeast. Installation, maintenance and repair employment grew in the Southwest and 

shrank the most in the Southeast and New England. Finally, protective service employment grew in the Midwest 

but declined in New York-New Jersey and the Southeast.

DURING THE PERIOD COVERED BY THIS STUDY, the 

economy experienced both the dot-com bubble burst and 

large numbers of layoffs in manufacturing. The occupations that were most affected in establishments with layoffs were those which generally involved nonanalytical 

skills and abilities and tended to represent business support functions, while the occupations whose employment 

level was relatively unaffected by the layoffs were those 

which involved analytical skills and abilities or were core 

to their business. Today’s labor market turbulence can be 

found in different industries, such as real estate and finance. Repeating the study with newer data would reveal 

whether the patterns observed in this analysis hold under 

different economic circumstances. In addition, using data 

further from the layoff date, rather than the first observation after the first layoff, might reveal different long-term 

restructuring outcomes. 

NOTES

ACKNOWLEDGEMENTS: The authors thank the following BLS 

employees for their guidance: Patrick Carey, Elizabeth Weber Handwerker, Michael Soloy, Dixie Sommers, George Stamas, Laura Train, 

and Zachary Warren.

1 According to the MLS, during the first quarter of 2009, there 

were 3,979 extended mass layoff events, resulting in the separation of 

705,141 workers from their jobs for at least 31 days. In the first quarter 

of 2011, there were 1,397 mass layoff events that resulted in 190,895 

separations. Extended mass layoff events and separations have shown 

an over-the-year decrease for six consecutive quarters. BLS Mass Layoff Statistics are available at http://www.bls.gov/news.release/mslo.

toc.htm (visited June 28, 2011). Extended mass layoff data have been 

available since second quarter 1995.
Mass Layoffs and Employment

28 Monthly Labor Review • June 2011

2

 Recessions are identified by the National Bureau of Economic Research (NBER). For a list of recession start and end dates, see “U.S. 

Business Cycle Expansions and Contractions” (Cambridge, MA, National Bureau of Economic Research, June 20, 2011), http:/www.nber.

org/cycles/cyclesmain.html (visited June 20, 2011).

3 This article uses the term “function” differently than does the MLS 

program, so the data are not comparable.

4

 Approximately 30 days after a mass layoff begins, the employer is 

contacted for additional information.

5

 The universe of OES establishments from which the study sample 

was drawn includes only usable units that passed all BLS tests and that 

reported all requested employment data and all or partial wage data.

6

 Farming, fishing, and forestry occupations were included in the total calculations, but were not included in the analysis, because MLS and 

OES data include only nonfarm industries.

7

 See “O*NET OnLine: Browse by O*NET Data,” http://online.

onetcenter.org/find/descriptor/browse/Abilities (visited July 26, 

2010). O*NET identifies six descriptors (categories of occupational information): knowledge, skills, abilities, work activities, interests, and 

work values. Each descriptor has a set of elements, and each element 

has importance scores for all O*NET occupations.

Occupations in the “analytical” group have high importance scores in 

skills such as reading comprehension; writing; speaking; math; science; 

critical thinking; complex problem solving; judgment and decision 

making; systems analysis; active listening; monitoring; social perceptiveness; coordination; persuasion; negotiation; instructing; service orientation; and management of time, financial, material, and personnel 

resources. “Analytical” occupations also have high importance scores in 

elements such as deductive and inductive reasoning, fluency of ideas, 

informative ordering, mathematical reasoning, memorization, number 

facility, oral and written expression and comprehension, perceptual 

speed, and problem sensitivity. 

Occupations in the “nonanalytical” group have high importance 

scores in nonanalytical skills such as equipment maintenance, troubleshooting, repairing, and quality control analysis. These occupations 

have high importance scores in nonanalytical abilities such as dynamic 

and extent flexibility; dynamic, explosive, static, and trunk strength; 

gross body coordination and equilibrium, and stamina. “Nonanalytical” occupations also tend to possess elements of psychomotor abilities, 

such as control precision and manual dexterity. 

8

 Education and training data come from “Employment Projections: 

EPP Tables—Occupations”(U.S. Bureau of Labor Statistics, no date), 

table 1.11, http://www.bls.gov/emp/#tables, (visited July 27, 2010). 

This observation references an occupation’s distribution of employment by educational attainment (found in the table). See also Occupational Outlook Handbook, 2010–11 Edition (U.S. Bureau of Labor 

Statistics, no date), http://www.bls.gov/oco (visited June 13, 2011). 

Information about general office clerks, customer service representatives, and secretaries and administrative assistants is also from the 

Handbook, at http://www.bls.gov/oco/ocos130.htm#training, http://

www.bls.gov/oco/ocos280.htm#training, and http://www.bls.gov/

oco/ocos151.htm#training, respectively (visited July 27, 2010).

9

 “Employment Projections: EPP Tables—Occupations.” 

10 See “Assemblers and Fabricators,” in Occupational Outlook Handbook, 2010–11 Edition, http://www.bls.gov/oco/ocos217.htm (visited 

July 27, 2010).

11 See Career Guide to Industries, 2010–11 Edition (Bureau of Labor Statistics, no date), http://www.bls.gov/oco/cg (visited June 13, 

2011).

12 See “Truck Drivers and Driver/Sales Workers: Training, Other 

qualifications, and Advancement,” in Occupational Outlook Handbook, 

2010–11 Edition, http://www.bls.gov/oco/ocos246.htm#training 

(visited July 27, 2010).

13 “Employment Projections: EPP Tables—Occupations.”

14 The reduced employment in management occupations overall may 

be, in part, a result of improvements in the classification of managers 

in the OES survey. The interpretation of the employment change in 

management occupations should be made with caution.

15 Layoffs because of contract completion or contract cancellation 

could be attributable to seasonal factors, but, for the purposes of this 

study, they were included in the “economic difficulties” category.

16 Because the OES program surveys the same establishment at the 

same time each year, staffing pattern changes in the seasonal layoffs category are long-term changes rather than the result of seasonal changes.

17 See “Mass Layoff Statistics” (U.S. Bureau of Labor Statistics, no 

date), http://www.bls.gov/mls (visited Mar. 18, 2010).

18 MLS ended the domestic/foreign relocation series with the 2003 

data. Relocation of work was then replaced by movement of work data. 

The category of controlling costs was added as a reason in 2007.

19 The rankings excluded seasonal layoffs—although they were included in the count of total mass layoffs. 

20 The rankings excluded seasonal layoffs, refusal to respond, “other,” 

and “does not know” as reasons for layoffs—although they were included in the count of total mass layoffs. 

21 The data relating to domestic relocation reflect occupations with at 

least 10 establishments reporting them initially.

22 From Geographic Profile of Employment and Unemployment, 

2004, “Table 7. Census regions and divisions: percent distribution of 

employed persons by industry, sex, race, and Hispanic or Latino ethnicity, 2004 annual averages” (U.S. Bureau of Labor Statistics, January 2009), also available online at http://www.bls.gov/opub/gp/pdf/

gp04_07.pdf (visited June 13, 2011).

23 The Midwest division is similar to the study’s Midwest region but 

also includes Missouri and Kansas.

24 The Pacific division is similar to the study’s West region but excludes Arizona, Idaho, and Nevada.

25 From Geographic Profile of Employment and Unemployment, 2004, 

“Table 20. States: percent distribution of employed persons by sex, 

race, Hispanic or Latino ethnicity, and industry, 2004 annual averages” 

(U.S. Bureau of Labor Statistics, January 2009), also available online 

at http://www.bls.gov/opub/gp/pdf/gp04_20.pdf (visited June 13, 

2011).

26 The Mountain division is similar to the study’s Mountain-Plains 

region but excludes Missouri and Kansas and includes Arizona and 

New Mexico. 

27 The South Atlantic and East South Central Census divisions are 

similar to the study’s Southeast region, except that the combined Census-defined divisions include Virginia, Delaware, the District of Columbia and West Virginia; these four jurisdictions are all in the study’s 

Mid-Atlantic region.

28 The West South Central division is similar to the study’s Southwest region but excludes New Mexico.
Monthly Labor Review • June 2011 29

Table A-1. Output for 21 regressions of occupational group on layoffi and control variables (industry, region, establishment 

size, and number of years between observations) 

Dependent variable (change in employment in occupational 

group)

Layoffi

parameter 

estimate

Standard error 

on layoffi

parameter 

estimate

t-value p-value

Production –20.76 1.00 –20.86 <.0001

Office and administrative support –10.97 1.23 –8.91 <.0001

Sales and related –7.66 .51 –15.05 <.0001

Management –6.13 .43 –14.27 <.0001

Transportation and material moving –6.04 .79 –7.69 <.0001

Architecture and engineering –4.29 .54 –7.96 <.0001

Installation, maintenance, and repair –3.38 .46 –7.41 <.0001

Construction and extraction –1.92 .43 –4.42 <.0001

Education –1.61 1.54 –1.05 .2938

Personal care and service –1.28 .61 –2.1 .0356

Arts, design, entertainment, sports, and media –1.13 .32 –3.51 .0004

Computer and mathematical science –.48 .67 –.71 .4792

Healthcare practitioner and technical –.05 .78 –.06 .9513

Protective service –.04 .40 –.09 .9263

Healthcare support –.03 .39 –.08 .9371

Life, physical, and social science –.03 .34 –.09 .9309

Legal .60 .11 5.32 <.0001

Community and social services .68 .44 1.54 .1225

Business and financial operations 1.28 .48 2.66 .0078

Building and grounds cleaning and maintenance 2.62 .37 7.09 <.0001

Food preparation and serving related 5.36 .58 9.27 <.0001

NOTE: Table excludes output for control variables to conserve space, and excludes farming, fishing, and forestry occupations. Degrees of freedom=1 

for all regressions.

Appendix tables: Output for regressions
Mass Layoffs and Employment

30 Monthly Labor Review • June 2011

Table A-2. Output for 21 regressions of occupational group on economici, seasonali, and control variables (industry, 

region, establishment size, and number of years between observations)

 Dependent variable 

(change in employment in 

occupational group)

Interaction term Coefficient Standard 

error t-value p-value

Are the two 

interaction terms 

significantly 

different?

Management layoffi

*economici 0.00 3.49 0 0.9996 no layoffi

*seasonali –.65 3.68 –.18 .8608

Business and financial 

operations

layoffi*economici 3.73 4.11 .91 .3633 no layoffi

*seasonali .12 4.33 .03 .9781

Computer and 

mathematical science 

layoffi*economici 1.36 5.43 .25 .8022 no layoffi

*seasonali –1.56 5.72 –.27 .7858

Architecture and 

engineering

layoffi*economici .69 5.80 .12 .906 no layoffi

*seasonali 3.53 6.12 .58 .5645

Life, physical, and social 

science

layoffi*economici –.32 2.05 –.16 .8767 no layoffi

*seasonali .68 2.16 .31 .7536

Community and social 

services

layoffi*economici –.33 .69 –.48 .6341 no layoffi

*seasonali –.14 .73 –.19 .8514

Legal layoffi

*economici .04 .38 .1 .9174 no layoffi

*seasonali .11 .40 .27 .7853

Education layoffi

*economici –1.31 2.13 –.61 .539 no layoffi

*seasonali .88 2.25 .39 .6963

Arts, design, entertainment, 

sports, and media 

layoffi*economici 6.11 4.15 1.47 .1408 no layoffi

*seasonali –3.47 4.37 –.79 .4279

Healthcare practitioner and 

technical

layoffi*economici 7.60 6.93 1.1 .2726 no layoffi

*seasonali 6.62 7.31 .91 .365

Healthcare support layoffi

*economici 3.03 1.85 1.64 .1007 no layoffi

*seasonali 2.45 1.94 1.26 .2078

Protective service layoffi

*economici 3.41 1.74 1.96 .0505 no layoffi

*seasonali –1.05 1.84 –.57 .5689

Food preparation and 

serving related

layoffi*economici –9.22 7.75 –1.19 .2341 no layoffi

*seasonali 6.21 8.16 .76 .4465

Building and grounds cleaning and maintenance

layoffi*economici –.18 2.80 –.07 .9476 no layoffi

*seasonali 4.36 2.95 1.48 .1393

Personal care and service layoffi

*economici 7.66 5.65 1.36 .1752 no layoffi

*seasonali –.60 5.96 –.1 .9197

Sales and related layoffi

*economici –4.80 5.90 –.81 .4167 no layoffi

*seasonali –4.70 6.22 –.76 .4499

Office and administrative 

support

layoffi*economici 7.87 9.58 .82 .4114 no layoffi

*seasonali 11.47 10.10 1.14 .2563

Construction and extraction layoffi

*economici –4.50 4.07 –1.11 .2686 no layoffi

*seasonali –6.86 4.29 –1.6 .1098

Installation, maintenance, 

and repair

layoffi*economici 3.26 5.28 .62 .5374 no layoffi

*seasonali 4.52 5.57 .81 .4169

Production layoffi

*economici –15.62 12.30 –1.27 .2041 no layoffi

*seasonali .10 12.96 .01 .9941

Transportation and material 

moving

layoffi

*economici –2.67 7.47 –.36 .721 no layoffi

*seasonali –4.55 7.88 –.58 .5631

NOTE: Table excludes output for control variables to conserve space, and excludes farming, fishing, and forestry occupations. Degrees of 

freedom=1 for all regressions.
Monthly Labor Review • June 2011 31

Table A-3. Output for 21 regressions of occupational group on layoffi goods–producing group, layoffi service–providing 

group, and control variables (region, establishment size, years between observations, and goods-producing 

group)

Dependent variable 

(change in employment in 

occupational group)

Interaction term Coefficient Standard 

error t–value p–value

Are the parameter 

estimates significantly different at 

the 5-percent significance level?

Management layoffi

*goodsi –4.94 0.55 –8.92 <.0001

yes layoffi

* servicei –7.12 .52 –13.77 <.0001

Business and financial 

operations

layoffi*goodsi .85 .62 1.36 .173 no layoffi

* servicei 1.65 .58 2.83 .0046

Computer and mathematical 

science 

layoffi*goodsi –.92 .86 –1.06 .2869 no layoffi

* servicei –.11 .81 –.13 .8948

Architecture and 

engineering

layoffi*goodsi –7.45 .69 –10.76 <.0001

yes layoffi

* servicei –1.66 .65 –2.57 .0102

Life, physical, and social 

science

layoffi*goodsi –.51 .44 –1.16 .2445

yes layoffi

*servicei .37 .41 .9 .3694

Community and social 

services

layoffi*goodsi .82 .57 1.44 .1503 no layoffi

* servicei .57 .53 1.07 .2838

Legal layoffi

*goodsi .62 .14 4.27 <.0001 no layoffi

* servicei .58 .14 4.31 <.0001

Education layoffi

*goodsi –1.13 1.98 –.57 .5685 no layoffi

* servicei –2.01 1.85 –1.09 .2761

Arts, design, entertainment, 

sports, and media 

layoffi*goodsi .01 .41 .01 .9898

yes layoffi

* servicei –2.06 .39 –5.34 <.0001

Healthcare practitioner and 

technical

layoffi*goodsi –1.29 1.01 –1.27 .203 no layoffi

* servicei .98 .94 1.03 .3015

Healthcare support layoffi

*goodsi –.11 .50 –.22 .8277 no layoffi

* servicei .03 .46 .07 .942

Protective service layoffi

*goodsi 1.12 .52 2.17 .0298

yes layoffi

* servicei –1.00 .48 –2.06 .0392

Food preparation and 

serving related

layoffi*goodsi 4.47 .74 6.01 <.0001 no layoffi

* servicei 6.09 .70 8.75 <.0001

Building and grounds 

cleaning and maintenance

layoffi*goodsi 2.17 .47 4.57 <.0001

yes layoffi

* servicei 2.99 .44 6.72 <.0001

Personal care and service layoffi

*goodsi 1.82 .78 2.32 .0204

yes layoffi

* servicei –3.84 .73 –5.24 <.0001

Sales and related layoffi

*goodsi –1.87 .65 –2.86 .0042

yes layoffi

* servicei –12.44 .61 –20.32 <.0001

Office and administrative 

support

layoffi*goodsi –1.28 1.58 –.81 .4202

yes layoffi

* servicei –18.99 1.48 –12.82 <.0001

Construction and extraction layoffi

*goodsi –4.33 .56 –7.76 <.0001

yes layoffi

* servicei .08 .52 .15 .8812

Installation, maintenance, 

and repair

layoffi*goodsi –5.16 .59 –8.77 <.0001

yes layoffi

* servicei –1.92 .55 –3.48 .0005

Production layoffi

*goodsi –48.17 1.28 –37.71 <.0001

yes layoffi

* servicei 1.91 1.19 1.6 .1093

Transportation and material 

moving

layoffi*goodsi –5.83 1.01 –5.77 <.0001 no layoffi

*servicei –6.22 .95 –6.58 <.0001

NOTE: Table excludes output for control variables to conserve space, and excludes farming, fishing, and forestry occupations. Degrees of freedom=1 for all regressions.
Mass Layoffs and Employment

32 Monthly Labor Review • June 2011

Table A–4. Output for regressions of change in employment in each occupational group on all 8 layoffregion interaction 

variables, and controls

Dependent variable 

(change in employment in occupational group)

Output for 

interaction 

terms

Interaction term

New 

England

New York/

New Jersey

Mid–

Atlantic Southeast Midwest Southwest Mountain–

Plains West

Management

Coefficient –12.26 3.81 –14.92 –7.06 –5.55 –10.03 –13.47 –5.80

Standard error 3.85 1.06 2.23 .84 .67 1.04 1.35 .67

p–value .00 .00 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Business and 

financial operations 

Coefficient 20.99 6.32 –4.36 –2.06 –.92 2.87 11.57 .16

Standard error 4.32 1.19 2.50 .95 .76 1.17 1.52 .75

p–value <.0001 <.0001 .08 .03 .23 .01 <.0001 .83

Computer and 

mathematical 

science

Coefficient –8.69 9.13 9.78 .50 –.15 –3.82 –1.00 –3.98

Standard error 6.02 1.66 3.49 1.32 1.05 1.62 2.12 1.04

p–value .15 <.0001 .01 .70 .88 .02 .64 .00

Architecture and 

engineering

Coefficient –11.21 –2.81 –2.65 –2.05 –5.25 –1.85 –3.89 –6.03

Standard error 4.82 1.33 2.79 1.06 .84 1.30 1.70 .84

p–value .02 .03 .34 .05 <.0001 .16 .02 <.0001

Life, physical, and 

social science

Coefficient –1.15 1.49 8.60 –.05 1.36 –2.04 –1.54 –1.40

Standard error 3.05 .84 1.77 .67 .53 .82 1.07 .53

p–value .71 .08 <.0001 .94 .01 .01 .15 .01

Community and 

social service

Coefficient .52 .23 1.74 1.30 .74 .49 .71 .45

Standard error 3.97 1.09 2.30 .87 .70 1.07 1.40 .69

p–value .90 .83 .45 .13 .29 .64 .61 .51

Legal 

Coefficient .61 1.01 .54 .56 .79 .22 .75 .39

Standard error 1.01 .28 .58 .22 .18 .27 .36 .17

p–value .54 .00 .35 .01 <.0001 .42 .03 .03

Education

Coefficient –2.69 3.00 –3.56 –4.65 –.61 –3.11 –3.36 –1.48

Standard error 13.76 3.79 7.97 3.01 2.41 3.71 4.84 2.38

p–value .85 .43 .66 .12 .80 .40 .49 .54

Arts, design, 

entertainment, 

sports, media

Coefficient –5.61 1.45 2.14 –6.31 .18 –1.03 –.56 –.81

Standard error 2.87 .79 1.67 .63 .50 .78 1.01 .50

p–value .05 .07 .20 <.0001 .72 .19 .58 .10

Healthcare 

practitioners and 

technical

Coefficient –1.11 –1.07 –2.99 –2.78 –3.12 17.20 –2.61 –.68

Standard error 7.02 1.94 4.07 1.54 1.23 1.90 2.47 1.22

p–value .87 .58 .46 .07 .01 <.0001 .29 .58

Healthcare support

Coefficient –.13 .79 .27 –.11 –1.19 4.74 –.20 –.90

Standard error 3.45 .95 2.00 .75 .60 .93 1.21 .60

p–value .97 .40 .89 .89 .05 <.0001 .87 .13

Protective service

Coefficient .38 –1.55 1.29 –1.63 1.30 –.02 –1.61 .34

Standard error 3.60 .99 2.09 .79 .63 .97 1.27 .62

p–value .92 .12 .54 .04 .04 .99 .20 .58

Food preparation 

and serving related

Coefficient 3.53 4.56 –2.83 15.58 2.29 4.68 2.11 4.77

Standard error 5.18 1.43 3.00 1.13 .91 1.40 1.82 .90

p–value .50 .00 .35 <.0001 .01 .00 .25 <.0001

Building and 

grounds cleaning 

and maintenance

Coefficient 1.45 .71 2.02 5.12 2.91 3.06 –.11 2.23

Standard error 3.30 .91 1.91 .72 .58 .89 1.16 .57

p–value .66 .44 .29 <.0001 <.0001 .00 .93 <.0001

Personal care and 

service

Coefficient 2.33 –3.43 –7.18 –10.05 1.76 .89 .40 .39

Standard error 5.45 1.50 3.16 1.19 .95 1.47 1.92 .94

p–value .67 .02 .02 <.0001 .07 .55 .83 .68

Sales and related

Coefficient –9.54 –16.01 –7.41 –10.39 –4.05 –9.25 –7.05 –6.20

Standard error 4.56 1.26 2.64 1.00 .80 1.23 1.60 .79

p–value .04 <.0001 .01 <.0001 <.0001 <.0001 <.0001 <.0001

See note at end of table.
Monthly Labor Review • June 2011 33

Table A–4. Output for regressions of change in employment in each occupational group on all 8 layoffregion interaction 

variables, and controls

Dependent variable 

(change in employment in occupational group)

Output for 

interaction 

terms

Interaction term

New 

England

New York/

New Jersey

Mid–

Atlantic Southeast Midwest Southwest Mountain–

Plains West

Office and 

administrative 

support

Coefficient –18.65 –13.33 –29.98 –15.98 –0.07 –14.21 –12.41 –15.12

Standard error 11.02 3.03 6.39 2.41 1.93 2.98 3.88 1.91

p-value .09 <.0001 <.0001 <.0001 .97 <.0001 .00 <.0001

Construction and 

extraction

Coefficient .80 –2.49 –2.62 .27 –4.14 –2.32 –3.54 –.16

Standard error 3.88 1.07 2.25 .85 .68 1.05 1.37 .67

p-value .84 .02 .25 .75 <.0001 .03 .01 .81

Installation, 

maintenance, and 

repair

Coefficient –8.15 –1.19 –7.78 –10.40 –3.12 9.23 –4.77 –4.52

Standard error 4.09 1.13 2.37 .89 .72 1.10 1.44 .71

p-value .05 .29 .00 <.0001 <.0001 <.0001 .00 <.0001

Production

Coefficient –29.86 –7.14 –53.37 –48.97 –23.78 –.55 –42.18 –6.93

Standard error 8.90 2.45 5.16 1.95 1.56 2.40 3.13 1.54

p-value .00 .00 <.0001 <.0001 <.0001 .82 <.0001 <.0001

Transportation and 

material moving

Coefficient 1.66 –12.61 –15.27 –14.35 –4.61 –4.59 7.54 –3.95

Standard error 7.03 1.94 4.08 1.54 1.23 1.90 2.48 1.22

p-value .81 <.0001 .00 <.0001 .00 .02 .00 .00

NOTE: Table excludes output for control variables to conserve space, and excludes farming, fishing, and forestry occupations. Degrees of freedom=1 for 

all regressions.