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The U.S. unemployment rate reached a postWorld War II high of 9.7% in 1982. The U.S. Department of Labor's Bureau of Labor Statistics (BLS), which tracks such data in Employment and Earnings (January 2008, http://www.bls.gov/cps/cpsa2007.pdf), further notes that unemployment remained high at 9.6% in 1983 as a result of the most severe economic recession since the Great Depression of the 1930s. The unemployment rate then dropped, approaching 5% in 1989, but again began increasing, reaching 6.8% in 1991 and rising to 7.5% in 1992. As the economy began to improve, the rate fell to 6.9% in 1993. By 1998 the U.S. unemployment rate had dropped to 4.5% and in 2000 dropped to 4%, the lowest level in three decades.

With a creeping recession, unemployment once again began to rise in early 2001 and rose significantly following the terrorist attacks on the United States on September 11, 2001. In June 2003, 9.4 million people were out of work, and the national unemployment rate spiked to 6%. Between 2003 and early 2007 the unemployment rate generally declined. It then began to rise again in 2007. In January 2008 the unemployment rate was 4.9%. (See Figure 3.1.) The unemployment rate measures people without jobs who are looking for work and thus does not count workers who have lost their jobs and eventually stopped looking for work, such as those who have become discouraged or decided to retire.


Where You Live

Unemployment rates in the United States vary from state to state. In December 2007 the unemployment rate nationwide was 5%. (See Table 3.1.) Unemployment rates in some states, however, were at least one percentage point higher, including in Michigan (7.6%), Mississippi (6.8%), South Carolina (6.6%), Alaska (6.5%), California (6.1%), the District of Columbia (6.1%), and Ohio (6.0%). Other states with higher unemployment rates than the nationwide average included Arkansas (5.8%) and Nevada (5.8%).

On the other hand, some states had unemployment rates as much as two percentage points below the national average. Both Idaho and South Dakota had unemployment rates of only 3%. (See Table 3.1.) Wyoming (3.1%), Hawaii (3.2%), Nebraska (3.2%), Utah (3.2%), North Dakota (3.3%), and Virginia (3.5%) had unemployment rates one and a half percentage points or more below the national average. Other states with lower than average unemployment rates included Delaware (3.8%), Iowa (4%), Kansas (4.4%), Maryland (3.8%), Montana (3.6%), New Hampshire (3.6%), New Mexico (3.7%), Texas (4.5%), and Vermont (4%).

Metropolitan areas tend to have lower unemployment rates than states as a whole, indicating that unemployment is a bigger problem in rural areas. For example, notes the BLS in Metropolitan Area Employment and Unemployment: December 2007 (January 29, 2008, http://www.bls.gov/news.release/archives/metro_01292008.pdf), in December 2007 Mississippi had a high unemployment rate, at 6.8%; in the Mississippi city of Jackson, however, the unemployment rate was close to the national average, at 5.1%. South Carolina also had a high unemployment rate, at 6.6%, but the unemployment rate in the cities of Charleston (5%) and Greenville (5.5%) were much lower. The same held true in Alaska, where overall the unemployment rate was 6.5%. In Alaska's cities of Anchorage and Fairbanks, however, the unemployment rate was 5.7%, significantly lower than statewide. The exception to this rule is in Michigan. The unemployment rate in that state was 7.6%, but the unemployment rate in its two principal cities, Detroit (8%) and Flint (8.3%), was much higher. This is mainly due to the loss of industrial jobs in these once dominantly industrial cities.

The same pattern of lower unemployment rates in cities holds true for states with low unemployment rates as well. The BLS notes that the unemployment rate in Idaho in December 2007 was a low 3%; the unemployment rates of Boise (2.9%) and Idaho Falls (2.2%) were even lower. In South Dakota the unemployment rate of Rapid City matched that of the state as a whole (3%), but the unemployment rate of Sioux Falls was half a percentage point lower (2.5%). In Wyoming, where the unemployment rate of the state was 3.1%, the unemployment rate in Casper was 2.9%.


Unemployment does not occur evenly in all occupations or sectors of society. Workers under twenty-five years of age are far more likely than older workers to be unemployed. The jobs held by younger workers are often more marginal, younger workers tend to have less education than older workers, and they leave their jobs more often than older workers. In addition, workers under age twenty-five also have less seniority to protect them against layoffs. These workers have less work experience than older workers as well, which can work against them when looking for a new job.

During 2007, for example, as noted by the BLS in Employment and Earnings, the unemployment rate among those sixteen years of age and older was greatest among sixteen- to nineteen-year-olds (15.7% of those in the labor force). It was higher for men in that age group (17.6%) than it was for women (13.8%). In contrast, the unemployment rate for men ages twenty to twenty-four years old was 8.9%, and for men ages twenty-five to fifty-four years it was only 3.7%. The unemployment rate for women ages twenty to twenty-four years was 7.3%, and for those ages twenty-five to fifty-four years it was only 3.8%.

States with unemployment rates significantly different from that of the United States as a whole, December 2007, seasonally adjusted
SOURCE: Table A. States with Unemployment Rates Significantly Different from That of the U.S., December 2007, Seasonally Adjusted, in Regional and State Employment and Unemployment: December 2007, U.S. Department of Labor, Bureau of Labor Statistics, January 18, 2008, http://www.bls.gov/news.release/archives/laus_01182008.pdf (accessed February 8, 2008)
United States5.0
District of Columbia6.1
New Hampshire3.6
New Mexico3.7
North Dakota3.3
South Carolina6.6
South Dakota3.0

Race, Gender, and Marital Status

Unemployment does not affect all demographic groups equally. Many African-Americans, for instance, work in occupations that have suffered as the American economy has changed from an industrial to a service economy, and, as in the overall unemployment picture, the youngest workers are particularly affected. In addition, African-Americans sometimes face discrimination in employment that can keep them from finding employment as quickly as members of other groups. According to the BLS in Employment and Earnings, African-American teenagers aged sixteen to nineteen experienced an unemployment rate of 29.4% in 2007, compared with an unemployment rate of 13.9% for white teenagers and 12.7% for Asian teens. African-American male teenagers (33.8%) were more likely to be unemployed in 2007 than were African-American female teenagers (25.3%), although the rates for both were very high.

Unemployed persons by marital status, race, Hispanic ethnicity, age, and sex, 200607
Thousands of personsThousands UnemploymentThousands of personsThousands Unemployment
Marital status, race, Hispanic or of persons rates of persons rates Latino ethnicity, and age20062007200620072006200720062007
Note: Estimates for the above race groups (white, black or African American, and Asian) do not sum to totals because data are not presented for all races. Persons whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually with the release of January data.
SOURCE: 24. Unemployed Persons by Marital Status, Race, Hispanic or Latino Ethnicity, Age, and Sex, in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, January 2008,http://www.bls.gov/cps/cpsaat24.pdf (accessed February 2, 2008)
Total, 16 years and over3,7533,8824.64.73,2473,1964.64.5
Married, spouse present1,1421,2062.42.51,0421,0492.92.8
Widowed, divorced, or separated5455445.25.37097244.95.0
Single (never married)2,0672,1328.68.81,4961,4227.77.2
White, 16 years and over2,7302,8694.04.22,2712,2744.04.0
Married, spouse present8969652.22.48398302.72.6
Widowed, divorced, or separated4074214.74.95235474.74.9
Single (never married)1,4281,4837.57.89098976.4 6.3
Black or African American, 16 years and over7747529.59.17756938.47.5
Married, spouse present1661564.74.31211234.44.3
Widowed, divorced, or separated105928.37.51551356.45.7
Single (never married)50250415.215.049943512.510.8
Asian, 16 years and over1101193.03.1951103.13.4
Married, spouse present49542.22.255612.93.1
Widowed, divorced, or separated1193.53.012122.72.7
Single (never married)49564.95.229373.84.4
Hispanic or Latino ethnicity, 16 years and over6016954.85.34805255.96.1
Married, spouse present2012473.03.51771914.54.7
Married, spouse present2012473.03.51771914.54.7
Widowed, divorced, or separated73854.25.1971105.56.0
Single (never married)3273637.88.32062248.28.3
Total, 25 years and over2,4262,5383.53.62,2212,1983.73.6
Married, spouse present1,0881,1522.42.59559592.72.7
Widowed, divorced, or separated5205155.15.16676834.84.9
Single (never married)8198716.16.35995565.75.2
White, 25 years and over1,7981,9073.13.31,5781,5793.33.3
Married, spouse present8549192.22.37657562.62.5
Widowed, divorced, or separated3894014.64.84945164.54.7
Single (never married)5555875.35.53203074.54.3
Black or African American, 25 years and over4734577.06.65054536.55.8
Married, spouse present1561494.54.11131134.24.1
Widowed, divorced, or separated102858.27.31451296.15.5
Single (never married)21422210.610.52472119.27.7
Asian, 25 years and over80922.42.775872.73.0
Married, spouse present48532.12.253592.83.0
Widowed, divorced, or separated1193.42.810122.32.7
Single (never married)21303.14.013172.73.1
Hispanic or Latino ethnicity, 16 years and over3874553.84.23303554.95.1
Married, spouse present1892343.03.51541664.24.3
Widowed, divorced, or separated67774.14.9911005.35.7
Single (never married)1311445.75.885896.46.1

Table 3.2 shows unemployment rates for men and women of different races and ethnicities. Among all ages, African-Americans and Hispanics were more likely than non-Hispanic whites or Asians to be unemployed. In 2007 African-American men age sixteen and older had an unemployment rate of 9.1%, while African-American women had an unemployment rate of 7.5%. Among Hispanics age sixteen and older, 5.3% of males and 5.9% of females were unemployed. In contrast, among non-Hispanic whites age sixteen and older, 4.2% of males and 4% of females were unemployed in 2007. Among Asians age sixteen and over, only 3.1% of males and 3.4% of females were unemployed. Among all men and women age sixteen and over, unemployment rates were similar in all ethnic groups except among African-Americans, where women were much less likely to be unemployed than were men in 2007.

Among men and women age twenty-five and over, unemployment rates for non-Hispanic whites were the same for men and women (3.3%). (See Table 3.2.) Among African-Americans, the unemployment rate for women was again lower than for men (5.8% and 6.6%, respectively). Among Asians and Hispanics, however, women age twenty-five and over were more likely to be unemployed than were men of the same age. Asian men had an unemployment rate of 2.7% in 2007, while Asian women had an unemployment rate of 3%. Hispanic men had an unemployment rate of 4.2%, while Hispanic women had an unemployment rate of 5.1%.

Marital status is also correlated with unemployment. A married person was much less likely to be unemployed than a single, widowed, or divorced individual in 2007. This observation held true across all races and ethnic groups. In 2007 single, never-married males aged sixteen and over had more than three times the unemployment rate of married males of the same age (8.8% and 2.5%, respectively). (See Table 3.2.) Widowed, divorced, or separated males (5.3%) had more than twice the unemployment rate of married men. Although only 2.4% of white, married men were unemployed in 2007, 7.8% of never-married white men and 4.9% of widowed, divorced, or separated white males were out of work. Among African-Americans, 4.3% of married men aged sixteen years and older were unemployed in 2007, compared with 7.5% of widowed, divorced, or separated men and 15% of never-married men.

The same correlation between marital status and employment was true for women. Although only 2.8% of married women age sixteen and over were unemployed in 2007, the unemployment rate of widowed, divorced, or separated women was 5%. Among never-married females, 7.2% were unemployed in 2007. (See Table 3.2.) This pattern held true across all races and ethnic groups. For example, never-married white women over the age of sixteen were markedly more likely to be out of work than married white women (6.3% and 2.6%, respectively), and never married African-American women were significantly more likely to be unemployed than married African-American women (10.8% and 4.3%, respectively).

Education and Unemployment

The more education an individual possesses, the less likely it is that he or she will be unemployed. In the Occupational Outlook Quarterly (More Education: Lower Unemployment, Higher Pay, Fall 2004, http://www.bls.gov/opub/ooq/2004/fall/oochart.pdf), the BLS theorizes that potential employers are more likely to hire more educated applicants because they see the ability to earn an academic degree as an indicator of assetssuch as organizational skills and aptitudethat a potential worker will bring to the job.

According to the BLS in Employment and Earnings, the unemployment rate of the population dropped with educational attainment. In 2007 high school dropouts with no diploma had an unemployment rate of 7.1%, while people with a bachelor's, master's, professional, and doctoral degrees had an unemployment rate of only 2%. High school graduates with no college had an unemployment rate of 4.4%, while those with some college, but no degree, had an unemployment rate of 3.8%.

People who achieve graduate degrees have even lower unemployment rates than people who have completed bachelor's degrees. The BLS notes in its Current Population Survey (Education Pays, April 15, 2008, http://www.bls.gov/emp/emptab7.htm) that in 2007 the unemployment rate for those with bachelor's degrees was 2.2%. People with master's degrees had an unemployment rate of 1.8%, people with doctoral degrees had an unemployment rate of 1.4%, and people with a professional degree had an unemployment rate of 1.3%.

Occupations and Industries

Workers in some occupations are more susceptible to unemployment than workers in others. In general, occupations that require more education have lower unemployment rates than occupations that have only basic educational requirements. For example, in 2007 the unemployment rate for people employed in managerial and professional specialties (2.1%) was much lower than the rate for people employed in production, transportation, and material moving occupations (5.8%). (See Table 3.3.)

Within each occupational grouping, significant differences can exist. In natural resources, construction, and maintenance occupations, 6.3% of workers were unemployed overall. (See Table 3.3.) The unemployment rate was highest in the farming, fishing, and forestry occupations (8.5%), while the rate was quite low in the installation, maintenance, and repair occupations (3.4%). Even within the professional ranks, unemployment was comparatively high for those in arts, design, entertainment, sports, and media occupations (4.4%), while it was comparatively low among health care techs and practitioners (1.3%).

Gender also plays a role in variations of unemployment rates in different occupations. In 2007 women in sales occupations had an unemployment rate of 5.7%, compared with a rate of only 3.8% for men. (See Table 3.3.) Although women and men had similar unemployment rates in managerial and professional specialty fields, more female workers in production, transportation, and material moving occupations were out of work (7.3%) than their male counterparts (5.4%). Similarly, 10.4% of women workers in natural resources, construction, and maintenance occupations were unemployed in 2007, compared with only 6.1% of men in that category. It may be that because women traditionally did not work in these fields, at times of high unemployment, women are more likely to lose their jobs and be unable to find another.

Unemployed persons by occupation and sex, 200607
Thousands of personsUnemployment rates
*Includes a small number of persons whose last job was in the Armed Forces.
Note: Updated population controls are introduced annually with the release of January data. Dash indicates no data or data that do not meet publication criteria.
SOURCE: 25. Unemployed Persons by Occupation and Sex, in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, January 2008, http://www.bls.gov/cps/cpsaat25.pdf (accessed February 2, 2008)
Total, 16 years and over*7,0017,0784.
Management, professional, and related occupations1,0651,0902.
Management, business, and financial operation occupations4274292.
Management occupations2792781.
Business and financial operations occupations1481512.
Professional and related occupations6386622.
Computer and mathematical occupations80762.
Architecture and engineering occupations49471.
Life, physical, and social science occupations27281.
Community and social services occupations50532.
Legal occupations22401.
Education, training, and library occupations1961982.
Arts, design, entertainment, sports, and media occupations1151274.
Healthcare practitioner and technical occupations98931.
Service occupations1,4851,5215.
Healthcare support occupations1521474.
Protective service occupations1051183.
Food preparation and serving related occupations5906267.
Building and grounds cleaning and maintenance occupations4023927.
Personal care and service occupations2352384.
Sales and office occupations1,6671,6384.
Sales and related occupations8128354.
Office and administrative support occupations8568044.
Natural resources, construction, and maintenance occupations1,0071,0526.
Farming, fishing, and forestry occupations101899.
Construction and extraction occupations6997816.
Installation, maintenance, and repair occupations2071823.
Production, transportation, and material moving occupations1,1271,1285.
Production occupations5445645.
Transportation and material moving occupations5835646.
No previous work experience616627
16 to 19 years435419
20 to 24 years90115
25 years and over9193

Unemployment also varies by industry. The BLS breaks down nonagricultural private wage workers into eleven industry categories: mining, construction, manufacturing (durable and nondurable goods), wholesale and retail trade, transportation and utilities, information services, financial activities, professional and business services, education and health services, leisure and hospitality, and other services. According to the BLS in Employment and Earnings, workers in leisure and hospitality suffered from the highest unemployment rate in 2007 (7.4%), while people in the finance industry (3%) and education and health services (3%) faced the lowest unemployment rates.

In the industries with low unemployment rates, women and men faced similar unemployment rates. On the other hand, in industries suffering from high unemployment rates, women usually had higher than average unemployment rates. For example, the BLS reports in Employment and Earnings that in manufacturing, the unemployment rate among women was 5.4% in 2007, while the unemployment rate for men was 3.8%. In the wholesale and retail trade, the unemployment rate for women was 5.4%, while the unemployment rate for men was only 4.2%. In transportation and utilities, the unemployment rate for women was 4.5%, while the unemployment rate for men was 3.8%. The exception to this rule was in construction, where women suffered a high unemployment rate of 6.6% in 2007, but men faced an even higher unemployment rate of 7.5%.

Unemployed persons by age, sex, race, Hispanic ethnicity, marital status, and duration of unemployment, 2007
Thousands of persons
15 weeks and overWeeks
CharacteristicTotalLess than 5 weeks5 to 14 weeksTotal15 to 26 weeks27 weeks and overAverage (mean) durationMedian duration
Note: Estimates for the above race groups (white, black or African American, and Asian) do not sum to totals because data are not presented for all races. Persons whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually with the release of January data.
SOURCE: 31. Unemployed Persons by Age, Sex, Race, Hispanic or Latino Ethnicity, Marital Status, and Duration of Unemployment, in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, January 2008, http://www.bls.gov/cps/cpsaat31.pdf (accessed February 2, 2008)
Age and sex
Total, 16 years and over7,0782,5422,2322,3031,0611,24316.88.5
16 to 19 years1,10150935523713110611.25.5
20 to 24 years1,24149240234717417414.47.6
25 to 34 years1,54455050149322926516.48.5
35 to 44 years1,22540338643619424217.99.4
45 to 54 years1,13533134046419626821.211.0
55 to 64 years64218619226411015421.911.0
65 years and over190735562283418.57.8
Men, 16 years and over3,8821,3701,2281,28457870617.38.7
16 to 19 years623288198137706711.65.5
20 to 24 years72127023821210111215.68.0
25 to 34 years85629727828113714416.48.7
35 to 44 years6342161982209812217.79.2
45 to 54 years5911691802419814422.011.1
55 to 64 years34992106150589223.411.9
65 years and over108372942172522.19.2
Women,16 years and over3,1961,1721,0041,02048353716.28.4
16 to 19 years478221157100613910.65.5
20 to 24 years520221164135736212.86.9
25 to 34 years6882522232139212116.38.3
35 to 44 years5911871882169612118.29.7
45 to 54 years5441611602229812420.410.9
55 to 64 years2939486114526220.09.9
65 years and over8135262011913.66.2
Race and Hispanic or Latino ethnicity
White, 16 years and over5,1431,9471,6341,56274781415.77.9
Black or African American, 16 years and over1,44541344259025333720.711.1
Asian, 16 years and over229817276294817.58.7
Hispanic or Latino ethnicity, 16 years and over1,22049538134416917514.97.3
Marital status
Men, 16 years and over Married, spouse present1,20641437941318822517.69.0
Widowed, divorced, or separated5441771681998711219.39.8
Single (never married) 2,13278068167130336816.78.2
Women, 16 years and over Married, spouse present1,04938533233214718516.68.2
Widowed, divorced, or separated72423122726613013718.29.8
Single (never married) 1,42255744542120621514.97.8


According to historical data compiled by the BLS from the Current Population Survey (http://www.bls.gov/webapps/legacy/cpsatab9.htm), the average length of unemployment in 2007 was 16.8 weeks, down from 19.6 weeks in 2004, but up from 12.6 weeks in 2000. The median duration of unemployment in 2007 was 8.5 weeks. (See Table 3.4.) In 2007 about 2.5 million of the nation's 7.1 million unemployed workers (35.9%) had been unemployed for less than five weeks, and 2.2 million (31.5%) had been out of work for five to fourteen weeks. About 2.3 million unemployed workers (32.5%) had been out of work for fifteen weeks or more. Over one million of these workers had been out of work for twenty-seven weeks or more. However, these statistics varied by gender, age, race and ethnicity, marital status, occupation, and industry.

Gender and Age

Both gender and age have some effect on the length of unemployment. Women typically spent less time unemployed than did men. In 2007 men were unemployed for an average of 17.3 weeks, while women were unemployed for an average of 16.2 weeks. (See Table 3.4.) In addition, generally the older the job seeker, the longer it took to find work. Because better-paying jobs usually take longer to find, men forty-five years and older, who are most likely to be seeking higher-paying employment than either women or younger people, remained unemployed longer. Young men and women aged sixteen to nineteen years old were unemployed an average of 11.6 weeks and 10.6 weeks, respectively, compared with 23.4 weeks for men and 20 weeks for women who were aged fifty-five to sixty-four.

Race and Ethnicity

Race and ethnicity also affects the duration of unemployment. Hispanic workers were unemployed, on average, for the least amount of time in 2007 (14.9 weeks), possibly because Hispanic workers tend to be concentrated in low-paid service occupations, a growing industry. Unlike in other race and ethnic groups, Hispanic women spent a longer average time unemployed than did Hispanic men15.5 and 14.6, respectively. (See Table 3.4.)

Whites spent the next shortest average time unemployed in 2007 (15.7 weeks). (See Table 3.4.) White women spent an average of 15.1 weeks unemployed, while white men spent an average of 16.3 weeks unemployed. Asians spent an average of 17.5 weeks unemployed; Asian men spent on average a slightly longer time than Asian women looking for work (17.8 weeks and 17.2 weeks, respectively). African-Americans spent the longest time looking for work; their average length of unemployment, 20.7 weeks, was nearly six weeks longer than the average length of unemployment for Hispanics. African-American men spent, on average, 21.6 weeks unemployed, while African-American women averaged 19.8 weeks unemployed.

Marital Status

Although never-married persons have a higher unemployment rate than married persons, married persons spend, on average, a longer time unemployed than do never-married persons. Widowed, divorced, or separated persons spend the longest average time unemployed of all. Among never-married men, the average length of a stint of unemployment was 16.7 weeks in 2007. (See Table 3.4.) Married men, on the other hand, spent an average of 17.6 weeks unemployed, while widowed, divorced, or separated men spent, on average, 19.3 weeks unemployed.

A similar pattern was true among women. Never-married women spent an average of 14.9 weeks unemployed in 2007. (See Table 3.4.) Married women spent, on average, a week and a half longer unemployed (16.6 weeks). Widowed, divorced, or separated women were unemployed longest of all; they spent, on average, 18.2 weeks unemployed, more than three weeks longer than never-married women did.

Occupation and Industry

People working in management, professional, and related occupations and production, transportation, and material moving occupations had a longer average duration of unemployment than did people in other occupations in 2007. Professionals averaged 18.1 weeks out of work, while managers averaged 17.6 weeks out of work. (See Table 3.5.) Those in production occupations averaged 18.1 weeks out of work, while those in transportation and material moving occupations averaged 17.7 weeks out of work. Natural resources, construction, and maintenance occupations had a relatively low average duration of unemployment (15.1 weeks). People working in sales averaged unemployment durations of 16.9 weeks, while those in service occupations averaged 16.3 weeks out of work. People with no previous work experience averaged a duration of 17.3 weeks looking for work.

The length of unemployment also varied by industry. The construction industry averaged the shortest duration of unemployment in 2007, at 14.1 weeks. The leisure and hospitality industry and other services industry also averaged fairly short durations of unemployment in that year (15.2 weeks and 15.9 weeks, respectively). The information industry averaged the longest duration of unemployment, at 22.1 weeks, followed by the public administration industry (20.3 weeks) and manufacturing (19.5 weeks). (See Table 3.5.)


In 2007 nearly 7.1 million workers experienced unemployment in the United States, according to the Bureau of Labor Statistics in Employment and Earnings. Nearly half of those had lost their jobs or had completed temporary jobs (3.5 million, or 49.7%), including 976,000 who were on temporary layoff. (See Table 3.6.) Another 2.1 million people (30.3%) had left the labor force at one time and were now returning and searching for work; the BLS calls these people reentrants. Another 793,000 people (11.2%) were unemployed because they had voluntarily left their jobs. Only 627,000 (8.9%) were new entrants to the labor force.

Reasons for unemployment varied greatly by gender. A larger proportion of men who were unemployed had lost their jobs permanently; 31.1% of unemployed men over the

Unemployed persons by occupation, industry, and duration of unemployment, 2007
Thousands of persons
15 weeks and overWeeks
Occupation and industryTotalLess than 5 weeks5 to 14 weeksTotal15 to 26 weeks27 weeks and overAverage (mean) durationMedian duration
a Includes wage and salary workers only.
b Data not shown where base is less than 35,000.
Note: Updated population controls are introduced annually with the release of January data.
SOURCE: 32. Unemployed Persons by Occupation, Industry, and Duration of Unemployment, in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, January 2008, http://www.bls.gov/cps/cpsaat32.pdf (accessed February 2, 2008)
Management, professional, and related occupations1,09037934436816120717.98.8
Management, business, and financial operations occupations429138141150688217.69.4
Professional and related occupations6622402032189312618.18.4
Service occupations1,52156047848223225116.38.3
Sales and office occupations1,63857050856027628516.98.9
Sales and related occupations83530225527713614216.28.7
Office and administrative support occupations80426825328314014317.59.2
Natural resources, construction, and maintenance occupations1,05240834529914215715.17.6
Farming, fishing, and forestry occupations89303227131316.58.6
Construction and extraction occupations78131625321210011214.47.1
Installation, maintenance, and repair occupations182626060283217.29.1
Production, transportation, and material moving occupations1,12838336138416421917.99.0
Production occupations5641871831938311018.19.2
Transportation and material moving occupations5641961771918110917.78.8
Agriculture and related industries82282925121417.68.7
Durable goods439139139162699319.19.8
Nondurable goods271908397395820.29.4
Wholesale and retail trade98734131133416317116.89.0
Transportation and utilities262838495375818.89.5
Financial activities29510194100505017.28.9
Professional and business services75226224224711713016.98.8
Education and health services80730025525312113216.28.1
Leisure and hospitality92936129427413214215.27.6
Other services245957180374315.98.1
Public administration133404251222920.310.0
No previous work experience6272371902018311817.38.1

age of twenty compared with 25.6% of unemployed women over the age of twenty were in this position, as indicated by Table 3.6. Women, on the other hand, were more likely to have voluntarily left their positions (12.9% of women who were unemployed and 11.4% of men who were unemployed). In addition, women were much more likely than men to be reentering the job market after some time away; 36.2% of unemployed women were reentrants, compared with only 22.2% of men. This reflects the reality that women workers often leave their jobs for a period of time to care for young children or elderly parents.

Duration by Reason of Unemployment

According to the BLS in Employment and Earnings, in 2007 people on temporary layoff were most likely to be out of work for less than five weeks, while people who had permanently lost their jobs were most likely to be out of work for fifteen weeks or more. In that year, slightly over one-third (37%) of workers aged sixteen and over who had lost their jobs or who had completed temporary jobs were unemployed less than five weeks, while 30.7% were unemployed fifteen weeks or more. More than one-half (55.8%) of those who were on temporary layoff were out of work for five weeks or less. Of those who had permanently lost their jobs in 2005, more than two out of five (41.3%) remained unemployed fifteen weeks or more.

A worker's age also had an impact on the amount of time spent unemployed. About two-thirds (62.9%) of all new entrants into the job market found work within four-teen weeks, 32.8% of them finding employment within five

Unemployed persons by reason for unemployment, sex, and age, 200607

[Numbers in thousands]
Total, 16 years and overMen, 20 years and overWomen, 20 years and overBoth sexes,16 to 19 years
Note: Updated population controls are introduced annually with the release of January data.
SOURCE: 27. Unemployed Persons by Reason for Unemployment, Sex, and Age, in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, January 2008,http://www.bls.gov/cps/cpsaat27.pdf (accessed February 2, 2008)
Number of unemployed
Total unemployed7,0017,0783,1313,2592,7512,7181,1191,101
Job losers and persons who completed
temporary jobs3,3213,5151,9272,0641,2491,276145176
On temporary layoff9219765405803243335763
Not on temporary layoff2,4002,5391,3871,48392594388113
Permanent job losers1,6861,7819481,0136856965372
Persons who completed temporary jobs7147584394702402473542
Job leavers8277933683713803517871
New entrants61662778101103107435419
Percent distribution
Total unemployed100.0100.0100.0100.0100.0100.0100.0100.0
Job losers and persons who completed
temporary jobs47.449.761.663.345.446.913.016.0
On temporary layoff13.213.817.317.811.812.25.15.7
Not on temporary layoff34.335.944.345.533.634.77.910.3
Job leavers11.811.211.711.413.812.97.06.5
New entrants8.
Unemployed as a percent of the civilian
labor force
Job losers and persons who completed
temporary jobs2.
Job leavers.
New entrants.

weeks, according to the BLS. Younger unemployed people and temporary workers tended to find jobs more quickly than older workers. Among sixteen- to nineteen-year-olds, nearly two-thirds (61.9%) of those who had lost their jobs or who had completed temporary jobs had found work within five weeks or less. Men age twenty and over were more likely than women of the same age to spend fifteen weeks or more unemployed (35.2% and 33.8%, respectively), while women were more likely than men to find work within five weeks (35% and 33.2%, respectively).


In addition to people who are working, the labor force includes all of the unemployed people who are looking for work. People who stop looking for work are considered to have left the labor force, regardless of their age or reason for not looking for work. They are not counted among the unemployed by the Department of Labor. In 2007 more than half of the 78.7 million people who were not in the labor force were aged fifty-five or older (42.2 million, or 53.6%). (See Table 3.7.) Approximately 21.3 million (27.1%) were between the ages of twenty-five and fifty-four and another 15.2 million (19.3%) were aged sixteen to twenty-four. More than six of every ten people not in the labor force (61.9%) were women, reflecting the fact that many women take care of their families rather than work.

Most of the people not in the labor force (74 million, or 94%) did not want a job at the time they were surveyed by the U.S. Department of Labor. (See Table 3.7.) Of the approximately 4.7 million people who were out of the labor force but did still want a job, nearly two million (41.6%) had looked for work during the previous year. Men who were not in the labor force were more likely to want a job (2.1 million of 30 million, or 7.1%) than were women who were not in the labor force (2.6 million of 48.7 million, or 5.3%).

Reasons for Not Working

The reasons people were not looking for work included school or family responsibilities, ill health or disability, and discouragement over job prospects. Women's reasons for being out of the labor force differed markedly from those reported by men. Of the 669,000 women who were available

Persons not in the labor force, by desire and availability for work, age, and sex, 2007

[In thousands]
Total16 to 24 years25 to 54 years55 years and overMenWomen
a Includes some persons who are not asked if they want a job.
b Persons who had a job in the prior 12 months must have searched since the end of that job.
c Includes believes no work available, could not find work, lacks necessary schooling or training, employer thinks too young or old, and other types of discrimination.
d Includes those who did not actively look for work in the prior 4 weeks for such reasons as child care and transportation problems, as well as a small number for which reason for nonparticipation was not ascertained.
Note: Updated population controls are introduced annually with the release of January data.
SOURCE: Adapted from 35. Persons Not in the Labor Force by Desire and Availability for Work, Age, and Sex, in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, January 2008, http://www.bls.gov/cps/cpsaat35.pdf (accessed February 2, 2008)
Total not in the labor force78,74315,19221,34342,20730,03648,707
Do not want a job nowa74,04013,51019,25641,27527,91446,126
Want a joba4,7031,6832,0889332,1222,581
Did not search for work in previous year2,7489311,1486681,1731,575
Searched for work in previous yearb1,9557519392649501,005
Not available to work now56027223453223336
Available to work now1,395479705211726669
Reason not currently looking
Discouragement over job prospectsc36911019961226143
Reasons other than discouragement1,026370506150500526
Family responsibilities160311092137123
In school or training18014927310278
III health or disability1141166375758

to work but not currently looking for a job in 2007, 123,000 (18.4%) cited taking care of their home or family as their reason for not working; 78,000 (11.7%) were in school; and 58,000 (8.7%) were either ill or disabled. (See Table 3.7.) In contrast, among the 726,000 men who were available to work but out of the labor force in 2007, only 37,000 (5.1%) were taking care of their family or home; 102,000 (14%) were in school or training; and 57,000 (7.9%) were ill or disabled. Nearly a third (226,000 of 726,000, or 31.1%) of the men available to work but not currently looking for work reported discouragement over job prospects as the reason. Discouragement over job prospects was reported by only 143,000 of 669,000 women (21.4%) not currently looking for work.


views updated Jun 11 2018


The United States unemployment rate reached a post–World War II high of 9.7 percent in 1982. It remained high at 9.6 percent in 1983 as a result of the most severe economic recession since the Great Depression of the 1930s. The unemployment rate then dropped, approaching 5 percent in 1989, but again began increasing, reaching 7 percent in 1991 and rising almost to 8 percent in 1992. As the economy improved, the rate fell to 6.9 percent in 1993. By 1998 the U.S. unemployment rate had dropped to 4.5 percent and in 1999 reached a low of 4.2 percent. By April 2000 unemployment had declined to 3.9 percent, the lowest level in three decades.

With a creeping recession, unemployment once again began to rise in early 2001 and rose significantly following the terror attacks on the United States on September 11, 2001. In June 2003, 9.4 million people were out of work, and the national unemployment rate reached a peak of 6.4 percent. As of February 2004 the national rate had gradually been reduced to 5.6 percent. (See Figure 3.1.) Not counted in the unemployment rate figures are some previously laid-off workers, especially those over 55 years old, who have stopped looking for work.


In the third quarter of 2003 the United States, as compared to eight other countries, had the fourth lowest unemployment rate (6.1 percent). The United Kingdom (5 percent), Japan (5.2 percent), and Australia (5.9 percent) all had lower unemployment rates. Countries with higher unemployment rates at that time include Canada (7.2 percent), Italy (8.7 percent), Germany (9.1 percent), and France (9.3 percent). (See Table 3.1.)


Unemployment rates in the United States vary from state to state. In December 2003, the states with the highest unemployment rate were Alaska (7.7 percent), Oregon and Michigan (each 7.2 percent), Washington (6.8 percent), and Washington, D.C. (6.6 percent). North Dakota (3.2 percent), South Dakota (3.4 percent), Virginia (3.6 percent), and Nebraska (3.7 percent) had the lowest rates. (See Table 3.2.) The rates in Colorado and Washington, D.C., remained unchanged from 2002 to 2003, while Michigan had the highest increase (from 6.2 percent to 7.2 percent). Unemployment rates in six states—Mississippi, Utah, Idaho, Georgia, Arizona, and Pennsylvania—dropped by one full point or more.


Unemployment does not occur evenly in all occupations or sectors of society. Younger workers under 25

PeriodUnited StatesCanadaAustraliaJapanFranceGermany1Italy2SwedenUnited Kingdom
R = Revised.
Note: For all countries except the United States and Australia, 2003 annual rates are preliminary.
1Unified Germany for 1991 onward. Prior to 1991, data relate to the former West Germany.
2Quarterly rates are for the first month of the quarter.
3Break in series. Series breaks for the United States in 1997, 1998, 1999, 2000, and 2003 had virtually no effect on unemployment rates.
source: "Unemployment Rates in Nine Countries, Civilian Labor Force Basis, Approximating U.S. Concepts, Seasonally Adjusted, 1990–2003," in Foreign Labor Statistics, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, February 6, 2004 [Online] ftp://ftp.bls.gov/pub/special.requests/ForeignLabor/flsjec.txt [accessed February 18, 2004]

years of age are far more likely to be unemployed than older workers. Their jobs are often more marginal, and younger workers leave their jobs more often than older ones. They also have less seniority to protect themselves against layoffs.

While the annual average unemployment rate was 5.8 percent in 2002, those 16 to 24 years old experienced a rate of 12 percent. Young adults 20 to 24 years old (9.7 percent) had more than double the unemployment rate of workers 25 to 54 years old (4.8 percent). (See Table 3.3.)


In September 2003 blacks (11.2 percent unemployment rate) and Hispanics (7.5 percent) were considerably more likely to be out of work than whites (5.3 percent). Many blacks work in occupations that have suffered as the American economy has changed from an industrial to a service economy. In September 2003 black teenagers experienced an unemployment rate of 32.8 percent as compared to 15.2 percent for white teenagers. Black male teenagers (34.2 percent) were more likely to be unemployed than black female teenagers (31.6 percent). (See Table 3.4.) In 2003, a greater percentage of men 16 years and older (6.3 percent) were unemployed than women (5.7 percent). (See Table 3.5.)

A married person was much less likely to be unemployed than a single, widowed, or divorced individual. This observation held true across all races. In 2003, single, never married males (11 percent) had nearly triple the unemployment rate of married males (3.8 percent). Widowed, divorced, or separated males (7.3 percent) had nearly two times the rate of married men. While only 3.5 percent of white married men were unemployed, 9.7 percent of single white men and 6.9 percent of widowed, divorced, or separated white males were out of work.

Monthly rankings seasonally adjusted
RankStateDec. 2002 rateDec. 2003 ratepChange
1Mississippi7.05.0− 2.0
2Utah6.34.7− 1.6
3Idaho6.14.8− 1.3
4Georgia5.34.1− 1.2
5Arizona5.94.8− 1.1
6Pennsylvania6.15.1− 1.0
7New Hampshire5.04.1− 0.9
8West Virginia6.15.3− 0.8
9Alaska8.47.7− 0.7
9New Jersey6.05.3− 0.7
9North Dakota3.93.2− 0.7
12Florida5.34.7− 0.6
12North Carolina6.76.1− 0.6
14California6.96.4− 0.5
14Louisiana6.35.8− 0.5
14Missouri5.55.0− 0.5
14Nevada4.94.4− 0.5
14Rhode Island5.55.0− 0.5
14Wisconsin5.75.2− 0.5
20Kansas5.24.8− 0.4
20Wyoming4.44.0− 0.4
22Illinois6.76.4− 0.3
23Delaware4.34.1− 0.2
23Montana4.74.5− 0.2
23New York6.46.2− 0.2
23Virginia3.83.6− 0.2
23Washington7.06.8− 0.2
28Alabama5.95.8− 0.1
28Kentucky5.55.4− 0.1
28Oregon7.37.2− 0.1
28South Carolina6.26.1− 0.1
28Texas6.56.4− 0.1
33District of Columbia6.66.60.0
43New Mexico5.45.70.3
49South Dakota2.83.40.6
Rates shown are a percentage of the labor force.
p = preliminary.
Note: Data refer to place of residence. All estimates are provisional and will be revised when new benchmark and population information becomes available.
source: "Over-the-Year Change in Unemployment Rates for States, December 2002–December 2003," in Regional and State Employment and Unemployment (Monthly), U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, January 2004 [Online] http://www.bls.gov/web/laumstch.htm [accessed February 18, 2004]

Among blacks, 5.9 percent of married men 16 years and older were unemployed, compared to 9.8 percent of widowed, divorced, or separated men and 19 percent of singles. (See Table 3.5.)

The same situation held true for married, single, divorced, widowed, and separated women. While only 3.7 percent of married women and 6.1 percent of widowed, divorced, or separated women were unemployed, 9.1 percent of single females over 16 years of age were unemployed. Single white women over the age of 16 (7.4 percent) were more than twice as likely to be out of work as married white women (3.5 percent). Single black women (15.2 percent) were almost three times as likely to be unemployed as married black women (5.5 percent). (See Table 3.5.)


The more education an individual has, the less likely it is that he or she will be unemployed. In 2002, while 8.4 percent of those with less than a high school diploma were unemployed, only 2.9 percent of college graduates were. High school graduates with no college had an unemployment rate of 5.3 percent, while those with some college, but no degree, faced a 4.5 percent unemployment rate. (See Table 3.4.)


Some occupations are more susceptible to unemployment than others. In 2003 the unemployment rates for people employed in managerial and professional specialties (3.1 percent) were much less than the rate for those in production, transportation, and material moving occupations (7.9 percent). Within each occupational grouping, differences exist. While sales and administrative service occupations had 5.5 percent of its workforce unemployed, those in sales occupations were more likely to be unemployed (5.9 percent) than those in office and administrative support occupations (5.2 percent). (See Table 3.6.)

Gender also plays a role. In 2003 women in sales occupations had an unemployment rate of 7.0 percent, compared to 4.8 percent for men. While women and men had similar unemployment rates in managerial and professional specialty fields, more female workers in production, transportation, and material moving occupations were out of work than were males in that occupational category. (See Table 3.6.)

In 2003 those working in management, administrative, and waste services occupations were most likely to find themselves without jobs. Those working in hospital health care occupations were among the least likely to be unemployed. (See Table 3.7.)


In 2003 the average length of unemployment was 19.2 weeks, up from 13.2 weeks in 2001. The median duration of unemployment was 10.1 weeks. Almost one-third (31 percent) of the unemployed had been unemployed

Annual average20022003
Sex and age20012002Sept.Oct.Nov.Dec.Jan.Feb.Mar.Apr.MayJuneJulyAug.Sept.
Total, 16 years and older4.
16 to 24 years10.612.011.911.812.211.911.811.911.712.713.113.513.012.313.0
16 to 19 years14.716.516.215.116.816.416.817.117.718.018.519.318.416.617.5
16 to 17 years17.218.819.416.219.417.618.317.916.718.718.521.620.818.719.4
18 to 19 years13.
20 to 24 years8.39.79.610.
25 years and older3.
25 to 54 years3.
55 years and older3.
Men, 16 years and older4.
16 to 24 years11.412.813.112.312.812.612.412.512.413.814.314.314.512.714.4
16 to 19 years16.018.118.316.018.017.518.219.520.820.620.820.120.916.920.0
16 to 17 years19.121.121.517.221.218.519.319.118.021.421.523.822.820.722.6
18 to 19 years14.016.416.315.216.116.717.619.321.520.120.917.719.515.318.3
20 to 24 years9.010.210.510.410.
25 years and older3.
25 to 54 years3.
55 years and older3.
Women, 16 years and older4.
16 to 24 years9.611.110.511.311.511.311.111.311.011.511.812.511.312.011.5
16 to 19 years13.414.914.014.115.615.215.514.814.615.516.218.516.016.415.1
16 to 17 years15.216.617.415.217.416.617.316.815.516.215.819.518.916.716.3
18 t0 19 years12.213.811.513.314.414.214.112.313.715.517.118.014.516.613.7
20 to 24 years7.
25 years and older3.
25 to 54 years3.
55 years and older2.
*Data are not seasonally adjusted.
source: "Table 9. Unemployment Rates by Sex and Age, Monthly Data Seasonally Adjusted," in Monthly Labor Review, November 2003 [Online] http://www.bls.gov/opub/mlr/2003/11/cls0311.pdf [accessed February 18, 2004]

for less than five weeks, and a little less than 30 percent had been out of work for five to 14 weeks. About 16 percent were out of work 15 to 26 weeks and 22 percent for 27 weeks and over. (See Table 3.8.)

Gender and Age

Men tended to stay unemployed somewhat longer (an average of 19.8 weeks) than women (18.4 weeks) in 2003. Generally, the older the job seeker, the longer it took to find work. Young adults 16 to 19 years old were unemployed an average of 11.7 weeks, compared to 26 weeks for those 55 to 64 years old. (See Table 3.8.)

Because better-paying jobs usually take longer to find, men 45 years and older, who were more likely to be seeking higher-paying employment than either women or younger people, remained unemployed longer.

Race and Ethnicity

Workers of Hispanic origin were unemployed for an average of 15.9 weeks in 2003, as compared to whites (18 weeks). African American workers remained unemployed 22.7 weeks on average. (See Table 3.8.)

Marital Status

Widowed, divorced, or separated women were unemployed somewhat longer (21.2 weeks) in 2003 than those who had never been married (16.2 weeks) or those who were living with their spouses (19.5 weeks). Married men living with their wives (21.4 weeks) and those who were widowed, divorced, or separated (21.5 weeks) were out of work longer than never-married men (18.1 weeks). (See Table 3.8.)


In 2003 more than 35 percent of those unemployed in service occupations were out of work less than five weeks, and more than a third were still looking for work after 15 weeks. Twenty-seven percent of those seeking managerial and professional positions were unemployed less than five weeks, and another 44 percent still lacked jobs after 15 weeks. (See Table 3.9.)

Workers in managerial and professional occupations had among the longest average duration of unemployment (22.6 weeks). Many of these had experienced the down-sizing of staff in major companies. They were often older workers looking for higher-paying jobs. Production workers

Annual average20022003
Selected categories20012002Sept.Oct.Nov.Dec.Jan.Feb.Mar.Apr.MayJuneJulyAug.Sept.
Total, 16 years and older4.
Both sexes, 16 to 19 years14.716.516.215.116.816.416.817.117.718.018.519.318.416.617.5
Men, 20 years and older4.
Women, 20 years and older4.
White, total14.
Both sexes, 16 to 19 years12.714.514.213.914.513.815.215.515.615.415.316.515.815.015.2
Men, 16 to 19 years13.915.915.614.715.814.916.217.318.017.717.017.818.216.017.9
Women, 16 to 19 years11.413.112.713.113.012.714.213.713.113.213.715.213.414.012.4
Men, 20 years and older3.
Women, 20 years and older3.
Black or African American, total18.610.29.89.910.811.210.310.510.210.910.811.811.110.911.2
Both sexes, 16 to 19 years29.029.828.023.930.533.230.430.233.433.137.039.336.030.032.8
Men, 16 to 19 years30.431.334.424.930.034.533.
Women, 16 to 19 years27.528.321.522.731.
Men, 20 years and older8.
Women, 20 years and older7.
Hispanic or Latino ethnicity6.
Married men, spouse present2.
Married women, spouse present3.
Full-time workers4.
Part-time workers5.
Educational attainment2
Less than a high school diploma7.
High school graduates, no college34.
Some college or associate degree3.
Bachelor's degree and higher42.
1Beginning in 2003, persons who selected this race group only; persons selected more than one race group are not included. Prior to 2003, persons reported more than one race were included in the group they identified as main race.
2Data refer to persons 25 years and older.
3Includes high school diploma or equivalent.
4Includes persons with bachelor's, master's, professional, and doctoral degrees.
source: "Table 6. Selected Unemployment Indicators, Monthly Data Seasonally Adjusted," in Monthly Labor Review, November 2003 [Online] http://www.bls.gov/opub/mlr/2003/11/cls0311.pdf [accessed February 18, 2004]

had an average duration of 22.4 weeks of unemployment; sales and office occupations workers were out of work for an average of 19.2 weeks; and employees in service occupations endured unemployment for an average of 16.5 weeks. (See Table 3.9.)


During 2003, 35 percent of construction workers, 29 percent of transportation and utilities laborers, and 27 percent of financial activities workers found employment within five weeks of being jobless. Almost one-third (31.9 percent) of construction workers still needed jobs after 15 weeks. Construction workers were out of work an average of 15.2 weeks. Durable goods manufacturing workers had the longest average duration of unemployment (25.4 weeks). The manufacturing labor force as a whole took an average of 24.5 weeks to find a job. Nearly one-quarter (24.6 percent) were unemployed for less than five weeks, and another 49.1 percent were unemployed more than 15 weeks. (See Table 3.9.)


In 2003 most of those classified as unemployed had lost their jobs or had completed temporary jobs (55.1 percent). Slightly less than one-third (28.2 percent) had left the labor force and were returning. Only 7.3 percent were new entrants to the labor force. About 39 percent of the unemployed men and 33.2 percent of the unemployed women had lost their jobs permanently. (See Table 3.10.)

Duration by Reason of Unemployment

Less than one-third (30.7 percent) of workers who had lost their jobs or who had completed temporary jobs were unemployed less than five weeks. More than 40 percent were unemployed 15 weeks or more. One-half (50.8 percent) of those who were on temporary layoff were out of work for five weeks or less. Less than one-fourth (22.3) percent) of those who had lost their jobs found work in five weeks or less. (See Table 3.11.)

Thousands of personsUnemployment ratesThousands of personsUnemployment rates
Marital status, race, Hispanic or Latino ethnicity, and age20022003200220032002200320022003
Total, 16 years and over4,5974,9065.96.33,7813,8685.65.7
Married, spouse present1,6501,7513.63.81,3231,3523.73.7
Widowed, divorced, or separated6416996.87.38378426.16.1
Single (never married)2,3062,45710.311.01,6211,6748.99.1
White, 16 years and over*3,4593,6435.35.62,6782,6684.94.8
Married, spouse present1,3191,3793.33.51,0481,0653.43.5
Widowed, divorced, or separated5055416.56.96246025.85.6
Single (never married)1,6351,7239.19.71,0061,0017.47.4
Black or African American, 16 years and over*83589110.711.68588959.810.2
Married, spouse present2162086.05.91651555.85.5
Widowed, divorced, or separated1061188.79.81711807.67.8
Single (never married)51456617.119.052156114.215.2
Asian, 16 years and over*2172046.16.21721625.75.7
Married, spouse present871114.05.385904.85.2
Widowed, divorced, or separated19136.45.425285.36.8
Single (never married)1118010.28.561447.96.2
Hispanic or Latino, 16 years and over7648097.27.25906318.08.4
Married, spouse present2863115.15.12332676.57.2
Widowed, divorced, or separated951057.37.41221278.18.2
Single (never married)38339410.410.523423710.510.5
Total, 25 years and over3,1053,3684.75.02,5902,6604.64.6
Married, spouse present1,5661,6763.53.71,2091,2333.53.6
Widowed, divorced, or separated6176756.77.37827925.95.9
Single (never married)9221,0177.78.35996346.56.7
White, 25 years and over*2,3812,5364.34.51,8541,8534.04.0
Married, spouse present1,2461,3153.23.49539693.33.3
Widowed, divorced, or separated4885226.46.85805645.55.4
Single (never married)6476986.97.43213215.15.0
Black or African American, 25 years and over*5145688.08.85555897.67.9
Married, spouse present2072035.95.91531435.55.3
Widowed, divorced, or separated1001148.49.71611717.37.6
Single (never married)20725111.713.924127510.111.2
Asian, 25 years and over*1551575.05.41301315.05.2
Married, spouse present861094.05.380854.65.0
Widowed, divorced, or separated18136.25.425285.46.9
Single (never married)51357.55.825185.94.3
Hispanic or Latino, 16 years and over4855365.85.93834396.67.2
Married, spouse present2592844.94.91982316.06.8
Widowed, divorced, or separated88967.27.21071187.57.9
Single (never married)1381567.57.777907.27.7
*Beginning in 2003, persons who selected this race group only; persons who selected more than one race group are not included. Prior to 2003, persons who reported more than one race group were included in the group they identified as the main race.
Note: Estimates for the above race groups (white, black or African American, and Asian) do not sum to totals because data are not presented for all races. In addition, persons whose ethnicity is identified as Hispanic or Latino may be of any race and, therefore, are classified by ethnicity as well as by race.
source: "Table 24. Unemployed Persons by Marital Status, Race, Hispanic or Latino Ethnicity, Age, and Sex," in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, January 2004 [Online] http://www.bls.gov/cps/cpsaat24.pdf [accessed February 18, 2004]

Younger unemployed people and temporary workers tended to find jobs more quickly than older workers. More than half (59.2 percent) of the 16- to 19-year-olds who had lost their jobs or who had completed temporary jobs had found work in five weeks or less. (See Table 3.11.)


Unemployed workers use different methods to find new jobs. In 2003 most tried an average of 1.96 different techniques. Almost two-thirds (63.8 percent) approached an employer directly. More than one-half (54.6 percent) sent out resumes or filled out applications. Approximately

Thousands of personsUnemployment rates
Total, 16 years and over*8,3788,7745.
Management, professional, and related occupations1,4821,5563.
Management, business, and financial operations occupations6226273.
Management occupations4474303.
Business and financial operations occupations1751983.
Professional and related occupations8599293.
Computer and mathematical occupations1601814.
Architecture and engineering occupations1221244.
Life, physical, and social science occupations42483.
Community and social services occupations49572.
Legal occupations37352.
Education, training, and library occupations2032252.
Arts, design, entertainment, sports, and media occupations1601715.
Healthcare practitioner and technical occupations87881.
Service occupations1,5441,6816.
Healthcare support occupations1441715.
Protective service occupations1111293.
Food preparation and serving related occupations6226838.
Building and grounds cleaning and maintenance occupations4054477.
Personal care and service occupations2612505.
Sales and office occupations2,1102,0705.
Sales and related occupations9989955.
Office and administrative support occupations1,1121,0765.
Natural resources, construction, and maintenance occupations1,1551,2447.
Farming, fishing, and forestry occupations14213612.011.410.59.116.418.7
Construction and extraction occupations7888149.
Installation, maintenance, and repair occupations2252954.
Production, transportation, and material moving occupations1,5301,5557.
Production occupations8488077.
Transportation and material moving occupations6827487.
No previous work experience536641
16 to 19 years368424
20 to 24 years83117
25 years and over85100
*Includes a small number of persons whose last job was in the Armed Forces.
source: "Table 25. Unemployed Persons by Occupation and Sex," in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, January 2004 [Online] http://www.bls.gov/cps/cpsaat25.pdf [accessed February 18, 2004]

18.8 percent sought the help of friends and relatives. Just over 20.6 percent went to public employment agencies, and 8.2 percent visited private employment agencies. (See Table 3.12.)

New entrants to the job market were somewhat less likely to seek out employers directly (61.5 percent) than those who had lost their jobs or who had completed temporary jobs (66.2 percent). They were also less likely to use employment agencies than those who had been working and lost their jobs. (See Table 3.12.)


The labor force includes those working and those unemployed who are still looking for work. In 2003 more than half (53.4 percent) of those people who were not in the labor force were age 55 or older. More than six of 10 (62.2 percent) were women. (See Table 3.13.)

Reasons for Not Working

In January 2004, approximately 21 million people between the ages of 25 and 54 had been out of the labor force for all of 2003. (See Table 3.14.) Women accounted for nearly two-thirds of those individuals who were not actively participating in the labor force. The reasons they were not in the labor market differed from those reported by men. Of the women who were available to work but not currently looking for a job, 11 percent cited taking care of their home or family as their reason for not working; 10 percent were either ill or disabled, and 19 percent were in school. In contrast, among the men who were available to work but currently out of the labor force, 7

Thousands of personsUnemployment rates
Total, 16 years and over8,3788,7745.
Nonagricultural private wage and salary workers6,9267,1316.
Durable goods7897626.
Nonmetallic mineral products31315.
Primary and fabricated metal products1441266.
Machinery manufacturing97847.
Computer and electronic products1541549.
Electrical equipment and appliances41406.
Transportation equipment1361515.
Wood products41437.
Furniture and fixtures53527.
Miscellaneous manufacturing93817.
Nondurable goods4164046.
Food manufacturing1081066.
Beverage and tobacco products5112.
Textile, apparel, and leather110999.
Paper and printing67805.
Petroleum and coal products1396.
Plastic and rubber products52526.
Wholesale and retail trade1,2021,2376.
Wholesale trade2052265.
Retail trade9971,0116.
Transportation and utilities2742834.
Transportation and warehousing2502565.
Publishing, except Internet36404.
Motion picture and sound recording industries384710.311.210.512.210.19.2
Broadcasting, except Internet27275.
Internet service providers and data processing services136.86.57.1
Other information services24510.24.812.
Financial activities3203193.
Finance and insurance2162173.
Real estate and rental and leasing1041014.
Real estate68713.
Rental and leasing services36307.
Professional and business services1,0091,0427.
Professional and technical services4193965.
Management, administrative, and waste services358964511.
Administrative and support services57162611.712.610.612.213.113.2
Waste management and remediation services16174.

percent were taking care of their family or home, and 21 percent were in school or training. Discouragement over job prospects was the reason reported by 31 percent of men and 20 percent of women who were out of the labor force. (See Table 3.14.)

Still Wanted a Job

In 2003 only about 6.4 percent of those who were no longer part of the labor force still wanted a job, and 43.8 percent of those who wanted a job had looked for work during the previous year. (See Table 3.14.)

Thousands of personsUnemployment rates
Education and health services5706403.
Educational services1261453.
Health care and social assistance4444943.
Health services, except hospitals2402783.
Social assistance1121255.
Leisure and hospitality9611,0068.
Arts, entertainment, and recreation1711558.
Accomodation and food services7898518.
Food services and drinking places6747258.
Other services3013475.
Other services, except private households2392734.
Repair and maintenance1131336.
Personal and laundry services63694.
Membership associations and organizations63713.
Private households62747.68.810.317.77.38.0
Agricultural and related private wage and salary workers13914010.
Government workers5125682.
Self-employed and unpaid family workers2652942.
No previous work experience536641
1Industry detail will not sum to total because of minor changes in the industry classification system between 2002 and 2003.
2Data not shown where base is less than 35,000.
3Includes other industries, not shown seperately.
source: "Table 26. Unemployed Persons by Industry and Sex," in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, January 2004 [Online] http://www.bls.gov/cps/cpsaat26.pdf [accessed February 18, 2004]
Thousands of personsWeeks
15 weeks and over
Age, sex, race, Hispanic or Latino ethnicity, and marital statusTotalLess than 5 weeks5 to 14 weeksTotal15 to 26 weeks27 weeks and overAverage (mean) durationMedian duration
Total, 16 years and over8,7742,7852,6123,3781,4421,93619.210.1
16 to 19 years1,25155939929416712711.75.9
20 to 24 years1,49554847247522824716.08.5
25 to 34 years1,96062960472733039717.99.9
35 to 44 years1,81549453079131147921.511.9
45 to 54 years1,35632837165825640124.113.9
55 to 64 years71317318335712223526.114.5
65 years and over183555376284923.310.7
Men, 16 years and over4,9061,5181,4461,9438091,13419.810.3
16 to 19 years697308218171977412.06.0
20 to 24 years84129626727912815016.78.8
25 to 34 years1,09735633540617922717.99.8
35 to 44 years98825828744216527822.512.5
45 to 54 years76417320638515123424.814.7
55 to 64 years412951022157514026.815.9
65 years and over107313146143224.810.5
Women, 16 years and over3,8681,2671,1661,43563380218.49.8
16 to 19 years554250180123705311.25.8
20 to 24 years6542522051971009715.18.0
25 to 34 years86327326932115117018.010.0
35 to 44 years82723624334814720220.311.3
45 to 54 years59215416527310616723.213.0
55 to 64 years3027881142479625.213.4
65 years and over76242230141721.310.9
Race and Hispanic or Latino ethnicity
White, 16 years and over*6,3112,1391,8872,2851,0091,27618.09.4
Black or African American, 16 years and over*1,78744952081832948922.712.9
Asian, 16 years and over*366941091625310923.912.3
Hispanic or Latino ethnicity, 16 years and over1,44153544446222623515.98.5
Marital status
Men, 16 years and over:
Married, spouse present1,75149250775229945321.411.6
Widowed, divorced, or separated69919419830712518221.512.0
Single (never married)2,45783274188438649918.19.3
Women, 16 years and over:
Married, spouse present1,35242939452922430519.510.2
Widowed, divorced, or separated84222826135314520821.211.6
Single (never married)1,67461051155326428916.28.6
*Beginning in 2003, persons who selected this race group only; persons who selected more than one race group are not included. Prior to 2003, persons who reported more than one race group were included in the group they identified as the main race. well as by race.
Note: Estimates for the above race groups (white, black or African American, and Asian) do not sum to totals because data are not presented for all races. In addition, persons whose ethnicity is identified as Hispanic or Latino may be of any race and, therefore, are classified by ethnicity as well as by race.
source: "Table 31. Unemployed Persons by Age, Sex, Race, Hispanic or Latino Ethnicity, Marital Status, and Duration of Unemployment," in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, January 2004 [Online] http://www.bls.gov/cps/cpsaat31.pdf [accessed February 18, 2004]
Thousands of personsWeeks
15 weeks and over
Occupation and industryTotalLess than 5 weeks5 to 14 weeksTotal15 to 26 weeks27 weeks and overAverage (mean) durationMedian duration
Management, professional, and related occupations1,55642144369226342922.612.3
Management, business, and financial operations occupations62714317930511319224.014.0
Professional and related occupations92927826338715023721.711.0
Service occupations1,68160051756426829516.58.9
Sales and office occupations2,07064661481035145919.210.2
Sales and related occupations99533130435916219717.69.4
Office and administrative support occupations1,07631531045118926220.611.1
Natural resources, construction, and maintenance occupations1,24442739342419522916.89.1
Farming, fishing, and forestry occupations136514342231915.28.4
Construction and extraction occupations81429826525112013115.48.3
Installation, maintenance, and repair occupations2957985131527821.412.3
Production, transportation, and material moving occupations1,55545544565426638820.811.3
Production occupations80721921537314922522.412.9
Transportation and material moving occupations74823723028111716419.010.0
Agriculture and related industries146564644232114.38.1
Durable goods76418319238913425525.415.1
Nondurable goods4051051141867511122.912.9
Wholesale and retail trade1,24238738347221126118.710.1
Transportation and utilities3169291132597420.311.5
Financial activities3268992144568821.512.2
Professional and business services1,05729931844017826220.911.5
Education and health services89932027930013316717.18.8
Leisure and hospitality1,04639132832816216615.48.2
Other services34811997132617117.99.8
Public administration154435259263319.910.6
No previous work experience6412261912259413118.68.7
*Includes wage and salary workers only.
source: "Table 32. Unemployed Persons by Occupation, Industry, and Duration of Unemployment," in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, January 2004 [Online] http://www.bls.gov/cps/cpsaat32.pdf [accessed February 18, 2004]
Total, 16 years and overMen, 20 years and overWomen, 20 years and overBoth sexes, 16 to 19 years
Number of unemployed
Total unemployed8,3788,7743,8964,2093,2283,3141,2531,251
Job losers and persons who completed temporary jobs4,6074,8382,7022,8991,7081,751197188
On temporary layoff1,1241,1217016863603676268
Not on temporary layoff3,4833,7172,0002,2131,3481,384136120
Permanent job losers2,7012,8461,5371,6671,0821,1028277
Persons who completed temporary jobs7838714645462652825443
Job leavers8668183863763893579185
New entrants5366416588102130368424
Percent distribution
Total unemployed100.0100.0100.0100.0100.0100.0100.0100.0
Job losers and persons who completed temporary jobs55.055.169.368.952.952.815.715.0
On temporary layoff13.412.818.016.311.
Not on temporary layoff41.642.451.352.641.741.810.89.6
Job leavers10.
New entrants6.
Unemployed as a percent of thecivilian labor force
Job losers and persons who completed temporary jobs3.
Job leavers.
New entrants.
source: "Table 27. Unemployed Persons by Reason for Unemployment, Sex, and Age," in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, January 2004 [Online] http://www.bls.gov/cps/cpsaat27.pdf [accessed February 18, 2004]
Total unemployedDuration of unemployment
15 weeks and over
Reason, sex, and ageThousands of personsPercentLess than 5 weeks5 to 14 weeksTotal15 to 26 weeks27 weeks and over
Total, 16 years and over8,774100.031.729.838.516.422.1
Job losers and persons who completed temporary jobs4,838100.030.729.
On temporary layoff1,121100.050.832.316.910.86.2
Not on temporary layoff3,717100.024.628.347.119.128.0
Permanent job losers2,846100.022.327.350.520.030.5
Persons who completed temporary jobs871100.032.431.536.116.219.9
Job leavers818100.035.331.633.216.516.7
New entrants641100.035.229.835.014.620.4
Men, 20 years and over4,209100.028.729.242.116.925.2
Job losers and persons who completed temporary jobs2,899100.029.129.641.317.024.3
On temporary layoff686100.046.934.718.312.16.3
Not on temporary layoff2,213100.023.628.048.518.530.0
Permanent job losers1,667100.021.326.851.919.432.5
Persons who completed temporary jobs546100.030.531.538.115.822.3
Job leavers376100.031.829.039.217.521.7
New entrants88100.024.324.151.615.935.7
Women, 20 years and over3,314100.030.729.739.617.022.6
Job losers and persons who completed temporary jobs1,751100.030.228.840.918.322.7
On temporary layoff367100.053.829.916.39.37.0
Not on temporary layoff1,384100.024.028.647.520.626.8
Permanent job losers1,102100.021.727.650.721.529.2
Persons who completed temporary jobs282100.
Job leavers357100.035.633.830.616.214.5
New entrants130100.025.927.047.115.431.7
Both sexes, 16 to 19 years1,251100.044.631.923.513.310.1
Job losers and persons who completed temporary jobs188100.059.226.814.09.64.4
On temporary layoff68100.073.420.
Not on temporary layoff120100.051.330.318.412.06.4
Permanent job losers77100.051.131.617.411.36.0
Persons who completed temporary jobs43100.051.827.920.313.37.0
Job leavers85100.049.133.417.513.54.0
New entrants424100.040.331.827.914.113.8
source: "Table 29. Unemployed Persons by Reason for Unemployment, Sex, Age, and Duration of Unemployment," in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, January 2004 [Online] http://www.bls.gov/cps/cpsaat29.pdf [accessed February 18, 2004]
Thousands of personsMethods used as a percent of total jobseekers
Sex and reasonTotal unemployedTotal jobseekersEmployer directlySent out resumes or filled out applicationsPlaced or answered adsFriends or relativesPublic employment agencyPrivate employment agencyOtherAverage number of methods used
Total, 16 years and over8,7747,65363.854.617.218.820.
Job losers and persons who completed temporary jobs*4,8383,71766.254.320.422.326.410.814.02.15
Job leavers81881864.156.417.717.118.27.411.21.92
New entrants64164161.552.511.814.811.
Men, 16 years and over4,9064,17565.252.316.920.
Job losers and persons who completed temporary jobs*3,0242,29367.151.919.323.025.810.414.42.12
Job leavers42242265.153.418.
New entrants32032061.250.411.515.512.
Women, 16 years and over3,8683,47862.257.317.617.
Job losers and persons who completed temporary jobs*1,8141,42464.758.322.121.327.311.313.22.19
Job leavers39739762.959.517.
New entrants32132161.854.512.
*Data on the number of jobseekers and the jobsearch methods used exclude persons on temporary layoff.
Note: The jobseekers total is less than the total unemployed because it does not include persons on temporary layoff. The percent using each method will always total more than 100 because many jobseekers use more than one method.
source: "Table 34. Unemployed Jobseekers by Sex, Reason for Unemployment, and Active Jobsearch Methods Used," in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, January 2004 [Online] http://www.bls.gov/cps/cpsaat34.pdf [accessed February 18, 2004]
16 to 24 years25 to 54 years55 years and overMenWomen
Total not in the labor force72,70774,65812,97613,80020,35820,98039,37339,87827,08528,19745,62146,461
Do not want a job now168,02969,93211,25412,07918,28618,85738,48938,99624,99426,07343,03543,859
Want a job14,6774,7261,7221,7212,0712,1248848822,0912,1242,5862,603
Did not search for work in previous year2,6732,6319108821,1121,1296516201,1351,1271,5381,503
Searched for work in previous year22,0042,0968128389609952332629569961,0481,099
Not available to work now5655642722742522484143227231338333
Available to work now1,4391,531540565708747191220729765710766
Reason not currently looking:
Discouragement over job prospects33694571101342092485175226266143190
Reasons other than discouragement1,0701,075430431499499141145503499567576
Family responsibilities1501533137999420223435116118
In school or training238239195194414223126125112114
Ill health or disability10711316156172302650515662
1Includes some persons who are not asked if they want a job.
2Persons who had a job in the prior 12 months must have searched since the end of that job.
3Includes believes no work available, could not find work, lacks necessary schooling or training, employer thinks too young or old, and other types of discrimination.
4Includes those who did not actively look for work in the prior 4 weeks for such reasons as child-care and transportation problems, as well as a small number for which reason for nonparticipation was not ascertained.
source: "Table 35. Persons Not in the Labor Force by Desire and Availability for Work, Age, and Sex," in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, January 2004 [Online] http://www.bls.gov/cps/cpsaat35.pdf [accessed February 18, 2004]
16 to 24 years25 to 54 years55 years and overMenWomen
CategoryJan. 2003Jan. 2004Jan. 2003Jan. 2004Jan. 2003Jan. 2004Jan. 2003Jan. 2004Jan. 2003Jan. 2004Jan. 2003Jan. 2004
Total not in the labor force74,59676,09314,13514,66620,93421,19239,52740,23528,46128,75246,13547,340
Do not want a job now169,81771,18012,38413,00018,75618,89638,67639,28426,25226,64443,56544,535
Want a job14,7794,9131,7511,6662,1772,2968519512,2092,1082,5702,805
Did not search for work in previous year2,6842,7409648191,1201,2786016431,2001,1221,4841,618
Searched for work in previous year22,0952,1737868471,0581,0182503081,0099861,0861,187
Not available to work now4975032152232442263953201200296303
Available to work now1,5981,670572624814792211255808786790884
Reason not currently looking:
Discouragement over job prospects34494321171422702236267243248205184
Reasons other than discouragement1,1491,238455481544569150188564537584701
Family responsibilities1531583926949820343257120101
In school or training2433392192692370128170114169
Ill health or disability12514211169376215060476595
1Includes some persons who are not asked if they want a job.
2Persons who had a job in the prior 12 months must have searched since the end of that job.
3Includes believes no work available, could not find work, lacks necessary schooling or training, employer thinks too young or old, and other types of discrimination.
4Includes those who did not actively look for work in the prior 4 weeks for such reasons as child-care and transportation problems, as well as a small number for which reason for nonparticipation was not ascertained.
source: "A-37. Persons Not in the Labor Force by Desire and Availability for Work, Age, and Sex," in Employment and Earnings, U.S. Department of Labor, Bureau of Labor Statistics, Washington, DC, January 2004 [Online] ftp://ftp.bls.gov/pub/suppl/empsit.cpseea37.txt [accessed February 18, 2004]

Unemployment Rate

views updated May 23 2018

Unemployment Rate







There are two dimensions of the unemployment rate that sit uneasily with each other. First, national statisticians produce the official unemployment rate that policy makers, lobby groups, and media commentators use to summarize the state of the labor market. Second, economists attempt to explain the unemployment rate using microeconomic and macroeconomic models, which do not correspond directly with the statisticians framework. The various explanations of unemployment remain highly contested.


Prior to the Great Depression, limited efforts were made to collect labor market data. For example, the gainful worker framework in the United States used the ten-year census to enumerate employment activities with little attention being paid to unemployment. A worker was defined as a person who works for money (Smuts 1960, p. 71).

The mass unemployment in the 1930s created a demand for a broader enumeration system, and the modern concept of the labor force framework emerged after World War II (19391945) in response. This framework is made operational through the International Labour Organization (ILO) and the conference of International Labour Statisticians. These conferences develop procedures (definitions) for generating national labor force data (see http://laborsta.ilo.org/ for sources and methods). National statistical agencies implement these definitions in periodic sample surveys (usually monthly) and publish labor force estimates. The application of these definitions varies from country to country.

Figure 1 sketches the labor force framework. The labor force concept has two components: (a) criteria defining activityspecifically, willingness and search; and (b) a time period for assessing activity. The working-age population (persons above fifteen years, although some countries exclude those above sixty-five years) dichotomizes into

active (the labor force) and nonactive (not in the labor force). The labor force divides between employment and unemployment. A person is considered employed if he or she works at least an hour during the survey week. A person not working and actively searching for and willing to work is classified as being unemployed.

The official unemployment rate is the number of unemployed persons as a percentage of the labor force. While a rising rate usually indicates the economy is wasting resources and sacrificing income by not utilizing willing labor, it may also reflect a strengthening economy if the labor force is growing faster than employment.

International comparisons are difficult because countries vary the ILO definitions. However, the Organization for Economic Cooperation and Development (OECD) publishes standardized unemployment rates that reflect common definitions, and the U.S. Bureau of Labor Statistics publishes labor force statistics that convert foreign aggregates into estimates consistent with U.S. definitions.


The official unemployment rates ability to portray accurately the condition of the labor market is challenged because it is a narrow measure of labor underutilization. Critics call for broader measures to be published. There are many issues relating to the labor force concept itself, including whether unpaid workers should be included in the labor force and whether defense personnel and persons who are institutionalized should be included in the working-age population. Decisions made by national statistical agencies with respect to these cohorts influence the size of the labor force estimate and in turn the unemployment rate estimate.

In this section we concentrate on the issues arising from marginal workers and underemployment. Total labor underutilization (wastage) of willing labor resources arises for a number of reasons that can be divided between two broad functional categories: (a) unemployment or its near equivalent, which includes the official unemployed under ILO criteria and those classified as being not in the labor force on search criteria (discouraged workers), availability criteria (other marginal workers), and more broadly still, those who take disability and other pensions as an alternative to unemployment (forced pension recipients). These workers share the characteristic that they are jobless and desire work if vacancies were available. They are, however, separated by the statistician on other grounds; (b) suboptimal employment relations, where workers are classified as being employed but suffer time-related underemployment, such that there are insufficient hours of work. Suboptimal employment also arises from an inadequacy of the employment situation when skills are wasted, income opportunities denied, and/or workers are forced to work longer than they desire.

The official unemployment rate captures only a portion of this wastage. Broadening the concept of labor wastage involves recognizing other cohorts within the working-age population that share some similarities with the official unemployed.

First, focus on the rise of underemployment in many countries is increasing. While both sources of underemployment (time-related and inadequacy of the employment situation) are possible to measure, in practice, estimates of time-related (or visible) underemployment are more easily obtained. Involuntary part-time workers face constraints similar to those confronting the unemployed. As estimated underemployment has risen around the globe, the official unemployment rate measured as the percentage of persons in the labor force not employed underestimates the extent of labor wastage. Governments that extol the virtues of employment growth generated under their watch rarely express it in terms of full-time equivalents and thus rarely admit that, in part, people are shifting from unemployment to underemployment.

Second, workers who are not working but have abandoned active search because they perceive there are insufficient job opportunities are classified as not in the labor force. These hidden unemployed or discouraged workers are similar to the official unemployed because they would accept a job offer immediately. They are also unlike others who are not in the labor force such as retirees.

A broad rule of thumb is that the true labor underutilization rate (including underemployment and hidden unemployment) is estimated by doubling the official unemployment rate.

We can consider two other working-age population cohorts that are less attached to the labor force but who nonetheless, by their size, provide some guide to the potential labor resources available to any country. First, persons who desire work but are unable to start immediately and are not actively searching are called marginal workers and are excluded from the labor force. But with some institutional changes (such as improved child or aged care) this cohort would accept immediate offers of employment. Second, in many countries the number of disability pension recipients has increased. These persons are excluded from the labor force on activity grounds. The increasing trend is arguably the result of health professionals and/or governments easing their interpretations of what constitute a disability when job prospects are low. Given that many of these persons are at the bottom of the labor queue (especially older males), pushing them out of the labor force reduces the unemployment rate and is thus politically beneficial in times of recession. In recent years, in strong employment-growth countries (for example, Australia) new measures have been introduced to induce this cohort back into the labor force in recognition that their disabilities may not preclude some capacity to work.

The justification for considering the broader underutilization concepts relates to the concept of labor efficiency. An economy that cannot provide enough hours of work to match the preferences of the available labor supply and/or institutional structures to maximize the participation of its potential labor resources is less efficient than one that can achieve these goals.


Is high unemployment a problem? Involuntary unemployment imposes heavy costs on the economy in the form of forgone output of goods and services and associated income. Economists typically ignore the social costs of unemployment. Unemployment also exacerbates social ills such as crime, family breakdown, and physical and mental health problems. Human capital (skills) atrophies when unemployment persists.

Strong spatial impacts reinforce the loss of income that accompanies unemployment. As a regions unemployment rate rises, more mobile workers (the youth and educated) leave such that skills are lost, making it hard to attract new business investment.

Many economists (mostly those who advocate a voluntarist conception) claim that unemployment is not a significant policy problem because it reflects the normal functioning of the labor market whereby job seekers use spells of unemployment to search for information about the career prospects that are available to them before settling into a career path. They specifically note that high youth unemployment is merely information-seeking behavior.

Focusing on short unemployment spells may be misleading given that many workers drop out of the labor force when they cannot find a job. Further, the transition by youth from a sequence of casual jobs to a higher paying career-oriented job is largely confined to those who combined schooling with casual employment while they acquire the skills necessary to satisfy entry into the chosen career path. While the casual work may have provided them with generic skills such as punctuality and grooming, the issue remains that those locked into the casual labor market and not combining work with schooling do not make such career transitions. Instead they sequence through a range of dead-end, low-paying jobs interspersed with spells of unemployment. For them unemployment provides no information.


Economists have used various taxonomies to help explain unemployment but remain in deep disagreement about its causes. A major debate during the Great Depression centered on the extent to which unemployed individuals were acting voluntarily (classical position) or whether macroeconomic spending deficiencies imposed systemic constraints (lack of jobs) on individuals who become involuntarily unemployed (Keynesian position). Marx had earlier provided analysis supporting the demand-deficient explanation. In his 1936 General Theory, Keynes turned this idea into a full-blooded rejection of classical employment theory, and Keynesian theory subsequently dominated macroeconomics until the mid-1970s. It provides the most accessible unemployment taxonomy for the layperson by distinguishing between frictional, structural, and cyclical unemployment.

Jobs are continuously being created and destroyed as industries grow and wane, and these processes generate huge flows of workers moving between jobs. So even when demand for goods and services is strong, there will be a coincidence of unfilled vacancies and unemployed persons. This unemployment is called frictional because it arises from frictions that accompany job turnover. Workers take time to find and move to new jobs, and firms take time to locate required labor. While it clearly represents an irreducible minimum level, there is some confusion between this level of unemployment, which is likely to be low, and the concept of natural rate of unemployment, which is explained below. Both have been referred to as the irreducible level of unemployment.

Keynesian theory considers firms supply output and hires workers in response to the demand for goods and services. Demand-deficient or cyclical unemployment arises when the demand for labor overall (indicated by unfilled vacancies) drops below the number of workers who desire employment. The lack of jobs is experienced across all regions and industries. Cyclical unemployment reflects a systemic failure, with individuals powerless to improve their job prospects. Most economists agree that cyclical fluctuations in unemployment are caused by changes in the demand for labor rather than shifts in workers attitudes to work. As a result, most would agree that mass unemployment is involuntary. The policy solution to demand-deficient unemployment is to use expansionary fiscal and/or monetary policy.

The concept of structural unemployment sits uneasily within this framework. It reflects a mismatch between the requirements of available jobs and the characteristics of job seekers and arises even if there is no overall demand deficiency. This mismatch could be in terms of skills and/or locations and is of concern because the retraining and relocation of labor take time and resources. Structural mismatch may arise as changes in industry composition, reflecting changing consumer spending patterns, cause regional dislocation as growing industries seek new labor skills and declining industries shed skills. Adjustment is slow because the social settlement (where people live) is less mobile than the economic settlement (where jobs are created).

Technological change also creates skill obsolescence and a demand for new skills. A particular variant of this idea is found in the emergence in the 1970s of the deindustrialization literature, which focused on manufacturing decline (and to some extent the decline of mining) and the simultaneous rise of services. The amorphous concept of globalization is interwoven into these discussions to explain job loss in particular regions and industries as a result of employment being exported to lower cost regions and countries. If there is structural unemployment, expansionary policies will come up against bottlenecks and invoke inflationary impulses. Instead, training and mobility incentives are required to ease the mismatch. In this sense, structural unemployment is a microeconomic problem.

However, the boundaries between cyclical and structural causes are blurred. For example, theories of hysteresis conclude that the current state of the economy reflects where it has been. Accordingly, cyclical fluctuations create structural imbalances, which can be reversed through macroeconomic expansion. For example, recession generates skill obsolescence as old capital is scrapped and/or long-term unemployment causes skills to atrophy. This structural problem is reversed as the economy resumes growth because firms lower their hiring standards and provide training opportunities as a way around perceived skill shortages.

Clearly, the idea that individuals can experience involuntary outcomes underpins this taxonomy and overlaps with the voluntary/involuntary taxonomy that was central to the Keynes versus Classics debates in the 1930s and persists today. The Great Depression spawned macroeconomics as a new and distinct field of study, and center stage was the concept of involuntary unemployment, which challenged the neoclassical orthodoxy. The neoclassical competitive model postulated that the equilibrium unemployment rate is determined by the intersection of labor supply and demand, both functions of the real wage. As labor supply reflects workers preferences between labor and leisure (real wage is the opportunity cost of leisure) and labor demand reflects the marginal productivity of labor (profit-maximizing firms equilibrate the real wage with the marginal product), flexible real wages guarantee full employment. At the full employment real wage, any firm can find a suitable worker and any worker can find a suitable job. Any observed unemployment is deemed voluntary (worker preference for leisure). When the real wage is above the full employment level, the resulting unemployment is caused by real wage rigidities such as excessive legislated minimum wages and trade unions wage setting power.

This type of unemployment is termed classical and is solved by real wage cuts to restore the equilibrium level where labor demand equals labor supply. During the Great Depression, the government tried neoclassical remedies without success. In the 1930s, Kalecki and Keynes, building on the earlier work of Marx, challenged this dominant view. They saw mass unemployment as a systemic failure in demand for goods and servicesthat is, cyclical. Deficient effective demand causes firms to lay off workers. Neoclassical remedies would exacerbate this Keynesian unemployment because real wage cuts reduce worker incomes, further eroding effective demand. As firms adjusted to the lower activity by producing and employing less, an exogenous force in the form of expansionary fiscal and/or monetary policy was needed to push the economy toward higher activity levels.

Keynesian unemployment is involuntary because an individual unemployed worker cannot improve his or her job prospects in the face of employment rations imposed by deficient effective demand. This concept challenges the centerpiece of neoclassical theory known as Says law, which holds that aggregate demand always absorbs production, given price flexibility. Keynes showed that even with flexible prices, unemployment would persist until the deficient demand was eliminated. This observation underpinned the so-called Keynesian revolution that dominated the next thirty years of policy making. The period of full employment up until the mid-1970s gave policy makers confidence that the business cycle had been tamed.

Neoclassical economists argue that the concept of involuntary unemployment is implausible because it implies irrational behavior by individuals. Why would workers not simply accept lower real wages? Keynesians respond by arguing that workers prefer higher money wages at each real wage level because they have large nominal commitments (such as mortgages). Resisting a money wage cut was rational even if real wages were falling (via general price-level rises) because nominal commitments could be maintained. Keynesians also argue that workers are unable to engineer a real wage cut by accepting a lower money wage because the lower costs would lead to competitive price-cutting with no guarantee of a lower real wage. But this was moot because even if real wages fell, firms would still not hire if the cheaper labor produced goods and services that could not be sold (given deficient demand).

The major policy challenge for Keynesians in a period of full employment was inflation as economies approached full capacity. A vast literature has emerged since the late 1950s examining the relationship between the unemployment rate and inflationthe so-called Phillips curve (Phillips 1958). Policy makers came to believe that they faced a stable trade-off between the twin evils of unemployment and inflation and sought to choose the combination that maximized social welfare.

The challenge to the Keynesian macroeconomic consensus was ignited by the monetarist contributions of Friedman (1968) and Phelps (1967). They disputed the existence of a stable Phillips curve that could be exploited using aggregate demand policy. For example, the misperceptions hypothesis (Friedman 1968) considers that workers possess less short-run information than employers about the relationship between relative and absolute price levels. Accordingly, workers can be induced to supply more labor than is optimal given their preferences for as long as they are confused about their real wage level. They thus believe that a nominal wage rise is a real wage rise and supply more labor accordingly. Once they learn the truth, they withdraw this supply and equilibrium is restored. So any policy-induced reductions in the unemployment rate bought by tricking workers into supplying more labor than was optimal would evaporate. The long-term implication was that there is a natural unemployment rate that reflects the underlying microeconomic structure of labor supply and labor demand, and any attempts by fiscal and monetary authorities to drive unemployment below this equilibrium generate ever increasing rates of inflation.

The essence of all supply-side explanations is that workers quit when times are bad despite all evidence to the contrary. The procyclicality of quitting challenges the very core of the neoclassical labor market model.

However, the rising inflation associated with the Vietnam War and the oil price hikes in the early 1970s provided a fortuitous empirical backdrop to the growing backlash against Keynesian demand management policies. While there was scant empirical support to associate rising inflation with the mechanisms that underpinned the natural rate hypothesis, the revival of Says law was broadly accepted by economists and policy makers.

Keynesians such as Clower (1965) and Leijonhufvud (1968) provided resistance to the natural rate hypothesis by showing that no market signals accompanied mass unemployment such that firms would hire more workers, even though these workers had notional demands for their products. The problem was that without income these demands were not effective. The market coordination implicit in Says law failed in these situations, leaving the economy stuck in an under-full-employment equilibrium.

By the 1970s, the new labor economics reinstated neoclassical notions of voluntarism in explaining unemployment, a view that still dominates labor market policy today. Accordingly, unemployment arises from workers need to search for new jobs and jobless spells are voluntary, maximizing strategies in pursuit of career improvement. Workers balance the costs of search (time and forgone earnings) with the gains in future earnings that emerge from successful search. Importantly, welfare benefits are seen as subsidizing search and encouraging longterm unemployment.

The reality is that while most job search activity is done on the job, many unemployed workers experience frequent spells of unemployment interspersed with low-skill, low-paid employment. Segmented labor market theory uses this observation to argue that structural rigidities, principally due to hiring policies of employers, discriminate against disadvantaged groups and confine them to marginal jobs and status (Doeringer and Piore 1971).


The breadth of the acceptance of the new labor economics by economists and policy makers was expressed in the influential 1994 OECD Jobs Study, which provided a policy blueprint for economic policy reform aimed at reducing unemployment following the deep recession in 1991. Its theoretical foundations can be found, for example, in Layard, Nickell, and Jackman (1991), LNJ for short. While, economists such as LNJ mimicked the traditional neoclassical concern about trade union power and legislated minimum wages, they also focused on welfare payments as a cause of persistent unemployment. They argued that the provision of unemployment assistance subsidized inactivity by reducing the intensity and effectiveness of job search. As a result of this subsidy, the wage necessary to induce the worker to abandon unemployment and accept work (the so-called reservation wage) is higher. Further, various government charges on employment (such as superannuation and termination payments) drive a wedge between what the worker receives and what the firm pays, which discourages firms from increasing employment. It was also argued that the long-term unemployed had deficient skills and required retraining to improve their employability.

The Jobs Study concluded that long-term unemployment was the outcome of government intervention, other institutions (such as trade unions), and/or negative attitudes to work of the unemployed that created rigidities in labor supply. The Jobs Study advocated extensive supply-side reform with a particular focus on the labor market to eliminate rigidities that were inhibiting the capacity of economies to adjust, innovate, and be creative. Governments variously adopted the reform agenda. It was typically accompanied by a narrowing of the focus of monetary policy to inflation control, which used unemployment as an instrument to achieve price stability rather than as a policy target. Further, governments adopted fiscal conservatism (for example, the Stability and Growth Pact in Europe) to passively support their inflation-first monetary policy emphasis. Policy makers believed that disinflation policy would allow the economy, after an adjustment phase, to settle at the natural rate optimum, and as a consequence they did not worry about any alleged short-run negative impacts of disinflation on unemployment. They considered that the micro focus of the Jobs Study would ensure there were no impediments to reaching this supposed natural rate.

However, high unemployment persisted in many countries during the 1990s, which prompted critics of the OECD position to say that governments had been encouraged to abandon full employment in favor of full employability. The critics said that unemployment existed long before unions had grown and welfare transfers operated. They also said that it was implausible to interpret the mass unemployment of the Great Depression as a sudden labor supply withdrawal.

In recent years, partly in response to the reality that active labor market policies have not solved unemployment and have instead created problems of poverty and urban inequality, some notable shifts in perspectives are evident among supporters of the OECD approach. Various econometric studies sought to establish the empirical veracity of the OECD Job Study relationships between unemployment, real wages, welfare payments, and the like. They also sought to evaluate the effectiveness of active labor market program spending. Many construct their analyses in ways that are most favorable to the null that the OECD view is valid. The overwhelming conclusion to be drawn from this literature is that there is no consensus view (see Freeman 2005; Baker, Glyn, Howell, and Schmitt 2005).

In the face of the mounting criticism and empirical argument, the OECD has now significantly shifted its position. In the 2004 OECD Employment Outlook, it admitted that the evidence underpinning the neoclassical relationship between real wages and unemployment was fragile. In the 2006 OECD Employment Outlook, which followed a comprehensive econometric study of employment outcomes across twenty OECD countries between 1983 and 2003, the OECD (2006) found that:

  • There is no significant correlation between unemployment and employment protection legislation;
  • The level of the minimum wage has no significant direct impact on unemployment; and
  • Highly centralized wage bargaining significantly reduces unemployment.

These conclusions undermine the basic causality in the Jobs Study. They also confound those who have relied on the OECDs previous work, including the Jobs Study, to push through harsh labor market reforms, retrenched welfare entitlements, and policies aimed at reducing the role of trade unions.

Internationally, sentiment is growing that paid employment measures must be a part of the employment policy mix if unemployment is to be reduced. The lack of consideration given to job creation strategies in the unemployment debate stands as a major oversight. Recognition is growing that programs to promote employability cannot, alone, restore full employment and that the national business cycle is the key determinant of regional employment outcomes (Mitchell 2001; Peck 2001).

SEE ALSO Business Cycles, Real; Natural Rate of Unemployment; Underemployment; Unemployable; Unemployment


Baker, D., A. Glyn, D. Howell, and J. Schmitt. 2005. Labor Market Institutions and Unemployment: A Critical Assessment of the Cross-Country Evidence. In Fighting Unemployment: The Limits of Free Market Orthodoxy, Chapter 3, ed. D. Howell. Oxford: Oxford University Press.

Clower, R. J. 1965. The Keynesian Counterrevolution: A Theoretical Appraisal. In The Theory of Interest Rates, eds. F. H. Hahn and F. P. R. Brechling. 103125. London: Macmillan.

Doeringer, P. B., and M. J. Piore. 1971. Internal Labor Markets and Manpower Analysis. Lexington, MA: Heath.

Freeman, R. 2005. Labour Market Institutions Without Blinders: The Debate over Flexibility and Labour Market Performance. International Economic Journal 19 (2): 129145.

Friedman, M. 1968. The Role of Monetary Policy. American Economic Review 58: 117.

Hauser, P. M. 1949. The Labor Force and Gainful Workers-Concept, Measurement, and Comparability. American Journal of Sociology 54 (4): 338355.

Layard, R., S. Nickell, and R. Jackman. 1991. Unemployment, Macroeconomic Performance and the Labour Market. Oxford: Oxford University Press.

Leijonhufvud, A. 1968. On Keynesian Economics and the Economics of Keynes: A Study in Monetary Theory. New York: Oxford University Press.

Mitchell, W. F. 2001. The Unemployed Cannot Find Jobs That Are Not There! In Unemployment: The Tip of the Iceberg, eds. W. F. Mitchell and E. Carlson, 85116. Sydney, Australia: CAER/UNSW Press.

Organisation for Economic Cooperation and Development. 1994. The Jobs Study. Paris: OECD.

Organisation for Economic Cooperation and Development. 2006. Employment Outlook: Boosting Jobs and Incomes. Paris: OECD.

Peck, J. 2001. Workfare State. New York: Guilford.

Phelps, E. S. 1967. Phillips Curves, Expectations of Inflation and Optimal Unemployment over Time. Economica 34: 254281.

Phillips, A. W. 1958. The Relation Between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 18611957. Economica 25: 283299

Smuts, R. W. 1960. The Female Labor Force: A Case Study in the Interpretation of Historical Statistics. American Statistical Association Journal 55(289): 7179.

William Mitchell


views updated May 14 2018




Unemployment occurs when an individual wants work but is unable to find it. It can be traumatic for the individual and other family members involved but is also a necessary part of the mature, modern economy. It is common for workers to move from job to job and many will experience (short) periods of unemployment. But when unemployment rises above this essential and unavoidable core, there emerge overlapping social, political, and economic problems that have bedeviled European economics and politics since World War I.


In practice unemployment is defined and measured by administrative systems that vary between countries. During the twentieth century European countries strengthened and broadened these systems, which makes it difficult to produce data that are comparable over time and between countries. Nevertheless, scholars such as the economist Angus Maddison and international bodies such as the International Labor Office have attempted to produce standardized unemployment rates. Although the figures in table 1 are generally accepted, economists Barry Eichengreen and T. J. Hatton have argued that they are probably underestimates for the interwar years. The national averages for standard historical periods in table 1 are complemented by figure 1, which shows unemployment in the big three European economies. From these two sources, there appear to be two separate effects and four different periods: two phases of relatively high unemployment (1920–1938 and 1979–2005 and after) surrounding the lower levels in the long postwar boom (1950–1973) and the transition phase (1973–1979), with cycles of high and low unemployment within each of these subperiods but especially in the first and last subperiods.

Unemployment was very low during World War I but soared during the international recession of 1920–1922, which hit Britain exceptionally hard. In the world slump of 1929–1932, European unemployment rates again rose dramatically, though France (for which there is very incomplete data) escaped the worst effects until 1933–1934. Historians agree that the slump was caused by a combination of the plunge of the U.S. economy after the Wall Street crash, instability in international finance, and plummeting prices for food and raw materials, though they disagree profoundly on their relative importance, and the slump's impact in each country depended upon the relative exposure to each. The worst-hit countries were the United Kingdom, which was heavily dependent on world trade and payments, and Germany and Austria, which were disrupted by the withdrawal of U.S. loans from Europe as U.S. share prices surged and collapsed in 1928–1929. The falling prices of major foodstuffs were especially damaging in Central and Eastern Europe, notably Yugoslavia, Romania, Bulgaria, Hungary, and Poland, where between one-half and three-quarters of the total

1 1924–1929 only
2 1921, 1926, and 1929 only
3 1939 only
4 1921–1929 only
5 1931, 1936, and 1938 only
6 1930–1934 and 1937–1938 only

population was dependent upon agricultural systems that were inefficient and badly organized. In the worst cases, agricultural product prices halved, hitting farms that had already gone heavily into debt, reducing spending and depressing the rest of these economies.

Unemployment was generally low in Western Europe during the 1940s as a result of wartime and then reconstruction demands, though Germany experienced high unemployment after 1945 as its fate was discussed among the victorious powers. Although European postwar recovery was aided by U.S. financial assistance (Marshall Plan) the dynamic of reconstruction and renewed growth drove the Western European economy (and, indeed, the Soviet bloc) to historically low unemployment rates. Low unemployment was all the more impressive in the light of significant migration into Western Europe and the widespread growth of paid employment for married women. The boom eventually burst in 1973 amid the massive oil-price rises associated with OPEC 1. The 1970s saw slowly rising unemployment, rising inflation, and intense social conflict over the direction of economic policy and marked a transition to the disturbed conditions of the late twentieth century. Since 1980 Europe has again suffered from the effects of international instability in finance and trade but has also been forced to make major technological changes to cope with rising energy costs and competition from lower-wage economies such as China, Brazil, and Mexico. The wealthier European economies have confronted these pressures with differing degrees of success but almost invariably have seen contraction of the number of jobs in manufacturing and relative expansion of the service sector. The availability of standardized unemployment statistics for peripheral Western European economies (since the early 1980s) and comparable data on the transition economies of the former Soviet bloc (for the 1990s) in table 2 should also be noted. Clearly unemployment in these countries has generally been higher than in Western Europe but their experiences have been very mixed.


The labor market is turbulent, with new workers joining (from school, university, or the household), older workers leaving (to pensions or family support), and some losing jobs while others find new employment. While any worker can experience unemployment, some groups are hit harder than others. Because the main slumps of 1920–1922, 1929–1932, 1979–1982, and 1991–1994 had their origins in the international economy, those parts of the economy (mainly manufacturing and agriculture) that depend on exports or compete with imports suffered highest unemployment. In the depression of 1929–1932, Britain experienced problems in shipbuilding, iron and steel, and textiles in addition to the longer-run problems of coal mining, while the German economy collapsed so spectacularly (full-time employment fell from 20 million in mid-1929 to 11.4 million in January 1933) that unemployment was spread comparatively evenly around the economy. In the recessions of the 1980s and 1990s, there were such substantial falls of manufacturing employment across Western Europe that commentators began to speak of "deindustrialization."

Vulnerability to unemployment has tended to vary with the worker's level of skills; managers are more secure than skilled manual workers who in turn are more secure than the unskilled. When older workers become unemployed it is often more difficult to find work, and unemployment is relatively high among workers over age fifty. Younger workers have also experienced problems. In interwar Britain, for example, those between ages fourteen (the school-leaving age) and twenty-one (when adult wages were paid) received low pay and experienced relatively low unemployment. But when unemployment rose in the 1970s and 1980s, younger workers were among the first to be displaced, and Britain, France, Italy, and Spain had high youth unemployment rates in the 1980s and 1990s.

In all countries, and throughout the period since 1914, female rates of unemployment have been lower than male. This reflects social convention whereby women have been expected to withdraw from paid labor to perform domestic duties. Furthermore, until the 1950s, women were supposed to work only until marriage. Indeed, in many countries welfare systems were organized around this assumption. Cultural attitudes reinforced these patterns; many married men regarded paid employment for their wives as a failure of their role as family protector and breadwinner. These attitudes began to change, however, as, led by the Scandinavian welfare economies, employment patterns shifted from "masculine" manufacturing to expansion of feminized work in the service sector (always allowing for the existence of feminized work in manufacturing and male jobs in the service sector), and full employment put more general pressure on the labor market. Nevertheless, even in the early twenty-first century, many married women losing their jobs continue to return to domestic duties rather than registering as unemployed and seeking new paid employment.

The other major variation in vulnerability to unemployment concerns ethnic group. For a

Czech RepublicHungaryIrish RepublicPolandPortugalSlovak RepublicSpain
1 1982–1990 only
2 1983–1090 only
3 1993–2004 only
4 1992–2004 only
5 1994–2004 only

variety of reasons French unemployment between the wars was less severe than in Germany and the United Kingdom, and the French managed to accommodate to the problem by restricting the entry into the country of foreign workers. As a result, Poles, Italians, Belgians, and Czechs bore the brunt of redundancies and the burdens of unemployment. Similar influences have operated since 1980. In Germany the Gastarbeiter ("guest" workers, i.e., immigrants) experienced higher rates of unemployment than Germans; the same was true of North Africans in France and Afro-Caribbeans and Asians in the United Kingdom. Ethnicity compounds other problems; in the early 1990s rates of unemployment among North African youths in France were well above the national average, as were rates among black youths in the United Kingdom.


Into the late nineteenth century the unemployed were regarded as feckless inadequates who should be treated by "relief" that was demeaning to the recipient and often separated the frequently unemployed "residuum" of society from the respectable majority. This harshness began to soften with a growing understanding of the regular pattern of booms and slumps and political pressure from trade unions and workers' parties. As a result, the wealthier and more progressive countries developed unemployment insurance to tide the unemployed over cyclical slumps. Some governments, notably the United Kingdom, extended unemployment insurance between 1914 and the mid-1920s under further pressure from organized labor, so that unemployment insurance covered much of the manual workforce.

The rapid rise in European unemployment in 1929–1932 resulted in lower tax revenues and increased public expenditures to support the unemployed. National budgets went into deficit and governments defaulted on international loans. They sought salvation in economic nationalism, limiting the convertibility of domestic currency into gold and foreign exchange and restricting imports, hoping to stimulate domestic employment. But this was not enough; European politics was gripped by crisis as unemployment rose rapidly in 1931–1932. In agricultural Eastern Europe, widespread rural distress forced governments to introduce emergency measures. Everywhere the unemployed protested and demanded support from governments, best summed up by the demand of the British Trades Union Congress for "work or maintenance." Simultaneously, employers demanded wage cuts and an end to public protests.

In Germany, rising unemployment forced the Heinrich Brüning and Franz von Papen governments into increasingly experimental "work creation," which had little impact on unemployment levels. The inability of democratic governments to cope drove opposition groups to the streets. The Nazis campaigned vigorously for more dynamic work creation and their electoral popularity increased. When they seized power in 1933, the Nazis used the full muscle of the state to generate recovery, insulating Germany from the world economy through elaborate controls over foreign exchange and trade. A massive program of public works, especially in building and road construction, and a comprehensive planning system helped unemployment fall rapidly (figure 1) and increased the government's popularity. From 1934 the central focus of Nazi planning switched to rearmament, though historians disagree on precisely which parts of public expenditure can be classified in this way. The Nazi government achieved the most spectacular reduction of unemployment in Europe in the 1930s but at the cost of a militaristic, dictatorial system that ruthlessly persecuted opponents and followed increasingly risky foreign policies.

In Britain, liberal democratic institutions remained in place despite widespread dissatisfaction with continuing mass unemployment. There were major policy changes, however, with withdrawal from the international financial system (the gold standard) and international free trade. The British political system emerged virtually unscathed from the mass unemployment of the 1930s despite marches of the unemployed and constant pressure in Parliament, thanks to the regional concentration of unemployment away from the prosperous Midlands and southeast and the early signs of recovery (late 1932), which was well sustained until 1937. Sweden combined innovative policies within a stable constitutional framework and established the foundations of the "Swedish model." Popular dissatisfaction over the pace of recovery brought a change of government in 1934, with the Social Democrats dominating a new coalition. The new government introduced loan-financed public works to accelerate recovery, as proposed by Swedish unions and Stockholm economists. This "deficit-financed" public works program subsequently received prominence as the first example of Keynesian policies in a democratic country.

The authority of Keynesian economics rose during World War II; John Maynard Keynes and his followers occupied key posts in British economic policy making, and Britain's wartime economic stability was widely noted by governments-in-exile. It now seemed possible for European governments to promise postwar "full employment" and a "welfare state" for the unemployed, the sick, and the old as long as trade unions pledged not to exploit full employment with big pay demands. This package seemed highly successful, with unemployment very low across Europe during the long boom (table 1, figure 1). It is now generally agreed, however, that the special conditions in the European economy (high investment, rapidly growing intra-European trade) were more important than economic policy in securing full employment. The commitment to Keynesian policies was tested for the first time in the 1970s, but against a background of rising prices (inflation) rather than the falling prices (deflation) that Keynes had imagined, and with very mixed results. In general, growth slowed and unemployment rose simultaneously with inflation, creating a new problem, "stagflation" (as identified by Michael Bruno and Jeffrey D. Sachs).

The most interesting response to stagflation was the further development of the Swedish model. Trade unions allowed managers to determine enterprise staffing levels and technologies of production and settled wages nationally at levels that would keep Swedish industry competitive, but they relied on governments to give generous benefits and retraining to displaced workers. Although formally described as the "Swedish" model, variants were found in all the small, open economies of Western Europe. This approach coped well with rising unemployment in the 1970s and early 1980s but came under increasing strain in country after country as wage pressures mounted and the cost of supporting and retraining displaced workers grew. Britain negotiated the 1970s with some difficulty, suffering seesawing inflation and rising unemployment. The apparent ineffectiveness of traditional Keynesian policies helped to win the 1979 general election for Margaret Thatcher, who promised to concentrate on reducing inflation rather than unemployment and cut state intervention. Her government blamed rising unemployment in the 1970s on increasing trade union power, which they attacked with a range of policies. Under Thatcherism unemployment remained very high throughout the 1980s, falling only in 1987–1989, when inflation began to rise once more. The Thatcher experiment is much studied and remains very controversial, but its analysis of British unemployment now seems limited.

Perhaps the most interesting response to rising unemployment was President François Mitterrand's experiment of 1981–1983 in France. Under the presidency of Valéry Giscard d'Estaing, French policy against stagflation concentrated on inflation reduction, allowing French unemployment to quadruple between 1974 and 1981 amid growing industrial problems. The resulting popular discontent contributed to the election in 1981 of a socialist government under Mitterrand, committed to Keynesian increases in public expenditure and radical industrial policies to reduce unemployment and modernize industry. However, unemployment continued to rise, inflation accelerated, and the French balance of payments deteriorated. After 1983 the focus of policy switched back to curbing inflation, and French unemployment has remained high. French governments, like their German counterparts, reacted to union pressures by granting employment protection and subsidies to industries in competitive difficulty. In an increasingly powerful analysis, based on the work of the U.S. economist Mancur Olson, both Germany and France are seen as examples of "Eurosclerosis," condemned by inflexible labor markets to persistent unemployment and "jobless growth," though this analysis is questioned in both France and Germany. Thus, in policy making, solving unemployment has involved calculating the impact of strategy on the price level, the balance of payments, the level of government spending, and the strength of the currency. The heady optimism of the 1960s that the unemployment problem had been banished can now be seen to have been misplaced.


Poverty was the most obvious consequence of unemployment in both the 1930s and 1990s. The jobless tended to be manual workers with few assets upon which to draw when redundancy struck. In the 1930s unemployment tended to hit male breadwinners with unfortunate consequences for family incomes. Benefits were paid to the unemployed in both periods, but in the 1930s they were well below even modest subsistence levels. In all countries insurance benefits lasted for a finite period, followed by various forms of less generous "dole." The fascinating study by Marie Jahoda and others of unemployment in the Austrian village of Marienthal in the early 1930s found that four-fifths of unemployed families had allotment gardens for vegetables. Diets were dull and boring, and nutritionists found poor standards of health in areas where unemployment was highest. A British survey of the 1930s found that one in three of the wives of the unemployed they visited was in poor health; the needs of husbands and children took priority at mealtimes. Where life was so bleak small luxuries helped to sustain morale, and George Orwell's Road to Wigan Pier (1937) illustrated the need for something "tasty" to brighten dreary lives. Interwar investigators also found complex links between unemployment and ill health. When recessions hit, employers tended to lay off their least productive workers first, and in general there were advantages to the unemployed in receiving sickness or disability rather than unemployment benefits. All countries experienced public demonstrations from the unemployed. These could often be violent, as in Germany during the early 1930s as rival political factions sought control of the streets. But political violence was worst in the early 1930s in those states that had been subject to the "victors' peace" at Versailles and where governing institutions had limited legitimacy.

Similar trends are evident in the 1980s and 1990s. All European countries have seen rising levels of poverty despite the growth of employment among married women since the 1950s. Social scientists have reaffirmed the impact of unemployment on mental and physical health, but the most interesting contrasts concern the impact on social stability. Europe was shaken in the late 1980s and early 1990s by a series of violent demonstrations against unemployment, often by ethnic groups who experienced high unemployment and limited concern from the political establishment over their plight. Many European governments have become concerned that the disaffection among these groups in the decaying industrial centers or on the fringes of major conurbations has provided a seedbed for Islamic fundamentalism.

In the 1930s the most common response from the unemployed was apathy. Governments bought social peace for the majority by benefit entitlements. When industrial depression was regionally concentrated, communities could "settle down" to "life on the dole" as unemployment became the norm and families struggled to cope in much-reduced circumstances. Of the five hundred families in Marienthal, more than four hundred had neither income from nor prospects of work. The vast majority of what little money these families commanded was spent on food, and the struggle for daily existence bred apathy. Orwell found similar conditions in the depressed coalfields of northern England. But the contrasts should not be exaggerated. Despite the growth of employment for married women after 1950, the vast bulk of Europe's unemployed since the 1980s have also faced drastically reduced lives and have coped by adapting to the reality of their conditions. In absolute terms, poverty is less intense and widespread than it was in the 1930s, but the unemployed remain on the fringes of European social and political life.

See alsoDepression; Inflation; Labor Movements; Strikes; Trade Unions.


Bruno, Michael, and Jeffrey D. Sachs. Economics of Worldwide Stagflation. Cambridge, Mass., 1985.

Eichengreen, Barry, and T. J. Hatton, eds. Interwar Unemployment in International Perspective. Dordrecht, Netherlands, 1988.

Jahoda, Marie, Paul F. Lazarsfeld, and Hans Zeisel. Marienthal: The Sociography of an Unemployed Community. New York, 1971. Translation of the original 1933 German edition.

Maddison, Angus. Dynamic Forces in Capitalist Development: A Long-Run Comparative View. Oxford, U.K., 1991.

Olson, Mancur. "The Varieties of Eurosclerosis: the Rise and Decline of Nations since 1982." In Economic Growth in Europe since 1945, edited by Nicholas Crafts and Gianni Toniolo, 73–94. Cambridge, U.K., 1996.

Orwell, George. The Road to Wigan Pier. London, 1937.

Alan Booth


views updated May 23 2018


Unemployment is widely regarded as a major social and economic global problem. When referring to someone as unemployed, most people have in mind a state consistent with the International Labour Office's (ILO) definition, namely a person who does not have a job, is available for work, and is actively looking for work (ILO 1998). This is certainly the case for government agencies, like the Bureau of Labor Statistics in the United States, that publish unemployment statistics. These statistics are used in a variety of situations, but mostly as an indication of the underuse of a nation's resources, and to inform on the economic and social hardship associated with the absence of employment. The data listed in Table 1 show that unemployment rates are high for most countries. These high unemployment rates have been extensively studied. See, for example, Richard Jackman (1997a) for OECD countries, Richard Jackman (1997b) for Central and Eastern Europe, Albert Berry, Maria Mendex, and Jaime Tenjo (1997) for Latin America, and Anh Le and Paul Miller (2000) for Australia. The findings from this research indicate that the unemployment experience is now very well documented.

Most of the people who become unemployed remain without work for very short periods. However, there is also a hard core of unemployed who remain without work for long periods of time. The adverse consequences of unemployment are much more acute for this group.

Consequences of Unemployment

Unemployment has obvious and well-documented links to economic disadvantage and has also been connected in some discussion to higher crime rates (Cantor and Land 1985; Ottosen and Thompson 1996), especially among the young (Britt 1994), suicide, and homicide (Yang and Lester 1994; Ottosen and Thompson 1996). Garry Ottosen and Douglas Thompson (1996) broaden the consequences of unemployment, relating it to increases in the incidences of alcoholism, child abuse, family breakdown, psychiatric hospitalization, and a variety of physical complaints and illnesses. Some researchers have emphasized the importance of preventing youth from falling into unemployment traps. Robert Gitter and Markus Scheuer (1997) suggest that unemployment among youth not only causes current hardship, but may also hinder future economic success. This is because unemployed youths are not able to gain experience and on-the-job training and because a history of joblessness signals that the individual may not have the qualities that are valued in the labor market.

Unemployment may impair the functioning of families (see, for example, Liker and Elder 1983; Barling 1990) by affecting the parents' interactions with their children and the interactions between partners. Although it has been shown that unemployed parents spend more time with their children, the quality of these interactions suffers in comparison with those of employed parents. Unemployment, particularly among male partners, is also likely to lead to major role changes in the home. For example, whether it is because they

table 1
Unemployment rates by region, 2000
regionunemployment rateregionunemployment rate
note: the unemployment data for south/central america include those who are aged 10 years and over.
the unemployment rates for egypt, suriname, and turkey are for the year 1999.
source: international labour office, http://laborsta.ilo.org/
north america middle east 
united sates4.0turkey7.3
south/central america united arab emirates2.3
costa rica5.2algeria29.8
europe asia 
france9.6korea, republic of4.1
sweden4.7sri lanka8.0
united kingdom5.5thailand2.4
eastern europe oceania 
czech republic8.3new zealand6.0

have more time or they feel that they have to undertake additional household duties when they are no longer the financial provider for the family, unemployed husbands are more likely to increase their participation in domestic activities (e.g., household tasks, shopping, meal preparation). In some circumstances, the loss of financial responsibility among husbands may lead to discontent within the marriage: unemployed husbands are more likely to have disagreements and arguments with their spouses than are employed husbands, and this has the potential to lead to spouse abuse and marriage dissolution.

It is very difficult to place a dollar figure on many of the social costs that unemployment imposes on the individual, his or her family, and society. Given the gravity of the problems created, the cost would seem to be enormous. Attempts have, however, been made to estimate the economic cost associated with unemployment. Ottosen and Thompson (1996, p.5) noted that "the United States loses a little less than one percentage point of potential gross domestic product (GDP) or output for each one percentage point of unemployment. This implies that an unemployment rate of 7 percent costs the United States at least $400 billion annually in foregone output. This is more than $2,000 for every man, woman, and child over 16 years of age." Similarly, in Australia, Peter Kenyon (1998) calculated that the loss of GDP associated with an unemployment rate above the full-employment rate is the equivalent of one year's worth of GDP over the past two decades.

In addition to the loss of GDP, high unemployment increases the burden on social welfare programs. These include unemployment insurance programs and other types of welfare, such as food stamps, Medicaid, Medicare, and Supplemental Security Income (Ottosen and Thompson 1996). There are also intergenerational effects, as unemployment of parents will limit their capacity to finance the schooling of their children. As education is the primary means of social mobility, this intergenerational effect will give rise to an inheritance of inequality.

Problems with the Statistics

Unemployment statistics are used in a variety of situations. Users of these statistics are usually well acquainted with at least some of their deficiencies. The main issue is that the conventional definition of unemployment does not capture some major categories of the underuse of labor. These include visible underemployment (i.e., an employed person who works fewer hours than desired), invisible underemployment (i.e., an employed person whose actual working time is not used to potential), and discouraged workers (i.e., those who no longer seek work due to their perception that suitable jobs are not available). Underemployment is typically a more important issue for developing countries than it is for industrialized countries. Unemployment statistics for developing countries are also difficult to interpret because of varying definitions (see also Berry et al. 1997).

Researchers in several countries have examined some alternative definitions of unemployment that might address these shortcomings of official unemployment statistics. John Bregger and Steven Haugen (1995) discussed alternative unemployment indicators that would allow certain groups to be added to the unemployment statistics, such as involuntary and voluntary part-time workers. Aldrich Finegan (1978) examined the importance of discouraged workers and argued that they represent losses for society and for themselves that are no different from the output foregone and income lost because of unemployment. Adjusting the unemployment statistics for these deficiencies makes a substantial difference. Mark Wooden (1996) quantifies these categories for the Australian labor market, with his estimates for September 1995 revealing that only 48 percent of the underused labor hours were in the "unemployment" category, 17 percent were in the "visible underemployment" category, 28 percent in the "invisible underemployment" category, and 10 percent were in "hidden unemployment." Haugen and William Parks (1990) calculated that if all individuals "who wanted a job" had been included in the definition of unemployment, this would have increased the unemployment rate in the U.S. labor market for the fourth quarter of 1989 from 5.3 percent to 9.1 percent.

Situations where the official unemployment category counts for only around one-half of the total underuse of labor suggest that the official unemployment rate does not reflect the true state of the labor market. It also means that forecasting change in the official unemployment count will be quite difficult. In many periods, the employment effects of increases in economic activity can be absorbed by higher rates of use of the employed, or by flows into the labor market of discouraged job seekers, rather than by reductions in the official unemployment category.

Who Becomes Unemployed?

The statistics show that the burden of unemployment is distributed unevenly across sections of society. To illustrate this point, Table 2 outlines the unemployment rate for various age groups, education levels, ethnic groups, and family status for the U.S. labor market in June 2001.

Age. The Table 2 data indicate that the unemployment rate varies considerably across age groups. The individual's age is generally used as a measure of the accumulation of knowledge of the labor market (best practice with respect to job search processes, information networks) that occurs through labor market activity. Individuals between the ages of sixteen and nineteen have the highest unemployment rate (15.9 percent for males and 12.7 percent for females). In comparison, individuals who are fifty-five years or over have the lowest unemployment rate, around 3 percent. The relatively high unemployment rate experienced by youth is the basis for arguments that they should be given priority in unemployment reduction policies. To the extent that unemployment today diminishes an individual's future job prospects (Le and Miller 2000), there is an additional reason to be concerned over the labor market difficulties encountered by many youth. In such a situation, failure to address the current unemployment problem may leave a large number of youth exposed to a process of cumulative disadvantage in the labor market over much of their working lives.

Education. The formal skills that may affect unemployment outcomes include schooling, qualifications, and language proficiency. Educational attainment is arguably the most important of these. The data in Table 2 indicate a pronounced, inverse association between unemployment and educational attainment. For example, the unemployment

Distribution of unemployment in the U.S. civilian population, June 2001
characteristicunemployment ratecharacteristicunemployment rate
note: a the figures are for individuals aged 25 years and over.
unemployment rate represented as a percentage of the labor force.
source:bureau of labor statistics, labor force statistics from the current population survey and monthly statistics from employment and earnings.
age family status 
males4.7married men, spouse present2.6
16 to 19 years15.9widowed, divorced, or separated4.2
20 to 24 years9.5single (never married)9.5
25 to 54 years3.5with own children under 18 years2.2
55 years and over3.0with own children 6 to 17 years, none younger2.1
  with own children under 6 years2.3
females4.4married women, spouse present3.0
16 to 19 years12.7widowed, divorced, or separated4.5
20 to 24 years6.7single (never married)8.5
25 to 54 years3.8women who maintain family6.3
55 years and over2.5with own children under 18 years4.3
  with own children 6 to 17 years, none younger3.4
educationa with own children under 6 years5.6
less than high school diploma6.8  
high school graduate, no degree3.9racial group 
less than a bachelors degree3.2white4.0
college graduate2.216 to 19 years12.6
  16 to 19 years28.2

rate of those who did not complete a high school diploma is three times higher than that of college graduates. There is also a notable difference between the unemployment rate of a non-high school graduate (7 percent) and one who has graduated (4 percent). The pattern in the data suggests that individuals can enhance their employment prospects considerably by completing high school.

Racial groups. Labor market performances also differ across racial groups. To illustrate this, data for the White, Black, and Hispanic racial groups are included in Table 2. The data show that the unemployment of white Americans (about 4 percent) is around one-half that of blacks and Hispanics (unemployment rates of 8 percent and 7 percent, respectively). Black youths (16–19) appear to be particularly disadvantaged, with an unemployment rate of 28 percent. These unemployment rate differentials paint a picture of disadvantage similar to that which emerges from study of other labor market indicators, such as earnings.

Family status. Most studies of unemployment recognize the importance of marital status as a determinant of labor market outcomes. The data in Table 2 indicate that married men and women with spouse present have the lowest rates of unemployment of the family states distinguished in the table. Individuals who have never married have the highest rate of unemployment, around 9 percent. The unemployment rate of women who maintain a family (spouse absent) is twice that of women who are married with their spouse present.

The Table 2 data illustrate that the presence of children increases the unemployment rate among women, but seems to have minimal impact on the employment outcome of men. This differential in impact presumably is associated with women still having primary responsibility for the care of children, with this responsibility limiting the types of work they obtain. Moreover, there appears to be an inverse relationship between the female unemployment rate and the age of the children. For example, the unemployment rate among women with children under six years is 6 percent compared to only 3 percent for women with children between six and seventeen years. Young children are relatively time-intensive for mothers, whereas older children tend to be more market-goods intensive. In addition, older children can provide care for younger siblings, enabling their mothers to pursue a greater range of market opportunities.

The characteristics such as a low level of education and membership of ethnic minorities that are associated with disproportionately high unemployment rates are also generally associated with low incomes among the employed and low-status occupations. Job instability will therefore compound the problems of the low-income groups in society. Not only is unemployment spread unevenly across sectors of society, but the burden of joblessness falls most heavily on those groups who do not have adequate resources to cope with the job loss.

Solutions to the Unemployment Problem

Much of the discussion on finding solutions to the unemployment problem has centered on the pivotal role of faster economic growth and cuts in real wages. Faster economic growth is viewed as a means of generating more jobs. Cuts in real wages are a reaction to the view that through their demands for higher wages, some groups of workers have priced themselves out of a job. How much growth and how large a fall in real wages would be required to reduce the size of the unemployment problem both remain matters for debate. Ottosen and Thompson (1996) suggest an overhaul of the National Labor Relations Act in the United States as a way of preventing unions from delivering the monopolistic wages and fringe benefit premiums that raise business costs and lead to unemployment. Such proposals are often very difficult to implement. Simulations by Guy Debelle and James Vickery (1998) for the Australian labor market are suggestive of manageable wage cuts only if the unemployment target is not set too low. Such advice is not very encouraging. Moreover, many researchers believe that the levels of economic growth required to make a major difference to the unemployment problem are unlikely to be sustained by most economies.

The United States and other countries could take other approaches to help reduce their unemployment rates (Ottosen and Thompson 1996). First, the methods of accumulation and dissemination of information on available jobs and workers could be improved. Ottosen and Thompson have suggested following the Swedish model, in which job centers have a nationwide, integrated database of jobs, employers, and available employees. This type of database could reduce the time spent by an average worker on the unemployment roll and thus reduce the unemployment rate. Second, unemployment agencies could tighten their job search and job acceptance requirements. Third, there could be improvements to the education and training provided to young people, with a greater focus on vocational skills. Finally, countries need to ensure that their welfare systems do not provide disincentives to work. Australia, for example, has strengthened the "Mutual Obligation" requirements (e.g., taking part in Work for the Dole projects) that eligible job seekers must meet in order to avoid loss of part of their income support.

There may also be a role for unemployment programs that target various groups of jobless persons. Carol West (1994) surveyed the unemployment programs aimed at reducing cyclical, frictional, seasonal, and structural unemployment in the United States. Some of these programs aim to change people to match existing jobs while others create jobs to match existing worker skills. The change in focus over time and the short duration of many programs make evaluation difficult. Many programs appear to do little more than reorder the line of unemployed people, though obviously they have the potential to fulfill an equity function in the labor market. John Piggot and Bruce Chapman (1995) suggest that labor market programs can be a cost-effective means of managing the pool of unemployment.

A number of other solutions to the unemployment problem have been advanced in the literature. For example, work sharing, early retirement, and reduced migration have been discussed. These policies affect the labor market by reducing the supply of labor. However, they have not won a great deal of support among economists.

See also:Family Policy; Family Roles; Homeless Families; Migration; Poverty; School; Stress; Work and Family


Barling, J. (1990). Employment, Stress, and Family Functioning. New York: John Wiley and Sons.

Berry, A.; Mendex, M. T.; and Tenjo, J. (1997). "Growth, Macroeconomic Stability and the Generation of Productive Employment in Latin America." In Employment Expansion and Macroeconomic Stability under Increasing Globalization, ed. A. R. Khan and M. Muqtada. London: Macmillan.

Bregger, J. E., and Haugen, S. E. (1995). "BLS Introduces New Range of Alternative Unemployment Measures." Monthly Labor Review 118(October):19–26.

Britt, C. L. (1994). "Crime and Unemployment among Youths in the United States, 1958-1990." American Journal of Economics and Sociology 53( January):99–109.

Cantor, D., and Land, K. C. (1985). "Unemployment and Crime Rates in the Post-World War II United States: A Theoretical and Empirical Analysis." American Sociological Review 50( June):317–332.

Debelle, G., and Vickery, J. (1998). "The Macroeconomics of Australian Unemployment." In Unemployment and the Australian Labour Market, ed. G. Debelle and J. Borland. Sydney: Reserve Bank of Australia.

Finegan, A. T. (1978). "Improving Our Information on Discouraged Workers." Monthly Labor Review 101(September):15–25.

Gitter, R. J., and Scheuer, M. (1997). "U.S. and German Youths: Unemployment and the Transition from School to Work." Monthly Labor Review 120(March):16–20.

Haugen, S. E., and Parks, W., II. (1990). "Job Growth Moderated in 1989 while Unemployment Held Steady." Monthly Labor Review 113(February):3–16

International Labour Office. (1998). Yearbook of Labour Statistics. Geneva: Author.

Jackman, R. (1997a). "Unemployment and Wage Inequality in Advanced Industrial (OECD) Countries." In Employment Expansion and Macroeconomic Stability under Increasing Globalization, ed. A. R. Khan and M. Muqtada. London: Macmillan.

Jackman, R. (1997b). "Macroeconomic Policies, Employment and Labour Markets in Transition in Central and Eastern Europe." In Employment Expansion and Macroeconomic Stability under Increasing Globalization, ed. A. R. Khan and M. Muqtada. London: Macmillan.

Kenyon, P. (1998). "Discussion of 'Dimensions, Structure and History of Australian Unemployment' by Jeff Borland and Steven Kennedy." In Unemployment and the Australian Labour Market, ed. G. Debelle and J. Borland. Sydney: Reserve Bank of Australia.

Le, A. T., and Miller, P. W. (2000). "Australia's Unemployment Problem." Economic Record 76(March):74–104.

Liker, J. K., and Elder, G. H., Jr. (1983). "Economic Hardship and Marital Relations in the 1930s." American Sociological Review 48( June):343–359.

Ottosen, G. K., and Thompson, D. N. (1996). Reducing Unemployment: A Case for Government Deregulation. Westport, CT: Praeger.

Piggott, J., and Chapman, B. (1995). "Costing the Job Compact." Economic Record 71(December):313–328.

West, C. T. (1994). "The Problem of Unemployment in the United States: A Survey of 60 Years of National and State Policy Initiatives." International Regional Science Review 1 and 2(16):17–47.

Wooden, M. (1996). "Hidden Unemployment and Underemployment: Their Nature and Possible Impact on Future Labour Force Participation and Unemployment," National Institute of Labour Studies, The Flinders University of South Australia, Working Paper No. 140.

Yang, B., and Lester, D. (1994). "Crime and Unemployment." Journal of Socio-Economics (Spring-Summer) 23:215–22.

other resources

Bureau of Labor Statistics. Monthly Data from Employment and Earnings. Available from http://stats.bls. gov/cpsee.htm

Bureau of Labor Statistics. Labor Force Statistics from the Current Population Survey. Available from http://stats.bls.gov/cpsatabs.htm

California State University, Fresno. Econ Data and Links. Available from http://www.csufresno.edu/Economics/econ_EDL.htm.

International Labour Office. Labour force statistics from ILO database LABORSTA. Available from http://laborsta.ilo.org/

International Labor Organization. Web site. Available from http://www.us.ilo.org/.

Organisation for Economic Co-Operation And Development. Labour Force Statistics. Available from http://www1.oecd.org/std/lfs.htm.

World Bank Group. Data by Topic. Available from http://www.worldbank.org/data/databytopic/databytopic.html




views updated May 11 2018


UNEMPLOYMENT. Few economic indicators are as important as the unemployment rate. A high unemployment rate, such as during the Great Depression, can precipitate tremendous political and legal change. Low unemployment is one of the surest signs of a healthy economy.

To be classified as unemployed by the U.S. Bureau of Labor Statistics (BLS), a person must be jobless, but must also be willing and able to take a job if one were offered, and must have actively looked for work in the preceding four weeks. The unemployment rate is calculated by dividing the number unemployed by the number in the labor force, where the labor force is the sum of the unemployed and the employed. The BLS calculates the unemployment rate monthly by surveying a random sample of about 50,000 households. The unemployment rate is criticized by some because it excludes "discouraged workers," that is, people who do not have jobs and are not actively seeking them because they believe that a job search would be fruitless.

Table 1 contains estimates of the average unemployment rate by decade beginning with the 1890s. The earliest figures come from Stanley Lebergott, who argues that unemployment in the early 1800s was very low—for example 1 to 3 percent in the 1810s—largely because the economy was dominated by agriculture, self-employment, and slavery. With the industrialization of the economy, the growth of wage labor, and the frequent occurrence of economic

Table 1

Decadal Estimates of the Average Unemployment Rate
 Lebergott/BLSAdjusted Figures

recessions and panics, unemployment became a serious problem after the Civil War. Lebergott guesses that unemployment averaged about 10 percent in the 1870s and 4 percent in the 1880s. Beginning in 1890, the decennial census asked questions about unemployment and Lebergott links these to other economic indicators to provide annual estimates of unemployment. Christina Romer argues that Lebergott's method overstates swings in the unemployment rate, because it incorrectly assumes that changes in employment mirror changes in annual output. This assumption contradicts a persistent relationship, known as Okun's Law, whereby changes in output are typically 2.5 to 3 times larger than changes in unemployment. Romer's estimates of unemployment (1890–1929) are given in the right-hand column. Her assumptions seem more realistic than Lebergott's but her estimates are still imprecise in comparison to estimates for later years. Figures after 1930 come from the BLS, but they have been criticized too. Michael Darby maintains that official figures vastly overstate unemployment between 1931 and 1942 because they improperly count millions of workers supported by federal work relief programs as unemployed. He argues that these jobs were substantially fulltime and paid competitive wages, so these workers should be counted as government employees. (On the other hand, there was substantial part-time work and work-sharing during the Great Depression, which is not reflected in unemployment figures.) Darby's estimates of unemployment for the 1930s and 1940s are given in the right-hand column.

The estimates in Table 1 show that the Great Depression was truly exceptional and that the second half of the twentieth century saw an upward drift in the unemployment rate, with a reversal at the end. Unemployment peaks were reached between 1894 and 1898 when the rate exceeded 10 percent for five years running. A strong spike occurred in 1921—11.7 percent by Lebergott's series, 8.7 percent according to Romer. In 1933 the official unemployment rate was 25 percent and exceeded 37 percent of non-farm employees. The highest postwar rate was 9.7 percent in 1982. The lowest rates have occurred during wartime, with a record low of 1.2 percent during World War II. Overall, the unemployment rate averaged about three percentage points lower than normal during wartime.

Economists distinguish among frictional, seasonal, structural, and cyclical unemployment. Frictional unemployment refers to the normal turnover of workers (and firms) in any dynamic market economy. Seasonal unemployment occurs because production in some sectors varies over the year. Structural unemployment refers to the mismatch between workers and jobs. The mismatch can be spatial—for example entry-level jobs in the suburbs may go begging because unemployed youths in central cities cannot easily get to them, or workers in the rust belt can be unable to find jobs while there are vacancies they could fill in sunbelt states. Structural unemployment can also be caused by skill-based mismatches—such as when blue-collar workers losing jobs in declining sectors cannot fill high tech white-collar job vacancies. Many commentators have worried, especially during the Great Depression era, that technological advances would cause continually increasing structural unemployment rates, as machines took away the jobs of people. The trends in Table 1 show that these fears were ill founded, especially in the long run, as rising productivity brought rising incomes and demands for new services. Together, frictional and structural unemployment define a natural rate of unemployment, one to which the economy tends in the long run. The natural rate is notoriously difficult to estimate, but seems to have risen and then fallen in the last four decades of the twentieth century. Part of this change was probably due to demographic forces. Because younger workers generally have higher unemployment rates, as the baby boom generation entered the labor force, the unemployment rate first climbed, and then dropped as boomers aged. Another probable part of this change was the restructuring of the economy with the move away from heavy industry and increased international competition. Cyclical unemployment arises during recessions.

There is no universally accepted theory of the causes of unemployment. Some economists argue that all unemployment is voluntary, because there are always job openings, even in a recession. Instead of taking such jobs, the unemployed rationally choose to wait for better offers. Other economists hold that unemployment arises because wages are too high in terms of supply and demand. Why don't wages fall to the point where the supply and demand for labor are equal and unemployment disappears? Wage "stickiness"—the failure of wages to fall when demand for labor falls—increased significantly in the late 1800s and has been attributed to rising bargaining power among workers and employers' fears that cutting wages during a recession would undermine worker morale, harm productivity, and spawn strikes. Furthermore, after World War I, firms shifted toward longer-term relationships with their employees and found that wage cutting could increase turnover and clashed with internal pay structures. Many firms, then, were unwilling to cut wages during a downturn in product demand and responded instead by laying off workers, protecting the majority of employees from the problem. In addition, some laws, such as the Fair Labor Standards Act, which established a minimum wage beginning in 1938, or the wage codes established temporarily under the National Recovery Administration in 1933, can keep wages above the equilibrium level and cause unemployment.

The duration and incidence of unemployment spells changed to a great extent between the late nineteenth century and the late twentieth century. Unemployment spells were much briefer in the earlier period, but the odds of any individual becoming unemployed were noticeably higher. Compared with workers in the late 1970s, those in 1910 faced a 37 percent higher monthly rate of entry into the ranks of the unemployed. On the other hand,

they also had a 32 percent higher rate of exiting unemployment, so the average spell of unemployment lasted less than four months. Data from the late 1800s suggest an even more rapid pace of workers entering and leaving unemployment, with an average unemployment spell lasting about seventy days, much less than the late 1970s rate of almost half a year. Evidence suggests that nearly 80 percent of employees laid off in the late 1800s were eventually recalled and rehired by their initial employers, a rate that was about the same in the late twentieth century. In the late 1800s and early 1900s, unemployment was influenced by personal characteristics, but to a much smaller degree than in the post–World War II period when educated, married, middle-aged, and experienced workers had significantly lower unemployment rates than others. Although unemployment was fairly indiscriminate in the earlier period, workers in industries with a high risk of layoff commanded higher wages—usually high enough to fully compensate them for the greater income risks they faced.

In the late twentieth century, the incidence of unemployment differed little by gender, but greatly by race. The nonwhite unemployment rate was 1.8 times higher than the white rate in 1950 and 1970 and 2.2 times higher in 1990. This gap opened up only after World War II—the nonwhite unemployment rate was slightly lower than the white rate in 1890 and 1930 and only 1.15 times higher in 1940. Another significant change has been the gradual decline in seasonal unemployment. In the late 1800s, employment in agriculture was very seasonal, as was manufacturing employment. In 1900 most industries saw considerable employment drops—often 10 to 15 percent—in the winter and again, to a smaller degree, in the summer. Seasonality faded slowly as America industrialized and as technology circumvented the vagaries of climate.

Until the Great Depression, federal and state governments did very little to explicitly combat or ameliorate the effects of unemployment. During the deep recession of the 1890s, for example, almost all the help to the unemployed came from the traditional sources, private charities and local governments. However, in 1935, as part of the Social Security Act, the federal government established a system of unemployment insurance, administered at the state level. The American system of unemployment insurance differs in important respects from that in other developed countries. The economists who framed this legislation, led by John Commons, believed that employers had enough leeway to substantially reduce seasonal and other layoffs, and constructed a system that included incentives to avoid layoffs. Unemployment insurance taxes were "experience rated," so that firms with higher layoff rates were taxed at higher rates. Evidence suggests that subsequently within the United States, seasonal differences in employment fell the most in states where experience rating was highest. Likewise, seasonality in the construction industry fell by two-thirds between 1929 and 1947 to 1963, a much faster rate than in Canada where firms were not penalized for laying off workers.

Unemployment insurance in the United States was designed to reduce unemployment and also to provide workers with extra income so that they could continue spending during a job loss and mount effective job searches, rather than accepting substandard jobs. By the standards of other countries, American unemployment insurance has covered a smaller portion of the work force and has provided benefits that are lower in comparison to average wages. Unemployed workers are normally eligible for benefits for twenty-six weeks, although this can be extended to thirty-nine weeks if unemployment in a state is unusually severe or if Congress votes an extension. In comparison, during the postwar period most countries in Western Europe established maximum benefit durations of a year or more. Many economists argue that the generosity of European unemployment insurance helps explain why unemployment rates there surged past the American rate in the 1980s and became about twice as high in the 1990s.

Another way in which government has combated unemployment is by taking an active role in managing the economy. The Employment Act, adopted in 1946, declared the "responsibility of the Federal Government to use all practicable means…to coordinate and utilize all its plans, functions, and resources for the purpose of creating and maintaining … conditions under which there will be afforded useful employment opportunities … and to promote maximum employment." Congress essentially committed itself to "do something" to prevent depression, recessions, and other macroeconomic malfunctions. During the Great Depression the intellectual underpinnings for such an activist policy were laid out in the writings of British economist John Maynard Keynes, who called for governments to cut taxes or boost spending at the appropriate time to reduce the negative effects of recessions. By the late 1950s some leading economists argued that there was a consistent, stable relationship between inflation and unemployment—the Phillips Curve—which allowed policymakers to keep unemployment perpetually at a low rate: a 3 percent unemployment rate was attainable if we accepted an inflation rate of 7 percent, according to one set of calculations by two future Nobel laureates. Beginning in the late 1960s, however, it was learned that the additional government spending on the Vietnam War and new social programs could not push down the unemployment rate much below its long-term trend and that additional spending fueled accelerating inflation. The U.S. Full Employment and Balanced Growth Act of 1978 (also known as the Humphrey-Hawkins Act) "required" the federal government to pursue the goal of an overall unemployment rate equal to 4 percent. The goal was achieved only briefly during 2000. By the 1980s the federal government had largely given up on using taxation and expenditures to steer the economy and the role of macroeconomic stabilization was left primarily to the Federal Reserve. The Federal Reserve's principal goal, however, appeared to be controlling inflation, rather than reducing unemployment.


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Romer, Christina. "Spurious Volatility in Historical Unemployment Data." Journal of Political Economy 94, no. 1 (February 1986): 1–37.

Vedder, Richard, and Lowell Gallaway. Out of Work: Unemployment and Government in Twentieth Century America. New York: Holmes and Meier, 1993.


See alsoEmployment Act of 1946 ; Great Depression ; Social Security andvol. 9:Advice to the Unemployed in the Great Depression .

Unemployment and Crime

views updated Jun 11 2018


Because criminal sources of income, such as theft and fraud, are alternatives to legitimate earnings, they tend to be associated with unemployment. This linkage, however, is far from clear and consistent.

Dependence on labor force attributes

One variable that greatly affects crime and employment relationships is the age of persons who are unemployed. Government statistics regularly indicate that juveniles and young adults greatly exceed older persons in rates of arrest for burglary, robbery, and other crimes of taking property belonging to others. A 1959 pioneer study found that this inverse relationship of such crimes to unemployment was most pronounced for young persons, and less evident for older persons out of work (Glaser and Rice). In 1968 a British study yielded similar findings, showing that the relations between crime and unemployment are most intense for youths who were out of school as well as work (Farrington et al.). The old adage that "idle hands are the devil's workshop" seemed to be confirmed.

These findings also appear to support the 1999 assertion by Bruce Western and Katherine Beckett that the U.S. penal system is "a labor market regulating institution." They justified this statement by pointing out that confinement institutions remove able-bodied but idle young men from the workforce, and that once these men have a record of imprisonment, their subsequent job prospects are greatly diminished (imprisoned women are not sufficiently numerous to affect the total female labor force significantly).

Unfortunatly, any statistical generalizations on the linkage of crime to unemployment, as well as to age or other personal attributes of offenders and nonoffenders, can only be tested with imperfect data. The completeness of our knowledge on lawbreakers necessarily varies with the extent to which they are caught, and with the use of imprisonment rather than alternative penalties for those convicted. Data on employment, age, and various other attributes of persons committing crimes is usually reported for those offenders who are arrested, but their total number, and information on them is somewhat diminished (although presumably made more accurate) if one studies only those arrestees who are subsequently convicted of the crimes for which they were arrested. Furthermore, data on the personal attributes of those convicted are often not compiled in as much detail for those fined or released on probation as for those who are imprisoned.

In 1939 two European scholars, Georg Rusche and Otto Kirchheimer, refugees from Hitler's Germany, published what proved to be a classic volume of historical scholarship, Punishment and Social Structure. In it they treated the variations in reaction to crime from ancient to contemporary periods, and generalized that the types of penalties usedsuch as executions, transportation to distant colonies, torture, mutilation, confinement in idleness, of forced labordepended greatly on economic conditions, particularly on the current value of labor. The cruelest penalties became most frequent, they asserted, when unemployment was extensive, making labor cheap.

These rather vague assertions were subsequently formalized and tested statistically by others, with diverse data, as can be illustrated by summarizing a few of the most methodologically sophisticated studies. Mathematician David Greenberg concluded from multivariate analysis in the 1970s, that "oscillations in the rate of admissions to prison in Canada in recent years have been governed almost entirely by changes in the unemployment rate. The same relationships appear to hold in the United States as well" (p. 651). Sociologists Andrew Hochstetler and Neal Shover, however, found in the 1990s that the intensity of this relationship in the United States varied greatly in different historical periods, and in various regions.

Hochstetler and Shover note that the incarceration rate in the United States, defined as the number of imprisoned adults per 100,000 population, was 462 for the southern states in 1994, but only 291 for the northeastern states. From a regression analysis of 1990 data for a sample of 269 U.S. counties that they showed were highly representative of all counties, they conclude that R-square (the variance explained) was only 0.14. But regressing 1990 imprisonment data for these counties with the 1980 unemployment rates of the same counties yielded an R-square of 0.74. They interpret this finding of a lag in the impact of unemployment on crime rates by pointing to the fact that the highest rates of known offenses, particularly unspecialized street crimes, occurs among teenagers, and in those whose childhood was spent in the most poverty-stricken urban areas, the slums. Their main conclusion is:

Change in violent street crime, in the proportionate size of the young male population, and in labor surplus, contribute to change in the use of imprisonment, while changing levels of property crime do not. These relationships persist even when street-crime rates and other presumed correlates of imprisonment are controlled. . . . The criminal justice system grows in creasingly punitive as labor surplus increases. The fact that our findings were achieved using both a unit of analysis more appropriate theoretically than measures employed by most investigators, and a longitudinal design, only strengthens confidence in them.

Other factors in crime-unemployment relationships

Of course, unemployment rates, age, and offense do not operate alone in determining sentences of imprisonment. In 1966 David Jacobs and Ronald E. Helms showed, in their multivariate analysis of historical and geographical fluctuations of incarceration rates in the United States, that unemployment's impact on crime seemed to have a close linkage with other conditions not noted in prior studies. They point out that rates of state plus federal imprisonment of adults per 100,000 population in the United States changed only from 43.4 to 50.9 from 1918 to 1965, dropped to 35.9 by 1968, then nearly doubled in 13 years to 69.7 in 1981, and rose by 84 percent to 127.9 in 1989. There was no such large movement in rates of crime known to the police, which increased only 12.1 percent in 19751991. They found that because of this small range in reported crime rates for this long time span, a multivariate regression only yielded very strong results if crime rates were squared. Also, in their regression analysis, out-of-wedlock birthsan index of the rate of breakdown in family relationswas one of the best predictors of imprisonment rates, especially when using birthrate data for five years before the prison figures, to allow for the time before such family conditions could promote higher imprisonment rates. Furthermore, rather than unemployment rates, income inequality, and votes for the Republican Partyan index of conservative political trendswere among the strongest predictors of crime rates.

In 1999 Michalowski and Carlson ascribed the diversity of findings in tests of "the Rusche-Kirchheimer hypothesis" to variations in the historical periods covered by these studies. Their analysis is based on theories about stages in what they call "social structures of accumulation," or "SSAs." The first stage is "Exploration," which occurs when new institutional arrangements emerge to cope with high levels of unemployment and with the displacement of both farm and industrial populations. The second is "Consolidation," an effort to maintain whatever new arrangements seem to help capital to preserve its profit margins. The third stage is "Decay," when conditions develop that impede consolidation policies, thus increasing unemployment and preventing children of workers from achieving upward status mobility.

The Michalowski and Carlson analysis of unemployment and imprisonment for crime focuses on what they and some others call the "Fordist" SSA of 19331992, for which they differentiate the three phases indicated. During Exploration, from 1933 to 1947, new types of "capital-labor accords" and welfare policies "socialized the costs of labor force displacement." Thus, unemployment insurance and old-age pensions, both adjusted for changes in the cost of living, shifted burdens of dealing with economic distress in this period from businesses to governments, and helped to maintain social order. In Consolidation, from 1948 to 1966, periods of unemployment were shortened by the bargaining strength of organized labor and its central position in the Democratic Party, which transferred government tax income to the poor, the disabled, and the elderly. These "profit-eroding" developments "made it difficult for capital to protect profit margins in the face of growing foreign competition," these authors claim, so that Decay occurred in 19671979, during which unemployment and inflation rose while profits declined and the United States lost the Vietnam War despite sending 3 million workers there.

These authors identify a new Exploration phase from 1980 until 1992, in which "cyber-technology" accelerates labor displacement, and also increases earning inequality between workers in digital information systems and those at "hamburger jobs." The latter were disproportionately from minority groups, and in this period there was a "shift away from placative social welfare strategies . . . toward repressive control strategies based on increased use of imprisonment" (p. 226). Consequentially, even with rates of arrest and of unemployment in the 1990s usually below those of prior decades, the rate of imprisonment nearly tripled, confining predominantly young men who had dropped out of school and work. The latter were disproportionately African Americans, whose rates of unemployment exceeded what one would predict for white workers of similar education and work experience. Although their higher rates of joblessness are doubtless due in large part to prejudice against them, it is also because they are below the growing number of Latinos in the labor market in their "reservation wage"the lowest pay rate for which they would be willing to work (Moss and Tilly, p. 9). Another major factor in their unemployment rates is that they exceed other racial and nationality groups in the proportion who have entered military service, or are enrolled full-time in schools, which probably removes those with the highest earning potential from the labor market (Mare and Winship). But still another influence was their ready opportunity, in the segregated slum areas in which most of the housing available to them was located, to gain much greater income from drug dealing than from the jobs available to them.

In summary, evidence and analysis indicate that unemployment is predictive of crime, but disproportionately for youth, the least educated, those in broken or disorganized families, and those segregated in poor minority residential areas. Also, these relationships of unemployment to crime are likely to continue unless unsegregated housing, special education, family unity, work experience, and appealing career jobs become more readily available to those who are unemployed.

Daniel Glaser

See also Class and Crime; Crime Causation: Economic Theories.


Chiricos, Theodore G., and DeLone, Miriam A. "Labor Surplus and Punishment: A Review and Assessment of Theory and Evidence." Social Problems 39, no. 4 (1992): 421446.

D'Alessio, Stewart J., and Stolzenberg, Liza. "Unemployment and the Incarceration of Pretrial Defendants." American Sociological Review 60, no. 3 (1995): 350359.

Farrington, David P.; Gallagher, Bernard; Morley, Lynda; St. Ledger, Raymond J.; and West, Donald J. "Unemployment, School-leaving, and Crime." British Journal of Criminology 26, no. 4 (1986): 335356.

Glaser, Daniel, and Rice, Kent. "Crime, Age and Employment." American Sociological Review 24, no. 5 (1959): 679686.

Greenberg, David F. "The Dynamics of Oscillatory Punishment Processes." Journal of Criminal Law and Criminology 68, no. 4 (1977): 643651.

Hochstetler, Andrew L., and Shover, Neal. "Street Crime, Labor Surplus, and Criminal Punishment, 19801990." Social Problems 44, no. 3 (1977): 358367.

Jacobs, David, and Helms, Ronald E. "Towards a Political Model of Incarceration: A Time-Series Examination of Multiple Explanations for Prison Admission Rates." American Journal of Sociology 102, no. 2 (1996): 323357.

Mare, Robert D., and Winship, Christopher. "The Paradox of Lessening Racial Inequality and Joblessness Among Black Youth: Enrollment, Enlistment, and Employment, 19641981." American Sociological Review 49, no. 1 (1984): 3955.

Michalowski, Raymond J., and Carlson, Susan M. "Unemployment, Imprisonment, and Social Structures of Accumulation: Historical Contingency in the Rusche-Kirchheimer Hypothesis." Criminology 37, no. 2 (1999): 217250.

Moss, Phillip, and Tilly, Chris. "Hiring in Urban Labor Markets: Shifting Labor Demands, Persistent Racial Differences." In Ivar Berg and Arne Kalleberg, eds. Sourcebook on Labor Markets: Evolving Structures and Processes. New York: Plenum, 2000.

Rusche, Georg, and Kirchheimer, Otto. Punishment and Social Structure. New York: Columbia University Press, 1929. Reprint, New York: Russell and Russell, 1967.

Western, Bruce, and Beckett, Katherine. "How Unregulated Is the U.S. Labor Market? The U.S. Penal System as a Labor-Market Regulating Institution." American Journal of Sociology 104, no. 4 (1999): 10301060.


views updated May 14 2018






A person is unemployed when he or she is willing and able to work given the prevailing terms and conditions of employment but does not currently have a job. Depending on its causes, unemployment can pose severe problems for both individuals and societies alike. For example, since most households derive most of their income from participation in the paid labor market, unemployment can be a source of considerable material hardship and distress. Furthermore, unemployment can challenge the sense of identity and self-worth that individuals derive from their jobs. Finally, unemployment represents, in the aggregate, a waste of productive resources: society as a whole would be better off if the unemployed were engaged in productive activity.


Because of its importance, economists have long had an interest in the accurate measurement of unemployment. But unemployment statistics are subject to several measurement problems. For example, official statistics usually measure unemployment by requiring that a persons willingness to work be demonstrated by evidence that they are actively searching for a job. Some economists, however, identify discouraged workers those who are willing and able to work, but have ceased to search for work because they do not believe that jobs are availableas being unemployed. Despite this, discouraged workers are not included in official measures of unemployment because they are not actively looking for work. They are instead categorized (along with full-time students, retirees, and others) as not participating in the labor force.

Another measurement problem is associated with disguised unemployment. This was originally identified as a condition afflicting developing countries, wherein individuals might engage in very low-productivity work in the agricultural sector for want of a job that would more fully utilize their productive potential. But some economists now identify involuntary part-time or temporary work that is, part-time or temporary work performed by those who would prefer a full-time, year-round jobas a form of disguised unemployment. Once again, disguised unemployment is not reflected in official measures of unemployment. This is because all those who have jobs are automatically categorized as employed, regardless of whether or not the jobs they perform fully utilize their productive potentials or satisfy their preferences with respect to the number of hours of paid work they perform.

Whatever their potential flaws, official unemployment statistics reveal a number of important stylized facts about unemployment. First, it has long been established that unemployment varies over the course of the business cycle, rising when the economy enters a recession and falling during a boom. Second, a more recently established stylized fact is that unemployment varies over time between lengthy episodes of higher or lower unemployment. For example, average annual unemployment rates in the major industrialized economies were much lower during the 19501973 period than they were during the preceding interwar period (19181939) or than they have been since 1973. Third, unemployment rates differ across countries at any particular point in time and during the episodes of high or low unemployment referred to above. Prior to 1973, for instance, it was commonly observed that unemployment in the United States was higher than that in Europe. Since the 1980s, however, the United States has tended to experience unemployment below the average rates witnessed in Europe. Finally, unemployment rates differ by age, sex, and race. Unemployment is typically highest among the young (ages sixteen to twenty-five) and members of racial or ethnic minorities. Older workers and women also frequently experience more unemployment than prime-age males, although since the 1970s, unemployment rates for women have become comparable to those for men in some of the major industrialized economies.


What factors cause unemployment and so explain the stylized facts described above? According to some economists, the labor market operates like any commodity market, with variations in the level of wages serving to clear the marketthat is, equate the supply of and demand for labor. But even when the labor market clears in this fashion, unemployment will exist. This frictional unemployment is often associated with the normal workings of the labor market. It arises because even if the number of jobs available is exactly sufficient for the number of job seekers at currently prevailing wages, the process of matching employers with employees takes time. It is therefore possible for some workers to be without jobs at any point in time. The amount of frictional unemployment is affected by a variety of factors, including the choices of workers themselves. For example, a currently unemployed person might decline to accept an offer of employment in anticipation of a superior subsequent offer. Unemployment that is the product of individual choice in this fashion is termed voluntary.

Other economists, however, claim that labor market clearing is a special case, sometimes called full employment (although not all economists who use the term full employment would agree that it is best conceptualized in terms of labor market clearing). These economists claim that observed unemployment is largely involuntary. According to the conventional definition, involuntary unemployment occurs when the labor market does not clear but is instead characterized by a surfeit of job seekers relative to the number of jobs available at currently prevailing wages. In this situation of too many workers chasing too few jobs, the unemployed are without work not by virtue of individual choice but as a result of a constraint that exists on their ability to sell labor. Some economists locate the source of this constraint on the supply side of the economy. For example, there may be impediments to the workings of the price mechanism that prevent wages from varying in the manner required to equate the demand for and supply of labor. Or alternatively, the stock of capital accumulated by firms may be insufficient to warrant the employment of all those willing to work, given the number of workers that need to be combined with a single unit of capital to produce a unit of output.

Other economists, however, locate the source of involuntary unemployment on the demand side of the economy. They argue that the demand for labor (and hence the quantity of employment offered by firms) is derived from the quantity of goods that firms can profitably produce and sell, so that the aggregate demand for goods is the ultimate determinant of employment and unemployment. According to this view, even when wages are free to vary and output can be produced by different combinations of capital and labor inputs (so that the existing stock of capital does not determine the level of employment that can be achieved), it is possible for an insufficient aggregate demand for goods to give rise to an insufficient derived demand for labor relative to the number of persons who wish to work at currently prevailing wages. This is the theory of employment and unemployment originally developed by John Maynard Keynes (18831946). Its most important feature is that it identifies as the source of involuntary unemployment a deficient demand for goods, rather than any problems with the functioning of the aggregate labor market that might prevent the latter from clearing.

The distinction between supply-side and demand-side theories of involuntary unemployment leads to a further distinction between classical and Keynesian unemployment. Classical unemploymentwhich includes frictional unemployment and supply-side involuntary unemploymentis determined on the supply side of the economy and is unresponsive to variations in aggregate demand. Classical unemployment is often associated with the concept of a natural rate of unemployment or a nonaccelerating inflation rate of unemployment (NAIRU). Keynesian unemployment, meanwhile, is determined on the demand side of the economy and does respond to variations in aggregate demand.

It is possible in principle for voluntary and involuntary or classical and Keynesian unemployment to exist within the same economy. However, economists disagree as to what extent observed unemployment is voluntary, involuntary, classical, or Keynesian, and hence on the extent to which these categories, and the theories of unemployment associated with them, are useful for explaining the stylized facts outlined earlier.


The policy implications of unemployment depend greatly on the theorized causes of unemployment. For example, if unemployment is a product of individual choice (i.e., a voluntary condition), it would not appear that any form of policy intervention is merited. Even if unemployment is entirely frictional, however, it may be prudent to use public policy to reduce unemploymentincluding voluntary unemploymentby improving the process whereby employers and employees are matched. In this case, supply-side, microeconomic policies designed to affect the choices or attributes of job seekers are appropriate. Unemployment insurance programs might be altered to influence the propensity of those searching for work to accept job offers, or training programs might be established in an effort to imbue the unemployed with the sorts of skills required by currently vacant jobs. If unemployment is involuntary and Keynesian, however, an altogether different approach to policy intervention is required. In this case, macroeconomic policies (such as a reduction in interest rates or an increase in government spending) are needed to raise aggregate demand in order to remedy the deficient demand for goods and hence the deficient derived demand for labor that is the ultimate cause of unemployment. As with the theories of unemployment from which these policy interventions derive, the appropriate policy response to unemployment is and will likely remaina subject of controversy among economists.

SEE ALSO Keynes, John Maynard; Lucas, Robert E.; Marx, Karl; Natural Rate of Unemployment; Underemployment; Voluntary Unemployment


Cornwall, John, and Wendy Cornwall. 2001. Capitalist Development in the Twentieth Century: An Evolutionary-Keynesian Analysis. Cambridge, U.K.: Cambridge University Press.

Davidson, Carl. 1990. Recent Developments in the Theory of Involuntary Unemployment. Kalamazoo, MI: Upjohn Institute for Employment Research.

Friedman, Milton. 1968. The Role of Monetary Policy. American Economic Review 58 (1): 117.

Keynes, John Maynard. 1936. The General Theory of Employment, Interest, and Money. London: Macmillan.

Lawlor, Michael S., William A. Darity, Jr., and Bobbie L. Horn. 1987. Was Keynes a Chapter Two Keynesian? Journal of Post Keynesian Economics 9 (4): 516528.

Lucas, Robert E., Jr. 1978. Unemployment Policy. American Economic Review 68 (2): 353357.

Mark Setterfield


views updated May 14 2018



A person is said to be unemployable if he or she is unsuitable for any job. More precisely, someone is unemployable if there exists an absolute mismatch between his or her personal characteristics or attributes (including, for example, education, skills, experience, or physical fitness) and those currently demanded by employers. Unemployable persons are either chronically unemployed or else do not participate in the labor force at all.

Three important features of the contemporary view of unemployability can be associated with the definition given above. First, as is obvious from the definition, being unemployable is identified with the inability to secure any employment. Second, it is commonplace to focus on acquired characteristics (that are amenable to change) rather than innate characteristics (that are taken as given) when accounting for an individuals status as unemployable. Third, following from the previous point, unem-ployability is viewed as a state that can and should be remedied by means of appropriate policy interventions. Unemployability has not always been viewed in this way, however. Historically, it has been associated with the ability to gain some employment, but for material reward that is insufficient to support an adequate standard of living. Moreover, there has, in the past, been a greater emphasis on innate rather than acquired characteristics when explaining the state of unemployable persons. This, in turn, encouraged a view of the unemployable as morally repugnant persons who needed to be dissociated from the mainstream labor force. These sentiments are evident to some degree even in the writings of radical or reformminded critics of capitalism, such as Karl Marx, William Beveridge, and Beatrice and Sydney Webb.

Because unemployable persons are defined as lacking the attributes desired by employersand in particular, the sort of human capital firms requirethe inevitable tendency is to focus on individual choice and behavior as the ultimate cause of unemployability. However, it is possible that events beyond an individuals control induce personal characteristics that make the individual unemployable. For example, the family environment and/or schooling that contribute to early childhood development may be responsible for making some individuals unemployable. Alternatively, chronic unemployment may cause skills to atrophy or even come to be regarded by employers as, in and of itself, a negative credential. This can result in an unemployed person eventually becoming unemployable. In this case, rather than unemployability resulting in chronic unemployment, it is chronic unemployment that causes a person to become unemployablea process anticipated by Beveridge that is now associated with modern views of hysteresis in the labor market. Finally, the mismatch between individual attributes and employers requirements characteristic of unemployability may not arise because of any literal deficiency of individual attributes at all. Consider, for example, the effects of the geography of deindustrialization coupled with the relative geographical immobility of labor. It is possible for the skill set in which a geographically concentrated group of workers has invested to be rendered redundant by the decline or relocation of a regionally concentrated industry. This leaves workers with the wrong skills rather than no skillstoo few for some remaining jobs, but too many for othersand thus suffering the absolute mismatch between individual attributes and employers requirements that renders them unsuitable for any job (at least within a regional context).

Whatever its causes, unemployability can be associated with a variety of social problems. Most obviously, since most individuals depend on the labor market for most of their income, unemployable persons may suffer severe material hardship. Since work also lends meaning and definition to the lives of most people, isolation from work caused by unemployability can lead to dissociative, antisocial behaviors such as violence and crime. It is not surprising, then, that numerous policy measures have been proposed to address the problem of unemployability. Some of thesesuch as welfare (or workfare) programs and earnings subsidiesare essentially permanent income maintenance schemes designed to offset the condition of unemployability. Other policies, however, seek to remedy the condition itself. For example, the strong association between unemployability and human capital deficiencies means that training schemes are a prominent feature of policy proposals to reduce the number of unemployable persons. If, however, former workers are made unemployable by geographically concentrated structural change as described earlier, broader regional development policies that focus not only on changing individual attributes (through retraining, for example) but also on the level and variety of economic activity in a region may have a role to play in redressing the problem of unemployability.

SEE ALSO Underemployment; Unemployment


Gray, D. 1996. Are Displaced Manufacturing Workers Unemployable? An Analysis of Sectorally Based Adjustment Costs in France. Canadian Journal of Economics 29 (Special Issue: Part 1): S84S88.

Komine, A. 2004. The Making of Beveridges Unemployment (1909): Three Concepts Blended. European Journal of the History of Economic Thought 11 (2): 255280.

LeBlanc, G. 2004. Optimal Income Maintenance and the Unemployable. Journal of Public Economic Theory 6 (3): 509535.

Scitovsky, T. 1996. My Own Criticism of The Joyless Economy. Critical Review 10 (4): 595605.

Mark Setterfield


views updated May 17 2018

unemployment The state of being unable to sell one's labour-power in the labour-market despite being willing to do so. In practice, unemployment is difficult to identify and measure, because willingness to be employed is partly affected by the extent and nature of demand for one's services. As a result, official definitions imposed by government employment agencies are affected by political theories about the causes of being unwilling or unable to be employed, on the one hand; and, on the other, by the rules allowing registration as out of work and eligible for such welfare benefits as may be on offer.

Unemployment was used by C. Wright Mills as a graphic illustration of the distinction between private troubles and public issues which he considered basic to sociology. Research on the unemployed has repeatedly shown that unemployment is rarely explicable simply as a private or individual problem of insufficient motivation and aptitude. It is, rather, a public issue caused by the failure of market processes.

Economists distinguish various causes of unemployment, the chief two of which are the structural decline of industry in a region or nation, and cyclical variations in economic activity. The former generates shifts in the occupational structure which render particular skills obsolete, as for example might follow technological innovation, changes in the market for goods and services, or the decision by companies to close or relocate their operations. The latter occurs when firms lay off workers (perhaps temporarily) during periods of economic recession. Other forms of unemployment include frictional unemployment (which is due to workers voluntarily switching jobs) and seasonal unemployment (when a change in the seasons reduces demand for particular types of workers, for example those involved in agriculture, or in the recreation and leisure industries).

Unemployment is a major factor in poverty, especially where the unemployed experience spells of joblessness alternating with so-called sub-employment, that is, low paid and uncongenial work with a high degree of insecurity of tenure. The unemployed must also endure the stigma of being unable to conform to the prevailing work ethic of Western societies—despite their own typically strong desire to find work.

There is an enormous sociological literature on the process of becoming unemployed and its social and individual consequences. A good place to start is Marie Jahoda's Employment and Unemployment (1982). An excellent series of empirical studies—examining the relationships between unemployment and (among other things) attitudes to work, household work strategies, psychological health, marital dissolution, welfare, deprivation, and involvement in social networks–is reported in Duncan Gallie et al. ( eds.) , Social Change and the Experience of Unemployment (1993