United States Government Accountability Office

GAO Report to the Chairman, Subcommittee 

on Human Resources, Committee on 

Ways and Means, House of 

Representatives 

UNEMPLOYMENT 

INSURANCE 

Factors Associated 

with Benefit Receipt 

March 2006 

 

GAO-06-341 
What GAO Found

United States Government Accountability Office

Why GAO Did This Study

Highlights

Accountability Integrity Reliability

www.gao.gov/cgi-bin/getrpt?GAO-06-341.

To view the full product, including the scope 

and methodology, click on the link above. 

For more information, contact Sigurd Nilsen at 

(202) 512-7215 or nilsens@gao.gov. 

Highlights of GAO-06-341, a report to the 

Chairman, Subcommittee on Human 

Resources, Committee on Ways and 

Means, House of Representatives 

March 2006

UNEMPLOYMENT INSURANCE 

Factors Associated With Benefit Receipt 

Unemployment Insurance (UI), 

established in 1935, is a complex 

system of 53 state programs that in 

fiscal year 2004 provided

$41.3 billion in temporary cash 

benefits to 8.8 million eligible

workers who had become

unemployed through no fault of 

their own.Given the size of the UI 

program, its importance in helping

workers meet their needs when

they are unemployed, and the little 

information available on what 

factors lead eligible workers to 

receive benefits over time, GAO

was asked to determine (1) the

extent to which an individual

worker’s characteristics, including

past UI benefit receipt, are 

associated with the likelihood of UI

benefit receipt or unemployment

duration, and (2) whether an 

unemployed worker’s industry is 

associated with the likelihood of UI

benefit receipt and unemployment

duration. Using data from a 

nationally representative sample of 

workers born between 1957 and

1964 and spanning the years 1979 

through 2002, and information on

state UI eligibility rules, GAO used

multivariate statistical techniques

to identify the key factors 

associated with UI benefit receipt 

and unemployment duration. 

In its comments, the Department of

Labor stated that while there are

certain qualifications of our

findings, the agency applauds our 

efforts and said that this report

adds to our current knowledge of

the UI program. 

Certain characteristics are associated with the likelihood of receiving UI 

benefits and unemployment duration. UI-eligible workers that GAO studied 

are more likely to receive UI benefits if they have higher earnings prior to 

becoming unemployed, are younger, have more years of education, or if they 

have a history of past UI benefit receipt when compared with otherwise 

similar workers. GAO found that past experience with the UI program has a 

particularly strong effect on the future likelihood of receiving UI benefits. 

However, some characteristics, such as receiving a higher maximum weekly

UI benefit amount, are not associated with a greater likelihood of receiving

UI benefits. UI-eligible workers who receive UI benefits have longer 

unemployment duration than workers with similar characteristics. Also, UIeligible workers are more likely to experience longer unemployment 

duration if they have lower earnings before becoming unemployed or have 

fewer years of education. Other characteristics associated with longer 

unemployment duration include being African-American, female, or not 

belonging to a union. GAO found no relationship between past UI benefit 

receipt and subsequent unemployment duration. 

UI-eligible workers from certain industries are more likely than similar 

workers in other industries to receive UI benefits and experience shorter 

unemployment duration. Specifically, GAO’s simulations show that the 

likelihood of receiving UI benefits during a first period of unemployment is 

highest among workers from the mining and manufacturing industries. 

Furthermore, the likelihood of receiving UI benefits when unemployed 

increases with each previous period of UI receipt across all industries, and 

the most notable increase occurs in public administration. First-time 

unemployed workers from construction and manufacturing experience 

significantly shorter unemployment duration than workers from other 

industries. 

Simulated UI Benefit Receipt Rates for UI-Eligible Workers during Successive Periods of 

Unemployment, by Past UI Receipt Status

0

20

40

60

80

100

First Second Third Fourth Fifth Sixth

Simulated likelihood of receiving UI (percent)

Source: GAO simulations based on GAO analysis of NLSY79 data.

Unemployment period

Always received UI previously Never received UI previously
Contents 

Letter 1

Results in Brief 4

Background 5

Certain Characteristics Are Associated with UI Benefit Receipt and 

Unemployment Duration 8 

Certain Industries Are Associated with UI Benefit Receipt and

Unemployment Duration 22 

Concluding Observations 31 

Agency Comments 32 

Appendix I Analysis of UI Benefit Receipt and Unemployment 

Duration 34 

Overview 34

Data Used 34 

Econometric Model 39 

Results 43

Limitations of the Analysis 80 

Appendix II Comments from the Department of Labor 82 

Appendix III GAO Contact and Staff Acknowledgment 83 

Bibliography 84 

Related GAO Products 86 

Tables 

Table 1: Simulated Unemployment Duration for UI-Eligible 

Workers by Current UI Receipt Status and Other 

Characteristics 21

Table 2: Simulated Likelihood of Receiving UI Benefits for UIEligible Workers from Different Industries 23 

Page i GAO-06-341 Unemployment Insurance 
Table 3: Simulated Likelihood of Receiving UI Benefits during 

Different Periods of UI-Eligible Unemployment for 

Workers with Past UI Receipt, by Industry 25 

Table 4: Simulated Unemployment Duration for UI-Eligible 

Workers, by Industry and UI Receipt Status 28 

Table 5: Simulated Likelihood of Receiving UI Benefits for UIEligible Workers from Different Occupations 29 

Table 6: Simulated Likelihood of Receiving UI Benefits During 

Different Periods of UI-Eligible Unemployment for 

Workers with Past UI Receipt, by Occupation 30 

Table 7: Simulated Unemployment Duration for UI-Eligible 

Workers, by Occupation and UI Receipt Status 31 

Table 8: Parameter Estimates for UI Receipt Equation from 

Industry-Interaction Specification 45 

Table 9: Parameter Estimates for Duration Equation from IndustryInteraction Specification 48 

Table 10: Parameter Estimates for UI Receipt Equation from 

Occupation-Interaction Specification 52 

Table 11: Parameter Estimates for Duration Equation from 

Occupation-Interaction Specification 55 

Table 12: Simulated Likelihood of Receiving UI Benefits for UIEligible Workers during Successive Periods of 

Unemployment, by Past UI Receipt Status 60 

Table 13: Simulated Likelihood of Receiving UI Benefits for UIEligible Workers from Different Industries 61 

Table 14: Simulated Likelihood of Receiving UI Benefits during 

Different Periods of UI-Eligible Unemployment for 

Workers with Past UI Receipt, by Industry 63 

Table15: Simulated Unemployment Duration for UI-Eligible 

Workers, by Industry and UI Receipt Status 70 

Table 16: Simulated Unemployment Duration for UI-Eligible 

Workers, by Education Level and UI Receipt Status 72 

Table 17: Simulated Unemployment Duration for UI-Eligible 

Workers, by Race/Ethnicity and UI Receipt Status 73 

Table 18: Simulated Unemployment Duration for UI-Eligible 

Workers, by Union Status and UI Receipt Status 73 

Table 19: Simulated Likelihood of Receiving UI Benefits for UIEligible Workers from Different Occupations 76 

Table 20: Simulated Likelihood of Receiving UI Benefits during 

Different Periods of UI-Eligible Unemployment for 

Workers with Past UI Receipt, by Occupation 78 

Page ii GAO-06-341 Unemployment Insurance 
Table 21: Simulated Unemployment Duration for UI-Eligible 

Workers, by Occupation and UI Receipt Status 79 

Figures 

Figure 1: Incidence of UI Benefit Receipt from 1979 through 2002, 

for Workers Born between 1957 and 1964 7 

Figure 2: Simulated Likelihood of Receiving UI Benefits for UIEligible Workers, by Prior-Year Earnings 9 

Figure 3: Simulated Likelihood of Receiving UI Benefits for UIEligible Workers, by Age 10 

Figure 4: Simulated Likelihood of Receiving UI Benefits for UIEligible Workers, by Education Level 12 

Figure 5: Simulated Likelihood of Receiving UI Benefits for UIEligible Workers during Successive Periods of 

Unemployment, by Past UI Receipt Status 15 

Figure 6: Simulated Unemployment Duration for UI-Eligible 

Workers, by Prior-Year Earnings and UI Receipt Status 18 

Figure 7: Simulated Unemployment Duration for UI-Eligible 

Workers, by Education Level and UI Receipt Status 20 

Figure 8: Distribution of All Periods of UI Benefit Receipt across 

Industries 24

Figure 9: Simulated Effect of Past UI Benefit Receipt on the 

Likelihood of Receiving UI in Subsequent Periods of 

Unemployment, for Selected Industries 26 

Figure 10: Simulated Likelihood of Receiving UI Benefits for UIEligible Workers, by Age 65 

Figure 11: Simulated Likelihood of Receiving UI Benefits for UIEligible Workers, by Prior-Year Earnings 67 

Figure 12: Simulated Unemployment Duration for UI-Eligible 

Workers, by Prior-Year Earnings and UI Receipt Status 75 

Page iii GAO-06-341 Unemployment Insurance 
Abbreviations 

BLS Bureau of Labor Statistics 

BPE base period earnings 

CPI-U Consumer Price Index for All Urban Consumers 

CPS Current Population Survey 

HQE high quarter earnings 

NLSY79 National Longitudinal Survey of Youth 1979 

OLF out of the labor force 

SIC Standard Industrial Classification 

SMSA Standard Metropolitan Statistical Area 

SOC Standard Occupational Classification 

UI Unemployment Insurance 

WBA weekly benefit amount

This is a work of the U.S. government and is not subject to copyright protection in the 

United States. It may be reproduced and distributed in its entirety without further 

permission from GAO. However, because this work may contain copyrighted images or 

other material, permission from the copyright holder may be necessary if you wish to 

reproduce this material separately. 

Page iv GAO-06-341 Unemployment Insurance 
United States Government Accountability Office

Washington, DC 20548 

March 7, 2006

The Honorable Wally Herger 

Chairman, Subcommittee on Human Resources 

Committee on Ways and Means 

House of Representatives 

Dear Mr. Chairman: 

Unemployment Insurance (UI), established in 1935, is a complex system of 

53 programs that provide temporary cash benefits to eligible workers who 

become unemployed through no fault of their own.1

 Eligibility for UI 

benefits, benefit amounts, and the length of time benefits are available are 

determined by state law, within broad federal guidelines. Benefits are 

financed through federal and state employer payroll taxes. In fiscal year 

2004, employers paid about $39.3 billion in UI taxes, and 8.8 million 

workers received UI benefits totaling $41.3 billion.

Decades of program experience and administrative data have resulted in a 

firm understanding of the composition of UI caseloads and the overall cost 

of the program. However, this understanding of the UI program has been 

based on snapshots of the UI beneficiary population at any given time. 

Additional research has provided limited information on the types of 

workers who are likely to receive UI benefits and on how UI requirements 

and benefits affect individuals’ movement into and out of the workforce, 

including how UI receipt affects the duration of unemployment. However, 

because of the difficulty of tracking the same workers over time, the 

circumstances that give rise to individual workers’ use or nonuse of the UI

program and how this may, in turn, affect individuals’ patterns of 

unemployment over the course of their entire working careers are still not 

well understood. 

In 2005, we reported on the results of our analysis of a unique database 

that tracked a single group of individuals over time.2

 Examining this 

 1

UI programs are administered by the 50 states, the District of Columbia, Puerto Rico, and 

the Virgin Islands. 

2

GAO, Unemployment Insurance: Information on Benefit Receipt, GAO-05-291 

(Washington, D.C.: Mar. 17, 2005). 

Page 1 GAO-06-341 Unemployment Insurance 
database, we found that 85 percent of a nationally representative sample 

of late baby boom workers (workers born between 1957 and 1964) had

experienced unemployment at least once between 1979 and 2002. Workers 

who experienced unemployment were unemployed an average of five 

times over this 23-year period. Moreover, we found that of those who were 

eligible for UI benefits at least once, only 38 percent at some point 

received UI benefits. About half of the workers receiving UI benefits 

received them more than once. Finally, we reported that the rate at which

unemployed workers received UI benefits varied across industries. 

As Congress reviews the ability of labor programs to meet the needs of the 

workforce in the new century, it will be important to understand why 

fewer than half of workers eligible for UI benefits receive them and the 

other half do not, as well as what factors cause workers in some industries 

to seek benefits multiple times over the course of their careers. In this 

context, you asked us to determine (1) the extent to which characteristics 

of individual workers, including a history of past UI benefit receipt, are 

associated with the likelihood of UI benefit receipt and unemployment 

duration, and (2) whether an unemployed worker’s industry is associated 

with UI benefit receipt or unemployment duration. 

To answer these questions, we analyzed data from the National 

Longitudinal Survey of Youth 1979 (NLSY79). This survey provides 

information that is not typically available from other data sources. The 

dataset contains information from ongoing periodic interviews with a 

nationally representative sample of individuals who were born between 

1957 and 1964. At the time of our analysis, the database contained 

information from interviews conducted between 1979 and 2002. There 

were 12,686 individuals in the sample in 1979. The survey provides a wide 

range of detailed information about these individuals, including their work 

histories, income, family composition, and education. Using the dataset,

we analyzed a single birth cohort over time; therefore, our findings do not

represent the experience of workers of all ages during this time period. 

Using this survey information and information on states’ UI program 

eligibility rules for each year from 1978 through 2002,3

 we estimated 

 3

We considered an individual to be UI-eligible if that individual experienced an involuntary

job loss, reported receiving a minimum amount of wages over a minimum period of time as 

defined by the state where the individual lived, and was actively looking for new 

employment. Our method of estimating eligibility tends to overestimate the number of UIeligible individuals. For a more complete discussion of our methodology, see appendix I. 

Page 2 GAO-06-341 Unemployment Insurance 
whether individuals from the sample were eligible for UI benefits 

following a job separation. We identified 5,631 workers who met the 

conditions for UI eligibility—a group that we refer to as “UI-eligible 

workers”—who collectively experienced 15,506 separate periods of 

unemployment during the study period (1979-2002). 

We used a multivariate statistical model to identify the key factors 

associated with UI benefit receipt and unemployment duration for our 

subsample of UI-eligible workers. The model allowed us to isolate the 

effect of a particular characteristic by statistically controlling for a number 

of other characteristics. In this report, we refer to the results for individual 

characteristics in comparison with “otherwise similar workers.” By this 

phrasing, we intend to show that we have controlled for all other 

characteristics that may be related to the characteristic being studied. For 

example, the test of the effect of age on benefit receipt was conducted 

while controlling, for example, for earnings and education—two 

characteristics that are correlated with age. In addition, we modeled UI 

benefit receipt and unemployment duration together to control for the 

likely correlation that exists between these two outcomes. 

To illustrate how changes in different characteristics affect the likelihood 

of UI receipt and unemployment durations, we used the results of our 

multivariate statistical model to simulate how changes in observable 

characteristics affect the likelihood of UI receipt and unemployment 

duration. The simulated results are calculated from our statistical model 

estimates, holding selected characteristics constant, as noted throughout 

the report. For example, to understand how changes in workers’ education

affect their likelihood of receiving UI benefits, we set the number of years 

of education at the same value for all workers in our sample and then used

the model estimates to simulate the likelihood of UI receipt for each 

worker. We then calculated the average likelihood of receiving UI benefits. 

We repeated this process for different years of education. Unless 

otherwise noted, simulated likelihoods of UI receipt and simulated 

unemployment duration are for workers experiencing unemployment for 

the first time. See appendix I for a more complete discussion of our 

methodology, including limitations of our analysis. 

We assessed the reliability of the NLSY79 dataset and found it to be 

sufficient for our analysis. Our work was conducted from May 2005 

through February 2006 in accordance with generally accepted government 

auditing standards. 

Page 3 GAO-06-341 Unemployment Insurance 
Certain characteristics are associated with the likelihood of receiving UI 

benefits and unemployment duration. Based on our analysis of workers 

during the first half of their working lives, UI-eligible workers are more 

likely than other workers to receive UI benefits if they have higher 

earnings, are younger or have more years of education, or, most notably, if 

they received UI benefits in the past. In particular, UI-eligible workers who 

received UI benefits before are more likely than other workers to receive

UI benefits again and this likelihood increases each time they are 

unemployed and receive UI. Other factors, including a high local 

unemployment rate, increase the likelihood of receiving UI. UI-eligible 

workers who receive UI benefits have longer periods of unemployment 

than workers who do not receive benefits. Similarly, workers who have 

fewer years of education, lower earnings, or no union membership 

experience longer unemployment than workers who do not have these 

characteristics. Workers who received UI benefits in the past, however, 

were unemployed about as long as similar workers who had not received

UI in the past. 

Results in Brief 

UI-eligible workers from certain industries are more likely than other 

workers to receive UI benefits and experience shorter unemployment 

duration, although no clear industry trend emerged. Specifically, our 

simulations show that 

• The likelihood of receiving UI benefits during a first period of 

unemployment is highest among workers from mining and 

manufacturing. Furthermore, the likelihood of receiving UI benefits 

when unemployed increases with each previous period of UI receipt 

across all industries, and the most notable increase occurs for workers 

from the public administration sector. 

• The unemployment duration for first-time unemployed workers from 

construction and manufacturing is significantly shorter than the 

unemployment duration experienced by workers from other industries. 

While unemployment duration varies across all industries, this 

variation is not affected by whether workers were unemployed in the 

past, or whether they received UI in the past. 

In its comments, the Department of Labor stated that, while there are 

certain qualifications of our findings, Labor applauds our efforts and said 

that this report adds to our current knowledge of the UI program. Labor 

also provided technical comments, which we incorporated where 

appropriate.

Page 4 GAO-06-341 Unemployment Insurance 
Background The UI program was established in 1935 and serves two primary 

objectives: (1) to temporarily replace a portion of earnings for workers 

who become unemployed through no fault of their own and (2) to help 

stabilize the economy during recessions by providing an infusion of 

consumer dollars into the economy. UI is made up of 53 state-administered 

programs that are subject to broad federal guidelines and oversight. In 

fiscal year 2004, these programs covered about 129 million wage and 

salary workers and paid benefits totaling $41.3 billion to about 8.8 million 

workers. 

Federal law provides minimum guidelines for state programs and 

authorizes grants to states for program administration. States design their 

own programs, within the guidelines of federal law, and determine key 

elements of these programs, including who is eligible to receive state UI 

benefits, how much they receive, and the amount of taxes that employers 

must pay to help provide these benefits. State unemployment tax revenues 

are held in trust by the Department of Labor (Labor) and are used by the 

states to pay for regular weekly UI benefits, which typically can be 

received for up to 26 weeks. During periods of high unemployment, the 

Extended Benefits program, funded jointly by states through their UI trust 

funds and by the federal government through the Unemployment Trust 

Fund, provides up to 13 additional weeks of benefits for those who qualify 

under state program rules. Additional benefits, funded by the federal 

government, may be available to eligible workers affected by a declared 

major disaster or during other times authorized by Congress. 

To receive UI benefits, an unemployed worker must file a claim and satisfy 

the eligibility requirements of the state in which the worker’s wages were 

paid. Although states’ UI eligibility requirements vary, generally they can 

be classified as monetary and nonmonetary. Monetary eligibility 

requirements include having a minimum amount of wages and 

employment over a defined base period, typically, about a year before 

becoming unemployed, and not having already exhausted the maximum 

amount of benefits or benefit weeks to which they would be entitled 

because of other recent unemployment. In addition to meeting states’ 

monetary eligibility requirements, workers must satisfy their states’ 

nonmonetary eligibility requirements. Nonmonetary eligibility 

requirements include being able to work, being available for work, and 

becoming unemployed for reasons other than quitting a job or being fired

for work-related misconduct. In all states, claimants who are determined 

to be ineligible for benefits are entitled to an explanation for the denial of

benefits and an opportunity to appeal the determination. 

Page 5 GAO-06-341 Unemployment Insurance 
Since UI was introduced, researchers and those responsible for overseeing 

the program have monitored the size, cost, and structure of the program 

and its effects on individuals’ movement into and out of the workforce, 

including which types of workers receive UI benefits. Much of what is 

known about the dynamics of the UI program has been based on 

snapshots of the UI beneficiary population at any given time. Labor 

regularly gathers UI program data from the states, including each state’s 

eligibility requirements, employers’ UI tax rates, program revenues and

costs, and numbers of claims received and approved. An extensive amount 

of research has been devoted to the effect of UI benefit receipt on 

unemployment duration. Specifically, researchers have found that 

receiving UI benefits increases unemployment duration. Much of this 

research is focused on measuring how changes in the amount of UI 

benefits increase the amount of time that an unemployed worker takes to 

find a new job.4

 Although much is known about UI caseloads and about the 

relationship between UI benefits and unemployment duration, less is 

known about the patterns of UI receipt among individual workers over 

their entire working careers. 

What is known about the patterns of UI benefit receipt over an extended

period for individual workers comes from a few studies that are fairly 

limited in scope. In one study, researchers analyzed 1980-1982 survey data

and found that among unemployed workers who were eligible for UI, 

younger or female workers were less likely to receive UI benefits, while 

union workers, workers from large families, or those with more hours of 

work from their previous jobs were more likely to receive UI.5

 In another 

study, using UI administrative data from five states, researchers found that 

between 36 and 44 percent of UI claims from 1979 to 1984 were from 

workers who had received UI benefits more than once and that middleaged workers and workers with higher earnings were more likely to be

repeat UI recipients.6

 Another study, based on survey data from the 

NLSY79, found that 16 percent of young adults had received UI benefits 

 4

Alan B. Krueger and Bruce D. Meyer, “Labor Supply Effects of Social Insurance,” NBER

Working Paper 9014 (Cambridge, Massachusetts: National Bureau of Economic Research,

2002). 

5

Rebecca M. Blank and David E. Card, “Recent Trends in Insured and Uninsured 

Unemployment: Is There an Explanation?” The Quarterly Journal of Economics, vol. 106, 

no. 4 (1991). 

6

Bruce D. Meyer and Dan T. Rosenbaum, “Repeat Use of Unemployment Insurance,” NBER

Working Paper 5423 (Cambridge, Massachusetts: National Bureau of Economic Research,

1996), p. 20. 

Page 6 GAO-06-341 Unemployment Insurance 
more than once between 1978 and 1991 and that as many as 46 percent of 

those who received UI were repeat recipients.7

 This study also found that

workers who were women or Hispanic or whose fathers had more years of 

education were less likely to become repeat recipients than workers who 

were men or non-Hispanic or whose fathers had fewer years of education.

In 2005, we analyzed the NLSY79 to determine the extent to which 

individual workers received UI benefits during their early working lives.8

We found that 38 percent of workers born between 1957 and 1964 received 

UI at least once between 1979 and 2002, with almost half of these 

individuals receiving UI benefits more than once. (See fig. 1.) We also 

found that the rate at which unemployed workers received UI benefits 

varied across industries, but we did not control for any of the other factors 

that may have helped to explain this variation. 

Figure 1: Incidence of UI Benefit Receipt from 1979 through 2002, for Workers Born between 1957 and 1964 

Source: GAO analysis of NLSY79 data.

56%

23% Twice

Three times

5%

Four times

5%

Five or more times

Received UI one time

12% 62% Received UI

Did not receive UI

38%

Note: Sampling errors were within plus or minus 5 percentage points at the 95 percent confidence 

level. 

 7

See Brian P. McCall, “Repeat Use of Unemployment Insurance,” in Laurie J. Bassi and 

Stephen A. Woodbury, editors, Long-Term Unemployment and Reemployment Policies 

(Stamford, Connecticut: JAI Press, Inc., 2000). 

8

GAO-05-291. 

Page 7 GAO-06-341 Unemployment Insurance 
Earnings, age, education, and most notably past UI benefit receipt are all 

associated with the likelihood of receiving UI benefits for UI-eligible 

workers. Education, earnings, and union membership, and current UI 

benefit receipt, are associated with unemployment duration.

Unemployed workers are more likely to receive UI benefits if they have 

higher earnings prior to becoming unemployed, are younger or have more 

years of education, or have a history of past UI benefit receipt, when 

compared to workers with similar characteristics.9

 We found that past 

experience with the UI program has a particularly strong effect on the 

future likelihood of receiving UI benefits. In addition, UI-eligible workers 

are more likely to receive UI when the local unemployment rate is high. 

However, some characteristics, such as the weekly UI benefit amount that 

a worker is eligible to receive, are not associated with a greater likelihood

of receiving UI benefits. 

Unemployed workers who have higher earnings or are younger or who are 

more educated are more likely to receive UI benefits than otherwise 

similar workers. With respect to earnings,10 our simulations show that the 

likelihood of receiving UI tends to increase as the amount earned in the 

year prior to becoming unemployed increases (see fig. 2). For example, a 

UI-eligible worker with earnings between $10,000 and $11,999 in the year

before becoming unemployed has a 36 percent likelihood of receiving UI, 

whereas a worker who earned roughly twice as much (between $20,000 

and $24,999) has a 45 percent likelihood of receiving UI.11 The likelihood of 

receiving UI is lowest among workers with the lowest earnings (i.e., less 

Certain 

Characteristics Are 

Associated with UI 

Benefit Receipt and 

Unemployment 

Duration 

Unemployed Workers with 

Higher Earnings, Younger 

Workers, Workers with

More Education, or Those 

Who Received UI in the 

Past Are More Likely to 

Receive UI Benefits 

Unemployed Workers Who 

Have Higher Earnings or Are 

Younger or Have More Years of 

Education Are More Likely to 

Receive UI 

 9

The results described in this report are statistically significant at the 95 percent confidence

level, unless otherwise noted. For a complete list of findings from our multivariate 

statistical model of the key factors associated with UI benefit receipt, see table 8 in 

appendix I.

10Earnings refers to base period earnings, which we define as the amount of earnings

received during the first four of the last five full calendar quarters before a worker becomes 

unemployed. This definition is consistent with the time frame states generally use to 

determine eligibility. 

11The average and maximum earnings for the unemployed workers in our sample are 

$15,524 and $597,950, respectively. 

Page 8 GAO-06-341 Unemployment Insurance 
than $10,000 in the year before becoming unemployed). There is generally

little difference in the likelihood of receiving UI among workers earning 

$18,000 or more. 

Figure 2: Simulated Likelihood of Receiving UI Benefits for UI-Eligible Workers, by 

Prior-Year Earnings

0

10

20

30

40

50

60

70

80

90

100

30,000 and higher

25,000-29,999

10,000-11,999

12,000-13,999

14,000-15,999

16,000-17,999

18,000-19,999

20,000-24,999

2,000-3,999

4,000-5,999

6,000-7,999

8,000-9,999

0-1,999

Simulated likelihood of receiving UI (percent)

Source: Simulations based on GAO analysis of NLSY79 data.

Earnings (2002 dollars)

Note: Simulations are for the average likelihood of receiving UI during first-time unemployment at 

different levels of earnings. The overall average likelihood of receiving UI during first-time 

unemployment is 33 percent. See appendix I for methodology and estimation results.

This result confirms our 2000 finding that low-wage workers are less likely 

to receive UI benefits than workers with higher earnings even when they

have worked for the same amount of time.12 Our current result also 

controls for other worker differences, such as which industries the 

workers were employed in or whether they were ineligible for benefits, 

which we had not previously been able to rule out as explanations for the 

variation in likelihood of receiving UI. The relationship between higher 

 12GAO, Unemployment Insurance: Role as Safety Net for Low-Wage Workers Is Limited,

GAO-01-181 (Washington, D.C.: Dec. 29, 2000). 

Page 9 GAO-06-341 Unemployment Insurance 
earnings and a higher likelihood of receiving UI benefits is also consistent 

with economic theory that predicts that workers with higher earnings 

prior to becoming unemployed will be more reluctant to accept lower 

reemployment wages and are therefore more likely to take advantage of UI 

benefits as a way to subsidize their job search efforts.13

Concerning age, our simulations show that the likelihood of receiving UI 

peaks at about age 25 and decreases thereafter (see fig. 3). More 

specifically, a 25-year-old unemployed worker who is eligible for UI is 

more than twice as likely to receive UI as an otherwise similar 40-year-old

unemployed worker. 

Figure 3: Simulated Likelihood of Receiving UI Benefits for UI-Eligible Workers, by 

Age 

0

10

20

30

40

50

60

70

80

90

100

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

Simulated likelihood of receiving UI (percent)

Source: Simulations based on GAO analysis of NLSY79 data.

Age when unemployment began

Note: Simulations are the average likelihood of receiving UI during first-time unemployment at 

different ages. The overall average likelihood of receiving UI during first-time unemployment is 

33 percent. See appendix I for methodology and estimation results.

 13For economic theory concerning the relationship between job search and unemployment

insurance, see Dale T. Mortensen, “Unemployment Insurance and Job Search Decisions,” 

Industrial and Labor Relations Review, vol. 30, no. 4 (1977). 

Page 10 GAO-06-341 Unemployment Insurance 
Previous studies have found that younger workers are less likely to receive 

UI benefits than older workers.14 However, these previous studies did not 

include as much information about workers’ past unemployment and UI 

benefit receipt histories as our current analysis. Therefore, because older 

workers have more of this experience than younger workers, it is possible 

that our analysis has controlled for the effect of this past experience more 

completely than these previous studies, resulting in a more precise 

estimate of the effect of age. We are unable to explain why younger 

workers are more likely to receive UI benefits than otherwise similar older 

workers. However, it is possible that older workers, who have had more 

time to accumulate financial assets, may have more private resources 

available to help them cope with unemployment than younger workers.15

Alternatively, younger workers may be less optimistic about how long it 

will take for them to become reemployed. 

Unemployed workers with more years of education are more likely to 

receive UI benefits than otherwise similar workers with fewer years of 

education. Specifically, our simulations show that the likelihood of 

receiving UI increases for each additional year of schooling that a UIeligible worker has completed before becoming unemployed (see fig. 4). 

For example, a UI-eligible worker with a college education (one who has 

completed 16 years of schooling) when he or she becomes unemployed is 

almost one-fifth more likely to receive UI than a UI-eligible worker with a 

high school education (12 years of schooling).16

 14See Blank and Card, and McCall. 

15See Jonathan Gruber, “The Wealth of the Unemployed,” October 2001, Industrial and 

Labor Relations Review, vol. 55, no. 1. 

16The average number of years of schooling completed by UI-eligible workers, at the time 

when they became unemployed, is 12 years. 

Page 11 GAO-06-341 Unemployment Insurance 
Figure 4: Simulated Likelihood of Receiving UI Benefits for UI-Eligible Workers, by 

Education Level 

0

10

20

30

40

50

60

70

80

90

100

20

and

higher

9 10 11 12 13 14 15 16 17 18 19

Simulated likelihood of receiving UI (percent)

Source: Simulations based on GAO analysis of NLSY79 data.

Years of education when unemployment began

Note: Simulations are the average likelihood of receiving UI during first-time unemployment at 

different education levels. The overall average likelihood of receiving UI during first-time 

unemployment is 33 percent. See appendix I for methodology and estimation results.

Although the impact of education on the likelihood of receiving UI benefits 

has been analyzed in other research, this research found no significant 

education effect.17 However, to the extent that workers with more years of 

education are better able to access and understand UI program rules, they 

may also be more likely to know when they are entitled to benefits and to

have the information that they need to file successful benefit claims. 

Other factors, including gender, marital status, job tenure, and the local 

unemployment rate are also associated with UI benefit receipt. Controlling 

for all other characteristics among this UI-eligible group, 

• a woman is 29 percent more likely to receive UI benefits than a man, 

 17See Blank and Card, p. 1185. 

Page 12 GAO-06-341 Unemployment Insurance 
• a married worker is 13 percent more likely to receive UI than an 

unmarried worker, 

• a longer tenured worker is more likely to receive UI—for example, a 

worker with 4 years of tenure at his or her most recent job is 

12 percent more likely to receive UI than a worker with 1 year of job 

tenure, and 

• being in an area with high unemployment raises the likelihood that an 

unemployed worker will receive UI—for example, a worker living in an 

area with an unemployment rate of 9 percent is 10 percent more likely 

to receive UI than a worker living in an area with an unemployment 

rate of 5 percent. 

Our finding that women are more likely to receive UI benefits than 

otherwise similar men differs from the results of previous research, which 

generally found no statistically significant differences. Nevertheless, our 

analysis controls for more worker characteristics than these previous 

studies, and it is likely that we have more carefully isolated the effect of 

gender from that of other characteristics related to gender, such as 

workers’ occupations or industries. It is not immediately clear why women 

are more likely to receive UI benefits, however. We are likewise unable to 

explain why married workers are more likely to receive UI benefits than 

otherwise similar unmarried workers.18 

Our findings on job tenure are consistent with previous research. 

However, the higher likelihood of UI benefit receipt associated with more 

years of job tenure is likewise difficult to explain. It might be that workers 

with longer job tenures have acquired more skills that are not as easy to 

transfer to another employer, relative to workers with less job tenure, and 

anticipate longer job searches. 

The higher likelihood of receiving UI benefits among workers living in 

areas with higher unemployment is likely due to the higher number of 

unemployed workers relative to available jobs, which may make workers 

more willing to apply for UI benefits as they engage in what are likely to be 

longer job searches. 

In contrast to our findings above, a key UI program element, the weekly UI 

benefit amount that UI-eligible workers are entitled to, is not associated 

 18We specifically tested for the effect of spousal income on the likelihood of receiving UI to

determine whether marital status was masking some underlying effect of additional family 

income, and found this not to be the case. 

Page 13 GAO-06-341 Unemployment Insurance 
with a greater likelihood of receiving UI benefits. Using our model 

estimates, we simulated increases in weekly UI benefit amounts of 

10 percent and 25 percent and a decrease of 10 percent and found that 

these changes had no effect on the likelihood of UI benefit receipt. This 

finding is consistent with the work of others, who have found that 

increases in the weekly benefit amount have mixed, but generally small 

effects on UI benefit receipt.19 Collectively, these results suggest that UI 

benefit levels have modest effects on individuals’ decisions about whether 

or not to receive UI benefits, after controlling for other factors. 

Unemployed workers who have received UI benefits during a prior period 

of unemployment are more likely to receive UI benefits during a current 

period of unemployment than otherwise similar workers who never 

received UI benefits (see fig. 5). For example, when workers experience 

their first UI-eligible period of unemployment, their likelihood of receiving

UI is 33 percent. During a second UI-eligible period of unemployment, the 

likelihood of receiving UI is 48 percent for workers who received UI 

during the first unemployment period but only 30 percent for workers who 

did not receive UI. Furthermore, the likelihood that these UI-eligible 

workers will receive UI benefits during successive periods of 

unemployment increases each time that they receive UI benefits and 

decreases each time that they do not.20

Unemployed Workers Who 

Received UI in the Past Are 

More Likely to Receive UI 

during Subsequent 

Unemployment 

 19See David E. Card and Phillip B. Levine, “Unemployment Insurance Taxes and the 

Cyclical and Seasonal Properties of Unemployment,” Journal of Public Economics, vol. 53, 

no. 1 (1994); Patricia M. Anderson and Bruce D. Meyer, “The Effect of Unemployment 

Insurance Taxes and Benefits on Layoffs Using Firm and Individual Data,” NBER Working 

Paper No. 4960, December 1994; and Robert H. Topel, “On Layoffs and Unemployment 

Insurance,” American Economic Review, vol. 73, no. 4 (1983). 

20As noted above, relatively few UI-eligible workers who receive UI benefits receive them 

multiple times. See GAO-05-291 for a more complete discussion of the incidence of repeat

UI benefit receipt. 

Page 14 GAO-06-341 Unemployment Insurance 
Figure 5: Simulated Likelihood of Receiving UI Benefits for UI-Eligible Workers 

during Successive Periods of Unemployment, by Past UI Receipt Status

0

10

20

30

40

50

60

70

80

90

100

First Second Third Fourth Fifth Sixth

Simulated likelihood of receiving UI (percent)

Source: GAO simulations based on GAO analysis of NLSY79 data.

Unemployment period

Always received UI previously

Never received UI previously

Note: Simulations are the average likelihood of receiving UI during a current unemployment period for 

two extreme cases: (1) workers who always received UI benefits during previous unemployment and 

(2) workers who never received UI during previous unemployment. The average likelihood of 

receiving UI during first-time unemployment for all UI-eligible workers is 33 percent. See appendix I 

for methodology and estimation results. 

This finding suggests that a worker’s first unemployment experience has a 

lasting and self-reinforcing effect. To the extent that workers know about 

the UI program and whether or not they are eligible, receiving or not 

receiving UI benefits may be a personal choice based on unobserved 

worker characteristics or preferences. Alternatively, if workers do not 

have good information about UI, those who receive UI benefits may know 

more about the UI program than those who do not receive UI, and their 

knowledge about the program could make it easier to apply for and 

receive benefits during a subsequent period of unemployment. 

Page 15 GAO-06-341 Unemployment Insurance 
Overall, unemployed workers who receive UI benefits have longer 

unemployment duration than otherwise similar workers who do not 

receive UI benefits.21 Several other characteristics are also associated with

unemployment duration. Specifically, UI-eligible workers are more likely 

to experience longer unemployment duration if they have lower earnings 

before becoming unemployed or have fewer years of education. Other 

characteristics associated with longer unemployment duration, after 

controlling for other factors, include being African-American or female or 

not belonging to a union. We found no relationship between past UI 

benefit receipt and subsequent unemployment duration. 

Whether or not an unemployed worker receives UI during a specific period 

of unemployment has the strongest effect on how long that period of 

unemployment is likely to last. Overall, UI-eligible workers who receive UI

benefits during a period of unemployment remain unemployed for about 

21 weeks on average, whereas otherwise similar workers who do not 

receive UI remain unemployed for about 8 weeks. This result is consistent 

with economic theory that predicts that receiving UI benefits reduces the 

costs associated with unemployment and allows workers to engage in 

longer job searches.22 That is, an unemployed worker who receives UI 

benefits faces less pressure to accept the first job offer they receive and 

can search longer for a more desirable job than an unemployed worker 

who does not receive UI. Another possible explanation for the strong 

association between UI receipt and longer unemployment duration may be

that workers who expect to experience longer unemployment may be 

more likely to apply for UI than those who expect to return to work 

quickly. 

Unemployed workers with lower earnings tend to have longer 

unemployment duration than otherwise similar workers with higher 

earnings. This finding holds for workers who are receiving UI benefits, and 

for workers who are not receiving UI benefits. Specifically, our 

simulations show that UI-eligible workers who receive UI benefits and 

have relatively high earnings ($30,000 and higher) in the year prior to 

becoming unemployed have unemployment duration that is as much as 

Receiving UI Benefits, 

along with Other Factors, 

Is Associated with 

Unemployment Duration 

Receiving UI Benefits Is 

Associated with Longer 

Unemployment Duration 

Unemployed Workers with 

Lower Earnings and Less 

Education Tend to Have Longer 

Unemployment Duration 

 21For the parameter estimates of these and other variables included in our multivariate 

statistical model of the key factors associated with unemployment duration, see table 9 in

appendix I. The variables reported here are those that were statistically significant at the 

95 percent confidence level. 

22See Mortensen. 

Page 16 GAO-06-341 Unemployment Insurance 
9 weeks shorter than workers with earnings that are below $16,000.23 The 

results are similar for UI-eligible workers who do not receive UI benefits 

(see fig. 6). 

 23The average prior-year earnings amount for this sample is $15,524. 

Page 17 GAO-06-341 Unemployment Insurance 
Figure 6: Simulated Unemployment Duration for UI-Eligible Workers, by Prior-Year 

Earnings and UI Receipt Status

0

5

10

15

20

25

30

30,000 and higher

10,000-11,999

12,000-13,999

14,000-15,999

16,000-17,999

18,000-19,999

20,000-24,999

25,000-29,999

2,000-3,999

4,000-5,999

6,000-7,999

8,000-9,999

0-1,999

Simulated unemployment duration (median weeks)

Source: Simulations based on GAO analysis of NLSY79 data.

Earnings (2002 dollars)

Receiving UI benefits

Not receiving UI benefits

Note: Simulations are the median duration of unemployment during first-time unemployment. Overall 

average duration is 21 weeks for UI-eligible workers receiving UI benefits and 8 weeks for UI-eligible 

workers not receiving UI benefits. See appendix I for methodology and estimation results. 

Our result is consistent with other research that has found that higher 

previous earnings tend to reduce unemployment duration.24 Researchers 

have suggested that the association between higher earnings and shorter 

unemployment duration may be due, in part, to the higher cost of 

unemployment for workers with higher earnings, relative to the cost for 

workers with lower earnings.25 Specifically, the cost of unemployment in 

 24See Karen E. Needels and Walter Nicholson, An Analysis of Unemployment Durations 

Since the 1990-1992 Recession, UI Occasional Paper 99-6, prepared for the Department of

Labor, 1999, p. 94. 

25See Bruce D. Meyer, “Unemployment Insurance and Unemployment Spells,” 

Econometrica, vol. 58, no. 4 (1990), p. 771. 

Page 18 GAO-06-341 Unemployment Insurance 
terms of lost wages is greater for workers with higher earnings, because 

they forego a higher amount of potential earnings in exchange for the time 

they spend on unpaid activities, such as job search, home improvement, or 

recreation. 

Our model estimates also indicate that unemployed workers who have 

more education tend to have shorter unemployment duration than 

otherwise similar workers with less education. For example, simulations 

show that on average, UI-eligible workers with a 4-year college education 

(16 years of schooling) who receive UI benefits remain unemployed about 

2 weeks less than workers with a high school education (12 years of 

schooling).26 (See fig. 7.) The results are similar for UI-eligible workers 

who do not receive UI benefits. This finding is consistent with past 

research indicating that less education is associated with longer 

unemployment duration, because workers with less education have fewer 

work-related skills.27

 26The average number of years of schooling completed by UI-eligible workers, at the time 

when they became unemployed, is 12 years. 

27Needels and Nicholson, p. 6.

Page 19 GAO-06-341 Unemployment Insurance 
Figure 7: Simulated Unemployment Duration for UI-Eligible Workers, by Education 

Level and UI Receipt Status

Simulated unemployment duration (median weeks)

Years of education when unemployment began

0

5

10

15

20

25

30

20

and

higher

9 10 11 12 13 14 15 16 17 18 19

Source: Simulations based on GAO analysis of NLSY79 data.

Receiving UI benefits

Not receiving UI benefits

Note: Simulations are the median duration of unemployment during first-time unemployment. Overall 

average duration is 21 weeks for UI-eligible workers receiving UI benefits and 8 weeks for UI-eligible 

workers not receiving UI benefits. See appendix I for methodology and estimation results. 

Unemployed workers’ race or ethnicity, gender, union membership status, 

and length of most recent job tenure are also associated with

unemployment duration. Specifically, simulations show that UI-eligible 

workers who are African-American or women, who do not belong to labor 

unions, or who have less years of job tenure before becoming unemployed 

tend to have longer unemployment duration than otherwise similar 

workers. As seen in table 1, these associations exist whether or not 

workers receive UI benefits. 

Page 20 GAO-06-341 Unemployment Insurance 
Table 1: Simulated Unemployment Duration for UI-Eligible Workers by Current UI 

Receipt Status and Other Characteristics

Unemployment duration (median weeks) 

Worker characteristics 

Receiving

UI benefits

Not receiving

UI benefits

Race or ethnicity 

White 19 8

Hispanic 21 8

African-American 25 11

Gender

Male 20 8

Female 22 9

Union membership status

Union member 19 8

Not a union member 21 9

Tenure at most recent joba

10 years 20 8

1 year 21 8

Overall average duration 21 8

Source: Simulations based on GAO analysis of NLSY79 data. 

Note: Simulations are the median duration of unemployment during first-time unemployment. See 

appendix I for methodology and estimation results.

a

Simulated decreases in median weeks of unemployment are less than 1 week per additional year of 

tenure at most recent job, regardless of whether workers received UI or not. 

Our findings are generally consistent with prior research. In particular, 

longer unemployment durations have been found to be associated with 

being African-American, female, or not belonging to a union.28 Two 

possible explanations for the differences in employment outcomes for 

African-American workers include labor market discrimination, and 

limited access to social networks that may enable these workers to find 

jobs more quickly.29 Likewise, longer unemployment duration among 

female workers may be due to labor market discrimination, or to 

 28See Needels and Nicholson. 

29See Antoni Calvó-Armengol, and Matthew O. Jackson, “The Effects of Social Networks on

Employment and Inequality,” The American Economic Review, Vol. 94, No. 3, (2004) for a 

discussion of the effects of individuals’ social networks on employment outcomes. 

Page 21 GAO-06-341 Unemployment Insurance 
differences in how they value paid work versus nonemployment activities,

relative to men.30 Likewise, the associations between shorter 

unemployment duration and union membership or longer job tenure may 

reflect the greater access of these workers to reemployment opportunities 

than otherwise similar workers or because of a greater likelihood of being 

recalled to their previous jobs.31

Past UI receipt has no significant effect on subsequent unemployment 

duration. Although receiving UI during a current period of unemployment 

is associated with longer unemployment duration, past UI receipt does not 

affect current unemployment duration. Specifically, simulations show that 

unemployment duration tends to decrease by about the same amount 

(typically, 1 week or less) from one unemployment period to the next, 

regardless of whether a worker received UI benefits in the past or not, and 

regardless of whether or not the worker receives UI benefits in the current

period. 

Unemployment Duration Is Not 

Associated with Past UI

Receipt 

Unemployed workers in certain industries are more likely to receive UI 

benefits and experience shorter unemployment duration than otherwise 

similar workers from other industries. Simulations show that first-time 

unemployed workers from mining and manufacturing are more likely to 

receive UI than workers from other industries. Moreover, the strength of 

the association between past and current UI benefit receipt varies across 

industries. The increase in the likelihood of receiving UI from one 

unemployment period to the next is highest for public administration and 

is lowest for agriculture and construction. Furthermore, simulations 

indicate that UI-eligible workers from industries with higher proportions 

of unemployment periods that result in UI receipt are no more likely to 

become repeat UI recipients than workers from other industries. With 

respect to unemployment duration, UI-eligible workers from construction 

and manufacturing have shorter unemployment duration than workers 

from other industries. 

Certain Industries Are 

Associated with UI 

Benefit Receipt and 

Unemployment 

Duration 

 30See Needels and Nicholson, and GAO, Women’s Earnings: Work Patterns Partially

Explain Differences between Men’s and Women’s Earnings, GAO-04-35 (Washington, D.C.:

Oct. 31, 2003). 

31See Needels and Nicholson. We did not control for the likely effect of an expected job 

recall. 

Page 22 GAO-06-341 Unemployment Insurance 
Unemployed workers from mining and manufacturing are more likely to 

receive UI than otherwise similar workers from other industries. For 

example, first-time unemployed workers from the manufacturing industry 

are about two-thirds more likely to receive UI benefits than workers from 

the professional and related services industry (see table 2). Although UIeligible workers from mining are more likely to receive UI benefits than 

workers from other industries, just 2 percent of the unemployment periods 

that result in UI benefit receipt come from the mining industry. (See fig. 

8.)32

Unemployed Workers from 

Mining and Manufacturing 

Are More Likely to Receive 

UI Benefits 

Table 2: Simulated Likelihood of Receiving UI Benefits for UI-Eligible Workers from

Different Industries 

Industry 

Simulated likelihood of 

receiving UI benefits (percent)

Mining 46

Manufacturing 40

Public administration 37

Wholesale and retail trade 35

Agriculture, forestry, and fishing 34

Business services 31

Construction 31

Finance, insurance, and real estate 31

Transportation and public utilities 29

Entertainment and recreation services 26

Professional and related services 24

Personal services 23

 All industries 33

Source: Simulations based upon GAO analysis of NLSY79 data. 

Note: Simulations are the average likelihood of receiving UI during first-time unemployment for 

workers from different industries. The parameter estimates for the mining, manufacturing, public 

administration, wholesale and retail trade, agriculture, forestry, and fishing, business services, and 

construction industries are statistically significant relative to the professional and related services 

industry at the 95 percent confidence level. See appendix I for methodology and estimation results. 

 32The percentages in table 2 and figure 8 are not comparable. The percentages in table 2 

represent an individual worker’s likelihood of receiving UI when UI-eligible unemployment 

occurs, whereas the percentages in figure 8 compare the relative proportions of 

unemployment spells with UI benefit receipt coming from different industries. 

Page 23 GAO-06-341 Unemployment Insurance 
Figure 8: Distribution of All Periods of UI Benefit Receipt across Industries 

Note: Total does not equal 100 percent due to rounding.

The Relationship between 

Past and Current UI 

Receipt Is Strongest for 

Public Administration 

Unemployed workers who have received UI benefits in the past are more 

likely to receive UI benefits during a current period of unemployment than 

otherwise similar workers who never received UI benefits, across each 

industry (see table 3). However, the increase in the likelihood of receiving

UI benefits associated with past UI benefit receipt is not the same across 

all industries. Specifically, this effect is strongest for workers from public 

administration and is weakest for workers from agriculture and 

construction.33

 33Although the association between past UI receipt and current UI receipt is statistically 

significant for all industries combined, differences in this association among industries 

were statistically significant only for public administration, agriculture, and construction. 

Page 24 GAO-06-341 Unemployment Insurance 
Table 3: Simulated Likelihood of Receiving UI Benefits during Different Periods of UI-Eligible Unemployment for Workers with

Past UI Receipt, by Industry 

Simulated likelihood of receiving UI benefits during current UI-eligible

unemployment period, given past UI receipt (percent) 

Industry

First

unemployment perioda

Second 

unemployment period 

Third

unemployment period

Mining 46 57 69

Manufacturing 40 52 65

Public administration 37 68 91

Wholesale and retail trade 35 52 70

Agriculture, forestry, and fishing 34 42 50

Business services 31 48 66

Construction 31 40 51

Finance, insurance, real estate 31 64 91

Transportation and public utilities 29 46 66

Entertainment and recreation services 26 45 67

Professional and related services 24 39 58

Personal services 23 38 56

All industries 33 48 64

Source: Simulations based upon GAO analysis of NLSY79 data. 

Note: Simulations are the average likelihood of receiving UI during a first unemployment period, a 

second unemployment period with UI receipt during the prior unemployment period, and a third 

unemployment period with UI receipt during both prior unemployment periods. The positive effect that 

each prior UI receipt period has on the likelihood of current UI receipt is statistically significantly larger

for the public administration industry relative to the professional and related services industry at the 

95 percent confidence level, and smaller for the agriculture and construction industries. The 

simulations also incorporate the industry effects and the industry interactions with the number of prior 

periods of unemployment. See appendix I for methodology and estimation results. 

a

Workers experiencing their first period of unemployment did not have past UI receipt. 

These results show that although UI-eligible workers in some industries 

are more likely to receive UI benefits when they experience 

unemployment for the first time, their likelihood of receiving UI benefits 

again when they become unemployed a second or third time is not 

necessarily higher than it is for workers from other industries. For 

example, the likelihood of receiving UI benefits for workers from the 

manufacturing industry who are unemployed for the first time is relatively 

high—about 40 percent. This likelihood increases to 52 percent during a 

second period of unemployment for workers who have already received 

UI benefits, and to 65 percent during a third period of unemployment for 

workers who received UI each time they were unemployed. By

comparison, the increase in the likelihood of receiving UI between the first 

Page 25 GAO-06-341 Unemployment Insurance 
and third periods of unemployment is higher for most other industries, 

especially public administration. Specifically, the likelihood of receiving

UI benefits for public administration workers who are unemployed for the 

first time is 37 percent. This likelihood increases to 69 percent during a 

second period of unemployment for workers who have already received 

UI, and to 92 percent during a third period of unemployment for workers 

who received UI each time they were unemployed. (See fig. 9.) 

Figure 9: Simulated Effect of Past UI Benefit Receipt on the Likelihood of Receiving

UI in Subsequent Periods of Unemployment, for Selected Industries

0

10

20

30

40

50

60

70

80

90

100

First Second Third

Simulated likelihood of receiving UI (percent)

Source: Simulations based on GAO analysis of NLSY79 data.

Period of unemployment

Public administration

Agriculture

Construction

Professional services

Note: Simulations are the average likelihood of receiving UI during a first unemployment period, 

second unemployment period with UI receipt during the prior unemployment period, and a third 

unemployment period with UI receipt during both prior unemployment periods. The positive effect that 

each prior UI receipt period has on the likelihood of current UI receipt is statistically significantly larger

for the public administration industry relative to the professional and related services industry at the 

95 percent confidence level, and smaller for the agriculture and construction industries. The 

simulations also incorporate the industry effects and the industry interactions with the number of prior 

periods of unemployment. See appendix I for methodology and estimation results. 

Administrative unemployment insurance data have shown that repeat UI 

recipients tend to be from industries that are more seasonal, such as 

manufacturing and construction. Our results, however, suggest that this is 

Page 26 GAO-06-341 Unemployment Insurance 
not because workers with past UI receipt from these industries are more 

likely to receive UI benefits when they become unemployed than 

otherwise similar workers from other industries. Rather, it may be that 

workers from such seasonal industries are unemployed more often on 

average than workers from other industries, or that a larger proportion of 

unemployed workers from such industries have collected UI previously. 

Unemployed Workers from 

Construction and 

Manufacturing Have Fewer 

Weeks of Unemployment 

Unemployed workers from construction and manufacturing have shorter 

unemployment duration than otherwise similar workers from other 

industries. (See table 4.) Furthermore, simulations based on our model 

estimates show that differences in unemployment duration across 

industries exist whether or not UI benefits are received. Specifically, UIeligible workers from construction who receive UI benefits have the 

fewest weeks of unemployment on average (17 weeks), when compared 

with workers from other industries. Likewise, UI-eligible workers from 

construction who do not receive UI benefits also have the fewest weeks of 

unemployment, on average (6 weeks). 

Page 27 GAO-06-341 Unemployment Insurance 
Table 4: Simulated Unemployment Duration for UI-Eligible Workers, by Industry and 

UI Receipt Status

Simulated unemployment duration (median 

weeks) 

Industry

Receiving UI 

benefits

Not receiving UI 

benefits

Construction 17 6

Mining 17 6

Business services 18 7

Manufacturing 19 7

Finance, insurance, and real estate 21 8

Wholesale and retail trade 22 9

Public administration 23 9

Professional and related services 24 10

Entertainment and related services 24 10

Personal services 24 10

Agriculture, forestry, and fishing 26 11

Transportation and public utilities 27 12

Overall average duration 21 8

Source: Simulations based upon GAO analysis of NLSY79 data. 

Note: Simulations are the median duration of unemployment during first-time unemployment. The 

parameter estimates for the construction and manufacturing industries are statistically significant

relative to the professional and related services industry at the 95 percent confidence level. See 

appendix I for methodology and estimation results.

Certain Occupations Are 

Associated with UI Benefit 

Receipt and Longer 

Unemployment Duration 

The likelihood of receiving UI benefits varies across occupations, but 

generally not as much as it does across industries. Specifically, UI-eligible 

managers are about one-fifth more likely to receive UI than otherwise 

similar transportation equipment operators, and one-half more likely to 

receive UI than professional and technical workers (see table 5). 

Page 28 GAO-06-341 Unemployment Insurance 
Table 5: Simulated Likelihood of Receiving UI Benefits for UI-Eligible Workers from

Different Occupations

Occupation 

Simulated likelihood of

receiving UI benefits (percent)

Managers and administrators 39

Farmers, farm laborers, and foremen 38

Machine operators (nontransportation) 38

Craftsmen 35

Laborers (nonfarm) 34

Transportation equipment operators 33

Clerical and unskilled workers 33

Service workers (excluding private 

household) 

28

Sales workers 28

Professional and technical workers 25

Overall average 33

Source: Simulations based upon GAO analysis of NLSY79 data. 

Note: Simulations are the average likelihood of receiving UI during first-time unemployment for 

workers from different occupations. The parameter estimates for managers and administrators, 

farmers, farm laborers, and foremen, machine operators, craftsmen, laborers, transportation 

equipment operators, and clerical and unskilled workers are statistically significant relative to 

professional and technical workers at the 95 percent confidence level. See appendix I for 

methodology and estimation results. 

UI-eligible workers who have received UI benefits in the past are more 

likely to receive UI benefits during a current period of unemployment than 

UI-eligible workers who never received UI benefits, across each 

occupation. Specifically, this effect is strongest for sales and service 

workers and weakest for transportation equipment operators and 

craftsmen (see table 6).34

 34Although the association between past UI receipt and current UI receipt is statistically 

significant for all occupations combined, differences in this association among occupations

were statistically significant only for sales and service workers, and for transportation

equipment operators and craftsmen. 

Page 29 GAO-06-341 Unemployment Insurance 
Table 6: Simulated Likelihood of Receiving UI Benefits during Different Periods of UI-Eligible Unemployment for Workers with

Past UI Receipt, by Occupation 

Simulated likelihood of receiving UI benefits during current UI-eligible

unemployment period, given past UI receipt (percent) 

Occupation 

First 

unemployment perioda

Second 

unemployment period 

Third

unemployment period

Managers and administrators 39 52 65

Farmers, farm laborers, and foremen 38 54 70

Machine operators (nontransportation) 38 50 62

Craftsmen 35 46 56

Laborers (nonfarm) 34 45 58

Transportation equipment operators 33 42 51

Clerical and unskilled workers 33 53 73

Service workers (excluding private household) 28 50 74

Sales workers 28 66 94

Professional and technical workers 25 39 56

Overall average 33 48 64

Source: Simulations based upon GAO analysis of NLSY79 data. 

Note: Simulations are the average likelihood of receiving UI during a first unemployment period, a 

second unemployment period with UI receipt during the prior unemployment period, and a third 

unemployment period with UI receipt during both prior unemployment periods. The positive effect that 

each prior UI receipt period has on the likelihood of current UI receipt is statistically significantly larger

for sales workers and service workers relative to professional and technical workers at the 95 percent

confidence level, and smaller for transportation equipment operators and craftsmen. The simulations 

also incorporate the occupation effects and the occupation interactions with the number of prior 

periods of unemployment. See appendix I for methodology and estimation results. 

a

Workers experiencing their first period of unemployment did not have past UI receipt. 

Unemployment duration also varies across occupations. UI-eligible 

professional and technical workers have longer unemployment duration 

than otherwise similar workers from other occupations. Specifically, 

professional and technical workers have unemployment duration that is 

5 weeks longer than average for workers receiving UI and 3 weeks longer

than average for workers not receiving UI (see table 7).35 Past experience 

with UI benefit receipt has no significant effect on unemployment 

duration, regardless of a worker’s occupation. 

 35The largest differences between industries in median weeks of unemployment are 

10 weeks for workers receiving UI and 5 weeks for workers not receiving UI. 

Page 30 GAO-06-341 Unemployment Insurance 
Table 7: Simulated Unemployment Duration for UI-Eligible Workers, by Occupation

and UI Receipt Status

Simulated unemployment duration 

(median weeks) 

Occupation Receiving UI 

benefits

Not receiving UI 

benefits

Craftsmen 16 6

Sales workers 18 7

Machine operators (nontransportation) 19 7

Transportation equipment operators 20 8

Laborers (nonfarm) 20 8

Service workers (excluding private 

household) 23 9

Managers and administrators 23 9

Clerical and unskilled workers 23 10

Farmers, farm laborers, and foremen 26 11

Professional and technical workers 26 11

Overall average duration 21 8

Source: Simulations based upon GAO analysis of NLSY79 data. 

Note: Simulations are the median duration of unemployment during first-time unemployment for 

workers from different occupations. The parameter estimates for craftsmen and machine operators 

are statistically significant relative to professional and technical workers at the 95 percent confidence 

level. See appendix I for methodology and estimation results. 

Although the UI program has existed for over 70 years and serves millions 

of workers each year, little is known about workers who receive UI 

benefits on a recurring basis or about workers who are eligible for UI 

benefits but never receive them. We found that UI-eligible workers during 

the first half of their working lives with certain demographic 

characteristics and from certain industries have a greater likelihood of 

receiving UI benefits multiple times and experiencing longer 

unemployment durations than otherwise similar workers. Although our 

results are generally consistent with past research, our analysis includes 

additional information about workers’ past experiences that provides new 

insight into the factors that distinguish workers who receive UI benefits 

from those who do not. In fact, the single most important factor associated 

with eligible workers receiving benefits is whether or not they received 

benefits during previous unemployment, suggesting that a worker’s 

perception of UI when they are faced with unemployment is key to 

whether that worker will ever use the program. Moreover, it does not 

Concluding 

Observations 

Page 31 GAO-06-341 Unemployment Insurance 
appear that previous UI recipients from industries where UI benefit receipt 

is more likely, such as construction and manufacturing, are any more 

likely to receive benefits if unemployed again than similar workers from 

other industries. Rather, it appears that workers from these industries are 

simply more likely to face the choice of whether or not to file for UI 

benefits more often than their counterparts in other industries. In addition,

while the patterns for UI receipt and unemployment duration we identified

for this group during the first half of their working lives may not change 

significantly as they enter the second half of their working lives, it remains 

to be seen whether the issues they face in the years leading up to their 

retirement will reshape their use of the UI program. 

We provided a draft of this report to Labor officials for their review and 

comment. Labor applauded GAO’s efforts to determine the extent to which 

an individual worker’s characteristics are associated with the likelihood of

UI benefit receipt and with unemployment duration and noted that the 

study adds to current knowledge of the UI program, particularly with 

regard to the impact of past UI benefit receipt on current UI receipt. 

However, Labor also noted that there are several issues related to our 

methodology that may limit the utility of our findings for policymaking. 

While we agree that there are limitations inherent in our methodology, we 

believe that these limitations have been noted throughout the report, and 

that they do not compromise the overall validity of our results. 

Nevertheless, we have provided additional clarifications, as appropriate, to

address Labor’s technical comments. 

Agency Comments 

Page 32 GAO-06-341 Unemployment Insurance 
As agreed with your office, unless you publicly announce the contents of

this report earlier, we plan no further distribution of it until 30 days from 

its date. At that time, we will send copies of this report to relevant 

congressional committees, the Secretary of Labor, or other interested 

parties. We will also make copies available to others upon request. The 

report will be available at no charge on GAO’s Web site at 

http://www.gao.gov. If you or members of your staff have any questions 

about this report, please contact me at (202) 512-7215. Other major 

contributors are listed in appendix III. 

Sincerely yours, 

Sigurd R. Nilsen, Director 

Education, Workforce and 

 Income Security Issues 

Page 33 GAO-06-341 Unemployment Insurance 
A

and U

ppendix I: Analysis of UI Benefit Receipt 

nemployment Duration 

Page 34 GAO-06-341 Unemployment Insurance 

Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

We analyzed the factors affecting unemployment insurance (UI) benefit 

receipt by statistically modeling the determinants of UI benefit receipt and 

unemployment durations simultaneously. We model UI benefit receipt in 

conjunction with unemployment durations to allow for correlations that 

may exist between the two outcomes for a given individual. For example, 

an unemployed person anticipating a lengthy unemployment period might 

be more likely to receive UI benefits than a person expecting a short 

unemployment period. Alternatively, the receipt of UI benefits may 

lengthen an unemployment period by allowing an individual to spend more 

time looking for new employment. In addition, our model controls for a 

number of observable factors about each unemployed worker’s situation, 

including recent employment experience, prior unemployment and UI 

benefit receipt experience, information about UI program factors, 

including benefit levels, and demographic characteristics. The model was 

developed and estimated by Dr. Brian McCall, Professor of Human 

Resources and Industrial Relations, University of Minnesota, under 

contract to GAO. 

This appendix describes (1) the data used in the analysis, including how 

the data were prepared, (2) the econometric model that was estimated, 

(3) the results from two specifications of the econometric model, and 

(4) the limitations inherent in the analysis. 

We used the Bureau of Labor Statistics’ (BLS) National Longitudinal 

Survey of Youth 1979 (NLSY79) for our analysis. The NLSY79 is an ongoing

longitudinal survey of individuals who were between the ages of 14 and 

22 in 1979, the first year of the survey.

1

 A primary focus of the NLSY79 is 

on individuals’ labor force patterns, and the data are collected at a very 

detailed level. This detail allows us to track the weekly employment, 

unemployment, and earnings histories of the individuals in the sample. 

The NLSY79 also contains less detailed information about individuals’ UI 

receipt during unemployment.2

 The NLSY79 does not contain direct 

information about an individual’s UI eligibility status, which is a function 

of previous employment and earnings, among other things, and varies by 

Overview 

Data Used 

 1

NLSY79 data begin in 1978. Interviews for the NLSY79 were conducted annually until 1994, 

and biennially beginning in 1996. We used data through 2002, which were the most recent 

NLSY79 data available. 

2

UI receipt information is provided on a monthly basis in the NLSY79. Because this 

information is only given on a monthly basis, it cannot be used to accurately measure the 

number of weeks of UI receipt during unemployment. 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

state of employment.3 We estimate an unemployed individual’s UI 

eligibility status using data that are available in the NLSY79.

There are three main reasons why the NLSY79 database provides the most 

suitable data for our analysis. First, the longitudinal nature and level of 

detail of the data allow us to control for an individual’s history of 

unemployment and UI receipt, which is a major contribution of this work. 

Second, respondents were first surveyed at a young age, which reduces 

the likelihood that we do not observe periods of unemployment and UI 

receipt early in a person’s working career. Third, the detailed data allow us

to estimate an individual’s UI eligibility status, allowing us to focus our 

analysis on unemployed individuals whom we estimated to be eligible for

UI benefits while also reasonably controlling for differences in UI program 

rules across states. A few limitations to the NLSY79 database should be 

mentioned. First, the sample began with 12,686 individuals in 1979, but has 

decreased in size over time due to attrition.4

 Second, the data are selfreported and thus subject to recall error. We assessed the reliability of the 

NLSY79 data by interviewing relevant BLS officials, reviewing extensive 

NLSY79 documentation, and performing electronic tests of the NLSY79 

data for missing or corrupt information that might negatively affect our 

analysis. On the basis of these reviews and tests, we determined that the

data were sufficiently reliable to be used in our analysis. 

We considered using administrative state UI data as an alternative to the 

NLSY79. Although such administrative data could provide information 

about all UI recipients in a state, these data could not provide information 

about UI-eligible unemployed workers who did not receive benefits. Also, 

because these data are not designed for research purposes, there is limited 

information available about individuals that can be used to control for 

differences, such as demographic characteristics. Finally, there is also no 

nationally representative data source for administrative UI data. 

For each individual in the NLSY79 database, we created a detailed weekly

history of employment and unemployment, including whether UI benefits

 3

State UI programs determine eligibility using a number of criteria, including the following 

conditions: (1) the unemployment must be the result of a job loss that was not caused by 

the individual, (2) the individual must have earned a specified amount of money during the 

time preceding the unemployment, and (3) the individual must be actively looking for new 

employment. 

4

See Center for Human Resource Research, Ohio State University, The National

Longitudinal Surveys NLSY79 User’s Guide, prepared for the Department of Labor, 2002. 

Page 35 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

were received during unemployment. Our definition of unemployment is 

not the strict definition used in the BLS’s Current Population Survey 

(CPS). We define unemployment to include both the weeks in which an 

out-of-work person is looking for work (the standard CPS unemployment 

definition) and the weeks during which the individual reports being out of 

the labor force (OLF). We did require that an individual spend at least 

1 week actively looking for work after a job loss to reduce the likelihood 

that the person had permanently left the labor force. Other research has 

addressed the effect that the UI program plays on the percentage of weeks 

of nonemployment that a person reports that he or she was looking for 

work.5

For each unemployment period experienced by an individual, we estimate 

the person’s UI eligibility status. Although states determine UI eligibility 

using a number of criteria, we focus on the following three: (1) the 

unemployment must be the result of a job loss that was not caused by the 

individual, (2) the individual must have earned a specified amount of 

money during the time preceding the unemployment, and (3) the 

individual must be actively looking for new employment. The NLSY79 

provides the information necessary to estimate whether these criteria are 

met by an unemployed individual. For criterion 1, the NLSY79 provides 

information about the reason that a job was lost. Only those unemployed 

individuals who lost a job through no fault of their own were deemed to be 

UI-eligible.6

 For the monetary eligibility criterion 2, we compiled a detailed

set of UI eligibility and benefit criteria for each of the 50 states and the 

District of Columbia over the period 1978 to 2002.

7

 When these criteria 

 5

R. Mark Gritz and Thomas MaCurdy, “Measuring the Influence of Unemployment 

Insurance on Unemployment Experiences,” Journal of Business and Economic Statistics, 

vol. 15, no. 2, (1997), examined the role that UI rules have on an individual’s choice to 

report himself or herself as unemployed (CPS definition) as opposed to out of the labor 

force. They found that, in addition to having longer nonemployment periods, UI recipients

report being unemployed in the CPS sense for a greater proportion of their nonemployment 

period. 

6

It appeared from the NLSY79 data that a number of respondents did not differentiate 

between being laid off and being discharged or fired. As a result, we include those who 

report being either laid off or discharged or fired as satisfying the first UI eligibility rule. 

The NLSY79 reports a number of other reasons for leaving a job, including having found 

better work, low pay, pregnancy, illness, change of job by spouse or parents, other family 

reasons, job’s interference with school, the end of a program, bad working conditions, and 

entrance into the armed forces. 

7

See U.S. Department of Labor. Employment and Training Administration, Significant 

Provisions of State Unemployment Insurance Laws (Washington, D.C., 1979-2002). 

Page 36 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

were combined with the NLSY79’s detailed employment and earnings 

histories, we were able to determine monetary eligibility for UI with 

reasonable accuracy, as well as the weekly benefit amount and the 

number of weeks of benefits a person was eligible to receive.

8

 For 

criterion 3, we considered as UI-eligible only those unemployed 

individuals who reported actively looking for work during at least 1 week

of their unemployment. We erred on the side of overestimating the 

eligibility based on criterion 3, because individuals who self-report 

information about nonemployment may not fully realize the impact that 

“looking for work” versus “being out of the labor force” has on UI 

eligibility, especially if they did not receive UI benefits. Although this 

estimation method is not perfect, we believe that it captures some of the 

most important features of UI eligibility. It is similar to the methods used 

by other researchers.9

 

In addition to estimating the UI eligibility status of individuals at the time 

of each of their unemployment periods, we also created the other 

variables used in our analysis. The empirical model outlined in the 

following subsection focuses on UI benefit receipt and unemployment 

duration. UI benefit receipt during unemployment was determined using 

the monthly measure provided in the NLSY79.10 The duration of

unemployment, as defined above, is measured in weeks from the week 

after a job was lost to the week a new job was begun. We censor duration 

to be no longer than 100 weeks. 

To isolate the impact that a variable has on the likelihood of UI benefit 

receipt and unemployment duration, our model controls for a great 

number of other factors that were observable at the start of, and 

throughout, the person’s unemployment. One set of variables relates to the 

employment experience of the individual immediately preceding 

unemployment, including industry and occupation of the lost job 

(measured at the one-digit Standard Industrial Classification [SIC] and 

 8

Although UI eligibility is based upon the rules in the state where an individual is employed, 

we used state of residence for our estimates because state of employment was not 

available in the NLSY79. Thus, people who work in one state but live in another may not be

classified correctly. However, we believe that only a small percentage of such data are

classified incorrectly and, thereby, our results should be only minimally affected. 

9

See Gritz and MaCurdy, 1997, and McCall 2000 for examples. 

10We consider only an individual’s first period of unemployment with UI receipt during a

person’s “benefit year.” A benefit year is the 52-week period during which UI benefits can

be claimed. 

Page 37 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

Standard Occupational Classification [SOC] level), union status and tenure

at the job lost, earnings (base period earnings [BPE] and high quarter 

earnings [HQE]), whether the job was lost because of a plant closing, and 

the calendar year and month the unemployment began.11 We group both 

earnings measures into brackets to allow for nonlinear effects. All dollar 

values are adjusted for inflation to 2002 dollars using the BLS’s Consumer 

Price Index for All Urban Consumers (CPI-U). We also control for the state 

unemployment rate during the month that unemployment began, and, in 

the duration equation, for the time-varying state monthly unemployment 

rate over the period of unemployment. 

A second set of variables summarizes UI program factors, such as the 

weekly benefit amount (WBA) a person is eligible to receive, the number 

of weeks of benefits a person is eligible to receive, whether the state has a

waiting period before benefits can be received, and whether permanent or 

temporary extended benefits are in effect.12 We also control for the 

percentage of new UI claims that are denied by a state (in the receipt 

equation) and the percentage of continuing UI claims that are denied by a 

state (in the duration equation). In the unemployment duration equation,

we also allow the parameter estimates for WBA, remaining weeks of 

benefits, and extended benefits to vary over the period of unemployment. 

This is done by interacting these variables with a cubic function of the 

number of weeks unemployed. Again, all dollar values are adjusted for 

inflation to 2002 dollars using the BLS’s CPI-U. 

A third group of variables relates to a person’s history of unemployment 

and UI benefit receipt as measured at the start of an unemployment 

period. This group of variables includes the number of times the person 

had been unemployed and the number of times a person had received UI 

benefits previously (in the receipt equation) and whether or not the person 

had been unemployed and whether or not the person had received UI 

benefits previously (in the duration equation). We also interact these 

variables with industry and occupation dummy variables to investigate 

whether previous unemployment and UI receipt affect the likelihood of 

 11The base period is the period of time during which earnings are counted toward UI

eligibility. It generally covers a year. We define the base period as the first four of the last

five completed calendar quarters. High quarter earnings refers to the quarter of highest 

earnings during the base period. 

12Permanent extended benefits are triggered by high unemployment rates in a state, and 

provide for up to 13 additional weeks of benefits to UI-eligible individuals. Temporary

extended benefits are available periodically, as authorized by Congress.

Page 38 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

current UI receipt and unemployment durations differently across 

industries. These interactions with industry and occupation are done in

separate specifications of the model.

A fourth group of variables relates to a person’s demographic 

characteristics at the time of unemployment. These include age, race, 

gender, marital status, number of years of schooling, health limitations, 

whether a spouse has used UI previously, family size, number of children,

number of children between the ages of 0 and 2, whether the person lives 

with his or her parents, state of residence, and whether the person lives in

a Standard Metropolitan Statistical Area (SMSA) as opposed to a rural 

area. 

We limit our analysis to the nonmilitary sample of NLSY79 respondents.13

In addition, we drop individuals with insufficient information to estimate 

UI eligibility with reasonable accuracy. Data for an individual were 

included up to their first missed interview.14 Individuals without any 

unemployment, and those without unemployment that was estimated to be 

UI-eligible, were not used in the analysis. Also, individuals who were 

missing data required by our econometric model were not used in the 

analysis. This yielded a sample of 5,631 individuals who had been 

unemployed and eligible for UI benefits at least once, resulting in a total of 

15,506 separate periods of UI-eligible unemployment. 

To investigate the key factors associated with UI benefit receipt, including

the role of prior UI benefit receipt (repeat UI recipiency), we used a 

dynamic econometric model that jointly determines UI benefit receipt and 

unemployment duration. As mentioned above, the reason for modeling 

these outcomes jointly is to allow for the likely correlations that exist 

between them.15 In addition to modeling UI receipt and unemployment 

duration jointly, our model allows prior unemployment and prior UI 

Econometric Model 

 13The NLSY79 began with 12,686 individuals in 1979, 1,280 of whom were part of the 

military subsample. The majority of the military subsample of the NLSY79 was eliminated 

in 1985. 

14The NLSY79 attempts to reconnect with individuals that missed an interview in the

previous year.

15For one example of an economic model of how the receipt of UI benefits can affect the 

expected length of unemployment by affecting a person’s reservation wage, see Mortensen. 

Page 39 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

receipt to influence current UI receipt and unemployment duration to 

allow for the correlations that possibly exist over time for an individual. 

We used a complementary log-log specification to model the probability of 

UI receipt during an individual’s kth unemployment period, k=1, ..., K, as:

Pr[ ( ) 1| ( ), , ( )] 1 exp( exp[ ( ) ( ) ]) u u u u u u u u UI k = x k ξ z k = − −ξ x k ′β + z k ′γ , 

where is a vector of exogenous variables measured at the start of 

the kth unemployment period, all of which are assumed to be independent 

of the unobserved random variable , which helps control for 

unobserved heterogeneity. Variables in include demographic 

characteristics, characteristics about the lost job, and UI program 

information. The vector is a vector of endogenous variables 

pertaining to past unemployment and past UI benefit receipt, which are 

measured at the start of an individual’s kth unemployment period and may 

be correlated to . 

(k) u x

u ξ

(k) u x

(k) u z

u ξ

We modeled unemployment durations using a discrete-time hazard 

function, which gives the probability of an event occurring during a 

discrete time period, conditional upon not having experienced the event

prior to that time. This can be thought of as the escape rate from 

unemployment during a specific time period. We assume that the 

conditional probability that an individual’s kth period of unemployment 

ends in the interval (m-1,m], given that it exceeds m-1, where m indexes 

the number of weeks, follows a complementary log-log specification: 

Pr[ ( ) | ( ), , ( )] 1 exp( exp[ ( ) ( ) ]) d

m

d d d d d d d d d k = m x k ξ z k = − −ξ x k ′β + z k ′γ + α , 

for m=1, ..., M, and where is a vector of exogenous variables 

measured at the start of the kth unemployment period, all of which are 

assumed to be independent of the unobserved random variable , which 

helps control for unobserved heterogeneity. Variables in include 

demographic characteristics, characteristics about the lost job, and UI 

program information. The vector consists of endogenous variables 

pertaining to current UI benefit receipt, past unemployment, and past UI 

benefit receipt, which are measured at the start of an individual’s kth 

(k) d x

d ξ

(k) d x

(k) d z

Page 40 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

unemployment period and may be related to . The parameter vector 

 is the baseline hazard function. 

d ξ

( , ,..., ) 1 2

d

M

d d d α = α α α

Letting i, i = 1, ..., I, index individuals and k index an individual’s 

unemployment periods, we define to be equal to 1 when individual i 

has a kth unemployment period, and 0 otherwise. Also, we define to 

be equal to 1 when individual i’s kth unemployment period is complete, 

and 0 otherwise. Using this notation, individual i’s contribution to the 

likelihood function can be written: 

q (k) i

c (k) i

{( ) [ ]

( ) [ ]

( ) [ ]

( ) [ ] ) ⎭

⎬

⎫ ×

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎜

⎜

⎝

⎛

× − =

× − =

= =

∏

∏

−

=

−

=

( ) ( )

( ) 1

1

1 ( )

1

( )

Pr ( ) | ( ), , ( )

1 Pr ( ) | ( ), , ( )

1 Pr ( ) 1| ( ), , ( )

( , , ) Pr ( ) 1| ( ), , ( )

q k c k d

i

d

i

d

i i

d k

m

d

i

d

i

d

i i

UI k u

i

u

i

u

i i

K

k

UI k u

i

u

i

u

i i

u d

i

i i

i

i

i

i

d k k k

d k m k k

UI k k k

L UI k k k

x z

x z

x z

θ x z

ξ

ξ

ξ

ξ ξ ξ

where the vector of parameters is to be estimated and contains , , 

, and , where j = d,u. We assume that the distribution of the 

unobserved random variables is such that there are 3 different 

types of individuals in the population, with the fraction of each type equal 

to , where: . Combining these possibilities, we write an 

individual’s likelihood contribution as: , 

where 

θ j β j γ

j ξ d α

( , ) d u ξ ξ

h p ∑=

=

3

1

1

h

ph

∑=

=

3

1

( , ) ( , , )

h

d

h

u

i hLi h L θ p p θ ξ ξ

( , , ) 1 2 3 p = p p p ′

. Taking logarithms and summing over all 

individuals yields the full log likelihood function for the sample: 

Page 41 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

∑=

=

I

i

L Li

1

log (θ,p) log (θ,p)

. This likelihood is computed in FORTRAN and 

maximized using the BHHH algorithm.16

A number of features outlined above are simplifications of a more general

version of this model, and were introduced to help reduce the number of 

parameters to be estimated by the model. First, the baseline hazard 

function, , was assumed to be independent of the unemployment period 

number, k. Second, the parameters associated with the exogenous ( , j = 

d,u) and endogenous ( , j = d,u) variables were assumed to be 

independent of the unemployment period number, k. Third, the 

unobserved random variables ( , j = d,u) were assumed to be 

independent of the unemployment period number, k. Although this 

assumption is not as general as allowing each individual to have different 

unobserved components over time, it does help control for unobserved 

differences between individuals that may influence UI receipt and 

unemployment durations. 

d α

j β

j γ

j ξ

Because of the complexity of the empirical model outlined above, 

interpreting the parameter estimates is difficult.17 As a result, we use the 

output from the model to simulate the effect that changes in certain 

variables have on the likelihood of UI receipt and the duration of 

unemployment for the average unemployed person in our sample. For 

example, to understand differences in UI receipt and unemployment 

durations by industry, we simulate the likelihood of UI benefit receipt and 

unemployment duration for the average person in our sample for each of

the possible industries, and then compare the results. To do this, we use 

the model’s output to calculate every person’s likelihood of UI receipt and

escape rate from unemployment—conditional upon receiving and not 

receiving UI—assuming all were in the first industry grouping when they 

lost their job. Averaging over all individuals yields the average probability 

 16See E. K. Berndt, B. H. Hall, R. E. Hall, and J. A. Hausman, “Estimation and Inference in

Nonlinear Structural Models,” Annals of Economic and Social Measurement, vol. 3, no. 4

(1974). The BHHH algorithm is a quasi-Newton method for finding maximums. 

17In addition to being a highly nonlinear model, the data were all normalized to help the

convergence of the parameter estimates. 

Page 42 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

of UI receipt and the averaged (week by week) survivor function.18 The 

averaged survivor function can be used to compute the expected median 

duration of unemployment.19 We then repeat this process, successively, 

assuming that all individuals were in another industry grouping when they 

lost their jobs, until all industry groups have been simulated. 

The simulated average likelihood of UI benefit receipt and median 

unemployment duration can then be compared across industries to 

estimate the differences by industry. Using all individuals for each 

simulation, and reporting results for the average unemployed person, 

helps insure that differences in the simulation results (e.g., industry 1 

versus industry 2) reflect only the variables (industry 1, industry 2) being 

simulated.20 To describe results that are not related to past experience with

unemployment and UI benefit receipt, we present simulations that are 

specific to first-time unemployment—a simple and clearly defined 

scenario (the observable trends also hold for unemployed individuals with 

prior unemployment and UI receipt experience). 

We report parameter estimates from two specifications of our model. The 

first specification includes interaction terms between industry and our 

measures of past UI benefit receipt and past unemployment. These results 

are presented in tables 8 and 9 for the UI benefit receipt equation and the

unemployment duration equation respectively. The second specification 

includes interaction terms between occupation and our measures of past 

UI benefit receipt and past unemployment. These results are presented in 

tables 10 and 11 for the UI benefit receipt equation and the unemployment 

duration equation respectively. We included the industry and occupation

interactions in separate specifications to avoid the issues brought about by

Results 

 18The survivor function at time t for an event is the probability of not having experienced 

that event prior to time t. The survivor function is mathematically related to the escape rate

(hazard rate). 

19We chose median rather than mean because of the skewed nature of our unemployment

duration data. 

20For our simulations, if we used only those individuals that reported losing a job from a

specific industry, as opposed to using all individuals, it is likely that a portion of the

differences we would observe in the likelihood of UI benefit receipt and unemployment 

duration would be due to differences in other observable factors between the individuals 

from the different industry groups. For example, it may be that professional services 

workers have higher average earnings than agricultural workers, which would be earnings

effect, not an industry-specific effect. 

Page 43 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

multicollinearity.21 Because the results for the noninteraction terms are 

similar between the two specifications, we focus on those from the 

industry-interaction specification (tables 8 and 9). After discussing these 

results, we discuss the results for the occupation-interaction specification 

(tables 10 and 11). 

Tables 8, 9, 10, and 11 are structured as follows. The first column in each 

table lists the variable names; the second column, the parameter 

estimates; the third column, the estimated standard errors; and the fourth

column, the t-statistics. The last column contains asterisks that signify 

statistical significance. One asterisk (*) signifies statistical significance at

the 90 percent confidence level (t-statistics greater than or equal to 1.65 in

absolute value); two asterisks signify statistical significance at the 95

percent confidence level (t-statistics greater than or equal to 1.96 in 

absolute value) and three asterisks (***) signify statistical significance at 

the 99 percent confidence level. Parameter estimates discussed below are 

statistically significantly different from zero at the 95 percent confidence

level unless stated otherwise.22 To conserve space, the tables do not 

present the parameter estimates for the unobserved heterogeneity ( and 

), state, year, and month effects. 

d ξ

u ξ

 21We also tried running a specification of the model that included these interactions for 

both industry and occupation. The parameter estimates and simulations were generally 

similar to those for the two separate specifications, but much of the statistical significance 

for individual parameters was lost due to the correlation between industry and occupation. 

However, a likelihood ratio test of the joint hypothesis that the interaction terms for both 

industry and occupation are all equal to zero is rejected at the 95 percent confidence level,

suggesting that there are both industry-specific and occupation-specific differences in the 

effects of past unemployment and past UI receipt on the likelihood of current UI receipt 

and current unemployment duration. 

22A statistically insignificant result indicates that the effect of a characteristic could not be 

precisely estimated using the sample data, and does not necessarily prove that the

characteristic is unimportant. 

Page 44 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

Table 8: Parameter Estimates for UI Receipt Equation from Industry-Interaction Specification

 Parameter estimate Standard error t-statistic 

Past unemployment and UI receipt

Number of previous UI receipt spells 0.714 0.086 8.26 ***

Number of previous unemployment spells -0.072 0.017 -4.27 ***

Industry

Agriculture, forestry, and fishing 0.438 0.211 2.07 **

Mining 0.868 0.242 3.59 ***

Construction 0.294 0.135 2.17 **

Manufacturing 0.672 0.108 6.20 ***

Transportation and public utilities 0.221 0.162 1.36 

Wholesale and retail trade 0.475 0.109 4.36 ***

Finance, insurance, and real estate 0.292 0.174 1.68 *

Business services 0.310 0.142 2.19 **

Personal services -0.077 0.198 -0.39 

Entertainment and recreation services 0.104 0.226 0.46 

Public administration 0.560 0.188 2.98 ***

Valid missing -0.030 0.095 -0.31 

Occupation 

Managers and administrators 0.614 0.100 6.15 ***

Sales workers 0.122 0.138 0.88 

Clerical and unskilled workers 0.296 0.088 3.39 ***

Craftsmen 0.261 0.092 2.85 ***

Machine operators (nontransportation) 0.187 0.090 2.07 **

Transportation equipment operators 0.244 0.115 2.12 **

Laborers (nonfarm) 0.075 0.101 0.75 

Farmers, farm laborers, and foremen 0.134 0.191 0.70 

Service workers (excluding private household) 0.081 0.092 0.88 

Industry * number previous UI receipt spells 

Agriculture, forestry, and fishing -0.301 0.101 -2.97 ***

Mining -0.115 0.201 -0.57

Construction -0.229 0.091 -2.53 **

Manufacturing -0.160 0.093 -1.72 *

Transportation and public utilities 0.033 0.120 0.27 

Wholesale and retail trade 0.010 0.109 0.10 

Finance, insurance, and real estate 0.565 0.344 1.64 

Business services -0.005 0.122 -0.04 

Page 45 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

 Parameter estimate Standard error t-statistic 

Personal services -0.011 0.161 -0.07 

Entertainment and recreation services 0.160 0.227 0.70 

Public administration 0.487 0.239 2.04 **

Valid missing 0.158 0.098 1.61 

Industry * number of previous unemployment 

spells 

Agriculture, forestry, and fishing -0.043 0.035 -1.23

Mining -0.129 0.056 -2.29 **

Construction -0.040 0.022 -1.83 *

Manufacturing -0.057 0.019 -2.97 ***

Transportation and public utilities -0.001 0.028 -0.02 

Wholesale and retail trade -0.047 0.020 -2.30 **

Finance, insurance, and real estate -0.013 0.032 -0.41

Business services -0.012 0.025 -0.48 

Personal services 0.023 0.035 0.65 

Entertainment and recreation services -0.052 0.045 -1.15 

Public administration -0.061 0.040 -1.54 

Valid missing -0.061 0.021 -2.90 ***

UI program variables 

Weekly benefit amount (WBA) 0.064 0.058 1.09 

Potential UI benefit duration -0.623 0.592 -1.05

Waiting week for UI benefits 0.053 0.124 0.43

Denial rate for new UI claims -1.448 0.836 -1.73 *

Extended UI benefits in effect 0.133 0.097 1.36

Personal characteristics 

Years of education 0.569 0.128 4.44 ***

Armed Forces Qualifying Test score -0.295 0.097 -3.06 ***

African-American 0.005 0.059 0.08

Hispanic -0.084 0.086 -0.98

Hispanic * male 0.225 0.098 2.30 **

Married 0.167 0.050 3.35 ***

Age 20.541 4.510 4.55 ***

Age-squared -41.787 8.035 -5.20 ***

Male -0.357 0.051 -7.01 ***

Lives in SMSA (urban) -0.111 0.050 -2.24 **

Health limitations 0.041 0.110 0.38 

Spouse used UI in past 0.270 0.100 2.70 ***

Page 46 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

 Parameter estimate Standard error t-statistic 

Spouse used UI in past * male -0.688 0.173 -3.97 ***

Live with parents -0.160 0.097 -1.65 *

Family size -0.478 0.177 -2.71 ***

Live with parents * family size 0.572 0.231 2.47 **

Children under age 2 0.091 0.060 1.51 

Number of children -0.017 0.025 -0.68 

Recent employment experience 

Union member -0.003 0.048 -0.05 

Tenure 0.140 0.029 4.85 ***

Tenure-squared -0.015 0.004 -4.20 ***

Lost job due to plant closing -0.263 0.085 -3.08 ***

State unemployment rate 0.314 0.150 2.09 **

Base period earnings brackets 

Under $2,000 -1.450 0.278 -5.21 ***

$2,000-$3,999 -1.383 0.196 -7.05 ***

$4,000-$5,999 -1.177 0.168 -7.02 ***

$6,000-$7,999 -0.799 0.152 -5.25 ***

$8,000-$9,999 -0.780 0.140 -5.56 ***

$10,000-$11,999 -0.528 0.127 -4.16 ***

$12,000-$13,999 -0.565 0.126 -4.50 ***

$14,000-$15,999 -0.381 0.116 -3.28 ***

$16,000-$17,999 -0.279 0.109 -2.56 **

$18,000-$19,999 -0.282 0.108 -2.62 ***

$20,000-$24,999 -0.143 0.089 -1.61

$25,000-$29,999 0.015 0.086 0.17

High quarter earnings

$0-$999 -0.180 0.260 -0.69

$1,000-$1,999 0.076 0.196 0.39

$2,000-$2,999 0.333 0.153 2.18 **

$3,000-$3,999 0.443 0.129 3.44 ***

$4,000-$4,999 0.410 0.113 3.65 ***

$5,000-$5,999 0.270 0.103 2.63 ***

$6,000-$6,999 0.096 0.092 1.05

$7,000-$7,999 0.170 0.090 1.89 *

$8,000-$8,999 0.030 0.092 0.33

Page 47 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

 Parameter estimate Standard error t-statistic 

Year effects Included

Month effects Included

State effects Included

Unobserved heterogeneity effects Included

Source: GAO analysis of NLSY79 data. 

Note: In the final column an asterisk signifies statistical significance at the 90 percent confidence 

level, two asterisks signify statistical significance at the 95 percent confidence level, and three 

asterisks signify statistical significance at the 99 percent confidence level. The notation X * Y signifies 

an interaction between the variables X and Y. The omitted category for industry is professional and 

related services and for occupation is professional and technical workers. The omitted category for 

BPE is $30,000 and above and for HQE it is $9,000 and above. Sample includes 5,631 individuals

with a total of 15,506 unemployment spells. The maximized log likelihood value is -63,438.514. 

Table 9: Parameter Estimates for Duration Equation from Industry-Interaction Specification 

 Parameter estimate Standard error t-statistic 

Past unemployment and UI receipt

Previous UI receipt 0.155 0.090 1.73 *

Previous unemployment 0.101 0.093 1.09

Industry

Agriculture, forestry, and fishing -0.088 0.198 -0.45

Mining 0.301 0.273 1.10

Construction 0.314 0.156 2.01 **

Manufacturing 0.213 0.107 1.99 **

Transportation and public utilities -0.135 0.233 -0.58

Wholesale and retail trade 0.069 0.104 0.67

Finance, insurance, and real estate 0.121 0.202 0.60

Business services 0.268 0.153 1.76 *

Personal services -0.031 0.191 -0.16

Entertainment and recreation services -0.024 0.299 -0.08

Public administration 0.029 0.236 0.12

Valid missing 0.005 0.084 0.06

Occupation 

Managers and administrators -0.046 0.062 -0.74

Sales workers -0.024 0.070 -0.33

Clerical and unskilled workers -0.106 0.044 -2.43 **

Craftsmen 0.030 0.050 0.61

Machine operators (nontransportation) -0.025 0.048 -0.51

Transportation equipment operators 0.005 0.061 0.08

Page 48 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

 Parameter estimate Standard error t-statistic 

Laborers (nonfarm) -0.030 0.052 -0.59

Farmers, farm laborers, and foremen -0.055 0.105 -0.52

Service workers (excluding private household) -0.112 0.044 -2.57 **

Industry * previous UI receipt 

Agriculture, forestry, and fishing -0.004 0.162 -0.02

Mining 0.006 0.287 0.02

Construction -0.123 0.111 -1.10

Manufacturing -0.136 0.099 -1.37

Transportation and public utilities -0.007 0.144 -0.05

Wholesale and retail trade -0.128 0.110 -1.16

Finance, insurance, and real estate -0.165 0.213 -0.78

Business services -0.104 0.138 -0.75

Personal services 0.101 0.188 0.54

Entertainment and recreation services 0.088 0.221 0.40

Public administration 0.303 0.221 1.37

Valid missing -0.080 0.098 -0.82

Industry * previous unemployment 

Agriculture, forestry, and fishing 0.053 0.196 0.27

Mining -0.455 0.295 -1.54

Construction -0.253 0.159 -1.60

Manufacturing -0.187 0.111 -1.69 *

Transportation and public utilities 0.139 0.239 0.58

Wholesale and retail trade -0.126 0.109 -1.16

Finance, insurance, and real estate -0.175 0.214 -0.82

Business services -0.276 0.163 -1.69 *

Personal services -0.199 0.199 -1.00

Entertainment and recreation services 0.012 0.305 0.04

Public administration -0.239 0.247 -0.97

Valid missing 0.224 0.090 2.48 **

UI program variables 

Receiving UI -1.256 0.195 -6.45 ***

Weekly benefit amount (WBA) 0.031 0.035 0.90

WBA * receiving UI 0.067 0.059 1.14

Remaining UI benefit duration -0.014 0.009 -1.64

Waiting week for UI benefits 0.030 0.064 0.47

Denial rate for continuing UI claims 0.488 0.215 2.27 **

Extended UI benefits in effect 0.042 0.054 0.78

Page 49 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

 Parameter estimate Standard error t-statistic 

Personal characteristics 

Years of education 0.235 0.069 3.40 ***

Armed Forces Qualifying Test score 0.251 0.056 4.49 ***

African-American -0.254 0.033 -7.74 ***

Hispanic -0.078 0.037 -2.13 **

Male -1.022 0.397 -2.57 **

Married -0.137 0.037 -3.73 ***

Married * male 0.294 0.049 6.05 ***

Age -5.371 2.796 -1.92 *

Age-squared 9.472 5.010 1.89 *

Age * male 7.759 2.905 2.67 ***

Age-squared * male -13.614 5.150 -2.64 ***

Lives in SMSA (urban) -0.040 0.027 -1.48

Health limitations -0.095 0.055 -1.73 *

Spouse used UI in past 0.136 0.051 2.68 ***

Live with parents -0.045 0.051 -0.87

Family size -0.118 0.089 -1.33

Live with parents * family size 0.114 0.136 0.84

Live with parents * family size * male 0.024 0.094 0.26

Children under age 2 -0.098 0.033 -2.93 ***

Number of children -0.004 0.014 -0.30

Recent employment experience 

Union member 0.084 0.029 2.86 ***

Tenure 0.050 0.018 2.75 ***

Tenure-squared -0.008 0.002 -3.07 ***

Lost job due to plant closing -0.179 0.046 -3.87 ***

State unemployment rate (time varying) -0.030 0.007 -4.04 ***

Base period earnings brackets 

Under $2,000 -0.389 0.103 -3.77 ***

$2,000-$3,999 -0.367 0.088 -4.18 ***

$4,000-$5,999 -0.360 0.080 -4.49 ***

$6,000-$7,999 -0.286 0.079 -3.64 ***

$8,000-$9,999 -0.239 0.074 -3.22 ***

$10,000-$11,999 -0.165 0.072 -2.29 **

$12,000-$13,999 -0.186 0.069 -2.71 ***

$14,000-$15,999 -0.137 0.066 -2.07 **

$16,000-$17,999 -0.125 0.064 -1.95 *

Page 50 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

 Parameter estimate Standard error t-statistic 

$18,000-$19,999 -0.101 0.066 -1.53

$20,000-$24,999 -0.039 0.056 -0.71

$25,000-$29,999 -0.135 0.053 -2.54 **

High quarter earnings

Under $1,000 -0.051 0.115 -0.44

$1,000-$1,999 0.103 0.096 1.07

$2,000-$2,999 0.060 0.083 0.72

$3,000-$3,999 0.088 0.074 1.20

$4,000-$4,999 0.031 0.066 0.46

$5,000-$5,999 0.021 0.062 0.33

$6,000-$6,999 0.009 0.057 0.17

$7,000-$7,999 -0.034 0.057 -0.60

$8,000-$8,999 -0.029 0.056 -0.51

Time interactions (t = number of weeks 

unemployed) 

UI receipt * Extended Benefits * (t-1) -1.536 0.740 -2.08 **

UI receipt * Extended Benefits * (t-1)-squared 3.351 2.822 1.19

UI receipt * Extended Benefits * (t-1)-cubed -0.179 0.246 -0.73

Remaining UI benefit duration * (t-1) 0.569 0.201 2.83 ***

Remaining UI benefit duration * (t-1)-squared -3.172 2.624 -1.21

Remaining UI benefit duration * (t-1)-cubed -0.256 0.956 -0.27

UI receipt * (t-1) 10.713 1.987 5.39 ***

UI receipt * (t-1)-squared -24.169 5.707 -4.24 ***

UI receipt * (t-1)-cubed 1.564 0.441 3.55 ***

UI receipt * WBA * (t-1) -1.052 0.683 -1.54

UI receipt * WBA * (t-1)-squared 2.425 2.065 1.17

UI receipt * WBA * (t-1)-cubed -0.141 0.163 -0.87

Year effects Included

Month effects Included

State effects Included

Unobserved heterogeneity effects Included 

Source: GAO analysis of NLSY79 data. 

Note: In the final column an asterisk signifies statistical significance at the 90 percent confidence 

level, two asterisks signify statistical significance at the 95 percent confidence level, and three 

asterisks signify statistical significance at the 99 percent confidence level. The notation X * Y signifies 

an interaction between the variables X and Y. The omitted category for industry is professional and 

related services and for occupation is professional and technical workers. The omitted category for 

BPE is $30,000 and above and for HQE it is $9,000 and above. Sample includes 5,631 individuals

with a total of 15,506 unemployment spells. The maximized log likelihood value is -63,438.514. 

Page 51 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

Table 10: Parameter Estimates for UI Receipt Equation from Occupation-Interaction Specification

 Parameter estimate Standard error t-statistic 

Past unemployment and UI receipt

Number of previous UI receipt spells 0.678 0.059 11.58 ***

Number of previous unemployment spells -0.094 0.016 -5.84 ***

Industry

Agriculture, forestry, and fishing -0.044 0.143 -0.30

Mining 0.302 0.171 1.77 *

Construction 0.004 0.095 0.04

Manufacturing 0.346 0.079 4.38 ***

Transportation and public utilities 0.256 0.106 2.41 **

Wholesale and retail trade 0.285 0.077 3.73 ***

Finance, insurance, and real estate 0.307 0.121 2.54 **

Business services 0.253 0.092 2.75 ***

Personal services 0.045 0.127 0.36

Entertainment and recreation services -0.090 0.157 -0.57

Public administration 0.426 0.122 3.48 ***

Valid missing -0.174 0.070 -2.48 **

Occupation 

Managers and administrators 0.573 0.154 3.72 ***

Sales workers 0.101 0.202 0.50

Clerical and unskilled workers 0.351 0.122 2.88 ***

Craftsmen 0.437 0.126 3.46 ***

Machine operators (nontransportation) 0.534 0.121 4.42 ***

Transportation equipment operators 0.353 0.167 2.12 **

Laborers (nonfarm) 0.364 0.135 2.70 ***

Farmers, farm laborers, and foremen 0.549 0.239 2.29 **

Service workers (excluding private household) 0.108 0.128 0.85

Occupation * number previous UI receipt spells 

Managers and administrators -0.155 0.082 -1.89 *

Sales workers 0.830 0.292 2.84 ***

Clerical and unskilled workers 0.141 0.097 1.45

Craftsmen -0.204 0.071 -2.89 ***

Machine operators (nontransportation) -0.100 0.069 -1.45

Transportation equipment operators -0.276 0.073 -3.79 ***

Laborers (nonfarm) -0.108 0.082 -1.31

Farmers, farm laborers, and foremen 0.035 0.182 0.19

Page 52 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

 Parameter estimate Standard error t-statistic 

Service workers (excluding private household) 0.283 0.091 3.13 ***

Occupation * number of previous unemployment 

spells 

Managers and administrators 0.025 0.025 0.98

Sales workers -0.013 0.036 -0.35

Clerical and unskilled workers -0.009 0.021 -0.44

Craftsmen -0.007 0.020 -0.36

Machine operators (nontransportation) -0.060 0.021 -2.91 ***

Transportation equipment operators 0.020 0.027 0.73

Laborers (nonfarm) -0.049 0.024 -2.03 **

Farmers, farm laborers, and foremen -0.073 0.052 -1.39

Service workers (excluding private household) -0.023 0.022 -1.02

UI Program variables 

Weekly benefit amount (WBA) 0.068 0.058 1.16

Potential UI benefit duration -0.529 0.589 -0.90

Waiting week for UI benefits 0.050 0.121 0.41

Denial rate for new UI claims -1.482 0.828 -1.79 *

Extended UI benefits in effect 0.126 0.095 1.33

Personal characteristics 

Years of education 0.537 0.128 4.19 ***

Armed Forces Qualifying Test score -0.265 0.096 -2.75 ***

African-American 0.008 0.058 0.14

Hispanic -0.084 0.086 -0.98

Hispanic * male 0.239 0.097 2.46 **

Married 0.164 0.050 3.30 ***

Age 18.880 4.432 4.26 ***

Age-squared -38.467 7.863 -4.89 ***

Male -0.362 0.051 -7.13 ***

Lives in SMSA (urban) -0.131 0.050 -2.64 ***

Health limitations 0.071 0.110 0.64

Spouse used UI in past 0.271 0.100 2.71 ***

Spouse used UI in past * male -0.696 0.170 -4.10 ***

Live with parents -0.170 0.097 -1.76 *

Family size -0.485 0.177 -2.74 ***

Live with parents * family size 0.577 0.232 2.49 **

Children under age 2 0.109 0.060 1.84 *

Number of children -0.028 0.024 -1.17

Page 53 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

 Parameter estimate Standard error t-statistic 

Recent employment experience 

Union member 0.000 0.048 0.01

Tenure 0.130 0.029 4.50 ***

Tenure-squared -0.014 0.004 -3.71 ***

Lost job due to plant closing -0.250 0.085 -2.94 ***

State unemployment rate 0.354 0.148 2.40 **

Base period earnings brackets 

Under $2,000 -1.423 0.278 -5.11 ***

$2,000-$3,999 -1.344 0.196 -6.85 ***

$4,000-$5,999 -1.135 0.168 -6.77 ***

$6,000-$7,999 -0.808 0.152 -5.32 ***

$8,000-$9,999 -0.778 0.140 -5.55 ***

$10,000-$11,999 -0.492 0.127 -3.87 ***

$12,000-$13,999 -0.564 0.126 -4.48 ***

$14,000-$15,999 -0.365 0.117 -3.12 ***

$16,000-$17,999 -0.292 0.109 -2.68 ***

$18,000-$19,999 -0.272 0.108 -2.51 **

$20,000-$24,999 -0.136 0.090 -1.50

$25,000-$29,999 0.019 0.086 0.22

High quarter earnings

$0-$999 -0.209 0.257 -0.81

$1,000-$1,999 0.080 0.195 0.41

$2,000-$2,999 0.339 0.152 2.23 **

$3,000-$3,999 0.441 0.129 3.43 ***

$4,000-$4,999 0.416 0.113 3.69 ***

$5,000-$5,999 0.275 0.104 2.65 ***

$6,000-$6,999 0.115 0.093 1.24

$7,000-$7,999 0.169 0.090 1.88 *

$8,000-$8,999 0.054 0.091 0.59

Year effects Included

Month effects Included

State effects Included

Unobserved heterogeneity effects Included 

Source: GAO analysis of NLSY79 data. 

Page 54 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

Note: In the final column an asterisk signifies statistical significance at the 90 percent confidence 

level, two asterisks signify statistical significance at the 95 percent confidence level, and three 

asterisks signify statistical significance at the 99 percent confidence level. The notation X * Y signifies 

an interaction between the variables X and Y. The omitted category for industry is professional and 

related services and for occupation is professional and technical workers. The omitted category for 

BPE is $30,000 and above and for HQE it is $9,000 and above. Sample includes 5,631 individuals

with a total of 15,506 unemployment spells. The maximized log likelihood value is -63,453.973. 

Table 11: Parameter Estimates for Duration Equation from Occupation-Interaction Specification

 Parameter estimate Standard error t-statistic 

Past unemployment and UI receipt

Previous UI receipt 0.111 0.076 1.47

Previous unemployment 0.270 0.095 2.83 ***

Industry

Agriculture, forestry, and fishing -0.031 0.081 -0.38

Mining -0.105 0.111 -0.95

Construction 0.052 0.051 1.03

Manufacturing 0.013 0.042 0.32

Transportation and public utilities -0.003 0.062 -0.05

Wholesale and retail trade -0.069 0.039 -1.75 *

Finance, insurance, and real estate -0.062 0.068 -0.92

Business services -0.007 0.050 -0.14

Personal services -0.205 0.058 -3.51 ***

Entertainment and recreation services -0.005 0.076 -0.07

Public administration -0.153 0.072 -2.14 **

Valid missing 0.190 0.033 5.71 ***

Occupation 

Managers and administrators 0.117 0.251 0.47

Sales workers 0.340 0.188 1.81 *

Clerical and unskilled workers 0.087 0.123 0.71

Craftsmen 0.444 0.146 3.03 ***

Machine operators (nontransportation) 0.287 0.118 2.42 **

Transportation equipment operators 0.240 0.206 1.16

Laborers (nonfarm) 0.231 0.136 1.70 *

Farmers, farm laborers, and foremen -0.002 0.234 -0.01

Service workers (excluding private household) 0.123 0.122 1.01

Occupation * previous UI receipt

Managers and administrators 0.056 0.147 0.38

Sales workers -0.074 0.241 -0.31

Page 55 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

 Parameter estimate Standard error t-statistic 

Clerical and unskilled workers -0.063 0.101 -0.63

Craftsmen -0.117 0.099 -1.18

Machine operators (nontransportation) -0.053 0.092 -0.58

Transportation equipment operators -0.141 0.118 -1.20

Laborers (nonfarm) 0.079 0.108 0.73

Farmers, farm laborers, and foremen 0.017 0.202 0.09

Service workers (excluding private household) -0.065 0.110 -0.59

Occupation * previous unemployment 

Managers and administrators -0.199 0.258 -0.77

Sales workers -0.387 0.197 -1.96 **

Clerical and unskilled workers -0.202 0.125 -1.62

Craftsmen -0.422 0.149 -2.84 ***

Machine operators (nontransportation) -0.338 0.120 -2.80 ***

Transportation equipment operators -0.222 0.213 -1.04

Laborers (nonfarm) -0.308 0.138 -2.24 **

Farmers, farm laborers, and foremen -0.085 0.238 -0.36

Service workers (excluding private household) -0.249 0.125 -2.00 **

UI program variables 

Receiving UI -1.247 0.195 -6.41 ***

Weekly benefit amount (WBA) 0.028 0.035 0.79

WBA * receiving UI 0.066 0.059 1.12

Remaining UI benefit duration -0.014 0.008 -1.68 *

Waiting week for UI benefits 0.030 0.064 0.47

Denial rate for continuing UI claims 0.474 0.214 2.21 **

Extended UI benefits in effect 0.042 0.054 0.77

Personal characteristics 

Years of education 0.240 0.070 3.43 ***

Armed Forces Qualifying Test score 0.242 0.056 4.31 ***

African-American -0.255 0.033 -7.74 ***

Hispanic -0.080 0.037 -2.16 **

Male -0.994 0.399 -2.49 **

Married -0.133 0.037 -3.59 ***

Married * male 0.289 0.049 5.91 ***

Age -5.011 2.825 -1.77 *

Age-squared 8.897 5.061 1.76 *

Age * male 7.571 2.917 2.60 *

Age-squared * male -13.275 5.164 -2.57 **

Page 56 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

 Parameter estimate Standard error t-statistic 

Lives in SMSA (urban) -0.041 0.027 -1.51

Health limitations -0.096 0.055 -1.75 *

Spouse used UI in past 0.133 0.051 2.62 ***

Live with parents -0.042 0.051 -0.83

Family size -0.120 0.089 -1.35

Live with parents * family size 0.112 0.135 0.83

Live with parents * family size * Male 0.023 0.094 0.24

Children under age 2 -0.095 0.033 -2.83 ***

Number of children -0.004 0.014 -0.30

Recent employment experience 

Union member 0.087 0.029 2.94 ***

Tenure 0.050 0.018 2.75 ***

Tenure-squared -0.007 0.002 -3.03 ***

Lost job due to plant closing -0.175 0.046 -3.80 ***

State unemployment rate (time varying) -0.029 0.007 -4.02 ***

Base period earnings brackets

Under $2,000 -0.387 0.103 -3.76 ***

$2,000-$3,999 -0.365 0.088 -4.16 ***

$4,000-$5,999 -0.359 0.080 -4.49 ***

$6,000-$7,999 -0.285 0.078 -3.65 ***

$8,000-$9,999 -0.236 0.074 -3.18 ***

$10,000-$11,999 -0.161 0.072 -2.24 **

$12,000-$13,999 -0.187 0.068 -2.73 ***

$14,000-$15,999 -0.133 0.066 -2.02 **

$16,000-$17,999 -0.134 0.064 -2.10 **

$18,000-$19,999 -0.097 0.066 -1.48

$20,000-$24,999 -0.048 0.055 -0.86

$25,000-$29,999 -0.134 0.053 -2.52 **

High quarter earnings brackets 

$0-$999 -0.063 0.115 -0.55

$1,000-$1,999 0.096 0.096 0.99

$2,000-$2,999 0.053 0.083 0.63

$3,000-$3,999 0.084 0.074 1.14

$4,000-$4,999 0.027 0.066 0.41

$5,000-$5,999 0.022 0.062 0.36

$6,000-$6,999 0.011 0.057 0.19

$7,000-$7,999 -0.033 0.057 -0.58

Page 57 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

 Parameter estimate Standard error t-statistic 

$8,000-$8,999 -0.032 0.056 -0.57

Time interactions (t = number of weeks 

unemployed) 

UI Receipt * Extended Benefits * (t-1) -1.582 0.734 -2.16 **

UI Receipt * Extended Benefits * (t-1)-squared 3.496 2.805 1.25

UI Receipt * Extended Benefits * (t-1)-cubed -0.191 0.244 -0.78

Remaining UI benefit duration * (t-1) 0.572 0.201 2.85 ***

Remaining UI benefit duration * (t-1)-squared -3.185 2.626 -1.21

Remaining UI benefit duration * (t-1)-cubed -0.257 0.955 -0.27

UI receipt * (t-1) 10.722 1.976 5.43 ***

UI receipt * (t-1)-squared -24.286 5.667 -4.29 ***

UI receipt * (t-1)-cubed 1.577 0.438 3.60 ***

UI receipt * WBA * (t-1) -1.049 0.680 -1.54

UI receipt * WBA * (t-1)-squared 2.455 2.051 1.20

UI receipt * WBA * (t-1)-cubed -0.145 0.161 -0.90

Year effects Included

Month effects Included

State effects Included

Unobserved heterogeneity effects Included 

Source: GAO analysis of NLSY79 data. 

Note: In the final column an asterisk signifies statistical significance at the 90 percent confidence 

level, two asterisks signify statistical significance at the 95 percent confidence level, and three 

asterisks signify statistical significance at the 99 percent confidence level. The notation X * Y signifies 

an interaction between the variables X and Y. The omitted category for industry is professional and 

related services and for occupation is professional and technical workers. The omitted category for 

BPE is $30,000 and above and for HQE it is $9,000 and above. Sample includes 5,631 individuals

with a total of 15,506 unemployment spells. The maximized log likelihood value is -63,453.973. 

Industry-Interaction 

Specification 

Table 8 summarizes the parameter estimates for the UI receipt equation of 

the industry-interaction specification. A positive parameter estimate for a 

variable implies that an increase in the variable increases the likelihood of

UI benefit receipt. A negative parameter estimate implies that an increase 

in the variable decreases the likelihood of UI benefit receipt. For example, 

the parameter estimate for years of education is 0.569, meaning that 

unemployed individuals with more years of education have a higher 

likelihood of receiving UI benefits than otherwise similar individuals with 

UI Receipt Equation 

Page 58 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

fewer years of education. The single asterisk signifies that the parameter 

estimate for years of education is statistically significant at the 95 percent 

confidence level. 

The results in table 8 show that the number of prior unemployment 

periods and the number of prior UI benefit receipt periods are strong 

predictors of an unemployed individual’s likelihood of receiving UI 

benefits. The parameter estimate for the number of prior unemployment 

periods is -0.072, which indicates that each additional prior unemployment 

period experienced by an individual reduces the likelihood of UI benefit 

receipt during current unemployment. Alternatively, the parameter 

estimate for the number of prior UI receipt periods is 0.714, which 

indicates that each additional prior UI receipt period experienced by an 

individual increases the likelihood of UI benefit receipt during current 

unemployment. 

The fact that the parameter estimate for the number of previous UI receipt 

periods is larger in absolute value suggests that this is the stronger of the 

two effects. To illustrate the magnitude of the effects, table 12 presents 

simulations of the likelihood of UI receipt by past unemployment and past 

UI receipt experience. According to the table, the average simulated 

likelihood of UI receipt for unemployed individuals with one previous 

unemployment period is 48 percent if UI was received in the previous 

unemployment period, but only 30 percent if UI was not received in the 

previous unemployment period.23 Thus, for individuals with one previous 

unemployment period, the average likelihood of UI receipt is 60 percent 

higher (18 percentage points) for those who received UI benefits in their 

previous unemployment period. The remainder of the table shows that UI 

receipt exhibits significant occurrence dependence. Specifically, an 

individual who does not receive UI benefits during unemployment 

becomes less likely to receive them during future unemployment, while an

individual who does receive UI benefits during unemployment becomes 

more likely to receive them during future unemployment.24 Our model and 

data do not allow us to determine the underlying reasons for these 

associations. There are several possible reasons for the strong relationship 

 23Note that the average simulated likelihood of UI receipt for first-time unemployed 

workers is 33 percent. 

24See Brian P. McCall, “Repeat Use of Unemployment Insurance,” in Laurie J. Bassi and 

Stephen A. Woodbury, editors, Long-Term Unemployment and Reemployment Policies 

(Stamford, Connecticut: JAI Press, Inc., 2000). 

Page 59 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

between past UI receipt and current UI receipt, however. If unemployed 

people do not know they are eligible for benefits, or think that UI benefits 

are not worth the effort to apply, or are overoptimistic about finding 

employment, then there may be a “learning effect” that results from having

received UI benefits which increases the likelihood of future use. 

Alternatively, if people do not apply for benefits because of a

misperception of UI as a welfare program, then having received benefits 

once may soften such an outlook and increase the likelihood of future use.

Table 12: Simulated Likelihood of Receiving UI Benefits for UI-Eligible Workers 

during Successive Periods of Unemployment, by Past UI Receipt Status

Simulated likelihood of receiving UI benefits (percent) 

Unemployment period 

Always received UI 

benefits previously 

Never received UI 

benefits previously

First — 33

Second 48 30

Third 64 28

Fourth 78 25

Fifth 88 23

Sixth 94 21

Source: GAO simulations based on GAO analysis of NLSY79 data. 

Note: Simulations are the average likelihood of receiving UI by unemployment period for two extreme 

cases: (1) individuals always received UI benefits during previous unemployment, and (2) individuals 

never received UI during previous unemployment. N/A denotes that there is no applicable value. See 

accompanying text for details. 

The results in table 8 also show that the likelihood of UI benefit receipt 

varies by the industry of the job lost by unemployed individuals. The 

industry variable is categorical in nature, so the parameter estimate for a 

particular category is an estimate of the effect of being in that category 

relative to an omitted category. The omitted category for industry is 

professional and related services. Table 8 shows that unemployed 

individuals from the mining, manufacturing, public administration, 

wholesale and retail trade, agriculture, forestry and fishing, business 

services, and construction industries are more likely to receive UI benefits 

than similar individuals from the professional services industry, because 

their parameter estimates are positive and statistically significant. To 

illustrate the magnitudes of these differences, table 13 presents the 

average simulated likelihood of UI receipt by industry under the specific 

assumption of first-time unemployment. The average simulated likelihood 

of UI receipt during first-time unemployment is 45.6 percent for 

unemployed miners, but only 24.3 percent for unemployed professional 

Page 60 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

service workers. Table 13 clearly demonstrates that there are significant 

differences across industries in unemployed individuals’ likelihoods of UI 

benefit receipt during first-time unemployment. 

Table 13: Simulated Likelihood of Receiving UI Benefits for UI-Eligible Workers 

from Different Industries 

Industry 

Simulated likelihood of

 receiving UI benefits (percent)

Mining 46

Manufacturing 40

Public administration 37

Wholesale and retail trade 35

Agriculture, forestry, and fishing 34

Business services 31

Construction 31

Finance, insurance, and real estate 31

Transportation and public utilities 29

Entertainment and recreation services 26

Professional and related services 24

Personal services 23

All industries 33

Source: GAO simulations based on GAO analysis of NLSY79 data. 

Note: Simulations are the average likelihood of receiving UI during first-time unemployment. The 

parameter estimates for the mining, manufacturing, public administration, wholesale and retail trade, 

agriculture, forestry, and fishing, business services, and construction industries are statistically 

significant relative to the professional and related services industry at the 95 percent confidence level.

See accompanying text for details. 

To test whether or not the effects of previous experience with

unemployment and UI receipt differ by industry, we also included the 

industry categories interacted with both the number of previous 

unemployment periods and the number of previous UI receipt periods. As 

was the case above, the parameter estimates are calculated relative to the 

omitted professional and related services industry. The parameter 

estimates for the industry interactions with the number of prior 

unemployment periods indicate that unemployed individuals from the 

mining, manufacturing, and wholesale and retail trade industries exhibit 

stronger occurrence dependence than unemployed individuals from the 

Page 61 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

professional services industry.25 That is, each additional previous 

unemployment period has a stronger negative effect on the likelihood of 

receiving UI benefits for unemployed individuals from these three 

industries relative to similar individuals from the professional services 

industry.26

The parameter estimates for the industry interactions with the number of 

previous UI receipt periods show that unemployed individuals from the 

agriculture and construction industries exhibit weaker occurrence 

dependence than individuals from the professional and related services 

industry.27 That is, each additional previous UI receipt period has a weaker

positive effect on the likelihood of receiving UI benefits for unemployed 

individuals from these three industries relative to similar individuals from 

the professional services industry. Unemployed individuals from the 

manufacturing industry also have weaker occurrence dependence, but the 

result is only statistically significant at the 90 percent confidence level. 

Unemployed individuals from the public administration industry exhibit 

stronger occurrence dependence than individuals from the professional 

services industry. A similar result occurs for unemployed workers from 

the finance, insurance, and real estate industry, but the result is only 

statistically significant at the 90 percent confidence level. The other 

industries showed no statistically significant effects compared to those 

from the professional services industry.28

To illustrate the magnitudes of these differences, table 14 presents the 

average simulated likelihood of UI receipt by industry and by the number 

of previous unemployment and UI receipt periods. Column 1 presents the 

simulations for first-time unemployment (see table 13). Column 2 presents 

 25As stated above, the occurrence dependence in this case relates to the fact that an

individual who does not receive UI benefits during unemployment becomes less likely to 

receive them during future unemployment. 

26Although the results for some industries were not individually statistically significant, a

likelihood ratio test of the joint hypothesis that all of the interaction terms between 

industry and past unemployment experience are equal to zero is rejected at the 95 percent 

confidence level. 

27As stated earlier, occurrence dependence relating to previous UI receipt means that an

individual who receives UI benefits during unemployment becomes more likely to receive

them during future unemployment. 

28However, a likelihood ratio test of the joint hypothesis that all of the interaction terms 

between industry and past UI receipt experience are equal to zero is rejected at the 

95 percent confidence level. 

Page 62 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

the simulations assuming one prior unemployment period with UI receipt. 

Column 3 presents the simulations assuming two prior unemployment 

periods, both with UI receipt. Table 14 shows that, although unemployed

individuals from the mining and manufacturing industries have the highest 

average simulated likelihoods of UI receipt for first-time unemployment, 

this is not the case if individuals have received UI benefits previously. For 

unemployed individuals with two prior UI receipt periods, those from the 

public administration, wholesale and retail trade, entertainment services, 

transportation, and business services industries are about as likely or are 

more likely to receive UI benefits again than similar individuals from the 

mining and manufacturing industries. Administrative unemployment 

insurance data have shown that repeat UI recipients tend to be from 

industries that are more seasonal, such as manufacturing and

construction. Our results, however, suggest that this is not because 

workers from these industries who have received UI before are more 

likely to receive UI benefits when they become unemployed than similar 

workers from other industries. Rather, it may be that workers from such 

seasonal industries are unemployed more often on average than workers 

from other industries, or that a larger fraction of unemployed workers 

from such industries have collected UI previously. 

Table 14: Simulated Likelihood of Receiving UI Benefits during Different Periods of UI-Eligible Unemployment for Workers 

with Past UI Receipt, by Industry 

Simulated likelihood of receiving UI benefits during a current UI-eligible 

unemployment period, given past UI receipt (percent) 

Industry

First

unemployment perioda

Second 

unemployment period

Third

 unemployment period

Mining 46 57 69

Manufacturing 40 52 65

Public administration 37 68 91

Wholesale and retail trade 35 52 70

Agriculture, forestry, and fishing 34 42 50

Business services 31 48 66

Construction 31 40 51

Finance, insurance, and real estate 31 64 91

Transportation and public utilities 29 46 66

Entertainment and recreation services 26 45 67

Professional and related services 24 39 58

Personal services 23 38 56

All industries 33 48 64

Source: GAO simulations based on GAO analysis of NLSY79 data. 

Page 63 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

Note: Simulations are the average likelihood of receiving UI during a first unemployment period, a 

second unemployment period with UI receipt during the prior unemployment period, and a third 

unemployment period with UI receipt during both prior unemployment periods. The positive effect that 

each prior UI receipt period has on the likelihood of current UI receipt is statistically significantly larger

for the public administration industry relative to the professional and related services industry at the 

95 percent confidence level, and smaller for the agriculture and construction industries. The 

simulations also incorporate the industry effects and the industry interactions with the number of prior 

periods of unemployment. See accompanying text for details. 

a

Workers experiencing their first period of unemployment did not have past UI receipt. 

Our model also controls for UI program factors, but the results in table 8 

show that after controlling for other observable characteristics, these 

factors had no statistically significant impact on an unemployed 

individual’s likelihood of UI receipt. These program factors include the 

estimated amount of weekly benefits an unemployed individual was 

eligible to receive, the estimated duration of those benefits, and the statespecific denial rate for new UI claims.29 Weekly benefits and the potential

duration of benefits are functions of earnings, which we controlled for 

(and are discussed below). 

The parameter estimates in table 8 also show that a number of personal 

characteristics are associated with an unemployed individual’s likelihood 

of UI benefit receipt, including education, age, and gender. For instance, 

the parameter estimate on years of education is 0.569, which indicates that 

each year of education increases an unemployed individual’s likelihood of

receiving UI benefits. The direction of the age effect on the likelihood of 

UI benefit receipt is difficult to interpret from the parameter estimates in 

table 8, because it is included as a polynomial to allow for nonlinear 

effects. Figure 10 presents a graph of the average simulated likelihood of 

UI receipt by age for the specific case of first-time unemployment. The 

graph shows that the likelihood of UI receipt increases until about the age 

of 25 and then decreases thereafter. For example, the average simulated 

likelihood of UI receipt during first-time unemployment for 25-year-olds is 

10 percentage points (39 percent) higher than for 35-year-olds. While other 

research has found that older individuals are more likely to receive UI 

benefits, other researchers generally do not control for individuals’ past 

unemployment and UI receipt experience as completely as we did.30

Because age and experience with both unemployment and UI receipt are

 29Other researchers have found that the weekly benefit amount does not affect UI receipt. 

See Gritz and MaCurdy. 

30See McCall. 

Page 64 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

correlated, age may act as a proxy for these experience measures when 

they are not controlled for. 

Figure 10: Simulated Likelihood of Receiving UI Benefits for UI-Eligible Workers, 

by Age

0

10

20

30

40

50

60

70

80

90

100

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

Simulated likelihood of receiving UI (percent)

Source: Simulations based on GAO analysis of NLSY79 data.

Age when unemployment began

Note: Simulations are the average likelihood of receiving UI during first-time unemployment at 

different ages. The overall average likelihood of receiving UI during first-time unemployment is 33 

percent. See accompanying text for details. 

Table 8 also shows that several measures relating to the recent

employment experience of unemployed individuals (excluding industry 

and occupation, which are discussed elsewhere) affect an unemployed 

individual’s likelihood of UI benefit receipt. For instance, table 8 shows 

that an unemployed individual’s likelihood of receiving UI benefits 

increases with earnings. We include two earnings measures: base period 

earnings and high quarter earnings. Each measure is grouped in earnings 

brackets and entered into the equation as a categorical variable to reflect 

nonlinear effects. As was the case with industry, each estimated effect is 

relative to an omitted category. For BPE the omitted earnings bracket is 

$30,000 and above and for HQE the omitted bracket is $9,000 and above.

The pattern of parameter estimates for BPE shows that an unemployed 

individual is more likely to receive UI benefits, the higher his BPE (at least 

up to $20,000). The pattern of parameter estimates for HQE shows that an 

unemployed individual is more likely to receive UI benefits if his HQE are 

Page 65 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

between $2,000 and $6,000. Figure 11 presents a graph of the average 

simulated likelihood of receiving UI benefits by base period earnings for 

the specific case of first-time unemployment. The level of HQE is varied to

maintain a ratio of HQE to BPE of about 25 percent to approximate steady 

employment during the base period. The figure shows that unemployed 

individuals who earned more than $14,000 in their base period had a 

likelihood of UI receipt of over 40 percent, while individuals who earned 

less than $6,000 had a likelihood of UI receipt of less than 20 percent. 

Page 66 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

Figure 11: Simulated Likelihood of Receiving UI Benefits for UI-Eligible Workers, by 

Prior-Year Earnings

0

10

20

30

40

50

60

70

80

90

100

30,000 and higher

25,000-29,999

10,000-11,999

12,000-13,999

14,000-15,999

16,000-17,999

18,000-19,999

20,000-24,999

2,000-3,999

4,000-5,999

6,000-7,999

8,000-9,999

0-1,999

Simulated likelihood of receiving UI (percent)

Source: Simulations based on GAO analysis of NLSY79 data.

Earnings (2002 dollars)

Note: Simulations are for the average likelihood of receiving UI during first-time unemployment at 

different levels of earnings. The overall average likelihood of receiving UI during first-time 

unemployment is 33 percent. See accompanying text for details. 

Table 8 shows that employment experience measures other than earnings 

also affect the likelihood of UI receipt. For instance, an individual’s 

likelihood of UI receipt increases with tenure up to 9 years, after which it 

decreases. Also, an individual’s likelihood of UI receipt increases as the 

state unemployment rate increases. Interestingly, the parameter estimate 

on the plant closing variable is a statistically significant -0.263, indicating

that unemployed individuals are less likely to receive UI benefits if they 

lost their jobs because of a plant closing. Union status does not have a 

statistically significant effect on an unemployed individual’s likelihood of 

UI receipt. 

Table 9 summarizes the parameter estimates for the unemployment 

duration equation of the industry interaction specification. A positive 

parameter estimate implies that an increase in a variable increases the 

escape rate from unemployment, thereby decreasing the duration of 

Unemployment Duration 

Equation 

Page 67 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

unemployment. A negative parameter estimate implies that an increase in

a variable decreases the escape rate from unemployment, thereby 

increasing the duration of unemployment. For example, the parameter 

estimate for years of education is a statistically significant 0.235, which 

implies that unemployed individuals with more years of education have

higher escape rates from unemployment than otherwise similar individuals

with fewer years of education. As a result, unemployed individuals with 

more years of education will tend to have shorter unemployment 

durations than those with fewer years of education. 

We found that after controlling for other observable characteristics, the 

single most important predictor of unemployment duration is whether or 

not an individual receives UI benefits during the current unemployment 

period. The parameter estimate on the dummy variable for UI receipt 

status is -1.256, which implies that receiving UI benefits while unemployed 

reduces an individual’s escape rate from unemployment, thereby 

increasing unemployment duration. Simulations show that the median 

duration of unemployment is 8 weeks for individuals who do not receive

UI benefits, but 21 weeks when they do receive UI benefits. We also 

allowed the effect of UI receipt to vary with the number of weeks of 

unemployment. These results indicate that a UI recipient’s escape rate

from unemployment increases until about the 33rd week of 

unemployment. After 33 weeks, the escape rate decreases again until 

about the 72nd week, and then increases until 100 weeks.31

The parameter estimates in table 9 show that having experienced prior 

unemployment or prior UI receipt has no statistically significant effect on

unemployment duration. This result, however, is conditional upon whether 

or not an individual currently receives UI benefits. The unconditional 

effect of having previously received UI benefits is to increase

unemployment duration. As stated earlier, we found that unemployed 

individuals who have previously received UI benefits are significantly 

more likely to receive UI benefits during current unemployment. Because 

those individuals who receive UI benefits during unemployment have

longer unemployment duration, the unconditional effect of having 

previously received UI benefits is to increase unemployment duration. 

 31Changes in the escape rate over an unemployment period are also affected by the other 

time-interaction effects included in the specification. However, these other effects do not 

affect the general shape of this overall trend. 

Page 68 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

Table 9 also shows that there is an association between the industry from 

which an individual lost a job and the duration of unemployment. As in the 

UI receipt equation, the omitted category for industry is professional and

related services. Table 9 shows that unemployed individuals from the 

construction and manufacturing industries have higher escape rates from 

unemployment than otherwise similar individuals from the professional 

services industry, because their parameter estimates are positive and 

statistically significant. The parameter estimate for business services is 

also positive, but is only statistically significant at the 90 percent 

confidence level. The effects for the other industries are not statistically 

significant relative to the professional services industry. To illustrate the 

magnitudes of these differences, table 15 presents the median simulated 

duration of unemployment by industry for the specific case of first-time 

unemployment. The median duration is about 17 and 19 weeks, 

respectively, for unemployed individuals from the construction and 

manufacturing industries who receive UI benefits, but is about 24 weeks 

for those from the professional services industry. 

Page 69 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

Table15: Simulated Unemployment Duration for UI-Eligible Workers, by Industry 

and UI Receipt Status

Simulated unemployment duration (median weeks) 

Industry Receiving UI benefits Not receiving UI benefits

Construction 17 6

Mining 17 6

Business services 18 7

Manufacturing 19 7

Finance, insurance, and 

real estate 21 8

Wholesale and retail trade 22 9

Public administration 23 9

Professional and related 

services 24 10

Entertainment and related 

services 24 10

Personal services 24 10

Agriculture, forestry, and 

fishing 26 11

Transportation and public 

utilities 27 12

Overall average duration 21 8

Source: GAO simulations based on GAO analysis of NLSY79 data. 

Note: Simulations are the median duration of unemployment during first-time unemployment. The 

parameter estimates for the construction and manufacturing industries are statistically significant

relative to the professional and related services industry at the 95 percent confidence level. See 

accompanying text for details. 

To test whether or not the effects of previous experience with

unemployment and UI receipt on the duration of unemployment differ by 

industry, we also included the industry categories interacted with the 

indicators for both previous unemployment and previous UI receipt. As 

stated above, the effects are relative to the omitted category of

professional and related services. The parameter estimates in table 9 

indicate that there are no statistically significant differences across 

Page 70 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

industry types by previous experience with unemployment or previous UI 

receipt, conditional upon current UI receipt status.32

Table 9 also shows that only one UI program factor (other than current UI 

receipt) has a statistically significant impact on an individual’s 

unemployment duration. Specifically, individuals who are unemployed in 

states with higher denial rates for continuing UI claims have higher escape 

rates from unemployment. That is, these individuals tend to become 

reemployed more quickly than those in states with lower denial rates. 

The parameter estimates in table 9 show that a number of personal 

characteristics affect an individual’s unemployment duration, including 

education, race, gender, and marital status. For example, the parameter 

estimate on years of education is 0.235, which indicates that each year of

education increases an individual’s escape rate from unemployment. The 

simulations reported in table 16 show that unemployed individuals with 

16 years of education (roughly a college education) have median 

unemployment duration that is about 1.9 weeks shorter than unemployed

individuals with 12 years of education when UI benefits are received, and 

1.1 weeks when UI benefits are not received. The parameter estimates for 

race show that African-Americans have significantly lower escape rate 

from unemployment than Hispanics, who in turn have slightly lower 

escape rates than whites. Table 17 displays simulations of median 

unemployment duration by race for the specific case of first-time 

unemployment. Simulations showed that the age effect, although 

statistically significant, did not have much of an impact on the median 

duration of unemployment. In table 9, the parameter estimates for gender 

are difficult to interpret because gender is interacted with other variables

in our specification, including age. Simulations show that unemployed 

men have median unemployment durations that are about 2 weeks shorter 

than for unemployed women when UI benefits are received; and about 

1 week shorter when UI benefits are not received. The parameter 

estimates for marital status show that married women tend to have longer

unemployment durations than do unmarried women and married men tend 

 32However, a likelihood ratio test of the joint hypotheses that all of the interaction terms 

between industry and past unemployment experience are equal to zero is rejected at the 95

percent confidence level. A similar test of the joint hypothesis that all of the interaction 

terms between industry and past UI receipt experience are equal to zero could not be 

rejected at the 95 percent confidence level. 

Page 71 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

to have shorter unemployment durations than do unmarried men.33

Although the age effects in table 9 are statistically significant, simulations 

showed that age had minimal effect on the median duration of

unemployment. 

Table 16: Simulated Unemployment Duration for UI-Eligible Workers, by Education 

Level and UI Receipt Status

Simulated unemployment duration (median weeks) 

Years of education when

unemployment began Receiving UI benefits Not receiving UI benefits

9 22 9

10 22 9

11 21 9

12 21 8

13 20 8

14 20 8

15 19 8

16 19 7

17 18 7

18 18 7

19 18 6

20 and higher 17 6

Source: GAO simulations based on GAO analysis of NLSY79 data. 

Note: Simulations are the median duration of unemployment during first-time unemployment. Overall 

average duration is 21 weeks for UI-eligible workers receiving UI benefits and 8 weeks for UI-eligible 

workers not receiving UI benefits. See accompanying text for details. 

 33In alternative specifications we explored whether an individual’s likelihood of UI benefit 

receipt and unemployment duration were affected by spousal income in the previous year. 

We found that spousal income had no statistically significant effect on an individual’s 

likelihood of UI benefit receipt, but did slightly increased the duration of unemployment.

Page 72 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

Table 17: Simulated Unemployment Duration for UI-Eligible Workers, by

Race/Ethnicity and UI Receipt Status

Simulated unemployment duration (median weeks) 

Race or ethnicity Receiving UI benefits Not receiving UI benefits

White 19 8

Hispanic 21 8

African-American 25 11

Source: GAO simulations based on GAO analysis of NLSY79 data. 

Note: Simulations are the median duration of unemployment during first-time unemployment. Overall 

average duration is 21 weeks for UI-eligible workers receiving UI benefits and 8 weeks for UI-eligible 

workers not receiving UI benefits. See accompanying text for details. 

The last set of parameter estimates in table 9 relates to the recent 

employment experience of unemployed individuals (excluding industry 

and occupation, which are discussed elsewhere). Most of the parameter 

estimates in this grouping are statistically significant at the 95 percent 

level. Specifically, unemployed individuals who belonged to a union at the

job that was lost had a higher escape rate from unemployment than 

otherwise similar individuals who were not union members. The 

simulations in table 18 show that union members had median

unemployment durations that were 2 weeks shorter than nonunion 

members when UI benefits were received and 1 week shorter when UI 

benefits were not received. Simulations also show that an individual’s 

unemployment duration decreases modestly with job tenure until 7 years, 

after which it increases slightly. 

Table 18: Simulated Unemployment Duration for UI-Eligible Workers, by Union 

Status and UI Receipt Status

Simulated unemployment duration (median weeks) 

Union memberships status

when unemployment began Receiving UI benefits Not receiving UI benefits

Union member 19 8

Not a union member 21 9

Source: GAO simulations based on GAO analysis of NLSY79 data. 

Note: Simulations are the median duration of unemployment during first-time unemployment. Overall 

average duration is 21 weeks for UI-eligible workers receiving UI benefits and 8 weeks for UI-eligible 

workers not receiving UI benefits. See accompanying text for details. 

Page 73 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

Of our two measures of an individual’s earnings, only the base period 

earnings proved to have a statistically significant effect on the duration of 

unemployment.34 The pattern of parameter estimates for BPE shows that 

unemployed individuals with low BPE have lower escape rates from 

unemployment than otherwise similar individuals with higher BPE. That 

is, lower-earning individuals tend to have longer unemployment periods. 

Figure 12 graphs simulations of median unemployment duration by BPE 

for the specific case of first-time unemployment.35 Individuals with BPE 

below $6,000 tend to have longer unemployment duration than 

unemployed individuals with higher BPE. 

 34Recall that each measure was broken into earnings brackets and entered into the equation 

as a categorical variable. See tables 8,9,10, or 11 for the brackets used. The omitted 

category for BPE is $30,000 and above and the omitted category for high quarter earnings is 

$9,000 and above. 

35For comparability, the simulations in figure 12 hold the ratio of HQE to BPE as closely as 

possible to 25 percent. 

Page 74 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

Figure 12: Simulated Unemployment Duration for UI-Eligible Workers, by Prior-Year 

Earnings and UI Receipt Status

0

5

10

15

20

25

30

30,000 and higher

10,000-11,999

12,000-13,999

14,000-15,999

16,000-17,999

18,000-19,999

20,000-24,999

25,000-29,999

2,000-3,999

4,000-5,999

6,000-7,999

8,000-9,999

0-1,999

Simulated unemployment duration (median weeks)

Source: Simulations based on GAO analysis of NLSY79 data.

Earnings (2002 dollars)

Receiving UI benefits

Not receiving UI benefits

Note: Simulations are the median duration of unemployment during first-time unemployment. Overall 

average duration is 21 weeks for UI-eligible workers receiving UI benefits and 8 weeks for UI-eligible 

workers not receiving UI benefits. See accompanying text for details. 

Occupation-Interaction 

Specification 

We also estimated a specification of our model with interaction effects 

between the occupation categories (as opposed to industry) and our 

measures of past unemployment and past UI receipt experience. These 

results are presented in tables 10 and 11. A comparison of these results 

with those from tables 8 and 9 shows that the overall results of the two 

specifications are very similar. Therefore, only the occupation estimates 

will be discussed here. 

Because occupation is included as a categorical variable, the parameter 

estimates are relative to an omitted group, which is professional and 

technical workers. The estimates in table 10 show that unemployed 

managers, machine operators, craftsmen, laborers, transportation 

Page 75 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

workers, and clerical workers are more likely to receive UI benefits than 

similar professional and technical workers. Table 19 presents the average

simulated likelihood of receiving UI benefits by occupation for the specific 

case of first-time unemployment. Although the range is not as wide as for 

industry (see table 13), the table shows that there are differences in the 

likelihood of UI receipt by occupation. 

Table 19: Simulated Likelihood of Receiving UI Benefits for UI-Eligible Workers 

from Different Occupations

Occupation 

Simulated likelihood of receiving UI 

benefits (percent)

Managers and administrators 39

Farmers, farm laborers and foremen 38

Machine operators (nontransportation) 38

Craftsmen 35

Laborers (nonfarm) 34

Transportation equipment operators 33

Clerical and unskilled workers 33

Service workers (excluding private 

household) 28

Sales workers 28

Professional and technical workers 25

Overall average 33

Source: GAO simulations based on GAO analysis of NLSY79 data. 

Note: Simulations are the average likelihood of receiving UI during first-time unemployment for 

workers from different occupations. The parameter estimates for managers and administrators, 

farmers, farm laborers, and foremen, machine operators, craftsmen, laborers, transportation 

equipment operators, and clerical and unskilled workers are statistically significant relative to 

professional and technical workers at the 95 percent confidence level. See accompanying text for 

details. 

The interactions between occupation and the number of previous 

unemployment periods in table 10 indicate that unemployed machine 

operators and laborers exhibit stronger occurrence dependence than

otherwise similar professional and technical workers.36 That is, each

additional previous unemployment period has a stronger negative effect

on the likelihood of receiving UI benefits for unemployed individuals from 

 36As stated above, the occurrence dependence in this case relates to the fact that an

individual who does not receive UI benefits during unemployment becomes less likely to 

receive them during future unemployment. 

Page 76 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

these two occupations relative to similar professional and technical 

workers.37

The parameter estimates for occupation interacted with the number of 

previous UI receipt periods show that unemployed transportation 

operators and craftsmen exhibit weaker occurrence dependence than

otherwise similar professional and technical workers.38 That is, each

additional previous UI receipt period has a weaker positive effect on the

likelihood of receiving UI benefits for unemployed individuals from these 

two occupations relative to otherwise similar individuals from 

professional and technical occupations. Managers also showed weaker 

occurrence dependence, but this estimate is only statistically significant at

the 90 percent confidence level. Unemployed sales workers and service 

workers exhibit stronger occurrence dependence than otherwise similar 

professional and technical workers. The other occupations showed no 

statistically significant effects compared with professional and technical

workers.39

To illustrate the magnitudes of these differences, table 20 presents the 

average simulated likelihood of UI receipt by occupation and by the 

number of previous UI receipt periods. Column 1 presents the simulations 

for first-time unemployment (as in table 19). Column 2 presents the 

simulations assuming one prior unemployment period with UI receipt. 

Column 3 presents the simulations assuming two prior unemployment 

periods, both with UI receipt. Table 20 shows that although unemployed

managers and machine operators have among the highest average 

simulated likelihoods of UI receipt for first-time unemployment, this is not 

the case if individuals have received UI benefits previously. In the case of

unemployed individuals with two prior UI receipt periods, sales workers, 

service workers, clerical workers, and farmers are about as likely, or are 

 37Although the results for some occupations were not individually statistically significant, a

likelihood ratio test of the joint hypothesis that all of the interaction terms between 

occupation and past unemployment experience are equal to zero is rejected at the 95

percent confidence level. 

38As stated earlier, occurrence dependence relating to previous UI receipt means that an

individual who receives UI benefits during unemployment becomes more likely to receive

them during future unemployment. 

39However, a likelihood ratio test of the joint hypothesis that all of the interaction terms 

between occupation and past UI receipt experience are equal to zero is rejected at the 

95 percent confidence level. 

Page 77 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

more likely, to receive UI benefits than otherwise similar managers and 

machine operators. 

Table 20: Simulated Likelihood of Receiving UI Benefits during Different Periods of 

UI-Eligible Unemployment for Workers with Past UI Receipt, by Occupation

Simulated likelihood of receiving UI benefits during a 

current UI-eligible unemployment period, given past UI 

receipt (percent) 

Occupation 

First 

unemployment 

perioda

Second 

unemployment 

period 

Third

unemployment 

period 

Managers and 

administrators 39 52 65

Farmers, farm laborers, 

and foremen 38 54 70

Machine operators 

(nontransportation) 38 50 62

Craftsmen 35 46 56

Laborers (nonfarm) 34 45 58

Transportation 

equipment operators 33 42 51

Clerical and unskilled 

workers 33 53 73

Service workers 

(excluding private 

household) 28 50 74

Sales workers 28 66 94

Professional and 

technical workers 25 39 56

Overall average 33 48 64

Source: GAO simulations based on GAO analysis of NLSY79 data. 

Note: Simulations are the average likelihood of receiving UI during a first unemployment period, a 

second unemployment period with UI receipt during the prior unemployment period, and a third 

unemployment period with UI receipt during both prior unemployment periods. The positive effect that 

each prior UI receipt period has on the likelihood of current UI receipt is statistically significantly larger

for sales workers and service workers relative to professional and technical workers at the 95 percent

confidence level, and smaller for transportation equipment operators and craftsmen. The simulations 

also incorporate the occupation effects and the occupation interactions with the number of prior 

periods of unemployment. See accompanying text for details. 

a

Workers experiencing their first period of unemployment did not have past UI receipt. 

Table 11 shows that there is also an association between the occupation

from which an individual lost a job and the duration of unemployment. 

Specifically, unemployed craftsmen and machine operators have higher 

escape rates from unemployment than similar professional and technical

Page 78 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

workers, because the estimates are positive and statistically significant. 

The effects for the other occupations were not statistically significant 

relative to professional and technical workers. To illustrate the 

magnitudes of these differences, table 21 presents the median simulated 

duration of unemployment by occupation for the specific case of first-time 

unemployment. The median duration is under 20 weeks for unemployed

craftsmen and machine operators who receive UI, but is almost 26 weeks 

for professional and technical workers. 

Table 21: Simulated Unemployment Duration for UI-Eligible Workers, by Occupation

and UI Receipt Status

Simulated unemployment duration 

(median weeks) 

Occupation Receiving UI benefits Not receiving UI benefits

Craftsmen 16 6

Sales workers 18 7

Machine operators 

(nontransportation) 19 7

Transportation equipment 

operators 20 8

Laborers (nonfarm) 20 8

Service workers (excluding private 

household) 23 9

Managers and administrators 23 9

Clerical and unskilled workers 23 10

Farmers, farm laborers, and 

foremen 26 11

Professional and technical 

workers 26 11

Overall average duration 21 8

Source: GAO simulations based on GAO analysis of NLSY79 data. 

Note: Simulations are the median duration of unemployment during first-time unemployment for 

workers from different occupations. The parameter estimates for craftsmen and machine operators 

are statistically significant relative to professional and technical workers at the 95 percent confidence 

level. See accompanying text for details. 

To test whether or not the effects of previous experience with

unemployment and UI receipt on the duration of unemployment differ by 

occupation, we also included the occupation categories interacted with 

the indicators for both previous unemployment and previous UI receipt. 

As stated earlier, the effects are relative to the omitted category of 

professional and technical workers. The parameter estimates in table 11 

Page 79 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

indicate that the interactions for prior unemployment are negative and 

statistically significant for craftsmen, sales workers, machine operators, 

laborers, and service workers. This suggests that unemployed workers 

from these occupations have lower escape rates from unemployment 

relative to professional and technical workers as the number of past 

unemployment periods increases.40 The parameter estimates for the 

interactions between occupation and past UI receipt showed no individual

statistical significance.41

Although our analysis was performed using the most appropriate dataset

and methodology available, there are a number of limitations to the 

analysis that could not be avoided and should be highlighted. Although the 

NLSY79 is the best available dataset for our purposes, it lacks some 

information that could have improved our analysis. It does not provide 

information about whether an unemployed individual attempted to collect

UI benefits or not, only whether the individual did collect benefits. It also 

does not provide information about whether an individual was aware of 

his or her eligibility for benefits. As a result, we had to estimate each 

unemployed individual’s UI-eligibility status. An unemployed worker’s 

awareness of the UI program and knowledge of its basic rules could have a 

large impact on his or her decision to apply for benefits. This awareness 

may also be correlated with other observable characteristics (education 

and earnings, for example). Not controlling for awareness may affect the 

estimates of such variables. 

Limitations of the 

Analysis 

The NLSY79 also lacks information about an unemployed worker’s former 

employer that could help estimate UI receipt and unemployment duration.

Although our results control for industry, firms within an industry have 

different labor turnover patterns that result in different UI tax rates 

through experience rating.

42 The lack of perfect experience rating may 

even encourage firms to use temporary layoffs and recalls as a way of 

 40However, a likelihood ratio test of the joint hypothesis that all of the interaction terms 

between occupation and past unemployment experience are equal to zero could not be

rejected at the 95 percent confidence level. 

41In addition, a likelihood ratio test of the joint hypotheses that all of the interaction terms 

between occupation and past UI receipt experience are equal to zero could not be rejected

at the 95 percent confidence level. 

42Experience rating describes the practice of making a firm’s UI tax rate a function of the 

amount of UI benefits paid to its former employees. 

Page 80 GAO-06-341 Unemployment Insurance 
Appendix I: Analysis of UI Benefit Receipt 

and Unemployment Duration 

managing its labor force during demand fluctuations.43 An individual who 

works for a firm with high labor turnover or with a high UI tax rate may be 

more aware of the UI program and, thus, more likely to receive benefits. 

Another limitation of the NLSY79 is that it includes only information about 

the specific group of individuals who were between the ages of 14 and 

22 in 1979. Thus, any findings based on the NLSY79 are specific to this 

group and do not represent the experiences of workers of all ages during 

the 1979-2002 period. 

A methodological limitation is that we assume that the time between 

unemployment spells is fixed. One might expect individuals who have 

been unemployed and received UI benefits to change their subsequent 

work behavior, either to increase or decrease their chances of using the 

program in the future. For example, a person who received UI benefits 

while unemployed may search for more stable employment in order to 

reduce the likelihood of experiencing a layoff in the future. We do not 

incorporate such possibilities into our model because this would require a 

third equation to model employment duration, which would be a more 

complex and time-consuming analysis. 

 43See Martin Feldstein, “Temporary Layoffs in the Theory of Unemployment,” The Journal 

of Political Economy, vol. 84, no. 5 (1976). 

Page 81 GAO-06-341 Unemployment Insurance 
Appendix II: Comments from the Department 

of Labor 

Page 82 GAO-06-341 Unemployment Insurance 

Appendix II: Comments from the Department 

of Labor 
Appendix III: GAO

A

 Contact and Staff 

cknowledgment 

Page 83 GAO-06-341 Unemployment Insurance 

Appendix III: GAO Contact and Staff 

Acknowledgment 

Sigurd R. Nilsen, Director, (202) 512-7215 

In addition to the contact named above, Brett Fallavollita, Assistant 

Director, Reginal Santucci, James Pearce, Bill Bates, Gale Harris, 

Gene Kuehneman, Jonathan McMurray, Edward Nannenhorn, 

Dan Schwimer, Shana Wallace, and Daniel G. Williams made major 

contributions to this report. 

We contracted with Dr. Brian McCall from the University of Minnesota for 

analysis of the NLSY79 and other technical assistance. 

GAO Contact 

Staff 

Acknowledgments 

Other 

Acknowledgments 
Bibliography Bibliography 

Anderson, Patricia M., and Bruce D. Meyer. “The Effect of Unemployment 

Insurance Taxes and Benefits on Layoffs Using Firm and Individual Data,” 

NBER Working Paper No. 4960. Cambridge, Massachusetts: National 

Bureau of Economic Research, 1994. 

Berndt, E. K., B. H. Hall, R. E. Hall, and J. A. Hausman. “Estimation and 

Inference in Nonlinear Structural Models,” Annals of Economic and 

Social Measurement, Vol. 3, No. 4 (1974): 653-665. 

Blank, Rebecca M., and David E. Card. “Recent Trends in Insured and 

Uninsured Unemployment: Is There an Explanation?” The Quarterly 

Journal of Economics, Vol. 106, No. 4 (1991): 1157-1189. 

Calvó-Armengol, Antoni and Matthew O. Jackson. “The Effects of Social

Networks on Employment and Inequality.” The American Economic 

Review, Vol. 94, No. 3 (2004): pp. 426-454. 

Card, David E., and Phillip B. Levine. “Unemployment Insurance Taxes 

and the Cyclical and Seasonal Properties of Unemployment,” Journal of 

Public Economics, Vol. 53, No. 1 (1994): 1–29. 

Feldstein,Martin. “Temporary Layoffs in the Theory of Unemployment,” 

The Journal of Political Economy, Vol. 84, No. 5 (1976): 937-958. 

Gritz, R. Mark, and Thomas MaCurdy. “Measuring the Influence of 

Unemployment Insurance on Unemployment Experiences,” Journal of 

Business and Economic Statistics, Vol. 15, No. 2 (1997): 130-152. 

Gruber, Jonathan. “The Wealth of the Unemployed,” Industrial and Labor 

Relations Review, Vol. 55, No. 1 (2001): 79-94. 

Krueger, Alan B., and Bruce D. Meyer. “Labor Supply Effects of Social 

Insurance,” NBER Working Paper 9014. Cambridge, Massachusetts: 

National Bureau of Economic Research, 2002. 

McCall, Brian P. “Repeat Use of Unemployment Insurance,” in Laurie J. 

Bassi and Stephen A. Woodbury, editors, Long-Term Unemployment and 

Reemployment Policies. Stamford, Connecticut: JAI Press, Inc., 2000. 

Meyer, Bruce D. “Unemployment Insurance and Unemployment Spells,” 

Econometrica, Vol. 58, No. 4 (1990): 757-782. 

Page 84 GAO-06-341 Unemployment Insurance 
Bibliography 

Meyer, Bruce D., and Dan T. Rosenbaum. “Repeat Use of Unemployment 

Insurance,” NBER Working Paper 5423. Cambridge, Massachusetts: 

National Bureau of Economic Research, 1996. 

Mortensen, Dale T. “Unemployment Insurance and Job Search Decisions,” 

Industrial and Labor Relations Review, Vol. 30, No. 4 (1977): 505-517. 

Needels, Karen E., and Walter Nicholson. An Analysis of Unemployment 

Insurance Durations since the 1990-1992 Recession. Prepared for the 

Department of Labor. 1999. 

O’Leary, Christopher J., and Stephen A. Wandner, editors. Unemployment 

Insurance in the United States: Analysis of Policy Issues. Kalamazoo, 

Michigan: W. E. Upjohn Institute for Employment Research, 1997. 

Topel, Robert H. “On Layoffs and Unemployment Insurance,” The 

American Economic Review, Vol. 73, No. 4 (1983): 541-559. 

Page 85 GAO-06-341 Unemployment Insurance 
Related GAO Products 

Page 86 GAO-06-341 Unemployment Insurance 

Related GAO Products 

Unemployment Insurance: Better Data Needed to Assess Reemployment 

Services to Claimants. GAO-05-413. Washington, D.C.: June 24, 2005. 

Unemployment Insurance: Information on Benefit Receipt. GAO-05-291. 

Washington, D.C.: March 17, 2005. 

Women’s Earnings: Work Patterns Partially Explain Difference between

Men’s and Women’s Earnings. GAO-04-35. Washington, D.C.: October 31, 

2003. 

Unemployment Insurance: States’ Use of the 2002 Reed Act Distribution. 

GAO-03-496. Washington, D.C.: March 6, 2003. 

Unemployment Insurance: Enhanced Focus on Program Integrity Could

Reduce Overpayments. GAO-02-820T. Washington, D.C.: June 11, 2002. 

Unemployment Insurance: Increased Focus on Program Integrity Could

Reduce Billions in Overpayments. GAO-02-697. Washington, D.C.: July 12, 

2002. 

Unemployment Insurance: Role as Safety Net for Low-Wage Workers Is 

Limited. GAO-01-181. Washington, D.C.: December 29, 2000. 

(130465) 
GAO’s Mission The Government Accountability Office, the audit, evaluation and 

investigative arm of Congress, exists to support Congress in meeting its 

constitutional responsibilities and to help improve the performance and 

accountability of the federal government for the American people. GAO 

examines the use of public funds; evaluates federal programs and policies; 

and provides analyses, recommendations, and other assistance to help

Congress make informed oversight, policy, and funding decisions. GAO’s 

commitment to good government is reflected in its core values of 

accountability, integrity, and reliability. 

The fastest and easiest way to obtain copies of GAO documents at no cost

is through GAO’s Web site (www.gao.gov). Each weekday, GAO posts 

newly released reports, testimony, and correspondence on its Web site. To

have GAO e-mail you a list of newly posted products every afternoon, go 

to www.gao.gov and select “Subscribe to Updates.” 

The first copy of each printed report is free. Additional copies are $2 each. 

A check or money order should be made out to the Superintendent of 

Documents. GAO also accepts VISA and Mastercard. Orders for 100 or 

more copies mailed to a single address are discounted 25 percent. Orders 

should be sent to: 

U.S. Government Accountability Office 

441 G Street NW, Room LM 

Washington, D.C. 20548 

To order by Phone: Voice: (202) 512-6000 

TDD: (202) 512-2537 

Fax: (202) 512-6061 

Contact: 

Web site: www.gao.gov/fraudnet/fraudnet.htm

E-mail: fraudnet@gao.gov

Automated answering system: (800) 424-5454 or (202) 512-7470 

Gloria Jarmon, Managing Director, JarmonG@gao.gov (202) 512-4400 

U.S. Government Accountability Office, 441 G Street NW, Room 7125 

Washington, D.C. 20548 

Paul Anderson, Managing Director, AndersonP1@gao.gov (202) 512-4800

U.S. Government Accountability Office, 441 G Street NW, Room 7149 

Washington, D.C. 20548 

Obtaining Copies of 

GAO Reports and 

Testimony

Order by Mail or Phone 

To Report Fraud,

Waste, and Abuse in 

Federal Programs 

Congressional 

Relations 

Public Affairs 

PRINTED ON RECYCLED PAPER