    Unemployment Insurance: Factors Associated with Benefit Receipt
    (07-MAR-06, GAO-06-341).

    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.
    -------------------------Indexing Terms-------------------------
    REPORTNUM:   GAO-06-341
        ACCNO:   A48477
      TITLE:     Unemployment Insurance: Factors Associated with Benefit
    Receipt
         DATE:   03/07/2006
      SUBJECT:   Labor statistics
    	     State programs
    	     Statistical data
    	     Unemployment insurance
    	     Unemployment insurance benefits
    	     DOL Unemployment Insurance Program

    ******************************************************************
    ** This file contains an ASCII representation of the text of a  **
    ** GAO Product.                                                 **
    **                                                              **
    ** No attempt has been made to display graphic images, although **
    ** figure captions are reproduced.  Tables are included, but    **
    ** may not resemble those in the printed version.               **
    **                                                              **
    ** Please see the PDF (Portable Document Format) file, when     **
    ** available, for a complete electronic file of the printed     **
    ** document's contents.                                         **
    **                                                              **
    ******************************************************************
    GAO-06-341

         * Results in Brief
         * Background
         * Certain Characteristics Are Associated with UI Benefit Recei
              * Unemployed Workers with Higher Earnings, Younger Workers, Wo
                   * Unemployed Workers Who Have Higher Earnings or Are Younger o
                   * Unemployed Workers Who Received UI in the Past Are More Like
              * Receiving UI Benefits, along with Other Factors, Is Associat
                   * Receiving UI Benefits Is Associated with Longer Unemployment
                   * Unemployed Workers with Lower Earnings and Less Education Te
                   * Unemployment Duration Is Not Associated with Past UI Receipt
         * Certain Industries Are Associated with UI Benefit Receipt an
              * Unemployed Workers from Mining and Manufacturing Are More Li
              * The Relationship between Past and Current UI Receipt Is Stro
              * Unemployed Workers from Construction and Manufacturing Have
              * Certain Occupations Are Associated with UI Benefit Receipt a
         * Concluding Observations
         * Agency Comments
         * Overview
         * Data Used
         * Econometric Model
         * Results
              * Industry-Interaction Specification
                   * UI Receipt Equation
                   * Unemployment Duration Equation
              * Occupation-Interaction Specification
         * Limitations of the Analysis
              * Order by Mail or Phone

    Report to the Chairman, Subcommittee on Human Resources, Committee on Ways
    and Means, House of Representatives

    United States Government Accountability Office

    GAO

    March 2006

    UNEMPLOYMENT INSURANCE

    Factors Associated with Benefit Receipt

    GAO-06-341

    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 UI-Eligible
    Workers from Different Industries 23
    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 UI-Eligible
    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
    Industry-Interaction 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 UI-Eligible
    Workers during Successive Periods of Unemployment, by Past UI Receipt
    Status 60
    Table 13: Simulated Likelihood of Receiving UI Benefits for UI-Eligible
    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 UI-Eligible
    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
    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 UI-Eligible
    Workers, by Prior-Year Earnings 9
    Figure 3: Simulated Likelihood of Receiving UI Benefits for UI-Eligible
    Workers, by Age 10
    Figure 4: Simulated Likelihood of Receiving UI Benefits for UI-Eligible
    Workers, by Education Level 12
    Figure 5: Simulated Likelihood of Receiving UI Benefits for UI-Eligible
    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 UI-Eligible
    Workers, by Age 65
    Figure 11: Simulated Likelihood of Receiving UI Benefits for UI-Eligible
    Workers, by Prior-Year Earnings 67
    Figure 12: Simulated Unemployment Duration for UI-Eligible Workers, by
    Prior-Year Earnings and UI Receipt Status 75

    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

    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.

    WBA weekly benefit amount

    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
    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.

    1UI programs are administered by the 50 states, the District of Columbia,
    Puerto Rico, and the Virgin Islands.

    2GAO, Unemployment Insurance: Information on Benefit Receipt, GAO-05-291
    (Washington, D.C.: Mar. 17, 2005).

    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
    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).

    3We 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 UI-eligible
    individuals. For a more complete discussion of our methodology, see
    appendix I.

    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.

                                    Results in Brief

    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.

    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

               o  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.

               o  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.

                                       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.

    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 middle-aged
    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 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.

    4Alan B. Krueger and Bruce D. Meyer, "Labor Supply Effects of Social
    Insurance," NBER Working Paper 9014 (Cambridge, Massachusetts: National
    Bureau of Economic Research, 2002).

    5Rebecca 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).

    6Bruce 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.

    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

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

    7See 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 .

    Certain Characteristics Are Associated with UI Benefit Receipt and Unemployment
                                        Duration

    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 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 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 Have More Years
      of Education Are More Likely to Receive UI

    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
    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.

    9The 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.

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

    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
    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

    12GAO, Unemployment Insurance: Role as Safety Net for Low-Wage Workers Is
    Limited, GAO-01-181 (Washington, D.C.: Dec. 29, 2000).

    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

    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).

    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 UI-eligible 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.

    Figure 4: Simulated Likelihood of Receiving UI Benefits for UI-Eligible
    Workers, by Education Level

    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,

               o  a woman is 29 percent more likely to receive UI benefits than a
               man,
               o  a married worker is 13 percent more likely to receive UI than
               an unmarried worker,
               o  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
               o  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.

    17See Blank and Card, p. 1185.

    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
    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.

    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.

      Unemployed Workers Who Received UI in the Past Are More Likely to Receive UI
      during Subsequent Unemployment

    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

    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.

    Figure 5: Simulated Likelihood of Receiving UI Benefits for UI-Eligible
    Workers during Successive Periods of Unemployment, by Past UI Receipt
    Status

    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.

    Receiving UI Benefits, along with Other Factors, Is Associated with Unemployment
    Duration

    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.

      Receiving UI Benefits Is Associated with Longer 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 and Less Education Tend to Have Longer
      Unemployment Duration

    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 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).

    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.

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

    Figure 6: Simulated Unemployment Duration for UI-Eligible Workers, by
    Prior-Year Earnings and UI Receipt Status

    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
    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.

    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.

    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.

    Figure 7: Simulated Unemployment Duration for UI-Eligible Workers, by
    Education Level and UI Receipt Status

    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.

    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 receivingUI 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.

    aSimulated 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 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

    28See Needels and Nicholson.

    29See Antoni Calvo-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.

      Unemployment Duration Is Not Associated with Past UI Receipt

    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.

    Certain Industries Are Associated with UI Benefit Receipt and Unemployment
                                        Duration

    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.

    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.

    Unemployed Workers from Mining and Manufacturing Are More Likely to Receive UI
    Benefits

    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
    UI-eligible 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

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

                                             Simulated likelihood of receiving UI
    Industry                                                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                                            26
    services
    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.

    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.

    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)
                                                       Second
                             Firstunemployment   unemployment   Thirdunemployment
    Industry                        perioda         period              period
    Mining                                       46              57         69
    Manufacturing                                40              52         65
    Public                                       37              68         91
    administration
    Wholesale and                                35              52         70
    retail trade
    Agriculture,                                 34              42         50
    forestry, and
    fishing
    Business services                            31              48         66
    Construction                                 31              40         51
    Finance,                                     31              64         91
    insurance, real
    estate
    Transportation and                           29              46         66
    public utilities
    Entertainment and                            26              45         67
    recreation
    services
    Professional and                             24              39         58
    related services
    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.

    aWorkers 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 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

    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
    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, UI-eligible
    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).

    Table 4: Simulated Unemployment Duration for UI-Eligible Workers, by
    Industry and UI Receipt Status

                                          Simulated unemployment duration (median
                                                          weeks)
                                                                 Not receiving UI
    Industry                            Receiving UI benefits         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).

    Table 5: Simulated Likelihood of Receiving UI Benefits for UI-Eligible
    Workers from Different Occupations

                                              Simulated likelihood ofreceiving UI
    Occupation                                              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                                      28
    household)
    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.

    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)
                                            First         Second            Third
                                     unemployment   unemployment     unemployment
    Occupation                         perioda         period           period
    Managers and                                           39       52      65
    administrators
    Farmers, farm laborers,                                38       54      70
    and foremen
    Machine operators                                      38       50      62
    (nontransportation)
    Craftsmen                                              35       46      56
    Laborers (nonfarm)                                     34       45      58
    Transportation equipment                               33       42      51
    operators
    Clerical and unskilled                                 33       53      73
    workers
    Service workers                                        28       50      74
    (excluding private
    household)
    Sales workers                                          28       66      94
    Professional and                                       25       39      56
    technical workers
    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.

    aWorkers 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.

    Table 7: Simulated Unemployment Duration for UI-Eligible Workers, by
    Occupation and UI Receipt Status

                                                  Simulated unemployment duration
                                                          (median weeks)
    Occupation                                  Receiving UI  Not receiving UI
                                                       benefits          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.

                                Concluding Observations

    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 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.

                                    Agency Comments

    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.

    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

    Aand U Appendix I: Analysis of UI Benefit Receipt and Unemployment
    Duration

                                        Overview

    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.

                                       Data Used

    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 state of employment.3 We estimate an unemployed individual's UI
    eligibility status using data that are available in the NLSY79.

    1NLSY79 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.

    2UI 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.

    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
    self-reported 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 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

    3State 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.

    4See Center for Human Resource Research, Ohio State University, The
    National Longitudinal Surveys NLSY79 User's Guide, prepared  for the
    Department of Labor, 2002.

    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 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

    5R. 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.

    6It 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.

    7See U.S. Department of Labor. Employment and Training Administration,
    Significant Provisions of State Unemployment Insurance Laws (Washington,
    D.C., 1979-2002).

    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 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.

    8Although 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.

    9See 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.

    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 current UI
    receipt and unemployment durations differently across industries. These
    interactions with industry and occupation are done in separate
    specifications of the model.

    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.

    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.

                                   Econometric Model

    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 receipt to influence current UI receipt and unemployment duration
    to allow for the correlations that possibly exist over time for an
    individual.

    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.

    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:

    , 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.

    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:

    ,

    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
    unemployment period and may be related to. The parameter vector is the
    baseline hazard function.

    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:

    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 . Taking logarithms and summing over all individuals yields the
    full log likelihood function for the sample: . 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.

    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 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.

    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.

    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).

                                        Results

    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 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).

    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.

    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.

    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.

    Table 8: Parameter Estimates for UI Receipt Equation from
    Industry-Interaction Specification

                                               Parameter Standard
                                                estimate    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
    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 ***
    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
    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 Standard
                                                estimate    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
    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
    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   *
    $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.

    Table 10: Parameter Estimates for UI Receipt Equation from
    Occupation-Interaction Specification

                                               Parameter Standard
                                                estimate    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
    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
    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.

    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 Standard
                                                estimate    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          0.123    0.122        1.01
    household)
    Occupation * previous UI receipt
    Managers and administrators                 0.056    0.147        0.38
    Sales workers                              -0.074    0.241       -0.31
    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         -0.065    0.110       -0.59
    household)
    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         -0.249    0.125       -2.00  **
    household)
    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  **
    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
    $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 *            3.496    2.805        1.25
    (t-1)-squared
    UI Receipt * Extended Benefits *           -0.191    0.244       -0.78
    (t-1)-cubed
    Remaining UI benefit duration * (t-1)       0.572    0.201        2.85 ***
    Remaining UI benefit duration *            -3.185    2.626       -1.21
    (t-1)-squared
    Remaining UI benefit duration *            -0.257    0.955       -0.27
    (t-1)-cubed
    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

      UI Receipt Equation

    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 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 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.

    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).

    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)
                                  Always received UI benefits   Never received UI
    Unemployment period                         previously 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 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

                                             Simulated likelihood of receiving UI
    Industry                                                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                                            26
    services
    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 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
    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.

    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.

    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)
                                                           Second           Third
                               Firstunemployment     unemployment    unemployment
    Industry                          perioda           period          period
    Mining                                 46               57              69
    Manufacturing                          40               52              65
    Public                                 37               68              91
    administration
    Wholesale and                          35               52              70
    retail trade
    Agriculture,                           34               42              50
    forestry, and
    fishing
    Business services                      31               48              66
    Construction                           31               40              51
    Finance,                               31               64              91
    insurance, and
    real estate
    Transportation and                     29               46              66
    public utilities
    Entertainment and                      26               45              67
    recreation
    services
    Professional and                       24               39              58
    related services
    Personal services                      23               38              56
    All industries                         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 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.

    aWorkers 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
    state-specific 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 correlated, age may act as a proxy for
    these experience measures when they are not controlled for.

    29Other researchers have found that the weekly benefit amount does not
    affect UI receipt. See Gritz and MaCurdy.

    30See McCall.

    Figure 10: Simulated Likelihood of Receiving UI Benefits for UI-Eligible
    Workers, by Age

    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
    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.

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

    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.

      Unemployment Duration Equation

    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. 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.

    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.

    Table 15: Simulated Unemployment Duration for UI-Eligible Workers, by
    Industry and UI Receipt Status

                                          Simulated unemployment duration (median
                                                          weeks)
    Industry                                                  Not receiving UI
                                           Receiving UI benefits         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
    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 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.

    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.

    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                                   Not receiving UI
    unemployment began                  Receiving UI benefits         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.

    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           Receiving UI      Not receiving UI
    unemployment began                          benefits              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.

    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.

    Figure 12: Simulated Unemployment Duration for UI-Eligible Workers, by
    Prior-Year Earnings and UI Receipt Status

    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 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

                                             Simulated likelihood of receiving UI
    Occupation                                              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
    these two occupations relative to similar professional and technical
    workers.37

    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.

    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
    more likely, to receive UI benefits than otherwise similar managers and
    machine operators.

    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.

    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)
                                           First           Second           Third
                                    unemployment     unemployment    unemployment
    Occupation                        perioda           period          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.

    aWorkers 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
    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)
                                                   Receiving UI  Not receiving UI
    Occupation                                      benefits          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
    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

                              Limitations of the Analysis

    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.

    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
    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.

    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.

    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).

    Appendix II: Comments from the Department of Labor Appendix II: Comments
    from the Department of Labor

    Appendix III: GAOA Appendix III: GAO Contact and Staff Acknowledgment

    GAO Contact

    Sigurd R. Nilsen, Director, (202) 512-7215

    Staff Acknowledgments

    In addition to the contact named above, Brett Fallavollita, Assistant
    Director, Regina 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.

    Other Acknowledgments

    We contracted with Dr. Brian McCall from the University of Minnesota for
    analysis of the NLSY79 and other technical assistance.

    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.

    Calvo-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.

    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.

    Related GAO Products 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.

    Obtaining Copies of GAO Reports and Testimony

    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."

    Order by Mail or Phone

    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

    To Report Fraud, Waste, and Abuse in Federal Programs

    Contact:

    Web site: www.gao.gov/fraudnet/fraudnet.htm E-mail: fraudnet@gao.gov
    Automated answering system: (800) 424-5454 or (202) 512-7470

    Congressional Relations

    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

    Public Affairs

    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

    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

    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, 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 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

    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.
    *** End of document. ***