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. 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. 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. 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. This study also found that workers who were women or Hispanic or whose fathers had more years of education were less likely to become repeat recipients than workers who were men or non-Hispanic or whose fathers had fewer years of education. In 2005, we analyzed the NLSY79 to determine the extent to which individual workers received UI benefits during their early working lives. 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. Earnings, age, education, and most notably past UI benefit receipt are all associated with the likelihood of receiving UI benefits for UI-eligible workers. Education, earnings, and union membership, and current UI benefit receipt, are associated with unemployment duration. Unemployed workers are more likely to receive UI benefits if they have higher earnings prior to becoming unemployed, are younger or have more years of education, or have a history of past UI benefit receipt, when compared to workers with similar characteristics. We found that past experience with the UI program has a particularly strong effect on the future likelihood of receiving UI benefits. In addition, UI-eligible workers are more likely to receive UI when the local unemployment rate is high. However, some characteristics, such as the weekly UI benefit amount that a worker is eligible to receive, are not associated with a greater likelihood of receiving UI benefits. Unemployed workers who have higher earnings or are younger or who are more educated are more likely to receive UI benefits than otherwise similar workers. With respect to earnings, 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. 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. 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. 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. 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. Previous studies have found that younger workers are less likely to receive UI benefits than older workers. 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. 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). 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. However, to the extent that workers with more years of education are better able to access and understand UI program rules, they may also be more likely to know when they are entitled to benefits and to have the information that they need to file successful benefit claims. Other factors, including gender, marital status, job tenure, and the local unemployment rate are also associated with UI benefit receipt. Controlling for all other characteristics among this UI-eligible group, a woman is 29 percent more likely to receive UI benefits than a man, a married worker is 13 percent more likely to receive UI than an a longer tenured worker is more likely to receive UI—for example, a worker with 4 years of tenure at his or her most recent job is 12 percent more likely to receive UI than a worker with 1 year of job tenure, and being in an area with high unemployment raises the likelihood that an unemployed worker will receive UI—for example, a worker living in an area with an unemployment rate of 9 percent is 10 percent more likely to receive UI than a worker living in an area with an unemployment rate of 5 percent. Our finding that women are more likely to receive UI benefits than otherwise similar men differs from the results of previous research, which generally found no statistically significant differences. Nevertheless, our analysis controls for more worker characteristics than these previous studies, and it is likely that we have more carefully isolated the effect of gender from that of other characteristics related to gender, such as workers’ occupations or industries. It is not immediately clear why women are more likely to receive UI benefits, however. We are likewise unable to explain why married workers are more likely to receive UI benefits than otherwise similar unmarried workers. 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. Collectively, these results suggest that UI benefit levels have modest effects on individuals’ decisions about whether or not to receive UI benefits, after controlling for other factors. Unemployed workers who have received UI benefits during a prior period of unemployment are more likely to receive UI benefits during a current period of unemployment than otherwise similar workers who never received UI benefits (see fig. 5). For example, when workers experience their first UI-eligible period of unemployment, their likelihood of receiving UI is 33 percent. During a second UI-eligible period of unemployment, the likelihood of receiving UI is 48 percent for workers who received UI during the first unemployment period but only 30 percent for workers who did not receive UI. Furthermore, the likelihood that these UI-eligible workers will receive UI benefits during successive periods of unemployment increases each time that they receive UI benefits and decreases each time that they do not. 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. Overall, unemployed workers who receive UI benefits have longer unemployment duration than otherwise similar workers who do not receive UI benefits. Several other characteristics are also associated with unemployment duration. Specifically, UI-eligible workers are more likely to experience longer unemployment duration if they have lower earnings before becoming unemployed or have fewer years of education. Other characteristics associated with longer unemployment duration, after controlling for other factors, include being African-American or female or not belonging to a union. We found no relationship between past UI benefit receipt and subsequent unemployment duration. Whether or not an unemployed worker receives UI during a specific period of unemployment has the strongest effect on how long that period of unemployment is likely to last. Overall, UI-eligible workers who receive UI benefits during a period of unemployment remain unemployed for about 21 weeks on average, whereas otherwise similar workers who do not receive UI remain unemployed for about 8 weeks. This result is consistent with economic theory that predicts that receiving UI benefits reduces the costs associated with unemployment and allows workers to engage in longer job searches. That is, an unemployed worker who receives UI benefits faces less pressure to accept the first job offer they receive and can search longer for a more desirable job than an unemployed worker who does not receive UI. Another possible explanation for the strong association between UI receipt and longer unemployment duration may be that workers who expect to experience longer unemployment may be more likely to apply for UI than those who expect to return to work quickly. Unemployed workers with lower earnings tend to have longer unemployment duration than otherwise similar workers with higher earnings. This finding holds for workers who are receiving UI benefits, and for workers who are not receiving UI benefits. Specifically, our simulations show that UI-eligible workers who receive UI benefits and have relatively high earnings ($30,000 and higher) in the year prior to becoming unemployed have unemployment duration that is as much as 9 weeks shorter than workers with earnings that are below $16,000. The results are similar for UI-eligible workers who do not receive UI benefits (see fig. 6). Our result is consistent with other research that has found that higher previous earnings tend to reduce unemployment duration. 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. 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. 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). (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. 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. 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. 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. 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. 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. 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. 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. 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.) 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. 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.) 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 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). 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). 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). 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). Past experience with UI benefit receipt has no significant effect on unemployment duration, regardless of a worker’s occupation. 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. 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. We analyzed the factors affecting unemployment insurance (UI) benefit receipt by statistically modeling the determinants of UI benefit receipt and unemployment durations simultaneously. We model UI benefit receipt in conjunction with unemployment durations to allow for correlations that may exist between the two outcomes for a given individual. For example, an unemployed person anticipating a lengthy unemployment period might be more likely to receive UI benefits than a person expecting a short unemployment period. Alternatively, the receipt of UI benefits may lengthen an unemployment period by allowing an individual to spend more time looking for new employment. In addition, our model controls for a number of observable factors about each unemployed worker’s situation, including recent employment experience, prior unemployment and UI benefit receipt experience, information about UI program factors, including benefit levels, and demographic characteristics. The model was developed and estimated by Dr. Brian McCall, Professor of Human Resources and Industrial Relations, University of Minnesota, under contract to GAO. This appendix describes (1) the data used in the analysis, including how the data were prepared, (2) the econometric model that was estimated, (3) the results from two specifications of the econometric model, and (4) the limitations inherent in the analysis. We used the Bureau of Labor Statistics’ (BLS) National Longitudinal Survey of Youth 1979 (NLSY79) for our analysis. The NLSY79 is an ongoing longitudinal survey of individuals who were between the ages of 14 and 22 in 1979, the first year of the survey. 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. 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. We estimate an unemployed individual’s UI eligibility status using data that are available in the NLSY79. There are three main reasons why the NLSY79 database provides the most suitable data for our analysis. First, the longitudinal nature and level of detail of the data allow us to control for an individual’s history of unemployment and UI receipt, which is a major contribution of this work. Second, respondents were first surveyed at a young age, which reduces the likelihood that we do not observe periods of unemployment and UI receipt early in a person’s working career. Third, the detailed data allow us to estimate an individual’s UI eligibility status, allowing us to focus our analysis on unemployed individuals whom we estimated to be eligible for UI benefits while also reasonably controlling for differences in UI program rules across states. A few limitations to the NLSY79 database should be mentioned. First, the sample began with 12,686 individuals in 1979, but has decreased in size over time due to attrition. 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. 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. 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. 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. 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. 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. 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 and Standard Occupational Classification level), union status and tenure at the job lost, earnings (base period earnings and high quarter earnings ), whether the job was lost because of a plant closing, and the calendar year and month the unemployment began. We group both earnings measures into brackets to allow for nonlinear effects. All dollar values are adjusted for inflation to 2002 dollars using the BLS’s Consumer Price Index for All Urban Consumers (CPI-U). We also control for the state unemployment rate during the month that unemployment began, and, in the duration equation, for the time-varying state monthly unemployment rate over the period of unemployment. A second set of variables summarizes UI program factors, such as the weekly benefit amount (WBA) a person is eligible to receive, the number of weeks of benefits a person is eligible to receive, whether the state has a waiting period before benefits can be received, and whether permanent or temporary extended benefits are in effect. 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. 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. 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. Individuals without any unemployment, and those without unemployment that was estimated to be UI-eligible, were not used in the analysis. Also, individuals who were missing data required by our econometric model were not used in the analysis. This yielded a sample of 5,631 individuals who had been unemployed and eligible for UI benefits at least once, resulting in a total of 15,506 separate periods of UI-eligible unemployment. To investigate the key factors associated with UI benefit receipt, including the role of prior UI benefit receipt (repeat UI recipiency), we used a dynamic econometric model that jointly determines UI benefit receipt and unemployment duration. As mentioned above, the reason for modeling these outcomes jointly is to allow for the likely correlations that exist between them. 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. ]) where the kth unemployment period, all of which are assumed to be independent is a vector of exogenous variables measured at the start of of the unobserved random variable , which helps control for unobserved heterogeneity. Variables in characteristics, characteristics about the lost job, and UI program information. The vector pertaining to past unemployment and past UI benefit receipt, which are measured at the start of an individual’s kth unemployment period and may is a vector of endogenous variables . ]) ) is the baseline hazard function. ) ] ) ) ] ) ) 1) ] ) ) ] ) ) ) ) ) ) ) ) ) . This likelihood is computed in FORTRAN and maximized using the BHHH algorithm. 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 , was assumed to be independent of the unemployment period number, k. Second, the parameters associated with the exogenous ( d,u) and endogenous ( independent of the unemployment period number, k. Third, the , j = d,u) variables were assumed to be unobserved random variables ( 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. , j = d,u) were assumed to be Because of the complexity of the empirical model outlined above, interpreting the parameter estimates is difficult. 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. The averaged survivor function can be used to compute the expected median duration of unemployment. We then repeat this process, successively, assuming that all individuals were in another industry grouping when they lost their jobs, until all industry groups have been simulated. The simulated average likelihood of UI benefit receipt and median unemployment duration can then be compared across industries to estimate the differences by industry. Using all individuals for each simulation, and reporting results for the average unemployed person, helps insure that differences in the simulation results (e.g., industry 1 versus industry 2) reflect only the variables (industry 1, industry 2) being simulated. To describe results that are not related to past experience with unemployment and UI benefit receipt, we present simulations that are specific to first-time unemployment—a simple and clearly defined scenario (the observable trends also hold for unemployed individuals with prior unemployment and UI receipt experience). We report parameter estimates from two specifications of our model. The first specification includes interaction terms between industry and our measures of past UI benefit receipt and past unemployment. These results are presented in tables 8 and 9 for the UI benefit receipt equation and the unemployment duration equation respectively. The second specification includes interaction terms between occupation and our measures of past UI benefit receipt and past unemployment. These results are presented in tables 10 and 11 for the UI benefit receipt equation and the unemployment duration equation respectively. We included the industry and occupation interactions in separate specifications to avoid the issues brought about by multicollinearity. Because the results for the noninteraction terms are similar between the two specifications, we focus on those from the industry-interaction specification (tables 8 and 9). After discussing these results, we discuss the results for the occupation-interaction specification (tables 10 and 11). Tables 8, 9, 10, and 11 are structured as follows. The first column in each table lists the variable names; the second column, the parameter estimates; the third column, the estimated standard errors; and the fourth column, the t-statistics. The last column contains asterisks that signify statistical significance. One asterisk (*) signifies statistical significance at the 90 percent confidence level (t-statistics greater than or equal to 1.65 in absolute value); two asterisks signify statistical significance at the 95 percent confidence level (t-statistics greater than or equal to 1.96 in absolute value) and three asterisks (***) signify statistical significance at the 99 percent confidence level. Parameter estimates discussed below are statistically significantly different from zero at the 95 percent confidence level unless stated otherwise. To conserve space, the tables do not present the parameter estimates for the unobserved heterogeneity ( ), state, year, and month effects. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Table 8 shows that employment experience measures other than earnings also affect the likelihood of UI receipt. For instance, an individual’s likelihood of UI receipt increases with tenure up to 9 years, after which it decreases. Also, an individual’s likelihood of UI receipt increases as the state unemployment rate increases. Interestingly, the parameter estimate on the plant closing variable is a statistically significant -0.263, indicating that unemployed individuals are less likely to receive UI benefits if they lost their jobs because of a plant closing. Union status does not have a statistically significant effect on an unemployed individual’s likelihood of UI receipt. Table 9 summarizes the parameter estimates for the unemployment duration equation of the industry interaction specification. A positive parameter estimate implies that an increase in a variable increases the escape rate from unemployment, thereby decreasing the duration of unemployment. 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. 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. 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. 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. 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. Although the age effects in table 9 are statistically significant, simulations showed that age had minimal effect on the median duration of unemployment. 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. 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. 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. Individuals with BPE below $6,000 tend to have longer unemployment duration than unemployed individuals with higher BPE. 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. 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. 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. 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. 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. 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. 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. 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. The parameter estimates for the interactions between occupation and past UI receipt showed no individual statistical significance. 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. 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. An individual who works for a firm with high labor turnover or with a high UI tax rate may be more aware of the UI program and, thus, more likely to receive benefits. Another limitation of the NLSY79 is that it includes only information about the specific group of individuals who were between the ages of 14 and 22 in 1979. Thus, any findings based on the NLSY79 are specific to this group and do not represent the experiences of workers of all ages during the 1979-2002 period. A methodological limitation is that we assume that the time between unemployment spells is fixed. One might expect individuals who have been unemployed and received UI benefits to change their subsequent work behavior, either to increase or decrease their chances of using the program in the future. For example, a person who received UI benefits while unemployed may search for more stable employment in order to reduce the likelihood of experiencing a layoff in the future. We do not incorporate such possibilities into our model because this would require a third equation to model employment duration, which would be a more complex and time-consuming analysis. 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. We contracted with Dr. Brian McCall from the University of Minnesota for analysis of the NLSY79 and other technical assistance. Anderson, Patricia M., and Bruce D. Meyer. “The Effect of Unemployment Insurance Taxes and Benefits on Layoffs Using Firm and Individual Data,” NBER Working Paper No. 4960. Cambridge, Massachusetts: National Bureau of Economic Research, 1994. Berndt, E. K., B. H. Hall, R. E. Hall, and J. A. Hausman. “Estimation and Inference in Nonlinear Structural Models,” Annals of Economic and Social Measurement, Vol. 3, No. 4 (1974): 653-665. Blank, Rebecca M., and David E. Card. “Recent Trends in Insured and Uninsured Unemployment: Is There an Explanation?” The Quarterly Journal of Economics, Vol. 106, No. 4 (1991): 1157-1189. Calvó-Armengol, Antoni and Matthew O. Jackson. “The Effects of Social Networks on Employment and Inequality.” The American Economic Review, Vol. 94, No. 3 (2004): pp. 426-454. Card, David E., and Phillip B. Levine. “Unemployment Insurance Taxes and the Cyclical and Seasonal Properties of Unemployment,” Journal of Public Economics, Vol. 53, No. 1 (1994): 1–29. Feldstein,Martin. “Temporary Layoffs in the Theory of Unemployment,” The Journal of Political Economy, Vol. 84, No. 5 (1976): 937-958. Gritz, R. Mark, and Thomas MaCurdy. “Measuring the Influence of Unemployment Insurance on Unemployment Experiences,” Journal of Business and Economic Statistics, Vol. 15, No. 2 (1997): 130-152. Gruber, Jonathan. “The Wealth of the Unemployed,” Industrial and Labor Relations Review, Vol. 55, No. 1 (2001): 79-94. Krueger, Alan B., and Bruce D. Meyer. “Labor Supply Effects of Social Insurance,” NBER Working Paper 9014. Cambridge, Massachusetts: National Bureau of Economic Research, 2002. McCall, Brian P. “Repeat Use of Unemployment Insurance,” in Laurie J. Bassi and Stephen A. Woodbury, editors, Long-Term Unemployment and Reemployment Policies. Stamford, Connecticut: JAI Press, Inc., 2000. Meyer, Bruce D. “Unemployment Insurance and Unemployment Spells,” Econometrica, Vol. 58, No. 4 (1990): 757-782. 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. 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.

Summary: Unemployment Insurance (UI), established in 1935, is a complex system of 53 state programs that in fiscal year 2004 provided $41.3 billion in temporary cash benefits to 8.8 million eligible workers who had become unemployed through no fault of their own. Given the size of the UI program, its importance in helping workers meet their needs when they are unemployed, and the little information available on what factors lead eligible workers to receive benefits over time, GAO was asked to determine (1) the extent to which an individual worker's characteristics, including past UI benefit receipt, are associated with the likelihood of UI benefit receipt or unemployment duration, and (2) whether an unemployed worker's industry is associated with the likelihood of UI benefit receipt and unemployment duration. Using data from a nationally representative sample of workers born between 1957 and 1964 and spanning the years 1979 through 2002, and information on state UI eligibility rules, GAO used multivariate statistical techniques to identify the key factors associated with UI benefit receipt and unemployment duration. In its comments, the Department of Labor stated that while there are certain qualifications of our findings, the agency applauds our efforts and said that this report adds to our current knowledge of the UI program. Certain characteristics are associated with the likelihood of receiving UI benefits and unemployment duration. UI-eligible workers that GAO studied are more likely to receive UI benefits if they have higher earnings prior to becoming unemployed, are younger, have more years of education, or if they have a history of past UI benefit receipt when compared with otherwise similar workers. GAO found that past experience with the UI program has a particularly strong effect on the future likelihood of receiving UI benefits. However, some characteristics, such as receiving a higher maximum weekly UI benefit amount, are not associated with a greater likelihood of receiving UI benefits. UI-eligible workers who receive UI benefits have longer unemployment duration than workers with similar characteristics. Also, 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.