U.S. Department of Labor 

Employment and Training Administration Occasional Paper 2005-12 

 

Systemic Disincentive Effects of the 

Unemployment Insurance Program 

Wayne Vroman 

Urban Institute 

This report has been funded, either wholly or in part, with Federal funds from the U.S. 

Department of Labor, Employment and Training Administration under Contract Number 

AF-12985-000-03-30. The contents of this publication do not necessarily reflect the 

views or policies of the Department of Labor, nor does mention of trade names, 

commercial products, or organizations imply endorsement of same by the U.S. 

Government. 
U.S. Department of Labor 

Employment and Training Administration

Table of Contents 

1.0 Introduction ........................................................................................................... 1 

2.0 A Taxonomy of Disincentive Effects ..................................................................... 2 

3.0 Analytic Frameworks Used to Examine UI Disincentive Effects ........................... 5 

3.1 Single Equation Analysis .................................................................................. 5 

3.2 Analysis that Decomposes the Unemployment Rate ........................................ 6 

3.3 General Equilibrium Studies.............................................................................. 6 

3.4 The UI Program and Labor Market Transitions ................................................. 8 

3.5 Analytic Approaches—Summary ...................................................................... 8 

4.0 Past Studies of UI and Unemployment Duration .................................................. 8 

5.0 UI Administration and Benefit Duration ............................................................... 12 

5.1 The Work Test ................................................................................................ 13 

5.2 Reemployment Bonuses ................................................................................. 15 

5.3 Profiling ........................................................................................................... 17 

5.4 Summary......................................................................................................... 18 

6.0 Determinants of the Number of UI Claims .......................................................... 19 

7.0 Studies of Incomplete Experience Rating ........................................................... 21 

8.0 Conclusions and Recommendations for Future Research ................................. 27 

Endnotes ....................................................................................................................... 28

Appendix A: Tables ....................................................................................................... 30
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Employment and Training Administration 1 

1.0 Introduction 

Unemployment Insurance (UI) provides short-term income support payments to eligible 

persons whose unemployment is caused by circumstances beyond their control. State UI 

Programs operate as Federal-state partnerships. States largely determine provisions affecting 

benefit levels and benefit duration as well as most provisions related to the UI payroll taxes paid 

by covered employers. The states also decide on the amounts to maintain in their trust funds at 

the U.S. Treasury, the source of monies to pay regular UI benefits. The Federal Government 

requires the states to designate a tax with an annual taxable wage base per employee of at least 

$7,000 and a maximum statutory tax rate of at least 5.4 percent of taxable wages. The Federal 

partner also imposes standards for state UI administrative performance, such as first payment 

promptness. 

In most situations UI benefits are paid following a “clean” or nonprejudicial separation 

by the employer; e.g., a layoff. The support payment replaces part of the lost earnings from 

unemployment and is of limited duration. In the United States during nonrecessionary years, 

maximum potential benefit duration is 26 weeks for a given 52-week benefit year. For persons 

who use up (or exhaust) their full benefit entitlement, the only way additional benefits may be 

received is from renewed eligibility in a later benefit year obtained through additional work. 

Benefit payments help support the income and living standards of the unemployed and 

provide automatic or built-in stability to the macro economy by automatically increasing during 

recessions. Payments during the strong labor markets of the late 1990s averaged about $20 

billion annually. During 2002 and 2003 annual payments have exceeded $50 billion including 

more than $10 billion of temporary Federal benefits (Temporary Extended Unemployment 

Compensation or TEUC) in each year. 

For some UI recipients, however, these same payments can lead to a prolonging of their 

time in benefit status and higher aggregate unemployment. The UI Program can adversely affect 

the labor market in other ways, such as increasing the volume of unemployment occurrences 

caused by employers. The combined effects on unemployment duration and unemployment 

occurrences cause the overall unemployment rate to be higher than it would otherwise be. Higher 

unemployment also implies, at least in the short run, lower balances in UI trust funds arising 

from both increased benefit payouts and reduced employment (and lower levels of payroll tax 

revenues). Lower balances then feed back upon employers as higher UI payroll tax rates arising 

through experience rating. Because most of the economic burden, or incidence, of payroll taxes 

ultimately rests with workers as lower wages, the higher unemployment and associated labor 

market distortions imply a reduction in the living standards of employed workers. 

Past research has examined several facets of the effects of the UI Program on the 

functioning of the labor market. This report summarizes and critically assesses the relevant 

research conducted since the mid-1970s. Because the volume of research is extensive, the review 

will be selective, reflecting the author’s judgement as to the most influential work. The review 

draws conclusions about the strength of research findings. It also identifies areas where our 
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understanding is too limited to draw strong conclusions. Finally, recommendations for further 

research are offered. 

Some specific areas for further research are highlighted as particularly important. These 

include the following: 

• Examine the effects of certain “natural experiments” reflecting large changes in 

benefit and tax provisions in states in recent years. Among the large changes in 

benefit provisions are the increase in weekly benefits in Indiana between 1991 and 

1995 and the increases in California and Virginia of 2002-2003. The effects of UI tax 

holidays in the 1990s on employer use of temporary layoffs in Georgia, Kansas, and 

North Carolina should also be studied. 

• Support for new research on the determinants of benefit duration in regular UI is 

recommended. 

• Because there have been relatively few studies that estimated the effects of profiling 

on UI benefit duration, the analysis should be extended to other states. The extant 

point estimates are based on studies from nine states, with the results from Kentucky 

showing much larger effects than other studies. The Kentucky analysis should be 

replicated in other states. 

More research on UI work test administration is also recommended. 

2.0 A Taxonomy of Disincentive Effects 

Paying unemployment benefits can affect labor market outcomes through several 

channels. Two that are the most obvious are: (1) the effects of payment levels on the duration of 

unemployment, and (2) the effects of potential benefit duration on actual benefit duration. 

Weekly payment levels are measured most commonly relative to weekly wages or as a 

replacement rate. Higher replacement rates reduce the reward from work and induce some 

claimants to prolong their spells of unemployment. In the language of labor economics, high 

replacement rates increase the appeal of not working (or leisure) since weeks in benefit status are 

compensated, albeit typically at a rate of half or less of the worker’s weekly wage. As the 

replacement rate is higher, the reward from work (the gap between the weekly wage and the 

weekly benefit) decreases. 

Several benefit provisions affect replacement rates. Replacement rates are higher when 

the minimum benefit is higher, the maximum benefit is higher, the statutory replacement rate is 

higher, the benefit schedule is weighted to give higher replacement to low-wage workers, and 

dependents’ allowances are available. These differences in UI statutes have measurable effects 

on the inter-state variation in replacement rates. 

In regular UI Programs, maximum potential benefit duration is limited to 26 weeks per 

benefit year. With legislation enacted during 2003 in Massachusetts and Washington, the UI 

Programs in all states will pay regular benefits for at most 26 weeks during nonrecessionary 

periods. During recessions potential benefit duration is extended under the Federal-state 
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Extended Benefits (EB) Program and/or under the provisions of temporary Federal benefits such 

as the TEUC Program of 2002-2003. Because maximum potential benefit duration has been 

stable in states for several years, research on the effects of increasing potential duration has 

frequently utilized data from periods when EB or temporary Federal benefit extensions have 

been in effect. 

For each claimant of regular UI benefits there is a direct link between previous earnings 

and the maximum potential benefit amount (or MBA). Most UI Programs determine the worker’s 

weekly benefit using earnings from the highest quarter or highest two quarters of the base period 

(usually the earliest four of the past five fully completed quarters prior to claiming benefits).1

The maximum potential benefit amount (MBA) is calculated as a percentage of base period 

earnings (BPE) up to a maximum determined by 26 times the weekly benefit maximum. This 

(MBA/BPE) percentage ranges from 25 percent to 50 percent across individual states with the 

largest concentration of states falling into the 30-33 percent range. The ratio of the maximum 

potential entitlement (MBA) to the weekly benefit (or WBA) then determines the maximum 

potential weeks of benefits that may be collected (subject to a limit of 26 weeks). Workers with 

steady quarterly earnings in the base period are entitled to longer potential durations than those 

with irregular quarterly earnings. Also, more workers qualify for 26 weeks of potential benefits 

in states where the maximum entitlement is a higher percentage of base period earnings.2

The connection between the weekly benefit amount and maximum weeks of potential 

benefit duration is important to understand because an increase in weekly benefits will cause a 

reduction in potential duration for many workers (and vice versa). Thus, a simulation of the 

effects of changing the replacement rate (and the WBA) on actual benefit duration should 

account for an offsetting effect on potential benefit duration for many workers. Past research, 

however, has treated these two determinants of actual benefit duration as independent. 

The replacement rate and maximum potential benefit duration are obvious factors 

affecting actual benefit duration. Estimates of their effects on benefit duration have been derived 

in numerous previous studies, many to be noted below. Several other aspects of UI Program 

statutes and administration also can affect benefit duration. While benefit payments are being 

received the claimant is to be able to work, available for work, and (in nearly all states) actively 

seeking work. These so called three A’s (able, available, and actively seeking) define 

unemployment (as opposed to inactive or not in the labor force). Someone who does not meet all 

three criteria is not unemployed and should not receive benefits in periods when these three 

criteria are not satisfied. 

From the perspective of UI Program administration, active monitoring of these aspects of 

claimant eligibility is needed. Most states have a work test to ensure that claimants are actively 

searching for work.3

 States typically require two contacts with potential employers per week and 

keeping records of such contacts (even though the agency rarely verifies the contacts). However, 

failure to have a record of the contact can result in suspension of benefits for the week. There 

may also be requirements to appear in person at a local office after a set number of weeks to 

review with a job counselor intervening activities to secure reemployment. These required 

meetings are termed eligibility review interviews or ERIs. Offers of suitable work (roughly, 

consistent with past work but defined differently in individual states) may not be refused (or are 
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limited to one or two refusals). Only limited amounts of earnings may be received without 

reducing weekly benefits. Active and ongoing monitoring of claimant eligibility can speed 

reemployment, both because useful information and services are supplied and because of 

sanctions when claimants do not satisfy ongoing eligibility criteria. 

Past research has shown that agency monitoring of ongoing eligibility can reduce benefit 

duration. The monitoring function and the benefit payment functions in states are often 

undertaken by separate administrative entities; e.g., benefits paid by UI agency but work search 

monitored by the Employment Service (ES). Thus, there is a need for close inter-agency 

coordination to ensure that work search is monitored effectively and appropriate sanctions are 

applied. Past research has shown that active administration of work search requirements can 

reduce benefit duration in a cost-effective manner. Examples of previous literature are noted 

below. 

Ongoing eligibility and benefit duration may also be influenced by the form that benefit 

payments take. Eligibility and payments are determined and administered on a weekly (or 

biweekly) basis. However, there have been experiments that tested the effects of paying 

reemployment bonuses. Bonuses were paid to claimants for some share of the remaining 

entitlement (say half) if a new job was secured within a set number of weeks from the date of the 

first benefit payment (and the new job was retained for a minimum period). The motivation for 

these experiments was both to speed worker reemployment and to save on UI benefit payments. 

The findings from the reemployment bonus experiments are reviewed below. 

The policy relevance of reemployment bonuses has increased by a recent (January 2003) 

Bush administration proposal to establish a system of Personal Reemployment Accounts (PRAs). 

These accounts make $3,000 available to unemployed persons identified through profiling as 

likely exhaustees of UI benefits. The accounts could be used for intensive services such as 

training. Additionally, persons with PRAs who become reemployed during the first 13 weeks of 

receiving UI benefits would be eligible for half the remaining balance in their PRA. States could 

make the bonus payments either as a single lump sum at the time of reemployment or 60 percent 

at that time with the remaining 40 percent paid after 6 months of work on the new job. This 

“bonus” element of the proposed PRAs will be reviewed in light of the structure of payments in 

the earlier bonus experiments. 

The preceding discussion shows that several different facets of UI benefit statutes and 

payments administration can affect duration in benefit status. The UI Program also can have 

effects on new occurrences of unemployment. Three effects can be identified: 

1) Some persons knowledgeable about UI eligibility criteria may join the labor force and 

secure jobs with the objective of subsequently receiving UI benefits. This so-called 

entitlement effect of UI was identified by Hamermesh (1979), but it has received only 

limited attention in research. 

2) UI entry requirements vary widely across states. To receive a UI first payment, the 

claimant must satisfy both monetary criteria and nonmonetary separation criteria. The 

latter refer to separations from work where there may be a question (issue) of either a 

voluntary quit or worker misconduct. The result of differing monetary and 
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nonmonetary requirements and administration is that the ratio of first payments to 

new initial claims for benefits routinely ranges from 0.50-0.55 in some states to 0.85-

0.90 in others. These state specific ratios are typically stable from one year to the next 

suggesting large and systematic differences in eligibility criteria and state 

administrative practices. There is the further implication that because entry is so easy 

in some states, there are needless occurrences of first payments and perhaps even 

occurrences of unemployment. This aspect of inter-state variation in benefits has not 

been researched in any major study. 

3) The experience rating of UI taxes may also affect unemployment occurrences. 

Experience rating is intended to assign the costs of benefit payments to those 

employers who cause UI costs through layoffs. Because experience rating as 

practiced in the states is only partial, situations frequently arise where a layoff and 

subsequent benefit charges are not effectively assigned to employers making the 

layoffs. Noncharged and ineffectively charged benefits become a shared 

responsibility of all UI covered employers. When benefit costs are not appropriately 

assigned, some employers initiate more layoffs than they otherwise would. The effect 

of “imperfect” experience rating has been studied by several authors, and their 

findings will be summarized below. 

The preceding taxonomy provides an introduction to the range of possible effects of UI 

on workers and employers. These effects in turn can influence the functioning of the labor 

market and aggregate outcomes. The next section describes analytic approaches used in past 

research to estimate the effects on worker and employer behavior. 

3.0 Analytic Frameworks Used to Examine UI Disincentive Effects 

Individual researchers and projects have employed different approaches in estimating the 

disincentive effects of UI Programs. Four will be identified and briefly described even though 

one approach is dominant, namely single equation analysis. 

3.1 Single Equation Analysis 

By far the most common approach has been to specify a single equation where a specific 

UI variable; e g., duration in benefit status, is examined using multiple regressions. Measures of 

UI benefit availability and generosity are included in equations that also have several other 

control variables. The data may be time-series, cross-section, or pooled time-series and crosssection. Typically, the cross-section data are microdata for individual workers while the time 

series and pooled data are more aggregative. 

Investigators often acknowledge potential problems of simultaneity and misspecification 

errors inherent in a single equation approach, but argue their estimates are reduced form 

estimates and valid for making inferences. Effects of the UI Program are inferred from the sign, 

size, and statistical significance of coefficients on UI explanatory variables like the replacement 

rate (weekly benefits divided by weekly wages) and potential benefit duration. Much of the 

research was completed prior to the 1990s. 
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A substantial amount of new data was generated by a series of social experiments 

undertaken in the 1980s and 1990s and by evaluations of temporary benefit extensions starting 

the temporary Federal Program of 1975-1977 (Federal Supplemental Benefits or FSB). This 

research was conducted with financial support mainly from the Employment and Training 

Administration of the U.S. Department of Labor. Experiments have explored the effects of 

changing the administration of work search requirements and paying cash bonuses intended to 

speed up the reemployment of claimants. The experiments have been conducted with control 

groups as well treatment groups using random assignment of persons into treatments and 

controls. Given the design of these studies, valid inferences as to effects of the treatments have 

been drawn from comparisons of means (treatment group mean less the control group mean) as 

well as multiple regression analysis. Since the associated regressions usually have rather low 

explanatory power, the comparisons of means usually have produced estimates very similar in 

size and statistical significance to the estimates based on multiple regressions. 

3.2 Analysis that Decomposes the Unemployment Rate 

As a matter of definition, the unemployment rate for any given period can be 

decomposed into frequency of occurrences and the average duration of individual spells. The 

basic identity is: (1) ur = f*d, where ur is the unemployment rate, f is the frequency of 

unemployment occurrences (number of occurrences per year as a ratio to the labor force) and d is 

unemployment duration (measured as a fraction of the year). One advantage of viewing the 

unemployment rate from an occurrences-duration perspective is that some UI claims phenomena 

that involve offsetting effects on f and d can be recognized and explicitly incorporated into the 

analysis. One study that uses this framework is Vroman (2002) where an analytic identity like (1) 

above is introduced and described (in report III). In report V of the same report, the offsetting 

effects of a high volume of employer-filed claims are documented empirically; i.e., raising the 

application rate for benefits but at the same time shortening average duration in benefit status 

because the workers have remained job-attached. 

The research approach that explicitly recognizes both frequency of unemployment 

occurrences and unemployment duration is helpful for furthering our understanding of interstate 

differences in the receipt of UI benefits. Vroman (2002) documents that low recipiency is 

consistently observed in some states, but achieved in individual states through differing 

combinations of low application rates and short duration in benefit status. The framework is also 

helpful in pinpointing the different ways that UI Program administration affects recipiency. 

Separation determinations and denials are important in influencing application rates and first 

payment rates among applicants while nonseparation determinations and denials influence 

continuation rates and average benefit duration. Within a frequency-duration framework, 

individual administrative activities can be assigned to that aspect of unemployment which they 

affect most directly. 

3.3 General Equilibrium Studies 

From the perspective of the full labor market, the effects of UI benefit payments are not 

confined to just beneficiaries. Effects on, say, unemployment duration among beneficiaries can 

affect the job search intensity and job-finding success among unemployed nonbeneficiaries. To 
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the extent that total employment is constrained by the overall levels of real output and 

technology, a slower return to employment by beneficiaries will enhance the job prospects of 

nonbeneficiaries. While this “general equilibrium” (or displacement) effect of the UI Program 

has long been recognized as potentially important, e.g., Welch (1977), very little empirical work 

on the question has been undertaken. The analytic problem is very challenging since it requires a 

model of the full labor market and parameter estimates to characterize the job search and job 

finding behavior of the unemployed (UI recipients and nonrecipients) and the employed. 

Two papers that address this “general equilibrium” problem are by Davidson and 

Woodbury (1993) and Levine (1993). Central to both papers is an explicit consideration of 

unemployed persons who are not recipients of UI benefits. In both papers, actions that change 

unemployment among UI recipients occur in a labor market where there are offsetting effects on 

nonrecipients. 

Davidson and Woodbury estimate the general equilibrium effects of a reemployment 

bonus program using a simulation approach with parameter estimates based partly on results 

from the Illinois Reemployment Bonus Experiment (Woodbury and Spiegelman 1987). There are 

four unemployed groups with groups 1 (those eligible for the bonus) and 4 (unemployed but 

ineligible for UI benefits) being of greatest interest. The bonus offer increases search effort and 

job finding among those eligible for the bonus, but there are offsetting effects on other groups, 

especially UI ineligibles. The latter group experiences lower success rates in job finding, an 

induced reduction in search effort, and increased unemployment duration. Under a range of 

parameter estimates, Davidson and Woodbury find the bonus program reduces overall 

unemployment, but the adverse effects of the bonus on the UI ineligibles offsets some 30 to 60 

percent of the employment gains realized by bonus recipients. Estimates of employment effects 

appear in table 2 of their paper. 

Levine (1993) estimates relationships for UI recipients and nonrecipients separately 

where duration for each group is a function of the UI replacement rate.4

 For recipients, a higher 

replacement rate increases unemployment duration, but for nonrecipients it reduces duration. The 

net overall effect of an increase in the replacement rate is estimated to cause a small decrease in 

aggregate unemployment. 

In sum, both papers find offsetting effects on nonrecipients. Davidson and Woodbury 

conclude that effects on nonrecipients offset some 30 to 60 percent of the employment gains 

among the reemployment bonus group. Levine finds the effects on nonrecipients from a higher 

replacement rate among recipients more than offsets the increase in unemployment duration 

among UI recipients. The range of conclusions reached by the two papers points up the need for 

more work on the general equilibrium implications of UI benefit payments. 

While the general equilibrium aspects of UI disincentive effects can be acknowledged, 

these will not be treated further in this review. The effect of this decision is to make the 

estimated effects of the UI Program on unemployment occurrences and duration appear to be 

larger than would be estimated based on a full general equilibrium characterization of the labor 

market. The analyses of Davidson and Woodbury (1993) and of Levine (1993) are useful for 

providing empirical grounding for this conclusion which has strong a priori logic. 
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3.4 The UI Program and Labor Market Transitions 

For the working age population, there are three labor market states: employed (E), 

unemployed (U), and not in labor force (N). Between any two points in time a large volume of 

labor market flows takes place; i.e., movement between E, U, and N. The shorthand description 

of the flows is given by six origin-destination descriptors: EU, EN, UE, UN, NE, and NU. The 

literature on the transitions between these three states (so-called “gross flows”) is limited and the 

subset of this literature that incorporates the UI Program is even more limited. 

While gross flows framework serves as the basis for at least two literature summaries 

(Gustman 1982 and Atkinson and Micklewright 1991) and is recognized in a third review article 

(Decker 1997), empirical estimation of the full set of flows incorporating effects of UI is present 

in just a single study (Clark and Summers 1982). 

Since the majority of unemployed persons do not collect UI benefits at any time during 

their spell of unemployment, this framework is difficult to implement. Each of the six gross 

flows involves flows by UI recipients and nonrecipients. The choice of empirical strategy for the 

researcher is either to estimate effects of UI on flows which combine recipients with 

nonrecipients or to separate the two components of each flow. To date, no research has attempted 

such a six-way decomposition for both recipients and nonrecipients, mainly because the Current 

Population Survey (CPS) and other data files that might be used do not routinely have relevant 

data for both UI recipients and nonrecipients. 

3.5 Analytic Approaches—Summary 

While this section has introduced and described four potential approaches for estimating 

the effects of UI on the labor market, the single equation approach has predominated in past 

research and will likely continue to predominate in future work. The three alternative approaches 

described in the preceding paragraphs merit serious consideration. It would seem that pursuing 

research utilizing a general equilibrium framework should be given some support in future 

research. At present, we have more information about parameters that show the effects of UI 

estimated from single equation analyses (to be summarized in the remainder of this report) than 

information on how the parts of the labor market fit together to yield aggregate labor market 

outcomes and the net effect of UI in influencing those outcomes. 

4.0 Past Studies of UI and Unemployment Duration 

Payment of UI prolongs unemployment duration. Among the program provisions whose 

effects on duration have been most thoroughly studied are the weekly benefit (and the associated 

replacement rate) and potential benefit duration. The single equation studies of UI and 

unemployment duration have included one or both of these key program provisions within the set 

of explanatory variables. While many other aspects of UI are important in determining benefit 

duration—particularly aspects of program administration—consideration of these other factors is 

reserved mainly for section 5.0. 

That UI should lengthen unemployment duration is not surprising, as it was part of the 

rationale for establishing UI. Those who lose jobs through no fault of their own are provided 
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cash support for a limited period while they search for new jobs. Better job matches can result 

when financial pressures on the unemployed are less pressing. 

Two distinct frameworks have been utilized in assessing the impact of UI on 

unemployment duration. The first posits a work-leisure choice where UI payments lower the 

price of leisure. With static preferences and with income (derived from employment) and leisure 

both being normal goods, the payment of UI benefits causes labor supply to decline and 

reemployment to be delayed (Moffitt and Nicholson 1982). The second framework focuses on 

worker behavior in searching for work. Within the job search framework (Mortensen 1977), 

receipt of UI lowers net utility of work, raises the minimum acceptable (reservation) wage for 

new jobs, and reduces job search intensity. Reduced search intensity, in turn, lowers the rate of 

job finding and increases unemployment duration. 

The two theories are difficult to distinguish empirically because they share three common 

predictions: (1) an increase in the level of benefits (or the replacement rate) will prolong 

unemployment duration, (2) an increase in potential UI benefit duration will increase actual UI 

benefit duration, and (3) the rate of job finding among UI recipients will increase in the weeks 

immediately prior to benefit exhaustion. Subsequent paragraphs will mainly emphasize these 

commonalities in predictions rather than findings that distinguish the two theories. 

Table 1 summarizes the results from 14 studies of the determinants of UI benefit 

duration. As noted above, the selection is not offered as fully exhaustive of the extant literature, 

but rather, it reflects a judgement as to the most important and influential studies. All utilize data 

from the U.S. and most allow for separate effects of the level of benefits (or replacement rate) 

and potential benefit duration on actual benefit duration. Duration is measured as weeks of actual 

benefits or as hazard (exit) rates for individual weeks within unemployment spells. Because the 

studies are diverse, it is not always obvious how they should be summarized so that the most 

meaningful comparisons are possible. To the extent possible, the two right-hand columns 

respectively show the effects on actual duration of a 10 percent increase in the WBA (or 

replacement rate) and the effects of a one-week increase in potential duration. 

It should also be noted that diverse measures of benefit levels (and replacement rates) 

were used. Newton and Rosen (1979) use the opportunity cost of unemployment measured as the 

gap between the weekly wage and the weekly UI benefit. UI benefits became partially taxable in 

late 1978 and fully taxable in late 1986. Several studies (Solon 1985; Meyer 1990) measured 

benefits on a “net” basis; i.e., both wages and UI benefits measured net of income and payroll 

taxes. These measures were based imputations whose accuracy could be questioned. 

Several studies (Moffitt and Nicholson 1982; Grossman 1989; and Card and Levine 

2000) estimate effects of potential duration in data where potential duration has been extended 

beyond the usual 26 weeks of the regular UI Program. These extensions in potential duration 

were temporary and occurred during recessions. Because the longer potential duration was due to 

a recession, caution must be exercised in assigning a causal nexus between the increase in 

potential duration and associated increase in actual duration. Moffitt and Nicholson (1982) 

estimated separate relationships for exhaustees and nonexhaustees. They found that duration for 

exhaustees was little influenced by the increase in potential duration. 
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n. 

The findings reported in table 1 display consistency in the estimated effects of benefit 

levels and potential duration. Both effects are consistently positive across these studies. While 

the point estimates for the two variables vary from study to study, a 10-percent increase in the 

weekly benefit (or replacement rate) increases duration an estimated 0.5 to 1.8 weeks. An 

increase in potential benefit duration increases actual duration by an estimated 0.1 to 1.2 weeks. 

When the individual point estimates for the latter are reviewed, note that all but two (Arizona in 

Classen 1979; Grossman 1989) fall into the range between 0.1 and 0.5 weeks. Given the 

differences in samples, estimation procedures, and time periods, this range is quite narrow. 

Because maximum benefit entitlement formulas cause an increase in the WBA to reduce 

potential benefit duration for many workers (at a given level of base period earnings), the 

estimated effects in table 1 should be treated as gross effects on actual duration, not net effects. 

The offsetting relationship between the WBA and potential duration is not accounted for in any 

of these studies. This “offset” effect is particularly important for workers with irregular base 

period earnings. They are much more likely to experience reductions in potential duration if the 

WBA (or replacement rate) were higher. 

Table 1 emphasizes average effects with detail by gender only shown for two studies 

(Moffitt and Nicholson 1982; Moffitt 1985b). Several studies have noted different effects of UI 

variables for demographic subgroups. Generally, the response of actual duration to potential 

duration is larger (hazard rates are lower) for women, nonwhites, older workers, and those with 

lower educational attainment. 

One illustration of differing sensitivity among demographic groups is provided by the 

analysis by Corson, Needles and Nicholson (1999) of the Emergency Unemployment 

Compensation (EUC) Program of the early 1990s. Table III.4 of their study compares 

characteristics of UI-only (short term) recipients with UI and EUC (long term) recipients. 

Women represented 40.8 percent of UI-only recipients but 43.8 percent of UI and EUC 

recipients. The corresponding shares for persons aged 55–64 were 9.1 percent and 12.5 percent, 

respectively. For African Americans, the analogous shares were 9.8 percent and 16.9 percent, 

respectively. As these authors note, similar patterns were also observed during the recession of 

1981–1983. 

Differing sensitivity has also been documented by type of separation (permanent or 

temporary) and duration in benefit status. Workers on temporary layoff are less sensitive to UI 

program variables than those on permanent layoff. Sensitivity to UI variables (hazard rates) also 

declines as duration in benefit status lengthens. The combination of these two findings points up 

the importance of knowing the type of separation experienced by the individual. Those on 

temporary layoff experience much shorter average durations than those on permanent layoff,5

but their spells are little affected by the replacement rate or potential duratio

A review of table 1 shows that 12 of the 14 studies were based on data that predate 1990. 

The Corson, Needles and Nicholson (1999) study was sponsored by the Office of Workforce 

Security (OWS) to examine the effects of the Emergency Unemployment Compensation (EUC) 

Program of 1991-1994. The size of the disincentive effects of both UI benefit levels and potential 

benefit duration estimated in this study fall within the range of estimates derived in the other 
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studies. Since the recent TEUC Program will pay benefits through March 2004 and followup 

data collection will extend for at least 1 year after its expiration, information on its labor market 

effects will not become available before 2005. 

Note also in table 1 that about half the studies utilized Claimant Wage and Benefit 

History (CWBH) data. The CWBH collected administrative data on both benefits and earnings 

from several states, but it was terminated in the early 1980s. Thus, the data from CWBH studies 

have no contemporary counterparts. Only Washington has maintained its CWBH data base in 

more recent years. 

The main benefit provisions in individual state UI Programs have exhibited strong yearto-year stability in recent years. Thus, potential duration of regular UI benefits of 26 weeks has 

been operative in all but two states (Massachusetts and Washington) since the mid-1980s and 

operative in 40 states since the late 1960s. Similarly, the provisions affecting average benefit 

levels (the statutory replacement rate and the maximum weekly benefit relative to the average 

weekly wage) have also been quite stable. 

One interesting change in benefit provisions occurred in New Jersey in 1996. During the 

last half of the year, persons who exhausted benefits in regular UI could claim an additional 13 

weeks (or half of regular benefit duration for those eligible for fewer than 26 weeks) under the 

New Jersey Extended Benefits (NJEB) Program. Card and Levine (2000) examined the effect of 

this temporary extension on actual duration. Relying on tabular data as well as regression 

analysis, they found that benefit duration did respond positively to the added eligibility from 

NJEB. The total estimated response was modest, 0.08 weeks per each week of extension, or 

roughly 1.0 week for 13 added weeks. 

Note in table 1 that the Card and Levin estimate falls at the bottom end of the range of 

estimated responses of actual weeks to an increase in potential weeks. Perhaps this would be 

anticipated in a temporary program. Unlike national benefit extensions under FSB, FSC, EUC 

and TEUC, the New Jersey extension was effective for a much shorter period (just 5 months), 

and it occurred in a reasonably robust labor market.6

 Interestingly, Card and Levine found large 

numbers of exits from UI just before 26 weeks continued to occur, even though most claimants 

were eligible for 39 weeks under NJEB. Perhaps information about the availability of NJEB 

benefits was not as widespread as in other programs of extended benefits. 

This was a most unusual extension of potential benefit duration. Since 1975, the only 

other increases in potential duration have occurred during periods when either temporary Federal 

benefits or Federal-state Extended Benefits (EB) were available. Because of this situation, the 

states do not provide a good source for natural experiments to test for effects of changes in 

potential duration on actual duration. 

Statutory provisions related to benefit levels provide more productive ground for finding 

and testing the effects of state-level natural experiments. Between 1990 and 2002 there were at 

least three instances of large changes in benefit generosity as reflected in statewide replacement 

rates. The changes occurred in California, Indiana, and Virginia. Other changes were identified, 

but they were much smaller than in these three states.7
U.S. Department of Labor 

Employment and Training Administration 12 

California increased the maximum weekly benefit from $230 in 2001 to $330 in 2002 (or 

43 percent) with three additional $40 annual increments slated for 2003–2005. The 2001 

legislation also changed (reduced) replacement rates for low-wage workers. Between 2001 and 

2002 California’s average replacement rate increased from 0.22 to 0.27, with smaller changes 

anticipated for 2003–2005. These changes follow an extended period of stability in the weekly 

maximum as California’s replacement rate decreased throughout the 1990s. Thus, California 

offers a recent history of both large increases and large decreases in its replacement rate as well 

as changes in wage loss replacement for those at the bottom of its benefit structure. 

Indiana implemented large increases in its maximum weekly benefit amount between 

1991 and 1995. The cumulative change saw the maximum increase from $96 to $202 per week, 

or from 22 percent of weekly wages to 41 percent. As a result, the replacement rate increased 

from 0.26 in 1991 to 0.37 in 1995. The higher maximum has been sustained since the mid-1990s. 

Following the terrorist attack of September 11, 2001, Virginia raised its weekly 

maximum in October from $268 to $368, or 37 percent, and then maintained the higher 

maximum throughout 2002. The maximum was reduced to $318 in 2003, still $50 higher than 

the earlier maximum of $268. In July of 2004, the maximum will increase to $326. 

The changes in the maximums in all three states have exceeded 35 percent. If the 

elasticity of duration to the replacement rate is 0.5, effects on actual duration should be apparent. 

For California and Virginia, the changes are sufficiently recent that relevant microdata should be 

readily available from the states. Examining the effects of the recent changes in these three states 

should be given high research priority. 

5.0 UI Administration and Benefit Duration 

The previous section showed that level of benefit payments and potential benefit duration 

are two important influences on the duration of unemployment and UI benefit duration. Base 

period earnings patterns interact with state statutes to determine the weekly benefit, the 

maximum benefit amount, and potential duration for individual claimants. In modeling the 

effects of payment levels and potential duration, both are taken as given for individual claimants 

who then try to maximize utility within either a static work-leisure framework or a job searchreservation wage framework. The focus is on decision making by the claimant within an 

environment characterized by constraints or imperfect information. 

Several aspects of UI Program administration also influence time in benefit status. 

However, studies of the effects of UI administrative decisions have been much less numerous. 

Millions of administrative decisions affecting eligibility are routinely made each year. These 

decisions can be grouped into three broad categories: monetary eligibility; nonmonetary 

separation issues (where quits and misconduct are at issue); and nonmonetary, nonseparation 

issues (typically where continuing eligibility is at issue). The jargon of UI administration refers 

to nonmonetary decisions simply as “seps” and “nonseps.” For a given claimant, the monetary 

and separation decisions must be favorable in order to receive an initial benefit payment while 

nonsep decisions typically take place after benefit payments have commenced. All decisions are 

made within tight time frames where claimants and their former employers often disagree about 
U.S. Department of Labor 

Employment and Training Administration 13 

the circumstances of the job separations and/or with agency decisions about eligibility. The 

combined effects of a large volume of decisions, tight time tables, and frequent claimantemployer disputes yield many occasions where redress is desired by one party. Thus, UI has 

appeal procedures where dissatisfied parties can try to reverse earlier adverse administrative 

decisions. 

States routinely report on their administrative activities in all three areas. For all three, the 

reporting shows the number of decisions and the breakdown of outcomes favorable and 

unfavorable to claimants. From these data, one can assess the frequency of determinations and 

denial rates. The data show wide inter-state variation in determination rates and in denial rates. 

For nonseps the issue areas are: (1) able, available, and actively seeking work, (2) disqualifying 

and/or deductible income, (3) refusal of suitable work, (4) reporting requirements (in-person or 

call-in), (5) refusal of profiling referrals, and (6) “other” which mainly relates to aliens, athletes, 

and school employees. Since an issue can arise in any of these areas in any week when benefits 

are claimed, UI agency exposure to potential issues is large. The volume of nonseps is typically 

measured relative to total weeks claimed (claimant contacts) during a given period (month or 

year). 

Besides UI agency activities in the three broad aspects of administration that are subject 

to periodic reporting to OWS (monetary eligibility, seps, and nonseps), several other activities 

potentially affecting benefit duration are not part of the OWS reporting system. Examples 

include: the content of eligibility reviews where claimants are required to appear in person or call 

agency staff to report job finding activities; the number of job search contacts per week; and 

agency procedures to verify job search activity. These activities have elements of both carrots 

and sticks in trying to encourage reemployment by claimants. Agencies differ in the pursuit of 

these activities. A sizeable fraction add state monies to Federal administrative allocations in 

order to enhance the effectiveness of program administration. 

Extant research on continuing eligibility and benefit duration falls into three broad areas: 

(1) the work test and work search verification, (2) reemployment bonus studies, and (3) profiling 

studies. All three have been conducted as state-level experiments, typically with randomized 

assignment of participants into treatment and control groups. 

5.1 The Work Test 

Nine studies of the work test are summarized in table 2A. Eight (all but Steinman 1978) 

utilize data from the 1980s and 1990s. It should be noted that studies using data from the mid1980s and earlier represented a major departure from earlier research which examined grouped 

data. Also, the earlier studies took place within an administrative environment where claimants 

had greater in-person contact with local UI offices. 

Typically, the studies tested more than a single treatment. Some treatments emphasized 

enhanced services while others emphasized increased work search requirements and/or work 

search verification. Thus, while the treatments usually reduced benefit duration, it often was not 

possible to separate the effects of carrots (enhanced services) from the effects of sticks. 
U.S. Department of Labor 

Employment and Training Administration 14 

Overall, the findings in table 2A are reasonably consistent. Short-run impact effects fall 

generally into the -0.40 to -1.00 weeks range and most treatment effects are significant.8

 The 

table displays 25 mean treatment effects of which 20 are negative and 17 are statistically 

significant at the 0.90 or 0.95 level. Of the 25 means, 16 fall into the range from -0.40 to -1.00. 

The estimated impacts are tightly bunched. 

Only two studies present major exceptions to the preceding. The Nevada program 

(Steinman 1978) reported much larger treatment effects (-3.90 weeks). This study had extensive 

involvement of experienced staff and close monitoring of claimant job search activities. It 

probably had the most intensive staff input per claimant of any study identified in the table. At 

the opposite extreme, Ashenfelter, Ashmore, and Deschenes (1999) found that work search 

verification had insignificant effects on duration in all four states. In two states—Massachusetts 

and Virginia—the point estimate was even positive, although not significant. Because the 

structure of the treatments in their study was different from others, the results do not speak 

directly to the effects of changing work search requirements as do the other studies. 

A second interesting finding relates to allowing greater claimant autonomy in search 

activities. Note in Johnson and Klepinger (1991) that exception reporting (Treatment 1) in 

Washington resulted in significantly increased benefit duration (3.34 weeks). A similar (but 

quantitatively smaller) finding was reported in the Maryland Work Search Demonstration where 

Treatment 2 did not require reporting of job contacts. Again, duration was significantly longer 

(0.4 weeks) than for the control group. 

Increased time in benefit status, however, did not lead to reduced earnings. This treatment 

was the one treatment in the Maryland Demonstration where earnings increased significantly. 

Woodbury (2001) also reports significantly higher quarterly earnings for the exception reporting 

group (conditional upon employment) in a 10-year followup of the Washington Demonstration. 

For those reemployed, the increase in quarterly earnings was as large as for those who received 

intensive services ($81-$82). It seems that longer benefit duration is associated with productive 

wait unemployment; e.g., an increased likelihood of reestablishing earlier job matches. Greater 

search autonomy permits some to wait longer for former jobs, and successful reunions explain 

the increased earnings. Note in Woodbury (2001), however, that these effects are conditional 

upon securing reemployment. The unconditional effects on earnings are significantly negative (-

$52). In sum, greater autonomy is productive for some but not for the majority. It would seem 

important to identify who benefits from increased search autonomy and who does not. 

It also appears in the Maryland study that increased search intensity (Treatment 1) and 

increased verification (Treatment 4) both help to shorten benefit duration. The latter finding 

directly conflicts with the findings of Ashenfelter, Ashmore, and Deschenes. The Maryland 

study is also interesting in that the treatments involve the number of contacts, reporting of 

contacts, and verification of contacts; i.e., sticks. The larger conclusion from the Maryland 

demonstration is that sticks have the intended reemployment effect, but faster reemployment 

does not translate into significant increases in earnings. 

There is also evidence in table 2A of the usefulness of carrots. Job search assistance 

speeded reemployment in both the District of Columbia and Florida (Decker et al. 2001), while 
U.S. Department of Labor 

Employment and Training Administration 15 

g period. 

intensive services had positive long-run effects on both employment and earnings in Washington 

(Woodbury 2001). 

Overall, carrots and sticks are both important tools for securing faster reemployment. 

Significant effects of both were found in Charleston (Corson, Long, and Nicholson 1985), and 

greater service intensity was productive (Treatment 3 versus Treatment 2 in Charleston). 

To summarize, changing work search requirements and providing enhanced support 

services are both effective in reducing unemployment duration and speeding reemployment. 

From a benefit-cost perspective, this is a useful finding, since the job search interventions are 

typically inexpensive. 

Improving earnings upon reemployment presents more of a challenge. The findings in 

table 2A indicate that longer benefit duration achieved through a relaxation of work search 

requirements (and reporting) is productive for some in their subsequent earnings. While this 

group seems to be a minority of claimants, it would be useful to try to identify them early in their 

spells of unemployment and then ascertain what makes their search so effective. Is it as simple as 

allowing some workers more time to try to return to jobs with former employers? 

5.2 Reemployment Bonuses 

During the mid-to-late-1980s, experiments were conducted in four states to test whether 

the payment of bonuses would speed the reemployment of UI claimants. The first was 

undertaken in Illinois in 1984 followed by experiments in New Jersey, Pennsylvania, and 

Washington over the next 5 years. All four experiments had treatment and control groups. 

Except in Illinois, the payments were made directly to claimants who secured jobs early 

in their spells and retained the new jobs for some minimum period, e.g., 4 months. The Illinois 

experiment tested two treatments, payments to claimants and payments to employers. Because 

the effects of paying claimants were found to be much stronger, payments to employers were not 

included in design of the later bonus experiments. The treatments in the other three experiments 

tested for effects of variation in qualification periods and the level of the bonus payments. 

Typically, claimants were told of eligibility for the bonus after receiving their first payment.9

The bonus payments were set as a fraction (40 to 60 percent) of the remaining entitlement. 

Typically, payments were made as two lump sum amounts. The first payment was made shortly 

after taking the new job and the second payment was made after the job had been retained for a

minimum qualifyin

Table 2B summarizes estimated effects of the four experiments. All estimated treatment 

effects on benefit duration were negative and roughly half (seven of 16) were statistically 

significant. The bulk of the point estimates (10 of 16) cluster in the range between –0.40 and –

1.00 with but one estimate larger than –1.00 and the other five between –0.06 and –0.36. The 

largest treatment effect was found for payments to claimants in Illinois (-1.15 weeks). Overall, 

the bonuses reduced unemployment duration by some 0.40 to 1.00 weeks. Note that this range is 

the same as the range for the estimated effects of the work search demonstrations summarized in 

table 2A. 
U.S. Department of Labor 

Employment and Training Administration 16 

The bonus experiments were not too successful in identifying the marginal effects of 

either the level of the bonus payment or the length of bonus qualification period. Of the two 

experiments that tested for variation in these treatments, the Pennsylvania results show a wider 

range of estimated effects of high bonuses (T3, T4 and T5) and of long qualification periods (T2, 

T4 and T5) vis-a-vis their opposites. In Washington, the relative ranges of estimated effects over 

these two dimensions of treatment were even wider. The one pattern that emerged in Washington 

was that high bonuses had the largest treatment effects (T3 and T6). A reader can speculate 

whether the experimental subjects understood the eligibility rules well enough to give reliable 

signals on these basic elements of the experimental designs. It seems clear that more can be 

learned about how claimants respond to the financial incentives offered through reemployment 

bonuses. 

Reemployment bonuses in Washington did raise claimant earnings in the long run. Note 

in the Woodbury (2001) analysis that all six treatment effects on quarterly earnings were positive 

over the 10 years following the experiments and that three estimated effects were statistically 

significant. When the six individual treatment effects are examined, however, there is no obvious 

pattern by the level of the bonus or the qualification period. Low and high bonuses were 

estimated to have larger effects than medium bonuses. 

The treatment groups within bonus experiments claim benefits under the traditional rules 

of continuing eligibility but with the added financial incentive provided by the bonus payment. 

For members of the treatment pool, four effects on claims can be identified that are undesirable. 

First, some who would have had short claims irrespective of the bonus payments will receive a 

windfall payment without any change in behavior. Second, the bonus payments increase the 

attractiveness of filing for UI benefits because of the windfall represented by the bonus. With the 

bonus in place, it becomes much more attractive to file and collect a few weeks of benefits 

because of the added financial reward for securing a new job. Third, some otherwise short-term 

claimants will delay exiting benefit status until the final weeks of the bonus eligibility period.10

Fourth, there are potential displacement effects as those responding positively to bonus payments 

secure jobs that otherwise would have been filled by other members of the claimant pool. 

To the extent that the preceding behavior occurs, it raises the costs of a bonuses program. 

Even with delays in paying the second part of the bonus amount, some would be financially 

rewarded for no change in behavior, while others would alter their behavior in ways that would 

increase benefit payouts. Several of these considerations were raised by Meyer (1995) in his 

review article. He also noted that these effects would likely become larger if a permanent bonus 

program were to be instituted. 

Interest in reemployment bonuses revived in 2003 through a proposal made by the Bush 

administration to establish PRAs. Persons identified through profiling as likely UI exhaustees 

would be granted $3,000 accounts to be used for intensive services like training. Additionally, 

those who become reemployed during the first 13 weeks in benefit status would receive a bonus 

equal to half of the remaining balance in their PRA. States could structure the payments as either 

a single lump sum at the time of reemployment or divided 60-40 with 60 percent paid at 

reemployment and 40 percent after 6 months on the new job. From the earlier research on 

reemployment bonuses, it would seem delayed payments should be a requirement of this 
U.S. Department of Labor 

Employment and Training Administration 17 

proposed program, not a state option. Making the delay a requirement is further supported by the 

disincentive considerations raised in the preceding discussion. 

From the review of the work search and reemployment bonus experiments, observe that 

the average effects of both treatments are similar. Most estimates in tables 2A and 2B fall into 

the –0.40 to –1.00 weeks range. If the two treatments achieve roughly comparable results, it 

would seem that work search interventions are superior because they are less costly. 

Additionally, changing work search requirements presents fewer possibilities for disincentives 

related to added benefit payments (bonuses) as noted above. 

5.3 Profiling 

The central idea behind profiling is straightforward. The pool of eligible claimants is 

heterogeneous and those most likely to collect unemployment benefits for long periods can be 

identified on the basis of observable characteristics. In most states, the profiling algorithm was 

developed using data from an earlier period where claimant characteristics and the actual 

duration of benefits were observed. The algorithm estimates parameters in a multivariate 

statistical model; e.g., a regression where the dependent variable is a 0-1 variable (= 1 when the 

person exhausts benefits or when benefit duration exceeds a predetermined threshold). The same 

algorithm is then used to assign scores to current applicants.11 Applicants are ranked and those 

with the highest scores are designated to receive enhanced services (where services are 

available). The treatments are intended to shorten benefit duration. Persons refusing to 

participate in the treatments may be denied benefits. Profiling is reserved for persons who are not 

job-attached. Persons with definite recall dates and those employed through union hiring halls 

are exempted from profiling. 

What are the profiling interventions? Data from 1999 were as follows. The total number 

profiled was 6.5 million or about 70 percent of all new initial claims for UI benefits.12 Almost 

2.2 million claimants were identified as likely exhaustees, while nearly 0.8 million were actually 

referred for services. The profiling reporting system identifies seven service categories. Total 

counts in 1999 were as follows: orientation—0.44 million; assessment—0.40 million; counseling 

—0.20 million; job search workshops—0.25 million; placement services—0.67 million; 

education and training—0.14 million; and the self-employment program (present in just a few 

states)—0.01 million. The total number of services offered totaled 2.1 million, but the bulk were 

either very short-term interventions (orientation, assessment, counseling, and job search 

workshops, 1.29 million) or placement services (0.67 million). Education and training measures, 

the most significant long-term intervention, totaled less than 0.15 million. Placement services 

and education and training measures had completion rates of about 70 percent compared to 

nearly 100 percent for the quartet of very short-term (typically one-shot) interventions. Thus, the 

typical profiling intervention was a small-scale intervention. 

Table 2C summarizes the results of four analyses of the effects of profiling, emphasizing 

estimated effects on unemployment duration. The table displays 16 estimates, and 15 are 

negative. The estimates cluster between –0.40 and –1.00 (10 of 15) with only two exceeding –1.0 

and three between –0.21 and –0.29. Note that 12 of the negative estimates are significant at the 
U.S. Department of Labor 

Employment and Training Administration 18 

95 percent level. These results suggest profiling has a small but significant effect in reducing 

unemployment duration. 

Of all the estimates displayed in table 2C, the estimate of –2.2 weeks from the Kentucky 

study (Black, Smith, Berger, and Noel 2003) is by far the largest. At least two factors seem to 

have been at work in Kentucky. First, Kentucky’s profiling algorithm utilizes more controls than 

in most states. Particularly important, the local unemployment rate enters with highly significant 

effects on unemployment claim duration. Second, the control group was drawn from the same 

predicted duration interval as the treatment group in each substate area. Both factors may have 

combined to yield a superior control group compared to the other profiling studies.13

Knowledge of the impact effects of profiling on duration is still limited. The four studies 

summarized in table 2C span just eight states with data for three studies drawn from the mid1990s.14 Given the universal application of profiling in UI Programs, there is an obvious need to 

undertake added research to broaden our understanding of how profiling works and which 

interventions are most effective. 

New research that provided answers to the following three questions would be especially 

valuable: 

1) Why are the estimated impacts of the Kentucky analysis so much larger than for the 

other studies? Pursuit of this question should involve a comparative analysis of 

control group methodologies and a repetition of the approach followed in Kentucky in 

at least one additional state. 

2) What are the effects of the carrots versus the sticks used in profiling? One aspect of 

this question is to note the experiences of persons sanctioned for failure to adhere to 

profiling requirements. A second aspect is to undertake an analysis of the 

comparative effects of the different profiling interventions identified above. 

3) How should resources be allocated among those identified as most likely to exhaust 

benefits (or experience long durations)? The question (as raised by the authors of the 

Kentucky analysis) is: Should those with the highest scores necessarily be first in line 

for receiving expedited services? Those with lower scores could, on average, benefit 

more from interventions than those with the highest scores. An alternative would be 

to follow a triage approach to resource allocation. Perhaps more resources should be 

devoted to those who will benefit more, not necessarily those with the highest 

profiling scores. 

The preceding questions give an idea of productive areas for added research. 

5.4 Summary 

In reviewing the studies of work search, reemployment bonuses and profiling, four 

findings seem especially noteworthy: 

1) All three interventions reduce duration in benefit status. 
U.S. Department of Labor 

Employment and Training Administration 19 

2) Surprisingly, the point estimates for all three interventions cluster in the same range: 

–0.40 to –1.00 weeks of reduced duration. 

3) Interventions that affect work search are more cost effective than reemployment 

bonuses. Their effects on duration are similar. However, bonuses are more expensive 

due to the payment of the bonuses, and they raise more issues of potential 

disincentive effects. 

4) More research on the effects of profiling is needed, with the three areas being 

especially important: a better understanding of the most appropriate control group 

methodology; a better understanding of the relative roles of carrots versus sticks in 

reducing duration; and how to best allocate intervention resources among those with 

high profiling scores. 

6.0 Determinants of the Number of UI Claims 

The terms and conditions of UI benefit availability can affect not only unemployment 

duration, but also the number of claims for benefits. This number depends upon both the number 

of underlying spells of unemployment and the rate at which persons file for benefits. Research on 

the link between UI and occurrences of unemployment has primarily emphasized the effects of 

imperfect experience rating on employer decisions to utilize temporary layoffs, a topic to be 

investigated in section 7.0. The focus here is worker decisions to file or apply for UI benefits, 

commonly termed the UI take-up rate in the literature. 

Although the UI Program has paid benefits for more than 60 years, the share of the 

unemployed who collect benefits is low and has declined substantially since the 1950s. National 

recipiency rates have fallen consistently below 0.40 in recent nonrecessionary years despite the 

fact that more than 95 percent of wage and salary employees work in UI-covered employment. 

Two other facets of UI recipiency are also noteworthy. Recipiency declined in the early 1980s 

and the cause(s) of the decline were investigated, most prominently by Corson and Nicholson 

(1988). Interestingly, there has been a noticeable increase in recipiency since the mid-1990s, but 

this has not yet received much attention. Recipiency varies widely by state, as noted by Vroman 

(2002, ch. 2) and by others. A large part of this variation is related to inter-state variation in takeup. 

In general, the determinants of UI application rates have been studied much less than the 

determinants of benefit duration. One research approach to the application decision has been to 

ask questions directly of unemployed persons in the CPS. Questions about UI applications were 

posed in CPS supplements of 1989–1990 and again in 1993 with summaries of findings available 

respectively in Vroman (1991) and Wandner and Stettner (2000). Two prominent reasons for not 

filing given in these surveys were “quit last job” and “did not work enough.” Because more than 

a decade has now elapsed since the most recent of these supplements, the need to repeat this data 

collection seems clear. 

Several approaches have been followed in examining the determinants of UI take-up. 

Table 3 identifies six studies undertaken since the late 1980s.15 The studies vary in the type of 
U.S. Department of Labor 

Employment and Training Administration 20 

 benefit status. 

data employed (state-year averages versus microdata), the definition of take-up, and the 

treatment of taxes. 

A common characteristic of all studies is the use of a UI benefits variable (either the level 

of weekly benefits or the benefit replacement rate) as a determinant of applications. Several 

aspects of monetary eligibility could affect applications including presence of weighted benefit 

schedules (which provide higher replacement for lower wage workers), dependents’ allowances, 

and the possibility of a second benefit determination for those initially found monetarily 

ineligible. These factors all make replacement rates quite variable at the microlevel. 

Although replacement rates are variable, it should be noted that factors besides benefit 

levels are of greater importance in determining take-up. Across individual states the variation in 

recipiency and take-up is much greater than the variation in replacement rates. For example, the 

coefficient of variation (the ratio of the standard deviation to the mean) for the application rate as 

measured by Vroman (2002) in state-level data for 1999 was 0.44 whereas for the replacement 

rate it was 0.15.16 In other words, many factors besides the level of benefits enter decisions to 

file for UI and the collective importance of these other factors greatly exceeds the importance of 

the benefit level in determining take-up. In the studies noted in table 3, some of the most 

important of these other factors are determination rates and denial rates for both seps and 

nonseps, reason for unemployment, unionization, industry, occupation, method of claims filing 

(in-person, mail, telephone, or employer), and duration in

Table 3 displays the key findings of these studies that pertain to the effect of benefit 

levels on take-up. The table gives only a small suggestion as to the number of important program 

variables that influence UI take-up. Note that the Corson-Nicholson (1988) study is included 

even though its dependent variable (the so-called IUTU ratio—Insured Unemployment/Total 

Unemployment) included not only elements of applications, but also benefit receipt and duration 

in benefit status. Besides the significant effect of the replacement rate, several aspects of 

nonmonetary determinations (the individual issues of quit,17misconduct, disqualifying income, 

and work test violations) all had significant effects. The decline of recipiency of the early 1980s 

in their analysis was found to have had several contributing factors. 

Blank and Card (1991) focused on the period from 1977 to 1987. They found that benefit 

levels had significant effects on take-up but the decrease between 1977–1980 and 1982–1987 

was fully attributed to a reduction in take-up. Thus, changes in claimant behavior rather than 

changes in UI Program variables carried most of the explanation for decreased application rates. 

Both analyses by Anderson and Meyer (1994 and 1997) found benefits had a significant 

effect on take-up, but with estimated effects that depended on the sample chosen and the method 

of estimation. They explicitly explored the changed tax treatment of UI benefits that was 

modified in 1979 (to partial taxation) and again 1987 (to full taxation) and found that the change 

exerted an important depressing effect on UI claims. 

The Vroman (2002) study is the only one of the six that did not find a positive effect of 

the replacement rate on applications. In certain specifications, it even found a negative 

coefficient for the replacement rate. Because the study did not use state-year dummy variables 

for controls, however, it is not persuasive on this question. Like the Corson-Nicholson study, 
U.S. Department of Labor 

Employment and Training Administration 21 

however, it highlighted the importance of other UI administrative variables, especially 

determination rates and denial rates for misconduct. This is the only one of the table 3 studies to 

explore the effects of the method of filing, finding significant effects. In states with a large 

volume of employer-filed claims, application rates were systematically higher than elsewhere. 

Overall, table 3 indicates that benefit levels have a significant positive effect on take-up. 

As Anderson and Meyer (1997) have observed, this means that estimates of the effects of the 

replacement rate on UI recipiency that focus just on benefit duration overlook an important 

second avenue through which the total volume of weeks in benefit status is affected by the level 

of UI benefits. 

Taxation of UI benefits reduces the attractiveness of filing for benefits and of long benefit 

duration. The avenue is through a reduction in the replacement rate. Consequently, larger effects 

of taxation on UI recipiency would be expected for claimants in higher tax brackets. Since 

taxation of UI benefits has been fully in place since 1987, the increases in duration and take-up 

rates since the mid-1990s must be attributable to other factors. Knowledge about the 

determinants of take-up would be enhanced with new information from another supplement to 

the CPS like the earlier supplements of 1989–1990 and 1993. 

7.0 Studies of Incomplete Experience Rating 

A unique feature of the UI Programs in the United States is reliance on experience rating 

to determine employer tax rates. Employers with higher payouts to current and former employees 

pay higher taxes than those with low payout. States typically operate with several tax rate 

schedules—higher schedules operative when trust fund balances are lower. For each schedule, 

however, higher payout rates are associated with higher tax rates. No other country uses 

experience rating to set UI tax rates. 

Experience rating as practiced is only partial. There is not a one-to-one correspondence 

between added payouts and added taxes. Three types of benefit payouts are not effectively 

assigned back to individual employers. The presence of maximum and minimum tax rates mean 

that some payouts (termed ineffective charges) are not recovered even though the responsible 

employer is known. Other payments are not charged to employers, either because the employer 

was not responsible for the separation (termed noncharges) or because the employer has ceased 

operations (termed inactive account charges). In the aggregate, these three ineffectively assigned 

charges are large, accounting for about 40 percent of all benefit payments between 1988 and 

2002 in data reported by the states to OWS.18 

Even if an employer pays dollar-for-dollar for added benefit payouts, there is an 

important difference in timing between the added payouts and the added taxes. Under a 3-year 

benefit-ratio experience rating system with a 10 percent annual discount rate, for example, three 

extra dollars of payments this year followed by one added dollar of taxes in each of the next 3 

years implies an effective experience rating percentage of 80 percent.19 This present value 

consideration is not reflected in the empirical measures of experience rating present in the ERIs 

published by OWS. Thus, experience rating is partial for two reasons. First, several categories of 

benefits are not assigned back to individual employers. Ineffective benefit charges are typically 
U.S. Department of Labor 

Employment and Training Administration 22 

ceding studies. 

financed by flat rate taxes levied on all covered employers. Second, the added the benefit 

payments that trigger increased taxes occur in years before the years when the associated higher 

taxes are collected.20

Several phenomena can arise under partial experience rating. Three merit explicit 

recognition. First, some employers; e.g., those taxed at the maximum tax rate and some taxed at 

the minimum tax rate, may face a marginal UI tax cost of zero when they release employees. 

This adds to the volume of employer-initiated worker turnover, turnover that has both cyclical 

and seasonal elements. This literature is examined below. Second, because charged benefits may 

increase labor costs, some employers may be more prone to contest claims to avoid the added 

costs. This aspect of experience rating has not been explored extensively.21 Third, several 

authors have examined the subsidies across industries and within industries that arise under 

partial experience rating. Industries such as agriculture and construction are consistently 

recipients of net subsidies while retail trade and finance are consistently subsidizer industries. 

This topic has been explored in the U.S. by Anderson and Meyer (1993b) and Woodbury (2003), 

and in Canada by Corak and Pyper (1995) and Corak and Chen (2002). Because of partial 

experience rating, inter-industry subsidies are smaller in the U.S. than in Canada. Cross-subsidies

within industries are also of substantial size, as shown in each of the four pre

Analysis of the effects of experience rating presents a greater data assembly challenge 

than does research on effects of UI statutes and administrative actions on unemployment 

duration. To study effects on temporary layoffs using microdata, for example, one needs three 

types of information: (1) worker turnover data (from the employer or the worker), (2) UI tax data 

for the employer, and (3) information on labor market outcomes for the worker (unemployment 

following a temporary or permanent layoff and (perhaps) receipt of UI benefits). Because one or 

more of these elements may be absent from available data, researchers have had to develop data 

constructs that approximate the desired information. Two important consequences are: (1) the 

use of aggregative data, e.g., by state or industry, and (2) measurement errors which add both to 

noise in estimated coefficients and perhaps downward biases in parameter estimates. 

Illustrative of these data issues is research using CWBH data. These data combine 

information on covered earnings (including employer ID number, industry, and UI tax rate) with 

information on benefit experiences (claimant data) in large samples from selected states during 

the years 1979 to 1983. Turnover and benefit experiences are inferred from quarterly earnings 

patterns and patterns of UI claims. While these data have much of the desired detail, they are 

samples (either 10 percent or 20 percent depending on the state). Thus, inferences about total 

employment at a given firm (an aggregation built up from all earnings records with a given 

employer number) will have errors—errors which are relatively larger for small firms. 

Despite shortcomings of the CWBH data, they represent a major improvement over 

previous data which was more aggregative. The earliest studies to be cited here used industry 

data on turnover and tax rates. These data came from separate reporting systems; e.g., BLS 

establishment data and UI state-industry tax information, and presented no possibility of 

examining the variability of experience rating within industries. 
U.S. Department of Labor 

Employment and Training Administration 23 

Tables 4 and 5 respectively summarize findings from studies of the effects of experience 

rating on worker turnover and on seasonality. Both tables are subdivided into distinct panels to 

help the reader distinguish important contrasts in research strategies. In both tables, there is an 

evolution from analysis of data measured as state-industry averages to microdata. A second 

evolution in table 4 is away from a detailed set of UI tax parameters (the maximum tax rate or 

TMax, the minimum tax rate, the average slope between the minimum and maximum rates, the 

taxable wage base) to a single measure that tries to capture all aspects of the marginal tax cost 

(MTC) of layoffs. The MTC measures incorporate the effects of tax rate minima and maxima as 

well as the inter-temporal linkage of benefits (first) and (then) taxes. Measurement of MTC also 

evolves from state-industry averages to micro-estimates for individual firms. 

Brechling was a pioneer in this research and one of his most influential pieces is 

identified in panel A of table 4. Note there were other dependent variables besides the two 

turnover measures, the layoff rate, and the rehire rate. The analysis was conducted for 

manufacturing industries where data were readily available. The summary in table 4 reflects his 

findings both for all manufacturing and for 16 two-digit industries. 

In results presented for all manufacturing (his table 6.1) and two-digit industries (his table 

6.2), the regression specifications include five UI tax variables as well as average wages, UI 

benefits, and UI coverage. Several detailed UI tax parameters were included and a number 

(TMax, TMin and Slope) were significantly linked to turnover measures. The tax rate on 

negative balance employers almost always entered with a negative effect (as expected) —a 

finding present in the all-manufacturing data and for the detailed industries. The use of year 

dummies as well as several tax variables makes it difficult to disentangle effects of individual tax 

variables. One suspects there is a very high degree of collinearity present in the regressions, 

adding to the imprecision of the parameter estimates. 

Saffer (1982) utilizes a bigger data set and a much simpler characterization of UI tax 

rates. He found that a wider range of rates (TMax—TMin) reduced the layoff rate in regressions 

that also included year and industry dummies. Because of the simpler characterization of UI tax 

parameters, Saffer could directly estimate the elasticity of turnover to increases in the maximum 

tax rate, an elasticity estimate of -0.61. 

Note in both studies there is no attempt to characterize the overall degree of UI 

experience rating and no attempt to estimate effects on turnover associated with moving to “full” 

experience rating. The main worth of these analyses is to show there are significant effects of UI 

tax parameters on turnover. 

The argument about imperfect experience rating and “excess” layoffs, however, is 

usually directed towards employer utilization of temporary layoffs; i.e., short-term separations 

followed by recall within 4 weeks or less. Unlike establishment data that do not distinguish 

temporary layoffs from other layoffs, this distinction is made in the CPS which also identifies 

other reasons for unemployment; i.e., among persons who quit and labor force reentrants and 

new entrants. 

Panel B of table 4 identifies nine studies that utilize microdata for workers, six employing 

the CPS. These data permit a 0-1 characterization for persons on temporary layoff and on 
U.S. Department of Labor 

Employment and Training Administration 24 

ce rating. 

permanent layoff. For the first seven, however, the UI tax variable (often the marginal tax cost or 

MTC) is a state-by-industry construct and does not refer to the employer that initiated the layoff 

for the individual worker. The two Anderson and Meyer (1994, 2000) studies are unique among 

this group in having microdata from the worker’s prelayoff employer. Thus, there is an unknown 

degree of measurement error in the employer tax variables used in the majority of these 

studies.22 Note that two studies (Halpin 1980; Saffer 1983) reflect the earlier practice of using 

parameters from UI tax schedules to characterize experien

The remaining studies in panel B, starting with Topel (1983), utilize the marginal tax cost 

(MTC) of layoffs to characterize the response of UI taxes per dollar of increased benefits 

(marginal increases for both benefits and taxes, each measured as present values). The downward 

deviation of MTC from 1.0 measures the UI tax subsidy per dollar of benefit payments. In 

specifications where layoffs are being explained, the MTC variable’s coefficient can be used to 

estimate the impact of moving to “full” experience rating; i.e., the product of multiplying the 

downward deviation of MTC from 1.0 times the coefficient on MTC. 

The six studies in panel B that use a MTC construct agree that the tax subsidy from 

partial experience rating has important effects on temporary layoffs. Overall, 10 to 50 percent of 

temporary layoff unemployment is attributed to partial experience rating. Topel estimates about 

half, Card and Levine some 20 to 50 percent, and Anderson and Meyer from 13 to 23 percent in 

six states and from 10 to 18 percent in Washington. Note that the estimates in the two Anderson 

and Meyer studies are the smallest even though they utilize an estimate of the MTC for 

individual firms. 

In contrast, effects on permanent layoffs are much smaller in work by Topel and Card 

and Levine. Insignificant effects on “other” unemployment were found by Card and Levine. 

Thus, in considering the various reasons for unemployment, the impacts of incomplete 

experience rating are concentrated among temporary layoffs in studies that use microdata (CPS 

and other) where “reason” can be identified. 

Panel C identifies three studies where the unit of observation is the firm. Since the firm is 

the entity that makes layoff decisions, these studies would have more face validity than ones 

where the firm and its actions are inferred from data about workers. 

Burgess and Low (1993) present results of an audit of Illinois employers in 1987, a 

project interested in tax compliance and the identification of factors associated with 

noncompliance. The study is included here because it identified limitations of research where the 

individual firm is not the unit of observation. A substantial share (27 percent) of all layoffs (in 

Illinois in 1987) were “free” layoffs. The firms were experience rated, but given the job mobility 

of workers, the base period employer (responsible for the benefit charges) was not the separating 

employer. Like other ineffectively assigned benefit charges, there was no added cost to the 

separating employer from these layoffs. Thus, a premise of the preceding studies (that separating 

employers and charged employers were identical) was not met for about one layoff in four. 

Burgess and Low note that 1987 was a year of strong employment growth so that the free layoff 

percentage would be lower when measured over all phases of the business cycle. More work on 

this aspect of experience rating (other states, other years) is warranted. 
U.S. Department of Labor 

Employment and Training Administration 25 

Betcherman and Leckie (1995) studied firms with plants in both the United States and 

Canada to see if differences in UI tax regimes affected reliance on temporary layoffs. Results 

from a mail survey of 331 firms coupled with data on layoff costs (computed MTCs) indicated 

that UI tax rates were not an important consideration in layoff decisions. Since this is the only 

extant study to examine temporary layoffs that uses a cross-border perspective, it is difficult to 

know the appropriate weight to assign to their findings. The sample of firms was modest (331), 

but the approach warrants replication; e.g., with firms in auto manufacturing. 

Woodbury (2003) examined temporary layoffs using large samples of longitudinal 

employer data from three states: Missouri, Pennsylvania, and Washington. In addition to 

studying measures of temporary and permanent layoffs, he also directly examined charged 

benefit payments associated with layoffs; e.g., benefit charges as a percent of payroll. The most 

important control variable was the MTC of layoffs measured at the firm level. Using three 

different estimation techniques (first differences, fixed effects, and instrumental variables) he 

found significant effects of a low MTC in increasing benefit charges. In his estimates, moving to 

full experience rating would lower benefit charges by 10-17 percent and reduce temporary 

layoffs. The estimated effects on temporary layoffs varied by state. They were largest in 

Missouri (10 percent), intermediate in Pennsylvania (5 percent) and essentially zero in 

Washington. This is a second finding (along with Anderson and Meyer 2000) of less 

responsiveness of temporary layoffs in Washington compared to other states. 

Because the studies summarized in panel C are based on data from employers, they 

should be given as much (if not more) weight as those from panel B. All three studies from panel 

C suggest the effects on temporary layoffs are smaller than previously estimated. Two avenues 

for the smaller effects are suggested: the presence of free layoffs, a factor not present in other 

studies, as well as the smaller point estimates of the effects of MTC on temporary layoffs. At a 

minimum, new analyses using longitudinal employer data should be conducted to determine if 

the effects of imperfect experience rating are as small as suggested by these three studies in panel 

C of table 4. 

Imperfect experience rating can also influence the seasonality of employment and 

unemployment for the same reasons as noted above; e.g., lowering the marginal cost of layoffs. 

Table 5 identifies five studies of seasonality. Three have been introduced earlier (because each 

study also examined the effects of UI taxes on layoffs). Table 5 displays just the findings related 

to seasonality. 

Halpin found seasonality to be lower when the ratio of taxable to total wages is higher. 

This effect of the taxable wage base has not been pursued in subsequent research, but as the 

wage base in many states is very low relative to average wages, there is a pronounced pattern of 

seasonality in UI tax receipts. Several states now derive upwards of half of total annual UI taxes 

in payments based on first quarter accruals; i.e., receipts in April–May. The effects of this 

growing seasonality in collections have not been pursued in more recent analysis. 

The estimated effects on employment seasonality range from measurable to zero. The 

largest estimate is from Anderson’s 1993 study where full experience rating would reduce 

employment seasonality in retail trade by an estimated 14 percent. Anderson and Meyer’s 
U.S. Department of Labor 

Employment and Training Administration 26 

estimate of reduced employment seasonality from full experience rating based on Washington 

State data is 11 percent and Woodbury’s estimate is essentially zero. Note that the latter two 

estimates are both based on Washington State data. Since this state may not be representative of 

the U.S. (based on the studies of temporary layoffs summarized in table 4), analysis of one or 

more other states is probably needed before accepting the low estimates shown in table 5. 

Significant unemployment seasonality related to imperfect experience rating is suggested 

by three studies summarized in table 5. Card and Levine, and Anderson and Meyer both find 

significant link to MTC, while Halpin finds a significant effect of the minimum UI tax rate. Card 

and Levine further find higher unemployment seasonality in construction and manufacturing 

than in trade and services. Since the Woodbury study had the most reliable employer data, it is 

unfortunate that he examined just the seasonality of employment but not unemployment. 

Considering the studies summarized in table 5, it is clear that the analysis of seasonality 

linked to UI taxes has received much less attention than analysis of temporary layoffs. Based on 

the five studies, however, three inferences may be drawn: (1) imperfect experience rating has a 

smaller effect on seasonal than on cyclical patterns of employment and unemployment, (2) 

imperfect experience rating has relatively larger effects on unemployment than on employment, 

and (3) imperfect experience rating has smaller effects on permanent layoffs than on temporary 

layoffs. 

What is the appropriate level of experience rating? Although the perspective of much of 

the research discussed here is that 100 percent (MTC = 1.0) is desirable, this overlooks several 

pertinent considerations: (1) noncharges and charges against inactive accounts will continue 

regardless of the tax treatment of layoffs, (2) new employers and those making free layoffs are 

not reached by UI experience rating, (3) as a practical matter, it seems likely that there will 

continue to be maximum tax rates even in UI Programs that operate with a high degree of 

experience rating, (4) recognizing that MTC = 1 implies a larger absolute amount of future taxes 

than the current increment in UI benefits, could this feature of a “reformed” UI tax system be 

sold to the employer community? It seems that partial or imperfect experience rating is destined 

to persist well into the future. Improvements in assigning benefit charges can and should be 

made, but achieving full experience rating (MTC = 1.0) is very unlikely. 

The experiences of three states during the 1990s provide three “natural experiments” for 

examining the effects of a major change in experience rating. Total or near-total tax holidays 

were provided to experience rated employers in Kansas (1995–1999), North Carolina (1996), and 

Georgia (1999–2003).23 If experience rating has the size of effects on layoffs suggested by 

earlier research, it would seem that (other things equal) large increases in temporary layoffs 

would have occurred during these periods. Since the Georgia tax holiday was so recent, it would 

seem most appropriate to undertake new research to determine the size of the increases in 

temporary layoffs. A project that examined layoffs in Kansas might also be feasible. Georgia and 

North Carolina are also relevant since they routinely have a major share of their claims arising 

from job-attached workers (upwards of one-half in North Carolina and upwards of one-fifth in 

Georgia) where the claims filing is done by the employer. Since the tax changes were large, 

evidence of effects might even be seen in aggregate data; e.g., turnover in manufacturing. 
U.S. Department of Labor 

Employment and Training Administration 27 

8.0 Conclusions and Recommendations for Future Research 

The preceding sections have covered many topics. Several summaries and suggestions for 

further research have already been offered. This section draws upon the earlier summaries and 

suggests priorities among topics for future research. 

The existing literature summarized above has amply demonstrated that UI affects the 

behavior of both workers and employers. At the most basic level is the question of: Which of 

these two major actors should receive higher priority for additional research? To this author it 

seems clear that added research on claimant behavior has the higher priority. 

The rationale for this inference is based on the following considerations: 

• Temporary layoffs are becoming much less important as a reason for unemployment. 

The change in temporary layoffs, especially the failure to grow as rapidly during the 

past two recessions as in earlier recessions, has occurred while time series indicators 

of the degree of experience rating (the ERI, available since 1988) have not 

demonstrated strong trends. 

• There is a long-run upward trend in both unemployment duration and the duration of 

UI claims. During the past 50 years, duration has increased in a variety of indicators 

both from the CPS and from UI Program data. In CPS data, the increase in duration 

seems to have accelerated after about 1980. 

• Exhaustions of UI benefits were at historic highs during 2002 and 2003—44 percent 

in each year—despite relatively low overall unemployment rates. 

These developments, documented in report 1 of Vroman and Woodbury (2003), suggest that 

major changes have been occurring in the labor market. Understanding how policy related both 

to UI statutes and administration and to other labor market interventions can influence 

unemployment (particularly unemployment duration) should be where future UI research is most 

heavily focused. 

Since claimant behavior is such a broad topic, several research areas could be considered 

for research emphasis. Three areas are recommended as being especially important. First, what 

policy interventions can be most effective in reducing the duration of benefits for the regular UI 

Program? This suggests added research on work search support/enforcement and more research 

on the effectiveness of profiling. From the earlier discussion, two areas may be especially worthy 

of new work: 

• Different approaches to work test administration may be appropriate for different 

types of workers. In particular, close monitoring may be appropriate for some, while 

others may benefit from greater search autonomy. Trying to develop indicators of 

these two situations could be productive.24
U.S. Department of Labor 

Employment and Training Administration 28 

 

• Because the estimated effects of profiling were so much larger in the analysis of the 

Kentucky data than in other studies, replication of this study in another state (or 

states) should be given high priority. 

Second, it was noted that all but two of the studies summarized in table 1 utilized data from years 

prior to 1990. It would be useful to undertake a new analysis of the effects of potential duration 

and the replacement rate on regular UI beneficiaries. This could be done using administrative 

microdata or new survey data from several states. Since past studies have shown differing 

sensitivity to UI provisions among different demographic groups (by age, gender, and ethnicity), 

the analysis could be designed to yield reliable parameter estimates for detailed demographic 

groups. Third, large changes in benefit levels and replacement rates took place in Indiana during 

the mid-1990s and in both California and Virginia in the past few years. Designing a study to 

examine effects of these changes on take-up and benefit duration should receive support. 

Endnotes 

1

 Like many UI statutory provisions there are exceptions, most commonly the use of average earnings over all 

weeks of the base period (six states in 2003). Also, for the eight states that have a uniform (26 week) maximum 

benefit duration, the link with base period earnings is less direct than in other states. 2

 These relationships are discussed in Woodbury and Rubin (1997), pp.213-220. 

3

 Which activities constitute work search is to some extent a definitional issue. Someone who only looks at help 

wanted ads in newspapers is not considered to be actively searching in the United States, but is so considered in 

Canada. The U.S. labor force survey of households (or CPS) considers this to be passive job search. Active search 

spans activities where the person undertakes active outreach to potential employers; e.g., sending resumes, 

responding to help wanted ads, and making other contacts with potential employers. 

4

 The published paper (Levine 1993) reports results for the effects of the replacement rate only for nonrecipients 

using microdata and notes that the results for recipients are generally insignificant. However, the same paper also 

reports results using aggregate state-year data where the replacement rate effects are as indicated in the text (positive 

for recipients but negative for nonrecipients). 5

 For those on temporary layoff in 2002, only 5.5 percent experienced unemployment duration of 27 weeks or 

longer compared to 23.5 percent for “other” job losers; e.g., those on permanent layoff. 6

 The annual unemployment rate in New Jersey in 1996 was 6.2 percent, more than 2 percentage points below the 

rate in 1992. 

7

 Three noticeable changes in replacement rates were the temporary decrease in Maine during 1997–1998, the 

decrease in Ohio in 1989, and the decrease in Louisiana between 1986 and 1989. When data from 2003 become 

available there may be additional instances of large increases in replacement rates during 2001–2003. However, 

these may reflect just a compositional shift related to job losses of senior workers. 

8

 Significance was tested in two ways in most studies: by comparisons of means and by regression analysis. Table 

2A shows results of tests for equality of means. Because regressions typically had only modest explanatory power, 

the regression tests yielded quite similar results to those shown in table 2A. 9

 In New Jersey, enrollment occurred later in the claim period; i e., 5 weeks after the first payment. 

10 Meyer (1995) reports evidence of increased exit rates (job finding) in both Illinois and Pennsylvania. 11 A few states rely just on claimant characteristics to assign scores without use of a regression equation. 12 These statistics refer to 1999 calendar year summaries for all 53 "state" UI Programs. 13 In each substate area, random selection into treatment and control group was done for persons with profiling 

scores in the same interval. Everyone in lower intervals was excluded from interventions while everyone in higher 

intervals was selected for interventions. The similarity of profiling scores between treatments and controls implies 

they are more “alike” than in the other profiling studies. 

14 The fourth, the New Jersey Reemployment Demonstration took place earlier and differed in the timing of the 

profiling intervention. Typically, claimants identified as likely exhaustees through profiling are contacted after 
U.S. Department of Labor 

Employment and Training Administration 29 

 

receiving a first payment. In New Jersey, the contact occurred later, after 5 weeks of benefits, and it did not have the 

same potential sanction since the New Jersey Demonstration preceded the implementation of profiling in the mid1990s. 

15 The table shows two studies by Anderson and Meyer. They are presented as separate even though both are based 

on the same data file. Note that the number of data points in these two studies are different and that different 

methods of estimation were used. 

16 The application rate was measured as the ratio of (new plus additional) initial claims to new occurrences of 

unemployment. The latter was measured as 12 times the annual average number of unemployment spells of less than 

5 weeks duration, data available from the CPS. For this application rate, the mean and standard deviation of 

(unweighted) state-level data were 0.496 and 0.194, whereas the respective counterparts for the replacement rate 

were 0.362 and 0.055. The replacement rates are much more tightly packed around their mean than are the 

application rates. These patterns are repeated in all years of data from that study; i.e., from 1977 to 1999. 

17 Surprisingly this variable had a positive coefficient, a finding repeated in Vroman (2002). 

18 States are to submit an annual report summarizing their experiences with benefit charging (the ETA 204 report). 

The report is used to develop an experience rating index (ERI) for each state. These reports were first submitted for 

tax rate year 1988. 

19 The present value of the future taxes for the next 3 years is calculated as 1/(1 + d) + 1/(1+ d)2

 + 1/(1 + d)3 for a 

discounted present value of 2.406 under a 10 percent discount rate (d = 0.1). The ratio of 2.406 to 3.00 yields an 

experience rating ratio of 0.802. To have a present value equal to the value of benefits, the employer would have to 

pay more in future taxes than the value of benefits paid out this year. 

20 These two categories are not exhaustive of the reasons for partial experience rating. New employers pay a flat 

rate tax in years before they are eligible for experience rating, effectively a zero experience rate on added layoffs. 

Also, some charges are not assigned to separating employers because the separating employer is not the base period 

employer to whom benefit charges are assigned. This latter phenomenon, termed “free layoffs,” has been identified 

and examined by Burgess and Low (1993). 21 Two analyses are found in Vroman (1996b) and Anderson and Meyer (2000). The latter study found evidence 

that movement to experience rating in Washington in 1985 was associated with a statistically significant increase in 

the rate of nonmonetary determinations (especially seps). 

22 Most unusual, the Feldstein (1978) study does not have a tax cost variable. It infers the effects of experience 

rating from the coefficient on the benefit replacement rate. 

23 In Kansas, 100 percent tax relief was provided to all positive balance employers for 5 years. In North Carolina, 

the relief was total in 1996 but also substantial in 1997. Rated employers in Georgia (except those at the top tax rate) 

were provided 99 percent relief of taxes for the 5 indicated years. 

24 Care will have to be taken to ensure that the “sorting” implicit in this suggestion is done in a nondiscriminatory 

manner. Since education is already used in some profiling algorithms, however, creative approaches probably can be 

developed. 
U.S. Department of Labor 

Employment and Training Administration 

Appendix A: Tables 

Table 1. Studies of the Determinants of Unemployment Insurance Benefit Durat

Study Data Estimation method 

Effect of increase in 

weekly benefits on 

duration 

Classen (1979) CWBH data: Arizona and 

Pennsylvania, 1967-1969 

Tobit duration estimates $10 increase in WBA 

increases dur. 1.1 weeks 

AriPen

Newton and Rosen (1979) UI beneficiaries in 

Georgia, 1974-1976 

Tobit duration estimates 10% increase in WBA 

increases dur. 1.8 weeks 

+0.

Katz and Ochs (1980) CPS microdata from 26 

states, 1968-1977 

Maximum likelihood estimates 

+0.

Moffitt and Nicholson (1982) 

Microdata from 15 states, recipients of EB or FSB, 1975-1977 

Labor supply model, maximum likelihood estimates 

10% increase in rep. rate men: +0.98 weeks, women: +0.84 weeks 

+0.

Moffitt (1985a) CWBH data: 15 states, 

men, 1978-1983 

Proportional hazards model 

10% increase in WBA increases duration 0.5 wks 

+0.

Moffitt (1985b) Four micro databases, 

1974-1983 

Proportional hazards models 

mewo

Solon (1985) CWBH: Georgia, 1978-

1979 

Proportional hazards model 

10% increase in rep. rate increases duration by 0.5-1.0 weeks 

+0.

Grossman (1989) CWBH: Three states, 

1981-1984 

Proportional hazards model 

10% increase in WBA increases duration 

+0.
U.S. Department of Labor 

Employment and Training Administration 

Table 1. Studies of the Determinants of Unemployment Insurance Benefit Duration (c

Study Data Estimation method 

Effect of increase in 

weekly benefits on 

duration 

Meyer (1989) CWBH, 1979–1984 OLS based on increases in 

min. and max. WBA 

10% increase in WBA increases duration 1.7 wks 

-- 

Meyer (1990) CWBH: men, 

1978–1983 

Proportional hazards model 

10% increase in rep. rate increases duration 1.5 wks 

-- 

Katz and Meyer (1990) CWBH: 12 states, 1978–

1983 

Proportional hazards model 

-- +0.

Davidson and Woodbury (1996) 

Reemployment bonus data from three states, 1984–1985 and 1988–89 

Impact estimates applied in a general equilibrium search model 

-- Illin

Pening

Corson, Needles and Nicholson (1999) 

Survey data (16 states) and administrative data (18 states) for EUC program, 1991–1994 

Weibull, natural log of duration (weeks to first job, weeks of UI benefits) 

$10 increase in WBA increases duration 

+0.

Card and Levine (2000) New Jersey Extended 

Benefit Program, June – Nov. 1996 

Probit and hazard rate models 

10% age point increase in replacement rate increases duration by 1.0 week 

+0.
U.S. Department of Labor 

Employment and Training Administration 

Table 2A. Studies of the Effects of Work Search 

Study and author(s) Design Sample Finding

Nevada Claimant Placement Program Steinman (1978) 

T: Increased staff attention, referrals, and eligibility checks 

Eligible Nevada claimants after three UI payments, Feb. 1977 to Mar. 1978 T = 2,371, C = 1,174 

T: -3.90** 

Wisconsin Eligibility Review Pilot Project Wisconsin Job Service (1984) 

T: Six hour job search workshop Wisconsin claimants indefinitely 

separated from most recent job, Mar. to Aug. 1983 T = 2,587, C = 2,277 

T: -0.62 w

Charleston Claimant Placement and Work Test Demonstration Corson, Long and Nicholson (1985) 

T1: Stronger work test T2: T1 plus enhanced placement services T3: T2 plus JSW (job search workshop) C: Customary work test 

Charleston, S.C., Feb. to Dec. 1983 T = 4,247, C = 1,428 

T1: -0.55* T2: -0.61 *T3: -0.76 *Effects largconstructio

New Jersey UI Reemployment Demonstration Project Corson, et al. (1989) Anderson, et al. (1991) Corson and Haimson (1996) 

T1: JSA T2: JSA plus training or relocation assistance T3: JSA plus cash bonus C: Eligibility: first UI payment, age, tenure, indefinite layoff 

New Jersey, July 1986 to June 1987 T = 8,675, C = 2,385 

T1: -0.47*T2: - 0.48 T3: -0.97*6 year T1: 6 year T2: 6 year T3: 

Key: T: treatment group, C: control group. *(**) Statistically significant at the 90 (95) percent level with a two-tailed test. 
U.S. Department of Labor 

Employment and Training Administration 

Table 2A. Studies of the Effects of Work Search (continued) 

Study and author(s) Design Sample Finding

Washington Alternative Job Search Experiment Johnson and Klepinger (1991) 

T1: Exception reporting T2: New work search policy T3: Intensive services C: Existing work search policy 

Tacoma, WA, July 1986 to Aug. 1987, T = 6,763, C = 2,871 

T1: +3.34*T2: +0.17 T3: -0.47* Exits increservice par

Maryland UI Work Search Demonstration Klepinger, Johnson, Joesh and Benus (1998) Klepinger, Johnson and Joesh (2002) 

T1: Report four employer contacts weekly T2: Two weekly contacts required, but no reporting T3: Report two contacts weekly plus a 4-day JSW T4: Report two contacts weekly and both verified C: Existing policy, two contacts weekly but not verified 

Maryland, six local offices, Jan. to Dec. 1994, Combined sample 23,758 monetarily eligible new initial UI claimants 

T1: -0.7 ** T2: +0.4* T3: -0.6** T4: -0.9** T1, T3, anearnings T2: raised Insignifica

Four-State Analysis of Work Search Enforcement Ashenfelter, Ashmore and Deschenes (1999) 

T1: Early eligibility review and work search plan, first visit T2: Review of monetary and job separation decisions, second visit T3: T2 + work search verification C1: Normal intake, first visit C2: Verification of reason for separation, second visit 

Connecticut, Massachusetts, Tennessee, and Virginia, Dec. 1984 to Apr. 1985 T1 = 1,852, C1 = 1,806 T2, T3 = 1,517, C2 = 1,499 

Effect of wCN T3 - TMA T3 - TTN T3 - T2VA T3 - T
U.S. Department of Labor 

Employment and Training Administration 

Table 2A. Studies of the Effects of Work Search (continued) 

Study and author(s) Design Sample Finding

Job Search Assistance Demonstration in District of Columbia and Florida Decker, Olsen, Freeman and Klepinger (2000) 

T1: Structured JSA T2: Individualized JSA, T3: T2 plus training C: Profiled as likely exhaustees 

District of Columbia, June 1996 to June 1996 D.C.: T + C = 8,071 Florida, Mar. 1995 to Mar. 1996 Florida: T + C = 12,042 

DC T1: -1DC T2: -0DC T3: -0FL T1: -0.4FL T2: -0.5FL T3: -0.5

Long-run Effects of Washington Alternative Work Search Experiment Woodbury (2001) 

T1: Exception reporting T2: New work search policy T3: Intensive services C: Existing work search policy 

Tacoma, WA, July 1986 to Aug. 1987 T = 6,763, C = 2,871 

Short-run esimilar to J(1991), see 10-year effT1: -$52 u+$82 **coT2: -$12 u+$43 condT3: +$137emp., +$81
U.S. Department of Labor 

Employment and Training Administration 

Table 2B. Reemployment Bonus Experiments 

Study and author(s) Design Sample Finding

Illinois UI Incentive Experiments Woodbury and Spiegelman (1987) 

Bonuses for early reemployment, about 4 times weekly benefit amount (WBA) T1: $500 paid to claimants T2: $500 paid to employers 

Illinois, July to Nov. 1984 T = 8,149 in two groups C = 3,952 

T1: -1.15*T2: -0.36 w

New Jersey UI Reemployment Demonstration Project Corson, et al. (1989) Anderson, et al. (1991) 

Bonus, half of remaining benefits, but declining 10% per week, avg. $1,644 or about 10* WBA T1: Bonus + JSA T2: JSA only T3: T1 - T2 

New Jersey, July 1986 to June 1987 T = 8,675 in six groups C = 2,385 

T1: -0.90*T2: -0.50*T3: -0.40 w

Pennsylvania Reemployment Bonus Demonstration Corson, et al. (1992) 

Two levels of bonuses (B), two qualification periods (Q), avg. about 3*WBA or $500, 

Pennsylvania, July 1988 to Oct. 1989 T = 10,694 in six groups C = 3,392 

T1: Low BT2: Low BT3: High BT4: High BT5: High-d

Washington Reemployment Bonus Expiriment Spiegelman, O’Leary and Kline (1991) 

Three levels of bonuses (B), two variable qualification periods (Q), avg. bonus about $562 

Washington, Feb. to Nov. 1988 T = 12,452 in six groups C = 3,082 

T1: Low BT2: Med. BT3: High BT4: Low BT5: Med. BT6: High B
U.S. Department of Labor 

Employment and Training Administration 

Table 2B. Reemployment Bonus Experiments (continued) 

Study and author(s) Design Sample Finding

Long-run Effects of Washington Reemployment Bonus Experiment Woodbury (2001)

Three levels of bonuses (B), two variable qualification periods (Q), avg. bonus about $562 

Washington, Feb. to Nov. 1988, 

Short-run esimilar to Sand Klein 10 year effT1: Low BT2: Med. BT3: High BT4: Low BT5: Med. BT6: High B
U.S. Department of Labor 

Employment and Training Administration 

Table 2C. Profiling Studies 

Study and author(s) Design Sample Finding

New Jersey UI Reemployment Demonstration Project Corson, et al. (1989) Anderson, et al. (1991) 

T1: JSA T2: JSA plus training or relocation assistance T3: JSA plus cash bonus C: Eligibility: first UI payment, age, tenure, indefinite layoff 

New Jersey, July 1986 to June 1987 T = 8,675, C = 2,385 

T1: -0.47*T2: - 0.48 T3: -0.97*

Six State Worker Profiling and Reemployment Services Evaluation Dickinson, Decker, Kreutzer and West (1999) 

P: Claimants profiled and referred to early JSA C: Claimants profiled but not referred to early JSA 

Connecticut, Illinois, Kentucky, Maine, New Jersey, and South Carolina, July 1995 to Dec. 1996, P = 92,401, C = 295,920 

CT: -0.25*KY:-0.21*NJ: -0.29 *

Job Search Assistance Demonstration in District of Columbia and Florida Decker, Olsen, Freeman and Klepinger (2000) 

T1: Structured JSA T2: Individualized JSA T3: T2 plus training C: Profiled as likely exhaustees but no services provided 

District of Columbia, June 1996 to June 1996 DC: T + C = 8,071 Florida, Mar. 1995 to Mar. 1996 Florida: T + C = 12,042 

DC T1: -1wks, T3: -0FL T1: -0.4wks, T3:-0

Kentucky Worker Profiling Evaluation Black, Smith, Berger and Noel (2003) 

T: Claimants profiled and referred to early JSA and reemployment services C: Profiled but not referred, same profiling score as treatments 

Kentucky, Oct. 1994 to June 1996 T = 1,236 C = 745 

T: -2.2 **wT: -$143** T: +$1,054 

Key: T: treatment group, C: control group, P: participant group. *(**) Statistically significant at the 90 (95) percent level with a two-ta
U.S. Department of Labor 

Employment and Training Administration 

Table 3. Studies of the Effect of UI Benefit Levels on Application Rates 

Study Data Estimation method Effect o

benefit

Corson and Nicholson (1988) IUTU ratios 

Quarterly data, 11 states 1971–1987 

OLS 10 % incre

increases Ior 2-6 perc

Blank and Card (1991) Beneficiary/eligible ratios 

50 state data, 1977–1987 

Weighted OLS 10 % incre

increases arate by 5-9 

Anderson and Meyer (1994) UI application (take-up) rates 

CWBH data, six states, 1979–-1983, small sample n = 80,331 

Logistic estimation 10 % incre

increases tpercent 

 Anderson and Meyer (1997) UI application (take-up) rates 

CWBH data, six states, 1979–1983, large sample n = 980,286 

Linear probability estimation 10 % incre

increases tpercent 

McCall (1995) CPS, dislocated worker data 

Applications/new spells ratios, 1984–1992 

Logistic estimation Positive ef

specificatioPositive qumaximum replacemen

Vroman (2002) Applications/new spells ratios, 

50 state data, 1977–1999 

OLS Increase in 

not increas
U.S. Department of Labor 

Employment and Training Administration 

Table 4. Studies of the Effects of Incomplete Experience Rating on Employee Tur

Study Dependent variable(s) Data and sample size Main independent variable(s) 

Panel A. Studies using state and industry data Brechling (1981) Annual layoff rate 

Annual rehire rate Avg. weekly hours Avg. unemployment duration 

State-year mfg. data for reserve ratio states, 1962–1969 n = 170 for all mfg., n = 48-96 for two digit industries 

Maximum, minimum and negative balance tax rates, slope of tax rate schedule, reserve ratio where maximum tax rate is reached, year dummies 

Incand turndurnegredand 

Saffer (1982) Annual layoff rate State-year data on allindustry layoff rates (first 

claims/emp.) 1967–1975, n = 468 

Maximum less minimum payroll tax rates (TMax – TMin), average weekly benefit, average weekly wage, taxable wage base, industry and year dummies 

IncTM(ela

Panel B. Studies using micro data for workers Feldstein (1978) 0-1 dummy variable, =1 if 

worker on temporary layoff 

Mar. 1971 CPS n = 24,545 

Imputed UI replacement rate 

10% rep5% lay
U.S. Department of Labor 

Employment and Training Administration 

Table 4. Studies of the Effects of Incomplete Experience Rating on Employee Turnover 

Study Dependent variable(s) Data and sample size Main independent variable(s) 

Panel B. Studies using microdata for workers Halpin (1980) 0-1 dummy variable, =1 if 

worker on temporary layoff 

1977 Survey of Income and Education n = 40,868 

Ratio of taxable to total wages, MAXGAP (gap between maximum tax rate and tax rate needed to fund benefits of negative balance firms), minimum and maximum tax rates, % of benefits noncharged 

10 incto tlaypoiredlowper

Saffer (1983) Two 0-1 dummy variables, 

=1 if worker on temporary layoff, and =1 if worker on permanent layoff 

March 1975 CPS linked to SIC two-digit data from ES202 reports n = 14,899 

Proportional deviation of industry average UI tax rate from schedule midpoint, imputed UI replacement rate, state taxable wage base 

Incdeginctemmaper

Topel (1983) Probability of temporary 

layoff 

March 1975 CPS linked to SIC two-digit data from ES202 reports n = 8,280 

Marginal tax cost (MTC) of layoffs per dollar of benefits (MTC imputed for 551 state-2 digit cells), imputed benefit replacement rate 

Inc(Mprolayratitem
U.S. Department of Labor 

Employment and Training Administration 

Table 4. Studies of the Effects of Incomplete Experience Rating on Employee Turnover 

Study Dependent variable(s) Data and sample size Main independent variable(s) 

Panel B. Studies using microdata for workers Topel (1984) Two 0-1 dummy variables, 

=1 if worker on temporary layoff, and =1 if worker on permanent layoff 

March CPS 1973-1976 linked to SIC two-digit data from ES202 reports n = 33,653 

Marginal Tax Cost (MTC) and imputed replacement rate as above in Topel (1983) 

Incinctemeffeper

Topel (1985) Two 0-1 dummy variables, 

=1 if worker on temporary layoff, and =1 if worker on permanent layoff (or quit) 

March CPS 1977–1981 linked to SIC two-digit data from ES202 reports n = 76,106 men 

Marginal Tax Cost (MTC) and imputed replacement rate as above in Topel (1983) 

Incinctemexpredfro

Card and Levine (1994) Three 0-1 dummy 

variables, =1 if worker on temp. layoff, =1 if worker on permanent layoff, =1 for “other” unemployment 

CPS outgoing rotation groups 1979–1987 n = 187,598 

Marginal tax cost of layoffs (MTC) for firms in 36 states and 5 industries, personal characteristics, dummies for industry, occupation, and year 

Ful(Mtem0.520-effelay“ot
U.S. Department of Labor 

Employment and Training Administration 

Table 4. Studies of the Effects of Incomplete Experience Rating on Employee Turnover 

Study Dependent variable(s) Data and sample size Main independent variable(s) 

Anderson and Meyer (1994) 

0-1 dummy variable, = 1 if worker laid off during the quarter 

CWBH, over 300,000 quarterly wage records from six states matched with UI claims records, 1979–1983 

Marginal tax cost of layoff (MTC) measured at firm level, amount and potential duration of benefits, other controls 

13% laydue rati

Anderson and Meyer (2000) 

Ratio of UI claims to employment 

Washington State, all U.S states, 1982 to 1997 n = 51 for difference-indifference estimates 

State dummy variables interacted with post 1984 dummy variables, MTC in Washington goes from 0 in 1984 to 0.99 in 1985 

Moraticlaiper18 

Panel C. Studies using data for individual firms Burgess and Low (1993) Layoffs where employers 

are not charged 

Illinois 1987, n = 611 firms and 84,000 workers 

Experience rating charges related to layoffs 

27% free cha

Betcherman and Leckie (1995) 

Firm layoff rate 331 firms in Ontario and 

three U.S. states 

Marginal tax cost of layoffs (MTC) 

No lay

Woodbury (2003) UI benefit charges as a 

percent of payroll, layoff rate (deviation from trend), temporary layoff rate, permanent layoff rate 

Longitudinal firm data from Missouri (6,812), Pennsylvania (12,792), and Washington (6,609), 1985 to 1994 

Marginal tax cost of layoffs (MTC) for individual firms 

Sigon expby lay
U.S. Department of Labor 

Employment and Training Administration 

Table 5. Studies of the Effects of Incomplete Experience Rating on 

Seasonality of Employment and Unemployment 

Study Dependent variable(s) Data and sample size Main independent variable(s) 

Panel A. Studies using state and industry data Halpin (1979) Spectral measure of 

seasonal variation in unemployment 

Monthly state data for three SIC 3-digit industries, 1960–1974 

Ratio of taxable to total wages, minimum and maximum tax rates (TMIN and TMAX) 

Seawitto tvarhig

Panel B. Studies with microdata Anderson (1993) Seasonal employment 

variability 

CWBH quarterly data for retail firms in six states, 1979 to 1983 n = 8,278 firms n = 37,749 firm-year obs 

Marginal tax cost of layoff (MTC) with fixed effects for firms and years 

Elavar0.1woem14% 

Card and Levine (1994) Seasonal unemployment 

variability. Three dummy variables in monthly data: = 1 for temp. layoffs, = 1 for perm. layoffs, =1 for “other” unemp. 

CPS outgoing rotation groups 1979-1987 n = 187,598 

Marginal tax cost of layoffs (MTC) for firms in 36 states and 5 industries, personal characteristics, dummies for industry, occupation and year 

Larlayseaeffemfgand 
U.S. Department of Labor 

Employment and Training Administration 

Table 5. Studies of the Effects of Incomplete Experience Rating on 

Seasonality of Employment and Unemployment (continued) 

Study Dependent variable(s) Data and sample size Main independent variable(s) 

Panel B. Studies with microdata Anderson and Meyer (2000) 

Seasonal UI claims variability and seasonal employment variability 

Washington State, all U.S states, 1982 to 1997 n = 51 for difference-indifference estimates 

State dummy variables interacted with post-1984 dummy variables, MTC in Washington goes from 0 in 1984 to 0.99 in 1985 

16-seaintrratiredsea

Woodbury (2003) Seasonal employment 

variability 

Longitudinal firm data from Missouri (6,812), Pennsylvania (12,792), and Washington (6,609), 1985 to 1994 

Marginal tax cost of layoffs (MTC) for individual firms 

Veron