Profiling UI Claimants to Allocate 

Reemployment Services: 

Evidence and Recommendations 

for States 

Final Report 

March 2003 

Principal Investigators 

Dan A. Black Jeffrey A. Smith

Center for Policy Research Department of Economics 

Syracuse University University of Maryland 

 

Miana Plesca Suzanne Shannon 

Department of Economics Center for Policy Research 

University of Western Ontario Syracuse University 

 
1

CONTENTS 

List of Tables and Figures 2 

Executive Summary 3 

1. Introduction 6 

2. Data 11 

3. Methodology 12 

 3.1 The WPRS Model 12 

 3.2 Additional Explanatory Variables 15 

 3.3 Assessing Predictive Performance 17 

 3.4 Comparing Profiling Models 21 

4. Use Simple Linear Models Estimated by OLS 23 

5. Results 25 

 5.1 The Dependent Variable Should Be Fraction of Benefits Exhausted 25 

 5.2 Variables Measuring Local Economic Conditions 25 

 5.3 Additional Variables: There is No Single Best Predictor 27 

 5.4 There is No Improvement from Estimating Separate Regional Models 29 

 5.5 Business Cycles and Data Quality 31 

 5.6 Another Covariate: Delayed Filing 33 

 5.7 Some Methodological Issues 34 

6. Conclusions 35 

References 38 

Tables and Figures 40 
2

LIST OF TABLES AND FIGURES 

Figure 1: Fraction Benefits Exhausted in 1994 

Figure 2: Fraction Benefits Exhausted in 1994 for Claimants Not Exhausting All of 

Their Benefits 

Table 1: WPRS model Covariates in the National, Maryland and Kentucky Datasets 

Table 2: Profiling Variables 

Table 3: The Ability of Profiling Models to Predict UI Benefit Duration/Exhaustion 

Table 4: Comparison of Alternative Functional Forms: OLS, Logit, Probit and Tobit 

Table 5: Comparison of Alternative Dependent Variables 

Table 6: Dropping Local Unemployment (UE) Rate and Industry Employment (IE) 

Changes from the WPRS Model Specification 

Table 7: Covariates Used in the 40 Model Specifications Analyzed for Kentucky 

Table 8: Predictive Performance from Different Model Specifications 

Table 9: Regional Analysis Results 

Table 10: Year-by-Year Predictive Performance from OLS Estimation 

Table 11: Definitions of Measures of Delayed Filing 

Table 12: Predictive Performance from Adding Delayed Filing Measures 
3

Executive Summary 

 This report does two important things. First, it develops and applies a state-of-theart methodology for constructing or modifying statistical profiling models for the 

allocation of reemployment services that states can apply to their own data. Second, it 

provides substantive guidance on model development and modification to states based on 

our analysis of UI data from the Commonwealth of Kentucky. Our recommendations 

include ways of simplifying existing models without reducing their ability to predict which 

claimants will have long spells of unemployment as well as suggestions for improving 

predictive performance. 

Simplifications of the Model: 

We have four recommendations for making profiling models easier to estimate and 

implement. Our findings suggest that such simplifications may actually improve the 

predictive performance of the models as well. 

Use Linear Models Estimated by Ordinary Least Squares: 

Following the lead of the original Worker Profiling and Reemployment Services (WPRS) 

model, many states have relied on the use of discrete choice models such as logits and 

probits. While estimation of these discrete choice models is now feasible in standard 

statistical packages such as SAS, these models are difficult to interpret and are relatively 

computationally burdensome. Our results suggest that Ordinary Least Squares (OLS) 

estimation of the linear probability model generally outperforms the discrete choice 

models. For continuous dependent variables, we find that OLS estimation of simple linear 

models outperforms more sophisticated Tobit models. In all cases, linear models are easier 

to interpret and estimate (using OLS) than the corresponding non-linear logit, probit and 
4

Tobit models. An additional advantage is that linear models allow researchers to look at 

an easily interpreted summary measure for goodness-of-fit (R2) while the non-linear logit, 

probit and Tobit models require researchers to use summary statistics that are much harder 

to interpret. 

Use Fraction of Benefits Exhausted as the Dependent Variable: 

Again, following the lead of the WPRS model, many states use a binary variable for the 

dependent model of their profiling models: whether or not claimants exhaust their UI 

benefits. Our analysis suggests that there is a modest improvement in performance if the 

fraction of UI benefits exhausted is used as the dependent variable. Unlike the binary 

exhaustion variable, the fraction of benefits exhausted variable distinguishes claimants 

who use 22 weeks of UI benefits from claimants who use 2 weeks of UI benefits. 

No Need to Use Local Unemployment Rates or Aggregate Industry 

Employment Growth Variables:

Our analysis suggests that the use of these two variables adds nothing to the predictive 

content of the model, which implies that they can be dropped from the model. The reason 

for this surprising finding is that all claimants who file a claim in a particular office in a 

particular week will have identical local unemployment rates. Many will also have the 

same industry employment growth rate variables. Thus, while including the local 

unemployment rate or industry employment variables improves the explanatory power of 

the model (e.g., the R2

 value), it does not affect the ordering of the claimants in terms of 

the likelihood that they will exhaust their benefits. Omitting these variables will ease 

implementation of the model as the remaining data needed for model estimation may 

simply be taken from the claimants’ application forms for UI benefits. 
5

Use of Regional Models:

Our analysis of the Kentucky data suggests that using separate models for regions within a 

state does not substantially improve the ability of the model to allocate reemployment 

services, relative to a model containing only regional dummy variables. Given the massive 

heterogeneity in the Kentucky economy, this suggests that other states with less 

heterogeneity may also not benefit from estimating regional models. Thus, a single state 

model (perhaps including regional dummy variables) will probably suffice. In addition, 

this result supports the external validity of our findings; that is, it suggests that they should 

apply to states other than Kentucky. 

Improving the Predictions of the Model: 

Richer Models Do Better: 

While we spend considerable time trying to identify individual variables that substantially 

improve the ability of a profiling model to predict benefit exhaustion, we find that no 

single variable has a substantial impact. Collectively, however, richer models that control 

for a larger number of covariates outperform models with fewer covariates. Whether the 

increased predictive power is worth the added complexity depends in a large part on the 

expertise available to the states. 

Model Performance Varies Over the Business Cycle: 

We find that the predictive performance of the profiling models we examine varies 

substantially between the relatively high-unemployment period of the early 1990s and the 

relatively low-unemployment period of the middle to late 1990s. This finding suggests 

that occasional re-estimation may improve the performance of profiling models. We leave 

an exact answer to the question of how often to best re-estimate profiling models to future 

research. 
6

1. Introduction 

 The Unemployment Compensation Amendments of 1993 (P.L. 103-152) amended 

the Social Security Act to require state agencies charged with administering state 

unemployment compensation laws to establish a Worker Profiling and Reemployment 

Services (WPRS) system. This system identifies Unemployment Insurance claimants 

“who will be likely to exhaust regular compensation and will need job search assistance 

services” and refers them to reemployment services to help them return to productive 

employment. The WPRS system consists of two major components: a profiling 

mechanism and a set of reemployment services. The U.S. Department of Labor (DOL) 

developed a “worker profiling” model that was first implemented at the state level in 

Maryland. 

 DOL guidelines originally recommended that states use the following variables in 

their profiling models: education, job tenure, aggregate industry-level employment 

changes, occupation, and local unemployment rate (Kelso 1998). We denote the model 

estimated using these five covariates as the “WPRS model” (Worden 1993). A Worker 

Profiling and Reemployment Services Policy Workgroup, however, recently recommended 

that “DOL should provide technical assistance to the States in improving their selection 

and referral processes” (Wandner and Messenger 1999). 

More detailed descriptions of the U.S. WPRS appear in Balducchi (1996) and 

Wandner (1997). Additional information on the U.S. experience, as well as the story of an 

ambitious Canadian attempt to profile based on predicted impacts from services, appears in 

Eberts, O’Leary and Wandner (2002). OECD (1998) documents experiences with 

profiling in Australia, Canada, and the United Kingdom. Outside the U.S., the 
7

Australians have had the most success with profiling. They have employed a sophisticated 

multivariate profiling system combining an automated model based on respondent 

characteristics with caseworker discretion to override the automated system based on 

factors not present in the administrative data, such as poor motivation. 

This research project aims to help states improve their selection and referral 

procedures in two ways. First, we provide a general methodology for the evaluation, 

development and modification of profiling models. Second, we provide substantive 

guidance regarding types of changes to profiling models that should improve the 

assignment of claimants to reemployment services. Because only a limited number of UI 

claimants can be assigned to employment and training services in each time period, the 

selection model ought to perform the best job of selecting, out of the entire pool of UI 

claimants, those individuals who are most in need of receiving reemployment services. 

Based on equity arguments, DOL has identified those UI claimants most likely to 

exhaust UI benefits as being the ones most in need of receiving employment and training 

services. Their argument is twofold. First, claimants who exhaust or nearly exhaust UI 

benefits face the strongest barriers to labor force reentry, and reemployment services 

should help them overcome such barriers. Second, if by participating in reemployment 

services UI claimants find a job and exit unemployment sooner, they will not collect the 

entire amount of entitled UI benefits and the states will economize on UI funds. 

Because states may differ in their data collection and in their ability to implement 

various profiling models, we provide guidance across a range of models that vary in 

complexity. In order to guide states in making incremental improvements to their profiling 

models, according to their respective data collection and technical capacity, we 
8

systematically examine the determinants of benefit recipiency duration. We consider how 

different model specifications regarding the regressors included in estimation, the 

dependent variable, and the functional form that the dependent variable implies, affect the 

models’ abilities to sort UI claimants. 

To assess the predictive performance of each profiling model, we forecast the 

dependent variable (usually the probability of benefit exhaustion or the expected fraction 

of benefits exhausted) for each claimant. We then sort UI claimants by their forecasted 

values and compute the fraction of benefits actually exhausted within groups of claimants 

with varying forecasts. In particular, we compute the fraction of benefits exhausted by UI 

claimants in each of the five quintiles from the distribution of forecasts. If a model is 

accurate, the upper quintiles should show a high proportion of the average fraction of UI 

benefits exhausted, while the opposite should be true for the claimants in the lower forecast 

quintiles. We provide a methodology to determine the impact of each additional variable 

on the predictive performance of the profiling models and we recommend the specification 

we consider preferable given the tradeoff between model performance and ease of 

implementation. 

In detail, our report carefully addresses the following questions: 

• What functional form should the estimated model have? Would a sophisticated 

nonlinear specification like logit, probit, or tobit bring improvement over the 

standard linear model estimated by ordinary least squares (OLS)? 

• What is the appropriate dependent variable? Is it the binary variable of whether 

or not the claimant exhausted UI benefits, or some other dichotomous 
9

dependent variable, e.g. whether the claimants exhausted up to a large fraction 

of their maximum allowable UI benefits? What is the improvement from using 

– instead of a binary variable – a continuous dependent variable such as the 

ratio of benefits drawn to benefits entitled? 

• Are all of the five explanatory variables used in the WPRS model, in particular 

the local unemployment rate and the change in aggregate employment in the 

claimants’ industry of employment, relevant in improving the performance of 

the profiling model in allocating claimants to services? 

• What additional variables might improve the assignment of claimants to 

reemployment services? Do the additional variables simply improve the 

statistical fit of the model, or do they alter the assignment of the claimants? 

Which are the variables that entail the largest improvement in predictive 

performance, and what is an optimal trade-off between model performance 

improvement and practical operational difficulty due to additional regressors? 

• How important are regional differences in predicting the duration of recipiency? 

• Is the assignment of claimants more accurate during recessions or during boom 

periods? Given existing capacity constraints, how do business cycles affect the 

performance of profiling models? 

 The analysis begins by estimating the WPRS model outlined in Kelso (1998) using 

the Kentucky data. We find that the linear model is the most versatile, and it has the added 

advantage of simple estimation by OLS. It also leads to the best results in most cases. We 

find that the local unemployment rate and the aggregate employment changes in the 

claimant's last industry variables included in the WPRS model do not improve the 
10

assignment of UI recipients. We find that a continuous dependent variable – fraction of 

benefits exhausted – does a better job at allocation than the dichotomous dependent 

variable – UI benefits exhausted or not – utilized in the WPRS model, because it 

incorporates the information contained in the durations of those claimants who do not 

exhaust their benefits. Adding explanatory variables improves the performance of the 

model, although at the cost of loss of simplicity. We examine how additional covariates, 

several of them currently used in some state models, improve the assignment of claimants 

to reemployment services. While we do not pinpoint one single best predictor, we make 

recommendations based on incremental improvements in the models’ predictive power that 

result from adding various regressors. We find that there is no improvement to be gained 

from estimating separate regional models in the Kentucky data, relative to just including 

regional dummy variables. Making this change would greatly simplify some states’ 

models. We also find that the predictive power of profiling models is sensitive to changes 

in the business cycle. The models predict best during periods of high unemployment, as 

there is greater heterogeneity among claimants. 

While we argue that our substantive findings generalize to states other than those 

used in our empirical work, our report has great value even to readers who disagree with 

this assessment. Our methodology provides a template for states interested in investigating 

potential improvements in their UI profiling models using their own data, rather than 

relying on our findings obtained using data from Kentucky. Such states can simply repeat 

our analysis using their own data and then draw the appropriate conclusions. 

2. Data 
11

Our recommendations are based on the analysis of UI administrative data from the 

Commonwealth of Kentucky for the fiscal years 1989 to 1995. These seven years include 

a variety of different periods relative to the business cycle. The data also encompass a 

substantial amount of economic diversity within Kentucky. These facts, coupled with the 

finding (described in detail in Section 5.5) that estimating separate regional models does 

not have a large payoff in terms of predictive power, lead us to believe that our main 

substantive findings likely generalize to other states. The Kentucky data we utilize here 

are also very clean; alternative measures of the same concept (e.g., weeks of benefits 

received) constructed using different elements of the data give about the same answer. 

The data we employ here are the same data utilized in Black, Smith, Berger and 

Noel (2002). They include all UI claims filed in the Commonwealth, except those on 

temporary layoff or hired from a union hall. UI claimants qualify for a maximum of 26 

weeks of UI benefit recipiency. As the maximum amount of UI benefits to which a 

claimant is entitled is known, as well as the amount he or she actually collected, we can 

measure the extent to which claimants exhausted UI benefits as the ratio of benefits 

collected over benefits entitled.1

The data are quite rich and allow us not only to replicate the five-covariate WPRS 

model, but also to investigate how the profiling of UI clients is improved with additional 

regressors pertaining to the claimants' backgrounds, characteristics of last employment, 

social assistance recipiency, and UI claim histories. 

The methodology for assessing the models' predictive performance, described in 

detail in the next section, requires that we withhold ten percent of the observations from 

the initial estimation; we use these observations for out-of-sample forecasts. Fortunately, 
12

the Kentucky sample is very large, about 330,000 observations, and therefore small sample 

sizes are never an issue, not even when the analysis is replicated on separate fiscal years. 

For some analyses, we report results using only data from fiscal year 1994, the most recent 

year for which we have data for the full fiscal year. In these cases, supplemental analyses 

not reported here indicate that our substantive conclusions hold in the other years of our 

data as well (which suggests that they also likely hold in years after our data run out in 

1995). 

3. Methodology 

3.1. The WPRS Model

The profiling procedure involves two steps. In the first stage, all claimants are 

screened and some of them are a priori excluded from the whole process. These excluded 

claimants are either laid-off claimants with a known recall date, whose ties with their 

employers need not be severed, or union workers, who are referred to employment by their 

union. In the second stage, statistical models are estimated, and their predictive 

performance is tested, using a validation procedure that we describe below. The Kentucky 

data we use already excludes claimants from the first step. 

Estimation of models to predict the length of spells of UI benefit receipt is a 

difficult task. Worden (1993) provided the baseline model for profiling UI claimants. She 

used a logit model with UI benefit exhaustion as the dependent variable and a 

parsimonious specification of the independent variables. Other researchers (e.g., Eberts 

and O’Leary 1996) have also used binary choice models in developing profiling systems. 

 1

 A ratio of one means the claimant exhausted his or her UI benefits. 
13

There is also variation among states in the choice of dependent variable. For instance, 

Washington State uses an indicator variable for whether the claimant collected at least 90 

percent of their benefit entitlement, while Idaho counts the number of weeks the claimant 

collected benefits (Kelso 1998). 

The problem with using a dichotomous dependent variable is that all of the data 

variation among individuals who do not exhaust their UI benefits is ignored. Figure 1 

presents a histogram of the fraction of benefits exhausted for claimants in Kentucky in 

1994. It reveals that the data have a large mass of observations at one. In other words, 

almost forty percent of UI claimants exhaust their benefits. Thinking of the story depicted 

by Figure 1 alone, one would be inclined to advocate the use of a dichotomous model that 

splits claimants into exhausters and non-exhausters, as in the WPRS model specification.2

 

Nevertheless, as documented in Figure 2, when only non-exhausters are considered, 

there is substantial variation in the duration of benefits for the group that does not exhaust. 

Thus, a dichotomous model that treats all claimants who do not exhaust as identical 

ignores much useful information in the data. Indeed, one might expect non-exhauster 

claimants who use up almost all of their benefits to be more similar to claimants who 

exhaust their benefits than to those with very short spells of UI receipt. Model 

specifications that employ a continuous dependent variable exploit this variation in benefit 

duration among non-exhausters, and thus would be expected to yield better predictive 

results than the dichotomous models. 

To document which specification of the dependent variable will result in the best 

assignment of claimants most in need of services, i.e., most likely to exhaust UI benefits, 

 

2

 Exhausters comprise the group of claimants who exhaust their UI benefits. Non-exhausters 

comprise the group of claimants who do not exhaust their UI benefits. 
14

we examine the following five specifications of the dependent variable as identified by 

Kelso (1998): 

(1) a dichotomous variable for whether or not the claimant exhausts his or her benefit 

entitlement; 

(2) a dichotomous variable for whether or not the claimant collects at least 90 percent 

of his or her benefit entitlement; 

(3) the ratio of benefits drawn to benefit entitlement (i.e., fraction of benefits claimed); 

(4) the number of weeks of benefits claimed; and 

(5) a dichotomous variable for at least 26 weeks of benefits claimed. 

For discrete variables we use the same logit model specification as the original 

WPRS model, that is, we use the dependent variables as described at (1), (2) and (5) above, 

and the five WPRS model regressors. Besides the logit model, we also estimate a similar 

probit specification on the same dependent and independent variables.3

 

For the continuous dependent variables – cases (3) and (4) above – we use OLS 

estimation of a simple linear model, along with a more sophisticated tobit model that 

accounts for masses of observations either at the lower or upper bounds of the support of 

the dependent variable.4

 For example, when the dependent variable is fraction of benefits 

exhausted, the observations are massed at one because many claimants exhaust their 

benefits. 

 

3

 The difference between the logit and probit models comes from distributional assumptions 

regarding the error term. In the logit case the error term is assumed to come from a logistic 

distribution, while the error term is assumed to have a normal distribution in the probit 

specification. 

4

 The support of a distribution of a random variable is the set of all possible values that the random 

variable takes on with positive (i.e., greater than zero) probability. In our case, the random variable 
15

3.2. Additional Explanatory Variables 

In addition to utilizing a binary dependent variable, the WPRS model includes only 

five covariates: education, job tenure, occupation, changes in aggregate employment in the 

industry of the claimant’s last employment, and the local unemployment rate.5

 The WPRS 

model has a poor predictive performance in part because of the very small number of 

covariates it includes. As we show in detail in Section 3.4, increasing the number of 

covariates improves profiling performance. The problem with adding extra covariates is 

that too many covariates make the model difficult to implement in day-to-day operations 

by UI staff. In practice, states make different choices regarding this tradeoff. For 

example, the model for the state of Pennsylvania uses only eight covariates, while the 

model for Washington State, which is one of the larger state models, includes 26 

covariates.6 At the other extreme, the Kentucky model contains over 140 different 

covariates.7

 

Table 1 describes the five independent variables in the WPRS model as they are 

coded in the national, WPRS, and Kentucky applications. In the Kentucky data, the job 

tenure and the occupation variables refer to the last main job of the claimants. The 

industry variable gives the percentage change in employment in the industry of the 

claimant’s last main job. The industry employment change is recorded as the percentage 

change from the previous month’s employment figures. The unemployment rate variable is 

recorded monthly at the county level. Both the industry employment change and the 

 

is the dependent variable, i.e. either the fraction of benefits exhausted or the number of weeks of 

benefits claimed. 

5

 We call a specification employing the five covariates defined here the WPRS model. We refer to 

a reduced specification that only includes three covariates: education, tenure, and occupation, as 

the basic (or reduced) WPRS model. 

6

 O’Leary, Decker, and Wandner (2002). 
16

unemployment rate enter the estimation with a lag. The rationale behind using lagged 

values is that, at the moment of application, only last month’s figures are known to the 

personnel who operate the profiling models. 

Table 2 lists the variables in the WPRS model along with variables included in 

other state models. We compiled this list using Kelso (1998) and Berger, Black, Chandra, 

and Allen (1997). With three exceptions described in the notes to Table 2, the Kentucky 

data allow us to examine the relative importance of each variable considered by other 

states. The list in Table 2 also includes some variables that have not been used by any 

state, but which we thought might improve the allocation of claimants to services. 

If some of the variables have missing values, rather than simply discarding those 

observations we use an imputation procedure. If the missing values come from a 

categorical variable, we create one more indicator variable for the category “missing.” If 

the missing values come from a continuous variable, we add an indicator variable equal to 

one for the observations that had missing values and equal to zero otherwise, and we 

replace the missing values in the original variable with zero. 

3.3. Assessing Predictive Performance

We use two different sets of measures to determine the performance and fit of our 

models. The first set of measures consists of the usual within-sample statistics reported for 

estimated models, such as R2 for the linear model estimated by OLS or the log-likelihood 

value for models estimated by maximum likelihood, such as the logit or probit. 

Unfortunately, statistics such as the R2

 or other within-sample forecast measures 

are often not compelling. First, within-sample statistics are not realistic tests given how 

 7

 Berger, Black, Chandra, and Allen (1997). 
17

profiling models are actually implemented. Profiling is necessarily a forecast of the 

claimant’s duration of recipiency, not a within-sample estimate. Indeed, this is the reason 

that Berger et al. (1997) exclude ten percent of the data from the estimation of their models 

and use these data to evaluate out-of-sample predictive performance. 

Second, the use of R2

 and other summary measures of the goodness-of-fit of a 

model may overstate the impact of variables on the predictive power of the model. A 

simple example illustrates why. Suppose that a researcher includes a dummy variable for 

the month in which the worker’s spell is initiated. Given the seasonal nature of 

unemployment, this might substantially improve the fit of the model. It will not 

necessarily improve, however, the assignment of workers to services because the variable 

will have the same effect on the predicted values of all workers beginning their spell of 

unemployment in the same month.8

 The inclusion of the dummy variable for the month 

will improve the statistical fit of the model, but may not alter the ranking of claimants for 

referral. 

To avoid this so-called false accuracy problem, we rely on portions of the sample 

excluded from estimation to simulate the assignment of workers to treatment. By 

simulating the actual assignment, we may examine whether the addition of a variable, or 

the choice of a particular estimation methodology, improves the assignment mechanism. 

Our validation methodology is an improvement over much of the existing literature, in the 

sense that we measure how well the models predict for claimants not included in the 

estimation. Thus, we follow Berger et al. (1997) and randomly exclude from the 

estimation ten percent of the sample. We keep this ten percent sample for validation 

 

8

 This is only approximately true in the case of non-linear models such as the logit or the probit. 
18

purposes. The models are estimated on the remaining ninety percent of the sample. The 

forecasting performance is then tested on the ten percent validation sample. 

Berger et al. (1997) create their validation sample by randomly excluding claimants 

from all weeks in which claims were filed. In contrast, we create our validation sample by 

excluding all claimants in a random sample of weeks. Claimants who file within the same 

week will face similar local economic conditions. Randomly selecting the validation 

sample on the basis of the weeks in which claims are filed limits the variation across these 

economic variables and thus avoids potentially inflating the predictive performance 

measures due to the variation in local economic conditions over time within the validation 

sample. 

In creating the validation sample we randomly exclude four weeks out of fifty-two 

in each fiscal year. After discarding the observations in the validation sample, we estimate 

the model on the remaining forty-eight weeks of observations.9

 We use the coefficients 

obtained from the estimation sample to compute the predictions for the validation sample. 

In the case of dichotomous models, we predict the probability that each claimant will 

exhaust his or her UI benefits (or the probability of exhausting at least ninety percent of UI 

benefits when that is the dependent variable). The predicted probabilities lie within the 

interval from zero to one. In the case of models with a continuous dependent variable, we 

predict either the fraction of UI benefits exhausted – out of one hundred percent – or the 

duration of the claim in weeks – with a maximum of twenty-six weeks. 

For assessing the predictive power of all of our models, we examine the actual UI 

benefit duration (as recorded in the data) at various percentiles of the distribution of 
19

predicted values of the dependent variable for the model under consideration. We use the 

term “predicted UI benefit duration/exhaustion” to denote, depending on the dependent 

variable in the model, the predicted probability of exhausting UI benefits, the predicted 

probability of collecting at least 90 percent of the benefit entitlement, the predicted fraction 

of benefits claimed, the predicted number of claimed weeks, or the predicted probability of 

consuming at least 26 weeks of benefits. 

Claimants from the ten percent validation sample are sorted into quintiles based 

upon the distribution of predicted values of UI benefit duration/exhaustion. We look at 

quintiles rather than some other, finer partition such as deciles because examining the 

quintiles provides sufficient information to assess the predictive performance of our 

models.10 Our measure of predictive performance is the average fraction of benefits 

exhausted for claimants in each quintile of the distribution of predicted values. If a model 

has good predictive performance, then the fraction of benefits exhausted by claimants from 

the top predicted quintiles should be large, and the fraction of benefits exhausted by 

claimants from the bottom predicted quintiles should be small. A useful performance 

benchmark is given by the average fractions of benefits exhausted by claimants in quintiles 

of the distribution of fraction of benefits received in the raw data (that is, quintiles of the 

distribution of realized values of fraction of benefits received). This comparison adjusts 

 9

 For the 1995 fiscal year we only have twenty-six weeks of data, so we exclude two random weeks 

and keep twenty-four weeks in estimation. Also, it may happen that a fiscal year has fifty-three 

weeks, so after excluding four weeks we keep forty-nine weeks in estimation. 

10 O’Leary, Decker, and Wandner (1998, 2002) assess predictive power in their application by 

inspecting deciles of predicted durations. Nevertheless, they only consider within-sample 

forecasts, rather than the more appropriate out-of-sample forecasts we examine here. 
20

for differences across years (or across states, for cross-state comparisons) in the mean 

fraction of benefits exhausted.11

To evaluate how much better we can profile claimants by using sophisticated 

statistical techniques, we also consider a random assignment mechanism. Under random 

assignment, claimants are not profiled based upon their predicted UI benefit 

duration/exhaustion. Instead, the claimants are simply put in a random order. Comparing 

the average fraction of benefits received for quantiles of predicted values from a profiling 

model with the same averages for quantiles of the randomly ordered distribution quantifies 

the improvement generated by the statistical profiling model relative to random assignment 

to services. If no significant improvements in assignment arise from the models relative to 

random assignment, it is difficult to justify the use of statistical profiling. 

 The top panels in all of the tables presenting results on the predictive performance 

of profiling models provide the predictive performance measures computed using the raw 

data and using the random assignment mechanism. These measures serve as baselines for 

the performance of each estimated model, with the measures based on the realized duration 

and exhaustion data at one extreme (the best case) and those based on the random 

assignment mechanism at the other extreme (the worst case). 

3.4. Comparing Profiling Models 

The ability of a model to sort claimants by their predicted values of UI benefit 

duration/exhaustion is measured as the difference in average fraction of benefits exhausted 

 

11 We believe the results reported here to serve as broad guidelines for all states implementing 

profiling models. Our experience with data from Kentucky and other states indicates that different 

data sets may have substantially different sample averages for variables that encode the same basic 
21

between the top and the bottom of the distribution of predicted values. Depending on the 

nature of the application at hand, we may be interested in knowing how the models 

perform at different points of the predicted distribution. Put differently, if approximately 

80 percent of clients are assigned to reemployment services, then we want to make sure our 

models are able to predict correctly the top 80 percent of the distribution of actual spell 

lengths. If instead only 40 percent of clients are assigned to reemployment services, we 

prefer that our models identify accurately the top 40 percent of actual spell lengths. 

Nevertheless, because of capacity and budgetary constraints and because of fluctuations in 

the number of unemployment claimants, there is no clear-cut threshold to use. 

Ideally, a model will perform well at all points of the distribution of predicted UI 

benefit duration/exhaustion values. To obtain an accurate picture of the predictive 

performance, we report results at different points in the distribution of predicted values. In 

particular, we compare the difference in the average fraction of benefits exhausted between 

the top 80 percent and bottom 20 percent of the distribution of predicted values, as well as 

the differences between the top 60 percent and bottom 40 percent, the top 40 percent and 

bottom 60 percent, and the top 20 percent and bottom 80 percent. We also report an 

average of the five differences. By reporting the results in this way we facilitate qualitative 

claims about the relative performance of various models. 

To begin, we compare the performance of existing profiling models as reported in 

the literature. Given the limited number of covariates, and the inherent difficulties in 

predicting the exhaustion of UI benefits, it is not surprising that the explanatory power of 

the WPRS model is modest. Table 3 reports the differences in predictive power between 

 

concept. This is why we recommend values relative to respective sample averages, rather than 

absolute values, for the predictive measures. 
22

the Pennsylvania, Washington, and the 140-covariates Kentucky models. It also reports 

the predictive power results we obtained from a model with the five WPRS covariates and 

from our preferred model specification estimated on Kentucky data, both using fraction of 

benefits exhausted as the dependent variable. For the Pennsylvania and Washington 

models we report differences between the top 25 percent and bottom 75 percent of the 

predicted distribution, taken from O’Leary, Decker and Wandner (1998). For the other 

three models (all estimated on Kentucky data) we report differences between the top 60 

percent and bottom 40 percent of the distribution of predicted UI benefit 

duration/exhaustion values, in order to be consistent with the way Berger et al. (1997) 

report the predictive results for the larger (140-covariates) Kentucky model. 

The predictions for the larger (140-covariates) Kentucky specification are far 

superior to those of the model with the WPRS covariates, as well as the Washington and 

Pennsylvania models. Our preferred model specification, estimated on the Kentucky data, 

fares much better than the model with the WPRS covariates, but not as well as the more 

elaborate Kentucky model.12

The results in Table 3 actually overstate the performance of the WPRS model 

because we have used fraction of benefits exhausted as the dependent variable in order to 

emphasize the effects of the covariate set in comparing the five WPRS model covariates to 

our preferred specification and to the Kentucky model. It is the 140 different covariates (or 

some subset thereof) in the Kentucky model that account for its superior performance in 

Table 3. In what follows, we strive to find a balance between model simplicity (fewer 

covariates) and model performance. 
23

4. Use Simple Linear Models Estimated by OLS 

The original model developed by Worden (1993) consists of a logit model with UI 

benefit exhaustion as the dependent variable. Since the imprecision in logit models is 

larger at the tails, we expected a similar probit model to perform marginally better that the 

logit. It turns out that for all the models with a dichotomous dependent variable, the probit 

model does not yield any performance improvement over the logit specification.13 

Moreover, the linear probability model estimated by OLS performed at least as well as the 

nonlinear logit and probit estimated by maximum likelihood. For models with a 

continuous dependent variable, we estimated both simple linear models using OLS and 

more sophisticated tobit models that account for mass points at either end of the 

distribution by maximum likelihood.14 Once again, none of the models outperformed the 

linear model estimated using OLS. 

Table 4 shows comparative predictive results from linear probability and simple 

linear models estimated by OLS, as well as logit, probit, and tobit models estimated by 

maximum likelihood, using the Kentucky data. Although for simplicity we only report 

results from models including the five covariates from the WPRS model, our finding that 

all else equal, linear models estimated by OLS are not outperformed by any other 

specifications holds more generally. Moreover, in numerous instances linear models 

 12 We use only the fiscal year 1994 in this set of estimation results for simplicity reasons. The 

detailed year-by-year analysis in Section 8 shows that 1994 is neither among the best years for 

prediction nor among the worst years for prediction. For the empirical results, see Table 11. 

13 The dichotomous dependent variables are UI benefits exhausted, at least 90 percent of UI 

benefits exhausted, and at least 26 weeks of claimed benefits. The continuous dependent variables 

are fraction of benefits exhausted and number of weeks of claimed benefits. 
24

estimated by OLS actually yield better predictive results. Given the simplicity of the linear 

model and OLS estimation, and given the fact that more sophisticated models such as the 

double limit tobit or probit do not bring any improvement to the predictive power of the 

models, we recommend the use of the linear regression model in all profiling model 

estimation. 

5. Results 

5.1. The Dependent Variable Should Be Fraction of Benefits 

Exhausted 

We consider five alternative definitions for the dependent variable: exhausted UI 

benefits (dichotomous); exhausted 90 percent of benefits (dichotomous); fraction of 

benefits exhausted (continuous); number of weeks claimed (continuous); and, finally, at 

least 26 weeks of benefits claimed (dichotomous). In Table 5 we report predictive results 

from estimating five linear regression models, each containing the five WPRS model 

covariates and using one of the dependent variables just listed. 

A continuous dependent variable – either the fraction of benefits exhausted or the 

number of weeks of claimed benefits – results in better predictive outcomes. Although not 

presented here, results from other model specifications indicate that the fraction of benefits 

exhausted is the best choice for a dependent variable. In all further analyses we estimate 

linear models using OLS with fraction of benefits exhausted as the dependent variable. 

 14 For instance, about 40 percent of the observations on the fraction of benefits exhausted equal 

one in the Kentucky data. 
25

5.2 Variables Measuring Local Economic Conditions

The WPRS model contains two variables measuring local economic conditions: the 

unemployment rate and aggregate employment growth rate in the claimant’s industry of 

last employment. These variables provide sources of “false accuracy”. It is quite possible 

that the local unemployment rate, while an important determinant of the duration of 

unemployment, will not provide useful information for sorting the claimants. While 

county level unemployment rates do vary somewhat at a point in time, most of the 

variation in unemployment rates is over time, not within relatively narrow geographic 

areas. Moreover, because virtually all of the claimants applying for UI at a given service 

delivery area (SDA) face the same unemployment rate, the regional variation in 

unemployment rates will not help separate among clients applying at the same SDA. The 

same points hold (although somewhat more weakly) for industry employment changes. 

Many claimants at a given office in a given week will often come from the same industry, 

due to the geographic sorting of industries and the occurrence of mass layoffs. Such 

claimants will all have the same value for the industry employment variable, which will 

therefore not aid in sorting among them, even though it may increase the fit of a profiling 

model. 

Table 6 reports the sensitivity of the predictive performance of a model with the 

five WPRS covariates and fraction of benefits exhausted as the dependent variable to 

dropping either the local unemployment rate or the industry employment change variable. 

We also report results from models that omit the local unemployment rate and industry 

employment change variables, while adding instead local or regional dummies. We 

observe some increase in the predictive performance when unemployment rates are used 
26

among the regressors, but the performance increase is more noticeable when the local 

unemployment rates are replaced by regional dummy variables. The most likely 

explanation is that local unemployment rates are a proxy for the omitted regional variables. 

In a properly specified model, with no omitted variables, there is no need to include either 

local unemployment rates or industry employment changes. Consequently, the basic 

model to which we add additional regressors in the remainder of the analysis will be a 

basic WPRS model that includes only three regressors: education, tenure, and occupation. 

We omit unemployment rates and industry employment changes from all future 

specifications. 

5.3 Additional Variables: There Is No Single Best Predictor 

Assessing the relative importance of each variable included in the estimation allows 

us to evaluate whether each variable provides sufficient information to warrant inclusion in 

the model. It is a bit of an art form to come up with a specification that is both 

parsimonious and at the same time does not leave out any variables that may improve the 

predictive power of the model. The questions we address here are: What variables might 

be added to improve the predictive power of the model? Do these variables simply 

improve the statistical fit of the model, or do they alter the ordering of claimants? 

In Table 7 we provide a description of the variables included in the 40 

specifications we estimate using the Kentucky data. Model 0 is the three-covariate WPRS 

basic model specification. Models 1-7 augment the base specification with various 

measures of past UI benefit take-up. Other specifications up to Model 28 include, one at a 

time, economic status and transfer payment variables, previous wages, tenure squared, 
27

reason for job separation, enrollment in school at the time of filing the claim and employed 

at the time of filing the claim.15 Model 29 adds local office variables. Models 31 to 38 

combine some of the more successful variables identified previously. For models 31 to 38, 

each even numbered specification consists of the preceding odd number specification plus 

regional dummy variables.16 The last specification, model 39, is the most elaborate one, 

including all of the available variables that might be expected to make a reasonable 

contribution to explaining UI benefit duration. 

In putting together a best specification we have to keep in mind that, although the 

more covariates we add, the better the within-sample fit (as shown by the R-squared 

measure), increased performance comes at the cost of a more complex model, which is less 

easy to operate by the states. Table 8 reports in-sample measures of fit (R-squared) and 

out-of-sample measures of fit (fraction of benefits exhausted by predicted quintiles) for all 

of our model specifications. In every case we estimate linear models by OLS with fraction 

of benefits exhausted as the dependent variable. For simplicity, we only look at Kentucky 

data for fiscal year 1994. 

It is clear that none of the specifications from Model 1 to Model 26 bring any 

improvements over the WPRS model. The first headway is made with the covariates 

“enrolled at time of claim” and “employed at time of claim.” With each of these two 

added covariates there is a significant jump in the R-squared as well. All specifications 

 

15 Models 18 and 19 include the weekly benefit amount received and the potential amount. We use 

potential (i.e., maximum allowable) weekly benefit amount because at the time of filing the claim 

the applicant will know the potential, but not the actual, benefits claimed. For conformity with 

other states, we tested specifications including either the potential or the actual weekly benefit 

amount claimed. All nominal variables are expressed in real terms using the CPI deflator with base 

year 1995. 

16 For example, model specifications 31 and 32 are equivalent, except that model 32 includes local 

office dummies while model 31 does not. 
28

from Model 31 to Model 40 bring a large improvement over the base specification, both in 

terms of R-squared (about four times larger), and in terms of the out-of-sample predictive 

performance of the model. Not surprisingly, adding local office dummies, all else equal, 

improves the R-squared and the predictive performance. 

Also not surprisingly, the more covariates we add to the model specification, the 

better the predictive performance. Nevertheless, the relationship between added regressors 

and performance increase is not linear, and at some point the cost of adding one more 

regressor will be less than the performance increase it entails. In other words, when 

picking the best specification we have to keep in mind that, although the more added 

covariates the better the fit, it all comes at the cost of a more complex model, less easy to 

operate by the states. 

We consider the specification that keeps the best balance between performance and 

simplicity to be Model 36. In terms of predictive performance, Model 36 does almost as 

well as the most complex model, Model 39. Looking at the fraction of benefits exhausted 

within each predicted quintile, the differences between Model 36 and Model 39 show only 

at the third decimal point. The average difference in prediction differences across 

quintiles, our preferred measure of predictive fit, again shows Model 36 second only to the 

elaborate Model 39. What tilts the balance in favor of Model 36 is that, while Model 39 

employs 28 covariates, Model 36 uses only about half as many – just 15 covariates.17 

 

17 When counting the covariates, dummies for the categories of a categorical variable are not 

counted as separate regressors. For instance, we count education as one regressor, although four 

dummies for educational categories enter in the estimation. 
29

5.4 There Is No Improvement from Estimating Separate Regional 

Models 

 The economy of Kentucky is quite heterogeneous, with large metropolitan areas 

with expanding economies (such as Lexington), older, industrialized metropolitan areas 

(such as the Northern Kentucky portions of Cincinnati and Louisville), and a variety of 

rural areas. How important are regional differences in predicting the duration of 

recipiency? Should states use distinct models for various regions or does a single state 

model adequately predict recipiency duration? To our knowledge, no one else has 

documented in writing the relative improvement in the assignment that arises from the use 

of distinct models for different geographic regions, though some states other than 

Kentucky find such models useful based on their own analyses. Clearly the use of 

regionally distinct models greatly increases the complexity of implementation. Our 

analysis of the regional models has broader implications: if regional models are not 

important within Kentucky, this provides support for the view that our substantive findings 

for Kentucky likely generalize to other states. 

The following question arises: How much does the added complexity improve the 

assignment of claimants to reemployment services? The answer, as evident from Table 9, 

is not very much.18 There is no gain from estimating separate regional models once 

regional dummies are included in the model. Additional interaction terms do not improve 

the assignment of clients. 

To provide evidence to this claim, we compare assignment results from two 

different experiments. In the first instance, we estimate separate models for the eight 

 

18 We estimate linear models by OLS using the 1994 Kentucky data with fraction of benefits 

exhausted as the dependent variable and the covariates from Model 36. 
30

regions of Kentucky in our data. In the second instance, we estimate a single model jointly 

on all eight regions, with controls for each separate region. Although there is significant 

heterogeneity across regions in terms of economic conditions and other factors, the 

predictive difference between the regional models and the joint model is remarkably small. 

Across regions, the control variables have the same effect on the fraction of benefits 

exhausted. 

5.5 Business Cycles and Data Quality 

In Table 10 we report the results from estimating various models pooling all of the 

years in our data, and also separately year by year. We report results from both the basic 

WPRS model with three variables and our preferred specification, Model 36 from Tables 7 

and 8.19 We find substantial swings in predictive performance from year to year. 

As a rule of thumb for identifying boom versus recession years we can compare the 

fraction of benefits exhausted in the entire sample during a particular year (as given by the 

random assignment measures) with the average for all years (reported in the column 

“All”). Recession years should have a larger than average fraction of benefits exhausted. 

Another rule of thumb indicates that during recession years about 60 percent of claimants 

(the top three quintiles in the Data panel) collect more than 90 percent of their benefits, 

while during boom years only about 40 percent of claimants (the top two quintiles in the 

Data panel) collect more than 90 percent of their benefits. 

 

19 We have already seen some of the results reported here. For instance, the first column from the 

base Maryland model estimated on pooled data (all years) is the same as column A from Table 6. 

The seventh column, 1994, from the best specification model is the same with the estimated model 

36 from Table 8. 
31

In terms of predictive success, profiling models for recession years like 1991 

usually perform better than profiling models for boom years like 1994. This result also 

holds when an average boom period, 1993 to 1995, is compared with an average recession 

period, 1989 to 1992. It is reassuring that the claimants most likely to exhaust UI benefits, 

those most in need of reemployment services, are more easily identified in periods of 

recession, since the number of claimants treated with reemployment services cannot be 

proportional to the number of claimants because of capacity constraints. These patterns 

likely arise from the increasing diversity among UI claimants during downturns, as many 

workers with long employment spells become unemployed. It is easier to predict well 

when the variation in the data increases. 

Nevertheless, one qualification is necessary. Models for recession years perform 

unambiguously better than models for boom years in the sparse 3-covariate basic WPRS 

specification. When the estimated model is our preferred specification – the 15-covariate 

Model 36 – the pattern of results changes somewhat. Although the average recession 

period of 1989 to 1992 continues to exhibit better predictive results than the average boom 

period of 1993 to 1995, the gap between the two periods becomes narrower. Moreover, 

examining the year-by-year results reveals that for a boom year like 1993, the predictive 

results in the best specification estimation (Model 36) can be as good as for most recession 

years and better than the results for 1992, a recession year. Because we know for a fact 

that, from 1993 on, the quality of the Kentucky data has improved, we believe that the 

better performance in 1993 comes from improved data quality. In the case of the sparsely 

specified basic WPRS model, improved data quality starting in 1993 did not help much as 

not much data was used in the first place. 
32

These estimates do not answer the important question of how often to optimally reestimate profiling models, but they do suggest that occasional re-estimation is useful, and 

that models with few covariates may optimally require more frequent re-estimation than 

models with many covariates. A complete answer to this question awaits future research. 

5.6 Another Covariate: Delayed Filing 

When presenting our initial draft at the Employment and Training Administration, 

a suggestion was made to examine the impact of delayed filing for UI benefits on the 

fraction of benefits exhausted. Unfortunately, the Kentucky data are not ideal for this task. 

For most claimants, we were able to construct this measure by examining the date last 

worked relative to the date the claimant filed for UI benefits. Some caseworkers, however, 

updated the date last worked variable when claimants had an employment spell within the 

benefit year and then had a subsequent unemployment spell. As a result, some of the 

estimated gaps between employment and filing for benefits are negative. With this caveat, 

however, we use the Kentucky data to examine the impact of filing late on the fraction of 

benefits exhausted. 

Table 11 presents the definitions of the alternative versions of the delayed filing 

variable that we examine. We consider six categorical measures and three continuous 

measures in separate regressions. Table 12 presents the results of our analysis, in which 

we include these delayed filing variables in four of the models described in Table 7: the 

basic WPRS model (Model 0), the WPRS model with covariates for the previous years UI 

activity (Model 1), our preferred model (Model 36), and the model with the most extensive 
33

set of covariates (Model 39). In each case, the dependent variable consists of the fraction 

of benefits exhausted and we estimate a linear model using OLS. 

For our preferred model with no delayed filing variable, claimants in the top 

quintile of predicted fraction exhausted actually collected an average of 74.1 percent of 

their benefits. Adding our measures of delayed filing generally improves the predictive 

performance of the model, with the fraction of benefits exhausted in the top quintile 

ranging from 74.0 percent to 75.1 percent. Using the difference between the top quintile 

and the bottom quintile, our preferred model does better with any of the delayed filing 

variables than it does without one. Moreover, the difference is often substantial. 

Finally, note that much of the improvement is obtained when we use a simple 

dummy variable for whether or not the claimant waited 30 days or more to file a claim 

(Measure A in Table 11). Given its ease of implementation, this would appear to be a 

desirable variable to include in profiling models, although the evidence in support of 

including it in sparsely specified models, such as Models 0 and 1, is weaker than for the 

richer models. 

5.7. Some Methodological Issues 

We believe one of the major contributions of this work is to outline a method of 

evaluating the performance of competing profiling models that states can apply to their 

own data. Traditional measures of whether variables should be included in the model are 

generally not appropriate for the analysis of profiling models because in profiling only 

relative prediction matters. That is, in a profiling model, individuals are selected based on 

the order of the predicted values, not based on the actual predicted values themselves. 
34

To see this point better, consider adding 0.3 to each of the predicted values for 

fraction of benefits exhausted from a model. In the case of OLS estimation, this would 

drastically reduce the R2 because the regression line would no longer go through the center 

of the data. It would not, however, change the ordering of claimants when it came to 

allocating scare reemployment services slots. As a result, this reduction in R2

 would have 

no impact on the allocation of services. It is, of course, necessary that the inclusion of 

additional covariates increase the R2

 of an estimated equation, but, importantly, the 

additional covariates do not necessarily improve the ordering of claimants and may 

actually worsen the ordering over parts of the distribution. 

Similarly, when evaluating the performance of a profiling model, it is crucial that 

the sample of claimants used for prediction consist of all claimants from randomly selected 

weeks, rather than a random sample of claimants from all weeks. In our empirical work, 

we randomly select a set of weeks and use all claimants from those weeks for prediction, 

leaving them out of the estimation entirely. This allows an evaluation of the profiling 

model in a realistic setting and minimizes any spurious improvement or degradation in the 

model’s performance because of differing economic conditions across weeks. 

6 Conclusions 

In this paper, we conduct a systematic investigation of alternative profiling models 

using UI records from the Commonwealth of Kentucky. Our analysis provides a 

methodological template for similar analyses of data from other states. In addition, we 

provide six substantive guidelines for the specification of UI profiling models that follow 

from the empirical findings in our analysis. Although based on data from a single state, we 
35

argue that these findings have relevance for other states, in part because of our finding that 

separate regional models are not needed to capture the (very) heterogeneous labor markets 

within Kentucky. Following these six guidelines should result in substantial improvement 

in the performance of state the profiling models. 

First, because of its simplicity and better performance, OLS estimation of linear 

models estimated is preferable to estimation of more sophisticated models such as the 

logit, probit or Tobit. 

Second, a continuous variable, such as the ratio of benefits claimed to benefits 

entitled, captures more of the variation among UI claimants who do not exhaust their 

benefits. As a result, a continuous measure should be used instead of a dichotomous one 

as the dependent variable in the profiling model. 

Third, there is no need to include either local unemployment rates (lagged) or 

employment changes in the industry of the claimant’s last job (lagged), because these 

variables do not help improve the allocation of claimants. 

Fourth, we suggest additional variables that improve the predictive performance of 

profiling models in the Kentucky data. We find no single best regressor that yields large 

improvements in predictive power. Instead, we find that improved performance beyond 

the basic WPRS model requires the addition of a set of additional variables. Although 

there is no scope for a miracle-working covariate, we can nevertheless identify relatively 

parsimonious specifications that substantially improve the fit of the model. Our preferred 

model specification from this analysis includes fifteen regressors, all of which are readily 

available on completed UI claim forms. These regressors include education, occupation, 

tenure and tenure squared, indicators for enrollment in school or employment at the time of 
36

claim, dummy variables for local offices, some measures for the economic status of the 

client and past UI claims, and a measure for wages in the year prior to unemployment.20 A 

supplementary analysis indicates the value of adding a simple measure of delayed filing. 

Fifth, there is no improvement in the quality of assignment to services from 

estimating separate regional models in Kentucky, once regional dummy variables are 

included. If this result generalizes to other states, it is good news, because it keeps the 

estimation and the assignment of claimants clear and simple. The evidence in favor of 

generalizability is the large degree of heterogeneity within the Kentucky economy – 

arguably larger than in most other states. If separate regional models do not add much in 

such a heterogeneous state, it suggests that they would add even less in more economically 

homogeneous states. The evidence against it is that some other states appear to find 

regional models helpful based on their own analyses. 

Sixth, the business cycle does affect model performance. Predictive performance 

decreases in booms and increases in downturns, probably because claimants become more 

heterogeneous during downturns. Our evidence on the cyclical behavior of the model is 

consistent with the view that profiling models should be re-estimated occasionally, but a 

definitive answer to the question of how often to re-estimate the model must await further 

analysis. 

 

20 In our preferred specification, the economic status indicators are welfare and food stamp 

recipiency, JTPA eligibility status, and public transport required for work. The public transport 

measure may not add as much in states that include cities such as New York or Chicago, where 

many white-collar workers use public transit. We include two measures of past UI receipt: 

previous UI claims and previous UI benefits exhausted. We also include a variable for shift type. 
37

References 

Balducchi, David (ed.) 1996. Worker Profiling and Reemployment Services (WPRS) 

Systems: National Colloquium, Selected Papers and Materials. Washington, DC: 

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

Berger, Mark, Dan Black, Amitabh Chandra, and Steve Allen. 1997. “Kentucky’s 

Statistical Model of Worker Profiling for Unemployment Insurance.” Kentucky 

Journal of Economics and Business 16: 1-18. 

Black, Dan, Jeffrey Smith, Mark Berger, and Brett Noel. 2002. “Is the Threat of 

Reemployment Services More Effective than the Services Themselves? 

Experimental Evidence from the UI System.” NBER Working Paper No. #8825. 

Eberts, Randall, and Christopher O’Leary. 1996. “Design of the Worker Profiling and 

Reemployment Services System and Evaluation in Michigan,” Research Working 

Paper #96-41. Kalamazoo, MI: W.E. Upjohn Institute for Employment Research. 

Eberts, Randall, Christopher O’Leary and Wandner, Stephen (eds). 2002. Targeting 

Employment Services. Kalamazoo, MI: W.E. Upjohn Institute for Employment 

Research. 

Kelso, Marisa L. 1998. “Worker Profiling and Reemployment Services Profiling 

Methods: Lesson Learned.” UI Occasional Paper 99-5. Washington, DC: U.S. 

Department of Labor. 

OECD. 1998. Early Identification of Jobseekers at Risk of Long-term Unemployment. 

Paris: Organization for Economic Co-Operation and Development. 

O’Leary, Christopher, Paul Decker and Stephen Wandner. 1998. “Reemployment Bonuses 

and Profiling.” W.E. Upjohn Institute Working Paper #98-51. Kalamazoo, MI: 

W.E. Upjohn Institute for Employment Research. 

O’Leary, Christopher, Paul Decker, and Stephen Wandner. 2002. “Targeting 

Reemployment Bonuses.” In Randall Eberts, Christopher O’Leary and Stephen 

Wandner, eds., Targeting Employment Services. Kalamazoo, MI: W.E. Upjohn 

Institute for Employment Research. 161-182. 

Wandner, Stephen. 1997. “Early Reemployment for Dislocated Workers in the United 

States.” International Social Security Review. 50(4): 95-112. 

Wandner, Stephen and Jon Messenger (eds.). 1999. “Worker Profiling and Reemployment 

Services Policy Workgroup: Final Report and Recommendations.” Working Paper. 

Washington, DC: U.S. Department of Labor. 
38

Worden, Kelley. 1993. “Profiling Dislocated Workers for Early Referral to 

Reemployment Services.” unpublished manuscript. Washington, DC: U.S. 

Department of Labor. 
39

Note: Authors’ calculations, Kentucky UI Claims data. 

Note: Authors’ calculations, Kentucky UI Claims data. 
40

Table 1. WPRS model Covariates in the National, Maryland, and Kentucky Datasets. 

 NATIONAL WPRS KENTUCKY 

EDUCATION 

Categorical 

Less than HS 

HS diploma 

Some college 

College 

Categorical 

Less than HS 

HS diploma 

Some college 

Bachelors degree 

Masters degree/ Ph.D. 

Categorical 

Less than HS 

HS diploma 

Some college 

Bachelors degree 

Masters degree/ Ph.D. 

JOB TENURE 

Categorical 

< 3 years 

3 - 5 years 

6 - 9 years 

> 10 years 

Continuous 

Years of job tenure 

Continuous 

Months of job tenure 

at main job 

(top coded at 99) 

INDUSTRY 

Employment % change 

(monthly figures) 

- SIC Division level 

- State level 

- SIC Division level 

- SDA level 

- SIC Division level 

(1 digit industry codes) 

OCCUPATION 

Binary 

1 = growing 

0 = declining or zero 

Categorical 

(1-digit codes) 

Categorical 

(1-digit codes) 

UNEMPLOYMENT 

RATE (%) 

(monthly figures) 

- State level - SDA level - SDA level 

Source: Kelso (1998) for the national and Maryland data sets, and authors’ manipulation of Kentucky data. 

Notes: 

SIC = Standard Industrial Classification 

SDA = Service Delivery Area 
41

Table 2. Profiling Variables 

Variable Used in our estimation on 

Kentucky Data? 

“WPRS” model variables: 

Education variables Yes 

Job tenure Yes 

Industry (employment % change) Yes 

Occupation Yes 

Unemployment rate Yes 

 

Additional variables used by at least some states:a

Weekly benefit amount Yes 

Wage replacement rate Yes 

Base year wage Yes 

Potential duration No 

Separation and claim filed dates No 

Ratio high quarter wage to base year wage Yes 

Number of base year employers No 

Month benefits began Yes 

Transfer payment recipiency Yes 

Pension recipiency Yes 

Claimant has phone Yes 

School enrollment Yes 

Separation from merger Yes 

Separation from plant closure Yes 

Worker has previous UI claim in recent past Yes 

Worker exhausted recent UI claim Yes 

Virtually all claimants in Kentucky data are eligible for 26 weeks of benefits and hence 

potential duration has no variation. In our sample, the separation date is too noisy to use, and 

we do not have precise information on the number of base year employers. 

Additional variables included in our specifications:

Past UI claims Average real dollar amount claimed in the past 

Number of weeks claimed in the past 

Average fraction of benefits exhausted in the past 

UI benefit exhaustion up to three year lags 

Welfare indicators Food stamps 

Welfare receipt 

JTPA eligibility 

Economically disadvantaged indicator 

Other variables Tenure squared 

Public transport required to get to work 

Type of shift worked 

Child support deductions 

 aKelso (1998) and Berger et al. (1997). 
42

Table 3. The Ability of Profiling Models to Predict 

UI Benefit Duration/Exhaustion 

Fraction of Benefits Exhausted at the Top and Bottom 

of the Predicted Benefit Duration/Exhaustion Distribution 

for Various Profiling Models 

Top of 

Distribution 

Bottom of 

Distribution Difference 

Pennsylvania model a 38.2 25.7 12.5 

Washington model a 35.3 24.6 10.7 

Kentucky model b

 78.3 53.5 24.8 

WPRS-type (Kentucky data, 1994) b, c 64.9 55.0 9.9 

Preferred specification (Kentucky data, 1994) b, d 67.5 51.1 16.4 

 a The division for the Pennsylvania and Washington models is the top 25 percent predicted benefit 

exhaustion probabilities versus the bottom 75 percent. These are the maximum differences reported 

by O’Leary, Decker, and Wandner (1998). 

 b For Kentucky, the division is the top 60 percent of predicted benefit receipt durations and the 

remaining 40 percent. 

 c

 The WPRS-type specification reported here includes five covariates: education, occupation, 

tenure, local unemployment rate, and aggregate employment change in the claimant’s industry. 

 d Our preferred specification, described as Model 36 in Table 7, includes education, occupation, 

tenure, plus a few more variables on past UI take-up, welfare recipiency and background. 

Estimation and prediction for the Pennsylvania and Washington model are done using the same 

samples; the models for Kentucky and Maryland on Kentucky data are estimated on a 90 percent subsample of claimants with prediction based on the remaining 10 percent. 
43

Table 4. Comparison of Alternative Functional Forms: OLS, Logit, Probit, and Tobit 

Predictive Performance from Estimation on Kentucky Data 1989-1995 

Quintiles of Predicted RANDOM 

Probability Distribution DATA ASSIGNMENT 

Q 5 0.997 0.637 

Q 4 0.997 0.637 

Q 3 0.742 0.636 

Q 2 0.372 0.638 

Q 1 0.102 0.638 

 

DEPENDENT 

VARIABLE EXHAUST FRACTION 

Quintiles of Predicted OLS LOGIT PROBIT OLS TOBIT 

Probability Distribution 

Q 5 0.758 0.758 0.757 0.757 0.759 

Q 4 0.699 0.698 0.698 0.696 0.697 

Q 3 0.651 0.649 0.650 0.666 0.662 

Q 2 0.624 0.625 0.625 0.617 0.617 

Q 1 0.575 0.577 0.576 0.570 0.572 

 

Differences Between Predicted Fractions of Benefits Exhausted 

 

Top 80% - Bottom 20% 10.83 10.52 10.71 11.46 11.13 

Top 60% - Bottom 40% 10.32 10.07 10.15 11.31 11.15 

Top 40% - Bottom 60% 11.16 11.12 11.08 10.89 11.12 

Top 20% - Bottom 80% 12.02 12.11 12.01 11.98 12.26 

 

Average difference 11.09 10.95 10.99 11.41 11.42 

Notes: 

In the first panel we report the actual fraction of benefits exhausted by quintiles in the actual data and 

by randomly generated quintiles. In the second panel the fraction of benefits exhausted is reported by 

quintiles of the predicted benefit duration/probability to exhaust UI benefits. The third panel reports 

differences in predictive results across different percentiles of the distribution of predicted benefit 

duration/ exhaustion probabilities. 

Explanatory variables 

All models use the standard WPRS-type regressors: education, occupation, tenure, 

local unemployment rate (lagged) and employment changes in claimant's industry (lagged). 

 

Dependent variables 

EXHAUST is a discrete variable indicating whether unemployment benefits were exhausted (=1) or not (=0).

FRACTION is a continuous variable equal to the share of benefits claimed out of maximum allowed benefits.
Table 5. Comparison of Alternative Dependent Variables 

Predictive Performance from OLS Estimation on Kentucky Data 1989-1995 

FRACTION EXHAUSTED EXHAUSTED NUMBER AT LEAST 

Quintiles of Predicted OF BENEFITS BENEFITS >= 90% OF WEEKS 26 WEEKS 

Probability Distribution EXHAUSTED BENEFITS CLAIMED CLAIMED 

 

Q 5 0.757 0.758 0.760 0.756 0.754 

Q 4 0.696 0.699 0.699 0.703 0.703 

Q 3 0.666 0.651 0.651 0.659 0.648 

Q 2 0.617 0.624 0.623 0.619 0.623 

Q 1 0.570 0.575 0.574 0.570 0.578 

 

Differences Between Predicted Fractions of Benefits Exhausted 

 

Top 80% - Bottom 20% 11.46 10.83 10.93 11.44 10.45 

Top 60% - Bottom 40% 11.31 10.32 10.52 11.15 10.14 

Top 40% - Bottom 60% 10.89 11.16 11.38 11.38 11.23 

Top 20% - Bottom 80% 11.98 12.02 12.38 11.89 11.61 

 

Average difference 11.41 11.09 11.30 11.47 10.86 

 

Notes: 

In the first panel the fraction of benefits exhausted is reported by quintiles of the predicted 

benefit duration/probability to exhaust UI benefits. The second panel reports differences in predictive results 

across different percentiles of the distribution of predicted benefit duration/ exhaustion probabilities. 

Explanatory variables 

All models use the standard WPRS model regressors: education, occupation, tenure, 

local unemployment rate (lagged) and employment changes in claimant’s industry of employment (lagged). 

 

Table 6. Dropping Local Unemployment (UE) Rate and 

Industry Employment (IE) Changes From the WPRS Model Specification 

Predictive Performance from Estimation on Kentucky Data 1989-1995 

 A B C D E F 

WPRS basic Adds both Adds Adds Adds regional Adds local office

Quintiles of Predicted Model UE rate and IE changes UE rate dummies dummies 

Probability Distribution IE changes 

 

Q 5 0.743 0.757 0.747 0.757 0.758 0.764 

Q 4 0.700 0.696 0.706 0.699 0.694 0.695 

Q 3 0.662 0.666 0.660 0.662 0.667 0.661 
Q 2 0.618 0.617 0.618 0.621 0.626 0.633 

Q 1 0.585 0.570 0.577 0.568 0.564 0.557 

Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 9.59 11.46 10.56 11.68 12.21 13.13 

Top 60% - Bottom 40% 10.05 11.31 10.65 11.18 11.10 11.17 

Top 40% - Bottom 60% 10.01 10.89 10.78 11.11 10.67 11.26 

Top 20% - Bottom 80% 10.19 11.98 10.63 11.94 11.95 12.76 

 

Average difference 9.96 11.41 10.65 11.48 11.48 12.08 

 

Notes: 

Linear model estimated by OLS. Dependent variable is fraction of benefits exhausted. 

All models include the basic (reduced) WPRS regressors: education, occupation and tenure. 

Each specification adds to the basic WPRS-type model variables as described below. 

 

Model A: Basic WPRS model specification: 

 includes neither local unemployment rates nor industry employment changes. 

Model B: Includes both local unemployment rates and industry employment changes. 

Model C: Does not include local unemployment rates, but includes industry employment changes. 

Model D: Does not include industry employment changes, but includes local unemployment rates. 

Model E: Does not include unemployment and industry, but adds regional dummies. 

Model F: Does not include unemployment and industry, but adds local office dummies. 

 
46

Table 7. Covariates Used in the 40 Model Specifications Analyzed for Kentucky 

Model 0 (WPRS) = Education 

 Occupation (1-digit codes) 

 Tenure with last employer (and dummy for missing tenure) 

Model 1 = WPRS + UI benefits exhausted last year 

 UI benefits claimed last year 

Model 2 = WPRS + UI benefits exhausted last year 

 UI benefits claimed last year 

 UI benefits exhausted two years ago 

 UI benefits claimed two years ago 

Model 3 = WPRS + UI benefits exhausted last year 

 UI benefits claimed last year 

 UI benefits exhausted two years ago 

 UI benefits claimed two years ago 

 UI benefits exhausted three years ago 

 UI benefits claimed three years ago 

Model 4 = WPRS + Indicator for previous UI benefit claims (in our sample) 

Model 5 = WPRS + Average fraction of UI benefits exhausted in the past 

Model 6 = WPRS + Average number of weeks claimed in the past 

Model 7 = WPRS + Average UI benefit amount (real 1995 dollars) claimed in the past 

Model 8 = WPRS + Welfare recipiency 

Model 9 = WPRS + Food stamps recipiency 

Model 10 = WPRS + Economically disadvantaged status 

Model 11 = WPRS + JTPA eligibility 

Model 12 = WPRS + Indicator for no phone 

 

Model 13 = WPRS + Public transport for getting to work 

Model 14 = WPRS + Quarterly wages within last year (real 1995 dollars) 

Model 15 = WPRS + Tenure squared 

Model 16 = WPRS + Type of working shift (shift 1,2,3, or rotating) 

Model 17 = WPRS + Ratio of high quarter wage to average (base) year wage 

Model 18 = WPRS + Weekly benefit amount (real 1995 dollars) 

 

Model 19 = WPRS + Maximum benefit amount (real 1995 dollars) 

Model 20 = WPRS + Wage replacement rule 
47

Table 7. (cont.) Covariates Used in the 40 Model Specifications Analyzed for Kentucky 

Model 21 = WPRS + Indicator for pension benefits recipiency 

Model 22 = WPRS + Indicator for child support deductions 

Model 23 = WPRS + Weekly amount of pension benefits received 

Model 24 = WPRS + Month benefits began (continuous) 

Model 25 = WPRS + Separation from plant closure 

Model 26 = WPRS + Separation from merger 

Model 27 = WPRS + Enrolled in school at the time of claim (+ dummy for missing 

enrollment) 

Model 28 = WPRS + Employed at the time of claim ( + dummy for missing employment) 

Model 29 = WPRS + Local office (32 dummies) 

Model 30 = WPRS + Local office 

 Claim filed out of state 

Model 31 = WPRS + Indicator for previous UI benefit claim 

 JTPA eligibility 

 Quarterly wages within last year (real 1995 dollars) 

 Tenure squared 

 Employed at the time of claim (+ dummy for missing employment) 

Model 32 = WPRS + Indicator for previous UI benefit claim 

 JTPA eligibility 

 Quarterly wages within last year (real 1995 dollars) 

 Tenure squared 

 Employed at the time of claim (+ dummy for missing employment) 

 Local office 

Model 33 = WPRS + UI benefits exhausted last year 

 UI benefits exhausted two years ago 

 Indicator for previous UI benefit claims 

 Welfare recipiency 

 Food stamps recipiency 

 Public transport for getting to work 

 Quarterly wages within last year (real 1995 dollars) 

 Tenure squared 

 Enrolled in school at the time of claim (+ dummy for missing 

enrollment) 

 Employed at the time of claim (+ dummy for missing employment) 

Model 34 = WPRS + UI benefits exhausted last year 

 UI benefits exhausted two years ago 

 Indicator for previous UI benefit claims 

 Welfare recipiency 

 Food stamps recipiency 

 Public transport for getting to work 

 Quarterly wages within last year (real 1995 dollars) 

 Tenure squared 

 Enrolled in school at the time of claim (+ dummy for missing 

enrollment) 

 Employed at the time of claim (+ dummy for missing employment) 

 Local office 
48

Table 7. (cont.) Covariates Used in the 40 Model Specifications Analyzed for Kentucky 

Model 35 = WPRS + UI benefits exhausted last year 

 Indicator for previous UI benefit claims 

 Welfare recipiency 

 Food stamps recipiency 

 Public transport for getting to work 

 JTPA eligibility 

 Quarterly wages within last year (real 1995 dollars) 

 Tenure squared 

 Enrolled in school at the time of claim (+ dummy for missing 

enrollment) 

 Employed at the time of claim (+ dummy for missing 

employment) 

 Type of working shift (shift 1,2,3, or rotating) 

Model 36 = WPRS + UI benefits exhausted last year 

 Indicator for previous UI benefit claims 

 Welfare recipiency 

 Food stamps recipiency 

 Public transport for getting to work 

 JTPA eligibility 

 Quarterly wages within last year (real 1995 dollars) 

 Tenure squared 

 Enrolled in school at the time of claim (+ dummy for missing 

enrollment) 

 Employed at the time of claim (+ dummy for missing employment) 

 Type of working shift (shift 1,2,3, or rotating) 

 Local office 

Model 37 = WPRS + UI benefits exhausted last year 

 Welfare recipiency 

 Food stamp recipiency 

 JTPA eligibility 

 High quarter wage to average (base) year wage 

 Quarterly wages within last year (real 1995 dollars) 

 Tenure squared 

 Enrolled in school at the time of claim (+ dummy for missing 

enrollment) 

 Employed at the time of claim (+ dummy for missing employment) 

Model 38 = WPRS + UI benefits exhausted last year 

 Welfare recipiency 

 Food stamp recipiency 

 JTPA eligibility 

 High quarter wage to average (base) year wage 

 Quarterly wages within last year (real 1995 dollars) 

 Tenure squared 

 Enrolled in school at the time of claim (+ dummy for missing 

enrollment) 

 Employed at the time of claim (+ dummy for missing employment) 

 Local office 

Model 39 = WPRS + Indicator for previous UI benefit claims Quarterly wages within 

last year 

 Average fraction claimed in the pas Weekly pension 

benefits 

 UI benefits exhausted last year Weekly child support 

deductions 

 UI benefits exhausted two years ago Tenure squared 
49

 UI benefits exhausted three years ago Enrolled in school at 

the time of claim 

 Welfare recipiency Employed at the 

time of claim 

 Food stamps recipiency Separation from 

plant closure 

 JTPA eligibility Separation from 

merger 

 Indicator for economically disadvantaged Month benefits 

began 

 Indicator for no phone Type of work shift 

(1,2,3, rotating) 

 Indicator for public transport Local office 

 Maximum benefits Claim filed out of 

state 
Table 8. Predictive Performance from Different Model Specifications 

Linear Model Estimated by OLS on 1994 Kentucky Data 

The dependent variable is fraction of UI benefits exhausted. 

Quintiles of RANDOM 

 Predicted Probabilities DATA ASSIGNMENT 

Q 5 0.998 0.629 

Q 4 0.998 0.636 

Q 3 0.721 0.631 

Q 2 0.344 0.629 

Q 1 0.101 0.635 

MODEL 0 1 2 3 4 5 6 7 

Q 5 0.681 0.683 0.684 0.680 0.677 0.677 0.680 0.679 

Q 4 0.643 0.639 0.643 0.647 0.651 0.645 0.641 0.641 

Q 3 0.600 0.603 0.599 0.603 0.590 0.605 0.608 0.603 

Q 2 0.597 0.596 0.599 0.595 0.610 0.600 0.600 0.602 

Q 1 0.527 0.528 0.524 0.524 0.521 0.522 0.521 0.523 

Differences Between Predicted Fractions of Benefits Exhausted 

 

Top 80% - Bottom 20% 10.33 10.24 10.68 10.68 11.12 11.00 11.10 10.82 

Top 60% - Bottom 40% 7.98 7.96 8.01 8.35 7.42 8.15 8.25 7.87 

Top 40% - Bottom 60% 8.78 8.53 8.94 8.94 9.04 8.52 8.42 8.40 

Top 20% - Bottom 80% 8.95 9.14 9.27 8.75 8.35 8.39 8.76 8.64 

 

Average Difference 9.01 8.97 9.22 9.18 8.98 9.01 9.13 8.93 

R-squared 0.031 0.031 0.031 0.032 0.031 0.031 0.031 0.031 

MODEL 8 9 10 11 12 13 14 15 

Q 5 0.679 0.686 0.681 0.682 0.681 0.682 0.677 0.681 

Q 4 0.641 0.642 0.637 0.644 0.643 0.644 0.645 0.644 

Q 3 0.605 0.600 0.604 0.602 0.600 0.597 0.616 0.592 

Q 2 0.598 0.595 0.609 0.588 0.597 0.601 0.585 0.607 

Q 1 0.525 0.525 0.518 0.535 0.527 0.524 0.525 0.523 

Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 10.60 10.61 11.44 9.37 10.32 10.70 10.58 10.83 

Top 60% - Bottom 40% 8.05 8.27 7.71 8.10 7.98 7.83 9.13 7.40 

Top 40% - Bottom 60% 8.40 9.06 8.20 8.77 8.77 8.89 8.56 8.88 

Top 20% - Bottom 80% 8.71 9.53 8.88 8.96 8.96 9.03 8.47 8.99 

Average Difference 8.94 9.37 9.06 8.80 9.01 9.11 9.18 9.03 

R-squared 0.031 0.032 0.031 0.033 0.031 0.031 0.033 0.031 
51

Table 8. (cont.) Predictive Performance from Different Model Specifications 

Linear Model Estimated by OLS on 1994 Kentucky Data 

The dependent variable is fraction of UI benefits exhausted. 

MODEL 16 17 18 19 20 21 22 23 

Q 5 0.674 0.674 0.675 0.675 0.678 0.681 0.683 0.681 

Q 4 0.652 0.648 0.652 0.652 0.650 0.644 0.640 0.644 

Q 3 0.589 0.612 0.593 0.593 0.609 0.599 0.604 0.599 

Q 2 0.610 0.591 0.616 0.616 0.597 0.598 0.593 0.598 

Q 1 0.521 0.523 0.512 0.512 0.528 0.527 0.528 0.527 

Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 10.98 10.77 12.19 12.19 10.52 10.33 10.24 10.33 

Top 60% - Bottom 40% 7.29 8.74 7.61 7.61 8.31 7.91 8.20 7.91 

Top 40% - Bottom 60% 9.01 8.57 9.04 9.04 8.61 8.78 8.64 8.78 

Top 20% - Bottom 80% 8.14 8.10 8.20 8.20 8.24 8.92 9.14 8.95 

Average difference 8.86 9.05 9.26 9.26 8.92 8.99 9.06 8.99 

R-squared 0.031 0.032 0.039 0.039 0.032 0.031 0.032 0.031 

 

MODEL 24 25 26 27 28 29 30 31 

Q 5 0.684 0.681 0.680 0.705 0.703 0.707 0.707 0.716 

Q 4 0.637 0.644 0.645 0.651 0.653 0.631 0.631 0.661 

Q 3 0.602 0.599 0.598 0.642 0.633 0.620 0.620 0.630 

Q 2 0.600 0.599 0.599 0.561 0.577 0.566 0.566 0.568 

Q 1 0.526 0.526 0.527 0.490 0.482 0.523 0.525 0.473 

Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 10.50 10.49 10.41 15.00 16.03 10.77 10.61 17.12 

Top 60% - Bottom 40% 7.83 7.87 7.80 14.05 13.41 10.78 10.72 14.84 

Top 40% - Bottom 60% 8.43 8.76 8.81 11.39 11.45 9.89 9.90 13.16 

Top 20% - Bottom 80% 9.26 8.88 8.83 11.89 11.72 12.16 12.17 13.29 

Average difference 9.01 9.00 8.96 13.08 13.15 10.90 10.85 14.60 

R-squared 0.031 0.031 0.031 0.096 0.099 0.049 0.049 0.110 

 

MODEL 32 33 34 35 36 37 38 39 

Q 5 0.737 0.731 0.752 0.726 0.741 0.724 0.754 0.749 

Q 4 0.676 0.664 0.677 0.660 0.684 0.650 0.646 0.680 

Q 3 0.597 0.631 0.587 0.639 0.600 0.635 0.613 0.593 

Q 2 0.570 0.540 0.558 0.550 0.560 0.554 0.564 0.568 

Q 1 0.468 0.482 0.474 0.473 0.463 0.485 0.470 0.458 

Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 17.71 15.92 16.98 17.07 18.31 15.59 17.41 18.94 

Top 60% - Bottom 40% 15.09 16.43 15.62 16.37 16.38 15.05 15.37 16.07 

Top 40% - Bottom 60% 16.15 14.63 17.52 13.94 17.20 12.93 15.09 17.48 

Top 20% - Bottom 80% 15.94 15.19 17.79 14.57 16.48 14.31 18.03 17.41 
52

Average difference 16.22 15.54 16.98 15.49 17.09 14.47 16.47 17.47 

R-squared 0.128 0.113 0.130 0.116 0.134 0.107 0.124 0.139 
Table 9. Regional Analysis Results 

from Linear Model with Model 36 Covariates Estimated by OLS on 1994 Kentucky Data 

Eight Models – One Estimated Separately for Each of the Eight Regions 

One Overall Ranking – Based on Predictions from the Eight Separate Regressions 

Quintiles of Predicted Overall Region Region Region Region Region Region Region Region 

Probability Distribution Ranking 1 2 3 4 5 6 7 8 

Q 5 0.738 0.742 0.685 0.669 0.719 0.787 0.773 0.784 0.743 

Q 4 0.683 0.709 0.652 0.635 0.660 0.768 0.736 0.693 0.619 

Q 3 0.597 0.609 0.605 0.589 0.550 0.678 0.684 0.656 0.578 

Q 2 0.558 0.517 0.613 0.578 0.528 0.553 0.665 0.594 0.537 

Q 1 0.472 0.429 0.538 0.474 0.441 0.499 0.494 0.546 0.413 

Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 17.17 21.53 10.11 14.38 17.36 19.78 22.06 13.60 20.57 

Top 60% - Bottom 40% 15.78 21.37 7.23 10.53 15.87 21.82 15.15 14.13 17.12 

Top 40% - Bottom 60% 16.85 20.72 8.36 10.54 18.32 20.06 14.02 13.99 17.10 

Top 20% - Bottom 80% 16.05 17.58 8.29 9.99 17.45 16.19 12.80 16.14 20.59 

 

Average difference 16.46 20.30 8.50 11.36 17.25 19.46 16.01 14.47 18.85 

One Model – Estimated Jointly for All Eight Regions, Including Regional Dummy Variables 

Quintiles of Predicted Overall Region Region Region Region Region Region Region Region 

Probability Distribution Ranking 1 2 3 4 5 6 7 8 

Q 5 0.741 0.740 0.709 0.656 0.693 0.793 0.729 0.771 0.741 

Q 4 0.673 0.709 0.618 0.638 0.676 0.733 0.758 0.737 0.645 

Q 3 0.606 0.623 0.639 0.617 0.642 0.680 0.710 0.609 0.536 

Q 2 0.557 0.517 0.570 0.553 0.462 0.558 0.624 0.635 0.545 

Q 1 0.471 0.418 0.556 0.480 0.425 0.521 0.530 0.521 0.423 

Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 17.36 22.91 7.77 13.63 19.27 17.03 17.54 16.63 19.44 

Top 60% - Bottom 40% 15.95 22.31 9.22 12.08 22.68 19.63 15.51 12.75 15.68 

Top 40% - Bottom 60% 16.27 20.55 7.50 9.69 17.49 17.68 12.20 16.56 19.18 

Top 20% - Bottom 80% 16.44 17.34 11.27 8.38 14.18 16.96 7.33 14.53 20.39 

Average difference 16.51 20.78 8.94 10.94 18.41 17.82 13.14 15.12 18.67 

Table 10. Year-by-year Predictive Performance from Linear Models Estimated by OLS 

on 1989-1995 Kentucky Data 

 Quintiles of Predicted YEARS 1989 1993 

Assignment 

Mechanism 

Probability 

Distribution ALL 1989 1990 1991 1992 1993 1994 1995 To 1992 To 1995

 

 Q 5 0.638 0.617 0.681 0.668 0.661 0.576 0.629 0.590 0.658 0.600 

1). Random Q 4 0.637 0.618 0.673 0.666 0.655 0.572 0.636 0.583 0.657 0.597 

 Assignment Q 3 0.636 0.619 0.674 0.673 0.661 0.574 0.631 0.579 0.657 0.598 

 Q 2 0.637 0.620 0.676 0.671 0.661 0.571 0.629 0.580 0.657 0.594 
 Q 1 0.638 0.620 0.679 0.670 0.658 0.581 0.635 0.588 0.660 0.603 

 

 Q 5 0.997 0.993 0.946 0.947 0.939 0.956 0.998 0.957 0.934 0.974 

2). DATA Q 4 0.997 0.993 0.946 0.947 0.939 0.956 0.998 0.957 0.934 0.974 

 Q 3 0.742 0.701 0.946 0.947 0.939 0.577 0.721 0.648 0.934 0.636 

 Q 2 0.372 0.346 0.431 0.426 0.392 0.307 0.344 0.315 0.406 0.329 

 Q 1 0.102 0.092 0.116 0.117 0.101 0.099 0.101 0.095 0.113 0.100 

 

 

 Q 5 0.781 0.745 0.882 0.820 0.801 0.803 0.741 0.714 0.803 0.744 

3). MODEL 36 Q 4 0.712 0.686 0.741 0.755 0.729 0.772 0.684 0.655 0.729 0.678 

BEST SPECIFICATION Q 3 0.665 0.641 0.685 0.729 0.693 0.675 0.600 0.625 0.689 0.649 

 Q 2 0.616 0.619 0.612 0.649 0.644 0.591 0.560 0.567 0.630 0.597 

 Q 1 0.533 0.540 0.543 0.464 0.532 0.482 0.463 0.508 0.529 0.508 

 Differences Between Predicted Fractions of Benefits Exhausted 

 Top 80% - Bottom 20% 16.02 13.30 18.69 27.39 18.45 22.80 18.31 13.23 18.34 15.86 

 Top 60% - Bottom 40% 14.46 11.14 19.19 21.14 15.31 21.34 16.38 12.71 16.06 13.73 

 Top 40% - Bottom 60% 14.16 11.58 19.84 17.35 14.25 20.48 17.20 11.78 14.99 12.57 

 Top 20% - Bottom 80% 14.97 12.37 23.73 17.10 15.19 17.32 16.48 12.52 15.88 13.55 

 

 Average difference 14.90 12.10 20.36 20.74 15.80 20.49 17.09 12.56 16.32 13.93 
Table 10. (cont.) Year-by-year Predictive Performance from Linear Models Estimated by OLS 

on 1989-1995 Kentucky Data 

 Quintiles of Predicted YEARS 1989 1993 

Assignment Mechanism 

Probability 

Distribution ALL 1989 1990 1991 1992 1993 1994 1995 To 1992

To 

 1995 

 Q 5 0.743 0.681 0.801 0.784 0.761 0.706 0.681 0.663 0.760 0.682 

 WPRS Q 4 0.700 0.657 0.765 0.723 0.702 0.700 0.643 0.656 0.720 0.639 

4). MODEL Q 3 0.662 0.648 0.727 0.724 0.678 0.682 0.600 0.602 0.693 0.626 

BASE Q 2 0.618 0.628 0.596 0.695 0.649 0.671 0.597 0.569 0.656 0.634 

SPECIFICATION Q 1 0.585 0.628 0.596 0.496 0.610 0.566 0.527 0.582 0.579 0.595 

 

 Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 9.59 2.55 12.64 23.54 8.76 12.33 10.33 4.10 12.77 5.03 

Top 60% - Bottom 40% 10.05 3.40 16.85 14.78 8.46 7.74 7.98 6.48 10.64 3.44 

Top 40% - Bottom 60% 10.01 3.43 14.34 11.51 8.59 6.35 8.78 7.49 9.70 4.20 

Top 20% - Bottom 80% 10.19 4.06 13.05 12.44 10.18 5.12 8.95 6.08 9.80 5.89 

 

 Average difference 9.96 3.36 14.22 15.57 9.00 7.89 9.01 6.04 10.73 4.64 

Notes:

For each of the estimated quintiles we report the fraction of benefits exhausted by claimants in that quintile. 

For the WPRS and best model specifications we also report differences between the predictions for the 

top 80 percent and bottom 20 percent , for the top 60 percent and bottom 40 percent, for the top 40 percent 

and bottom 60 percent, and for the top 20 percent and bottom 80 percent of the predicted values. 

1. The random assignment mechanism forms quintiles at random. 

2. The data distribution is computed using on the distribution of fraction of exhausted UI benefits in the raw data. 

3. The best model specification is Model 36 described in Table 7. 

4. The WPRS model base specification uses as covariates education, tenure, and 1-digit occupation codes. 

 Year dummies are included in the models estimated using the pooled data (All, 1989 to 1992 and 1993 to 1995) . 
56

Table 11: Definitions of Measures of Delayed Filing 

Categorical measures of delay 

A 0: # Days Elapsed <=30 

1: # Days Elapsed > 30 

B 

1: 0 <= # Days elapsed <=14 

2: 15 <= # Days elapsed <=30 

3: 31 <= # Days elapsed <=60 

4: 61 <= # Days elapsed <=180 

5: 181<= # Days elapsed 

C 1: 0 <= # Weeks elapsed <=1 

2: 2 <= # Weeks elapsed <=6 

3: 7 <= # Weeks elapsed 

D 

1: 0 <= # Days elapsed <=10 

2: 11 <= # Days elapsed <=15 

3: 16 <= # Days elapsed <=30 

4: 1 <= # Months elapsed <=2 

5: 3 <= # Months elapsed 

E 

1: 0 <= # Weeks elapsed <= 1 

2: 2 = # Weeks elapsed 

3: 3 <= # Weeks elapsed <= 4 

4: 5 <= # Weeks elapsed <= 6 

5: 7 <= # Weeks elapsed <= 8 

6: 9 <= # Weeks elapsed 

F 1: # Months elapsed = 0 

2: # Months elapsed = 1 or 2 

3: # Months elapsed >= 3 

Continuous measures of delay 

G # Days elapsed 

H # Weeks elapsed 

 

# Months elapsed 
57

K 
Table 12. Predictive Performance from Adding Delayed Filing Measures. 

Four Selected Model Specifications. OLS Estimation of Linear Model on 1994 Kentucky Data 

(Delay = Time Gap Between Losing a Job and Filing for UI benefits) 

Model 0 (WPRS) 

Categorical measures of delay Continuous measures of delay

Model 0 A B C D E F G H K 

Q5 0.681 0.695 0.695 0.691 0.700 0.693 0.700 0.691 0.691 0.694 

Q4 0.643 0.643 0.648 0.649 0.641 0.653 0.643 0.653 0.651 0.653 

Q3 0.600 0.609 0.607 0.610 0.610 0.600 0.606 0.602 0.606 0.593 

Q2 0.597 0.561 0.556 0.558 0.556 0.561 0.560 0.563 0.561 0.567 

Q1 0.527 0.540 0.542 0.541 0.542 0.542 0.541 0.540 0.540 0.541 

Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 10.33 8.78 8.45 8.61 8.44 8.45 8.61 8.75 8.75 8.55 

Top 60% - Bottom 40% 7.98 9.88 10.07 10.05 10.09 9.71 9.92 9.76 9.88 9.24 

Top 40% - Bottom 60% 8.78 9.93 10.33 10.01 10.10 10.55 10.22 10.37 10.22 10.60 

Top 20% - Bottom 80% 8.95 10.69 10.67 10.11 11.27 10.42 11.23 10.10 10.12 10.48 

 

Average difference 9.01 9.82 9.88 9.69 9.97 9.79 10.00 9.75 9.75 9.72 

 

R-squared 0.031 0.044 0.044 0.044 0.045 0.044 0.045 0.044 0.044 0.044 

Model 1 

 Categorical measures of delay Continuous measures of delay

Model 1 A B C D E F G H K 

Q5 0.683 0.693 0.693 0.690 0.700 0.692 0.700 0.691 0.693 0.694 

Q4 0.639 0.642 0.648 0.648 0.639 0.653 0.642 0.651 0.649 0.649 

Q3 0.603 0.612 0.608 0.611 0.611 0.600 0.606 0.605 0.605 0.596 

Q2 0.596 0.562 0.557 0.558 0.556 0.561 0.560 0.560 0.561 0.568 

Q1 0.528 0.540 0.542 0.541 0.542 0.542 0.541 0.541 0.541 0.541 

Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 10.24 8.69 8.45 8.59 8.43 8.45 8.61 8.63 8.64 8.53 

Top 60% - Bottom 40% 7.96 9.80 10.05 10.05 10.10 9.68 9.90 9.86 9.83 9.17 

Top 40% - Bottom 60% 8.53 9.64 10.16 9.92 10.00 10.49 10.24 10.29 10.25 10.33 

Top 20% - Bottom 80% 9.14 10.47 10.45 10.07 11.29 10.29 11.29 10.20 10.48 10.54 

 

Av. Dif. 8.97 9.65 9.78 9.66 9.96 9.73 10.01 9.75 9.80 9.64 

R-squared 0.031 0.044 0.044 0.044 0.045 0.044 0.045 0.044 0.044 0.044 
Table 12 (cont. ) Predictive Performance from Adding Delayed Filing Measures. 

Four Selected Model Specifications. OLS Estimation of Linear Model on 1994 Kentucky Data 

(Delay = Time Gap Between Losing a Job and Filing for UI benefits) 

Model 36 (Best specification) 

 Categorical measures of delay Continuous measures of delay

Model 36 A B C D E F G H K 

Q5 0.741 0.746 0.742 0.744 0.744 0.740 0.743 0.751 0.749 0.746 

Q4 0.684 0.674 0.681 0.675 0.678 0.679 0.675 0.674 0.675 0.678 

Q3 0.600 0.592 0.591 0.597 0.595 0.597 0.597 0.592 0.594 0.590 

Q2 0.560 0.580 0.578 0.573 0.578 0.576 0.578 0.575 0.574 0.574 

Q1 0.463 0.456 0.456 0.459 0.453 0.457 0.455 0.456 0.456 0.460 

Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 18.31 19.24 19.22 18.88 19.61 19.13 19.33 19.23 19.16 18.69 

Top 60% - Bottom 40% 16.38 15.29 15.46 15.60 15.68 15.59 15.51 15.69 15.75 15.38 

Top 40% - Bottom 60% 17.20 16.73 17.02 16.64 16.89 16.64 16.54 17.12 17.06 17.04 

Top 20% - Bottom 80% 16.48 17.08 16.55 16.81 16.75 16.28 16.66 17.65 17.44 17.02 

 

Av. Dif. 17.09 17.09 17.06 16.98 17.23 16.91 17.01 17.42 17.35 17.03 

R-squared 0.134 0.146 0.146 0.145 0.146 0.146 0.146 0.145 0.145 0.145 

Model 39 (Most covariates) 

 Categorical measures of delay Continuous measures of delay

Model 39 A B C D E F G H K 

Q5 0.749 0.762 0.758 0.762 0.764 0.759 0.768 0.751 0.749 0.746 

Q4 0.680 0.663 0.665 0.657 0.656 0.667 0.653 0.674 0.675 0.678 

Q3 0.593 0.597 0.599 0.605 0.604 0.600 0.603 0.592 0.594 0.590 

Q2 0.568 0.576 0.576 0.574 0.573 0.572 0.575 0.575 0.574 0.574 

Q1 0.458 0.450 0.450 0.450 0.451 0.450 0.449 0.456 0.456 0.460 

Differences Between Predicted Fractions of Benefits Exhausted 

Top 80% - Bottom 20% 18.94 20.00 19.94 19.89 19.82 19.99 20.11 19.23 19.16 18.69 

Top 60% - Bottom 40% 16.07 16.13 16.11 16.24 16.26 16.44 16.27 15.69 15.75 15.38 

Top 40% - Bottom 60% 17.48 17.21 17.03 16.59 16.75 17.25 16.85 17.12 17.06 17.04 

Top 20% - Bottom 80% 17.41 19.10 18.59 18.99 19.28 18.67 19.80 17.65 17.44 17.02 

 

Av. Dif. 17.47 18.11 17.91 17.93 18.03 18.09 18.26 17.42 17.35 17.03 

R-squared 0.139 0.151 0.152 0.151 0.152 0.152 0.152 0.151 0.151 0.151