Is the Threat of Reemployment Services More Effective Than the Services Themselves? Evidence from Random Assignment in the UI System Author(S): Dan A

American Economic Association

Is the Threat of Reemployment Services More Effective than the Services Themselves? Evidence from Random Assignment in the UI System Author(s): Dan A. Black, Jeffrey A. Smith, Mark C. Berger, Brett J. Noel Source: The American Economic Review, Vol. 93, No. 4 (Sep., 2003), pp. 1313-1327 Published by: American Economic Association Stable URL: http://www.jstor.org/stable/3132290 Accessed: 01/09/2010 17:55

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http://www.jstor.org Is the Threat of Reemployment Services More Effective Than the Services Themselves? Evidence from Random Assignment in the UI System

By DAN A. BLACK, JEFFREYA. SMrIT, MARK C. BERGER,AND BRETr J. NOEL*

We examine the effect of the WorkerProfiling and ReemploymentServices system. This program "profiles" UnemploymentInsurance (UI) claimants to determine their probability of benefit exhaustion and then provides mandatory employment and training services to claimants with high predictedprobabilities. Using a unique experimentaldesign, we estimatethat the program reduces mean weeks of UI benefit receipt by about 2.2 weeks, reduces mean UI benefits received by about $143, ana increases subsequent earnings by over $1,050. Most of the effect results from a sharp increase in early UI exits in the treatmentgroup relative to the control group. (JEL J650)

The UI system is widely believed to provide unemploymentby providing a subsidy to their incentives for workersto lengthen their spells of job search and leisure. This paper examines the behavioral effects of a new programthat "profiles" Unemployment Insurance(UI) claimants * Black: Center for Policy Research, 426 Eggers Hall, Syracuse University, Syracuse, NY 13244 (e-mail: based on the predicted length of their unem- [email protected]);Smith: Department of Eco- ployment spell or the predictedprobability that nomics, 3105 Tydings Hall, University of Maryland,Col- they will exhaust their UI benefits. Established lege Park, MD 20742 (e-mail: [email protected]); in 1993 and formally called the "WorkerPro- Berger (Mark Berger passed away on April 30, 2003): and Services" Departmentof Economics, Gatton College of Business and filing Reemployment (WPRS) Economics, University of Kentucky,Lexington, KY 40506; system, the programforces claimants with long Noel: American Express-TRS, 10030 North 25th Avenue, predictedUI spells or high predictedprobabili- Building 10400, Phoenix, AZ 85021 (e-mail: Brett.J. ties of benefit exhaustion to receive employ- [email protected]). We thank the U.S. Departmentof Labor ment and trainingservices early in their spell in for financial support through a contract between the Ken- tucky Departmentof Employment Services and the Center order to continue receiving benefits.1 for Business and Economic Research at the University of We consider the effects of the profiling pro- Kentucky. Smith also thanks the Social Science and Hu- gram on claimant behavior using data from manities Research Council of Canada and the CIBC Chair Kentucky. Our data embody a unique experi- in Human and Capital Productivity at the University of mental The randomizationin our WesternOntario for financialsupport. We thankBill Burris, design. data Donna Long, and Ted Pilcher of the Kentucky Department occurs only to satisfy capacity constraints and of Employment Services for their assistance, and Steve only at the margin. UI claimants are assigned Allen, Susan Black, Amitabh Chandra,and Roy Sigafus for "profiling scores" that take on integer values research assistance. Seminar participantsat AustralianNa- tional University, Boston University, the U.S. Bureau of Labor Statistics, University of Colorado, Cornell Univer- sity, the Econometric Society meetings, University of Es- sity College Dublin, the Upjohn Institute,and University of sex, University of Houston, IndianaUniversity, the Institute Western Ontarioprovided useful comments. We especially for Fiscal Studies, the Institute for Research on Poverty thankJaap Abbring, Joshua Angrist, ChristopherTaber, and SummerConference, Louisiana State University, University Bruce Meyer for their suggestions, along with three anon- of Maryland, MIT, University of Missouri, University of ymous referees. 1 New South Wales, Ohio State University, the Society See Stephen Wandner(1997), U.S. Departmentof La- of Labor Economists meetings, the Stockholm School of bor (1999), or RandallEberts et al. (2002) for more detailed Economics, SUNY-Buffalo, Syracuse University, the Tin- descriptions of the program and of how it varies across bergen Institute, University of Toronto, UBC, Univer- states.

1313 1314 THE AMERICANECONOMIC REVIEW SEPTEMBER2003 from 1 to 20, with higher scores indicating claimants to exit quickly, thereby reducing the claimants with longer expected durations.The extent of moral hazardin the UI program. requirementto receive reemployment services Third,we evaluate the use of profiling scores is allocated by profiling score up to capacity. based on expected UI claim durationas a means Within the marginalprofiling score-the one at of allocating the treatment.If this is an efficient which the capacity constraint is reached- method of treatmentallocation, we would ex- randomassignment allocates the mandatoryser- pect to find that the impact of treatment in- vices. Thus, if there are ten claimants with a creases in the profiling score. Instead, we find profiling score of 16 but only seven slots re- little evidence of any systematic relationship main, seven claimantsare randomlyassigned to between the estimated impact of treatmentand the treatmentgroup and three are assigned to the the profiling score. This suggests that such pro- control group. Donald T. Campbell (1969) filing does not increase the efficiency of treat- terms this experimentaldesign a "tie-breaking ment allocationand indicates the potentialvalue experiment." Donald L. Thistlethwaite and of furtherresearch on econometric methods of Campbell(1960) first advocatedit as a means of treatmentallocation before extending profiling evaluating the impact of receiving a college to other programs. scholarship.2To our knowledge, our experiment The paper proceeds as follows: In the next is the first to use this design. section, we describe the Kentucky WPRS sys- We have threemajor findings. First, using our tem and the design of the experiment.Section II unique data, we evaluate the WPRS system for lays out the econometric framework for our persons at the profiling score margin. We esti- investigation. Section III presents our empirical mate that for this group, the program reduces findings and Section IV concludes. mean weeks of UI benefit receipt by about 2.2 weeks, reduces mean UI benefits received by I. How the WPRS SystemWorks about $143, and increases subsequentearnings by about $1,000. Given its very low cost, the States are afforded a great deal of leeway in program easily passes standard cost-benefit the design and implementationof their WPRS tests. systems. In Kentucky, the Departmentof Em- Second, the dynamics of the treatmenteffect ployment Services contracted with the Center provide importantevidence about how the pro- for Business and Economic Research (CBER) gram works. The treatment group has signifi- of the University of Kentucky to develop an cantly higher earnings in the first two quarters econometric model of expected UI spell after filing their UI claims than the control duration. group, while there are no significantdifferences CBER estimated the profiling model using in the thirdthrough sixth quarters.This suggests five years of UI claimant data and variables that the earnings gains result primarily from obtainedfrom various administrativeand public earlier return to work in the treatmentgroup. use data sets. The profilingmodel contains local Moreover, examinationof the exit hazardfrom economic and labor market conditions along UI suggests that much of the impact results with worker characteristics.3U.S. Department from persons in the treatmentgroup leaving UI of Justice regulationsprevent states from using upon receiving notice of the requirementthat receive services, ratherthan they reemployment 3 or after the receipt of those services. See Berger et al. (1997) for a more detailed description during The model has moderatesuccess in the induces some of the model. profiling Thus, program job-ready predicting claimants who will exhaust their UI benefits. Berger et al. report that selection based on the profiling model results in a treated group whose members receive 2 We thankJoshua Angrist for bringingthese citations to 78.3 percentof their possible benefits while randomassign- our attention. Campbell (1969) notes the relationship be- ment would result in a treated group whose members re- tween the "tie-breakingexperiment" and the regressiondis- ceive only 66.6 percent of their possible benefits. "Perfect" continuity design. See Angrist and Alan B. Krueger(1999) assignment based on realized spell lengths would yield a and James J. Heckman et al. (1999) for discussions of the treatmentgroup whose membersreceive about93 percentof regression discontinuitydesign. their potential benefits. VOL.93 NO. 4 BLACKET AL: REEMPLOYMENTSERVICES 1315 sex, age, race, ethnicity, and veteran status in ment. We will assess your needs and work their profiling models. While the econometric with you to decide which services may profiling model provides a continuous measure increase your chances of finding a good of the expected number of weeks of benefit job. Services may include counseling, job CBER the of Em- search workshops, testing, job referral receipt, provides Department and or if referral to Services with a discrete score placement, needed, ployment profile more intensive services, such as training. ranging from 1 to 20. Claimants predicted by the profiling model to exhaust between 95 and 100 percent of their unemploymentbenefits re- Because of capacity constraints,local offices ceive a score of 20, claimants predicted to ex- at some times during the year are not able to haust between 90 and 95 percent of their serve the entirepopulation of claimants,making unemploymentbenefits receive a 19, and so on. it necessary to ration entry into the program. The WPRS system was implementedin October CBER allocates program slots at each local of 1994; we make use of UI spells starting office, serving those claimants with the highest between that date and June 30, 1996. profiling scores. In the marginal score group, The Kentucky WPRS system begins with where there are enough slots to serve some but claimantsproviding information about their em- not all claimants with a given score, CBER ployment history and characteristicswhile filing randomly assigns persons to either a treatment their claims. For claimants found to be eligible group required to participatein reemployment for profiling, the Kentucky DES provides services as a condition of continued UI receipt CBER with data from the claimants' intake or a control group exempt from this require- forms.4 CBER then provides local Department ment. We call these sets of claimants "profiling of Employment Services' offices with the pro- tie groups,"or PTGs-groups of claimantsin a filing scores of claimants in their area and the given office filing claims in a given week who list of those 'chosen to receive reemployment have the marginalprofiling score for that office services. Finally, those claimants selected to in that week. This design differs from typical receive reemployment services are contacted experimental evaluations of employment and throughthe mail to inform them of their rights training programs wherein all program appli- and responsibilitiesunder the program.A copy cants are randomly assigned. of the letter sent by the Departmentof Employ- Unfortunatelyfor the sample size available ment Services appearsas Exhibit 1 on the AER for our analysis but fortunately for the claim- web site (http://www.aeaweb.org/aer/contents/). ants, the Kentucky economy was extremely In the letter, the claimant is told: strong from October 1994 to June 1996, the period for which we currentlyhave data. As a You have been indentifiedas a dislocated result, local offices were often able to treat the worker and selected under the UI Claim- entire claimant population. Indeed, of the ant Profiling Program to receive job 57,779 claimants in this period, 48,002 were search assistance services. You are obli- selected for treatment,or slightly over 83 per- underthe law to Failure gated participate. cent. Of the 2,748 potential PTGs, there are to report or in participate reemployment 286 actual in size from 2 to services withoutjustifiable cause may re- only PTGs, ranging sult in denial of your unemploymentin- 54. The mean size of a PTG is 6.9, with a surance benefits. median of 4, a 25th percentile of 3, and a 75th percentileof 8. Profilingscores within the PTGs This program is designed to provide job range from 6 to 19, with the median and the search assistance services to those UI mode at 16.5 Combining all of the PTGs yields claimants identified as being most likely to need assistance in finding new employ- 5 Most of the variation in the marginal profiling score among the PTGs consists of variationacross local offices. A regression of the marginal profiling score on a vector of 4 Individuals who have a definite recall date or who are local office indicatorsusing PTGs as the unit of observation hired through a union hall are exempt from profiling. explains 64 percent of the variation in profiling scores. 1316 THE AMERICANECONOMIC REVIEW SEPTEMBER2003

TABLE1-DEMOGRAPHIC CHARACTERISTICS OFTREATMENT AND CONTROL GROUPS: KENTUCKYWPRS EXPERIMENT,OCTOBER 1994 TOJUNE 1996

Population p-values with profiling Treated for tests of score 6 to 19, population, Control Treatment differences not in not in group group in means experiment experiment Age 37.0 37.1 0.717 37.2 37.4 (10.9) (11.1) (11.3) (11.2) Years of schooling 12.3 12.6 0.221 12.4 12.4 (2.10) (2.14) (2.03) (2.06) White male 0.564 0.518 0.095 0.519 0.517 White female 0.352 0.372 0.060 0.394 0.399 Nonwhite male 0.040 0.055 0.433 0.044 0.042 Nonwhite female 0.044 0.055 0.691 0.044 0.042 Earningsin year before $19,759 $19,047 0.666 $18,612 $19,171 claim (13,677) (13,636) (13,344) (14,612) Weekly benefit amount $168.35 $167.36 0.747 $169.90 $173.26 (68.90) (64.70) (66.01) (64.76) N 745 1,236 - 54,649 46,766

Notes: Standard deviations are given in parentheses. Means are unweighted. Tests for differences in means are for the treatmentand control groups and are based on a linear regression that also conditions on the 286 PTGs. The treated population consists of all claimants assigned to the profiling treatment,not just those in the PTGs. All claimants are eligible for 26 weeks of UI benefits. Source: Authors' calculations are from the Kentucky WPRS Experiment. a treatment of 1,236 claimants and a group Week 0 1 2 3 4 5 control group of 745 claimants. Thus, the ex- < I I I I I I I ) ' A perimentaldesign uses only about 6 percentof t and 7.6 of the ClaimC i fildfiled Services end the treated population percent Letter untreated population. Table 1 compares the received and control First check Orientation characteristics of the treatment received and other groups as well as the populations of treated services received; claimantswho are not randomlyassigned and of second other claimantswith profiling scores between 6 check and 19. Based on these characteristicsall of the received groups look very similar. FIGURE 1. TIMELINE FORTYPICAL UI CLAIMANT IN Figure 1 provides a time line for the typical KENTUCKYWPRS PROGRAM claimant, althoughthere is considerablehetero- geneity among claimants in the timing of these events. Unemployment insurance checks are continuing eligibility in order to receive the usually sent fortnightly in Kentucky. The first second check. Thus, if the letters, and the man- check is received in week two of the spell. The datoryreemployment services they imply, are to letter in Exhibit 1 is typically received after the have a deterrent effect, we would expect to first check but before the second-that is, in observe it between weeks two and four. Within week three or four. Claimants need to contact ten working days following notification of the the UI office in week three or four to verify their program,claimants selected for treatmentreport to a local office for an orientation where they learn about the program and complete a ques- There are, however, at least two different offices for each tionnaire. Using this information,Employment profiling score among the PTGs. Services staff assesses the claimants and then VOL.93 NO. 4 BLACKETAL: REEMPLOYMENTSERVICES 1317 refers them to specific services, such as assisted a random disturbanceterm. (3* and /j are pa- job search, employment counseling, job search rametersto be estimated. workshops, and retrainingprograms. Conditioning on the PTG fixed effects has Among those claimantswho attendedthe ori- two importantconsequences for the estimates. entation, 76.7 percent were referred to less First, because the proportionof claimantsin the expensive job search and job preparationactiv- treatmentgroup varies among PTGs, failure to ities. These less expensive services are also less include PTGs fixed effects would bias the esti- intensive, typically consuming from four to six mated impacts if expected earnings in the ab- hours of claimant time. In contrast, only 13.8 sence of treatmentvary among PTGs in a way percent were referred to (relatively) more ex- that is correlated with the random assignment pensive, and intensive, education and training ratio. Second, because each PTG consists of programs.6The average numberof services re- individuals with a specific profiling score at a ceived following orientation was 1.02. Condi- particularlocation on a particularweek, includ- tional on completing at least one service, the ing the /ij implicitly conditions on the profiling average numberof additionalservices received score, location, and time period. Conditioning was 2.10. Of those referred to services, only on these factors substantiallyreduces the resid- 61.3 percentcompleted at least one. Another5.7 ual variationin these data and therebyincreases percent startedat least one service but returned the precision of our estimatedtreatment effects. to employment before completing any. Overall, When the impact of treatmentis the same for 31.8 percent of those referredreceived no ser- each PTG, unweighted estimation of equation vices because they had returnedto employment, (1) provides efficient estimates of the impact of chose not to claim benefits, or were exempted treatment for claimants in a PTG. When the because their previous employer provided sim- impact of treatmentvaries across PTGs, how- ilar services.7 ever, the situation is more complex. Let Aj = Yl - Yoj denote the estimated impact for the H. Estimation jth PTG. Following Angrist (1998), it can be shown that the ordinary least-squares (OLS) In this section, we present experimentalesti- estimate of (3* from unweighted estimation of mates of the mean impact of treatment for equation (1) is: claimants in a PTG. In particular,we estimate versions of: (2) 3*= wij j = (1) Yij '+j+ P*Tij + Vij, r - = (I rj)Nj where w , Nj is the - where Yijis the outcome for the ith individualin k-1 rk(l rk)Nk thejth PTG, Tijis a binaryindicator for whether numberof claimantsin thejth PTG, and rj is the or not the ith individualin thejth PTG received probabilitythat a member of jth PTG receives treatment,/j is a vector of PTG fixed effects to treatment.In this case, estimating equation (1) control for differences in expected earnings in produces a weighted average of the PTG- the absence of treatmentacross PTGs, and vij is specific treatment effects, where the weights correspondto the conditional variance of treatment in each PTG. 6 Two features of the Individualscould be referredto more than one service implicit weights on the and some persons were referred to miscellaneous other Ai are of interest in this context. First, for a services. See Noel (1998) for a detailed description of the given random assignment ratio within a PTG, available services. 7 increasesin the numberof claimantsin the PTG The fractionexempted due to receiving similar services increases the implicit weight on that PTG in the from their previous employer is not precisely known, but estimation of (*. program staff indicate that it is small. The remaining 1.2 Second, for a given size of percent was referred in error or had incomplete data on PTG the weight is larger the closer the random service completion. assignment ratio is to 0.5. As Angrist (1998) 1318 THEAMERICAN ECONOMIC REVIEW SEPTEMBER2003 emphasizes, this weighting tends to reduce the recordsare only for the Commonwealthof Ken- variance of the estimates because Aj is more tucky, no earnings are recorded for claimants precisely estimated when Nj is larger and the who crossed state lines to begin employment. assignmentrate rj is closer to 0.5. If the assign- This is likely to be particularlyproblematic in ment rate is the same for all PTGs (whether or the urbanareas of Kentucky.Of the seven Met- not it equals 0.5) as in a classic experimental ropolitan Statistical Areas in Kentucky, only design, then the weighting is simply based on Lexington is not located on the border of an the size of the PTG. adjoining state. Thus, if the WPRS treatment To examine the importanceof this issue, we affects the probability of taking a job outside present estimates based on an alternateweight- Kentucky, this will bias our results. ing scheme that provides consistentestimates of Second, earnings are only observed for the impact of treatmentfor claimants in a PTG claimants who work in jobs covered by the UI even if the impact of treatmentvaries by PTG. system.9 Third, UI records do not include any This estimator treats each PTG as a separate "informal"activities. To the extent that claim- experiment.It consists of a weighted average of ants work "off the books," the UI records un- the mean differences in outcomes between the derstatetotal earnings.If the treatmentincreases treatmentgroup and control group members in participation in the formal labor market and each PTG, with the weights proportionalto the reduces participationin the informallabor mar- number of treated individuals (rjNj) in each ket, then our measure of earnings will tend to PTG. We refer to this estimator as the "match- overstate the earnings impact of treatment. ing" estimator,because it has the same structure These problemsare standardin all analyses that as a nonexperimentalcell matching estimator, use earnings variables constructed from UI with the crucial difference that in this case we records. know that the conditional independence as- Table 2 presents the basic impact estimates. sumption that justifies the matching holds be- In column (1) we report the results from esti- cause of the random assignment within each mating the fixed-effects regression in equation PTG. In a world where the impacts do not vary (1) above. We find that the treatment group among PTGs, the matching estimator remains collects payments for about 2.2 fewer weeks consistent, but is inefficient relative to estimat- than the control group. The treatment group ing equation (1) by OLS. receives about $143 less in benefits than the control group, but this difference is statistically III. EmpiricalAnalyses significant only at the 10-percent level.10 We

A. Aggregate Estimates Bloom (1999) for a comparisonof UI data and survey data We focus on four outcomes of interest:the in an evaluation context. 9 numberof weeks thata claimantreceives benefits, Certain wages and salaries are exempt from the UI the amountof benefitsthat the claimantreceives, system, althoughthe U.S. Departmentof Commerce (1994) salaries are the fractionof claimantsexhausting benefits, and estimates that about 98 percent of wages and included in the system for all but eight industries. The the claimant'searnings in the quartersfollowing industries that are not well covered are railroads, farms, initiationof the UI claim. All data elements are farm contractors,private household, privateelementary and takenfrom administrative records of the Kentucky secondary educational institutions, religious organizations, of U.S. citi- Departmentof EmploymentServices. the military, and "other,"which is comprised the startof the zens working for exempt internationalagencies and foreign The measureof earnings after consulates and embassies. Combined, these industries ap- unemployment insurance claim is less than pear to account for less than 3 percent of wage and salary ideal for three reasons.8 First, because UI earnings.Thus, the coverage rate appearsto be in excess of 95 percent of wage and salary earnings. 10The reductionsin weeks paid and amount of benefits paid, however, give conflicting estimates of the magnitude 8 See V. Joseph Hotz and Karl Scholz (2000) for a of the treatmenteffect. The mean weekly benefit paymentis general discussion of the advantages and disadvantagesof approximately$168, which suggests that a 2.2-week reduc- administrative data and Robert Komfeld and Howard tion in weeks paid should reduce the amountpaid by about VOL.93 NO. 4 BLACKET AL.: REEMPLOYMENTSERVICES 1319

TABLE2-IMPAcr OF TREATMENTON DURATIONOF ing initiation of the Ul claim.11Thus, in terms BENEFITSAND EARNINGS: of mean impacts, the WPRS treatmentshortens KENTUCKYWPRS EXPERIMENT, OCTOBER1994 TOJUNE 1996 the durationof UI claims, reduces benefits paid, and raises earnings.12Column (2) presents the (1) (2) matching estimates described at the end of Sec- Fixed-effect tion II. They tell the same substantive story as regression Matching the estimates in column (1), with slightly Outcome measures estimates estimates smaller estimated impacts on the number of Number of weeks receiving -2.241 -2.045 weeks of benefits received and the dollar value UI benefits (0.509) (0.411) of benefits received, but larger estimates of the [0.000] [0.000] UI benefits received -143.18 -81.44 impact of treatmenton the fraction exhausting (100.3) (81.6) benefits and earnings in the year after the start [0.077] [0.159] of the claim. Fraction exhausting benefits -0.024 -0.030 As discussed in Section II, our unique data (0.023) (0.0019) the of treatmentfor [0.152] [0.0055] directlyidentify only impact Earnings in the year after 1,054.32 1,599.99 claimants in a PTG. Given the similarity in the start of the UI claim (588.0) (475.2) characteristics we show in Table 1 among [0.037] [0.001] claimants in PTGs, all claimants with profiling N 1,981 1,981 scores between 6 and 19, inclusive, and all claimants the WPRS Notes: Each of the regressionscontrols for the ProfilingTie receiving treatment, our Group (PTG) of the recipients. There are 745 claimants in estimates may generalize to these larger popu- the control group, 1,236 claimants in the treatmentgroup, lations, and so also provide guidance on broader and 286 PTGs. Standarderrors are in parentheses and p- policy questions regarding retaining or scrap- values from one-tailed tests are in brackets. The "Fixed- the entire WPRS system.13 effect regression"estimates result from OLS estimation of ping equation (1) in the text. The "Matching"estimates represent weighted averages of the mean differences in treatmentand B. Putting the Aggregate Estimates in control group outcomes within each PTG, with the weight Perspective for each PTG proportionalto the numberof treatmentgroup members it contains. have Source: Authors' calculationsare from the KentuckyWPRS Economists studied several policies that Experiment. modify the U.S. unemploymentinsurance system by reducing the incentives for excess benefit receipt while at the same time not punishing claimants for whom a longer search is optimal. estimate that 2.4-percent fewer claimants in the The reemploymentbonus experimentssurveyed treatmentgroup exhaust their benefits, but this in Meyer (1995) tested one such policy. In these difference is statistically insignificant. Finally, studies, claimants who find a job quickly and the treatmentgroup earned, on average, $1,054 more than the control group in the year follow-

l We wonderedif the impact of treatmentmight dimin- ish over time as later cohorts of claimantslearned about the modest time commitmentthat the programusually requires. $370. In contrast,a savings of $143 suggests a reductionof We found, however, no systematic patternover time. 12 only 0.85 in weeks paid. This latter estimate is similar to Interestingly, Katherine P. Dickinson et al. (1997) estimates from other programsin the existing literature;see evaluate the WPRS using nonexperimental methods for Bruce D. Meyer (1995). We examine this discrepancy in three states: Delaware, Kentucky, and New Jersey. For detail in the Appendix to Black et al. (2002). We find Kentucky, they find that the program reduced weeks of evidence of more repeat UI spells in the treatmentgroup. benefit receipt by 0.72, reduced benefits paid by $96, and Our evidence suggests that for some of these repeat spells, had no impact on earnings. Overall, their estimates suggest the benefits paid variable was updatedin the administrative that simple nonexperimentalestimators have trouble repli- records to reflect the second spell but the weeks paid vari- cating the experimentalimpact estimates, which is consis- able was not. As a result, the analysis suggests that the tent with the usual findings in the literature. weeks paid impact estimates in Table 2 may have a modest 13 Black et al. (2002) provide additionalevidence on this upwardbias. point. 1320 THEAMERICAN ECONOMIC REVIEW SEPTEMBER2003 keep it receive a cash payment.14 These exper- nus experimentthat Woodburyand Spiegelman iments indicate that the unemploymentspells of (1987) present.They estimate that a $500 bonus UI claimants can be shortened without loss of to UI claimants who found a job within 11 post-programearnings. Though reemployment weeks resulted in a reductionin the durationof bonuses reduce the length of UI spells, many of UI spells of about 1.1 weeks. The earnings of the claimants who receive bonuses would have those offered a bonus were comparableto the exited quickly without them. Moreover, Meyer earnings of those not offered a bonus. Thus, argues that the permanent adoption of reem- relative to the Illinois bonus experiment, the ployment bonuses would substantiallyincrease KentuckyWPRS appearsto have had a substan- the UI take-up rate as eligible persons who tial impact on claimants. This may reflect the expect shortspells and who do not at presentfile fact that claimants have until week 11 to find for benefits would do so in order to collect the alternative employment under the Illinois bo- bonus. This response would furtherincrease the nus, but to avoid reemploymentservices under cost of the program without increasing its WPRS claimantsmust find a job within the first benefits. 15 few weeks of their unemployment spell. The While the UI bonus schemes representa "car- WPRS programhas the furtheradvantage that it rot" designed to lure claimants back into em- is unlikely to increase the UI take-up rate. ployment,other experimentsused "sticks,"such The WPRS impacts reportedhere also tend to as greaterenforcement of UI job searchrequire- be larger than those from experimentalevalua- ments, to push claimants who could find work tions of job search assistance programsfor UI back into employment by raising the costs of claimants summarized in Meyer (1995).16 Most staying on UI. Orley Ashenfelter et al. (1999) of these programs (see his Tables 5A and 5B) present experimentalevidence on small "stick" have estimatedimpacts equal to or less than one programsin four states. These programsinclude week of benefit receipt. Decker et al. (2000) detailed eligibility reviews at the start of the analyze the recent Job Search Assistance (JSA) claim, more informationabout the work search experiment, which used profiling to assign requirementand, for a randomsubsample of the workersto job search assistance in Washington, treatmentgroups, randomwork search verifica- DC and Florida. They find that structuredjob tion very early in the spell. The findings suggest search assistance in Washington lowered the that the treatment, particularly the eligibility number of weeks receiving benefits by 1.13 reviews, had a small but noticeable effect on weeks and reducedpayments by $182, while the qualification rates (see the top of their Table impacts in Florida were -0.41 weeks and $39, 6). Meyer (1995) reviews other experiments respectively. The larger impacts we find here that examined programsthat combined stricter are consistent with the somewhat more inten- enforcement with job search assistance. These sive employment and training services being programshad strongereffects and passed stan- offered, which presumablyraise the cost of con- dard cost-benefit tests. Such "stick" policies tinued UI receipt for those who do not value have the potential to shorten UI spells without them and raise the benefits of service receipt for causing the increases in the take-up rate gener- those who do. ated by reemploymentbonuses. Finally, we consider the costs and benefits of To compare our estimated impacts of the the profiling programfrom the point of view of WPRS program with those of other programs, the UI system. Our estimates from Table 2 in- consider the estimates from the Illinois UI bo- dicate that treated claimants receive, on average, $143 less in benefits than untreated claimants. We can compare these average ben- 14 See Stephen Woodbury and Robert Spiegelman efits with the average cost per treated claimant (1987), Patricia Anderson (1992), Paul T. Decker (1994), and Decker and ChristopherJ. O'Leary (1995) for analyses of the individual bonus experiments. 16 15O'Leary et al. (2002) propose using profiling to allo- See Walter Corson et al. (1985), Anderson et al. cate eligibility for reemployment bonuses to avoid these (1991) and Terry R. Johnson and Daniel H. Klepinger problems. (1994) for analysesof the individualjob searchexperiments. VOL.93 NO. 4 BLACKETAL: REEMPLOYMENTSERVICES 1321

0.12

0.1

0.08 0.06

0.04

0.02 C.~ ~~ ~ ~ ~ ~ ~ ~ ~ - 0 -, ,- I x , , ,. , , / ,*-. r , , :-: X , i , 1 2 3;4 5; 6 .?-4-t'0.1l;<'2, 14.5 1617 22 24 -0.02 If19'2021 Y'4-8-t 5-.6-1-.1 V P- 2 1 2 3 4 5 6 7 8 9 1011 1213141516171819202122 2324 -0.04 Week -0.06 - --Con-ol Grow---Trntt rop | |---Treatnt effect - - 95-peret confidenceband - FIGURE2. HAZARDFUNCTIONS OF THETREATMENT AND 1 95-perentconfidece band CONTROLGROUPS, KENTUCKY WPRS EXPERIMENT, OCTOBER1994 TOJUNE 1996 FIGURE 3. IMPACT OF TREATMENT ON PROBABILITY OF EXITINGUI PROGRAM,KENTUCKY WPRS EXPERIMENT, Note: Triangles denote significant differences at the 5- OCTOBER1994 TOJUNE 1996 percent level. Sources: Authors' calculations are from Kentucky WPRS Sources: Authors' calculations are from Kentucky WPRS Experiment.The parameterestimates used to constructthe Experiment. The parameter estimates used to construct graph appear in Table B1 of Black et al. (1999). the graph appearin Table B1 of Black et al. (1999). in the Kentucky UI system. To construct the C. The Effect of TreatmentOver Time average cost per treated claimant, we use data on the average hours spent per week on profil- Figure 2 displays hazardrates for leaving UI ing in each of the 28 local offices and the state for the experimental treatment and control UI office, the average compensation per hour groups, and Figure 3 displays the difference for employees of the Kentucky Departmentfor between the two with the corresponding 95- Employment Services, the annual cost of the percent confidence bands. They document a contract with CBER at the University of Ken- large impact of treatmentafter claimants receive tucky to maintain the profiling model and data the letter notifying them of their obligation to system, and the number of treatedclaimants in receive reemployment services. About 13 per- the first 86 weeks of profiling.17These costs cent of the treatmentgroup exits after the first sum to $11.93 per treatedclaimant. Even if one two weeks but only about 4 percent of the adds approximately$0.5 million in startupcosts control group exits. Subsequently, the hazard and initial model development and spreads rate of the treatment group is almost always them over the treatedclaimants from the first 86 higher than that of the control group, although weeks of profiling, the costs are still only the difference is statistically significant only a $22.35 per recipient. Thus, the profiling system couple of times. We may use these estimates to appears to save the UI program a substantial calculate the survivor function. The maximum amount of money.18 difference between the treatment and control group survivor functions is 0.11, which is achieved in week 12. The difference after just 17 two weeks is 0.083 or about 75 of the These data were provided by Ted Pilcher of the Ken- percent tucky DES. maximum difference.19 18 The costs included here do include short-termtraining providedby UI staff but do not include the cost of long-term 19 training referrals to outside providers. A full cost-benefit Parameterestimates are presented in Appendix Table analysis would include these additionalcosts. A cost-benefit B1 of Black et al. (1999). Most benefits are paid biweekly analysis from the standpointof society (ratherthan of the UI (every other week). Technically, these data are not true system) would also include the increased earnings of the hazards because we do not observe whether the weeks of treated claimants and some measure of the value of their benefit receipt are consecutive. Rather, they represent forgone leisure. counts of the numberof weeks within the benefit year that 1322 THE AMERICANECONOMIC REVIEW SEPTEMBER2003

5000O 1.000- 6- a

618OO-

4,000 0QSC- Qan- Sano IL OD 2,000 a3nt

o ? ? ? ? ? ? ? ? I Qaio -4 -3 -2 -1 1 2 3 4 5 6 UU.lI I I I l -4 -3 -2 -1 1 2 3 4 5 6 --- Cntrol Group -- Treatment GroWp

O'-*-nrl (c;p - -Ea,ate.t Group FIGURE4. EARNINGSOF THETREATMENT AND CONTROL GROUPS,KENTUCKY WPRS EXPERIMENT, FIGURE6. EMPLOYMENTOFTHE TREATMENT AND CONTROL OCTOBER1994 TOJUNE 1996 GROUPS,KENTUCKY WPRS EXPERIMENT, OCTOBER1994 TOJUNE 1996 Note: Triangles denote significant differences at the 5- percent level. Note: Triangles denote significant differences at the 5- Sources: Authors' calculations are from Kentucky WPRS percent level. Experiment.The parameterestimates used to constructthe Sources: Authors' calculations are from Kentucky WPRS graph appearin Table B2 of Black et al. (1999). Experiment.The parameterestimates used to constructthe graph appearin Table B2 of Black et al. (1999).

0.2

0.15 -

0.1 - ' '.

0.05 -

-0.05 - ..

-0.1

Treatmenteffect - 95-percentconfidence band - 95-percentconfidence band

FIGURE 5. IMPACT OF TREATMENT ON EARNINGS, KENTUCKYWPRS EXPERIMENT, FIGURE7. IMPACT OF TREATMENT EMPLOYMENT OCTOBER1994 TOJUNE 1996 PROBABILITIES, KENTUCKY WPRS EXPERIMENT, OCTOBER1994 TOJUNE 1996 Sources: Authors' calculations are from Kentucky WPRS Sources: Authors' calculations are from Kentucky WPRS Experiment. The parameter estimates used to construct Experiment. The parameter estimates used to construct the graph appear in Table B2 of Black et al. (1999). the graph appearin Table B2 of Black et al. (1999).

The exit hazard in the treatmentgroup con- tinues to lie above that for the control group for This explanationis consistent with the evidence most of the eligibility period. This could result of modest but detectable impacts in the AFDC from a positive impact of employment and work/welfare experiments documented in Ju- training services on those who receive them. dith Gueronand EdwardPauly (1991). Alterna- tively, it is possible that persons with low hazardrates in the treatmentgroup exit UI in the first few weeks at a higher rate than similar a claimantreceives payments.Over 80 percentof claimants in PTGs, of treatedclaimants, and of all claimantshad &ither persons in the control group. no interruptionor one of two weeks or less. In Figures 4 and 6, we graph mean earnings VOL.93 NO. 4 BLACKET AL.: REEMPLOYMENTSERVICES 1323 and employmentby quarterafter the startof the on some claimants comports with the findings UI spell for the treatmentand control groups, in the literaturethat UI reduces the incentive to and in Figures 5 and 7, we graphthe differences find a job quickly. For example, Meyer (1990) between the treatmentand control groups along documents spikes in the hazard function as with 95-percentconfidence bands. The earnings workers approach the exhaustion of their UI estimates illustratethe impact of early exit from benefits, and David Card and Phillip B. Levine unemployment in the treatment group. In the (2000) document that increasing the length of first quarter,treatment group members average time that claimants may receive benefits causes $525 more in earningsthan control group mem- the hazardfunction to fall substantially.20Look- bers, indicating that about half of the earnings ing at search behavior directly, John M. Barron gain occurs in the first quarter.In the second and Wesley Mellow (1979) find that those quarterthe earnings impact is about $344. By workers receiving UI searched 1.6 fewer hours the third quarter,the difference, while positive, per week than unemployed workers not receiv- is no longer statisticallysignificant, and for sub- ing payments. Robert D. St. Louis et al. (1986) sequent quartersthere is virtually no difference offer evidence that claimants systematically vi- in mean earnings. The impact of treatmenton olate the search requirementsthat UI imposes. employment-where employment is defined as Our results are consistent with the idea that positive earnings during a quarter-indicates a the WPRS system lowers the worker's reserva- substantial increase in the probability of em- tion wage and increases searchintensity early in ployment in the first quarter,a modest increase the unemploymentspell. A faster returnto em- in the second quarterand little effect after that. ployment implies worse matches on average in Only the first quarter effect is statistically the treatmentgroup. This in turnimplies that we significant. should observe treatmentgroup members hav- Experimental evaluations of mandatoryjob ing more interruptedspells of unemploymentas searchassistance in othercontexts reportsimilar more of their matches fail to result in stable results. Corson and Decker's (1989) analysis of employment. To test this prediction, we esti- the New Jersey search experimentand Johnson mated a linear probability model based on and Klepinger's (1994) analysis of the Wash- equation (1) with an indicator for the presence ington search experimentboth find evidence of of an interrupted spell as the dependent vari- early return to work. Decker et al.'s (2000) able. The results indicate that the treatment analysis of JSA experimentsin Washington,DC group had a 0.06 higher probability of having and Florida also find sharp increases in the an interrupted spell than the control group hazard rate in the second and third weeks of (with a p-value of 0.003), which corresponds the JSA program. Peter Dolton and Donald to about a 36-percent increase in the number O'Neill's (1996) experimental examination of of interruptedspells.21 The absence of signif- the Restart component of Britain's UI system icant earnings impacts in quarters three parallels our findings on a different dimension. throughsix after the startof the claim, however, After receiving benefits for six consecutive months, the Restartprogram requires recipients to participatein an interview with a caseworker. Dolton and O'Neill (1996) document a sharp 20 See also Ronald Ehrenberg and Ronald Oaxaca spike in the hazard rate of the treatmentgroup (1976), Robert Moffitt (1985), Lawrence Katz and Meyer relative to the control when claimants (1990), and many others. group 21 receive notice of the interview. Johnson and If the WPRS program lowers claimants' reservation in Table find a similar in wages early their unemployment spells, then treatment Klepinger(1991, 4) spike group members who exit early should have lower earnings the UI exit hazard in response to a letter noti- than control group memberswho exit early. To test this, we fying the recipient of an eligibility review in- interacted the treatment indicator with an indicator for terview in the Washington Alternative Work whetheror not the claimantexits early-that is, within four Search Experiment. weeks of the startof the UI claim. We find strong evidence of lower earnings among treatmentgroup members exiting Our evidence that WPRS reduces moral haz- early compared to control group members who do so. See ard in the UI system by acting as a "leisuretax" Black et al. (1999) for these estimates. 1324 THE AMERICANECONOMIC REVIEW SEPTEMBER2003

TABLE 3-ESTIMATES OF THE IMPACT OF TREATMENT ON THE TREATED BY PROFILING SCORE CATEGORY: KENTUCKYWPRS EXPERIMENT,OCTOBER 1994 TOJUNE 1996

Fraction Weeks Amount exhausting Annual paid paid benefits earnings Profiling score between 6 and 13 -2.238 -$270.08 -0.055 $939.51 (0.913) (179.74) (0.041) (1052.05) [0.007] [0.067] [0.090] [0.186] Profiling score between 14 and 15 -1.891 -$14.42 0.030 -$1,257.14 (1.050) (206.77) (0.0049) (1,210.25) [0.036] [0.472] [0.771] [0.851] Profiling score of 16 -3.057 -$465.73 -0.095 $4,175.83 (1.102) (216.94) (0.051) (1,269.76) [0.003] [0.016] [0.032] [0.001] Profiling score between 17 and 19 -1.861 $182.09 0.027 $689.71 (1.039) (204.60) (0.047) (1,197.55) [0.037] [0.813] [0.784] [0.283] p-value for test of equal impacts 0.851 0.132 0.174 0.021 across approximateprofiling score quartiles

Notes: Each of the regressions controls for the Profiling Tie Group (PTG) of the recipients. There are 745 claimantsin the control group, 1,236 claimantsin the treatmentgroup and 286 PTGs. The approximatequartiles for the profiling scores are scores 6 to 13 (515 members), scores 14 and 15 (390 members), score 16 (424 members), and scores 17 to 19 (652 members). Source: Authors' calculations are from the Kentucky WPRS Experiment.

indicates that there is no long-term harm from allocate the treatment?22If the goal of profiling the treatment. is to increase the efficiency of treatmentalloca- In sum, we have strong evidence that the tion then, assuming that the costs of the treat- earnings gains we document result from more ment do not vary across persons, it should early exits from UI in the treatmentgroup. Most allocate the treatmentto those for whom it has of these exits take place prior to the receipt of the largest impact. To addressthis question, we reemployment services. Earnings are signifi- examine impact estimates for profiling score cantly higher in the first and second quarters subgroups.If the profilingmechanism enhances after claimants' file their claims. We find no the efficiency of treatmentallocation, then we evidence that claimants ever suffer reduced should find larger impacts for claimants with earningsthrough the first six quartersafter their higher scores, as claimants with higher scores claims. This evidence suggests that the "leisure are much more likely to get treated. tax" implicit in the WPRS treatmentrepresents Table 3 presentsimpact estimates for our four an effective tool for reducing the moral hazard outcome variables, with the claimants divided in the UI program. into four subgroups based on their profiling scores: 6-13 (about 26 percent of the treatment D. Evaluating Profiling as an Allocation group), 14 or 15 (about 20 percent of the treat- Mechanism ment group), 16 (about 21 percent of the treatment group) and 17 to 19 (about 33 percent of In addition to evaluating the impact of the profiling treatmenton those assigned to it, we also brieflyconsider a differentevaluation ques- 22 See Berger et al. (2000) for an extended discussion of tion: How well does the profiling mechanism these issues. VOL93 NO. 4 BLACKET AL: REEMPLOYMENTSERVICES 1325 the treatmentgroup). The results suggest that higher wages conditional on employment. We the impact varies nonlinearlywith the profiling find no evidence that the earnings of the treat- score, but we can reject the null of equal im- ment group are lower throughthe first six quar- pacts across profiling score subgroupsonly for ters after the start of the unemployment spell, earnings. suggesting that there is no long-termharm from The assumptionunderlying the WPRS is that the treatmentprovided by the program. those with the longest expected UI spells benefit The reduction in the length of recipiency in the most from the profiling treatment.The esti- the treatmentgroup is largely accomplished by mates in Table 3 provide little justification for early exits from UI. Many of these early exits this assumption, as there does not appearto be coincide in time with the letters sent out to a monotonic relationshipbetween the profiling treatmentgroup members to notify them of their score and the impact of treatment. Thus, the obligations under the program. These findings evidence in Table 3 calls into question the wis- suggest thatthe gains from the programresult in dom of using expected UI spell duration(rather large part from removing claimantsfrom the UI than, say, predicted impacts) as a means of rolls who were job ready and had little trouble allocating treatment. locating employment. Hence, the WPRS treatment appears to be successful at reducing the IV. Conclusion moral hazard associated with the Ul program. Moreover, from the perspective of the UI sys- We use unique data to examine the impact of tem, and likely from that of society as well, it the Worker Profiling and Reemployment Ser- produces a wide excess of benefits over costs. vices (WPRS) initiative. Our experimentaldata Finally, the underlying assumption of the are for persons in marginal profiling groups- WPRS program is that those with the longest that is, persons whose expected UI spells are expected UI spell durations would benefit the just long enough to put them in the group re- most from the requirementthat they participate quired to receive reemployment services in re- in reemployment services in order to continue turn for continued receipt of benefits. This receiving their UI benefits. It is also assumed design, which Thistlethwaite and Campbell that treating these claimants will result in the (1960) call a tie-breaking experiment, allows largest budgetarysavings for state UI systems. the introductionof random assignment without Our results provide little justification for either disrupting the program and without denying assumption, as we do not find a monotone re- services to those most in need. In so doing, it lationship between the profiling score and the may reduce resistance to randomassignment by impact of treatment.If the goal of profilingis to line workers and program administrators(and allocate the treatmentto those claimants with politicians) and also reduce the negative public- the largest expected impact from it, or to save ity sometimes associated with random assign- the state UI system the most money, then our ment in the social services. findings call into question the wisdom of using For claimants in the profiling tie groups, we the expected benefit duration as a means of find that randomassignment to the WPRS treat- allocating treatment. They also suggest the ment results in a 2.2-week reduction in benefit value of further thought and study before ex- receipt relative to the control group. This rep- tending profiling to other programs. resents a reduction in mean benefits payments of slightly over $143 per recipient. 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