Internet Job Search and Unemployment Durations

Transcription

1 Internet Job Search and Unemployment Duratons Peter Kuhn Department of Economcs Unversty of Calforna, Santa Barbara Santa Barbara CA Mkal Skuterud Famly and Labour Studes Dvson Statstcs Canada P.O. Box 134 Ottawa, Canada K1Y 4S Ths draft: June 2, 2003 Frst draft: November 26, 2000 After decades of stablty, the technologes used by workers to locate new jobs began to change rapdly wth the dffuson of nternet access n the late 1990 s. Whch types of persons looked for work on lne, and dd searchng for work on lne help these workers fnd new jobs faster? We address these questons usng measures of nternet job search among unemployed workers n the December 1998 and August 2000 CPS Computer and Internet Supplements, matched wth job search outcomes from subsequent CPS fles. In our data, nternet searchers have observed characterstcs that are typcally assocated wth shorter unemployment spells, and do spend less tme unemployed. Ths unemployment dfferental s however elmnated and n some cases reversed when we hold observable characterstcs constant. We conclude that ether nternet job search s neffectve n reducng unemployment duratons, or nternet job searchers are negatvely selected on unobservables.

2 Internet Job Search and Unemployment Duratons Ths draft: June 2, 2003 Frst draft: November 26, 2000 After decades of stablty, the technologes used by workers to locate new jobs began to change rapdly wth the dffuson of nternet access n the late 1990 s. Whch types of persons looked for work on lne, and dd searchng for work on lne help these workers fnd new jobs faster? We address these questons usng measures of nternet job search among unemployed workers n the December 1998 and August 2000 CPS Computer and Internet Supplements, matched wth job search outcomes from subsequent CPS fles. In our data, nternet searchers have observed characterstcs that are typcally assocated wth shorter unemployment spells, and do spend less tme unemployed. Ths unemployment dfferental s however elmnated and n some cases reversed when we hold observable characterstcs constant. We conclude that ether nternet job search s neffectve n reducng unemployment duratons, or nternet job searchers are negatvely selected on unobservables.

3 Usng CareerBulder to fnd a job s lke drvng n the carpool lane. -half-page ad for an nternet job ste n the Los Angeles Tmes, Frday March 1, (p. C5) Thnk Monster for the best resumes, the best canddates. -Monster.com web ste, Sept. 19, Introducton After decades of stablty, the technologes used by workers to locate new jobs began to change rapdly wth the dffuson of nternet access n the late 1990 s. As early as August 2000, one n four unemployed U.S. jobseekers reported that they regularly used the nternet to look for jobs; one n ten employed persons sad they regularly looked for other jobs on lne. The use of nternet job and recrutng stes s generally free of cost for workers and much cheaper for frms than tradtonal prnt advertsements. In addton, these servces offer frms and workers the promse of nstant access to a much larger number of possble matches than tradtonal channels, as well as the potental for the exchange of much more detaled nformaton about both worker and job attrbutes. 1 Not surprsngly, economsts have begun to speculate on the potental effects of the above developments on labor markets. For example, commentators have argued that the hgher contact rate, lower cost, and greater nformaton content provded by ths technology could lead to lower frctonal unemployment (Mortensen 2000), hgher average match qualty (Krueger 2000), a reducton of noncompettve wage dfferentals (Autor 2001), and an amplfcaton of abltyrelated wage dfferentals (Kuhn 2000). If even some of these clams are correct, the advent of nternet job search wll have mportant mplcatons for both labor- and macroeconomc polcy. 2 Ths artcle has two man goals. The frst s to examne the ncdence and dffuson of nternet job search: who looks for work on lne? Second s to estmate the effect on search 1 For example, at frms request WebHre wll check the followng worker credentals: socal securty numbers; current and prevous addresses; references; educaton; crmnal, cvl and bankruptcy court records; drvng and credt reports; and workers compensaton clams. Also offered are on-lne sklls and personalty testng. The combnaton of nternet applcaton procedures and tradtonal database management software also dramatcally smplfes the process of searchng through submtted resumes for approprate matches. Fnally, workers can now gan much more nformaton about workng condtons and job requrements from job boards and company webstes. 2 One potentally relevant aspect of labor market polcy s the ratonale for government-provded job matchng servces such as the states Employment Servces. Macro polcy mplcatons could follow from any change n the natural unemployment rate caused by nternet job search technology.

4 2 outcomes, for an ndvdual worker, of ncorporatng the nternet nto hs or her job search strategy. We are of course well aware that even f nternet search has prvate, ndvdual benefts, t does not follow that the equlbrum effects of ntroducng ths technology on unemployment rates, wages and other outcomes are socally benefcal. 3 However, snce n most equlbrum models, some frst-order, or prvate effects are a necessary condton for any general equlbrum effect to occur, the questons posed n ths paper seem to be the rght ones to ask frst. 4 In order to answer our questons we use measures of nternet search derved from the December 1998 and August 2000 CPS Computer and Internet Use Supplements, matched wth job search outcomes from all subsequent CPS fles that contan some of the same survey respondents. Throughout our analyss we focus on the search methods and outcomes of unemployed persons only. Ths s because the regular monthly CPS does not collect data on nonnternet job search by employed persons. 5 Thus, for those wth jobs, CPS data does not allow one to dstngush nternet job search actvty from the decson to look for work n the frst place. We also restrct our attenton to one partcular outcome of the job search process jobless duraton. In part, ths s drven by data consderatons: n the CPS, job qualty (.e. wage) nformaton s not avalable for a suffcent sample of jobseekers 6 ; thus we cannot ascertan from our data whether nternet search produces better job matches. For many polcy purposes, however, unemployment duratons are the outcome of greatest nterest, justfyng our focus here. Ths paper contrbutes to an emergng lterature on the effects of nternet technology on product market performance (e.g. Brown and Goolsbee 2002 n lfe nsurance markets; Brynjolfsson and Smth 2000 on book and CD markets, and Carlton and Chevaler 2001 on 3 For example, Lang (2000) has suggested an asymmetrc-nformaton model n whch a reducton n the costs of applyng to jobs can be Pareto-worsenng, n part by reducng the average match qualty n every frm s applcant pool. 4 The nternet has, of course, affected frms recrutng strateges as well as workers search strateges. To the extent that vacances posted on lne can only be dscovered by job-searchng on lne, such changes n frms recrutng strateges should ncrease the estmated effectveness of nternet job search n ths paper. 5 See Skuterud (2001) for a recent analyss of trends n on-the-job search usng the occasonal CPS surveys that do collect ths nformaton. 6 CPS wage nformaton s of course only avalable for persons who fnd new jobs, and who are n the outgong rotaton groups. Further, a credble analyss of re-employment wages also requres controls for pre-unemployment wages, a restrcton whch reduces the sample to non-useful levels.

5 3 varous consumer goods); to our knowledge ours s the only study of the effects of nternet technology on the functonng of the labor market. The current paper also contrbutes to an older lterature on the relatve effectveness of dfferent job search methods. For example, Holzer (1987, 1988), Bortnck and Ports (1992), Osberg (1993) and Addson and Portugal (2001) compare the job-fndng rates of unemployed workers usng a varety of search methods. Thomas (1997) focuses specfcally on the effectveness of publc employment agences. Fnally our work also relates to a recent lterature on the dgtal dvde, whch asks whether dfferental access to computer or nternet technology aggravates nequalty along varous dmensons (e.g. Farle 2001). In our data, we fnd that nternet job searchers are better educated, prevously worked n occupatons wth lower unemployment rates, and had several other characterstcs whch are usually assocated wth shorter unemployment duratons. Thus t s unsurprsng that, overall, nternet searchers had shorter unemployment duratons than workers who dd not use the nternet to locate new jobs. Once observable dfferences between nternet and other searchers are held constant, however, we fnd no dfference n unemployment duratons, and n some specfcatons even sgnfcantly longer duratons among nternet searchers. We conclude that ether (a) nternet job search s neffectve n reducng unemployment duratons or (b) nternet job searchers are adversely selected on unobservable characterstcs; further research s needed to dsentangle these two possbltes. In ether case, however, nternet search frms who smultaneously clam to employers that ther applcants are postvely selected (on hard-to-observe characterstcs) and to ther applcants that nternet search wll reduce ther search tme are makng clams that are nconsstent wth our evdence. 2. Data and Descrptve Statstcs As noted, our data on nternet job search come from the December 1998 and August 2000 Computer and Internet Use Supplements to the Current Populaton Survey. These supplements

6 4 ncluded the followng queston: Do(es) (you) (any one) REGULARLY use the Internet... to search for jobs?. As always, the regular monthly CPS survey n these months also asked unemployed ndvduals whch out of a lst of nne tradtonal job search methods they used. Internet job search rates n these two surveys, classfed by labor force status, are shown n Table 1. As already noted, the fracton of unemployed jobseekers 7 lookng for work on lne was 25.5 percent n August 2000, up from 15.0 percent n November 1998, less than two years earler. As Table 1 also shows, much of ths ncrease was assocated wth a large rse n home nternet access among unemployed persons (from 22.3 to 39.4 percent), but nternet use for job search condtonal on nternet access also rose over ths perod. By August 2000, regular nternet job search was also surprsngly common among the employed (around 11 percent) and among labor force nonpartcpants, at least those who were nether retred nor dsabled (around 6 percent). 8 In order to measure the job-fndng success of nternet versus other job searchers, we matched observatons n the December 1998 supplement wth the same persons n the ten subsequent CPS regular monthly surveys (January-March 1999, September 1999 through March 2000) n whch some of the same ndvduals were re-ntervewed. Smlarly the August 2000 survey was matched wth September-November 2000, and May through November Matchng was done usng establshed methods (see for example Madran and Lefgren 1999); detals about our procedure are avalable from the authors. 9 To be n our sample, a person had to be unemployed accordng to the offcal Bureau of Labor Statstcs defnton n a Computer/Internet supplement month (December 1998 or August 7 All unemployed workers not expectng to be recalled to ther former employer are classfed by the BLS as jobseekers. 8 Kuhn and Skuterud (2000) compare these recent rates of on-the-job nternet job search (IJS) to hstorcal measures of on-the-job search (OJS) va any method. They are sgnfcantly hgher, suggestng that the nternet may have contrbuted to an ncrease n total OJS. 9 See an earler verson of ths paper posted at Only 10.4 percent of observatons were not matched n any month after the Supplement date. The match rate for nternet searchers and others were very smlar. For example, n January 1999 the match rate for nternet searchers s 93.6 compared to 91.5 for those not reportng nternet search n the prevous month. In order to assess the possblty that our results mght be drven by nternet searchers who were not matched because they moved to take jobs, we replcated our entre analyss treatng all ndvduals whose spells were censored due to a falure to match as becomng re-employed n the month followng the censorng. There was very lttle change.

7 5 2000), yeldng a sample of 4139 persons. 10 Accordng to ths defnton, unemployed persons must not be workng, and ether on layoff from a job to whch they expected to be recalled or searchng for work usng at least one of nne recognzed actve methods. 11 These methods are lsted n Table 2; the most common are contacted employer drectly, sent resumes/flled applcatons, and contacted publc employment agency. It s noteworthy that these tradtonal measures of job search actvty --used for decades by the BLS to defne unemployment could themselves nvolve nternet use, n whch case they may be natural complements wth nternet search. For example, a jobseeker could emal resumes to employers or fll out an on-lne job applcaton form. Because of the possblty of complementartes (and, of course, substtutabltes), nterpretaton of the nternet search coeffcent n a search outcome regresson requres some care, as s dscussed n Secton 3 below. Sample means of all the varables used n our regressons are presented n Table 2, separately for unemployed persons who searched for a new job on the nternet and those who dd not. In most cases, unemployed workers who look for jobs on lne have observable characterstcs that are usually assocated wth greater job search success than other unemployed workers. For example, n the Computer/Internet Supplement month, the average unemployed nternet searcher had already been unemployed for 3.44 months, somewhat less than the month retrospectve duraton of the non-nternet searchers. Internet searchers resded n states wth somewhat lower unemployment rates than other unemployed workers, and had prevously worked n occupatons wth consderably lower unemployment rates. They were more lkely to have been employed pror to the current unemployment spell, were much better educated, and were more lkely to be n ther prme workng ages (26-55) (versus under 26 or over 55). Internet job searchers were less lkely to be black, Hspanc or mmgrant and more lkely to be homeowners than other unemployed persons. Fnally, on average, unemployed workers who 10 Our sample ncludes a small group of persons who were never matched wth an observaton after those dates. Whle these observatons contrbute no nformaton on unemployment duratons, they do contrbute nformaton on the determnants of nternet search, and are retaned n our analyss for that reason.

8 6 looked for work on lne were more lkely, not less lkely, to use tradtonal job search methods than other unemployed workers. In all, they used an average of 2.17 tradtonal search methods, compared to 1.67 for other unemployed workers, suggestng an overall complementarty of nternet and non-nternet methods. By constructon, no one n our sample was workng n the month n whch we observe whether ther job search strategy ncorporated the nternet (December 1998 or August 2000). The fracton of our sample observed n employment at varous ponts after these dates s reported near the bottom of Table 2. For example, among those ndvduals whose labor market status was observed one month after the Supplement date (.e. n January 1999 or September 2000), 29.1 percent were employed. Two months after the supplement date, 37.5 percent were employed, and a year later 55.9 percent were employed. If we pool all ndvduals who were re-ntervewed at least once after the date n whch we observe ther nternet search actvty, the same share, 55.9 percent, were seen n re-employment at some tme after the Supplement date. Comparng nternet job searchers wth other unemployed workers, essentally no dfference n employment rates s evdent one or two months after an ndvdual s nternet job search actvty s observed. A year later, however, 64.6 percent of unemployed nternet searchers are re-employed, compared to 53.3 percent of other unemployed workers. Ths dfference, lke the dfference n re-employment at any tme after the Supplement date, s statstcally sgnfcant. On the surface, Table 2 thus seems to suggest that nternet search facltates re-employment, at least f one allows a few months to elapse for ths method to yeld results. 3. Conceptual Framework To help nterpret our estmates of the effect of nternet job search actvty, suppose that an outcome of job search, R (for example the log of the ntegrated baselne hazard see equaton 6) s a lnear functon of a vector of exogenous observables, Z ; a vector of 9 endogenously-chosen 11 We conducted some analyses that excluded workers expectng recall, as well as some analyses that ncluded margnally-attached workers (nonpartcpants who engaged n passve job search only). In nether case were the results substantally dfferent.

9 7 tradtonal search methods, M ; an ndcator varable for the use of nternet methods, IJS ; and a random term µ. In other words, the producton functon for re-employment s gven by 12 : R = θ Z + γijs + δm + µ. (1) Let ndvdual s total cost of usng the nternet to look for work be: C = b + ε (2) 0 0Z 0 and the cost of usng tradtonal job search method j (j = 1, 9) be: C j = b Z + c IJS + ε (3) j j j Accordng to ths formulaton, use of the nternet can be ether complementary ( c < 0) or a substtute ( c > 0) wth tradtonal methods such as sendng resumes. If workers choose a j vector of search methods to maxmze U = kr C (where k s a scalng parameter convertng search outcomes nto dollars), they wll use the nternet for job search whenε 0 < kγ b0z, and wll use method j whenε j < kδ j b jz c jijs. Gven the above structure, equaton (1) can be consstently estmated by OLS (or ts sngle-equaton equvalent) as long as the vector of shocks to search costs, ε, s uncorrelated wth shocks to re-employment rates, µ, where the latter may nclude a permanent person-specfc effect,.e. unobserved re-employablty. Identfyng the parameter of nterest γ n the presence of correlaton betweenε and µ requres an nstrument.e. a varable that enters (2) but not (1)--; unfortunately we do not have credble canddates for such a varable n our dataset. In the absence of such an nstrument, our prors when we started ths research were that abler workers would have lower nternet use costs, mplyng that sngle-equaton estmates of (1) wll overstate the productvty of nternet job search. j j j 12 Note that ths framework does not allow for heterogenety n the margnal effectveness of nternet search across ndvduals. If anythng, gnorng the possblty that ndvduals choose those search methods whose dosyncratc productvty effects are the greatest mples that our estmates n ths paper wll overstate the effectveness of nternet search for a randomly-selected ndvdual.

10 8 Fnally, n the case where ε and µ are uncorrelated, consder estmatng equaton (1) excludng measures of tradtonal search methods, M. Approxmatng the dstrbuton ofε j by a unform dstrbuton (wthout loss of generalty wth densty 1), the omtted-varable bas formula mples that: where φ = c ^ 9 γ = γ + δ j φ j, (4) j j j= 1,.e. the margnal effect of nternet use on the use of tradtonal search method j for a total-search-cost-mnmzng ndvdual, estmated from a lnear probablty model for each of the 9 tradtonal search methods. Equaton (4) thus defnes two nternet search effects of potental nterest: the drect effect (γ), and the total effect ( ^ γ ). The former gves the effect of nternet search on outcomes holdng all other search methods fxed; the latter gves ts effect when all other search methods are adjusted optmally to the adopton of nternet search, allowng for both substtutabltes and complementartes among methods. Snce these are both nterestng questons, we shall present results for both specfcatons n the ensung tables. 4. Probt Analyss As suggested by equaton (1), any credble analyss of both the determnants and effects of nternet search would, of course, control for observable dfferences between unemployed workers who look for work on lne and those who do not. To that end, Table 3 reports estmates of probt models for nternet job search, as well as for the outcomes of job search. Regressors nclude characterstcs of the ndvdual, hs/her unemployment spell, and the ndvdual s actvty before enterng the current unemployment spell. Throughout Table 3, we present specfcatons of each equaton wth and wthout a control for home nternet access. In the nternet-search probts, home nternet access clearly has a strong estmated effect, but t s possble (especally among the unemployed) that nternet access was obtaned n order to assst wth job search,

11 9 makng specfcatons wthout ths control of some nterest. Lkewse, whle we do not beleve home nternet access has a causal effect on the job-fndng rate --what should matter s whether the nternet s used for job search--, we can thnk of plausble arguments for and aganst controllng for nternet access n the re-employment probts. 13 Lookng frst at the determnants of nternet search n columns 1 and 2 of Table 3, most of the results from the unvarate comparsons n Table 2 are confrmed. For example, nternet search grew rapdly between 1998 and 2000, was more common n occupatons wth low unemployment rates, among young and well-educated workers, and among persons who entered unemployment ether from work or school. Snce all of these characterstcs are usually assocated wth shorter unemployment spells, ths confrms our strong mpresson of postve selecton on observables. Columns 1 and 2 also show that nternet job search tends to be used n conjuncton wth publc employment agences, prvate employment agences, sendng resumes, usng ads, and other actve measures. In fact, whle not always statstcally sgnfcant, use of every tradtonal search method s postvely assocated wth nternet search, suggestng a complementarty rather than substtutablty between these methods. Turnng now to the effects of nternet search on unemployment duratons, do the apparently benefcal effects of nternet search n the Table 2 means also survve controllng for observable dfferences between nternet searchers and others? As a frst step n answerng ths queston, the remanng columns of Table 3 present probt estmates of the probablty an unemployed ndvdual s employed 12 months after we observe ther nternet job search actvty n the CPS Computer/Internet Supplement. We focus on 12 months because ths s where the largest apparent nternet effect was observed n Table As dscussed n Secton 3, we present 13 On the for sde, home nternet access may be correlated wth other unobserved characterstcs (for example wealth, whch n turn s correlated wth past employment) that do affect job-fndng rates. On the other hand, home nternet access s a very powerful predctor of on-lne search among the unemployed, and much of the varaton n home access may be drven by genunely exogenous dfferences n the rate of nternet dffuson across space, tme and ncome groups; n ths case controllng for access could be dscardng a large amount of useful varaton. 14 Smlar analyses were performed for re-employment wthn a month, wthn two months, or at any tme after nternet search actvty s observed. (In the latter specfcaton, we added a control for the number of months n whch the ndvdual s observed after the Supplement month). We also replaced the state unemployment rate by a state fxed effect. In all cases, the results were smlar to those n Table 3: whenever even a relatvely parsmonous set of demographc controls are used, the nternet search coeffcent s ether nsgnfcant or negatve.

12 10 estmates wth and wthout controls for the use of tradtonal search methods, dentfyng respectvely the drect and total effects of ncorporatng the nternet nto one s job search strategy. Effects of the control varables n Table 3 s employment probts are generally n lne wth expectatons. For example, we see that ndvduals wth hgh retrospectve duratons are less lkely to be re-employed a result that mrrors the common fndng of declnng re-employment hazards n duraton studes. 15 Workers on layoff are more lkely to be re-employed than those not expectng to be recalled to ther former employer. A hgh occupatonal unemployment rate depresses job-fndng rates, and ndvduals who worked or went to school mmedately before the onset of ther current unemployment spell are much more lkely to be re-employed than those who dd nether. Persons whose last job was n the prvate sector fared better n re-employment than those whose last job was n the publc sector or n self-employment, or who dd not work just pror to the current unemployment spell. 16 Younger workers are re-employed more quckly; lesseducated and black workers more slowly. Although the effect s not qute sgnfcant at conventonal levels, sngle men are less lkely to be re-employed than sngle women. Marred men are however much more lkely to be re-employed than marred women, possbly reflectng greater geographcal search constrants among marred women (Crossley, Jones and Kuhn, 1994). The remanng varables n Table 3 are controls for the use of other, tradtonal job search methods. Interestngly, when these varables are ncluded (columns 5 and 6 only) we detect sgnfcant postve effects on re-employment for three such methods: drect employer contact, sent resumes and publc employment agences, whch ncdentally are also the search methods most commonly used by unemployed persons n our data. For the remanng methods, no statstcally sgnfcant effects on the job-fndng rate are detected. 15 In a prevous verson of ths paper we modelled the effects of left-censorng n our duraton data more formally, usng a technque ntroduced by Lancaster (1979): essentally we condton each observaton s contrbuton to the lkelhood functon on the fact that t lasted long enough to be observed n our sample. There was very lttle change n the results. 16 Note that n a substantal number of cases the ndvdual s last job preceded a spell of nonpartcpaton, so that these sector ndcators do not smply subdvde the group who entered unemployment drectly from a job.

13 11 Most surprsng, and of greatest nterest to us here s the nternet job search coeffcent n Table 3. In contrast to the unvarate results n Table 2, Table 3 shows that addng the nternet to one s job search strategy appears not to ncrease re-employment rates. Ths s true whether or not we hold constant an ndvdual s nternet access from home, and whether or not we allow the use of tradtonal search methods to be adjusted optmally when an nternet search component s ntroduced. As noted, f as our data suggest-- nternet and other job search methods are complementary, ths latter result s especally strong, snce no effect s seen even when we allow workers to adjust ther use of tradtonal methods when the nternet s ncorporated nto ther search strategy. In sum, when we control for the postve selecton of nternet job searchers on observed characterstcs, no evdence of an unemployment-reducng effect of nternet search s evdent n our data. 5. Duraton Analyss Whle Table 3 certanly suggests that ncorporatng the nternet nto one s job search strategy s neffectve n reducng jobless duratons, one reason why ths concluson mght be premature s an neffcency n the estmaton procedure. In partcular, any probt focusng on a worker s labor force status at only a sngle date --n the above case 12 months after hs/her search actvty s observed-- dscards a consderable amount of nformaton on the actual duraton of unemployment. It s therefore possble that those probts mght fal to reveal a true, benefcal effect of nternet job search. To address ths ssue, we estmate a duraton model that ncorporates all the avalable nformaton about a worker s jobless spell followng the Supplement date. Of course, the nformaton avalable to us on duratons n the CPS s hghly dscrete: at best, we only know the month n whch re-employment occurred; n some cases (the gap between the two four-month CPS observaton wndows ), we only know that re-employment occurred durng an eght-month perod. Ths makes contnuous-tme duraton models hghly napproprate. For ths reason we

14 12 develop and estmate a dscrete-tme hazard model that takes nto account the partcular features of CPS duraton data (.e. potentally large falure wndows whose structure vares across observatons), whle stll allowng for a fully flexble form of the baselne hazard functon. 17 We begn, as s common, by assumng the hazard rate nto re-employment, λ (τ ), s separable nto a baselne component that depends on elapsed duraton λ ( ), and a component that depends on a lnear combnaton of observed characterstcs X and estmated parameters β: 0 τ λ( τ ) = λ 0 ( τ ) exp( X β ) (5) From assumpton (1) t follows that (see Kefer 1988, pp ): logλ 0 ( t ) = β + µ (6) X where the random varable Λ ( t ) s the ntegrated baselne hazard up to each observaton s realzed duraton,.e.: 0 0 ) 0 t 0 Λ ( t ) = λ ( τ dτ (7) and where µ follows a type-1 extreme-value dstrbuton. 18 Thus the transformed duraton varable, logλ 0 ( t ),--whch s monotoncally ncreasng n t -- can be thought of as the dependent varable n a lnear regresson. Suppose now that a partcular unemployment spell s known to have ended between two dates, t a > t b. Defnng δ a log Λ 0 ( t a ) and δ b logλ 0 ( t b ), the lkelhood of such a spell s just: F ( δ + X β ) F( δ + X β ), (8) a b where F s the cdf of µ. Duratons known only to have ended after, say, t a (.e. rght-censored duratons) have a lkelhood of 1 F ( δ + X β ) ; duratons known to have ended between t=0 and, say, t b, have a lkelhood of F ( δ + X β ). 19 b a 17 Exstng dscrete-tme hazard models, such as Meyer s (1990) requre the structure of ntervals to be the same across observatons. 18 The cdf for the extreme-value dstrbuton s gven by F µ ) = exp( exp( µ )) ( 19 Unlke observed duratons whch must be postve, note that the transformed duratons and the error term µ occupy the entre real lne.

15 13 In our data, job searchers are observed no more frequently than once per month. Recognzng ths dscreteness, we dvde the set of possble jobless duratons nto dsjont ntervals. 20 Denote the number of such ntervals by T+1; n the results reported n Table 5 (whch focus on post-supplement duratons only), we used eght ntervals: 0-1, 1-2, 2-3, 3-10, 10-11, 11-12, and more than 13 months. For some of our observatons (for example those persons observed as unemployed n one month and employed the next), we know n exactly whch of these ntervals ther unemployment spell ended. Others are rght-censored, due to attrton or rotaton out of the sample. For yet others (ncludng, but not lmted to, persons who were not matched n a perod before they are frst observed n employment) we know only that they became employed at some pont wthn a set of adjacent ntervals. To allow for the latter types of observatons, defne V as a 1xT vector of lower bound dummy varables (thnk of these as applyng, n order, to each of the T+1 ntervals defned above except the hghest one). Set V equal to zero for all ntervals except the one precedng the nterval n whch worker s unemployment spell s known to have ended. 21 Defne V as a 1xT vector of upper bound dummy varables, equal to zero for all ntervals except the one durng whch we knew the unemployment spell ended. 22 Fnally, let δ be a Tx1 coeffcent vector correspondng to the cut ponts between the above ntervals. Because the elements of δ correspond to the log of the ntegrated baselne hazard at the upper end of each nterval, and becauseδ s estmated, ths procedure allows for an unrestrcted baselne hazard functon. Puttng all the above together, the log lkelhood for the entre sample can be expressed as: 20 An appendx descrbng how we constructed unemployment duratons from the matched CPS fles s avalable from the authors. See footnote If the observaton s rght-censored ths s the nterval before t became rght-censored; f the observaton became re-employed durng the frst nterval V s a vector of zeroes. 22 If the observaton s rght-censored, V s a vector of zeroes.

16 14 log L = Cens= L Cens= Cens= R log[ F( V δ + X β)] + [ F( V δ + X β ) F ( V δ + X β) ] log 0 log[ 1 F( V δ + X β) ]. + (9) where Cens = L, 0 and R ndcates the observaton s left-censored, not censored, or rghtcensored, respectvely. (Note that we refer to observatons that became re-employed n the frst month of ther unemployment spell as left-censored because the transformed duraton varable, log Λ 0 ( t ), has no lower bound for ths group). Table 4 presents the values of β that maxmze (5) for the same set of control varables (X) used n Table 3 (not all coeffcents are reported to save space). Note that a postve coeffcent n Table 4 ndcates a postve effect on the hazard rate, so that coeffcent sgns and sgnfcance but not magntudes are comparable wth Table 3. That sad, Table 4 results for the control varables are very smlar to those n Table 3. For example, persons who are far nto ther unemployment spells (.e. wth hgh retrospectve duratons n the Supplement month) have lower re-employment hazards (longer remanng unemployment duratons) after that date. Reemployment rates were hgher n the 2000 Supplement, reflectng the tghter aggregate labor market condtons prevalng around the tme of that survey. Hgh state unemployment rates retard re-employment. One nterestng dfference from Table 3 s that the postve partal correlaton between home nternet access and re-employment rates becomes statstcally sgnfcant. The most surprsng fndng from Table 3, however, s that nternet job search now appears to be not smply neffectve, but n fact sgnfcantly counterproductve. In other words, holdng constant observable characterstcs of the person and the prevous duraton of the unemployment spell, persons who searched for work on lne actually entered re-employment more slowly than those who dd not, durng the perod after we observe whether they search on lne. Incorporatng all the avalable nformaton on duratons n our sample therefore only strengthens the case aganst an unemployment-reducng effect of nternet job search; ths s true

17 15 whether we consder total effects of nternet job search, allowng the use of other methods to adjust optmally to the use of the nternet, or partal effects that hold all other methods fxed. 6. Dscusson Accordng to our best estmates, nternet job search s more common among workers wth observed characterstcs that are usually assocated wth faster re-employment. At the same tme, holdng these observed characterstcs constant, unemployment duratons are longer among workers who look for work on lne than among workers who do not. What explans ths? One possblty, of course, s that nternet search s n fact counterproductve at the ndvdual level, perhaps because of the sgnals t sends to employers. Workers mght stll use ths method, however, ether because t s so much easer and cheaper than tradtonal methods or because they were unaware of these drawbacks. Alternatvely, nternet job search mght sgnfcantly mprove search outcomes on dmensons such as job qualty that we cannot measure here, whch could more than compensate for an estmated ncrease n search tme. A thrd possblty s that nternet job search does speed re-employment, but that (despte the relatvely rch set of observables avalable n ths data) our results are contamnated by selecton nto nternet search on unobservable worker characterstcs that are correlated wth the workers re-employablty. Our prors when we started ths research, n fact one of our chef concerns, was that nternet searchers would be postvely selected on unobservables, as they are on observables. Clearly, f we were to mantan our belef n ths plausble noton that, for example, nternet searchers are lkely to be more motvated or better-connected than other jobseekers, then our estmates n Table 4 exaggerate the benefts of nternet job search, thus strengthenng the case that nternet job search does not reduce unemployment duratons. 23 But what of the possblty of negatve selecton nto nternet search on unobservables? We can thnk of at least four mechansms that could generate ths. Frst, as suggested by Holzer (1987) n another context, 23 Snce we have no measures of advance notce of job loss, one example of postve selecton would nvolve a greater amount of preunemployment search among nternet searchers search whch could yeld job offers durng the perod n whch we observe workers.

18 16 persons who use formal and anonymous job search channels (such as the nternet) may be dong so because ther nformal contacts and socal networks are poor. 24 Second, and related, s the possblty of prvate nformaton about re-employablty: persons usng a larger number of search methods ncludng the nternet may do so n response to prvate nformaton that ther search prospects are partcularly poor. Thrd, our data do not allow us to control for UI recept. If nternet searchers are more lkely to apply and qualfy for UI, ths omtted varable mght also account for ther longer duratons. 25 Fnally, especally among workers wth home nternet access, nternet job search strkes us as a very low-cost job search method. The costs of engagng n t are therefore unlkely to screen out ndvduals wth only a very margnal nterest n fndng a new job. Ths source of adverse selecton s apparently a major concern for practtoners currently workng n the nternet recrutng ndustry. In a personal ntervew, a professonal recruter nformed us that he avods nternet job boards altogether because of a concern about negatve selecton. Ths s echoed by a recrutng executve quoted n Autor (2001), who observed that nternet job boards are populated wth four types of resumes: the unhappy (and thus probably not a desrable employee); the curous (and therefore lkely to be a job-hopper ); the unpromotable (probably for a reason); and the unemployed (probably for a worse reason). It s also echoed n the development of software tools such as resume spders and resume robots, whose man am s to crcumvent job boards by trollng the nternet for passve job seekers who have not decded to look for work on lne. 26 In sum, unemployed nternet job searchers do not become re-employed more quckly than observatonally-equvalent unemployed persons who do not look for work on lne. A number of factors, ncludng smple neffectveness of nternet job search methods and negatve selecton on unobservables, could account for ths fndng. Whle dsentanglng these remanng possbltes Other omttted varables nclude chldren, ncome and prevous unemployment though t s unclear n what drecton these mght bas our results. 24 In partcular, Holzer suggests that mnorty youth dsproportonately use formal and anonymous job search networks n part due to low access to nformal contacts n the world of work, and that ther relance on formal methods n part explans ther lower job-fndng rates. 25 We thank an anonymous referee for ths suggeston. 26 See Kuhn (2003, forthcomng) for a more detaled descrpton of these ndustry developments.

19 17 remans an mportant topc for further research, our results n ths paper are clearly nconsstent wth a scenaro n whch nternet searchers are postvely selected (on hard-to-observe characterstcs) and n whch nternet search speeds re-employment. Snce nternet search companes often make both clams smultaneously, some re-evaluaton of these clams may be necessary.

20 18 References Addson, J. and P. Portugal. Job Search Methods and Outcomes Insttute for the Study of Labor (IZA), Dscusson Paper No (August 2001). Autor, D. Wrng the Labor Market Journal of Economc Perspectves 15 (1) (Wnter 2001): Bortnck, S. M and M. H. Ports, Job Search Methods and Results: Trackng the Unemployed Monthly Labor Revew; 115(12), December 1992, pages Brown, Jeffrey and Austan Goolsbee. Does the Internet Make Markets More Compettve? Evdence from the Lfe Insurance Industry Journal of Poltcal Economy 110(3) (June 2002): Brynjolfsson, Erk and Mchael D. Smth, Frctonless Commerce? A Comparson of Internet and Conventonal Retalers. Management Scence 46(4), (Aprl 2000): Carlton, Denns W. and Judth A. Chevaler. Free Rdng and Sales Strateges for the Internet NBER Workng Paper No. W8067, January Crossley, T., S. Jones and P. Kuhn. "Gender Dfferences n Dsplacement Costs: Evdence and Implcatons" Journal of Human Resources 19 (Sprng 1994): Farle, Robert W. Race and the Dgtal Dvde unpublshed paper, Unversty of Calforna, Santa Cruz, December Holzer, H. J. Informal Job Search and Black Youth Unemployment. Amercan Economc Revew 77 (June 1987): Holzer, H. J. Search Method Use by Unemployed Youth. Journal of Labor Economcs; 6(1), January 1988, pages Kefer, N. Economc Duraton Data and Hazard Functons Journal of Economc Lterature 26 (2) (June 1988): Krueger, Alan B. The Internet s Lowerng the Cost of Advertsng and Searchng for Jobs. New York Tmes July 20, 2000, p. C2. Kuhn, P. Polces for an Internet Labour Market. Polcy Optons, October 2000: pp Kuhn, P. and M. Skuterud, Job Search Methods: Internet versus Tradtonal. Monthly Labor Revew, October 2000, pp Kuhn, P. The New Economy and Matchng n Labor Markets, n Derek Jones, ed. Handbook of Economcs n the Electronc Age. San Dego, CA: Academc Press. (forthcomng 2003). Lancaster, Tony (1979). Econometrc Methods for the Duraton of Unemployment Econometrca 47(4):

21 19 Lang, Kevn. Panel: Modellng How Search-Matchng Technologes Affect Labor Markets, talk gven to IRPP and CERF conference on Creatng Canada s Advantage n an Informaton Age, May Madran, Brgtte and Lars Lefgren (1999). A Note on Longtudnally Matchng Current Populaton Survey (CPS) Respondents NBER Techncal Workng Paper Seres no Meyer, Bruce. Unemployment Insurance and Unemployment Spells. Econometrca 58 (July 1990): Mortensen, Dale T. Panel: Modelng How Search-Matchng Technologes Affect Labor Markets, talk gven to the IRPP and CERF conference on Creatng Canada s Advantage n an Informaton Age, May Osberg, L. Fshng n Dfferent Pools: Job Search Strateges and Job-Fndng Success n Canada n the Early 1980s. Journal of Labor Economcs; 11(2), Aprl 1993, pages Skuterud, Mkal "Causes and Consequences of the Upward Trend n On-the-Job Search: ", unpublshed paper, McMaster Unversty, Thomas, J. M. Publc Employment Agences and Unemployment Spells: Reconclng the Expermental and Nonexpermental Evdence. Industral and Labor Relatons Revew; 50(4), July 1997, pages

22 20 Table 1: Fracton of persons wth nternet access and engagng n nternet job search, by labor force status, December 1998 and August Fracton wth home nternet access Fracton lookng for work on lne Fracton lookng for work on lne, gven home nternet access Employed - at work absent Unemployed - on layoff jobseeker Not n LF - retred dsabled other Total Notes: 1. Does not equal the rato of prevous columns because some ndvduals wthout home nternet access search on lne.

23 21 Table 2: Sample means by nternet search actvty. Internet Search Total Yes No Retrospectve duraton * 2000 supplement * On layoff State unemployment rate Occupatonal unemployment rate * Worked pror to unemployment * School pror to unemployment Lost job * Temporary job Prvate sector Publc sector * Self-employed Age * Age Age Age * Male Marred * Male and marred * Spouse employed * Prmary school * Incomplete hgh school * Completed hgh school * Incomplete college * Assocate degree * Black * Hspanc * Home owner * Immgrant * Contacted employer drectly Contacted publc employment agency * Contacted prvate employment agency * Contacted frends or relatves Contacted school employment center * Sent resumes / flled applcatons * Checked unon/professonal regsters * Placed or answered ads * Other actve search method * Number of tradtonal search methods * Internet access at home * Employed n the month followng the Computer/Internet Supplement 1 Employed 2 months after Computer/Internet Supplement 1 Employed 12 months after Computer/Internet * Supplement 1 Observed n Employment, n any postsupplement month * Number of months observed * Notes: * ndcates f means are statstcally dfferent at a 5% sgnfcance level whch s obtaned by regressng each varable on a constant and the nternet search dummy varable. Sample szes are 860 nternet searchers and 3279 non-nternet searchers. 1. Share of persons observed at that date 2. Share of all observatons

24 22 Table 3: Probt Estmates of Internet Search Determnants and Outcomes Dependent Varable Looked for Work on Lne Employed One Year Later? (1) (2) (3) (4) (5) (6) Internet job search (0.095) (0.107) (0.097) (0.109) Retrospectve duraton * * * * (0.005) (0.006) (0.007) (0.007) (0.007) (0.007) 2000 supplement 0.429* 0.194* (0.052) (0.058) (0.075) (0.076) (0.076) (0.077) On layoff * 0.328* 0.316* 0.315* (0.089) (0.100) (0.135) (0.135) (0.138) (0.137) State unemployment rate (0.027) (0.030) (0.039) (0.039) (0.039) (0.040) Occupaton unemployment rate * * * * * * (0.016) (0.017) (0.021) (0.021) (0.021) (0.021) Worked before unemployment 0.164* 0.225* 0.414* 0.415* 0.418* 0.420* (0.082) (0.091) (0.122) (0.122) (0.123) (0.123) School before unemployment * 0.271* 0.266* 0.258* 0.251* (0.084) (0.092) (0.121) (0.121) (0.122) (0.122) Lost job (0.080) (0.089) (0.123) (0.123) (0.125) (0.125) Temporary job (0.096) (0.105) (0.142) (0.142) (0.145) (0.145) Prvate sector 0.281* 0.244* 0.422* 0.419* 0.440* 0.437* (0.110) (0.121) (0.148) (0.148) (0.149) (0.149) Publc sector 0.319* 0.302* (0.135) (0.148) (0.195) (0.195) (0.196) (0.196) Self-employed 0.378* 0.413* (0.163) (0.180) (0.241) (0.241) (0.242) (0.243) Age * 0.458* 0.571* 0.572* 0.584* 0.585* (0.121) (0.135) (0.160) (0.160) (0.161) (0.161) Age * 0.451* 0.466* 0.471* 0.443* 0.450* (0.116) (0.129) (0.153) (0.154) (0.155) (0.156) Age * 0.288* 0.507* 0.511* 0.511* 0.516* (0.115) (0.128) (0.149) (0.149) (0.151) (0.151) Age * 0.355* (0.119) (0.132) (0.155) (0.155) (0.157) (0.157) Male * * * (0.063) (0.069) (0.094) (0.095) (0.095) (0.095) Marred (0.104) (0.117) (0.151) (0.151) (0.153) (0.153) Marred male * 0.336* 0.339* 0.319* 0.322* (0.105) (0.118) (0.154) (0.154) (0.157) (0.157) Spouse employed (0.093) (0.104) (0.135) (0.135) (0.137) (0.137) Prmary school * (0.210) (0.233) (0.204) (0.205) (0.207) (0.208) Incomplete hgh * * * * * * (0.094) (0.104) (0.144) (0.144) (0.147) (0.147) Complete hgh * * (0.077) (0.085) (0.126) (0.127) (0.129) (0.130) Incomplete college * * (0.080) (0.088) (0.136) (0.136) (0.138) (0.138) Assocate degree * (0.110) (0.122) (0.187) (0.187) (0.190) (0.190) Black * * * * * (0.070) (0.079) (0.095) (0.095) (0.096) (0.097) Hspanc *

25 23 (0.087) (0.097) (0.114) (0.115) (0.116) (0.117) Home owner * (0.052) (0.059) (0.078) (0.079) (0.079) (0.080) Immgrant (0.088) (0.096) (0.117) (0.117) (0.118) (0.118) Contact employer * 0.160* (0.053) (0.058) (0.079) (0.079) Contact publc employment agency 0.196* 0.338* 0.257* 0.262* (0.062) (0.069) (0.097) (0.098) Contact prvate employment agency 0.283* 0.284* (0.091) (0.101) (0.153) (0.153) Contact frend/relatve (0.074) (0.082) (0.111) (0.111) Contact school employment agency (0.141) (0.158) (0.220) (0.220) Sent resumes 0.358* 0.372* 0.220* 0.220* (0.051) (0.056) (0.077) (0.077) Check unon (0.167) (0.186) (0.292) (0.292) Used ads 0.304* 0.340* (0.067) (0.075) (0.108) (0.108) Other actve 0.441* 0.363* (0.102) (0.112) (0.179) (0.180) Constant * * * * (0.226) (0.252) (0.308) (0.309) (0.320) (0.322) Home Internet Access 1.468* (0.062) (0.096) (0.097) Log lkelhood N Notes: Standard errors are n parentheses. * ndcates sgnfcance at the 5% level. The reference category for actvty before unemployment (worked or school) s nether worked nor attended school before unemployment.