Phoenix Center Policy Paper Number 39: Internet Use and Job Search. (January 2010)

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1 PHOENIX CENTER POLICY PAPER SERIES Phoenx Center Polcy Paper Number 39: Internet Use and Job Search T. Randolph Beard, PhD George S. Ford PhD Rchard P. Saba, PhD (January 2010), T. Randolph Beard, George S. Ford, and Rchard P. Saba (2010).

2 Phoenx Center Polcy Paper No. 39 Internet Use and Job Search T. Randolph Beard, PhD George S. Ford, PhD Rchard P. Saba, PhD ( Phoenx Center for Advanced Legal & Economc Publc Polcy Studes, T. Randolph Beard, George S. Ford, and Rchard P. Saba (2010).) Abstract: Wth unemployment levels at record hghs, polcymakers are strugglng to fnd any means possble to put Amercans back to work. In ths PAPER, we use the 2007 Computer and Internet Use Supplement of the Census Bureau s Current Populaton Survey to estmate the effect of Internet use on job search, and we fnd ths effect to be sgnfcant. Our emprcal model, whch combnes multnomal logt and propensty score methods, explots the dstncton between the unemployed and the dscouraged, where both desre employment but the latter has ceased actve job search due to negatve belefs about the labor market. We fnd that broadband use at home or at publc locatons reduces defecton from the labor market due to dscouragement by over 50 percent (50%). Dalup Internet use also has a statstcally sgnfcant effect, reducng labor market dscouragement by about one-thrd. These results provde useful nsghts for polcymakers: on the demand-sde, our results show that programs to promote Internet use keep the jobless actve n job search and may equate to more employment; and, on the supply-sde, our results demonstrate that the promoton of shared connectons, such as at lbrares, n unserved and underserved areas may, n fact, produce substantal socetal benefts. Senor Fellow, Phoenx Center for Advanced Legal & Economc Publc Polcy Studes and Professor of Economcs, Auburn Unversty. Chef Economst, Phoenx Center for Advanced Legal & Economc Publc Polcy Studes. Adjunct Fellow, Phoenx Center for Advanced Legal & Economc Publc Polcy Studes and Assocate Professor of Economcs, Auburn Unversty. The vews expressed n ths paper are the authors alone and do not represent the vews of the Phoenx Center, ts Adjunct Fellows, or any of ts ndvdual Edtoral Advsory Board members. The authors would lke to thank Phoenx Center Presdent Lawrence J. Spwak for hs helpful nput n preparng ths paper for publcaton.

3 2 PHOENIX CENTER POLICY PAPER [Number 39 TABLE OF CONTENTS: I. Introducton... 2 II. Lterature Revew... 7 III. Emprcal Strategy A. Data Internet Use Labor Market Status B. Causal Effects C. Specfcaton Detals: Treatment and Outcomes IV. Results A. Uncondtonal Treatment Effect B. Condtonal Treatment Effect Ignorng Overlap C. Condtonal Treatment Effect ncludng Overlap Condton D. Summary of Emprcal Results E. Addtonal Analyss V. Concluson and Extensons I. Introducton The Internet s wdely vewed as one of the most mportant forces n socal, poltcal, and economc development. Because the dffuson of the Internet n socety s a relatvely recent phenomenon, however, formal research on ts mpacts and consequences remans lmted. Economsts have not been able, for the most part, to document ts mpact n any partcularly convncng manner, though not due to any lack of effort. 1 The consequences of the Internet for the labor market, for example, have garnered hgh nterest for some tme. Reductons n frctonal unemployment, lower wage dsperson, and other phenomena one mght assocate wth ncreased market effcency have all been expected, hoped-for, or analyzed n papers ncludng, for example, 1 The lack of credble evdence s, n our opnon, largely due to the focus on macro-level mpacts of broadband. Macro-effects are dffcult to quantfy, even wth large datasets and mature technologes, nether of whch characterze Internet servce. For a revew of some of the lterature on the economc effects of the Internet, see L. Holt and M. Jamson, Broadband and Contrbutons to Economc Growth: Lessons from the US Experence, 33 TELECOMMUNICATIONS POLICY (2009). Mcro-level studes are ncreasngly prevalent and not restrcted to ssues narrowly construed as economcs. See, e.g., Sherry G. Ford and George S. Ford, Internet Use and Depresson Among the Elderly, PHOENIX CENTER POLICY PAPER NO. 38 (October 2009) (avalable at: and the ctatons theren.

4 Wnter 2010] INTERNET USE AND JOB SEARCH 3 Krueger (2000), Mortenson (2000) and Autor (2001). 2 Gven the present economc crss and resultng double-dgt unemployment and sgnfcant underemployment, 3 of obvous and mmedate nterest s the possblty that the Internet mght reduce the costs of job search, leadng to lower unemployment through reductons n the typcal length of jobless epsodes. Labor market research has not provded much n the way of postve evdence for the economc effects of the Internet. Indeed, the wdely dscussed fndngs of Kuhn and Skuterud (2004) are ndcatve of the typcal fndng: [w]e conclude that ether Internet job search s neffectve n reducng unemployment duratons, or Internet job searchers are negatvely selected on unobservables. 4 Ths lack of strong evdence on the value of the Internet on labor markets, partcularly job search, s somewhat surprsng. As many, ncludng Autor (2001) have noted, the Internet surely reduces the drect costs of search, both by job seekers and employers. 5 In most plausble crcumstances, ths wll lead to ncreased job search and more effcent matchng of employers and employees. 6 However, the Internet also serves as a source of nformaton about jobs, employers, and relevant economc condtons. In general, Internet search actvtes may, or may not, generate nformaton leadng the searcher to update hs or her belefs about relevant statstcs, such as the prevalence of openngs n partcular ndustres or trades. Vewed n ths way, use of the Internet could dscourage or encourage job seekers, dependng on the nature of the nformaton they fnd there. 2 A. Krueger, The Internet s Lowerng the Cost of Advertsng and Searchng for Jobs, NEW YORK TIMES (July 20, 2000) at C2; D. Mortensen, Panel: Modelng How Search-Matchng Technologes Affect Labor Market, Presentaton to IRPP and CERF Conference on Creatng Canada s Advantage n an Informaton Age, Ottawa, Canada (May 2000); D. Autor, Wrng the Labor Market, 15 JOURNAL OF ECONOMIC PERSPECTIVES (2001). 3 Unemployment measures only those lookng for work but wthout jobs, whereas underemployment measures nclude those nterested n work but have ceased actve job search. 4 P. Kuhn and M. Skuterud, Internet Job Search and Unemployment Duratons, 94 AMERICAN ECONOMIC REVIEW (2004). Contrary fndngs are found n B. Stevenson, The Internet and Job Search, NBER WORKING PAPER NO (2008). 5 Supra n For example, recent work by Weber and Mahrnger (2008) begns the task of evaluatng not whether certan forms of job search lead to employment, but how dfferent modes of job seekng affect the degree of job ft obtaned. A. Weber and H. Mahrnger, Choce and Success of Job Search Methods, 35 EMPIRICAL ECONOMICS (2008).

5 4 PHOENIX CENTER POLICY PAPER [Number 39 Table 1. The Labor Force: December 2008, 2009 Total Persons ( 000) Dec Dec Cvlan Labor Force.. 154, ,059 Employed , ,792 Unemployed... 11,400 15,267 Not n the Labor Force ,686 84,231 Persons who currently want a job... 5,180 5,939 Margnally attached to the labor force 1,908 2,486 Dscouragement over job prospects Reasons other than dscouragement... 1,266 1,588 Source: Bureau of Labor Statstcs, The Employment Stuaton - December 2009, USDL (Jan. 8, 2010) at Table A-2 and A-13. It s addtonally the case that employment status tself s mult-faceted, and t s often mportant to dstngush between employment, under-employment, unemployment, margnal attachment to the labor force, and so on. 7 Of partcular socal mportance n ths regard s the ssue of the margnally attached, defned by the Bureau of Labor Statstcs ( BLS ) as those persons not n the labor force who want and are avalable for a job, and who looked for work n the past 12 months, but who are not currently lookng (.e., wthn the past four weeks). 8 These workers are dvded nto two classes: dscouraged, and margnally attached but not dscouraged. A dscouraged person s no longer seekng work because they beleve ether that there are no jobs avalable, or else no jobs for whch they are qualfed. 9 As shown n Table 1, dscouraged workers amounted to around 929,000 ndvduals as of the fnal quarter of 2009, or about 37% of the margnally attached and 16% of those who wanted a job. 10 The remander of the margnally attached ncludes those persons not lookng for work due to reasons such as famly responsbltes or transportaton problems. In the fnal quarter of 2009, these margnally attached but not dscouraged ndvduals were 2.5 mllon strong, or about 26% of those who wanted a job. 11 Clearly, the margnally attached are economcally and socologcally sgnfcant, and ther problems should be an ssue of publc polcy concern. 7 See, e.g., Bureau of Labor Statstcs, How the Government Measures Unemployment (avalable at: 8 Id. Notably, casual Internet search for jobs does not count as actve job search. 9 Id. 10 Bureau of Labor Statstcs, The Employment Stuaton - December 2009, USDL (Jan. 8, 2010) (avalable at: at Table A Id.

6 Wnter 2010] INTERNET USE AND JOB SEARCH 5 In ths POLICY PAPER, we examne the effects of Internet use on worker status by analyzng the pool of workers who are jobless and currently want a job. Ths group ncludes the unemployed (jobless but actvely seekng a job or awatng layoff recall) and the margnally attached (avalable for work, but not actvely seekng employment at present). In BLS terms, the margnally attached are not currently searchng because they are ether dscouraged about ther job prospects, or else face some other challenge such as carng for an elderly relatve or lackng transportaton. In our vew, Internet use provdes three sorts of servces relevant to these categores of the jobless, and potentally affects the probabltes wth whch a person may fall nto one or another of them. Snce onlne job searches are nexpensve, Internet use should encourage actve search. Addtonally, Internet use provdes nformaton on jobs, wages, and the lke. Ths nformaton may nfluence the workers belefs about job avalablty and requrements. Fnally, the Internet s wdely used by persons n dffcult crcumstances (such as joblessness) to obtan support and emotonal renforcement. Such encouragement may prevent job seekers from gvng up,.e., becomng dscouraged. The nformatonal and supportve roles of the Internet can be crudely evaluated by examnng the dfferental mpacts of access on the sortng of the margnally attached workers nto varous categores, where ths sortng reflects belefs about the labor market, versus those categores that reflect largely external crcumstances such as chldcare dutes, poor health, or the lack of relable transportaton. Although we wll also examne our results usng a modfed defnton of dscouragement, even when usng the formal BLS defnton, we fnd evdence that the jobless are more lkely to be dscouraged when they do not use the Internet. Ths evdence suggests that support and nformaton obtaned from the Internet reduces the lkelhood that they feel there are no jobs, or no jobs for whch they could qualfy. Ths fndng s consstent wth, for example, those provded by Stevenson (2008), who reports that numerous jobseekers clam to have found useful job market nformaton on webstes. 12 Importantly, we get results that are smlar when we use a modfcaton of the BLS defnton of dscouragement that, n our opnon, better reflects the expected mpact of Internet use. For example, the Internet may provde nformaton relevant to solvng transportaton problems, or may allow the dsabled to work from home. These outcomes do not lead to the affected persons beng classfed as dscouraged by the BLS, but we suspect that Internet use may alter the 12 Supra n. 4.

7 6 PHOENIX CENTER POLICY PAPER [Number 39 probabltes wth whch these challenges drve jobless persons out of the labor force. The problem of nferrng the causal effect of varous sorts of Internet use on worker unemployment status, as consdered here, wll be approached usng the general framework of Rubn (1974), the modern detals of whch were revewed recently n Imbens and Wooldrdge (2009). 13 Other excellent treatments of the topc nclude Cameron and Trved (2005) and Angrst and Pschke (2009). 14 In partcular, we seek to obtan average treatment effects wth a causal nterpretaton usng multvarate regresson methods modfed to satsfy the requrements of unconfoundedness and propensty score methods to address the ssue of covarate overlap. 15 Ths approach s outlned n detal n Secton III and s based on the dea that one may remove the bases nherent n smple means comparsons of outcomes between groups by accountng for confoundng factors and adjustng the groups to reflect observed dfferences n covarate dstrbutons. Our task s made more nterestng and dffcult by the presence of multple treatments Dalup Internet use at home, Broadband Internet use at home, and Internet use n publc settngs and the trchotomous nature of the outcome unemployed, dscouraged, or margnally attached but not dscouraged. We therefore combne the approaches of Lechner (2002) and Crump et al. (2009) to estmate the average treatment effects n ths relatvely complex envronment. 16 Specfcally, we estmate propensty scores à la Lechner (2002) and then trm the sample to exclude extreme values of these scores n an effort to mprove covarate balance à la Crump et al. (2009). 17 Ths approach 13 D. Rubn, Estmatng Causal Effects of Treatments n Randomzed and Non-Randomzed Studes, 66 JOURNAL OF EDUCATIONAL PSYCHOLOGY (1974); G. Imbens and J. Wooldrdge, Recent Developments n the Econometrcs of Program Evaluaton, 47 JOURNAL OF ECONOMIC LITERATURE 5-86 (2009). 14 A. Cameron and P. Trved, MICROECONOMETRICS: METHODS AND APPLICATIONS (2005); J. Angrst and J. Pschke, MOSTLY HARMLESS ECONOMETRICS (2009). 15 Imbens and Wooldrdge (2009), supra n. 13 at 26; Angrst and Pschke (2009), supra n. 14 at Ch M. Lechner, Program Heterogenety and Propensty Score Matchng: An Applcaton to the Evaluaton of Actve Labor Market Polces, 84 REVIEW OF ECONOMICS AND STATISTICS (2002); R. Crump, V. Hotz, G. Imbens and O. Mtnck, Dealng wth Lmted Overlap n Estmaton of Average Treatment Effects, 96 BIOMETRIKA (2009); see also M. Lechner, Identfcaton and Estmaton of Causal Effects of Multple Treatments under the Condtonal Independence Assumpton, n ECONOMETRIC EVALUATION OF LABOR MARKET POLICIES (M. Lechner and F. Pfeffer eds. 2001) at Id.

8 Wnter 2010] INTERNET USE AND JOB SEARCH 7 allows all the treatment effects to be estmated n a sngle model (rather than only n a par-wse fashon) and facltates hypothess testng across estmated treatment effects. Our paper s organzed as follows: Secton II revews some lterature on three relevant strands n the lterature Internet use for job seekng actvtes, dscouraged workers, and the role of Internet connectvty n solaton and depresson. Secton III outlnes our emprcal strategy for estmatng causal effects. Secton IV summarzes the results. Conclusons are provded n Secton V. We also provde an appendx ncludng more detal on the data and econometrc results. II. Lterature Revew An overvew of the extent of employment-related webstes lends credence to the dea that the Internet mght fundamentally alter the dynamcs of the labor market. As detaled by Nakamura et al. (2007), the Internet has facltated a large number of employment nnovatons for both job seekers and those seekng employees. 18 Specfc webstes (e.g., Monster.com), employment portal webstes for major corporatons, streamlned onlne applcaton systems, and many other nnovatons have greatly reduced the costs of lookng for jobs, lookng for employees, and exchangng resumes or fllng out applcatons. Although t s slghtly hazardous to generalze from such frst order effects to characterstcs of the resultng equlbra, t would be qute surprsng f these cost-reducng nnovatons dd not result n mproved job matchng and decreased search cost and duraton. (Complcatng ths smple pcture somewhat, however, s the overwhelmng evdence suggestng the majorty of Internet job seekers are currently employed.) Nakamura et al. (2007) provde a battery of statstcs suggestng onlne job seekng s wdespread and vewed as effectve by the users: Monster.com receved over 18 mllon dstnct vstors n September, 2004; 92% of the largest North Amercan corporatons had employment sectons on ther corporate webstes as early as 2000; 87.6% of surveyed men ages 25-34, and 93.8% of women of the same ages, reported usng an Internet jobste n 2007; 41.8% of surveyed men ages 25-34, and 39.3% of women from the same cohort, reported they successfully used the Internet n fndng ther current or most recent job n Usng a dfferent sample, Stevenson (2008) reported that,...workers 18 A. Nakamura, K. Shaw, R. Freeman, E. Nakamura and A. Pyman, Jobs Onlne, n D. Autor ed. STUDIES OF LABOR MARKET INTERMEDIATION (2009) at Id.

9 8 PHOENIX CENTER POLICY PAPER [Number 39 beleve that the Internet s helpng them fnd jobs. [A]mong those that began a job n md-2002, 22% credted the Internet as the prmary means by whch they found ther job [O]ver half of those surveyed felt that the Internet was an effectve method of job search. 20 In stark contrast to these general observatons, specfc studes usng employment data suggest that the Internet s ether of lmted effectveness, or else s worse than useless. The wdely dscussed fndngs of Kuhn and Skuterud (2004), whch utlzed longtudnal observatons on Internet use and subsequent employment for a group of unemployed persons, found that once allowance s made for dfferent values of relevant covarates, use of the Internet actually appears to reduce the prospects of job seekers slghtly. 21 They remark, [o]nce observable dfferences between Internet and other searchers are held constant, however, we fnd no dfferences n unemployment duratons, and n some specfcatons even sgnfcantly longer duratons among Internet users, and later, [w]e conclude that ether (a) Internet job search s neffectve n reducng unemployment duratons or (b) Internet job searchers are adversely selected on unobservable characterstcs: further research s needed to dsentangle these two possbltes. 22 The analyss of Fountan (2005: 1253) offers only a very slghtly more postve assessment: [r]esults suggest the Internet s contrbuton to an unemployed searcher s nformaton pool may afford a small advantage only to the extent that other job searchers are not usng t. 23 The perceptons of job searchers appear qute at varance wth the (admttedly lmted) evdence on the effectveness of the Internet for obtanng employment. Assumng that the poor performance of the Internet n facltatng job search s confrmed by later research, one could say that ths msalgnment of percepton and realty presents a pattern famlar n the lterature of psychology, especally wth regard to the notons of well-beng and depresson. Feelngs of powerlessness and an nablty to control events or one s envronment are conventonal features of psychologcal descrptons of depressve dsorder. 24 If, as s often alleged, the Internet provdes users wth vrtual communtes that 20 Stevenson (2008), supra n. 4 at Kuhn and Skuterud (2004), supra n Kuhn and Skuterud (2004), supra n. 4 at C. Fountan, Fndng a Job n the Internet Age, 83 SOCIAL FORCES (2005). 24 B. Frey and A. Stutzer, What Can Economsts Learn from Happness Research?, 40 JOURNAL OF ECONOMIC LITERATURE (2002) at 51.

10 Wnter 2010] INTERNET USE AND JOB SEARCH 9 offer support, encouragement, and connecton, then use of the Internet mght lead to hgher subjectve evaluatons of the job search process than a factual readng of the record would mert. Ths explanaton would depend, of course, on the ablty of the Internet to provde such affrmaton. Although many studes have establshed the danger of excessve or compulsve use of the Internet, especally among young people, the potental therapeutc value of onlne actvtes has also receved attenton. 25 Ths focus has arsen from the wdely-accepted fndngs of Fernandez and Harrs (1992) documentng the effects of socal networks on perceved well-beng, mental health, and lfe success, ncludng employment status. 26 In general, socal bondng and communcaton, whch s facltated by the use of the Internet for some people, ncreases the percepton of control of the personal envronment, and reduces the severty and duraton of epsodes of depresson assocated wth ether unemployment or unsatsfactory job performance. Studes by Hoybye, Johansen, and Thomsen (2009), Houston, Cooper, and Ford (2002), Shaw and Gant (2002) and many others strongly suggest that, when used correctly, the Internet can sgnfcantly mprove mental health and outlook for many people facng traumatc events. 27 Most recently, Ford and Ford (2008), n POLICY PAPER NO. 38, employ a wde varety of emprcal tests on a large sample of aged persons n the U.S. and fnd that Internet use by ths group reduces depresson to a szeable degree. 28 Depresson and joblessness are strongly lnked because the transton from work to joblessness s hghly stressful, and s therefore a trgger for depresson and other emotonal dffcultes. Prause and Dooley (2001), for example, fnd that depresson at tme t s a vald predctor of unemployment at tme t + 1, and smlarly that employment at tme t s assocated wth less 25 See, e.g., Sajjadan and Nad, Depresson & Socal Isolaton n Adolescent and Young Adult Internet Users, Correlaton wth Tme Duraton of Internet Use, 4 JOURNAL OF RESEARCH IN BEHAVIOURAL SCIENCES (2006) and the extensve ctatons n Ford and Ford (2008), supra n R. Fernandez and D. Harrs, Socal Isolaton and the Underclass, n A. Harrell and G. Peterson eds. DRUGS, CRIME, AND SOCIAL ISOLATION: BARRIERS TO URBAN OPPORTUNITY (1992) at M. Hoybye, C. Johansenand T. Tjornhoj-Thomsen, Onlne nteracton: Effects of Storytellng n an Internet Breast Cancer Support Group, 14 PSYCHO-ONCOLOGY (2005); T. Houston, L. Cooper, D. Ford, Internet Support Groups for Depresson: A 1-year Prospectve Cohort Study, 159 AMERICAN JOURNAL OF PSYCHIATRY (2002); L. Shaw and L. Gant, In Defense of the Internet: The Relatonshp Between Internet Communcaton and Depresson, Lonelness, Self-Esteem and Perceved Socal Support, 5 CYBERPSYCHOLOGY & BEHAVIOR (2002). 28 Supra n. 1.

11 10 PHOENIX CENTER POLICY PAPER [Number 39 depresson at tme t + 1, more-or-less confrmng the mutually causatve role of mental state and employment status. 29 Thus, job loss could trgger depresson, whch reduces the prospects for re-employment, resultng n a vcous cycle and hgher healthcare and socal support costs. III. Emprcal Strategy As observed by Autor (2001), Stevenson (2008) and others, t s possble that the Internet, whle not necessarly producng an ndependently sgnfcant number of job matches, does tend to keep the jobless searchng (perhaps usng other, more effectve technologes). 30 In lght of our dscusson above, the queston mmedately arses: does access and use of the Internet prevent job seekers from becomng dscouraged,.e. non-searchers? In response, our focus here s on the effects of Internet connectvty and use on the probablty that the jobless become dscouraged,.e., cease lookng for work for reasons arsng from ther perceptons of the nature of the labor market, rather than perhaps temporary and exogenous barrers such as llness n the famly. Because the defnton of a dscouraged worker used by the BLS s qute strngent, dated and not necessarly reflectve of the potental mpacts of Internet use, we wll addtonally examne a somewhat modfed noton of dscouragement of our own constructon. A. Data Data on Internet use, (un)employment, and other covarates of nterest comes from the 2007 Internet and Computer Use Supplement to the Current Populaton Survey. 31 Ths data allows Internet use to be measured n three ways: Dalup use at home, Broadband use at home, and Publc use (such as at a publc lbrary). Ths same data permts the classfcaton of respondents as employed, unemployed, or margnally attached, ncludng whether those persons ndentfed as margnally attached are dscouraged or not. The survey also 29 J. Prause and D. Dooley, Effect of Favorable Employment Change on Psychologcal Depresson: Two-Year Follow-Up Analyss of the Natonal Longtudnal Survey of Youth, 50 APPLIED PSYCHOLOGY: AN INTERNATIONAL REVIEW (2001). Also see D. Dooley, J. Prause and K. Ham-Rowbottom, Underemployment and Depresson: Longtudnal Relatonshps, 41 JOURNAL OF HEALTH AND SOCIAL BEHAVIOR (2000). 30 Autor (2001), supra n. 2; Stephenson (2008), supra n The data s accessed va DataFerrett (avalable at: The supplement to the Current Populaton Survey has been collected n 1994, 1997, 1998, 2000, 2001, 2003, and 2007.

12 Wnter 2010] INTERNET USE AND JOB SEARCH 11 contans a large number of demographc and geographc varables on respondents. 1. Internet Use For Internet use, we defne an Internet user by a Yes response to the queston Internet use - any locaton. 32 Of all Internet users, we categorze home users by a Yes response to Connect to Internet from home. 33 Fnally, for home users, we dstngush between dalup and broadband use by responses to the queston Currently access - Internet usng (1) A regular dalup telephone; (2) DSL, cable modem, satellte,.; and (3) somethng else. 34 We assume broadband users are all home users not usng dalup. We thus have three treatments: (1) homes users wth dalup (11% of the observatons); (2) home users wth broadband (48% of observatons); and (3) Internet users usng publc connectons (14% of observatons), and denote these varables as DIALUP, BROADBAND, and PUBLIC, respectvely. About 27% of the sample respondents do not use the Internet. There s no nherent orderng between these treatments (e.g., s home dalup more or less ntense than publc Internet use?) and we do not mpose one. 2. Labor Market Status In formng our sample, we exclude all employed respondents. Our nterest focuses on the effect of Internet use on search efforts by the jobless (rather than use by those employed persons who access the Internet to look for more appealng jobs), and on whether Internet use keeps jobless persons n the labor force. Of the jobless persons n our sample, we classfy them varously as unemployed, dscouraged, or margnally attached n the same manner as the Bureau of Labor Statstcs ( BLS ) and Natonal Bureau of Economc Research ( NBER ). 35 Unemployed persons are n the labor force, whle the margnally attached are not. In the data, the unemployed are dentfed by an Unemployed-On Layoff or Unemployed-Lookng response to the Labor Force-employment status survey queston. 36 The dvson of the margnally 32 Survey varable: HENET1. 33 Survey varable: HENET3. 34 Survey varable: HENET4. 35 Bureau of Labor Statstcs, supra n Survey varable: PEMLR.

13 12 PHOENIX CENTER POLICY PAPER [Number 39 attached nto the dscouraged and just margnal classes s summarzed n Table 2. Followng the BLS defntons, the dscouraged are answerng the queston reason not lookng wth one of the offered responses: a) beleves no work avalable; b) couldn t fnd any work; c) lacks necessary schoolng or tranng; d) beleves employers thnk he/she s too young or too old; and e) beleves other types of dscrmnaton preclude fndng a job. 37 Those classfed as just margnal, on the other hand, face problems such as: a) can t arrange for chld care; b) has famly responsbltes precludng work; c) s n school or recevng tranng; d) has ll-health or a dsablty; e) has transportaton problems; and f) all other responses. Table 2. Jobless Classfcatons Response n BLS Informaton-related (Authors) Beleves No Work Avalable 71 Dscouraged Dscouraged Couldn t Fnd Any Work 113 Dscouraged Dscouraged Lacks Schoolng/Tranng 23 Dscouraged Dscouraged Emp s Thnk Too Young/Old 31 Dscouraged Just Margnal Other Dscrmnaton 8 Dscouraged Just Margnal Can t Arrange Chld Care 35 Just Margnal Dscouraged Famly Responsbltes 296 Just Margnal Just Margnal In School/Tranng 213 Just Margnal Just Margnal Ill-Health, Dsablty 187 Just Margnal Dscouraged Transportaton Problems 42 Just Margnal Dscouraged Other 422 Just Margnal Just Margnal Sum 1,441 Sample Sze 4,229 Unemployed 2,788 2,788 Dscouraged Just Margnal 1, After excludng observatons wth mssng data, the full sample conssts of 4,229 responses. For Internet use, the summary statstcs are: 27% do not use the Internet at all, 48% use Broadband at home, 14% access the Internet at publc (not home) stes, and 11% use Dalup servce. As for employment, 66% are unemployed and 44% are margnally attached. Of ths latter group, 83% are just margnal (.e., margnally attached but not dscouraged) and 17% are 37 The full documentaton for the queston s: PEDWRSN, Labor Force-(not n dscouraged) reason not lookng: (-1) Not n Unverse; (1) Beleves No Wrk Avl In Lne Lk Or Area; (2) Couldn t Fnd Any Work; (3) Lacks Necessary Schoolng/Tranng; (4) Employers Thnk Too Young Or Too Old; (5) Other Types Of Dscrmnaton; (6) Can t Arrange Chld Care; (7) Famly Responsbltes; (8) In School Or Other Tranng; (9) Ill-Health, Physcal Dsablty; (10) Transportaton Problems; (11) Other Specfy.

14 Wnter 2010] INTERNET USE AND JOB SEARCH 13 dscouraged. Of the jobless, 66% are unemployed, 28% are just margnal, and 6% are dscouraged. The BLS defnton of a dscouraged worker, although well-known, extensvely studed, and thoroughly debated, s not self-evdently useful f one seeks to analyze the potental role of the Internet n job search actvtes. In partcular, we beleve that the Internet lkely has a rather complex effect on the status of the unemployed worker, as dscussed n secton II. Thus, to examne better the potental effects of the Internet on the job search process, we offer an alternatve defnton of dscouragement that hghlghts the potental nformatonal effects of Internet use. In the fnal column of Table 1, we have reclassfed the responses to construct a new measure of dscouragement that s ntended to better reflect these factors. For example, belevng no work avalable or couldn t fnd any work are clearly employment obstacles that Internet use may help overcome. Smlarly, nformaton on schoolng and tranng requrements are avalable onlne, as are educatonal programs. These three responses are ncluded n the dscouraged defnton n the BLS classfcaton and clearly fall nto a class of crcumstances that the Internet may ad n resolvng. In our new, alternatve defnton, three responses are moved nto the dscouraged class, ncludng the nablty to fnd chldcare, havng poor health or a dsablty, and transportaton problems. Fndng chldcare and transportaton can be facltated by Internet use, and the dsabled can work from home usng an Internet connecton. We queston whether a person s belefs about age or other forms of dscrmnaton can be modfed by Internet use. Thus, we move age-related and other dscrmnaton out of the dscouraged class. In ths modfed defnton of dscouraged, 11% of the sample s dscouraged and 23% s just margnal. 38 We wll perform our statstcal analyss usng both the tradtonal BLS defnton of dscouragement, and our proposed alternatve defnton. B. Causal Effects Labor markets have been the focus of much of the treatment effects lterature n economcs. Ths paper s partally ntended as an addton to that lterature, so we vew Internet use as the treatment (T) and jobless categorzaton as the outcome (Y). As wth the bulk of ths exstng lterature, we do not have expermental data; the data just descrbed s observatonal, collected by the Census Bureau. As a consequence, the assgnment of the treatment s not 38 Increasng the sample sze of dscouraged also has benefts of a purely emprcal nature.

15 14 PHOENIX CENTER POLICY PAPER [Number 39 random (as one would of course wsh), but s based on the choces of the ndvduals n the sample. If the factors nfluencng treatment choce also nfluence the outcome, then there s a great rsk of obtanng a based measure of the treatment effect usng smple statstcal tests that gnore ths characterstc of the sample. The prmary techncal challenge of ths PAPER then s to develop credble estmates of the treatment effects of the varous types of Internet use gven the nature of the sample. Selecton bas s most easly llustrated n the potental outcomes framework based on the Rubn Causal Model. 39 Let there be a dchotomous treatment T, such as partcpaton n a program (or use of the Internet). Suppose that ths treatment wll affect some outcome, say wages, Y. The observed outcome for ndvdual can be wrtten as Y Y Y Y f f ( Y 1 T T Y ) T (1) where Y 1 - Y 0 s the causal effect of the treatment T. (Presumably, the outcomes follow some dstrbuton n the populaton). The core dffculty wth measurng the causal treatment effect s that (n most cases) only Y 1 or Y 0 s observed n a sample snce an ndvdual ether receved, or dd not receve, the treatment. In other words, the treatment effect of nterest s Y 1 versus Y 0 for ndvdual, but n practce ndvdual s ether treated or untreated, so ether Y 1 or Y 0 s not observable. Consequently, the drect computaton of the treatment effect s precluded, and we are forced to compare the outcomes of a sample of ndvduals recevng the treatment to a sample of ndvduals not recevng the treatment. After some algebrac manpulaton, the average effect of the treatment n a sample s E[ Y T 1] E[ Y T 0] E[ Y E[ Y 1 0 Y T 0 T 1] 1] E[ Y 0 T 0] (2) where E[ ] s the expectaton. The frst term on the rght hand sde s the causal effect of nterest, whch s the dfference n outcomes for ndvdual upon 39 Rubn (1974), supra n. 13; Imbens and Wooldrdge (2009), supra n. 13; Angrst and Pschke (2009), supra n. 14.

16 Wnter 2010] INTERNET USE AND JOB SEARCH 15 recevng the treatment. Added to ths causal effect, however, s an addtonal term that equals the average dfference n the untreated states of those who were treated and those who were not. The second term on the rght hand sde can be thought of as a selecton bas. If the treated have a dfferent value of Y 0 from the untreated, then the average dfference n outcomes n a sample wll be based. For example, t s sensble to expect more ntellgent persons wll earn hgher ncomes, at least on average. It s also true that more ntellgent persons are more lkely to get a college educaton. The dfference n average ncomes between those wth and wthout a college degree ncludes a selecton bas component snce, wthout a college degree, the ncomes of the more ntellgent wll be hgher. Generally, f the treated are lkely to do better (worse) than the untreated n any case, then the selecton bas s postve (negatve) and the estmated treatment effect wll be too large (small). Resolvng the problem requres some procedures and/or assumptons that ensure the selecton bas s zero. As s well known, random assgnment of the treatment solves the problem snce the assgnment s ndependent of potental outcomes. Ths s the bass of sample desgn n laboratory scence. In observatonal data, however, there s no a pror reason to expect ndependence between the assgnment of the treatment and the outcomes, so somethng must be done ex post to account for the bas. As detaled by Imbens and Wooldrdge (2009), the most common way to proceed when estmatng the causal treatment effect n observatonal studes s to appeal to the concepts of (1) unconfoundedness (or condtonal ndependence) and (2) covarate overlap. 40 Unconfoundedness mples that, condtonal on observed covarates X, the treatment assgnment probabltes are ndependent of potental outcomes, or { Y 0, Y1 } T X, (3) where the symbol denotes ndependence, so the expresson reads, the outcomes are ndependent of the treatment gven the condtonng covarates. If we have a suffcently rch set of observable covarates, regresson analyss ncludng the varables X leads to vald estmates of causal effects. Snce the X must be observed to be ncluded n the model, ths approach s often referred to as selecton-on-observables. In ths PAPER, we employ regresson analyss to estmate the treatment effects. Selecton bas n a regresson framework s drectly analogous to the 40 Imbens and Wooldrdge (2009), supra n. 13, at 23-8.

17 16 PHOENIX CENTER POLICY PAPER [Number 39 potental outcomes approach. Consder the regresson approach to measurng the causal effect, Y T e (4) where e s the random dsturbance term. 41 expectatons, we see that Evaluatng the condtonal E[ Y T 1] E[ Y T 0] E[ e T 1] E[ e T 0] (5) where the rght hand sde equals the treatment effect () plus the selecton bas term n parenthess. The smlartes between Expressons (2) to (5) are apparent. In regresson, the selecton bas appears (or can be wrtten as) as a correlaton between the dsturbance term, e, and the treatment varable, T. Ths selecton arses because of the non-zero dfference between the no-treatment potental outcomes between those recevng and those not recevng the treatment, or E [ Y0 T 1] E[ Y0 T 0], whch s the same as n the potental outcomes approach. Appealng to unconfoundedness, we can decompose the dsturbance term e nto a lnear functon of observable covarates X, so that e X v, (6) then by constructon of the least squares estmates (v s uncorrelated wth X ) and by the condtonal ndependence from Expresson (3), we can estmate the regresson model Y T X v, (7) and nterpret as the causal treatment effect, snce the resdual v s uncorrelated wth T and X. A second condton for the measurement of the causal effect s covarate overlap. Imbens and Wooldrdge (2009) observe that, once one commts to the unconfoundedness assumpton, the ssue of covarate overlap s the man 41 See, e.g., Angrst and Pschke (2009), supra n. 14 at 58-9; Imbens and Wooldrdge (2009), supra n. 13, at 26.

18 Wnter 2010] INTERNET USE AND JOB SEARCH 17 problem facng the analyst. 42 Imbens and Wooldrdge (2009) defne the overlap condton as 0 p( T 1 X x) 1, for all x (8) where p ndcates probablty. 43 Ths condton mples that the support of the condtonal dstrbuton of X gven T = 0 overlaps completely wth the condtonal dstrbuton of X gven T = 1. Put smply, covarate overlap mples that the covarate dstrbutons for the treated and untreated groups are suffcently alke, whch s mportant, gven the nherent extrapolatons between the groups made n regresson analyss. From above, recall that n practce we only observe Y 1 or Y 0 for any ndvdual. Thus, we must use the observed outcomes from the untreated observatons to project Y 0 onto the treated observatons n order to compute an average treatment effect (Y 1 - Y 0 ). If the untreated observatons are very dfferent demographcally and geographcally from the treated, the projecton wll be a poor one. A number of studes have shown that a lack of covarate overlap s a major concern n estmatng treatment effects. 44 Unlke the unconfoundedness assumpton for whch there s no drect emprcal test, covarate overlap can be evaluated n a relatvely straghtforward manner. Imbens and Wooldrdge (2009), for example, recommend evaluatng the normalzed dfferences for each covarate, X 1 X 0 x, (9) V V 1 0 where the X and V are the sample means and varances for the treated and untreated groups. 45 If these normalzed dfferences exceed 0.25, then the regresson estmates tend to be senstve to model specfcaton. 46 Ths threshold 42 Imbens and Wooldrdge (2009), supra n. 13, at Imbens and Wooldrdge (2009), supra n. 13, at R. Deheja and S. Wahba, Causal Effects n Nonexpermental Studes: Reevaluatng the Evaluaton of Tranng Programs, 94 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (1999); J. Heckman, H. Ichmura, and P. Todd, Matchng as an Econometrc Evaluaton Estmator, 65 REVIEW OF ECONOMIC STUDIES (1988). 45 Imbens and Wooldrdge (2009), supra n. 13, at Id.

19 18 PHOENIX CENTER POLICY PAPER [Number 39 value s exceeded for a few covarates n our data. As detaled below, n order to remedy ths problem and satsfy the overlap assumpton we follow Deheja and Wahba (1999) and Crump et al. (2009) and employ propensty score methods to trm the data pror to estmaton. 47 Ths technque deletes observatons that exhbt a hgh lkelhood of exhbtng the presence of any gven treatment, snce such observatons are unlke observatons one would observe n a sample n whch treatments were randomly assgned. Duplcatng the character of such a sample s the goal. C. Specfcaton Detals: Treatment and Outcomes In the prototypcal framework, a sngle treatment (e.g., a labor program) s evaluated on an outcome that s often contnuous n nature (.e., wage or ncome). Our task s a bt dfferent n that we have a trchotomous outcome unemployed, dscouraged, and just margnal and multple treatments Internet use at home usng ether dalup or broadband and publc Internet use. The trchotomous outcome s handled by use of multnomal logt estmaton (unordered), whch s an establshed and wdely used estmaton technque. 48 The multnomal logt model specfes that p j m l exp T j X j exp T j X 1 j, (10) where p j s the probablty observaton falls nto outcome category j (j = 1,, m), the X are the covarates for each observaton, and the j the estmated coeffcents for each outcome category j. 49 There are three possble treatments n T, and thus three coeffcents j for each j. To dentfy the model, the j are set to zero for one of the outcomes. The unemployed outcome s set as the base case, so all coeffcents measure the probablty of fallng nto ether the dscouraged or just margnal category relatve to the unemployed category. If Internet use reduces job search costs and keeps jobless people n the labor force, then the coeffcents on the Internet use varables wll be negatve. 47 Deheja and Wahba (1999), supra n. 44 ; Crump et al. (2009), supra n Cameron and Trved (2005), supra n. 14 at Ch Id.

20 Wnter 2010] INTERNET USE AND JOB SEARCH 19 The problem of multple treatments s challengng. In the presence of multple treatments, Lechner (2002) proposes par-wse comparsons of treatment types and outcomes usng propensty score methods. 50 The propensty score, whch s the condtonal probablty of recevng the treatment (gven the X s), can be estmated ether structurally usng multnomal or ordered logt (or probt) models, or usng reduced form sngle equaton logt models n parwse comparsons. Matchng algorthms are then appled to the estmated propensty scores to compute the par-wse treatment effects. Estmaton of propensty scores for our three treatments s not problematc; multnomal logt can be used for that purpose. Gven our trchotomous outcome, however, the second-stage matchng approach s not deal; our trchotomous outcome s perhaps better modeled wth multnomal logt. Therefore, we choose to combne propensty score methods and regresson, whch s an ncreasngly common approach n emprcal research. 51 Specfcally, we trm the data (renderng an estmaton sample A) usng the propensty score to mprove covarate overlap. Our trmmng s based on the rule of thumb proposed by Crump et al. (2009). 52 Specfcally, we frst estmate propensty scores, ŝ(x ), then trm the sample to mprove covarate balance by keepng only observatons where 0.10 ŝ(x ) As dscussed below, by trmmng the data n ths way, the normalzed dfferences are below threshold for all covarates. Ths estmaton strategy s not wthout costs, snce trmmng the data changes what s beng estmated. 53 Specfcally, our approach does not estmate the average causal effect for the populaton, but rather estmates the condtonal average treatment effect ( CATE ) for the subpopulaton A ( CATE-A ). Nevertheless, Crump et al. (2009) recommend estmatng CATE-A because these are easer to estmate and (potentally) more precse than populaton estmates. 54 In any case, care must be taken when extrapolatng CATE-A to the general populaton. 50 M. Lechner, supra n Imbens and Wooldrdge (2009), supra n. 13 at Crump et al. (2009), supra n. 16. The same approach s dscussed n Imbens and Wooldrdge (2009), supra n. 13 at and Angrst and Pschke (2009), supra n. 14 at Imbens and Wooldrdge (2009), supra n. 13 at Crump et al. (2009), supra n. 16; see also Imbens and Wooldrdge (2009), supra n. 13 at 45-6; Angrst and Pschke (2009), supra n. 14, at

21 20 PHOENIX CENTER POLICY PAPER [Number 39 IV. Results Our emprcal strategy s mplemented as follows. Frst, we dscuss the uncondtonal treatment effects that are estmated by consderng only means dfferences n outcomes across the three treatments and outcomes. By the dscusson above, there s a rsk that ths measure of the average treatment effect s based, snce nether unconfoundedness nor overlap s addressed. Second, n an effort to correct for ths bas, we add covarates to the model and compute the condtonal average treatment effect ( CATE ). Thrd, we trm the sample to mprove covarate overlap followng Crump et al. (2009), thereby potentally addressng both unconfoundedness and overlap. 55 Ths approach requres a twostep estmaton technque. In the frst stage, we estmate a propensty score, whch s smply the condtonal expectaton of a sample respondent recevng the relevant treatment (the predcted probablty of a logt regresson). In the second stage, we estmate the multnomal logt model, but trm the sample to exclude observatons exhbtng extreme values of the propensty score (thus creatng subsample A) usng the rule of thumb proposed by Crump et al. (2009). 56 The estmated treatment effect from ths model s CATE-A, the condtonal average treatment effect for the subsample A. A. Uncondtonal Treatment Effect To begn, we estmate the uncondtonal treatment by regressng the outcomes on the Internet use varables alone. Ths uncondtonal treatment s estmated by ncludng only the treatment dummy varables n the multnomal logt model, whch can be wrtten as, p j m l exp T exp T 1 j j. (11) The predcted outcomes from the uncondtonal estmates are summarzed n Table 3. Detaled results are provded n the Appendx Table A-1 and A-2. These results should be nterpreted wth care, snce by the argument above the estmated effects could be substantally based. 55 Id. 56 Id.

22 Wnter 2010] INTERNET USE AND JOB SEARCH 21 For the BLS defnton of dscouragement, the coeffcents on the Internet use varables (.e., the treatment effects) are all negatve and mostly statstcally dfferent from zero. Internet use reduces both dscouraged and just margnal classfcatons, but the response for dscouraged s larger. (Conversely, one may conclude that Internet use ncreases the probablty that the ndvdual wll be actvely searchng for work.) The average treatment effects, measured as the percentage dfference n the predcted outcomes, are very large and consstently statstcally sgnfcant n the case of the dscouragement outcome. Broadband and Publc use are found to reduce dscouragement by over 60%, and dalup has a treatment effect of about 40%. The effects of Internet use on just margnal are small and not all statstcally dfferent from zero. Jont tests on the results usng the BLS defntons are as follows. Frst, we can reject the null hypothess that Internet use of any type has no effect (prob( 2 ) < 0.01) n the entre model. Second, we can reject the null hypothess that Internet use has no effect n the dscouraged equaton (prob( 2 ) < 0.01) and the just margnal equaton (prob( 2 ) < 0.01). Thrd, n the dscouraged equaton, we can reject the null hypothess that the effect of Dalup equals that of Broadband or Publc use (Dalup has a smaller effect) (prob( 2 ) < 0.01 n both cases), but we cannot reject the null hypothess that the effect of Broadband equals that of Publc Use (prob( 2 ) < 0.45). We can summarze as follows: Internet use of all types reduce dscouragement, wth Broadband and Publc use havng the same effect, whch s larger than the effect of Dalup.

23 22 PHOENIX CENTER POLICY PAPER [Number 39 Table 3. Uncondtonal Treatment Effects (n = 4,229) Bureau of Labor Statstcs Defntons (n = 4,229; Pseudo-R 2 = 0.011) ML Results Treatment Effect Coef. St. Err. T-Stat Untreated Treated Dfference Dscouraged Dalup * % Broadband * % Publc * % Just Margnal Dalup % Broadband * % Publc * % Informaton-related (Author) Defntons (n = 4,229; Pseudo-R 2 = 0.016) Dscouraged Dalup * % Broadband * % Publc * % Just Margnal Dalup % Broadband % Publc % * Sgnfcant 5% level or better. The results are dfferent for the Informaton-related defntons of jobless status. Whle Internet use reduces Informaton-related dscouragement (as we defned t above), t has no effect on the just margnal type. These results are consstent wth our earler conjecture, snce the new defnton of dscouragement was motvated by the expected effects of Internet use. Despte the change n defnton, the treatment effects for dscouragement are smlarly szed. Broadband use at home and Publc use by over 60%, and Dalup use reduces dscouragement by nearly 40%. Jont tests are as follows. Frst, we can reject the null hypothess that Internet use of any type has no effect (prob( 2 ) < 0.01) n the entre model. Second, we can reject the null hypothess that Internet use has no effect n the dscouraged equaton (prob( 2 ) < 0.01), but not the just margnal equaton (prob( 2 ) = 0.29). Thrd, n the dscouraged equaton, we can reject the clam that the effect of Dalup equals that of ether Broadband or Publc use (Dalup has a smaller effect) (prob( 2 ) < 0.01 n both cases), but we cannot reject the null hypothess that the effect of Broadband equals that of Publc Use (prob( 2 ) < 0.23). We can agan summarze as follows: Internet use of all types reduces dscouragement (but not just margnal attachment), wth Broadband and Publc use havng the same effect, whch s larger than the effect of Dalup.

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