Offshoring, Trade in Tasks and. Occupational Specificity of Human Capital

Transcription

1 Offshorng, Trade n Tasks and Occupatonal Specfcty of Human Captal Mortz Rtter October 2008 Abstract Ths paper makes three contrbutons to our understandng of the mpact of offshorng on aggregate productvty and on labour market outcomes. Frst, I document that workers n tradable servce occupatons attan more general and more specfc human captal. Second, I develop a dynamc general equlbrum model n whch workers acqure human captal specfc to the task they complete. Thrd, I calbrate the model to the U.S. economy, quantfy the output effect of trade n tasks, and descrbe the occupatonal reallocaton of workers. The dynamc nature of the model allows for dfferentaton between short and long run effects. The welfare effects of ncreased offshorng are unambguously postve; ther magntude depends on the dfference between autarky and world relatve prces, but not on the skll-content of offshored and nshored tasks. For reasonable terms of trade, the calbrated steady state welfare gans are found to be between 1.8% and 4%. The dstrbuton of the gans from trade crtcally depends on the tme horzon: n the short term, workers wth human captal specfc to the nshored occupatons gan, whle workers wth human captal specfc to the offshored occupatons lose. In the long run, the gans from trade are equally dstrbuted among ex-ante dentcal agents. Keywords: Offshorng, Sectoral Labour Reallocaton, Human Captal JEL classfcaton: E24, F16, J24, J62 I am grateful to Shouyong Sh and Gueorgu Kambourov for ther support and gudance. I have also benefted from dscussons wth Bernardo Blum, Andres Erosa, Peter Morrow, and Dego Restucca. I receved valuable comments from partcpants at the 2008 Mdwest Macroeconomcs Meetngs, the 2008 Annual Meetng of the Canadan Economc Assocaton and the 2008 Research on Money and Markets Workshop. Fnancal support from Shouyong Sh s Bank of Canada Fellowshp s gratefully acknowledged; the opnons expressed n ths paper are my own and not those of the Bank of Canada. All remanng errors and shortcomngs are mne. Department of Economcs, Unversty of Toronto. Emal: m.rtter@utoronto.ca

2 1 Introducton Technologcal progress has led to consderable changes n the organzaton of the producton process tasks tradtonally completed n close physcal proxmty can now be spatally separated and carred out ndependently, thus spurrng offshorng of ntermedate processes or tasks. 1 Dfferently from past trade experences, trade n tasks affects not only manufacturng but also hgh-skll servce occupatons. 2 Ths has spurred a debate between two opposng vewponts, one of whch focuses on the long term gans and mantans that offshorng s productvty-enhancng; the other outlook stresses potental short term losses and warns about the dsruptve effects the offshorng of hgh skll tasks may have. Prevous work evaluatng clams of ether sde of the debate has mostly reled on statc models to address the mpact of offshorng on productvty and wages and consequently could not jontly evaluate both short and long term mpacts as well as the transton between the two. Ths paper ascertans that usng a dynamc model n whch workers accumulate specfc human captal s mperatve for assessng the potental devaluaton of human captal due to offshorng of hgh skll tasks and for quantfyng the magntude of ts short and long term effects on aggregate productvty and wages. Dfferentatng between specfc and general human captal s partcularly relevant n the context of worker reallocaton due to hgh-skll offshorng. Were reallocated workers human captal mostly general, ther loss n productvty would lkely be small as they would be able to apply most of ther knowledge to the new task. However, f workers who are exposed to ncreased offshorng have relatvely more occupaton specfc human captal, swtchng occupatons may cause a sgnfcant loss n workers productvty and wages. Motvated by ths observaton, I develop a dynamc general equlbrum model n whch workers acqure human captal specfc to the task they are completng. Openng up the economy to trade trggers a reallocaton of workers out of offshored and nto nshored occupatons, causng a loss of specfc human captal. Both the ncrease n unemployment durng the reallocaton process and the loss of human captal have a negatve mpact on aggregate productvty. At the same tme, ncreased trade allows the economy to explot ts comparatve advantage, thereby generatng a postve productvty effect. In the short run, the total effect depends on the relatve magntude of the negatve reallocaton and the postve comparatve advantage effects. In the long run, workers reacqure 1 Offshorng s the reallocaton of producton stes to foregn countres to take advantage of lower nput costs. Ths phenomenon s often mslabelled as outsourcng, a term whch refers to the organzatonal structure of the frm nstead. Offshorng s the choce over where to produce, whle outsourcng s the choce over what selecton of tasks are to be performed outsde the frm; offshorng may or may not nvolve outsourcng. 2 In the context of trade n tasks, an occupaton s the relevant labour market counterpart; a task s the output of an occupaton. 2

3 human captal and unemployment falls to ts pre-trade level, so the postve productvty effect prevals. The magntude of the productvty effect depends on dfferences between autarky and world market prce, but not on the characterstcs of the traded tasks. The frst part of ths paper documents dfferences n the occupatonal specfcty of human captal across occupatons and relates ths to offshorng. As such, t bulds on the work of Kambourov and Manovsk (2008), who fnd that returns to occupatonal tenure are hgher than returns to job or ndustry tenure, ndcatng that workers acqure substantal amounts of occupaton-specfc human captal. Usng occupaton descrptons from the O*NET database, 3 I frst dentfy tradable occupatons. Classfyng occupatons by educatonal attanment of ther workers reveals that newly tradable occupatons are more frequently hgh skll than low skll. Mare than 53% of all employment n tradable servce occupatons s n college occupatons (manageral, professonal and techncal), ndcatng that newly exposed workers have relatvely more human captal than prevously exposed producton workers. Subsequently, usng data from the Survey of Income and Program Partcpaton (SIPP), I establsh that workers employed n tradable servce occupatons have relatvely hgh returns to occupatonal tenure. These hgh returns ndcate that workers n these occupatons acqure almost double as much specfc human captal as workers n tradable producton occupatons. In other words, workers n newly tradable occupatons not only accumulate general human captal, but also a sgnfcant amount of specfc human captal; as a consequence, these workers may be more negatvely affected n the short run by offshorng than producton workers have been n the past. Buldng on these fndngs, the second part of ths paper ntroduces occupaton specfc human captal nto a dynamc general equlbrum model wth trade n tasks. To depct trends n globalzed producton, the economy conssts of a large number of dstnct occupatons producng dfferentated tasks. Workers are free to move between occupatons, though labour market frctons may delay the arrval of an offer and cause the worker to stay unemployed. Specfcally, the dfferent occupatons are modeled as slands as n Lucas and Prescott (1974); workers choose an occupaton to whch to apply and enter the occupaton wth some probablty or else stay unemployed. The model developed n ths paper features four sources of heterogenety n workers: 3 The Occupatonal Informaton Network (O*NET) s beng developed under the sponsorshp of the US Department of Labor and s desgned to assst both career counsellors and the general publc n the process of choosng or changng careers. However, the entre database s also avalable to researchers who are nterested n detaled descrptons of occupatons and work envronments. At the centre of the database s the O*NET Content Model, whch descrbes over 800 occupatons usng 277 descrptors n 6 major domans. 3

4 educatonal attanment, level of occupaton specfc human captal, 4 a match-specfc productvty draw, and labour market status. Ths structure allows the model not only to evaluate aggregate welfare effects, but also dstrbutonal effects. Frst, the fracton of educated workers s fxed, whch allows an assessment of the possble dstrbutonal effects arsng from a skll bas n trade. 5 Second, the dstrbuton of specfc human captal s endogenous, whch generates short run dstrbutonal effects whch dffer from the long run effects. Thrd, snce the dstrbuton of specfc human captal s endogenous and ts accumulaton s explctly modelled, the transton from short to long run can be evaluated usng the calbrated model. Lastly, labour market frctons generate unemployment n equlbrum. In the long run, trade n tasks ncreases overall productvty by allowng the economy to explot ts comparatve advantage. The socal welfare effects of the tradablty revoluton are thus unambguously postve: ther magntude depends on dfferences between autarky and world relatve prces (.e. ts comparatve advantage), but not on the skll-content of offshored and nshored tasks. For reasonable terms of trade, the steady state welfare gans of ncreased offshorng are found to be between 1.8% and 4%. Yet, workers dffer n ther specfc human captal and match-specfc productvty, so ncreased trade does have short-run dstrbutonal effects. Movng from a state of autarky to a new trade equlbrum n whch hgh skll tasks are also tradable, workers employed n mport-competng occupatons see ther ncome reduced, whle workers employed n exported tasks see ther ncome ncrease. In the same smulaton as above, the lfe-tme expected utlty of a worker wth human captal specfc to the offshored occupaton falls by 3.1%, whle the lfe-tme expected utlty of a worker wth human captal specfc to the nshored occupaton ncreases by 3%. Ths change n the relatve values between occupatons causes workers to mgrate to the exportng sector. Because of labour market frctons, unemployment ncreases temporarly and swtchng of sklled workers also leads to a loss n specfc human captal. Over tme, reallocated agents attan specfc human captal anew, whch elmnates most of the dstrbutonal effects of reallocaton. In the long run, the gans from trade wll be shared by all agents through the compettve nature of the labour market. The envronment most smlar to that n ths paper s Kambourov (2008), who assesses the mpact of labour market rgdtes on the success of trade reforms and calbrates the model to the Chlean and Mexcan trade lberalzaton. 6 As the goal of the present paper s to examne the mpact 4 For brevty, specfc human captal n the present envronment always denotes occupaton specfc human captal. 5 The fracton of educated workers need not to be fxed; as long as workers dffer n ther cost of acqurng an educaton, dstrbutonal effects may arse. 6 A smlar envronment wth occupaton specfc human captal s also used n Kambourov and Manovsk (2004), who nvestgate the mpact of an ncrease n occupatonal moblty on wage nequalty. 4

5 of task offshorng on the U.S. economy, the model used here ntroduces addtonal heterogenety to capture mportant features of the U.S. labour market. Agents dffer n ther levels of educaton to allow the model to capture a possble skll bas n task trade. To model the lengthy search process n the labour market, agents receve dosyncratc match-specfc productvty draws upon enterng an occupaton. Labour market frctons, on the other hand, are modelled much more parsmonously; most mportantly, there are no frng costs n ths model. An alternatve approach to study the dynamc nature of the reallocaton of workers s presented n Cameron et al. (2007), who develop a model wth movng costs for workers; ther model s estmated and the dstrbutonal effects of a trade reform are studed n Artuc et al. (2007). Also, earler work on the dynamcs of adjustment after a trade shock ncludes Mussa (1978) and Matsuyama (1992). Ths paper also touches on a varety of other lteratures. On the emprcal sde, Amt and We (2006) and Lu and Trefler (2008) have studed employment consequences of offshore outsourcng n servces and found the employment effect s (stll) small. Usng Swedsh data, Ekholm and Hakkala (2006) fnd a small negatve effect for workers wth ntermedate levels of educaton. 7 On the theoretcal sde, Grossman and Ross-Hansberg (2008a) provde a model of trade n tasks n whch producton requres a contnuum of tasks to be completed, an ncreasng fracton of whch becomes tradable; Grossman and Ross-Hansberg (2008b) extends ths framework to trade n tasks between smlar countres where offshorng arses as a result of ncreasng returns. These studes mostly am to provde a settng whch consders fragmentaton and ncorporates t nto trade models. A related lterature focuses on explctly modellng frctons n the labour market whch gve rse to equlbrum unemployment and allow to consder the mpact of trade on employment and dstrbutonal consequences of trade beyond a skll premum. Davdson et al. (1999, 2008), Helpman and Itskhok (2008), Helpman et al. (2008) and Mtra and Ranjan (2007) ntroduce labour market search frctons nto nternatonal trade models; Davs and Harrgan (2007) and Amt and Davs (2008) generate unemployment through effcency wages. Ths paper dffers from the aforementoned lterature n two mportant ways. Whle prevous work on trade and the labour market was mostly statc n nature and typcally ether studed the short or the long run, ths paper explctly focuses on the dynamc nature of factor accumulaton and the 7 Of course, there s a large lterature on nternatonal trade and nequalty, both across skll groups and resdual nequalty. However, most of ths lterature does not focus on recent developments, but rather on earler epsodes. The fndngs n ths lterature are mxed: see for example Feenstra and Hanson (1999, 2003) for evdence on the mportance of trade n ntermedate nputs for the ncrease n the skll premum. Yet, Katz and Autor (1999) and Autor et al. (2008), among others, stress the mportance of skll-based techncal change for the wage gap between sklled and unsklled workers. Also, see the survey by Goldberg and Pavcnk (2007) for the mpact of trade lberalzaton on ncome nequalty n developng countres. 5

6 redstrbuton of workers across occupatons and skll levels. Furthermore, the goal of ths paper s to provde a model whch captures key features of the labour market observed n the data and can be calbrated to quantfy the mpact of trade n tasks on labour market outcomes. As such, t does not am to explan the actual pattern of trade, but rather takes t as gven. The remander of the paper s structured as follows: Secton 2 provdes evdence that newly tradable occupatons requre more specfc human captal compared to tradtonally tradable tasks. Secton 3 then presents a model n whch the dstrbuton of workers across occupatons and skll levels s endogenously determned. The model s calbrated and several quanttatve exercses are undertaken n secton 4, secton 5 concludes. 2 Trade n Tasks and Specfc Human Captal - Evdence To analyze and dscuss the labour market mplcatons of ncreased trade n tasks, three questons must be addressed frst. Frst, whch occupatons are actually tradable; second, what are the characterstcs of workers employed n tradable occupatons; and thrd, whch of these tradable occupatons face the rsk of offshorng and whch stand to gan from nshorng. The frst and second queston are the focus of ths secton. The frst part develops a method for dentfyng tradable occupatons and the second provdes a more detaled overvew of the labour market by analyzng some nformatve statstcs for tradable occupatons. The thrd porton nvestgates whether workers n these occupatons acqure comparatvely more general or specfc human captal, and contrasts the fndngs for tradable servce tasks wth results obtaned from studyng manufacturng tasks whch were part of earler waves n offshorng. 2.1 Identfyng Tradable Occupatons To dentfy whch occupatons are tradable, I analyze the characterstcs and requrements of ndvdual occupatons. 8 Detaled descrptons of each occupaton can be found n two sources: the Dctonary of Occupatonal Ttles (DOT) and the Occupatonal Network Database (O*NET). For the 8 Two other approaches to denty tradable occupatons have been proposed by Lu and Trefler (2008), who lnk servce mport and export data (as reported by the Bureau of Economc Analyss, BEA) to the assocated occupaton, and Jensen and Kletzer (2004), who construct a geographc concentraton ndex for occupatons to classfy tradable and non-tradable occupatons. Whle both approaches gve valuable nsghts nto occupatons potentally affected by trade n servces, they both suffer from some mportant shortcomngs. Hgh geographc concentraton of occupatons can be an ndcaton of tradablty, but s not a necessary condton. Usng BEA data on currently traded servces does not dentfy every potentally tradable occupaton snce ths type of trade s only n ts early stages. 6

7 purpose at hand, the O*NET database s the better choce; unlke the DOT, t s frequently updated and contans sgnfcantly more nformaton on servce sector occupatons. Snce the latest update of the O*NET database s more recent (wth a frst release n 1998 and the latest revson n 2007) than that of the DOT (the 4th edton was revsed n 1991), t also better reflects the current condtons and requrements of each occupaton. Furthermore, O*NET provdes a more detaled account of each occupaton through 227 dstnct occupaton descrptors n 6 major categores. I frst focus on the Occupatonal Interest Profles, whch descrbe the work envronment of each occupaton. Occupatons labelled as Socal or Artstc, for example, are unlkely to be tradable. If the work envronment s socal, the occupaton nvolves a hgh degree of personal nteracton, wth examples such as teachers, therapsts, and chld care workers. Smlarly, occupatons descrbed as artstc have a hgh degree of nteracton wth the audence or customer and the qualty of the work output most often s hghly subjectve; examples of such occupatons nclude dancers, actors, and reporters. I then use the nformaton provded on the typcal actvtes performed by workers n an occupaton. For every occupaton, O*NET lsts the level and the mportance for a varety of typcal actvtes (e.g. Montorng Processes, Materals, or Surroundngs; Analyzng Data or Informaton). Usng crtera for tradablty commonly dscussed n the lterature and socety, such as lttle or no face-to-face customer nteracton; standardzed work output; and hgh nformaton content, I defne occupatons as non-tradable f they nvolve hgh levels of Assstng and Carng, Sellng and Workng wth the Publc. More specfcally, f an occupaton nvolves delverng standard arguments or sales ptches to convnce others to buy popular product, I regard t as potentally tradable; conversely, f t requres delverng a major sales campagn n a new market, I regard the degree of sophstcaton necessary as too hgh for ths occupaton to be tradable. The cut-offs for the other actvtes are smlarly defned. In the fnal step, I reclassfy occupatons whch would be tradable accordng to the above crtera, but are unlkely ever to be traded because the cost assocated wth offshorng them are too hgh. Ths group conssts mostly of low-skll servce occupatons whch could be offshored n prncple, but for all practcal purposes cannot be - launderers, roners and certan repar and mechancs occupatons fall n ths group. Ths approach results n a lst of 61 servce occupatons (see next subsecton and appendx A for detals) that are lkely tradable. Irrespectvely of the rule used to assgn occupatons to groups accordng to ther tradablty, there are gong to be debates about the classfcaton of some occupatons (e.g. archtects are classfed as non-tradable, whle secretares are classfed as tradable). The drect 7

8 approach allows me to classfy occupatons based on ther characterstcs alone and s hence ndependent of actual trade observed today, whch s crucal n assessng the possble mplcaton of an expandng and ncreasng trade n tasks. 2.2 Characterstcs of Tradable Occupatons In order to depct the extent to whch occupatons that requre dfferent levels of (general or specfc) human captal n the US economy are offshorable, the Census % sample s used here to break down the labour force by occupaton group, educatonal attanment (the smplest proxy for skll), and offshorablty. Restrctng the sample to ndvduals who report partcpatng n the labour force and consderng the occupatonal groups of the Census 1990 (for consstency wth data later used n the estmaton of returns to (occupatonal) tenure), Table 2.1 below presents the composton of the labour force by occupaton group and hghest educatonal level completed. Indvduals are classfed nto four groups: hgh school dropouts, hgh school graduates, ndvduals wth some college educaton, and college graduates. Fgures n columns [a] through [d] show the number of ndvduals n each occupaton group by educatonal attanment. To get a rough dea of the share of employment n each broad occupaton group wth hgh human captal, I group hgh school graduates and dropouts nto the lower educaton category and consder ndvduals wth at least some college educaton hgher educaton, as they arguably complete ther educaton wth a hgher level of both general and specfc human captal. As the last column demonstrates, workers n manageral, professonal and techncal occupatons (hgh skll occupatons) tend to have the hghest educatonal attanment, whle workers n producton and transportaton occupatons, helpers, and labourers have the lowest attanment. Table 2.2 breaks down the employment n tradable occupatons nto the same major occupaton groups. The frst column lsts the total employment for each group and the second column the total employment wthn that group that s employed n tradable occupatons. In total, there are about 29.6 mllon workers employed n occupatons classfed as tradable, out of a total of mllon non-farm employment. The thrd column gves the fracton of employment that s potentally tradable n each group. Not surprsngly, techncal and producton occupatons are the most tradable; wthn these categores, more than 2/3 of total employment s n tradable occupatons, though techncal and producton occupatons make up only 13.7% of overall employment. On the other end, sales, servces, craft and repar and transportaton occupatons are generally non-tradable and jontly represent about 8

9 42% of total employment. Overall, 22.2% of the U.S. labour force s employed n tradable occupatons, though ths share falls to 16.7% f only non-producton occupatons are taken nto consderaton. Manageral, professonal and techncal occupatons together represent 36.6% of all employment n tradable occupatons, whle makng up about 44% of total employment. Dsregardng producton tasks (whch have been traded n the past) these hgh skll occupatons account for 53.5% of tradable employment, whle makng up 48% of the total non-producton employment. Combnng the nformaton from Tables 1 and 2, and agan dsregardng producton occupatons, t appears that tradable servce occupatons are more frequently hgh human captal occupatons than low human captal occupatons. In attemptng to assess the labour market mplcatons of heghtened nternatonal trade, t s mportant to keep n mnd that these tasks can potentally be traded and that, as a consequence, the U.S. wll not necessarly become a net mporter of hgher skll tasks. Ths analyss provdes a prelmnary ndcaton that workers n newly tradable occupatons possess more human captal than workers prevously exposed to offshorng. However, t does not dstngush between specfc and general human captal. The next secton addresses ths queston. 2.3 Estmates of Specfc Human Captal In order to dscern whether occupatons ncreasngly exposed to offshorng requre hgh specfc or general human captal, I nvestgate returns to occupatonal tenure usng a rch dataset on survey respondents job, occupaton and ndustry experence. Once I account for the contrbuton of observable characterstcs such as age, gender, job and ndustry tenure and overall work experence n explanng wage levels, the remanng ncrease n wages over tme should reflect knowledge obtaned through experence n the occupaton.e. occupaton (or task) specfc human captal. The extent to whch occupatonal tenure contrbuton to wages, n turn, can help dscern the extent to whch workers n dfferent occupatons acqure specfc human captal. A rch emprcal lterature studes the returns to overall labour market experence, job, and ndustry tenure (see for example Altonj and Shakotko, 1987; Neal, 1995; Parent, 2000; and Altonj and Wllams, 2005). Recently, Kambourov and Manovsk (2008) stressed the mportance of occupaton specfc human captal, notng that after controllng for occupatonal tenure, employer and job tenure do not contrbute sgnfcantly to wage growth. Ths fndng led them to conclude that workers accumulate sgnfcant occupaton-specfc human captal durng ther careers. However, as n most of the prevous analyses, the paper does not nvestgate how occupaton-specfc human captal vares across groups. 9

10 Usng the Natonal Longtudnal Survey of Youth 1979, Sullvan (2008) showed that there s substantal heterogenety across occupatons n the relatve mportance and magntude of occupaton and ndustry specfc human captal. Fnally, Connolly and Gottschalk (2006) demonstrate that college graduates experence hgher returns to general experence, whle hgh school graduates receve hgher returns to ndustry tenure The Model and Data Followng the emprcal lterature measurng returns to tenure, I estmate the followng earnngs equaton: ln w jmnt = β 1 EmpT en jt + β 2 OccT en mt (1) + β 3 IndT en nt + β 4 W orkexp t + αx jmnt + κ jmnt, where w jmnt s the real hourly wage of worker at employer j n occupaton m and ndustry n. W orkexp denotes overall labour market experence, whle EmpT en, OccT en and IndT en denote tenure wth the current employer, occupaton and ndustry, respectvely. X s a set of observables whch nfluence wages ndependently of tenure: gender, race, educatonal attanment, unon status, frm sze, 1-dgt ndustry and occupaton afflaton, and state and year fxed effects. κ jmnt an error term decomposed as follows: κ jmnt = µ + λ j + ξ m + ν n + ɛ t, where µ s an ndvdual-specfc component and λ j, ξ m, ν n are job-match, occupaton-match, and ndustry-match components, respectvely. These unobserved components pose a potentally serous challenge to consstently estmate the returns to tenure; workers wth good employer (occupaton/ ndustry) matches, for example, may be more lkely to have remaned wth ther employer (occupaton/ ndustry) longer whle at the same tme recevng a hgher wage due to the excellent match qualty. Estmatng (1) usng Ordnary Least Squares wll therefore lkely result n upward-based estmates. Followng the approach developed by Altonj and Shakotko (1987), whch has been wdely adopted n the lterature, I estmate (1) usng an nstrumental varable estmaton strategy. The standard nstruments for experence and the three tenure varables are the devatons of experence/tenure for ndvdual from the ndvdual s mean experence/tenure n the observed spell. If T t s the current tenure of worker, the correspondng nstrument s T t = ( ) T t T, where T s the average tenure of ndvdual n the current spell. The nstruments are orthogonal to ther respectve 10

11 match components by constructon. Unfortunately, they are not necessarly orthogonal to the other match components; e.g. the nstrument for occupaton tenure, OccT en mt = ( OccT en mt OccT en m ), s potentally stll correlated wth the job-match unobserved effect λ j. For example, an ndvdual wth a good employer, but a bad occupaton match mght be less nclned to swtch occupatons than an otherwse dentcal ndvdual wth a bad job match because swtchng occupatons most lkely also results n loosng the good employer match. The dataset of ndvdual employment profles used to estmate (1) comes from the 1996 and 2001 waves of the Survey of Income and Program Partcpaton (SIPP). 9 The advantage of usng the SIPP s ts relatvely large cross-sectonal sample sze n comparson wth other panel data sets, but t comes at the cost of havng a relatvely short panel length (4 and 3 years, respectvely). The sze of the dataset allows to estmate the returns despte the relatvely short sample and justfes departure from usng data from the 1980s and early 1990s, whch s advantageous for three reasons. Frstly, many of the occupatons now exposed to offshorng were nether fully developed nor common some 20 years ago; secondly, snce there s no reason to beleve that the returns to tenure are constant over tme even as the returns to schoolng have evolved, ncludng earler years of data would lkely not produce estmates most relevant to current dscussons on offshorng. Fnally and most mportantly the SIPP data was collected at a monthly frequency, wth ndvduals respondng to one ntervew every four months. Ths allows a much more relable dentfcaton of job swtchers somethng that posed a sgnfcant challenge n prevous studes usng the Panel Study of Income Dynamcs, PSID (Brown and Lght, 1992), and the Natonal Longtudnal Survey of Youth, NLSY. The relablty of the survey responses s also ncreased through an mplementaton of computer-asssted ntervews, whch reduces the rsk of mscodng through dependent ntervewng (.e. questons and skp-patterns are based on the prevous answers of the respondent.) Respondents n the SIPP are asked to gve the start- (and end-) dates for every job, allowng me to obtan very relable nformaton on employer tenure and thus crcumvent the ssue of ntalzaton. In the frst ntervew, the respondent s asked about how long she has been workng n the current lne of work, whch allows me to ntalze occupatonal tenure as well. There s, unfortunately, no nformaton on ntal ndustry tenure; I therefore ntalze ndustry tenure together wth occupatonal tenure. Fnally, snce I do not observe an ndvdual from the tme she enters the labour market, I have no nformaton on her actual acqured overall work experence. However, the SIPP provdes detaled 9 The 2004 wave was recently completed and unfortunately s not yet avalable n ts entrety. 11

12 nformaton on schoolng, so I use potental experence - age less 6 less numbers of years of schoolng - as a proxy for actual experence. To mnmze the resultng bas, I restrct the sample to male full-tme workers. In each ntervew, the respondent s asked retrospectvely about the past four months, and the responses are recorded for each month ndvdually. The ndvdual reports employer, occupaton and ndustry classfcatons, hours worked, and total ncome. She also reports start- and end-dates for each job, whch allows me to dentfy job swtches and calculate employer tenure wth comparatvely hgh precson. 10 Followng Kambourov and Manovsk (2008), occupaton and ndustry swtches are only coded as true swtches f they concde wth employer swtches. Usng ths conventon, 20.2% of partcpants swtch ther employers at least once per 12 months; 14.5% swtch occupatons, and 13.5% ndustres. These shares are somewhat lower than ther PSID equvalents n Kambourov and Manovsk (2008) and Sullvan (2008). A possble explanaton s that workers who lose ther job may be more lkely to leave the sample. Snce the SIPP has relatvely hgh sample attrton, ths could explan fewer job, occupaton, and ndustry swtches n ths sample Results Table 2.4 presents coeffcent estmates of a specfcaton of (1) whch ncludes quadratc and cubc terms for all tenure (3-dgt classfcaton level) and experence terms. Returns to occupatonal tenure can then be computed from these results. Frst, I calculate the returns for male, full-tme employees and present these n Table 2.4[a]. For comparson, Table 2.5 lsts the returns to overall labour market experence. I fnd that stayng n an occupaton for two, fve or ten years ncreases wages by about 2.0, 4.6 and 7.8%, respectvely. 11 Next, I estmate the returns to occupatonal tenure focusng only on hgher skll occupatons (as defned n Table 2.1) and present them n Tables 2.4[c]-[f]. I fnd that the returns to tenure n these occupatons are ndeed sgnfcantly hgher than n the full sample of occupatons, ndcatng that 10 Nevertheless, there s a sgnfcant seam bas n the data; more swtches happen at the seam, or between ntervews (e.g. between months 4 and 5, 8 and 9) than wthn ntervews (e.g. between months 1 and 2, 2 and 3). However, snce I am not nterested n estmatng a hazard functon, ths bas s a mnor ssue and causes only a small error when calculatng tenure - at the most 3 months. 11 These returns are lower than those reported by Kambourov and Manovsk (2008), where 5 years n an occupaton ncrease wages by 12.0% and Sullvan (2008), who reports 5-year returns of 13.3% f occupatonal tenure s computed comparably. Several factors are potentally responsble, not least of whch the fact that the returns to occupatonal tenure may have dmnshed snce the 1980s, whch represent a szeable porton of the PSID. If the wage ncrease s largest for workers swtchng employers and not occupatons, and f these swtches are correlated wth extng the sample, the hgh attrton rate n the SIPP wll cause a downward bas n the returns to tenure as well. 12

13 ndvduals workng n hgher skll occupatons not only accumulate more general human captal, but also more occupaton-specfc human captal. The hghest returns are found for techncal occupatons, wth 30.3% for 10 years n a techncal occupaton. Recall that ths group also contans the hghest fracton of potentally tradable occupatons (see Table 2.2). I also estmate returns to occupatonal tenure n manufacturng occupatons and fnd that they are about the same as the returns n the full sample: 3.0%, 6.0%, and 7.4% for 2, 5, and 10 years, respectvely. Ths s n lne wth the argument that workers n occupatons prevously exposed to offshorng acqure less specfc human captal. Furthermore, the returns to tenure n manufacturng occupatons that I estmated for the second half of the 1990s and early 2000s may actually be hgher than the returns n already offshored manufacturng occupatons.e. the manufacturng jobs that we stll observe today are more human captal ntensve then the average manufacturng job n the 1970s and 80s, whch have been offshored n the past. Ths argument s consstent wth conventonal wsdom s that US mports have (slghtly) less skll content than exports (e.g. Wolff, 2003). The parameter estmates presented above are useful n classfyng occupatons as those requrng comparatvely more or less specfc human captal. The results provde strong ndcaton that workers n newly tradable occupaton acqure sgnfcantly more specfc human captal than n prevously tradable producton occupatons. 3 A Model of Trade n Tasks wth Specfc Human Captal In ths secton, I present a model of trade n tasks (ntermedate goods) whch ncorporates workers specfc human captal. As a key feature of the model, the dstrbuton of specfc human captal s not exogenously fxed, but rather arses endogenously as agents choose whch task to produce and for whch to acqure specfc human captal. Every perod, workers may swtch occupatons and forego ther current specfc human captal, whle over tme acqurng t agan for the new task. Consequently, the dstrbuton of workers across occupatons and levels of specfc human captal responds to shocks the economy experences, such as technologcal progress and trade. 13

14 3.1 The Envronment The economy s populated by a measure 1 of rsk-free, nfntely lved agents (workers). Thus, the agent maxmzes β t c t, t=0 where c t s the consumpton of the fnal good n perod t and β < 1 s the tme dscount factor. The fnal consumpton good Y s a CES-aggregate of N dstnct tasks: Y = where κ s a share parameter for each task. [ N κ y ρ =1 For each task, there s a large number of producers, so both nput and output market are compettve. Labour s the only varable nput n the producton; there s also a fxed factor for each task to whch each agent holds an equal share. The fxed factor s mpled by the decreasng returns technology, whch s needed to asure that occupaton task wll have a postve mass of workers. The representatve task producer s technology s gven by: ] 1 ρ, y (z, l) = z (l ) α, α < 1, where z s a tme-nvarant task-specfc productvty parameter and l s the total effectve labour employed n the occupaton. Human Captal Ex ante, agents dffer only by ther general human captal, the level of educaton; a fracton E has hgh educaton and a fracton (1 E) low educaton. Hghly educated workers can be employed n any occupaton, whle low educated workers can only be employed n some. After enterng an occupaton, there are two addtonal sources of heterogenety between agents. Frst, upon enterng, agents draw ther worker-occupaton specfc productvty θ from some dstrbuton F (θ); a worker provdes θ unts of productve tme each perod. Second, agents dffer by ther level of specfc human captal. In each occupaton, there are two skll-types of workers, those wth acqured specfc human captal (sklled workers) and those stll unsklled. At the end of each perod (except the frst one) the worker may acqure the specfc human captal necessary to become a hgh skll worker; the arrval rate of the skll 14

15 shock for an unsklled worker s γ. 12 After becomng sklled, a worker remans sklled untl she leaves the sector. Ths captures the human captal that s specfc to the occupaton. The ncrease n productvty upon becomng sklled vares between occupatons, but wthn an occupaton all agents experence the same relatve ncrease n ther productvty. Whle an unsklled worker has θ unts of productve tme each perod, a sklled worker has a θ, a > 1. A worker can ether choose to leave the occupaton or she can get separated exogenously at rate π; however, t s assumed that at the end of her the frst perod n the occupaton the worker wll not get separated. At the begnnng of each perod, an employed worker decdes whether to stay n the current occupaton and keep the current productvty draw θ or become unemployed and search for a new offer (.e. try to sample a new productvty draw). There s no tme gap between quttng and searchng; a worker who elects to leave her occupaton begns searchng n the same perod. An unemployed worker chooses the sector to whch to apply and wth probablty (1 ɛ) receves an offer θ. 13 A worker who receves a productvty draw remans n the occupaton for the current perod before decdng whether or not to search agan. For an educated worker, the applcaton process conssts of 2 stages. Frst, an educated worker apples to a hgh educaton occupaton; f she receves an offer, the search has ended. However, f she does not receve an offer, she apples to a low educaton occupaton. Ths structure captures the emprcal observaton that many college graduates start ther career n a noncollege occupaton but stay there only for a short perod of tme (see Fgure 3.1). The non-educated and unsklled worker s problem s summarzed n Fgure 3.2, the educated and unsklled worker s problem s summarzed n Fgure 3.3. Ths structure generates a rch pattern of heterogenety and allows the model to capture key features of the data, beyond the already dscussed specfc human captal. It enables me to address three key concerns regardng the dstrbuton of the gans from trade. The partton between educated and non-educated generates an educaton premum whch s potentally affected by structural changes. 12 For the purposes of ths paper, an unsklled worker s a worker wthout specfc human captal, whereas a non-educated worker s one wth low educaton. The occupatons that employ (hgh) educated workers are referred to as hgh educaton occupatons. Incdentally, n the data, these are also the occupatons n whch workers acqure the most specfc human captal. 13 Whle there s evdence that workers do not always start workng n the occupaton they are seekng n ther search process, the longer the tme frame, the more lkely t s that they arrve n an occupaton they are targetng. Furthermore, I am nterested n the worker relocaton resultng from a large, permanent shock and t s more lkely agents wll specfcally target occupatons wth a postve shock and avod those wth a negatve one; n the steady state, agents are ndfferent between all occupatons, so they would be wllng to apply for postons n any occupaton; only along the transton path s the assumpton of drected search crtcal. 15

16 Because of the match-specfc productvty draw t takes tme for workers to fnd a good match. It also ntroduces resdual ncome nequalty, whch has been argued to be affected by ncreased trade, a clam that can be nvestgated usng ths model. The labour market frcton generates unemployment, both along the transton path and n equlbrum The Agent s Problem a. Non-Educated Workers The value of beng an unsklled worker n occupaton wth productvty shock θ at the begnnng of a perod s gven by: where V u (θ, Σ) = max {J u (θ, Σ); U(Σ)}, (2) J u (θ, Σ) = θw (Σ) + β(1 π) ( (1 γ )V u (θ, Σ ) + γ V s (θ, Σ ) ) + βπu(σ ) (3) s the value of stayng n occupaton for an unsklled worker, s the value of beng unemployed, and { ( U(Σ) = max (1 ɛ )E θ J 1 (θ, Σ) ) + ɛ βu(σ ) } (4) J 1 (θ, Σ) = θw (Σ) + βv u (θ, Σ ) (5) s the value of enterng the occupaton wth draw θ. w denotes the real wage per effectve unt of labour n occupaton, so the worker s ncome s θw. Wages are determned compettvely and agents take them as gven. Σ(θ) = (σ1 u(θ), σu 2 (θ)..., σs 1 (θ), σs 2 (θ)...) denotes the dstrbuton of workers across sectors and productvtes at the begnnng of the perod. E θ denotes the expectaton operator over the possble draws of the productvty shock θ. Smlarly, the value of beng a sklled worker n occupaton wth productvty θ at the begnnng of a perod s gven by: V s (θ, Σ) = max {J s (θ, Σ); U(Σ)}, (6) wth J s (θ, Σ) = θa w (Σ) + β(1 π)v s (θ, Σ ) + βπu(σ ). (7) 16

17 Search s drected, so any occupaton that wshes to attract applcants must offer them the same expected value, so U(Σ) (1 ɛ )E θ ( J 1 (θ, Σ) ) + ɛ βu(σ ). (8) If the value of applyng to occupaton s less than that of other occupatons,.e. (8) s not satsfed as equalty for occupaton, no worker wll apply and employment wll shrnk due to the exogenous separaton and possble quttng. However, due to a decreasng returns technology, every sector wll have a postve mass of workers and (8) wll eventually be satsfed wth equalty for all occupatons. Workers are dentcal, so t s natural to assume that all follow the same applcaton strategy. However, ths mples that f one worker apples to an occupaton wth probablty 1, all workers would apply to ths one occupaton and employment n that occupaton would ncrease drastcally whle t decreases n all the others. Snce wages are determned compettvely, (8) would be volated. Therefore, n equlbrum, workers must use a mxed strategy and apply to each occupaton wth some probablty. Let g A (Σ) denote the polcy functon descrbng ths optmal applcaton strategy and A(Σ) the total number of applcants; then A (Σ) = g A (Σ)A(Σ) s the number of applcants for occupaton. Snce each worker takes the value of search, U(Σ), and the future values V u and V s as gven, the workers optmal quttng decson can be descrbed by a smple reservaton productvty strategy: f the productvty draw exceeds the reservaton level, the worker remans n the occupaton, otherwse the worker leaves and searches for a better match. These reservaton productvty levels (ˆθ u, ˆθ s ) satsfy J u (ˆθ u, Σ) = U(Σ), and (9) J s (ˆθ s, Σ) = U(Σ). (10) Let g u (θ, Σ) denote the polcy functon for unsklled workers descrbng the optmal quttng decsons, wth the conventon g u(θ, Σ) = 1 f θ ˆθ u. Smlarly, gs (θ, Σ) denotes the polcy functon for sklled workers. In a statonary equlbrum (see that defnton below), two types of workers wll be employed n each occupaton temporary and permanent. Temporary workers are those who entered at the begnnng of the current perod, receved a low draw and wll search agan n the next perod, whle permanent workers wll reman and only leave after an exogenous separaton. As a result, n a statonary envronment, sklled workers are always permanent workers. 17

18 b. Educated Workers A fracton E of all workers are educated. Only educated workers can apply to hgh educaton occupatons. Furthermore, f an educated worker s employed n a low educaton occupaton she s more productve than a non-educated worker condtonal on the occupaton-specfc productvty draw. An educated worker employed n a low educaton occupaton provdes a c θ effcency unts of labour f she s unsklled and a c a θ f she s sklled, where a c > 1 s the relatve productvty of an educated to a non-educated worker who s otherwse dentcal. Alternatvely, one can vew the educated worker as drawng from a dstrbuton whose mean s shfted by a c relatve to non-educated workers. For notatonal convenence, I wll adopt the conventon E E θ = a ce θ n low educaton occupatons. 14 The value of beng unemployed for an educated worker s gven by { U E (Σ) = max h H (1 ɛ h )E θ ( J 1 h (θ, Σ) ) + ɛ h max l L { (1 ɛ l )E E θ ( ) J E,1 l (θ, Σ) + ɛ l βu(σ )} }, (11) where H s the set of hgh educaton occupatons to whch the worker apples frst and L s the set of low educaton occupatons to whch the worker apples f she fals to secure an offer n a hgh educaton occupaton. Usng the same notaton as for non-educated workers, J E,1 h and J E,1 l enterng hgh and low educaton occupatons, respectvely. Then, denote the value of wth and J E,1 (θ, Σ) = θw (Σ) + βv E,u (θ, Σ ), (12) { V E,u (θ, Σ ) = max J E,u } (θ, Σ); U E (Σ), (13) ( ) J E,u (θ, Σ) = θw (Σ) + β(1 π) (1 γ l )V E,u (θ, Σ ) + γ V E,s (θ, Σ ) + βπu E (Σ ). (14) After enterng a sector and drawng the specfc productvty shock, the only dfference between an educated and non-educated worker s the contnuaton value n the case of separaton. As a result, the reservaton productvty levels for educated and non-educated workers dffer; the reservaton productvty levels (ˆθ E,u, ˆθ E,s ) for the educated satsfy: Let g E,u (θ, Σ), g E,s (θ, Σ) denote the resultng polcy functons. 14 A superscrpt E denotes educated, whle no superscrpt denotes non-educated. J E,u (ˆθ E,u, Σ) = U E (Σ), (15) J E,s (ˆθ E,s, Σ) = U E (Σ). (16) 18

19 Agan, due to the drected nature of the search process, any hgh educaton occupaton whch attracts a postve number of applcants must offer at least U E (Σ). Ths condton apples to hgh educaton occupatons only; low educaton occupatons whch attract non-educated applcants satsfy (8). Snce the productvty premum for educated workers, a c s the same across occupatons and educated and non-educated workers only dffer by ths constant, (8) also assures that educated workers are ndfferent between all low-educaton occupatons n the second stage. Snce educated agents are ndfferent between occupatons, I assume they follow the same applcaton strategy as the non-educated n low educaton occupatons n the second stage. c. Labour Supply Let g E,A (Σ) denote the polcy functon descrbng the optmal applcaton strategy for educated workers and A E H (Σ) the total number of educated applcants to hgh skll occupatons. Then the total number of educated agents applyng to low skll occupatons s A E L (Σ) = ɛ ha E H (Σ). occupaton, Total labour supply n each occupaton s equal to the total productve tme avalable n the l s = a θ θ g s (θ, Σ) dσ s (θ) + θ g u (θ, Σ) dσ u (θ) + (1 ɛ )A θ θ + a θ θ g E,u (θ, Σ) dσ E,s (θ) + θ θ g E,u (θ, Σ) dσ E,u (θ) + (1 ɛ )A E θ df (θ) (17) θ θ df E (θ). Recall that Σ(θ) = (σ1 u(θ), σu 2 (θ)..., σs 1 (θ), σs 2 (θ)...) denotes the dstrbuton of workers across sectors and productvtes at the begnnng of the perod and g j (θ, Σ), j = u, s denotes the polcy functon ndcatng whether the worker wth draw θ stayed or qut the occupaton n the current perod. Fnally, the resultng law of moton for the dstrbuton of workers s gven by σ s = (1 π) (g s (θ, Σ) σ s + γ g u (θ, Σ) σ u ), (18) σ u = (1 π)(1 γ ) g u (θ, Σ) σ u + (1 ɛ )A (Σ), ( ) (19) σ E,s = (1 π) g E,s (θ, Σ) σ E,s + γ g E,u (θ, Σ) σ E,u, and (20) σ E,u = (1 π)(1 γ ) g E,u (θ, Σ) σ u + (1 ɛ )A E (Σ), (21) where the prme denotes the begnnng of next perod s element. 19

20 3.1.2 The Producer s Problem The Producer s problem n ths envronment s a smple statc problem. Let p denote the prce of each task n terms of the numerare good; then the demand for each task s gven by ( ) 1 y d κ P 1 ρ = Y, (22) where P = p ( N p =1 ρ 1 ρ ) ρ 1 1 ρ 1 ρ κ. (23) where P, the prce ndex for the fnal good, follows from the zero-proft condton for the fnal good s producer. Labour markets n each occupaton are compettve, so the real wage per effectve unt of labour s equal to the value of the margnal product n terms of the numerare good: w = p αz (l ) α 1, (24) where p s the prce of each task n terms of the numerare good. As normalzaton, let w 1 = Statonary Equlbrum Before studyng the mpact of ncreased trade n ths envronment, t s nstructve to study the statonary equlbrum. A statonary equlbrum s characterzed by a tme-nvarant dstrbuton of workers across skll levels and occupatons,.e. Σ = Σ. Frst, notce that n a statonary envronment the crtcal level of the match specfc productvty s constant. As a result, a worker ether quts after the frst perod, or stays wth the occupaton untl the match s exogenously separated. Further recall that an unsklled worker s ncome s θw, and that the wage pad per effectve unt of labour s a constant determned n a compettve market. Consequently, one can regard the productvty draw as an ncome draw as well: n a statonary envronment the model reduces to a varant of the stochastc job matchng model wth a constant matchng rate. a. Non-Educated Workers Usng the fact that a sklled worker never quts n a statonary equlbrum, the steady state value of beng a sklled worker n occupaton wth shock θ s gven by J s (θ, Σ) = a θw 1 β(1 π) + βπ U(Σ). (25) 1 β(1 π) 20

21 Smlarly, for an nexperenced worker n occupaton, t s: J u 1 β(1 π)(1 γ a ) (θ, Σ) = w (1 β(1 π))(1 β(1 π)(1 γ )) + βπ U(Σ). (26) (1 β(1 π)) Here, U(Σ) denotes the value of searchng. Substtutng (5) nto (4) and usng the optmal reservaton productvty strategy, the value of applyng at any occupaton can be wrtten as [ ( (1 ɛ) U (Σ) = U(Σ) = E θ, w + β F ( 1 βɛ ˆθ ) + θ ˆθ J u (θ, Σ)dF (θ) Usng (26), the condton for the reservaton productvty level (9) can be rearranged to yeld )]. (27) ˆθ w = (1 β)u(σ) 1 β(1 π)(1 γ ) 1 β(1 π)(1 γ a ). (28) Lastly, by substtutng (26) nto (27), the fundamental reservaton productvty equaton can be obtaned: ˆθ = (1 ɛ ) [ E θ, (θ) ] 1 β(1 π)(1 γ ) β(1 π) θ + (θ 1 β(1 π)(1 γ a ) 1 β(1 π) ˆθ )df (θ). (29) ˆθ Note that the reservaton productvty level s ndependent of the wage rate. In a statonary equlbrum, each occupaton offers a tme-nvarant wage per effectve unt of labour. Snce all sectors offer the same value to each applcant, a worker who quts after the frst perod s wllng to resample n the same occupaton agan and receve the same wage rate per effcency unt (her ncome θw wll only change because θ changes). Therefore, the wage rate reduces to a scalng parameter and does not have an mpact on the reservaton productvty level. The nterpretaton of (29) s easest after multplyng both sdes wth the wage rate w. Then, the left-hand sde s the utlty per perod from mantanng the job at the reservaton productvty, whle the rght-hand sde s the expected utlty from quttng: the expected draw n the current perod plus the dscounted expected mprovement. The optmal reservaton level equates these two values. as Fnally, usng that U = U j, (28) allows solvng for the relatve wage between two occupatons w w j = ˆθ j ˆθ 1 β(1 π)(1 γ ) 1 β(1 π)(1 γ a ) 1 β(1 π)(1 γ j a j ) 1 β(1 π)(1 γ j ). (30) Recall from (29) that the reservaton levels are ndependent of the wage pad n the occupaton. Thus (30) states that the steady state relatve wage between sectors depends on parameters alone; output 21