Abstract Measurng the specfcty of human captal: a skll-based approach Krstan-Olar Lepng In ths artcle a skll-based measure for human captal specfcty wll be constructed. Ths measure s based on the possbltes of makng use of sklls on labour market and t wll depend on the number of obs, where the partcular skll s requred. It s assumed that the specfcty of human captal depends on the specfcty of sklls. In order to calculate the levels of specfcty of dfferent sklls emprcally, the data from the skll requrements of vacant obs wll be used. The valdty of ths measure s tested by usng t as an estmator of the probablty that on-the ob tranng s offered to employees. The dfferences n the specfcty of requred human captal between dfferent ndustres and occupatons are also nvestgated n ths paper. The proposed ob specfcty measure can be used for plannng the publc sector support to on-the-ob tranng as the companes decsons to pay for tranng depend on the specfcty of requred human captal. Introducton Dvdng human captal nto general and specfc human captal has been a common ssue n the research of on-the ob tranng snce the development of human captal theory by Becker (1964). Usually human captal s consdered to be frm-specfc, but some authors have also used the concepts of skll-specfc and ndustry-specfc human captal (Neal 1995). A recent development n ths feld s the skll-weghts approach, whch emphaszes the pont that the sklls are not frm-specfc, but the combnatons of the sklls requred on dfferent obs are frm-specfc. (Lazear 2003) There have been developed lots of theoretcal models about general and specfc human captal, whch have covered topcs lke bearng the costs of on-the-ob tranng (Hashmoto 1981), labour turnover (Joanovc 1979) and tenure effect on wages (Topel 1991). Human captal has also been used for explanaton of economc growth, labour moblty, nvestment n physcal captal and several other ssues. Although the dstncton between general and specfc human captal has been wdely used n theoretcal lterature, there has not been turned enough attenton to the queston how to measure the specfcty of human captal. In the earler studes, t has been common to use the years of formal schoolng or level of formal educaton as the measure for general human captal and the length of tenure as the measure for specfc human captal. But n realty n most cases general sklls are also acqured through onthe-ob tranng, so tenure s not a satsfactory measure for specfc human captal. More recently there have been made some attempts to measure the specfcty of human captal usng alternatve approaches. For example Frank (2003) has measured t ndrectly by the length of vocatonal adustment of new employees. Ingram and Neumann (1999) have proposed a measure based on observed skll characterstcs of the ob, but the am of ther measure s to dstngush between dfferent levels of human captal through the sklls of workers (low-sklled and hgh sklled) nstead of general and specfc human captal. Ths knd of approach could be used to dstngush between general and specfc too. Therefore the am of ths paper s to develop a skll-based measure of the specfcty of 1
human captal. In ths paper, ths measure s based on possbltes of gettng use of a partcular skll and t wll depend on the number of obs, where ths skll s requred. The smaller the number of obs, where the skll s requred the hgher the level of specfcty of that partcular skll s. To calculate the levels of specfcty of dfferent sklls emprcally, the data from the skll requrements of vacant obs wll be used. For ths purpose data from one Estonan Internet-based ob advertsement database wll be used. For testng the valdty of ths measure, t wll be used as an estmator of the probablty that on-the ob tranng s offered to employees. If ths measure s correct, then accordng to the human captal theory, n case of the obs, whch requre more specfc sklls, tranng s offered wth hgher probablty. The artcle s organsed as follows. Frst, there wll be gven an overvew of the theoretcal background of ths topc focusng on the optons of measurng human captal and t specfcty. Next, accordng to the human captal theory and especally the skllweghts approach, there wll be proposed dfferent types of measures for human captal specfcty. Then there wll be gven a descrpton of the data used n the emprcal analyses. After that there wll be an analyss of the specfcty of dfferent sklls and the specfcty of requred human captal across dfferent occupatons and ndustres. Followng, the valdty of human captal specfcty measures prevously proposed wll be tested. Fnally, conclusons on the results wll be drawn. Lterature overvew Human captal can be defned as sklls and knowledge acqured by people durng ther lfetme and whch can be used for producton of goods (Fredrksen 1998). It s most often assocated wth educaton and on-the ob tranng. Dependng on the type of sklls acqured by schoolng, tranng or work experence human captal can be dvded nto general and specfc human captal. General human captal affects the productvty of the tranee n all companes, whereas specfc human captal rases the productvty only n one enterprse. General human captal s acqured the general educaton and tranng programs, whch mprove the sklls and knowledge of the workers n a way that they can work more productvely n many enterprses. Specfc human captal on the other hand s acqured through specfc tranng, whch conssts of enterprse-specfc tranng programs, whch mprove the sklls n a way that ther productvty s ncreased only n that partcular frm. For example they are taught to handle some specfc machnery, whch s not used n other companes. In realty, however many tranng programs have both elements of general and specfc nature. One of the most mportant fndngs of the human captal theory s that n case of perfect competton on labour market, where the wage pad to employees equals the margnal product of labour, frms do not have motvaton to fnance ther employees general tranng, but t wll be proftable for them to fnance specfc tranng. (Becker 1962) Dstngushng between general and specfc human captal has been a standard approach durng many decades and untl recently assumng human captal to be frmspecfc has not been crtcsed very wdely. Although n practse human captal appears to be mostly not frm-specfc and accordng to human captal theory t should not be fnanced by the frms, t has been a common knowledge that n realty frms at least partly fnance tranng programs of ther employees. Ths fact has lead to development of several models, whch try to gve explanaton why t s proftable for the companes to fnance general tranng. Exstence of transacton costs and mperfect competton on 2
labour markets are one of the most common reasons that have been proposed as the reasons for frms to pay for general tranng. In presence of these factors the employers poston on wage negotatons may be stronger than the employees poston, whch leads to the stuaton where the workers are pad for ther work less than ther margnal product and n that case frms can earn rents on the usage of employees labour, whch makes for companes proftable to fnance general tranng. (Acemoglu and Pschke 1999) Imperfectons on the market, whch make t proftable for frms to pay for general tranng need not to be lmted to mperfect competton, but they also nclude nformatonal mperfectons. Katz and Zderman (1990) suggest that, n practce, nformaton about general sklls provded by one employer s not readly avalable to other employers. Whle such sklls are valuable to other employers, they do not have nformaton about them and t means that the other employers do not know the true margnal product of other frm s workers. They assume t do be lower than n realty and therefore are not wllng to pay them a hgher wage as n the ntal frm. Employers can therefore provde general tranng because those sklls wll rase the workers productvty but not the marketablty and wages of ther employees. The other possble reason for employers to fnance general tranng can be the exstence of the external effects of tranng. In case of external effects the tranng wll not only ncrease the productvty of the partcpants of tranng program, but also the productvty of other workers, who dd not partcpate. For example, f some workers are taught to use nformaton and communcaton technology, then t s n most cases general tranng as such technology s usually smlar n dfferent companes. After the completon of tranng program worker s ncreased knowledge n handlng such technology could mprove the overall speed and qualty of communcaton n the company and so the productvty of other employees may ncrease too. Ths knd of external effect s called network externalty. (Ercson 2005) Some other reasons for the fnancng the general personnel tranng by employers are based on the complementary relatonshp between general tranng and other nvestments or actvtes undertaken by the employer or the employee. These complementary nvestments/actvtes ncrease the return on general tranng. General tranng can be complementary to both physcal captal nvestment and specfc tranng, as the hgher sklls of workforce wll ncrease the rate of return on nvestments n machnery and the acquston of general sklls may ncrease the productvty growth from specfc human captal. (Galor and Moav 2003) But the reason for companes to fnance tranng can also be the fact that the tranng s not general. The typcal examples of frm-specfc human captal are sklls to handle equpment or methods, whch are dosyncratc to a sngle frm, knowledge about products specfc to a sngle frm, knowledge about the frm s structure, knowng the phone numbers of other employees and so on. The frst two of these arguments can have a sgnfcant mportance n case of some specfc enterprses, for example nuclear plants, but n most cases the equpment used and products produced are not very specfc to a sngle company. For example, same or smlar types of cars are used by tax-drvers n dfferent companes, smlar software s used by accountants n dfferent frms and dfferent unverstes teach qute smlar courses of mcroeconomcs. So t means that human captal s usually not frm-specfc and accordng to the standard theory t has to be general then. But what f human captal s nether frm-specfc nor general? 3
Although even Becker acknowledged that much on-the-ob tranng s nether frmspecfc nor completely general, he argued that such tranng can be consdered as the sum of two components, one completely general, the other completely specfc (Groen 2005) More recently t has been proposed that human captal s mostly not frm-specfc but ndustry-specfc, occupaton-specfc or skll-specfc. Frm-specfc human captal has been argued to be a reason for the tenure coeffcents to be sgnfcant n wage regressons. There have been some other explanatons to ths fact, lke the ob-matchng process (Mortensen 1988), but frm-specfc human captal has been the standard case. The possblty of presence of ndustry-specfc human captal was already ponted out by Helwege (1992) but t receved more attenton when Neal (1995) argued that n many frms ndustry-specfc sklls are an mportant component of workers human captal stock. For example, n a food-processng ndustry all frms value a common set of sklls but same knd of sklls may not be valued n electroncs ndustry. The reason for that s that frms n one ndustry use smlar technology but frms n dfferent ndustres may use very dfferent technologes. In hs artcle, Neal (1995) shows that usng wage data from dsplaced workers, wages n part reflect compensaton for ndustry-specfc sklls. Most of the dsplaced workers suffer a wage loss, but those, who swtch an ndustry, suffer a bgger wage loss n comparson wth the dsplaced workers, who fnd a new ob n the same ndustry. Furthermore, hs results pont out that among those dsplaced workers, who swtched ndustry wage losses are more strongly correlated wth tenure than among those, who dd not swtch ndustres. Parent (2000) also comes to smlar results. Usng data from a longtudnal survey, he fnds that controllng for workers, who change ndustry when changng obs, the tenure effect s reduced by 40-60%. Accordng to these results he makes a concluson that ndustryspecfc human captal plays a larger roll n the wage growth process than frm-specfc human captal. The other pont of vew s that human captal s not frm-specfc but occupatonspecfc. Kamburov and Manovsk (2002) provde evdence of the consderable returns to occupatonal tenure and fnd that when ndvdual s experence n an occupaton s taken nto account, hs tenure n an ndustry or wth an employer have lttle mportance n explanng the wage he receves. Accordng to ther vew t s true that the ndustry can affect the ob one performs, but t seems mplausble that the human captal of these workers s specfc to the ndustry they work n rather than to the type of work they do (ther occupaton). The reason for human captal to be occupaton-specfc can be the fact that there are very dfferent occupatons wthn a sngle ndustry and at the same tme there can be qute smlar occupatons n dfferent ndustres. For example, both welders and accountants work n the metal ndustry. It s qute natural to thnk that the dfference between sklls requred for an accountant n metal ndustry and accountant n unversty are smaller than between accountant and welder wthn the same ndustry. The vew of occupaton-specfc human captal s also supported by Groen (2005), who explores the relatonshp between occupaton-specfc human captal and local economc envronment. Poletaev and Robnson (2003) have argued that human captal s not frm-specfc nor ndustry-specfc but skll-specfc. Accordng to ther vew human captal can be represented by a small number of sklls that are largely general across frms and ndustres. They have used the same data as Neal (1995). They fnd that fnd that when skll status s taken nto account there s lttle evdence of ndustry-specfc human 4
captal. To some extent the skll-specfc and occupaton-specfc vews are smlar as n many cases dfferent occupatons are assocated wth dfferent sklls. But n some cases same sklls are productve n dfferent occupatons, lke the ablty to use econometrc methods can be used both by economc analysts and professors. A further development of skll-specfc human captal approach s the skll-weghts approach by Lazear (2003). In ths case t s assumed that sklls are all general but they are valued dfferently on dfferent obs. It s supposed that n each ob there s a varety of sklls that s used there and each of these sklls s general n the sense that t s used at other frms as well. The dfference s that frms vary n ther weghtng of the dfferent sklls. For example workng as a chef executve of a wood processng company requres manageral sklls and knowledge of the producton technologes used n the company. Workng as a producton manager requres the same sklls, but n ths occupaton, the knowledge about producton technologes s more mportant than n case of the chef executve, whose occupatonal requrements emphasse more on the manageral sklls. Accordng to that theory the wage loss of a worker when changng obs depends on the labour market thckness. When the labour market s relatvely thn, then t s not possble to fnd a new ob, whch s smlar to the prevous one and the worker cannot make a good use of hs sklls and ths s true even f each of those sklls s general n the sense that there are other employers who make use of the same skll. Ths theory makes a number of predctons, whch have been tested and found support by Backers-Kellner and Mure (2004). Those are the followng: 1. Tenure effects on the wages should be smaller n thck markets than n thn markets. Thck markets n ths context are the markets, where there are lots of ob offers or where frms have qute smlar skll-weghts. 2. Departures from large frms should result n larger wage losses than departures from small frms. That s because n larger frms there are more dfferent obs wthn the same frm and so these obs match better workers sklls than obs n small frms. 3. Hghly dosyncratc skll-weghts of the frm cause large wage losses when worker swtches frms. Thus, n new ndustres that use sklls n uncommon combnaton the tenure coeffcent should be relatvely large. 4. The more dosyncratc the skll-weghts of the frm are the larger share of the tranng the frm wll pay for. Ths mples that frms are more lkely to pay for general tranng n ndustres and occupatons where tenure effects are largest because those are the ndustres wth the largest losses assocated wth movng from one frm to another. 5. Tenure effects are tme-dependent. Because of the way that uncertanty gets resolved over tme, the tenure effects should be largest at the tme that a separaton occurs and should de out over tme. Besdes consderng the human captal to be frm, ndustry, occupaton or skll-specfc, the other mportant ssue s measurng the specfcty of human captal. In realty t s lkely that n some portons human captal s specfc and n same portons general. There have been proposed a number of methods for measurng the specfcty of human captal. For example, Frank (2003) has used the length of vocatonal adustment of new employees for ths purpose. In hs survey a followng queston was asked from the frms: "For new employees, who are experenced n ther occupaton but new to your 5
frm, typcally a certan tme span wll pass untl ther productvty s comparable to that of ther ncumbent colleagues. Please try to estmate the number of month whch s necessary for ths." The longer the adustment perod, the hgher the level of specfcty wll be. One possblty of measurng the specfcty of human captal s dong by measurng by the specfcty of sklls, whch of course needs assumng human captal to be skllspecfc. Backers-Kellner and Mure (2004) have used two dfferent methods to do ths. The frst one s based on the employees own opnon about the employer s possbltes to replace them. The workers were asked whether they thnk they are easly replaceable at ther current ob or not. It was assumed that f a worker thnks he s not easly replaceable, he uses farly specfc sklls on hs ob that cannot be found elsewhere n the nternal or external labour market. So that knd of workers human captal s assumed to be specfc. The second measure of human captal specfcty proposed n ths artcle s the number of past ob changes. In accordance wth the skll-weghts approach (and standard human captal theory) t s assumed that workers only leave ther prevous employer f the outsde wage offer exceeds hs ntal wage. In other words, wth every ob change the degree of specfcty ncreases. Measure for human captal specfcty Whle these prevous measures of human captal specfcty are ndrect, n ths paper there wll be developed a more drect measure for human captal specfcty, whch s based on the skll-weghts approach by Lazear (2003). In order to develop such measure there s a necessty to clarfy what the sklls exactly are and how to measure them. As Ingram and Neumann (1999) have notced, among economsts there s lttle agreement on what consttutes skll. Usng the years of educaton s a standard approach to measure skll n economcs, n a great number of theoretcal models measurng skll s lmted to dvdng employees nto low-sklled and hgh-sklled categores (for example Topel (1994)). Years of educaton s a very smplfed measure for skll because t does not take the qualty and type of the educaton nto account. For example, dfferent unverstes have dfferent currcula and the number of years of schoolng can be same for one person, who has attaned vocatonal educaton, and for other person, who has attaned secondary educaton. One soluton to that problem can be usng the hghest attaned level of schoolng as a measure for skll, but for measurng human captal specfcty a more detaled measure for skll s needed. It should be also mentoned that an other drawback of usng a skll measure lke years of educaton or educatonal level s that t does not take the sklls attaned through on-the-ob tranng nto account. Ingram and Neumann (1999) have proposed an alternatve measure of skll based on observed characterstcs of obs held by workers. For developng ths measure, they have used skll nformaton n the U.S. Dctonary of Occupatonal Ttles data. Ths data provdes nformaton on ffty-three characterstcs of specfc occupatons, whch can be broadly grouped nto fve categores: general ntellectual development, temperaments, apttudes, physcal demands and envronmental condtons. They have ponted out that ther data represents ffty-three potental dmensons of skll heterogenety among workers, but t s unlkely that each dmenson represents a unque worker skll trat. For that reason they have used factor analyss n order to reduce the number of dmensons. They end up wth four dfferent factors: ntellgence, fne motor skll, coordnaton and strength. 6
The startng pont for developng the human captal specfcty measure n ths paper wll be the skll-weghts approach by Lazear (2003). There t was assumed that the workers wage depends on the value of the weghts that the frm poses on the employee s sklls. In the standard model there was assumed that employees have only two sklls A and B and each frm poses weghts and 1 to these sklls. So a worker wth the skll set (A, B) has potental earnngs n frm y A 1 B In the realty the number of sklls, whch are requred on dfferent obs s usually hgher than two. So ths model can be extended to the case where there are s a total number of m sklls and each frm poses a weght to a partcular skll, so the potental wage, whch n case of perfect competton and absence of other frctons on the labour market s equal to the margnal productvty of worker s labour, n frm wll be where m y A, 1 A s the level of the skll owed by the worker. Whle the skll-weghts n dfferent frms can be dfferent, t wll be dffcult to estmate them emprcally. At the same tme t s qute obvous that when the sklls are defned qute narrowly, whch means that the total number of sklls n the economy s hgh, then only a small number of them affects the employees productvty and wage on one partcular ob sgnfcantly. For example the skll of preparng meals s hghly crtcal for cooks, but the have no sgnfcant effect on the productvty of dentsts. So t can be assumed that for each frm there s a number of sklls whch affect the employee s productvty sgnfcantly, these sklls can be called crtcal sklls. It can be assumed that frm poses a zero-weght to all other sklls, whch do not affect the productvty sgnfcantly. As t s dffcult to estmate the skll-weghts emprcally, then t s assumed here that frms pose equal weghts to all crtcal sklls. So f the number of crtcal sklls n frm s m, then each of these sklls s valued by a weght m 1. The potental wage wll be then y m 1 1 m A. So f a worker has a level A of a skll then he can get a return 1 n frm from t f ths skll s crtcal to ths frm and he wll get a return 0 from t s ths skll s not crtcal n that frm. As the sets of crtcal sklls are dfferent n dfferent frms, employees wages n dfferent frms are dfferent too. For employees t s optmal to be employed n a frm, whch pays hm the hghest wage and as the wage depends on the crtcal sklls, then t s optmal to be employed n a frm, whch requres the set of sklls, whch match the employees sklls the best. The employees sklls can be developed by tranng, whch can be fnanced both by the employer or employee. It s natural to assume that employers are only nterested n developng employees crtcal sklls as nvestng n other sklls wll be clearly waste of m 7
resources as these sklls do not affect workers productvty. But t s also the employees optons to make use of ther sklls n other frms, whch affect the frm s decsons to nvest n these sklls. It means that f the possbltes for employees to use ther sklls n other companes are hgh, then the rsk of a separaton s also hgh and therefore the frm s ncentves to nvest n worker s human captal are low. The possbltes for employees to use a skll depend on the number of frms, where that skll s crtcal. If a partcular skll s crtcal only n one frm, then t s completely frmspecfc and n that case employees cannot beneft from that skll n other companes and therefore employers have ncentve to nvest nto these sklls. The opposte case occurs when a partcular skll s crtcal n all frms, n ths case that skll s completely general and workers can beneft from t everywhere and employers have no ncentve to nvest n t (n case of no market mperfectons). Therefore the number frms, where a skll s crtcal can be used to determne a measure for skll specfcty. To make ths measure comparable for dfferent labour markets where the total number of frms can be dfferent, the share of the frms where the skll s crtcal s used, so the measure for skll specfcty s c s, where s s the specfcty of skll and crtcal and k s the total number of frms. k c s the number on frms where skll s As there s usually more than one crtcal skll for each frm, the ncentves for frms to pay for tranng do not depend only on a specfcty of ust one partcular crtcal skll, but on the specfcty of all crtcal sklls. In the Lazear s model, one of the results was that the more dosyncratc the skll-weghts of the frm are the larger share of the tranng the frm wll pay for. As frms pay more lkely for nvestment nto specfc human captal, then t can be concluded from the prevous statement that more dosyncratc skll-weghts correspond to the requrement of more specfc human captal n that frm. So t can be sad that frms decsons about fnancng employees tranng are based on the ob specfcty, whch depends on the skll specfctes of ts crtcal sklls and also on the number of crtcal sklls. It s qute obvous that the hgher the specfcty of crtcal sklls s, the hgher the ob specfcty wll be. But t s also assumed here that the hgher the number of specfc sklls the hgher the ob specfcty wll be. The ntuton for ths s that obs wth greater number of crtcal sklls are lkely to be more dfferent from other obs as the number of possble combnatons of sklls rses when the number of sklls, whch can be combned, rses. Accordng to these two factors, whch affect ob specfcty a followng measure for ob specfcty s proposed m s s. 1 So the ob specfcty s the product of the skll specfctes of all crtcal sklls n that frm. Ths measure for ob specfcty can be nterpreted as a measure for human captal for two reasons. Frst, t expresses the specfcty of crtcal sklls, as n case of more specfc crtcal sklls the ob specfcty s hgher and f the crtcal sklls are more specfc then requred human captal n that frm s more specfc. Second, as frms only offer tranng of crtcal sklls, then over the tme of employment worker s sklls wll become more smlar to the frm s crtcal sklls and so the requred and actual human captal of a worker become more and more smlar. So t can be sad that the ob specfcty measures the worker s human captal specfcty and over the tme ths measure becomes more precse. 8
Data The data used n ths artcle comes from an nternet-based ob vacances database, whch s stuated on the webste www.hyppelaud.ee. Ths web-ste s the bggest on-lne ob search ste n Estona. On ths ste employees can advertse about ther vacances and ob searchers can apply to these vacances through the webste. Most of the servces provded by ths webste are free. In ths artcle nformaton about 1268 ob advertsments, whch were actve n the perod from August 10 th 2005 to August 20 th 2005, s used. In order to avod the possble seasonalty problems, t would be deal to use data from the perod whch nclude a whole year, but t was not possble to use that knd of data as ths webste does not provde nformaton about past vacances. So the queston whether the specfcty of obs depends on the month, when the vacancy advertsement s actve, remans to be nvestgated n future. The sample ncludes vacances posted both by prvate and publc sector nsttutons, but advertsements on vacances abroad were dropped. For each vacancy, there s nformaton about the occupaton, locaton of the ob, ndustry requred educatonal level and prevous work experence, length of hours, salary, requred skll and provson of on-the ob tranng by employee. In case of requred prevous work experence two types of experence can be dstngushed: general and occupaton-specfc. In case of some vacances t s only requred that the applcants have some prevous work experence on any ob, but other vacances requre ob experence on the same occupaton. The data however does not contan nformaton about the requred prevous work experence. As sklls are often acqured by on-the-ob tranng and learnng by dong, then t can by assumed that when the applcants are requred to have work experence, they are ndrectly requred to have sklls relevant to that experence. The problem here s that t s not possble to detect, whch sklls the work experence actually represents and therefore ths nformaton can not be used for estmaton of specfcty of dfferent sklls. Stll, t s possble to use ths nformaton on the testng of the valdty of the ob specfcty measure as t s possble to use the requrement of prevous work experence as the estmator of provson of one-the ob tranng. The requred sklls, about whch the database consst nformaton, and whch are used n the followng analyss belong to three broad categores: computer sklls, language sklls and drvng sklls. Although t s clear that these sklls represent only a small part of sklls whch belong to the crtcal skll on dfferent obs or frms, the data stll makes t possble to evaluate the specfcty of these sklls and the fact that the data does not consst nformaton about all sklls, does not affect the estmaton process of skll specfctes of sklls, whch belong to these three categores. For the computer sklls n some cases there s announced detaled nformaton about dfferent types of software, whch the applcant should be able to use, but on other cases t s only announced that knowledge to use computers s requred. As these requrements are very heterogeneous, only one type of computer skll s dstngushed here. Sx dfferent language sklls are dstngushed here. These are sklls for Estonan, Russan, Englsh, German, French and Fnnsh languages. Although some advertsements provde nformaton about the requred level and type (oral, wrtten) of language profcency, t s only taken nto account whether a ob requres some type of command of a partcular language or not. Fve dfferent type of drvng sklls are dstngushed and the classfcaton of these 9
sklls n based on the drvng lcence categores. Accordng to the Estonan Traffc Law A category lcence allows drvng motorcycles, B category lcence automobles wth kerb weght no more than 3500 kg and no more than 8 passenger seats, C category lcence automobles wth kerb weght more than 3500 kg, but wth no more than 8 passenger seats, D category lcence automobles wth more than 8 passenger seats and E category lcence automobles wth a traler wth kerb weght more than 750 kg. Results In order to calculate the specfcty of those prevously mentoned sklls, there has to be made an assumpton that n case of the ob advertsements all the requred sklls are crtcal sklls and all those sklls that are not mentoned to be requred on the ob advertsement are not crtcal sklls for that ob. It could be qute natural for frms to menton only those sklls that affect productvty sgnfcantly on the ob advertsements, but n practce, there could some reasons why frms could announce some non-crtcal sklls to be requred and on the other hand n some cases not all crtcal sklls could be lsted as requred sklls. For example, f the frms want to reduce the number of potental applcants to the ob, then they may announce some other sklls, whch n realty do not affect the productvty sgnfcantly, to be requred for applyng for that ob. Reducng the number of applcant could reduce the costs of fllng the vacancy, but t can also decrease the chances of hrng good workers as t s possble that the best sutable worker for that ob does not apply as he or she does not have a requred skll, whch n fact does not affect hs or her productvty. There s also a possblty that not all crtcal sklls are announced to be requred. One reason for that s that f frms reduce the number of requred sklls then they can ncrease the number of applcants. However, ncreasng the number of applcants n such way should not ncrease the number of those applcants, who possess all the crtcal sklls, but t attracts also such workers, who do not possess all crtcal skll and whose productvty should be lower f the productvty s determned only by the crtcal sklls. But f there are some other factors, lke the loyalty of workers, whch affect productvty, then t may be ratonal for frms to announce not all crtcal sklls to be requred. The other reason for such possblty s the fact that there are some nformatonal problems and frms do not exactly know whch sklls are crtcal to that partcular ob, whch could be the case for the startng frms or new and uncommon occupatons. Whle that knd of problems exst and these ssue need to be nvestgated n the future, t s not lkely that these factors have a very bg nfluence on the results of the analyss. In some cases the number of requred sklls can be hgher than the number of crtcal sklls and n some cases the stuaton can be converse, but on the average the number of requred and crtcal sklls should be equal and probably n most cases requred and crtcal sklls should be the dentcal. The sklls specfctes of dfferent sklls, whch are calculated by usng the prevously descrbed methodology, are presented n the table 1. 10
Table 1. Estmated skll specfctes Skll Number of vacances, where crtcal Specfcty Computer 276 4.59 Estonan 937 1.35 Russan 625 2.03 Englsh 384 3.30 German 27 46.96 French 6 211.33 Fnnsh 141 8.99 A category 4 317.00 B category 230 5.51 C category 35 36.23 D category 3 422.67 E category 12 105.67 Source: author s calculatons These results show that Estonan and Russan language sklls are the most general ones, Englsh sklls are more specfc. Computer sklls are wth medum specfcty. Drvng sklls are hghly specfc, expect B category. Other foregn language sklls lke Fnnsh, German and French are also hghly specfcty. Accordng to the human captal theory frms should be more lkely to pay for the tranng of the sklls wth hgh specfcty. Unfortunately the data does not nclude nformaton about the types of tranng so t s not possble to check ths proposton drectly. These skll specfctes are used for estmatng the ob specfctes for every vacancy. Vacances about whch no requred sklls were announced are assumed to be completely general and the specfcty of these obs s assumed to be 1. Accordng to estmated ob specfctes the average ob specfctes for dfferent sectors and occupatons are calculated. The estmated skll specfctes of dfferent vacances range from 1 to 112 mllon, whch the average of 189 131 and medan 4.47. As there are very few vacances wth very hgh skll specfcty and these few outlers are lkely to have a bg nfluence on the results of the analyss, then the natural logarthm of ob specfcty s used n the followng analyss. Ths s called log-ob specfcty and t s calculated as follows: m m 1 ln s ln s ln s. If there are no requred sklls announced then the log-ob specfcty for ths vacaton s 0 as ln1 0. 1 11
Table 2. Average log-ob specfctes for dfferent occupatonal categores Occupatonal category Log-ob specfcty Legslators, senor offcals and managers 3.79 Professonals 1.89 Techncans and assocate professonals 3.23 Clerks 2.99 Servce workers and shop and market sales workers 1.99 Sklled agrcultural and fshery workers 2.8 Craft and related workers 0.97 Plant and machne operators and assemblers 3.34 Elementary occupatons 0.94 Source: author s calculatons In table 2 calculated average log-ob specfctes for dfferent occupatonal categores are presented. These occupatonal categores are based on the ISCO 88 classfcaton. It can be seen that the vacances, whch belong to the category of legslators, senor offcals and managers, have the hghest ob-specfcty. On the general, occupatons, whch requre hgher qualfcaton, requre more specfc sklls and low-skll occupatons lke craft and related workers or elementary occupatons, requre less specfc sklls. There are some exceptons to that pattern, for example, vacances for plant and machne operators and assemblers have hgh ob-specfcty. Ths s caused by the fact that occupatons of truck and bus drvers belong to that category and these occupatons need hghly specfc drvng sklls (lcence categores C, D and E). Jobspecfcty for sklled agrcultural and fshery workers s also relatvely hgh, but ths s probably to due the fact that there are very few vacances that belong to these occupatonal categores n the dataset. Table 3 presents the average log-ob specfctes for dfferent ndustres. It can be seen that dfferences n ob specfctes are remarkable. It s worth mentonng that dfferences across ndustres are bgger than across occupatonal categores. Ths can be caused by the fact that the number of ndustres s bgger than the number of occupatonal categores and n many cases very dfferent occupatons belong to the same occupatonal categores. For example, both truck drvers and wood-processngplant operators belong to the same category of plant and machne operators and assemblers. Agrculture, huntng and forestry and fnancal ntermedaton are the ndustres wth the vacances, whch requre the most specfc human captal. Educaton and manufacturng are the ndustres, where the ob specfctes are the lowest and that means that the requred human captal s most general there. 12
Table 3. Average log-ob specfctes for dfferent ndustres Industry Log-ob specfcty Agrculture, huntng and forestry 4.49 Manufacturng 1.21 Constructon 1.93 Wholesale and retal trade; repar of motor vehcles, motorcycles and personal and household goods 2.70 Hotels and restaurants 2.33 Transport, storage and communcaton 3.95 Fnancal ntermedaton 4.48 Real estate, rentng and busness actvtes 2.18 Publc admnstraton and defence; compulsory socal securty 1.44 Educaton 0.76 Health and socal work 3.98 Other communty, socal and personal servce actvtes 2.56 Source: author s calculatons For testng the valdty of the ob specfcty measure, ths measure s used as the estmator of probablty of company fnanced tranng. Frms are probably lkely to pay for tranng of the crtcal sklls and as t was proposed prevously n ths paper that frms decsons about fnancng employees tranng are based on the ob specfcty, whch means that the hgher the ob specfcty of a vacancy the hgher the probablty of offerng tranng. Unfortunately, the data from the ob advertsement does not consst nformaton about whether the companes actually offer tranng for the employee, whch s hred to that ob. Nether does the nformaton about the ob advertsements tell anythng about, who pays for the tranng. But t s qute natural to assume that f the frm announces on the ob advertsement that the employee wll receve tranng then the frm wll pay for t. Although t mght be possble that after hrng the worker, who has promsed to receve tranng wll not be offered company fnanced tranng, ths case wll be not lkely as n the database only n case of 41 vacances out of 1268 tranng s offered. The problem s lkely to be the other way round as s qute clear that actually a frms pay for employees tranng n case of a bgger number of obs than they announce on advertsements. If ths s true then only a fracton of the frm, whch offer tranng announce t n the ob advertsement and t can be assumed that frms only announce tranng f they are absolutely sure that they wll offer t, n other cases the do not announce t as they do not want to rsk Besdes ob specfcty, there can be several other factors that nfluence the probablty of offerng on-the-ob tranng. These factors can be dvded nto human captal, ndustry-specfc, occupaton-specfc and ob-specfc factors. Employees prevous ob experence can be one of them as t s part of the employees human captal. The dataset ncludes nformaton about requred prevous work experence, whch can be dvded nto general and occupaton-specfc experence. Formal educaton s the other component of human captal, whch wll probably have an effect on the possbltes of recevng tranng. Usually workers wth hgher educatonal level receve more tranng from employers. Three educatonal levels are dstngushed between n the model for 13
tranng offerng probablty here. The educatonal levels are based on the ISCED97 classfcaton. So educatonal level 1 conssts here of the ISCED97 levels 0-2, level 2 of the ISCED97 levels 3-4, and educaton level 3 of the ISCED97 levels 5-6. Industry and occupatonal specfc factors can also have an effect of offerng tranng because besdes dfferences n the ob-specfctes n dfferent ndustres and occupatons, whch were nvestgated prevously, there can exst ndustry or occupaton specfc effects of offerng on-the-ob tranng. The emprcal research on Estonan data has ndcated that n secondary and tertary sectors tranng s more offered at greater extent (Lepng and Eamets 2005). Industry and occupaton specfc effects can also nclude the frm-sze effect on the offerng of tranng as the dataset does not nclude nformaton about the sze of the companes. The probabltes of offerng tranng are estmated for each vacancy by a logt-model. The dependent varable s used offerng tranng, whch s assumed to have value 1 f n the advertsement t s sad that the company wll provde tranng for the employee, and s assumed have value 0 n other cases. The explanatory varables used n ths model are lsted n the table 4. Table 4. Explanatory varables of tranng offerng model Varable LNJOBSPEC EXPERIENCE SPECEXP EDUC3 EDUC2 MANAGER PROFESSIONAL TECHNICIAN CLERK SERVICEWORKER SKILLAGRI CRAFT OPREATOR CONSTRUCTION TRADEHOT TRANSPORT FINANCE PUBLIC TALLINN Descrpton log-ob specfcty of vacancy dummy varable for requred prevous ob experence dummy varable for requred prevous occupaton-specfc experence dummy varable for level 3 educaton dummy varable for level 2 educaton dummy varable for legslators, senor offcals and managers dummy varable for professonals dummy varable for techncans and assocate professonals dummy varable for clerks dummy varable for servce workers, and shop and market sales workers dummy varable for sklled agrcultural and fshery workers dummy varable for craft and related workers dummy varable for plant and machne operators and assemblers dummy varable for constructon dummy varable for wholesale and retal trade, repar of motor vehcles, hotels and restaurants dummy varable for transport, storage and communcaton dummy varable for fnancal servces, real estate, rentng and busness actvtes dummy varable for publc admnstraton and defence, compulsory socal securty, educaton, health and socal work dummy varable for the locaton of employment (TALLINN=1 f the vacancy s located n the captal, TALLINN=0 otherwse) 14
Those explanatory varables are log-ob specfcty, two dummy varable for requred prevous ob experence, two educatonal dummes, where level 1 educaton s selected as a bass, eght occupatonal dummes (elementary occupatons s selected as a bass), fve ndustry dummes (agrculture, forestry, fshng, mnng and quarryng, manufacturng and electrcty, gas and water supply ndustres are selected as a bass) and one locaton dummy. Table 5. Estmaton results Varable Parameter Standard error LNJOBSPEC 0.060 0.068 EXPERIENCE 0.737 0.457 SPECEXP 0.075 0.428 EDUC3-0.488 0.588 EDUC2 0.335 0.398 MANAGER -0.076 0.833 PROFESSIONAL 0.204 0.794 TECHNICIAN 1.161 0.627 CLERK -0.235 1.152 SERVICEWORKER 0.190 0.680 CRAFT -0.305 0.682 OPREATOR -0.068 1.157 CONSTRUCTION 0.012 0.587 TRADEHOT -0.304 0.544 TRANSPORT -0.751 1.097 FINANCE -0.374 0.475 PUBLIC -0.972 0.727 TALLINN -0.848 0.333 CONSTANT -3.146 0.637 Note: Varable SKILLAGRI s dropped as t predcts falure perfectly Source: author s calculatons The estmaton results are presented n table 5. As the number of vacances, where offerng of tranng was announced s small, maorty of the parameter estmates of the model are not statstcally sgnfcant. It seems to be that that those obs that requre prevous work experence offer on-the-ob tranng more often and tranng s provded wth hgher probablty on the obs requrng secondary or lower educaton and especally n case of the occupaton of techncans. There are some sector-specfc and occupaton-specfc effects on the tranng probablty and sector-specfc effects seem to be bgger, but t must be kept n mnd that these effects are statstcally nsgnfcant. An nterestng result s that wth other thng equal frms are much less lkely to pay for tranng n the ob s located n the captal of Estona. The parameter for ob-specfcty s postve but nsgnfcant and therefore t s dffcult to draw conclusons about the valdty of ths ob specfcty measure. If t had been sgnfcant, then t could be sad that t corresponds to the human captal theory and predcts the probablty of offerng tranng by frms. In ths case the nsgnfcance of the parameter s lkely to be caused by the poor qualty of dataset as there are few observatons, where tranng s offered. 15
Conclusons The am of ths artcle was to construct a skll-based measure for human captal specfcty. For that reason the number of frms, where a partcular skll s affectng the productvty, was used for defnng the skll specfcty, whch descrbes the specfcty of sklls and as human captal conssts of skll also the specfcty of human captal. All the sklls, whch affect the productvty n partcular frm, are announced crtcal sklls for that frm. Accordng to the crtcal sklls a measure for ob specfty was developed. Job specfcty can be nterpreted as a measure for human captal as f the crtcal sklls are more specfc then requred human captal n that frm s more specfc. As frms offer only tranng of crtcal sklls, then over the tme of employment worker s sklls wll become more smlar to the frm s crtcal sklls and so the requred and actual human captal of a worker become smlar. In the emprcal part of the artcle the skll specfctes and ob specftes of dfferent sklls and obs were calculated by usng the data from the ob advertsements. The results ndcate that Estonan and Russan language sklls are the most general one and some type of drvng sklls are the most specfc one. On the general more specfc human captal s requred on the occupatons, whch requre hgher qualfcaton lke legslators, senor offcals and managers. There exst also remarkable dfferences n the specfcty of requred human captal between dfferent ndustres. For testng the valdty of the ob specfcty measure, ths measure s used as the estmator of probablty of company fnanced tranng. Unfortunately, the qualty and sze of the dataset s not very good and therefore the estmaton results are nsgnfcant and therefore t s dffcult to draw conclusons about the valdty of ths ob specfcty measure. So t remans for the future work to test the valdty of the human captal specfty measure by usng better data. That knd of data should nclude better nformaton about the actual frm-fnanced tranng as well as more detaled and more complete nformaton about sklls. One possblty to acqure that knd of data s to conduct a questonnare survey among companes. The ob specfcty measure can be used for plannng the publc sector support to onthe-ob tranng. As mprovng the qualty of labour s an acute topc n the labour market polces, then t s necessary to fnd out whch ndustres and occupatons are the one s where the governmental support s most urgently needed. As frms are more lkely to offer tranng for workers, who work on the more specfc obs, then the role of the publc sector support should be bggest n the ndustres and occupatons, where the skll specfctes are low. References 1. Acemoglu, D., Pschke, J.-F. The Structure of Wages and Investment n General Tranng - The Journal of Poltcal Economy, Vol. 107, Issue 3, 1999, pp. 539-572. 2. Backes-Gellner, U., Mure, J. The Skll-Weghts Approach on Frm Specfc Human Captal: Emprcal Results for Germany, Workng Paper, Zurch, October 2004, 16 p. 3. Becker, G. Investment n Human Captal: A Theoretcal Analyss - Journal of Poltcal Economy, Vol.70, No. 5, 1962, pp. 9-49. 4. Ercson, T. Personell tranng: a theoretcal and emprcal revew. IFAU Workng Paper 2005:1, 76 p 16
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