Decomposing productivity and wage effects of intra establishment labor restructuring

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1 ömmföäflsäafaäsflassflassflas ffffffffffffffffffffffffffffffffffff Dscusson Papers Decomposng productvty and wage effects of ntra establshment labor restructurng Mka Malranta The Research Insttute of the Fnnsh Economy (ETLA) and Pekka Ilmakunnas Helsnk School of Economcs and HEER Dscusson Paper o. 85 ovember 2005 ISS HEER Helsnk enter of Economc Research, P.O. Box 17 (Arkadankatu 7), FI Unversty of Helsnk, FILAD, Tel , Fax , E mal nfo hecer@helsnk.f, Internet

2 HEER Dscusson Paper o. 85 Decomposng productvty and wage effects of ntra establshment labor restructurng* Abstract Aggregate productvty growth can be decomposed nto growth wthn establshments, between establshments, and the mpact of enterng and extng establshments. We demonstrate that such a productvty decomposton formula can also be used for studyng ntra establshment restructurng through the nflow and outflow of workers. There are, however, three requrements: Frstly, comprehensve longtudnal lnked employeremployee data are needed. Secondly, now the productvty decomposton formula cannot be used for accountng but must be used as an estmaton model. Thrdly, the decomposton formula should be such that a meanngful nterpretaton of ts components s possble. The decomposton can also account for dfferent worker types, e.g. age groups. We apply such a method to study the productvty growth and wage growth wthn the establshments of the Fnnsh busness sector. The results provde emprcal evdence on economc ncentves for hrng young workers and separatng older workers. JEL lassfcaton: 43, J23, J24, J63, M51 Keywords: employer employee data, labor productvty, productvty decomposton, wage determnaton, worker turnover Mka Malranta Pekka Ilmakunnas The Research Insttute of the Department of Economcs Fnnsh Economy (ETLA) Helsnk School of Economcs Lönnrotnkatu 4B, P.O. Box 1210 FI Helsnk FI Helsnk FILAD FILAD e mal: mka.malranta@etla.f e mal: pekka.lmakunnas@hse.f * Both wrters acknowledge fnancal support provded by Tekes wthn the Research Programme for Advanced Technology Polcy (ProAct) and Malranta fundng by Yrjö Jahnsson Foundaton for studyng decomposton methods. The data work and computatons have been carred out at Statstcs Fnland, n accordance wth the terms and condtons of confdentalty. We have benefted from comments by Rta Asplund. Of course, the usual dsclamer apples. We thank Pekka Vanhala for research assstance. We are also thankful to many ndvduals at Statstcs Fnland for ther gudance regardng the propertes of the data. Please contact the Research Laboratory of the Busness Structures Unt, Statstcs Fnland, FI 00022, Fnland, for access to these data. The SAS and Stata codes used n ths study are avalable from the authors upon request.

3 I. Background The ncrease of GDP per labor nput s the drvng force of mprovng lvng standards. The role of nput reallocaton n productvty growth has been studed by usng varous knds of decomposton methods. Some studes have examned nter-ndustry restructurng (e.g. Bernard & Jones, 1996) and some other. ntra-ndustry restructurng wth frm or establshment data (e.g. Baly et al., 1992; Baly et al., 2001; Foster et al., 2001). 1 Emprcal analyss of ntra-ndustry restructurng, n partcular, has expanded rapdly n recent years n parallel wth the avalablty of large-scale mcro data sets and developments n the theoretcal lterature. ew theores go beyond the representatve frm framework by emphaszng the role of frm (and establshment) heterogenety n the economc development (e.g. Grossman & Helpman, 1991; Hopenhayn, 1992; Meltz, 2003; Helpman et al., 2004; Klette & Kortum, 2004). However, there may be heterogenetes even at a deeper level. Evdently, the employment structures of the frms/establshments and ntra-frm/ntra-establshment restructurng through hrng and separaton of workers s potentally an mportant source of heterogenety for productvty levels and growth rates between frms/establshments. hanges n the employment structures affect the skll structures, whch n turn, accordng to the human captal lterature (Becker (1962)), should be reflected n productvty and wage growth at the dfferent levels of aggregaton (see e.g. Krueger & Lndahl, 2001). We are, however, unaware of any attempts to go nsde the black boxes wth lnked employer-employee data by usng a smlar decomposton approach. We suggest n ths paper a new method that can be used for decomposng productvty growth wthn establshments to the effect of enterng, extng and stayng workers. As opposed to famlar analyses of ntra-ndustry restructurng, the productvty decomposton formula cannot be used for accountng but must be used as an estmaton model for productvty growth

4 2 usng lnked employer-employee data. We llustrate the method usng data from the Fnnsh busness sector. Ths paper s structured as follows. In secton II we develop the productvty decomposton method. In secton III we descrbe the data set that we are usng and present the results. In the last secton we present conclusons and suggest how the method could be used for sheddng new lght on the connecton of educaton and productvty growth. II. Decomposng aggregate productvty change n the dfferent layers of the economy It s useful to start our dscusson from nter-ndustry restructurng and then proceed to ntra-ndustry and, fnally, to ntra-establshment restructurng. In ths way the dfferences nvolved at the dfferent levels of analyss are made clear. A. Inter-ndustry restructurng follows: Output (Y) per labor (L), an economy s labor productvty (), s obtaned as Y Y t t Lt Yt t = = = = LSH t t, (1) L L L L t t t t where and t denotes ndustry and year, respectvely. LSH t s the labor share of ndustry n year t, and t ts productvty level. Productvty change from year s to t ( t decomposed n a famlar way: where ) can be = LSH + LSH (2) t t LSH s the average labor share of ndustry n year s and t, and correspondng average productvty level. t s the Expresson (2) can be turned nto a rate of change form by dvdng by the average of productvty n years s and t, =. 5 ( + ) 0 s t :

5 3 t = LSH t + LSH t (3) The frst term on the rght-hand sde of (3) s the wthn ndustry productvty growth component. The second term s the between component whch measures aggregate productvty growth due to changes n the employment shares of the ndustres. It should be noted that (3) s approxmately equal to the log-change n productvty; t t ln s. From the standpont of nterpretaton, un unattractve feature n (3) s that the denomnator n the sums on the rght-hand sde s the average aggregate productvty,, not the average productvty of the respectve ndustry, the wthn component of (3) further as. Malranta (2003) decomposes t t t LSH = LSH + LSH 1 (4) where the frst term on the rght-hand sde can be called the pure wthn component, whch s the labor share weghted average of ndustry productvty growth rates. The second one can be ascrbed as a convergence term. It s negatve when the ndustres of low productvty levels have hgher productvty growth rates than the ndustres of hgh productvty levels. B. Intra-ndustry restructurng ext we go down to the next layer, the ndustry level. Industry productvty growth rate t can be decomposed to ts establshment-level sources n an analogous manner wth one major excepton: ntra-ndustry restructurng nvolves entres and exts, whch rarely s the case when restructurng between ndustres s studed. In what follows, we dstngush between three types of establshments: ontnuers (or stayers) (denoted by ), who exst both n the ntal

6 4 and the end year, entrants () that appear n the end year t but not n the ntal year s, and exts (X) that appear n the ntal year s but not anymore n the end year t. ow ndustry productvty growth can be expressed usng the ndustry-level counterparts of (3) and (4). We nsert (3) nto (4) and develop t a bt further to obtan: p LSH t t LSH =, p p X ( ) ( ) t LSH p t p + pt p LSH + X s p LSH s p s p 1 pt p + (5) where p denotes establshment. LSH t and exts n year s n ndustry, respectvely. X LSH s are the labor shares of entrants n year t and, and X are aggregate productvty levels among contnuers, entrants and exts, respectvely. Equaton (5) decomposes productvty growth nto the pure wthn, convergence, between, entry, and ext effects. Ths exact form has been proposed by Malranta (2003) 2. Earler Malranta (1997) and Vanomäk (1999) have proposed formulas that have an analogous treatment of entres and exts. Vanomäk s decomposton was desgned for decomposng skll upgradng of establshments and, thus, t was not presented n a rate of change form. Recently Dewert and Fox (2005) have suggested a formula that s exactly the same as the one n Vanomäk (1999). Equaton (5) bears some resemblance wth the now so popular formula advocated by Foster, Haltwanger and Krzan (2001). However, at least two mportant dfferences are worth notng. Frstly, n (5) the productvty level of entrants s compared to the current aggregate productvty of contnuers whle ths s not the case n the formula proposed by Foster, Haltwanger and Krzan. In contrast to (5), they compare the entrants to the aggregate

7 5 productvty level n the ntal year (5 years back n the past n ther case). They therefore compare the entrants to both contnuers and destned extors n the past. In (5) the entry component tells how much lower (or hgher) the aggregate productvty rate would have been had the new establshments not entered between years s and t. The nterpretaton of the ext component s qute analogous n (5), but the counterfactual s now that extng establshments had stayed and had had the same productvty growth rate as the contnung establshments. 3 Secondly, n (5) the pure wthn component (the frst component on the rght-hand sde) has a natural nterpretaton. It s a labor share weghted average of productvty growth rates of the contnung establshments. Ths s not the case n the popular formulas, where the sum of the weghts of the contnung establshments s less than one f some establshments have exted durng the perod. The pure wthn component s lkely to be a useful ndcator of dsemboded technologcal change (especally when ths formula s appled to the total factor productvty measure). The dfference between the ndustry productvty growth rate and the pure wthn component ndcates the magntude of productvty-enhancng restructurng through entres, exts and job creaton and destructon among contnung establshments. The productvty decompostons presented above and elsewhere n the lterature use ether ndustry level or the establshment/frm level data, but stop there. However, ths stll leaves the sources of a large share of productvty growth hdden n the black box of frms or establshments. For example, Malranta (2003) fnds that slghtly less than half of productvty growth can be attrbuted to mcro-level restructurng, a major part of whch takes place wthn narrowly defned ndustres. Slghtly more than half of productvty growth s due to productvty growth of the establshments. Ths pure wthn establshments productvty growth effect s clearly below the 100% that s assumed n the representatve frm models, but stll seems to be an

8 6 mportant source of ndustry productvty growth that deserves a closer look. In partcular, there may be nterestng varaton n establshment productvty growth rates.. Intra-establshment restructurng These consderatons have motvated us to dve deeper nto the productvty growth dynamcs. We want to have a look at the sources of productvty growth at the mcro-mcro level. More specfcally, we examne ntra-establshment labor restructurng through hrng and separaton of employees and how ths s reflected n establshment productvty growth p pt. For that purpose we wll use a formula that s the establshment-level analogue to (5), wth hrng and separaton playng the role of entry and ext. We dstngush between dfferent types of workers that may have varyng productvty levels as well as dfferent productvty growth rates over tme. The man dfference between mcro level (.e. establshment level) and mcro-mcro level (.e. worker level) analyss s that we cannot observe the productvty levels of the ndvduals drectly. Therefore we cannot use (5) as an accountng dentty for productvty growth but we must use t as an estmaton model nstead. 4 Ths s feasble when productvty can be measured at the level of establshments and we observe the labor shares of dfferent types of workers establshment by establshment. Ths means that lnked employer-employee data are needed. We assume that the type of the worker s tme nvarant. For example, below we wll classfy the workers by ther age n the end year,.e. n practse we compare dfferent age cohorts that are tme nvarant. Therefore there cannot occur a structural change among contnung workers. Ths s to say that, among the contnung workers, the labor share of each worker group s the same n the ntal and the end year. As a consequence, the thrd term on the rght-hand sde

9 7 of equaton (5) drops out. Unable to measure, we cannot defne the pure wthn component nor dstngush the convergence component. Then we have the followng formula: g LSH p pt pgt = pgt LSH pg + g p X ( pgt pt ) ( pgs ps ) X LSH pgs p g p (6) where the sums are over worker types g, denotes hrng (enterng workers) and X separaton (extng workers). So, equaton (6) has three knds of components: a wthn contnung workers component, a hrng component and a separaton component. The frst component measures productvty growth, whereas the second and thrd measure dfferences n productvty levels (productvty gaps). From ths formula we obtan an estmaton model whch can be used for measurng the productvty gaps and growth rates between the worker groups: p pt = X θ g g LSH gpt + θ g g LSH gpt θ g gx LSH gps + β ' X pt + ε pt (7) where LSH gpt s the share of stayng workers of type g of all stayng workers n establshment p n year t (note that LSH = LSH = LSH and LSH = 1), gps gpt pg g gpt LSH gpt s the share of hred workers of type g of all workers (.e., newly hred plus stayng workers) n establshment p n year t, and X LSH gps s the share of extng workers of type g of all workers (.e., extors plus stayng workers) n establshment p n year s. In addton, varous controls X may be needed n order to avod spurous relatonshps between worker flows and productvty growth. The nterestng coeffcents to be estmated are θ g, θ g, and θ gx for each worker type g The frst parameters measure the productvty growth rates of the stayng workers by type durng the

10 8 perod. The second parameters are the relatve productvty levels of the newly hred workers by type, and the thrd ones the relatve productvty levels of those leavng. As can be seen from (6), the nterpretaton of the coeffcents of (7) s not qute straghtforward. Let us assume that,.e. the productvty level of stayng workers s approxmately equal to the average productvty of all workers. Ths may be a reasonable approxmaton f the tme nterval s not very long. We defne the productvty gap between enterng workers of types g = 1 and g = 2 as follows: GAP(1,2) = 0.5 1, t ( + ) 1, t Assumng that t we obtan 2, t 2, t 1, t ln (8) 2, t θ1, GAP(1,2) 0.5 ( θ + θ ) + 1 1, θ 2, 2, (9) The productvty gap between extng workers of types g = 1 and g = 2 s defned analogously (assumng that s ) θ1, XGAP(1,2) 0.5 X ( θ + θ ) + 1 1, X θ 2, X 2, X (10) Both of these measures are symmetrc,.e. GAP ( 1,2) GAP(2,1) = 0 and XGAP ( 1,2) XGAP(2,1) = 0. After the θ parameters have been estmated, standard errors and confdence levels of these gap measures can be calculated by usng the delta method. 5 The same knd of decomposton methods can be used for estmatng the mpact of worker flows on average wage growth at the establshment level. Relatve wage gaps, whch are analogous to the productvty gaps, can then be calculated for the dfferent worker types. In addton, the dfference between the productvty and wage gaps tells us the relatve proftablty

11 9 of hrng and layng off certan types of workers. 6 For example, a hgh productvty-wage gap for a worker group on the hrng sde ndcates that hrng that knd of workers rases productvty more than t rases average wages (n comparson to the reference group) and consequently mproves proftablty. III. An emprcal applcaton of the productvty decomposton method ext we use the productvty decomposton formula (7) to study how worker flows affect the productvty growth of establshments. As t wll turn out, ths knd of analyss may provde fresh nsghts about the role of varous labor characterstcs n productvty growth at the mcro level. A. Data Lnked employer-employee data are needed for applyng the estmaton model (7) for examnng productvty-enhancng ntra-establshment restructurng through worker flows. Fnnsh Longtudnal Employer-Employee Data (FLEED) s excellently suted for such an exercse for a number of reasons. The data on ndvduals orgnate from the Employment Statstcs (ES) that covers practcally the total workng age populaton n the years and has annually about 3.5 mllon observatons. The data have a rch content, ncludng nformaton about age, educaton, gender, employment status, earnngs etc. The workers are lnked to ther employer companes and establshments on the bass of the employment relatonshp n the last week of the year. onsequently, we are able to dentfy worker flows from the year t-s to year t n each establshment by comparng the work force at the end of these years. Furthermore, we can examne the flows by dfferent labor characterstcs, We have classfed the workers nto three age groups: years (young), years (prme-aged) and years (old) on the bass of ther age n the end year of the respectve perod. The entry and ext of

12 10 workers n these age groups s measured n 3-year perods: , and It should be noted that the entry of a worker that s followed by the ext wthn the same 3-year perod s not observed n our data because of the way our varables are constructed. There s also some ncompleteness n the lnkng of employers and employees, so that for some ndvduals no establshment s found and for some establshments not all workers can be found n the data on ndvduals. Informaton on companes and establshments can be found from dfferent data sources. In ths study we use the Busness Regster (BR) of establshments 7 and companes. These data cover bascally all companes and establshments n Fnland. The varables of ths data set nclude sze, the date of entry, ndustry, regon etc. There s also nformaton on sales and employment that can be used for measurng productvty as sales/employee. Because the data are based on admnstratve regsters they should be representatve cross-sectonally from the pont of vew of both employees and employers, n addton to beng representatve longtudnally (see Abowd & Kramarz, 1999). We study productvty growth n 3-year ntervals, , and By usng long ntervals we try to reduce the effects of measurement errors and to allow some tme for productvty effects that may be delayed. We lnk the establshment-level data of worker flows (wth 3-year ntervals) to productvty growth data usng a one year lag. Ths s because the employer-employee lnks concern the stuaton at the end of the calendar year whereas productvty s measured as an average of the current calendar year, the md-pont beng at the end of June. So, essentally we use half a year lags of the worker flows. The so-called manucentrsm stll very much characterzes the lterature of worker flows and productvty analyss (see Hamermesh, 2000). 8 Ths work ams to contrbute to correct that

13 11 mbalance by examnng productvty growth n the whole busness sector, not n manufacturng only. We focus on establshments that employ at least 20 persons (the average of the ntal and the end year). Ths s because nformaton on these establshments s generally obtaned from the Drect Survey of Structural Busness Statstcs whereas for small establshments nformaton s often mputed by use of varous admnstratve regsters. Ths makes the data for the small establshments unrelable for our current purpose. Furthermore, the measurement accuracy of the dependent and explanatory varables s lkely to be substantally better for larger establshments. We have also lmted our estmaton sample by requrng that the number of ndvduals n FLEED that can be lnked to an establshment s at least 75% of the average employment n the establshment accordng to the Busness Regster. Our sample comprses otherwse the whole non-farm busness sector, but the fnancal sector s dropped because of lack of an approprate output measure. Establshments that lack some varables of our nterest are also excluded. 9 Fnally, we have removed some potentally nfluental outlers that were detected by usng the method proposed by Had (1992; 1994). The method s useful for detectng of multple outlers n multvarate data. Identfcaton of outlers was made on the bass of three varables: 1) the growth rate of average monthly earnngs calculated from the data on ndvduals n the Employments Statstcs, 2) the growth rate of average wage calculated from the establshmentlevel data n the Busness Regster, and 3) the productvty growth rate of the establshment. The frst two varables should be hghly correlated wth each other because they are essentally gaugng the same thng, but may stll sometmes dffer n the data due to mperfect lnks between workers and establshments. Wage growth s usually correlated wth productvty growth, but sometmes they may be very dfferent because of measurement errors n output and/or labor

14 12 nput. We dentfed 210 outler establshments out of orgnal observatons (that employ at least 20 employees). Table A.1 n the Appendx gves some descrptve summary statstcs of our basc sample that s used n the regresson analyss below. It ncludes observatons. The average number of lnked employees per establshment s 86.2, whch s close to the average number of employees n these establshments accordng to the Busness Regster (83.6 employees, measured n full-tme equvalents). In other words, our regressons are based on 1.4 mllon (16089*86.2) ndvdual observatons that we lnked to the establshments of our basc sample. Because we nvestgate three perods, our sample covers annually about ndvduals employed n the non-farm busness sector. Ths s more than one thrd of the total employment n ths sector. The average nomnal productvty growth rate n the 3-year perods s 12.2%. Accordng to the Busness Regster the average wage growth rate s 11.5%,whch s reasonably close to the average growth of monthly earnngs of the lnked employees (10.6%), obtaned from the Employment Statstcs. The average level of monthly earnngs s 2099 and the average level of sales per employee s The average hrng rate s 37.7% (= the sum of the hrng rates of the three worker age groups) and the average separaton rate 33.6 % (= the sum of the separaton rates of the three worker age groups). Young workers account for 12.0% of the ncumbents (.e. those who have been employed n the same establshment now and three years earler). However, ther share s 44.3% of all newly hred workers and 26.4% of all leavng workers.

15 13 B. An econometrc nvestgaton We have estmated equaton (7) wth our sample of establshments. Because we study the factors of productvty growth, unobservable establshment-specfc effects on productvty levels are dfferenced out and there s no need to nclude fxed establshment effects n the estmaton model. As dstnct from a standard dfference form estmaton, we are, however, able to dstngush the two sdes of the employment change of an establshment,.e. the hrng and separaton sdes, that may be sometmes asymmetrc. The productvty growth and wage growth models are estmated wth seemngly unrelated regresson (SURE). Both models have a common set of regressors and hence the SURE estmates of the parameters are equal to the OLS estmates. The advantage of usng SURE n ths case s that we are able to test for dfferences n the productvty and wage gaps. The estmatons have been done by usng labor weghts (average of employment n years t-3 and t). Table A.2 n the Appendx shows the regresson results of sx dfferent models (Model (1) Model (6)). In addton to varables for the entres and exts of workers by worker type, and the labor shares of stayng workers by type, all the models have a wde varety of controls. They nclude the log of average labor productvty (sales per person) and the log of the average wage level n year t-4. Further, we have ncluded dummes for establshment sze (4 groups), regonal dummes (20 regons), and dummes for 2-dgt ndustres (47 ndustres) that are nteracted wth perod dummes (3 perods). The latter allow us to use undeflated sales n the productvty measure, snce the nteracton varables can be regarded as ndustry-specfc prce ndces. Besdes these, Model (6) ncludes also share varables for entres, exts and stayers by three educaton groups. Before gong to the results of our man nterest, the productvty and wage gaps between dfferent groups of workers, t s nterestng to take a look at some estmates gven n Table A.2

16 14 n the Appendx. As expected, we fnd that the ntal levels of productvty and wage (measured n year t-4) have a negatve relatonshp wth subsequent productvty and wage growth, respectvely. Somewhat more nterestngly, we fnd evdence that a hgh ntal wage level has a postve mpact on subsequent productvty growth, especally among expandng establshments (Model (3)) and n the servce sector (Model (5)). One nterpretaton of these fndngs s that a hgher wage level generates some addtonal pressure for greater productvty growth. Qute analogously, we fnd that a hgh ntal productvty level boosts wage growth. Hgh productvty yelds profts that may partly be used to reward workers through hgher wage growth. The man results of ths paper are reported n Table 1. It shows the estmates of the productvty gaps and wage gaps calculated usng equatons (9) and (10). In addton, the table ncludes productvty-wage gaps, whch are the dfferences of productvty and wage gaps. All gaps, ther standard errors and sgnfcance levels are calculated by applyng the delta method. [ISERT TABLE 1 ABOUT HERE] These results ndcate that t s more proftable for employers to hre young workers than older workers. When all establshments are ncluded n the estmatons (Model (1)) the productvty-wage gap between the young and prme-aged (between the young and old workers) workers n the hrng sde s 22.2% (28.0%). Ths productvty-wage gap between the young and prme-aged workers s for the man part derved from the wage gap, whch ndcates that the young workers obtan 13.3% lower wages. On the other hand, the productvty-wage gap between the young and old workers t s manly due to the 28.8% productvty gap n favor of the young. The results also show that t s more proftable to lay off older workers than young workers, snce the productvty-wage gap of the young vs. prme-aged s 29.2% and that of the young vs. old s 33.1% on the separaton sde. In both of these cases the productvty-wage gap s

17 15 due to the productvty gap. In other words, separated young workers are more productve than separated prme-aged workers or old workers. It s worth notng that the productvty gaps between young and prme-aged workers are more favorable to young workers on the separaton sde than on the hrng sde. So, the separatng young workers are relatvely more productve compared to the separatng prme-aged workers, than the newly hred young workers are compared to the newly hred prme-aged workers. An explanaton for ths s that young workers learn new productve sklls as soon as they have become employed, often for the frst tme. Qute consstently wth ths, Ilmakunnas, Malranta and Vanomäk (2004) fnd evdence that at the begnnng the senorty-productvty profle s upward-slopng but reaches a peak after a few years of senorty. It should be noted that snce we use 3-year ntervals, an average newly hred worker has worked for one and half years at the same establshment. The results ndcate that employers have opportuntes for mprovng proftablty by ther personnel polces. Varous nsttutonal factors, however, lmt the possbltes of makng proftable adjustments on the separaton sde, but less so on the hrng sde. The best opportuntes for makng use of productvty-wage gaps should be avalable when markets are growng and there s more hrng of new workers. To examne ths, we estmated the models separately for growng (.e. employment has ncreased at least 10% durng the perod) and declnng (.e. employment has declned at least 10%) establshments. We fnd that the productvty-wage gaps are partcularly favorable to young workers, when the analyss s lmted only to the growng establshments (Model (3) n Table1). Among the declnng establshments the potental gans seem to be smaller, as expected. The prme-aged workers, nstead, appear to have a hgh productvty level and a favorable productvty-wage gap n the latter crcumstances.

18 16 All n all, the productvty gap estmates ndcate qute consstently that young workers are more productve than older workers. One explanaton mght be the superor educatonal level of the young that has ncreased rapdly n Fnland over tme. We have tred to take ths nto account n Model (6) by ncludng also the hrng and separaton rates classfed by three educaton groups (prmary, secondary and tertary level educaton). The productvty gaps and productvty-wage gaps are qute smlar to those calculated from Model (1). The results of Model (6) ndcate that the productvty gap between the newly hred hghly educated workers and the newly hred low educated workers (prmary educaton only) s economcally large and statstcally sgnfcant (see Table A.2 n Appendx). In addton, the wage gaps appear to have about the equal wdth. As for the separaton sde, the coeffcent estmates for the productvty and wage effects are qute mprecse, whch prevents us from makng any defntve conclusons. We have made varous robustness checks that we do not report but comment here brefly. Frstly, the estmatons have been done separately for the three perods. These results do not provde us any evdence of noteworthy changes n the gaps over tme. Secondly, Model (1) has been estmated wth unweghted regresson. On the separaton sde, the productvty gap between the young and prme-aged workers s 15.0% (standard error s 5.2%) and the between young and old workers 20.5% (standard error s 5.0%), but these gaps are nsgnfcant on the hrng sde 10. The smaller coeffcents are lkely due to attenuaton bas especally among smaller establshments that have a larger mpact on the results n the unweghted regresson than n the employment weghted regresson. Thrdly, we have examned the robustness of our results to the way outlers are handled. Bollnger and handra (2005) provde qute a crtcal analyss of the standard methods of removng outlers and show that the results may be qute senstve to the method. Although we

19 17 beleve that our cleansng procedure s qute sutable for the current purpose, t s stll comfortng to fnd that the conclusons are the same also when those 210 outlers that were dentfed wth Had s method are ncluded. For example, accordng to Model (1) the productvty-wage gap between the young and old workers s now 30.0% (standard error s 8.0%) on the hrng sde and 27.9% (standard error s 6.5%) on the separaton sde. Above we have reported results related to the two components of equaton (6), that s, the hrng and separaton components. ext we brefly consder the thrd component, whch s the productvty growth of contnung workers. Wth the help of ths component we can study dfferences n productvty and wage growth rates between dfferent worker groups. These results are reported n Table 2. It can be hypotheszed that young workers have greater productvty growth rates than older workers due to more rewardng learnng-by-dong n the begnnng of the career. Although the results from the analyss of enterng workers supported ths argument, our emprcal evdence on the contnung workers s, however, not supportve. On the other hand, we mght expect that those young workers who stay are those who enjoy hgher wage growth. Ths would be consstent wth Lazear s (1981) theory of deferred payments, for nstance. Young workers are underpad and therefore employers offer them large pay rses n order to reduce ther costly quts. Some support for such conjectures are found n the results of Table 2, especally for the expandng establshments. Fnally, the growth effects of educaton shown n the Appendx ndcate that a large proporton of hghly educated workers ncreases productvty growth of the establshment. One nterpretaton of ths s that the hghly educated workers generate or mplement new nnovatons, whch s reflected n the productvty growth rate of the establshment (see e.g. Benhabb & Spegel, 1994). [ISERT TABLE 2 ABOUT HERE]

20 18 IV. oncludng remarks We have demonstrated that productvty-enhancng ntra-establshment labor restructurng can be studed by a method that s qute smlar to the one that has been used ncreasngly n nvestgatng ntra-ndustry restructurng,.e., by means of a productvty decomposton formula. The man dfference s that at the mcro-mcro level the productvty decomposton method cannot be used for accountng but must be used as an estmaton model. Further, comprehensve longtudnal lnked employer-employee data are requred for the mcromcro level analyss. Fnally, the propertes of the productvty decomposton method deserve close attenton. Ths s because an approprate comparson group (or counterfactual) needs to be specfed before an economc nterpretaton of the results of the decomposton exercse can be done approprately. Our emprcal nvestgaton wth Fnnsh data demonstrates the merts of the use of good qualty mcro data and an approprate productvty decomposton method. For example, our results for the age effects help us to understand why employers mght be unwllng to hre old workers, and try to persuade them to early retrement. Economcally and statstcally sgnfcant productvty-wage gaps between age groups cast also doubt on the relablty of the standard growth accountng computatons that are bult on the assumpton that wage gaps reflect gaps n margnal productvtes. Our results suggest that growth accountng projectons mght exhbt excessvely favorable prospects regardng economc growth n the future. In addton to that we found young workers to have hgh productvty, we also found them to have hgh hrng and separaton rates,.e. they are hghly moble. A less moble workforce would be a bad news for the new nnovatve companes that wll fnd t ncreasngly dffcult to hre new workers and grow. Thus, a less moble workforce poses an

21 19 mmnent threat to mcro-level dynamsm that arguably s a key factor of sustaned economc growth. All thngs consdered, an agng workforce may be an mpedment for both productvtyenhancng ntra-ndustry restructurng and productvty growth of the establshments n the future. Ths paper also provdes a promsng approach to analyze the dynamcs of the productvty effects of educaton. A puzzlng fndng of earler emprcal analyses has been that wthn establshment varaton n the data (.e. the use of fxed effects models or dfferenced specfcatons) seems to ndcate an nsgnfcant or even negatve relatonshp between the educaton of the personnel and establshment productvty (e.g. Haltwanger et al., 1999; Ilmakunnas & Malranta, 2005a). It may be hypotheszed that ths follows from delayed postve effects of hgh educaton. Hgh sklls may not be partcularly valuable n current producton but are needed n nnovatons or to mplement new technologes that make an establshment s less educated workers more productve n the future (see Greenwood & Jovanovc, 2001). As a consequence, the separatons of hghly educated workers may have an mmedate productvty effect whch s nsgnfcant or even negatve. Ths s an ssue that we wll pursue n future work.

22 20 References Abowd, J. M. and Kramarz, F. (1999). The Analyss of Labor Markets Usng Matched Employer-Employee Data. In O. Ashenfelter & D. ard (Eds.), Handbook of Labor Economcs. Volume 3b (pp ). Amsterdam; ew York and Oxford: Elsever Scence, orth-holland. Baly, M.., Bartelsman, E. J. and Haltwanger, J. (2001). Labor Productvty: Structural hange and yclcal Dynamcs. Revew of Economcs and Statstcs, 83(3), Baly, M.., Hulten,. and ampbell, D. (1992). Productvty Dynamcs n Manufacturng Plants. Brookngs Papers on Economc Actvty, Mcroeconomcs 1992, Bartelsman, E. J. and Doms, M. (2000). Understandng Productvty: Lessons from Longtudnal Mcrodata. Journal of Economc Lterature, 38(3), Becker, G. S. (1962). Investment n Human aptal: A Theoretcal Analyss. Journal of Poltcal Economy, 70(5, Part 2: Investment n Human Bengs), Benhabb, J. and Spegel, M. M. (1994). The Role of Human aptal n Economc Development - Evdence from Aggregate ross-ountry Data. Journal of Monetary Economcs, 34, Bernard, A. B. and Jones,. I. (1996). Productvty across Industres and ountres: Tme Seres Theory and Evdence. Revew of Economcs & Statstcs, 78(1), Bollnger,. R. and handra, A. (2005). Iatrogenc Specfcaton Error: A autonary Tale of leanng Data. Journal of Labor Economcs, 23(2), Dewert, W. E. and Fox, K. A. (2005). On Measurng the ontrbuton of Enterng and Extng Frms to Aggregate Productvty Growth. Department of Economcs, Unversty of Brtsh olumba, Dscusson Paper o Vancouver. Foster, L., Haltwanger, J. and Krzan,. J. (2001). Aggregate Productvty Growth: Lessons from Mcroeconomc Evdence. In. R. Hulten, E. R. Dean & M. J. Harper (Eds.), ew Developments n Productvty Analyss (pp ). hcago and London: Unversty of hcago Press. Greenwood, J. and Jovanovc, B. (2001). Accountng for Growth. In. R. Hulten, E. R. Dean & M. J. Harper (Eds.), ew Developments n Productvty Analyss (pp ). hcago: Unversty of hcago Press. Grossman, G. M. and Helpman, E. (1991). Innovaton and Growth n the Global Economy. ambrdge, MA and London: MIT Press. Had, A. S. (1992). Identfyng Multple Outlers n Multvarate Data. Journal of the Royal Statstcal Socety, Seres (B), 54, (1994). A Modfcaton of a Method for the Detecton of Outlers n Multvarate Samples. ournal of the Royal Statstcal Socety, Seres (B), 56, Haltwanger, J.., Lane, J. I. and Spletzer, J. R. (1999). Productvty Dfferences across Employers: The Roles of Employer Sze, Age, and Human aptal. Amercan Economc Revew, 89(2), Hamermesh, D. S. (2000). The raft of Labormetrcs. Industral and Labor Relatons Revew, 53(3), Helpman, E., Meltz, M. J. and Yeaple, S. R. (2004). Export Versus FDI wth Heterogeneous Frms. Amercan Economc Revew, 94(1), Hopenhayn, H. A. (1992). Entry, Ext, and Frm Dynamcs n Long Run Equlbrum. Econometrca, 60(5),

23 21 Ilmakunnas, P. and Malranta, M. (2005a). Technology, Labour haracterstcs and Wage- Productvty Gaps. Oxford Bulletn of Economcs and Statstcs, 67(5), (2005b). Worker Inflow, Outflow, and hurnng. Appled Economcs, 37, Ilmakunnas, P., Malranta, M. and Vanomäk, J. (2004). The Roles of Employer and Employee haracterstcs for Plant Productvty. Journal of Productvty Analyss, 21, Klette, T. J. and Kortum, S. (2004). Innovatng Frms and Aggregate Innovaton. Journal of Poltcal Economy, 112(5), Krueger, A. B. and Lndahl, M. (2001). Educaton for Growth: Why and for Whom? Journal of Economc Lterature, 39(4), Laukkanen, T. (2004). Admnstratve Data n Busness Regster, Semnar on the use of admnstratve sources. Pars, 4-6 October 2004 (avalable at Lazear, E. P. (1981). Agency, Earnngs Profles, Productvty, and Hours Restrctons. Amercan Economc Revew, 71(4), Malranta, M. (1997). Plant-Level Explanatons for the atch-up Process n Fnnsh Manufacturng: A Decomposton of Aggregate Labour Productvty Growth. In S. Laaksonen (Ed.), The Evoluton of Frms and Industres. Internatonal Perspectves (pp ). Helsnk: Statstcs Fnland.. (2003). Mcro Level Dynamcs of Productvty Growth. An Emprcal Analyss of the Great Leap n Fnnsh Manufacturng Productvty n : The Research Insttute of the Fnnsh Economy (Etla), A 38 Seres (avalable at Meltz, M. J. (2003). The Impact of Trade on Intra-Industry Reallocatons and Aggregate Industry Productvty. Econometrca, 71(6), Vanomäk, J. (1999). Technology and Skll Upgradng: Results from Lnked Worker-Plant Data for Fnnsh Manufacturng. In J. Haltwanger, J. Lane, J. R. Spletzer, J. J. M. Theuwes & K. R. Troske (Eds.), The reaton and Analyss of Employer-Employee Matched Data (pp ). Amsterdam; ew York and Oxford: Elsever Scence, orth-holland.

24 22 Appendces Table A.1. Descrptve statstcs of the sample Varable Mean p5 p50 p95 Employees (ES) Employees (BR) Prod. growth rate Wage growth rate (ES) Wage growth rate (BR) Sales per person ( ) Average monthly earnngs ( ) Entry shares Young Prme-aged Old Ext shares Young Prme-aged Old Dstrbuton of contnuers Young Prme-aged Old Entry shares Prmary educaton Secondary educaton Tertary educaton Ext shares Prmary educaton Secondary educaton Tertary educaton Dstrbuton of contnuers Prmary educaton Secondary educaton Tertary educaton ote: The data consst of three 3-year perods and nclude non-farm busness sector establshment employng at least 20 persons (average of the ntal and end year). ES refers to Employment Statstcs and BR to Busness Regster.

25 23 Table A.2. Seemngly unrelated regressons for productvty and wage growth log of productvty level log of wage level Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) prod. growth wage growth prod. growth wage growth prod. growth wage growth prod. growth wage growth prod. growth wage growth prod. growth wage growth *** 0.010*** *** 0.016*** *** 0.009*** *** 0.010*** *** 0.008*** *** 0.009*** (0.004) (0.002) (0.009) (0.003) (0.006) (0.003) (0.006) (0.002) (0.005) (0.002) (0.004) (0.002) 0.081*** *** *** 0.100*** *** *** 0.089*** *** 0.048*** *** (0.017) (0.006) (0.039) (0.013) (0.022) (0.009) (0.028) (0.010) (0.020) (0.008) (0.018) (0.007) n t-4 Entry of young *** ** *** 0.213*** *** *** *** 0.169** 0.159*** (0.039) (0.014) (0.106) (0.036) (0.059) (0.023) (0.062) (0.022) (0.050) (0.019) (0.084) (0.031) Entry of prme *** 0.372*** 0.111*** *** ** 0.081*** *** (0.046) (0.017) (0.118) (0.040) (0.064) (0.025) (0.074) (0.026) (0.057) (0.022) (0.078) (0.029) Entry of old *** *** *** *** *** *** *** *** (0.055) (0.020) (0.141) (0.048) (0.073) (0.029) (0.085) (0.031) (0.071) (0.028) (0.093) (0.034) Ext of young * *** (0.053) (0.020) (0.127) (0.043) (0.075) (0.030) (0.108) (0.039) (0.060) (0.023) (0.667) (0.246) Ext of prme 0.227*** *** *** ** 0.209*** (0.043) (0.016) (0.094) (0.032) (0.066) (0.026) (0.071) (0.025) (0.053) (0.020) (0.665) (0.245) Ext of old 0.282*** *** 0.058** 0.149* *** *** (0.042) (0.016) (0.083) (0.029) (0.077) (0.031) (0.062) (0.022) (0.057) (0.022) (0.669) (0.246) Share of young ** (0.040) (0.015) (0.093) (0.032) (0.051) (0.020) (0.065) (0.023) (0.048) (0.019) (0.041) (0.015) Share of prme * ** 0.027* ** (0.027) (0.010) (0.062) (0.021) (0.036) (0.014) (0.040) (0.015) (0.035) (0.014) (0.028) (0.010) Share of old ref. ref. ref. ref. ref. ref. ref. ref. ref. ref. ref. ref. Entry of prmary educated Entry of second. Educated Entry of tert. educated Ext of prmary educated Ext of second. educated Ext of tert. educated Share of prmary educated Share of second *** *** (0.089) (0.033) *** (0.083) (0.031) (0.000) (0.000) (0.668) (0.246) (0.666) (0.245) (0.671) (0.247) ** *** (0.042) (0.015) * *** (0.041) (0.015) educated Sh. of tert. edu. ref. ref. Observatons R-squared orrelaton Standard errors n parentheses Sgnfcance level: * 10%; ** 5%; *** 1% ote: orrelaton refers to the correlaton of the resduals of the productvty and wage equatons. Accordng to Breusch-Pagan test we can reject the hypothess that ths correlaton s zero. Other varables nclude ndustry dummes (47 ndustres) that are nteracted wth perod dummes (3 perods), dummes for regons (20 regons) and dummes for sze groups (4 groups). The models are estmated by weghtng by establshment employment (average of the ntal and end year).

26 24 Table 1 Productvty, wage and productvty-wage gaps between age groups, n %-unts Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) All Declnng Expandng Manufacturng Servces All (wth educaton) Prod. Entry Ext Entry Ext Entry Ext Entry Ext Entry Ext Entry Ext Young vs *** * ** 31.5 *** 34.6 *** 56.7 *** * 13.5 ** 22.9 prme (6.7) (6.9) (13.3) (14.5) (9.0) (9.8) (11.0) (16.0) (8.8) (7.1) (6.1) (15.5) Prme vs 20.0 * ** 26.4 ** *** old (10.3) (5.7) (19.8) (11.1) (14.6) (9.1) (15.3) (9.1) (15.1) (6.9) (9.2) (6.2) Young vs 28.8 *** 33.5 *** ** 37.2 *** 28.6 *** 23.2 ** 54.6 *** 38.8 *** 22.1 *** 18.3 *** 25.6 old (7.7) (6.5) (18.2) (12.5) (10.6) (10.1) (9.8) (14.7) (13.0) (6.9) (6.6) (16.2) Wage Entry Ext Entry Ext Entry Ext Entry Ext Entry Ext Entry Ext Young vs *** *** *** *** -9.3 * -8.4 *** ** -8.6 ** prme (2.4) (2.7) (5.4) (5.1) (3.3) (3.8) (4.0) (5.1) (3.1) (3.1) (2.1) (3.8) Prme vs 14.1 *** *** ** ** 9.2 ** 15.5 *** -6.3 * old (3.3) (2.7) (7.6) (4.5) (4.4) (4.1) (4.9) (4.2) (4.5) (3.5) (2.8) (3.4) Young vs * * old (2.6) (2.5) (6.6) (4.6) (3.5) (4.0) (3.9) (4.3) (3.7) (3.3) (2.1) (2.9) Prod-wage Entry Ext Entry Ext Entry Ext Entry Ext Entry Ext Entry Ext Young vs 22.2 *** 29.2 *** *** 27.5 *** 52.4 *** 66.0 *** *** 31.5 * prme (6.3) (6.6) (12.6) (13.7) (8.6) (9.3) (10.4) (15.1) (8.4) (6.8) (5.8) (16.2) Prme vs ** ** 16.1 ** old (9.7) (5.4) (18.7) (10.5) (13.9) (8.7) (14.4) (8.7) (14.3) (6.7) (8.7) (6.1) Young vs 28.0 *** 33.1 *** ** 42.6 *** 27.7 *** 30.3 *** 54.8 *** 31.7 ** 23.4 *** 20.3 *** 29.0 * old (7.3) (6.1) (17.2) (11.8) (10.1) (9.7) (9.2) (13.9) (12.4) (6.7) (6.3) (16.4) Standard errors n parentheses Sgnfcance level: * 10%; ** 5%; *** 1% ote: The gap (n productvty level or n wage level) between the young and the prme-aged plus the gap between the prme-aged and the old s equal to the gap between the young and the old. The productvty-wage gap s the productvty level gap mnus the wage level gap. The standard errors have been calculated by the delta method. All numbers may not, however, add up n all cases due to roundng.

27 25 Table 2 Productvty, wage and productvty-wage growth dfferences of contnung workers by age groups, n %-unts Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Prod. All Declnng Expandng Manufacturng Servces All (wth educaton) Young vs prme (4.3) (10.1) (5.5) (7.2) (5.1) (4.3) Prme vs ** old (2.7) (6.2) (3.6) (4.0) (3.5) (2.8) Young vs old (4.0) (9.3) (5.1) (6.5) (4.8) (4.1) Wage Young vs prme (1.6) (3.5) (2.2) (2.6) (2.0) (1.6) Prme vs 1.7 * * ** -0.3 old (1.0) (2.1) (1.4) (1.5) (1.4) (1.0) Young vs ** old (1.5) (3.2) (2.0) (2.3) (1.9) (1.5) Prod-wage Young vs prme (4.1) (9.5) (5.3) (6.8) (4.9) (4.1) Prme vs * *** -6.3 * old (2.5) (5.8) (3.5) (3.8) (3.4) (2.7) Young vs old (3.7) (8.7) (4.9) (6.2) (4.6) Standard errors n parentheses (3.8) Sgnfcance level: * 10%; ** 5%; *** 1% ote: The dfference n growth rate between the young and the prme-aged plus the dfference between the prme-aged and the old s equal to the dfference between the young and the old. The productvty-wage growth dfference s the productvty growth dfference mnus the wage growth dfference. The standard errors have been calculated by the delta-method. All numbers may not, however, add up n all cases due to roundng.

28 26 EDOTES 1 For a revew of such studes, see e.g. Bartelsman and Doms (2000). 2 The only dfference s that Malranta (2003) measured entry and ext effects n log terms. 3 An addtonal advantage of (5) s that prce ndces are not needed to compute the entry or ext effects, because cross-sectonal comparsons between the entrants and the ncumbents and between the extors and the ncumbents can be made here usng the current prces. 4 The fact that the productvty of ndvduals cannot be measured s the background also n recent research where the productvty effects of age, educaton, and experence are nvestgated by estmatng plant-level producton functons wth average worker characterstcs or shares of workers n varous age etc. groups ncluded (see e.g. Hellersten, eumark, and Troske, 1999). 5 For example, when there are three groups (g) of workers, nne parameters need to be estmated. 6 The proft margn over worker costs of type g s (S-w g L g )/w g L g = (S/L)/w g 1 = g /w g 1, where S s sales, productvty s measured by S/L, and w g s wage. The relatve gap n the term g /w g between groups 1 and 2 s ln( 1 / 2 ) ln(w 1 /w 2 ), whch can be approxmated by the dfference of a productvty gap ((9) for enterng or (10) for extng workers) and a wage gap calculated n an analogous way. 7 In the Fnnsh Busness Regster the delneaton of establshment s made n conformty wth the local knd-ofactvty unt concept (see Laukkanen, 2004). 8 A few recent exceptons notwthstandng (see e.g. Ilmakunnas & Malranta, 2005b, 2005a), 9 For example, our data set ncludes only those establshments that appear n the data n the years t-4 (the ntal productvty level s measured at ths pont), t-3 (the ntal year of productvty growth) and t (the end year of productvty growth). 10 In unweghted regresson, R-squared of Model (1) for productvty growth and wage growth s and , respectvely.

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