INTER-INDUSTRIAL VALUE MIGRATION

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1 QUANTITATIVE METHODS IN ECONOMICS Vol. XV, No. 2, 2014, pp INTER-INDUSTRIAL VALUE MIGRATION Darusz Sudak Insttute of Socal Scences and Management of Technologes Lodz Unversty of Technology e-mal: Abstract: In ths paper there s dscussed value mgraton from the perspectve of all economc sectors. It was ntroduced the method for measurng the sectoral value mgraton and the algorthm for classfcaton wth respect to three stages value mgraton model. The value mgraton measurement was conducted employng multvarate comparatve analyses and n partcular lnear orderng to construct a synthetc varable of development. On the bass of the proposed measure, the rankng of value mgraton development and classfcaton of sectors to the partcular phases of value mgraton processes were delvered. Keywords: value mgraton, synthetc varable, ndustry INTRODUCTION Value mgraton s defned as the shft n value-creatng forces [Phllps 2012, p.36]. The degree of realzaton of the companes goals amed at value creaton for the shareholders causes ts mgraton between ndvdual companes and ndustres [Szczepankowsk 2007, p. 36]. Hence value mgraton analyss can be carred out n an aggregate way at the level of ndvdual ndustres. The analyss of the value mgraton process can be performed usng the three stages of value mgraton model, proposed by A. Slywotzky n hs theoretcal framework [Slywotzky 1996, p ]. The essence of the model s the assumpton that every company can be n one of the three stages of value mgraton [Sudak 2001, p. 195], whose short descrpton s provded n table 1.

2 Inter-ndustral value mgraton 233 Table 1. Descrpton of the ndvdual stages of value mgraton Phases of value mgraton Inflow stage Stablty stage Outflow stage Source: own based on [Slywotzky 1996, s. 50] Descrpton Lmted competton, hgh ncrease n market share, hgh proftablty. Compettve stablty, stable market share, stable margns. Compettve ntensty, declnng sales, low profts, competences, resources, talent, and customers leave at an acceleratng rate. The purpose of the artcle s a classfcaton of the ndustres based on the presented three stages of value mgraton model and the value mgraton analyss n the relaton company-ndustry. The study ncludes all companes quoted on the Warsaw Stock Exchange n 2007, 270 companes n total. A dvson nto separate ndustres s based on the ndustry classfcaton proposed by the Warsaw Stock Exchange and documented n the offcal bulletn The Man Lst of the Warsaw Stock Exchange [2007]. The number of companes assgned to the ndvdual ndustres s provded n table 2. Table 2. The number of companes assgned to the ndvdual ndustres Industry Number of companes 1 Buldng ndustry 22 2 Developers 9 3 Power ndustry 5 4 Fnance-other 19 5 Fnancal ndustry 16 6 Retal 17 7 Wholesale 21 8 Hotels and restaurants 5 9 Computer scence Constructon materals Meda Chemcal ndustry Wood and paper ndustry 7 14 Electromechancal ndustry Lght ndustry Metal ndustry Food ndustry Telecommuncatons 7 19 Servces 14 Total: 270 Source: own work based on The Man Lst of the Warsaw Stock Exchange [2007]

3 234 Darusz Sudak METHOD OF THE INDUSTRY VALUE MIGRATION ANALYSIS The measurement of the value mgraton can be performed by adoptng the lnear orderng method, constructng an approprate synthetc varable based on three ndependent varables actng as stmulant [Sudak 2013b]: 1. Share n the economy mgraton balance n MVA SHARE IN THE MIGRATION BALANCE = MVA 0 (1) n = 1 MVA where: MVA market value added of company (=1,, n). 2. Share n the ndustry mgraton balance MVA SHARE IN THE INDUSTRY MIGRATION BALANCE = MVA 0 (2) I s MVA where: MVA market value added of company ncluded n s ndustry, ( I s, =1,, s). 3. Change MVA/K ( MVA/ K ) MVA = K where: K book value of nvested captal. = 1 I s MVA K (K 0) (3) Market value added (MVA) s expressed wth the followng formula [Steward, 1991] MVA = V K (4) where: V gross market value. Both categores market value added and nvested captal on whch ndependent varables are based, are addtve. Hence the measurement of the value mgraton can be carred out among companes as well as n an aggregate way at the level of ndvdual ndustres. To measure value mgraton process at the ndustry level, market value added and nvested captal were aggregated separately for each ndustry. The constructon of the synthetc varable requres that the followng parameters are determned: (1) a system for weghtng varables, (2) a varable normalzaton method, and (3) an aggregaton functon. The nfluence of the ndvdual varables on the nvestgated process was expressed wth dfferentated weghts, whose values were as follows: share n the economy mgraton balance 25%, share n the ndustry mgraton balance 25%, change MVA/K 50%. T T 1

4 Inter-ndustral value mgraton 235 The varable normalzaton was carred out wth the followng equaton [Sudak 2013b]: xj zj = ( max { x j } mn { x } 0) (5) j max { x } mn { x } j where: z j normalzed value of j varable for company, x j value of j varable for company. The aggregaton was carred out employng the pattern method whch used weghted coeffcents and was based on Eucld s dstance m j= 1 j ( ) 2 z z d = w (6) where: d value of the synthetc varable n company, w j weghted coeffcent of j varable (j=1, 2,, m), z j normalzed value of j ndependent varable n company (j=1, 2,, m; =1, 2,, n), between the analysed objects and an element whch s an ant-pattern (lower development pole for the parameters above workng as a stmulant) determned by the relaton z = mn{ z } (7) j j 0 j 0 j j The constructed synthetc varable was named the synthetc ndex of value mgraton (SIOVM). Its values fall wthn the range 0 1. The constructon s based on the concept of the taxonomc measure of development ntroduced for the frst tme by [Hellwg 1968]. Lnear orderng of ndustres n relaton to the synthetc varable s non-growng. Lower values of SIOVM correspond to a lower level of value mgraton. Remarks on the ways of creatng synthetc varable can be found n the followng studes: Hellwg [1968], Gatnar, Walesak [2004], Grabnsk, Wydmus, Zelaś [1989], Wtkowska [2010], Jaworska, Kożuch [2012], Łunewska, Tarczyńsk [2006], Łunewska [2008], Malna [2004], Młodak [2006], Nowak [1990], Ostrowska [2007], Panek [2009], Pocecha, Podolec, Sokołowsk, Zając [1988], Walesak [1996], [2006], and Zelaś [2000]. The problem of the normalzaton of ndependent varables s addressed n the works of: Kukuła [2000], [2012], and Pawełek [2008]. The grounds for synthetc varables (abbrevaton SIOVM) wth regard to the ratonale for choosng: dagnostc varables, appled system of weghts, methods of normalzaton and aggregaton can be found n Sudak s work (2013a).Hgh estmates of the dscrmnatory property of the synthetc ndex of value mgraton (SIOVM) usng the measure analyss (G) were provded n [Sudak, 2013a, p ]. The descrpton of the measure (G) can be

5 236 Darusz Sudak found n the followng studes [Pocecha Podolec, Sokołowsk, Zając 1988], [Nowak 1990]. Fgure 1. Algorthm of the classfcaton of the analyzed objects n relaton to the three stages of value mgraton Decreasng lnear orderng of ndustres based on synthetc ndex of value mgraton (SIOVM =d ) (=1,2,,n) (=1,2,,n) d < u No Yes Stablty stage Yes No Inflow stage Outflow stage Me - medan Source: based on D. Sudak [2013a, s. 162] As dagnostc varables contan outlers, there cannot be appled standard procedures for analyss of the consdered set usng avalable methods of cluster analyss. The applcaton of medan approach n the presented algorthm of classfcaton makes the classfcaton robust.

6 Inter-ndustral value mgraton 237 RANKING AND CLASSIFICATION OF INDUSTRIES IN TERMS OF THE DEVELOPMENT OF VALUE MIGRATION Table 3 presents the rankng and dvson of the analyzed ndustres n terms of the development of value mgraton. The number of ndustres belongng to the stablty stage s 9. In 7 ndustres value mgrated to 3 other ndustres respectvely n non-growng order of SIOVM DEVELOPERS; POWER INDUSTRY; MEDIA. Table 3. Rankng and dvson of ndustres nto three stages of value mgraton Threshold value (u) Medan d Industry SIOVM =d d d u Mgraton stage 1 Developers 0,9434 0,3775 Larger 2 Power ndustry 0,9241 0,3583 Larger Inflow stage 3 Meda 0,8326 0,2668 Larger 4 Fnancal ndustry 0,6988 0,1330 Smaller 5 Retal 0,6268 0,0610 Smaller 6 Chemcal ndustry 0,6268 0,0609 Smaller 7 Metal ndustry 0,6062 0,0404 Smaller 8 Wholesale 0,6040 0,0382 Smaller Stablty stage 9 Food ndustry 0,5665 0,0007 Smaller 10 Hotels and restaurants 0,5658 0,0000 Smaller 11 Electromechancal ndustry 0,5377 0,0281 Smaller 12 Buldng ndustry 0,5145 0,0513 Smaller 13 Telecommuncatons 0,3263 0,2395 Equal 14 Servces 0,1892 0,3767 Larger 15 Constructon materals 0,1772 0,3886 Larger 16 Lght ndustry 0,1626 0,4032 Larger Outflow stage 17 Fnance-other 0,0727 0,4931 Larger 18 Wood and paper ndustry 0,0693 0,4966 Larger 19 Computer scence 0,0195 0,5463 Larger source: own calculatons What follows s a verfcaton of the hypothess for the equalty of means of the synthetc varable ( SIOVM ) among the three classes of ndustral value mgraton usng one-way analyss of varance (one-way ANOVA). Before performng the analyss of varance, ths method s assumpton of equalty of varances n groups s tested. Lavene s test for equalty of varances provdes the followng result: F(2, 16)=1,516; p=0.249, whch mples that the varances n the ndvdual groups are equal at the level of sgnfcance α=0.05. Ths concluson s confrmed by the test statstcs: (1) Hartley s F-max=3.435, (2) Cochran s C=0.615 and (3) Bartlett s

7 238 Darusz Sudak Ch-square=2.546; p=0.280 (the varances n the three sets are equal at the level of sgnfcance α=0.05). The formal representaton of the hypotheses for the equalty and nequalty of the values of the means for the synthetc varable s as follows: H 0: µ 1=µ 2=µ 3 Η 1: j j : µ µ 1 2 j j 1 2 Table 4 presents the statstcs of the F-test. Table 4. Statstcs of the F-test Specfcaton Sum square (SS) df Mean square (MS) F p-value Between groups 1, ,715 Wthn groups 0, , ,38 0,000 Total 1, Source: own calculatons The F-test statstcs s F(2; 16)= (a value whch s much hgher than one) and s statstcally sgnfcant at the level of sgnfcance α=0.01. As a result we the hypothess H 1 s supported, whch unambguously ponts at a statstcally sgnfcant dfference n the values of the means of the synthetc varable ( SIOVM ) between at least two groups. Wth multple comparsons usng post-hoc HSD tests proposed by Turkey (for dfferent N n groups) and Scheffe, we determne between whch classes there are statstcally sgnfcant dfferences n the values of the synthetc varable whch cause the support of the hypothess H 1. Table 5 presents approxmate p-levels for Turkey s and Scheffe s HSD tests. Table 5. Approxmate p-levels for post-hoc tests Test Phases of value mgraton Inflow stage Stablty stage Outflow stage Inflow stage 0,0006 0,0002 HSD Stablty stage 0,0006 0,0002 Turkey Outflow stage 0,0002 0,0002 Phases of value mgraton Inflow stage Stablty stage Outflow stage Inflow stage 0,0001 0,0000 Scheffe Stablty stage 0,0001 0,0000 Outflow stage 0,0000 0,0000 Source: own calculatons Both tests show statstcally sgnfcant dfferences n the values of means for all comparsons between the ndvdual groups of the ndustral value mgraton, at the level of sgnfcance α= The mean values of the synthetc varable n the dstnct stages of the ndustral value mgraton are as follows: (1) nflow stage: ; (2) stablty stage: and (3) outflow stage: Obvously the largest dfference between the mean

8 Inter-ndustral value mgraton 239 values of the synthetc varable s n two extreme classes (nflow stage-outflow stage), whch results from the non-growng lnear orderng of ndustres n relaton to SIOVM. The proper taxonomc dvson should have a hgh dversty of objects between varous groups and a low dversty wthn the ndvdual classes [D. Wtkowska, 2002, p. 90]. For the evaluaton of the results of the classfcaton we use between groups dssmlarty (hgh values denote a hgh degree of dssmlarty of objects between groups) and wthn group dssmlarty (low values denote a low degree of dssmlarty and smultaneously low dversty of objects wthn the ndvdual classes), usng respectvely [Wtkowska, 2002, p. 91; Nowak, 1990, p. 190]: 1. Average between groups dstance D 1 pq = d( O, O j N N ) (8) p q O A p O A where: D pq average between group dstance, A p concentraton of objects O (=1, 2,, Np),, A q concentraton of j objects O j (j=1, 2,, Nq),, N p number of objects n group A p, N q number of objects n group A q, d(o, O j) dstance between element of group A p and j object of group A q. 2. Average wthn group dstance D 1 pp = d( O, O j N N 1 ) (9) p ( ) O A O A q where: D pp average wthn group dstance, A p concentraton of O, O j (, j=1, 2,, Np), N p number of objects n group A p, d(o, O j) dstance between ndvdual elements of group A p. Table 6 shows measures of the evaluaton of the classfcaton based on mean between groups dstance and average wthn group dstance. Table 6. Average between groups dstance and average wthn group dstance Phases of value mgraton Inflow stage Stablty stage Outflow stage Inflow stage 0,4964 0,7677 1,4198 Stablty stage 0,7677 0,2986 1,0691 Outflow stage 1,4198 1,0691 0,1848 Source: own calculatons We observe lower values of the average wthn group dstance as compared to the values of average between groups dstance. Objects are more smlar to each other wthn the ndvdual groups (stages of value mgraton) and smultaneously more p j j p q

9 240 Darusz Sudak dversfed between the stages n queston. It proves that the dvson of the ndustres n queston nto the three stages of value mgraton s correct. It should be emphaszed that the dversty of ndustres between the extreme groups,.e. nflow and outflow of value s hgher than n the two other pars (1) nflow stage-stablty stage and (2) stablty stage-outflow stage. It proves that the dvson s vald. SUMMARY The current study has proven the valdty of the ntroduced dvson of the analysed ndustres n terms of the three stages of value mgraton usng measures to evaluate the classfcaton and the test of the dfferences n the values of the means of the synthetc varable n the ndvdual groups. Importantly, t should also be emphaszed that there are more ndustres at the outflow stage than those at the nflow stage. Three ndustres captured the value flowng out of seven others, whch ndcates a concentraton of an ndustral allocaton of captal. REFERENCES Ceduła Gełdy Warszawskej, (2007), Ofcjalny Buletyn, Nr 249/2007 (3710). Gatnar E., Walesak M. (2004), Metody statystycznej analzy welowymarowej w badanach marketngowych, Wydawnctwo Akadem Ekonomcznej m. Oskara Langego we Wrocławu, Wrocław. Grabńsk T., Wydymus S., Zelaś A. (1989), Metody taksonom numerycznej w modelowanu zjawsk społeczno-gospodarczych, PWN, Warszawa. Hellwg Z. (1968), Zastosowane metody taksonomcznej do typologcznego podzału krajów ze względu na pozom ch rozwoju oraz zasoby strukturę kwalfkowanych kadr, Przegląd Statystyczny, nr 4. Jaworska M., Kożuch A. (2012), Ocena przydatnośc wybranych metod WAP w analze samodzelnośc fnansowej gmn, Metody loścowe w badanach ekonomcznych. T. XIII/1, s , Warszawa. Kukuła K. (2000), Metoda untaryzacj zerowanej, PWN, Warszawa. Kukuła K. (2006), Propozycja budowy rankngu obektów z wykorzystanem cech loścowych jakoścowych, Metody loścowe w badanach ekonomcznych, T. XIII/1, s. 5-16, Warszawa. Łunewska M., Tarczyńsk W. (2006), Metody welowymarowej analzy porównawczej na rynku kaptałowym, PWN, Warszawa. Łunewska M. (2008), Ekonometra fnansowa, Analza rynku kaptałowego, PWN, Warszawa. Malna A. (2004), Welowymarowa analza przestrzennego zróżncowana struktury gospodark Polsk według województw, Wydawnctwo Akadem Ekonomcznej w Krakowe, Kraków. Młodak A. (1990), Analza taksonomczna w statystyce regonalnej, Dfn, Warszawa.

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