MEDDELANDEN FRÅN SVENSKA HANDELSHÖGSKOLAN SWEDISH SCHOOL OF ECONOMICS AND BUSINESS ADMINISTRATION WORKING PAPERS

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1 MEDDELANDEN FRÅN SVENSKA HANDELSHÖGSKOLAN SWEDISH SCHOOL OF ECONOMICS AND BUSINESS ADMINISTRATION WORKING PAPERS 3 Jukka Liikanen, Paul Soneman & Oo Toivanen INTERGENERATIONAL EFFECTS IN THE DIFFUSION OF NEW TECHNOLOGY: THE CASE OF MOBILE 2001

2 Swedish School of Economics and Business Adminisraion, Jukka Liikanen, Paul Soneman & Oo Toivanen Jukka Liikanen, Paul Soneman & Oo Toivanen Swedish School of Economics and Business Adminisraion P.O.Box Helsinki, Finland Disribuor: Library Swedish School of Economics and Business Adminisraion P.O.Box Helsinki Finland Phone: , Fax: hp://www.shh.fi/link/bib/publicaions.hm SHS inressebyrå IB (Oy Casa Securiy Ab), Helsingfors 2001 ISBN

3 Inergeneraional Effecs in he Diffusion of New Technology: The Case of Mobile Phones Jukka Liikanen *, Paul Soneman + and Oo Toivanen ++ Absrac The radiion in he empirical lieraure on echnology diffusion is o rea he echnology as generic. In several echnologies, clear generaional shifs can be idenified. We use daa from he mobile phone indusry o sudy he effecs of he old generaion (1G) on he speed of diffusion of he new generaion (2G), and vice versa. The generaions share he general nework effecs, bu may exhibi generaion specific nework exernaliies. We find ha 2G slows down 1G diffusion, and evidence ha 1G has a posiive effec on 2G diffusion speed. Generaion specific resuls differ significanly from hose of a generic model. * Helsinki School of Economics, and Mobirex Ld, + Warwick Business School, ++ Helsinki School of Economics. Corresponding auhor: Oo Toivanen, Deparmen of economics, Helsinki School of Economics, PO Box 1210, FIN 00101, Helsinki, FINLAND. Jukka Liikanen: Paul Soneman: The auhors graefully acknowledge financial suppor from he Yrjö Jahnsson foundaion. The usual cavea applies.

4 1. Inroducion Technical change happens boh in discree seps and in a more coninuous fashion. For some producs or echnologies, clear generaions can be idenified. Mobile phones are an example of such a echnology: one generaional shif, from analog o digial (1G o 2G) has aken place, and we are in he early sages of he nex one. Mobile phones have anoher imporan characerisic: hey are nework goods. This paper addresses he quesion of mobile phone diffusion during a period when a generaional shif was under way. In paricular, we firs use he sandard approach of neglecing echnological generaions, and compare hose esimaes o ones obained by esimaing generaion specific diffusion models. The radiion in he diffusion lieraure has been o rea all versions of he same good idenically. According o Karshenas and Soneman (1995, pp. 266), 1 no oher reason han radiion exiss for his. The effec ha an old generaion of a echnology (he sock of i) has on he diffusion speed of he new generaion has hus no been sudied. This can however be of crucial imporance o enire indusries: hink for example of European elecommunicaions operaors huge invesmens ino he hird generaion of mobile phone echnology. The presumpion here seems o be ha despie high peneraion raes of second generaion (2G) mobile phones, diffusion of hird generaion (3G) will be swif. Our specific ineres is in he nework exernaliies wihin and beween he old and he new generaion, and beween an obvious subsiue ha shares a leas some of he nework effecs: fixed line elephony. One could argue ha all phones, mobile and fixed, share he same direc exernaliy of nework size, bu ha indirec nework effecs such as lower prices due o economies of scale in producion are reaped wihin 1

5 echnologies and/or generaions. In his paper, we employ an inernaional daa-se comprising 80 counries and up o seven years per counry. The exising sudies on mobile phone diffusion (see in paricular Gruber and Verboven 2001a,b, henceforh GV) uilize sandard mehods of he empirical lieraure on echnology diffusion (surveyed recenly by Geroski, 2001, and Soneman, 2001). Our paper joins ha radiion. We differ from he aforemenioned sudies in he following respecs: firs, we measure nework effecs direcly. Second, we make a disincion beween 1G and 2G mobile phones, and use more informaion on echnology han previous sudies. Finally, our esimaion mehods, while based on he same epidemic model (see Geroski, 2001) as GV, differ significanly from heirs. Mobile phone generaions, while offering he same basic produc, voice, differ significanly from each oher echnologically and oherwise, as we will deail in he nex secion. They herefore are imperfec subsiues, and alhough hey share mos of he direc nework effecs, he indirec ones (emanaing from economies in scale in producion, for example) are generaion specific. Similarly, mobile phone services vary from counry o counry even hough he base produc is again he same. Governmens make differen insiuional choices: examples are he number of licenses, or wheher he licenses are naionwide or local. Also, conracual erms offered o cusomers differ beween counries. In some counries, for example, he socalled calling-pary-pays sysem is in use. We discuss hese issues in he following secion. The daa is presened in Secion 3, and Secion 4 is devoed o specifying our economeric model which is a varian of he sandard epidemic model of echnology diffusion. Our economeric resuls are presened in secion 5, and secion 6 offers conclusions. 2

6 2. Mobile Phone Generaions A. Sandards and Inergeneraional Differences Differen mobile phone generaions are easily idenifiable by he echnology hey use. Similarly, all mobile phone sandards can be allocaed ino a echnological generaion. The firs public mobile communicaion sysems were aken ino use in he US in he lae 40 s and in Scandinavia in he early 70 s. These sandards relied on manually swiched calls and used large and heavy radio ransmiers in vehicles and are herefore usually viewed as predecessors o modern mobile communicaion echnologies raher han a par of hem. They were followed by analog sandards ha feaured auomaic dialing o and from exernal neworks. Various sandards were designed simulaneously in differen counries, and US and Japan were he firs counries o adop an analog (1G) sandard in 1978 (Advanced Mobile Phone Sysem, AMPS, in he US) and 1979 (Nippon Telegraph and Telephone, NTT, in Japan), respecively. While AMPS developed ino a dominan sandard in he Americas, he Japanese NTT sandard was NTT s proprieary sandard, and was never adoped in any oher counry. Scandinavian counries all adoped a common (Nordic Mobile Telephones, NMT) sandard in he early 80 s. This sandard was o be adoped by several oher counries over he nex decade and a half. In oal, eigh differen 1G sandards were adoped in a leas one counry (see Table 1). Whils he analog sandards made wide-spread use of mobile phones possible, echnical feaures se consrains on heir applicabiliy. The early analog phones were large and heavy and were beer suied o communicaion from a base such as a car raher han for mobile communicaion as we currenly undersand he word. In addiion, analog echnologies use radio specrum inefficienly, and when he diffusion of he echnology speeded up, 3

7 capaciy consrains were me in many counries. In pracice his mean ha cusomers could no necessarily make calls a he ime hey waned. Already in early 80 s, i was realized ha he nex generaion of mobile phones would be based on digial echnology (2G). The main benefis of digial over analog echnology are a more efficien use of radio specrum, and clariy of voice. European counries coordinaed heir sandards choice on GSM in he 80 s, several years before he firs GSM nework was opened (in Finland in 1991). 2G inroducion was also ofen accompanied by oher measures. Mos noably, several counries did no inroduce compeiion in 1G, bu only issued licenses for he exising (governmen owned) elecom-monopolies (see e.g. GV). Wih 2G, compeiion was inroduced in several counries. Similarly, innovaions in conracing erms offered o cusomers evolved over ime. Two mos criical ones were he so-called calling-pary-pays pracice, and he inroducion of prepaid cards. The laer have proved especially imporan in counries ha lack credi insiuions. We will conrol boh for compeiion, and conracing feaures in our empirical analysis. Table 1 illusraes he diffusion of differen sandards over counries, and he degree of adopion in erms of numbers of subscribers. One can see ha of he analog sandards, he AMPS sandard (originaing in he US) is mos widely in use by lae 90 s boh in erms of number of counries, and number of subscribers. Wha is also visible is ha analog mobile phones are being replaced by digial phones: for example, he number of counries ha have an NMT nework is declining, as is he number of NMT subscribers. [TABLE 1 HERE] 4

8 B. Nework Effecs wihin and Beween Generaions Phones are of course one of he mos prominen examples of a good ha has posiive nework exernaliies. As one can use phones of any one sandard, wheher mobile of fixed, o call a phone using anoher sandard, one is inclined o hink ha hese nework exernaliies are shared beween echnologies. The decision of wha ype of a phone o adop is hen driven by he sandard economics of demand for differeniaed goods (see e.g. Verboven, 1996). However, some of he indirec nework exernaliies can be echnology specific. For example, if here are economies of scale in producion, hese are likely o be echnology specific. These will affec pricing, and hereby he relaive araciveness, of phones of differen generaions, in addiion o heir relaive echnical qualiy. As he digial phones in mos aspecs echnically dominae analog phones (i.e., here is verical produc differeniaion), i is o be expeced ha if here are indirec, echnology specific, nework exernaliies, hen an increase in he degree of digial diffusion will slow down analog diffusion. In Figure 1, we plo he wihin-sample (see Secion 2 for a discussion of sample consrucion) diffusion curves for 1G and 2G. 2 As can be seen, he 1G curve is close o he radiional sigmoid curve documened in numerous diffusion sudies. I clearly levels off by he ime 2G is inroduced. The 2G curve, on he oher hand, has a very seep slope up o he end of our observaion period. Figure 1 nealy illusraes how he diffusion of differen generaions can be very differen a a poin in ime, and ha hey mos likely affec each oher. The seep increase in he number of digial phones coincides wih he leveling off, and decline of, analog phones. The more ineresing quesion is how he degree of 1G diffusion affecs he diffusion speed of 2G. This is also he quesion ha bears resemblance o he curren 5

9 generaional shif. The oncoming 2.5G and 3G offer echnological superioriy over 2G, 3 bu do no offer generaion specific nework exernaliies in he beginning. There are wo opposing forces. I is characerisic for nework goods ha consumers make decisions (parly) based on heir expecaions of oher consumers behavior. On he one hand, a larger insalled base of he old echnology means ha he general nework exernaliy on which phone demand depends is sronger. Also, in he spiri of epidemic models of diffusion (see Soneman, 2001, or Geroski, 2001), a larger insalled base means ha informaion abou he new echnology is available o a larger proporion of poenial adopers. On he oher hand, he relaive size of old generaion specific nework exernaliies are larger, weakening he relaive qualiy of he new generaion. In oher words, he old generaion is a beer subsiue for he echnically superior new echnology, due o a larger insalled base. Also, he higher is he peneraion rae of he old echnology, he larger is he degree o which demand for he phones of he new generaion is replacemen demand, again meaning ha he degree of diffusion of he old echnology slows down he diffusion of he new echnology. If he former dominaes, a higher degree of 1G peneraion should speed 2G diffusion; if he laer dominaes, he effec is reversed. Here again, he crucial aspec is how consumers expecaions are affeced by he size of he insalled base. In our empirical analysis, we concenrae on inergeneraional effecs. [FIGURE 1 HERE] 3. The Daa and he Markes Our daa come mainly from wo sources: he daa on mobile phones comes from EMC. The demographic and economic daa come from WDI of he World Bank. These are currenly only available unil 1998, which herefore deermines he observaion period for us. As we are ineresed in he generaional shif ha has aken 6

10 place, we condiion our daa on he exisence of boh generaions. In oher words, our sample consiss of hose counry-year observaions for which we know ha boh 1G and 2G were available for consumers. This leaves us wih 316 observaions, afer deleing observaions for which we do no have daa on explanaory variables. Table 2 presens sample descripive saisics on our variables. We measure counry size boh in erms of populaion and geographic area; variaion in boh dimensions is subsanial. Populaion, also used by eg. GV, is an obvious measure of marke size for a echnology used on a personal basis. The geographic size of a counry may have an effec for a leas wo reasons. Firs, he larger he counry, he longer he disances, and he more likely i is ha people are no reachable by fixed line phones as ofen. Second, he larger he counry, he more expensive i is o build an mainain a fixed line nework. 4 In addiion o he level of populaion, we observe he following demographic variables: proporion of over-65 year olds (mean 9.7%; sandard deviaion 4.9), age-dependency raio (56.3%; 12.8), and he proporion of urbane populaion (66.5%; 21.8). Older people are generally regarded as less likely o adop new echnologies; people in urban areas are, on he one hand, ofen beer informed of new echnologies, and on he oher hand, have a lower opporuniy cos of using fixed line phones (due o shorer disances). [TABLE 2 HERE] On he echnology side we observe he number of adopers of analog, digial, and fixed line elephones a he end of each calendar year. In addiion, we know which mobile phone sandards are in use in each of he counries: here, we only use informaion on wheher he mos widely used sandard is in use in a paricular counry or no. The mos widely used analog sandard (in erms of number of counry-year observaions) is NMT, used in 40.8% of our observaions, and 28 ou of 80 counries 7

11 in 1998 use NMT. 5 GSM is he mos widely used digial sandard. I is used in 84.5% of our observaions and 63 ou of 80 counries in In erms of regulaion, we know hree hings: he number of licenses, wheher or no he counry has spli he licenses geographically (e.g. Brazil is spli ino 10 regions), and he number of analog and digial sandards ha are in use. The number of analog and digial licenses varies beween one and hree. A oal of nine counries spli licenses regionally. The number of sandards varies beween one for each echnology o maximum of hree per echnology, wih a maximum of five in oal (in wo counries: he oher has hree analog and wo digial sandards in use, he oher vice versa). Finally, we observe wheher he so-called calling-pary pays-sysem is in use (25% of observaions); wheher prepaid-phones are used (42% of observaions); and we measure he concenraion of he marke using he herfindahl index calculaed for he hree larges firms (on average 0.62). Alhough mobile phones as a generic echnology have diffused fas during he 90 s, heir peneraion raes sill lag far behind hose of fixed line phones. This is illusraed in Figure 2, which shows hisograms of all hree phone echnologies peneraion raes in our sample counries in 1998 (1G, 2G, mainline: he bin widh is five percenage poins). A ha poin in ime, 1G peneraion raes were already declining in a number of counries, 2G peneraion raes were increasing fas, and main line peneraion raes were much more sable in comparison, bu a a higher level. As can be seen, in he vas majoriy of counries, 1G peneraion raes are below five per cen, wih a maximum of 19%. 2G peneraion raes are much more widely disbursed, wih a maximum of 50%. This saemen holds even more srongly for fixed line elephones, where he maximum peneraion rae is as high as 67.5% (recall, peneraion raes are calculaed in relaion o populaion). We have calculaed he 8

12 sample correlaions of he 1998 peneraion raes. Analog and digial peneraion raes are posiively correlaed, bu he coefficien is only The correlaion beween analog and mainline peneraion raes is also posiive, a 0.44, and ha beween digial and mainline These sugges he presence of nework exernaliies beween mobile phone generaions and mobile and fixed line elephone sysems. In conras, GV found a negaive relaionship beween mainline (fixed) nework size and diffusion speed of mobile phones. [FIGURE 2 HERE] 4. Economeric Models Some recen papers on inernaional diffusion of echnology (Caselli and Coleman, 2001) have esimaed a simple linear model (see equaion (2)), where he dependen variable is he number of adopers of echnology j in counry i a he end of period. We esimae a varian of his approach, using he change in he sock of adopers as he dependen variable: (1) j S = X ' β + η. In (1), S = S S ; S (-1) is he sock of adopers of mobile phones of generaion j in counry i a he end of period (-1); S is a vecor of explanaory variables which includes he lagged sock of adopers o measure nework exernaliies; η is an i.i.d. error erm; and β j is a echnology specific vecor of parameers o be esimaed. As so ofen wih daa on new echnology, we lack daa on prices. 6 This is a furher reason for esimaing reduced form models of his kind. In addiion o esimaing linear models, we esimae nonlinear diffusion models (see e.g. Zeelmayer and Soneman, 1995). In line wih GV, we ake as our saring poin he sandard epidemic diffusion model (see e.g. Geroski, 2001). In ha 9

13 model, one posis ha he number of adopers of echnology j a he end of period is affeced by hree facors: he number of poenial adopers N ; he number of acual adopers in he previous period, and a muliplier. The muliplier ells us which proporion of hose who could poenially adop, bu have no by beginning of period, will adop during ha period. This leads o he following equaion: (2) S [ g N S S ] = ) 1 1 (.)(. In (2), N is he number of poenial adopers of echnology j in counry i a (end of year ; g(.) is he learning coefficien ha measures he srengh of he epidemic effec (aking on values in he uni inerval; for a deailed discussion, see Geroski, 2001). The lieraure on mobile phone diffusion (GV) has used a version of (2) where is allowed o go o zero. We assume ha = 1, where one refers o a calendar year. 7 i j j Rearranging (2), and assuming ha N = POP f ( X ' β ) and g = g( ' γ ) we arrive a our esimaing equaion: S = g( ' γ ) POP f ( X ' β ) S + ε. j j (3) [ ] In (3), POP refers o he size of he populaion in counry i in year ; X are exogenous or predeermined explanaory variables; is a (counry and possibly echnology specific vecor of) ime-based variable(s); ε is an i.i.d., zeroauocorrelaion error erm; and β j and γ j are (echnology specific) parameer vecors o be esimaed. These models (equaions (1) and (3)) can be used o esimae generic diffusion, i.e., no accouning for generaional differences wihin a echnology, or o esimae generaion specific diffusion. As menioned in he Inroducion, we will do boh. In (3), we hus posulae ha he number of poenial adopers is deermined, in he case of mobile phones, by populaion. As mobile phones are used on an individual 10

14 basis, and he number of people wih more han one phone is small especially wihin our observaion period, i seems sensible o assume ha populaion deermines an upper bound on N. We herefore assume ha f(.) is a funcion ha akes on values in he uni inerval. Similarly, g(.) is a funcion ha akes on values in he uni inerval. We specify our empirical model as follows: (4) g(.) = 1 exp( ( γ o + γ 1TIME )), (he exponenial pdf) where TIME is he number of years ha echnology j has been available in counry i. The idea is ha informaion diffusion o a large exen is populaion (hence counry) specific, and ha differen generaions may necessiae differen processes of informaion diffusion. For he generic model, we specify TIME as ime from firs inroducion of mobile phones. We specify ha he proporion of populaion ha is a poenial adoper of mobile phones is given by (we omi he j superscrip from he β coefficiens) (5) 1 f (.) = 1+ exp( h(.)) where h(.) is given by (6) i i2 β 0 + β1gdpcap + β 2gdpcap i i i + β 3urbpopp + β 4pop65 + β 5agedep + β 6surface i + β 7ownpen -1 + β 8ohpen -1 + β 9fixpen -1 h(.) = ( 1) i i i + β10prepaid + β11ech_d + β12ech_a i i i + β13spli + β14hhi3 + β15licence + β16tech k + β16tech + β17year i. In oher words, f(.) akes he form of a logisic pdf. The funcional forms for g(.) and f(.) have been chosen parly for heir analyic simpliciy: given our limied sample size, explici funcional forms yield added efficiency. We muliply he vecor of explanaory variables wih minus one in order o have posiive coefficiens increase 11

15 he share of populaion ha are poenial adopers. We arrive a our nonlinear specificaion by insering (4), (5) and (6) ino (3). Esimaions of he linear model (equaion (1)) ac as a specificaion check. The explanaory variables in (6) can be divided ino hree caegories. Firs, here are demographic and economic variables. Of hese, gdpcap is he GDP per capia and gdpcap 2 is square, urb is he percenage of populaion ha lives in ciies, pop65 is he proporion of populaion aged 65 or more, agedep is he age-dependency raio, and surface is he geographical area of counry i. All oher bu surface are imevarying. Second, here are variables measuring nework effecs wihin and beween echnologies. These are he peneraion raes of mobile (pen_m for generic mobile, pen_a for analog phones, and pen_d for digial phones. Pen_M=pen_A+pen_D), he oher mobile echnology (eiher pen_a of pen_d) and fixed line elephones (pen_fixed), all defined as he number of phones of a given echnology, divided by populaion. The inerpreaion of he coefficiens of hese variables is as follows: The coefficien of he peneraion of he own echnology measures nework exernaliies wihin he mobile phone echnology. The coefficien of he oher mobile echnology s peneraion rae measures boh subsiuabiliy and nework exernaliies beween he echnologies. We herefore expec he firs one o have a posiive effec, and he second eiher a posiive or a negaive effec (depending on wheher subsiuabiliy or nework exernaliies dominae), on he number of poenial adopers. For mainlines, he coefficien measures nework effecs on he one hand, and subsiuabiliy on he oher hand. A negaive coefficien for fixed line peneraion, for example, would imply ha alhough here undoubedly are nework effecs, hese are more han ouweighed by he effec of subsiuabiliy. An argumen can be made for including 12

16 peneraion raes in he g(.) funcion in addiion o having hem in f(.): if diffusion of informaion is no only affeced by ime, bu also by peneraion effecs, hen one should include hem ino g(.). However, separaely idenifying hese effecs would pose grea problems. We have herefore oped for including peneraion raes ino (5) only. Finally, he hird caegory of variables aims o capure he (relaive) cos of adoping and using a mobile phone (of a paricular generaion). The former is measured by he number sandards in use, boh wihin he own generaion, and in he oher generaion (ech_m, ech_a and ech_d). These are defined as he number of mobile, analog and digial sandards, respecively, in use in counry i in year. More sandards mos likely means ha some sandard specific nework effecs (such as economies of scale in producion of services) are los, leading o higher usage prices. For he generic model, we pool he number of analog and digial sandards. The variable spli refers o a siuaion where digial licenses are no counry-wide, bu defined over smaller regions. Again, his can lead o los (indirec) nework effecs, and hereby o higher marginal coss of producion of services of 2G phones. We measure marke srucure hrough he 3-firm herfindahl index, and he number of licenses. The laer corresponds o he number of firms in he marke, and herefore, he inerpreaion of he hhi3 coefficien is how concenraion affecs prices, condiional on he number of firms. Finally, we include a echnology dummy in boh 1G and 2G equaions. For boh, we allow hose counries wih he mos widely adoped sandard (in erms of he number of counries using ha sandard: NMT for 1G, GSM for 2G) o have a differen consan erm in f(.). This is o allow for differences in prices and qualiy of he service ha differen mobile phone sandards yield cusomers. The variable YEAR is included o capure world-wide economies of 13

17 scale and learning. I is he calendar year minus 1991 divided by seven (o normalize he variable ono he uni inerval). 5. Resuls A. Linear Esimaions We presen he resuls from he linear models (equaion (1)) firs, in Table 4. We firs look a he generic model (column (1)). The lagged sock of mobile adopers has a posiive and significan coefficien of 0.34, indicaing posiive nework effecs. The number of mainlines obains a negaive, bu insignifican coefficien. Populaion has a posiive effec, proporion of urban and old no effec, as has he age-dependency raio. Surface has a negaive effec, indicaing ha ceeris paribus, mobile phones are adoped more slowly, he larger he geographic area of a counry. Turning o echnology variables, we find ha boh echnology dummies (for he mos popular 1 s and 2 nd generaion sandards) obain negaive coefficiens, wih he digial dummy coefficien being significan. The inerpreaion is ha boh NMT and GSM sandards diffuse slower han oher sandards wihin counries ha have adoped hem. The number of echnologies (sandards) in use also slows down diffusion. The more compeiive he marke, measured by he number of firms (licenses), he faser is diffusion. Condiional on he number of firms, concenraion seems no o affec diffusion. Of he conrac erm variables, cpp (calling pary pays) carries a significan posiive coefficien, prepaid a highly insignifican (negaive) coefficien. Having spli license speeds up diffusion: he coefficien is only significan a he six per cen level, hough. Of our ime variables, we find ha ime in he marke (TIME) has no effec, bu calendar ime a posiive effec on diffusion. There migh be a problem in separaely idenifying hese: idenificaion ress solely on cross-secion variaion. 14

18 Looking hen a he analog resuls (column (2)) and he effecs of demographics, we see ha he coefficien of populaion is posiive and significan. None of he oher demographic or economic variables carries a significan coefficien, nor does he geographic area of he counry. Our conrols for informaion diffusion (he epidemic effec: TIME. Noe ha TIME is generaion specific) does no impac on diffusion speed, whereas YEAR carries a significan negaive coefficien (in conras o he generic model). This mos likely reflecs he worsening relaive qualiy of analog phones o digial phones ha ook place in he 90 s as firms invenive effors were more and more direced owards he laer, and he wihin echnology nework exernaliies of digial echnology grew larger. [TABLE 4 HERE] Turning o he socks of adopers variables, we find ha he coefficien of analog sock is As he dependen variable is he change in he sock of adopers, his suggess posiive nework exernaliies wihin analog echnology. The coefficien of he sock of digial adopers is negaive and significan (-0.13), meaning ha he subsiuion effecs beween generaions dominae any posiive nework exernaliies. Fixed line phones seem o have no impac on analog diffusion. Calling pary pays seems o slow down analog diffusion. This mos likely reflecs furher he negaive effecs of digial phones on analog diffusion, as inroducion of cpp ofen coincided wih he inroducion of digial phones. Prepaid has no effec on analog diffusion, nor does he number of analog echnologies. The number of digial echnologies carries a negaive coefficien which is highly insignifican. The number of licenses, and he hree-firm concenraion raio boh have a negaive impac on he adopion speed. Whils he laer is in line wih expecaions, he former is no. One possible inerpreaion is ha here are wihin-operaor nework effecs ha are no reaped 15

19 when he number of licenses increases. The oher one is ha his variable parially capures he compeiiveness of he digial phone marke, again worsening he relaive price-qualiy raio of analog phones in comparison o digial phones. Counries wih geographically spli 2G licenses have faser diffusion. This mos likely capures worse relaive qualiy of 2G services in hose counries. Those counries ha have adoped he NMT sandard do no differ in heir analog diffusion speed from ohers. Turning hen o column hree, which presens he digial diffusion resuls, we find ha populaion again carries a posiive coefficien. Again, no oher demographic variable seems o affec he diffusion speed; in conras, geographic area has a posiive effec on he speed of diffusion. Our conrols for ime work similarly as in columns (1): whereas TIME again carries an insignifican coefficien, YEAR carries a subsanial and significan posiive coefficien. This indicaes ha over ime, he pricequaliy raio of digial phones (over subsiues such as analog) have considerably improved. The resuls on nework effecs are differen, oo. This ime, boh analog and digial sock variables obain posiive and significan coefficiens. The inerpreaion is ha 1) here are nework exernaliies wihin digial mobile echnology and 2), here are nework exernaliies beween generaions, and hese dominae subsiuion effecs. Noe however ha he coefficien of analog sock is (saisically significanly) smaller han ha of he digial sock, indicaing ha he wihin generaion nework effecs are sronger han hose beween generaions. Main line elephone peneraion rae has a negaive coefficien ha is highly insignifican. We find ha an increase in he number of digial echnologies decreases he diffusion speed, bu ha he number of analog echnologies has no effec. Geographically spliing licenses has no effec on digial diffusion, even hough i had a posiive impac on analog diffusion. Of 16

20 conracing erms, calling pary pays has a large posiive impac, bu prepaid no impac. GSM phones diffuse slower, keeping everyhing else consan. Turning o marke srucure, we find ha whereas concenraion in he form of hhi3 has no significan effec on diffusion speed, he number of licenses has a large posiive effec. These resuls already show ha esimaing generaion specific diffusion models produces resuls ha would be los if he sandard approach of using (solely) a generic model is used. Differen generaions are differenly affeced by demographic, echnology, marke srucure, and informaion variables. Mos imporanly, he diffusion speeds of differen echnologies are differenly affeced by he peneraion raes of oher generaions of he same echnology. B. Nonlinear Esimaions In Table 5, we presen he resuls from our nonlinear esimaions: column (1) repors resuls for he generic model, column (2) for 1G, and column (3) for 2G. Recall ha now he inerpreaion of mos variables (hose in f(.)) is as an effec on he proporion of populaion who are poenial adopers. We will alernaively say ha a posiive coefficien means ha a variable has a posiive effec on (speeds up) diffusion. Using he generic model, we find firs of all ha he coefficien of ime in he marke is posiive and significan. Now, boh ime variables carry significan (posiive) coefficiens. We find ha gdp per capia has a nonlinear effec: he linear erm has a posiive, he squared erm a negaive effec on diffusion speed. Urban populaion decreases he share of populaion ha are poenial adopers; he share of over 65-year olds has a similar effec. The age-dependency raio has he opposie effec, increasing he populaion share of poenial adopers. And conrary o he resuls from he linear model, he larger he geographical surface, he larger he 17

21 populaion share of poenial adopers. More generally, he linear and nonlinear resuls are mosly in line wih each oher (regarding significan coefficiens). NMT (1 s generaion) mobile phones diffuse faser han average. The diffusion speed of GSM (2 nd generaion) phones is no differen from ha of phones using oher sandards. The number of echnologies in use has a negaive effec on diffusion speed. Calling pary pays-conracs speed up diffusion, and prepaid phones slow i down. We find ha he number of licenses has no effec on diffusion speed, bu more highly concenraed markes have slower speed of diffusion (a smaller proporion of populaion belonging o he se of poenial adopers). Having spli licenses has no effec on diffusion. Finally, we find srong evidence for nework exernaliies wihin mobile phones. In conras, any nework exernaliies beween fixed line phones and mobile phones is more han ouweighed by subsiuion effecs (corroboraing GV s resul). For 1G, we find ha TIME has a posiive impac on he proporion of nonadopers ha adop, i.e., on he learning effec. The value of g(.) goes from 1% in he year of digial phone inroducion o 9% in he 7 h year. Similarly, YEAR has a posiive impac on he share of populaion of poenial adopers. The laer capures unobserved improvemens in he price-qualiy raio of (analog) phones. Noe ha he laer resul is in conradicion wih he linear esimaion resuls, which suggesed a negaive impac of YEAR on adopion speed. [TABLE 5 HERE] Turning hen o he demographic and economic variables ha affec he number of poenial adopers, we find ha gdpcap has no effec on analog diffusion. The share of elderly increases he populaion share of poenial adopers, counering he convenional view of old people being relucan o adop new echnologies, and 18

22 he resuls from he generic model. The age-dependency raio and he geographic area of a counry obain insignifican coefficiens. We find ha he peneraion rae of digial mobile phones has a significan negaive effec on he share of populaion ha are poenial adopers of analog phones. Thus, subsiuion effecs dominae any nework effec. We find no evidence of he laer wihin he analog echnology eiher, as he coefficien of analog peneraion rae is of he wrong sign and highly insignifican. Subsiuion effecs dominae also beween analog and fixed line elephony, alhough he coefficien is insignifican. Regarding our conracing variables, calling-pary pays has a significan negaive effec on he populaion share of poenial adopers of analog mobile phones, as in he linear model. Of echnology variables, we find ha he number of digial echnologies decreases, and he number of analog echnologies increases he populaion share of poenial adopers (he laer is only significan a he 9% level, hough). These resuls are conrary o expecaions. NMT diffusion does no differ from ha of oher analog echnologies. The geographical spliing of digial licenses is beneficial o analog diffusion jus as in he linear esimaions, whereas he number of licenses decreases analog diffusion. On digial diffusion, we find ha he ime ha a populaion has had access o digial echnology decreases he share of poenial adopers who acually adop. This runs couner he sandard epidemic effec: he value of g(.) varies beween 19% and 6%. Of he deerminans of he populaion share of poenial adopers, we find ha he ime ha digial echnology has been in use worldwide (YEAR) has a posiive impac, whereas gdpcap has a nonlinear impac. The linear gdpcap erm carries a posiive, he squared a negaive coefficien. This is in conras o he analog resuls, bu in line wih he generic resuls. Conrary o analog, he more urban he populaion, he smaller he 19

23 share of poenial adopers. The share of over 65-year olds decreases he populaion share of poenial adopers, he age-dependency raio and he geographical surface increase i. As o conracing variables, prepaid decreases he populaion share of poenial adopers, bu calling-pary-pays increases i. The former is in line wih analog resuls, he laer in conras wih hem. In he linear esimaion, prepaid carried a negaive bu insignifican, cpp a posiive and significan coefficien. An increase in he number of digial echnologies has a negaive impac, an increase in he number of analog echnologies no impac on he populaion share of poenial adopers. Geographical spliing of digial licenses has again no effec on digial diffusion (hey did have a posiive effec on analog diffusion). More licenses leads o a higher populaion share of adopers, whereas (condiional on he number of firms), concenraion has no effec. We find posiive nework exernaliies wihin digial echnology: he coefficien on analog peneraion rae suggess nework effecs beween echnologies, bu i is no significan. Noe ha in line wih he linear esimaions, he coefficien of analog peneraion is smaller han ha of he digial peneraion rae. For mainlines, subsiuion effecs dominae. Comparing he generic resuls wih he generaion specific resuls shows ha he former are a mix of he laer. For example, he gdpcap, surface, urb, and pop65 coefficiens of he generic and digial models are close o each oher; he fixed line and YEAR poin esimaes in he generic and analog models are almos idenical. The linear and nonlinear resuls are mosly similar, corroboraing each oher. Mos imporanly however, our resuls show ha he esimaed nework exernaliies are very differen when one allows for generaional differences. Boh in he linear and he 20

24 nonlinear esimaions, he esimaed nework effec is significanly smaller han ha esimaed for he digial mobile phones wihin generaion. Similarly, i is larger han hose esimaed beween generaions (when comparing significan coefficiens), and larger han ha esimaed for analog mobile phones wihin generaion. 6. Conclusions The objecive of his paper was o shed ligh on how echnology diffusion akes place under a period when wo consecuive generaions of a echnology are on offer. In he indusry under sudy, mobile phones, his is a imely quesion as he indusry is abou o ener he nex generaional shif, having invesed heavily in he fuure (hird) generaion o come. We find srong evidence ha he new generaion impedes he diffusion of he old generaion, whereas he (peneraion rae of he) old generaion has no effec or a posiive effec on he diffusion of he new one. We hus find ha an inermediae echnology such as 1G (i was know by early 80 s ha 1G is going o be replaced by digial 2G) can have a posiive impac on he diffusion speed of a superior (new) echnology. Thus lock-in ino an inferior echnology seems no o have been a major problem regarding mobile phones. I clearly emerges ha he nework effecs are larges wihin echnological generaions. Wih regard o fixed line elephony, subsiuion effecs dominae any nework effecs (as found by GV). Undersanding he effecs of wihin and beween generaion nework exernaliies and subsiuabiliy will be of grea imporance also in he ongoing generaional shif from 2G o 2.5G and 3G. We find ha boh economic variables and demographics srongly influence he diffusion process, and ha even he geographic srucure of a counry plays a role. Furher, conracing feaures, marke srucure, and governmens decisions as o licensing and he number of sandards and heir form (wheher or no hey are 21

25 geographically spli) all influence he diffusion speed in expeced ways. Calling pary pays and use of he prepaid-echnology enhance diffusion, as does an increase in he number of licences, and keeping he licenses a a counry level. The number of sandards seems o be an impedimen o diffusion. 22

26 References Caselli, F., and Coleman, J., 2001, Cross-Counry Technology Diffusion: The Case of Compuers, American Economic Review, Papers and Proceedings, 91, 2, Geroski, P., 2001, Models of Technology Diffusion, CEPR wp Gruber, H., and F. Verboven, 2001a, The evoluion of markes under enry and sandards regulaion he case of global mobile elecommunicaions, Inernaional Journal of Indusrial Organizaion, (19), 7, pp Gruber, H., and F. Verboven, 2001b, The diffusion of mobile elecommunicaions services in he European Union, European Economic Review, (45), 3, pp Karshenas, M., and P. Soneman, 1995, Technological Diffusion, Handbook of he Economics of Technological Change, P. Soneman (ed.), Edwar Elgar. Soneman, P., 2001, The Economics of Technological Diffusion, Oxford, Basil Blackwell, Sepember. Verboven, F., 1996, Inernaional price discriminaion in he new car marke, RAND Journal of Economics, 27, 2, Zeelmayer, F., and P. Soneman, 1995, Tesing alernaive models of new produc diffusion, Economics of Innovaion and New Technology, 2,

27 Table 1 Technology Sysem Analog AMPS Counries Operaors Subscribers NMT Counries Operaors Subscribers TACS Counries Operaors Subscribers C-450 Counries Operaors Subscribers Comvik Counries Operaors Subscribers NTT Counries Operaors Subscribers RC2000 Counries Operaors Subscribers RTMS Counries Operaors Subscribers Digial GSM Counries Operaors Subscribers TDMA Counries Operaors Subscribers CDMA Counries Operaors Subscribers PDC Counries Operaors Subscribers LEGEND: Counries = number of counries ha have adoped sandard k by year. Operaors = number of mobile phone operaors using sandard k in year. Subscribers = number of subscribers using sandard k in year (in millions). 24

28 Table 2 Descripive Saisics Variable Mean (Sandard Deviaion) Pop. Populaion (000 s) ( ) Urb. Proporion of urban populaion (0.218) Pop65. The proporion of over 65-year olds (0.050) Agedep. Age-dependency raio (0.128) Surf. Geographical area ( sq. km) (0.235) YEAR. Calendar year 1991 divided by 7 in order o normalize ono he uni inerval. (0.234) Time (analog). Years from inroducion of analog phones. (3.540) Time (digial). Years from inroducion of digial phones. (1.634) License. Number of mobile phone licenses (0.140) HHI3. Herfindahl index of hree larges firms (max. 1). ( 0.277) Spli. An indicaor variable for geographically spli licenses. (0.302) Tech_A. Number of analog sandards in use Normalized by dividing by he maximum number (0.169) in he sample (3). Tech_D. Number of digial sandards in use. Normalized by dividing by he maximum number in he sample (3) (0.165) Cpp. Indicaor variable for calling pary paysconracs. (0.494) Prepaid. Indicaor variable for prepaid conracs (0.434) Subsa1. Number of analog phone subscribers (000 s), lagged by one year. ( ) Subsd1. Number of digial phone subscribers (000 s), lagged by one year. ( ) Dsubsa. Change in he number of analog phone subscribers from end of year -1 o end of year. ( ) Dsubsd. Change in he number of digial phone subscribers from end of year -1 o end of year. ( ) Pen_A. Peneraion rae of analog phones (# subscribers/populaion). (0.034) Pen_D. Peneraion rae of digial phones (# subscribers/populaion). (0.052) NMT. An indicaor variable for he NMT analog phone sandard. (0.493) GSM. An indicaor variable for he GSM digial phone sandard. (0.363) NOTE: All variables defined on a year-counry basis. 316 year-counry observaions, 80 counries. Number of observaions per counry varies beween one and seven. 25

29 Table 3 Linear Model Resuls Generic Analog Digial Consan ( ) ( ) ( ) Gdpcap ( ) ( ) ( ) Gdpcap ( ) ( ) ( ) Popul (0.0004) (0.0003) (0.0005) Urb ( ) ( ) ( ) Pop ( ) ( ) ( ) Agedep ( ) ( ) ( ) Surface ( ) ( ) ( ) NMT ( ) (76.169) GSM ( ) ( ) Tech_M ( ) Tech_D ( ) ( ) Tech_A ( ) ( ) Cpp ( ) ( ) ( ) Prepaid ( ) (83.999) ( ) License ( ) ( ) ( ) Spli ( ) ( ) ( ) HHI ( ) ( ) ( ) Subsm (0.015) Subsa (0.012) (0.020) Subsd (0.017) (0.0292) Main ( ) ( ) ( ) Year ( ) ( ) itime (23.107) (14.851) R Noes: repored numbers are coefficien and (s.e.). itime is generaion specific. ( ) (54.684) 26

30 Table 4 Non-Linear Model Resuls Variable Generic Analog Digial Learning coeff. g(.) Consan (0.027) itime (0.004) Populaion proporion f(.) Consan (0.939) (0.003) (0.0002) (0.014) (0.003) (32.402) (1.535) Gdpcap (9.944) ( ) (12.659) Gdpcap (21.535) ( ) ( ) Urb (0.722) (29.011) (0.804) Pop (2.531) ( ) (4.536) Agedep (1.166) (19.535) (1.442) Surface (0.647) (4.817) (0.771) GSM (0.107) (0.354) NMT (0.130) (4.746) Tech_M (0.167) Tech_A (14.434) (0.133) Tech_D (2.088) (0.297) Cpp (0.502) (9.503) (0.561) Prepaid (0.100) (1.294) (0.137) License (0.522) (8.420) (0.835) Spli (0.210) (1.360) (0.370) HHI (0.281) (4.862) (0.381) Pen_M (0.658) Pen_A (19.607) (2.562) Pen_D (15.877) (3.362) Pen_fixed (1.389) (9.048) (1.727) Year (0.313) (2.481) (0.537) R LogL Noes: repored numbers are coefficien and (s.e.). itime is generaion specific. 27

31 Diffusion of Mobile Phones 250 # Users, in millions Analog Phones Digial Phones Year Figure 1 28

32 Peneraion Raes of Differen Phone Technologies in 1998 # Counries Analog Digial Main 5 Peneraion Rae (%) Figure 2 FOOTNOTES: 1 Karshenas and Soneman (1995) acually raise he possibiliy of sudying echnology diffusion generaion by generaion, aking ino accoun inerdependencies. 2 The world-wide diffusion curves are similar in shape 3 Of course, he differences beween 1G and 2G, and 2G and 3G are no idenical. 4 This is visible e.g. in Finland, where he companies providing fixed line services have sared o replace damaged fixed lines wih mobile connecions in he counry side. 5 Noe ha he difference beween he sample and world-wide saisics is explained by he win facs ha 1) here are more counries in he world-wide saisics and 2) counries adoping GSM were fas in adopion, yielding more counry-year observaions. One canno herefore compare he sample saisics and Table 1 direcly. 6 Price daa would be paricularly difficul o obain and handle in he case of mobile phones. Firs, here are hundreds of phones available. Second, heir prices o consumers vary considerably wihin and beween counries, and wihin a year. In addiion, in some counries, phones are bough bundled wih a conrac for mobile phone service (from a mobile phone operaor); in ohers, no bundling akes place. Finally, operaors offer several ypes of conracs, meaning ha here are elemens of price discriminaion. 7 Our observaions are annual. In oher words, we measure he change in he sock of adopers beween 31 s December in years and

33 The Yrjö Jahnsson s Working Paper Series in Indusrial Economics is funded by The Yrjö Jahnsson Foundaion. Edior of he Working Paper Series: Professor Rune Senbacka 1. Anhony Dukes & Esher Gal-Or: Negoiaions and Exclusiviy Conracs for Adverising 2. Oz Shy & Rune Senbacka: Parial Subconracing, Monioring Cos, and Marke Srucure 3. Jukka Liikanen, Paul Soneman & Oo Toivanen: Inergeneraional Effecs in he Diffusion of New Technology: The Case of Mobile

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