of New Technology: The Case of Mobile Phones

Transcription

1 Inergeneraional Effecs in he Diffusion of New Technology: The Case of Mobile Phones Jukka Liikanen *, Paul Soneman + and Oo Toivanen ++ Absrac Many new echnologies exhibi clear generaional changes. The empirical lieraure on echnology diffusion radiionally analyses he spread of new echnologies generically. We use daa from he mobile phone indusry, where firs-generaion (1G) and second-generaion echnologies (2G) can be clearly idenified, o analyze he role of generaional effecs in diffusion. The resuls from a generaion specific approach differ significanly from hose of a generic model. There are posiive wihingeneraion nework effecs. 1G (2G) has a posiive (negaive) effec on 2G (1G) diffusion. Boh generaions are subsiues for fixed phones. Effecs of compeiion and paymen schemes are analyzed. * Helsinki School of Economics, and Mobirex Ld, + Warwick Business School, ++ Helsinki School of Economics, and ETLA, he Research Insiue of he Finnish Economy. Corresponding auhor: Oo Toivanen, Deparmen of economics, Helsinki School of Economics, PO Box 1210, FIN 00101, Helsinki, FINLAND. oivanen@hkkk.fi. Jukka Liikanen: jukka.liikanen@mobirex.com. Paul Soneman: paul.soneman@warwick.ac.uk. We would like o hank he edior (Denicoló), wo anonymous referees, and seminar paricipans in Oulu for commens. The auhors graefully acknowledge financial suppor from he Yrjö Jahnsson foundaion. This research is par of he wireless communicaion research program (brie-ela.org) of BRIE, he Berkeley Roundable on he Inernaional Economy a he Universiy of California a Berkeley, and ETLA, he Research Insiue of he Finnish Economy. Financial suppor from Nokia and Tekes is graefully acknowledged. All opinions expressed are hose of he researchers. The usual cavea applies.

2 1. Inroducion Technical change can occur boh as discree seps and in a more coninuous fashion. For some producs or echnologies, clear generaions can be idenified. Mobile phones are one 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. This paper analyzes mobile phone diffusion during a period when he generaional shif was under way. The radiion in he diffusion lieraure has largely been o eiher ignore such generaional changes or o ake hem ino accoun only as represened by associaed changes in he price or qualiy of he generic echnology being diffused. Karshenas and Soneman (1995, pp. 266), 1 sugges ha here is no oher reason han radiion for proceeding in his way. The oucome of his is ha he (empirical) lieraure largely has no explicily considered he effec ha an old generaion (s) of a echnology (he sock of i) will have on he diffusion of new generaion (s) of ha echnology and vice versa. Such iner-generaional effecs can 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. There seems o be lile basis in he lieraure o suppor such a presumpion. In paricular, employing an inernaional daa-se covering 80 counries for up o seven years per counry, in his paper we illusrae he imporance of an explici consideraion of generaional change by comparing he resuls from a diffusion model ha uses he sandard approach, neglecing echnological generaions, wih esimaes obained by esimaing generaion specific diffusion models. In doing his we place emphasis upon one imporan characerisic of mobile phones: hey are nework 1

3 goods. Our specific ineres is in he nework exernaliies wihin and beween generaions, and beween an obvious subsiue ha shares a leas some of he nework effecs: fixed line elephony. The case of mobile elephony has some parallels o he famous QWERTY example (David, 1985, Liebowiz and Margolis, 1990), and he well documened sandard bales in video recorders. The oucome however seems raher differen for in his case he marke has no go locked ino an inferior echnology. Exising sudies of mobile phone diffusion (see in paricular Gruber and Verboven 2001a,b, henceforh GV; Dekimpe e al., 1998) uilize he sandard epidemic model ofen found in he empirical lieraure on echnology diffusion (surveyed recenly by Geroski, 2000, and Soneman, 2001). Oher papers (Kaz and Aspden, 1997) employ survey daa. This paper follows GV in uilizing sandard diffusion models, bu we differ from he aforemenioned sudies in he following respecs: firs, we measure nework effecs direcly (for a recen survey on nework effecs, see Gandal, 2002); 2 second, we make a disincion beween 1G and 2G mobile phones; hird, we use more informaion on echnology han previous sudies; and finally, our esimaion mehods differ significanly from hose used in hese oher sudies. Firs and second mobile phone generaions, while offering he same basic produc, voice ransfer, differ significanly from each oher echnologically and oherwise, as we will deail in he nex secion. They herefore are imperfec subsiues. Also, alhough one could argue ha all phones, mobile and fixed, share he same direc exernaliy of nework size, indirec nework effecs such as lower prices due o economies of scale in producion may be specific o echnologies and/or generaions. Similarly, mobile phone services vary from counry o counry even 2

4 hough he base produc is again he same. Governmens make differen insiuional choices, e.g. 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 so-called calling-pary-pays sysem is in use. Such issues are discussed furher in he following secion. The daa is presened in secion 3, and secion 4 is devoed o specifying he economeric model. Economeric resuls are presened in secion 5, and secion 6 provides conclusions. 2. Mobile Phone Generaions Nework Effecs Wihin and Beween Generaions When wo generaions of a echnology co-exis, we may expec ha hey affec he diffusion of each oher. In Figure 1, we plo he wihin-sample diffusion curves for 1G and 2G (see below on daa sources and samples). 3 The 1G curve approximaes he radiional sigmoid curve documened in numerous diffusion sudies. By he ime 2G was inroduced, diffusion of 1G had clearly levelled off. 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 relaive diffusion of differen generaions can be very differen a differen poins in ime. As he seep increase in he number of digial phones coincides wih he levelling off and decline in he diffusion of analog phones, i also suggess ha he diffusions of he wo differen generaions are likely o be inerdependen. [FIGURE 1 HERE] Iner-generaional diffusion effecs may come eiher direcly from he compeiion beween goods in a marke, or more indirecly from he impac of accumulaed socks. In he absence of sock effecs, he decision of wha ype of phone o adop is driven 3

5 by he sandard economics of demand for differeniaed goods (see e.g. Verboven, 1996). As digial phones in mos aspecs echnically dominae analog phones (i.e. hey are superior in a verically differeniaed sense), i is o be expeced ha for a given price and in he absence of nework exernaliies, consumers would purchase digial raher han analog phones. The radiional lieraure on echnology diffusion has emphasised he role ha accumulaed socks play in he diffusion of generic echnologies. The earlier lieraure ofen incorporaes informaion spreading effecs arising from he growing familiariy wih a echnology ha comes from use (e.g. Geroski, 2000, Soneman, 2001). Thus, as more individuals use mobile phones, he number of people ha know of heir exisence, performance characerisics, and coss of operaion will increase. This would sugges a posiive link beween mobile phone generaions, as a larger sock of 1G adopers means beer informed consumers and increased demand for 2G phones. More recen lieraure emphasises he role of nework exernaliies (Kaz and Shapiro, 1985, Farrell and Shapiro, 1985, Arhur, 1989, Choi, 1997, Choi and Thum, 1998). The essence of nework exernaliies in his conex is ha he benefis o phone users increase as he nework size increases. As one can use phones of any one sandard, wheher mobile or fixed, o call a phone using anoher sandard, one is inclined o hink ha such nework exernaliies will be shared beween all phones. However, some feaures such as ex (SMS) messages and picure ransmission are generaion specific. I may herefore be he case ha alhough here are sysem wide nework exernaliies ha affec 1G, 2G and fixed line phone demand, here exis also nework exernaliies ha are specific o mobile phones, or o a paricular mobile phone generaion. 4 The general posiive effec from a larger insalled base would 4

6 sugges a posiive iner-generaional effec. These posiive exernaliies we subsequenly label nework effecs. On he negaive side old-generaion specific nework exernaliies may weaken he relaive appeal of he new generaion 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 may slow down he diffusion of he new echnology (in ha he acquisiion decision for he new echnology will be based upon he addiional benefis of changing from 1G o 2G raher han he absolue benefis of 2G versus no mobile phone). These negaive exernaliies we subsequenly label subsiuion effecs. If he posiive effecs dominae, a higher degree of 1G peneraion should speed 2G diffusion; if he negaive ones dominae, he effec will be reversed. In our empirical analysis, we concenrae on wha effecs dominae. This issue also has relevance for he nex generaional shif. The oncoming 2.5G and 3G mobile phones will be echnologically superior o 2G, 5 bu will no enjoy generaion-specific nework exernaliies iniially. For marke forecasing purposes, i is hus necessary o have some indicaion as o he imporance of such exernaliies in he ake up of a new generaion 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 appeared in he US in he lae 40 s and in Scandinavia in he early 70 s. These relied on manually swiched calls 5

7 and used large and heavy radio ransmiers in vehicles. Therefore, hey are usually viewed as predecessors o modern mobile communicaion echnologies raher han a par of hem. They were followed by analog echnologies ha feaured auomaic dialing o and from exernal neworks. Various sandards for hese echnologies were designed simulaneously in differen counries, wih he US and Japan being 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 (NTT s proprieary sandard) 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 fifeen years. 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 as he echnology spread, capaciy consrains were me in many counries. In pracice his mean ha cusomers could no necessarily make calls a he ime hey waned. Even early in he 1980 s, i was realized ha he nex (2G) generaion of mobile phones would be based on digial echnology. 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). Table 1 illusraes he 6

8 diffusion of differen 1G and 2G sandards over counries, and he exen of heir adopion in erms of numbers of subscribers. One can see ha of he analog sandards, he AMPS sandard (originaing in he US) was mos widely in use by lae 90 s boh in erms of number of counries and number of subscribers bu wih 2G he GSM sandard was mos widely used. Wha is also apparen from his daa 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] 2G inroducion was also ofen accompanied by oher measures. Mos noably, several counries who did no inroduce compeiion in 1G, bu only issued licenses for he exising (governmen owned) elecom-monopolies, licensed compeiive suppliers wih 2G (see e.g. GV). Whereas in 1991, here were 56 monopoly markes ou of a oal of 69, in 1998 here were 90 monopoly markes ou of 177 markes (EMC figures). Similarly, conracing erms offered o cusomers evolved over ime. The wo 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. In our empirical analysis we ake accoun of boh compeiion and conracing feaures. 3. Daa Sources and Diffusion Paerns Our daa come mainly from wo sources: he daa on mobile phones comes from EMC. 6 Our demographic and economic daa comes from World Developmen Indicaors (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 7

9 generaional shif ha has aken 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. From a poenial sample of 80 counries each wih a maximal seven-year observaion period we are lef wih 316 observaions, afer deleing observaions for which we do no have daa on explanaory variables. Table 2 presens sample descripive saisics on all he variables used in he economeric analysis below. In paricular 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 (alhough we only use informaion on wheher he mos widely used sandard is in use in a paricular counry or no). [TABLE 2 HERE] The daa indicaes ha alhough mobile phones as a generic echnology have diffused fas during he 90 s, heir peneraion raio sill lags far behind hose of fixed line phones. This is illusraed in Figure 2, which shows hisograms of he peneraion raios of all hree phone echnologies in our sample counries in 1998 (1G, 2G, mainline: he bin widh is five percenage poins). A ha poin in ime, 1G peneraion raios were already declining in a number of counries, 2G peneraion raes were increasing fas, and main line peneraion raios were much more sable in comparison, bu a a higher level. In he vas majoriy of counries, 1G peneraion raes are below five per cen, wih a maximum of 19%. 2G peneraion raios are much more widely disribued, wih a maximum of 50%. This saemen holds even more srongly for fixed line elephones, where he maximum peneraion raio is as high as 67.5% (peneraion raios are calculaed in relaion o populaion). We have calculaed he sample correlaions of he 1998 peneraion raios. Analog and digial 8

10 peneraion raios are posiively correlaed, bu he coefficien is only The correlaion beween analog and mainline peneraion raios is also posiive, a 0.44, and ha beween digial and mainline is These values sugges he presence of posiive 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] 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 in 1998 use NMT. 7 GSM is he mos widely used digial sandard. I was used in 84.5% of our observaions and 63 ou of 80 counries in Economeric Models We concenrae on esimaing nonlinear diffusion models (see e.g. Zeelmeyer and Soneman, 1995). Some recen papers on inernaional diffusion of echnology (e.g. Caselli and Coleman, 2001) have esimaed a simple linear model, where he dependen variable is he number of adopers of echnology j in counry i a he end of period. As a specificaion check, we esimae varians of his approach, and commen on he resuls briefly. 8 In line wih GV, we ake as our saring poin he sandard epidemic diffusion model (see e.g. Geroski, 2000). In ha model, one posis ha he number of adopers of echnology j in counry i a he end of period is affeced by hree facors: he number of poenial adopers ji N ; he number of acual adopers in he previous period, and a muliplier. The muliplier ells us he proporion of hose who could poenially adop, bu have no by beginning of period, who will adop during ha period. This leads o he following equaion: 9

11 ji ji ji ji (1) S [ g N S + S ] = ) 1 1 (.)(. In (1), ji 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, 2000). The lieraure on mobile phone diffusion (GV) has used a version of (1) where is allowed o go o zero. We assume ha = 1, where one refers o a calendar year. 9 ji i ji j ji j Rearranging (1), and assuming ha N = POP f ( X ' β ) and g = g( ' γ ) we arrive a our esimaing equaion: S ji ji j ji ji j ji ji (2) [ ] = g( ' γ ) POP f ( X ' β ) S + ε. 1 In (2), ji POP refers o he size of he populaion in counry i in year ; ji X are exogenous or predeermined explanaory variables; is a (counry and possibly echnology specific) vecor of ime-based variables; ji ε is an i.i.d., zeroauocorrelaion error erm; and β j and γ j are (echnology specific) parameer vecors o be esimaed. The linear and nonlinear models can be used o esimae eiher generic diffusion, i.e., no accouning for generaional differences wihin a echnology, or generaion specific diffusion. We will do boh. In (2), 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 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 ji 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 as i measures he proporion of poenial adopers who adop. We specify our empirical model as follows: 10

12 ji (3) g(.) = 1 exp( ( γ + γ TIME + γ ( TIME )), (he exponenial disribuion) where o 1 2 ji2 ji TIME is he number of years ha echnology j has been available in counry i, and ji2 TIME is is square). 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 ji 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 (4) 1 f (.) = 1+ exp( h(.)) where h(.) is given by (we omi he j superscrip from he β coefficiens) (5) h(.) = β + β NETWORK + β DEMECON + β TECHCOST 0 j' 1 ji j' 2 ji j' 3 ji. In oher words, f(.) akes he form of a logisic disribuion. 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 h(.) by minus one (see equaion (4)) in order o have posiive coefficiens increase diffusion speed. We arrive a our nonlinear specificaion by insering (3), (4) and (5) ino (2). The explanaory variables in (5) can be divided ino hree caegories, each consising of a vecor of variables. Firs, here are variables measuring nework effecs wihin and beween echnologies (NETWORK). These are: (i) he peneraion raes of mobiles (pen_m for generic mobile, pen_a for analog phones, and pen_d for digial phones where Pen_M = pen_a+pen_d) (ii) he peneraion of he oher mobile echnology (eiher pen_a or pen_d) and (iii) peneraion of fixed line elephones 11

13 (pen_fixed). These are all defined as he number of phones of a given echnology, divided by populaion. We also include heir squared values o capure any nonlineariies, and o miigae he effecs of our funcional form assumpions. 10 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 given 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 of hese 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 fixed lines, 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 nework effecs are ouweighed by he effec of subsiuabiliy. An argumen can be made for including 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 (4) alone. Second, here are demographic and economic variables (DEMECON). We include he following: GDP per capia (gdpcap) and is square (gdpcap 2 ), he percenage of he populaion ha lives in ciies (urb), he proporion of populaion aged 65 or more (pop65), he age-dependency raio (agedep), and he geographical area of counry i (surface). All bu surface are ime-varying. The reasons for inclusion of hese variables are probably obvious, bu a few remarks are in order. The geographic size of a counry may have an effec for a leas wo reasons. Firs, he 12

14 larger he counry, he longer he disances, and he more likely i is ha people are no reachable by fixed line phones as easily. Second, he larger he counry, he more expensive i is o build and mainain a fixed line nework. 11 The proporion of over-65 year olds and he proporion of urban populaion are included because older people are generally regarded as less likely o adop new echnologies whereas people in urban areas ofen are beer informed of new echnologies, bu on he oher hand have a lower opporuniy cos of using fixed line phones (due o shorer disances). Finally, he hird caegory (TECHCOST) of variables aims o capure he (relaive) cos of adoping and using a mobile phone (of a paricular generaion). Unforunaely, as so ofen wih daa on new echnology, we lack daa on prices. 12 This is a furher reason for esimaing reduced form models of his kind. In he absence of prices he cos of using and adoping is measured by he number of sandards in use, boh wihin 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 coss. 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 hhi3 coefficien measures how concenraion affecs prices, condiional on he number of firms. Finally, we include a echnology dummy in boh 1G and 2G equaions ( TECH and ji TECH k ji in equaion (6)). For boh, we 13

15 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 allows for differences in prices and qualiy of he service ha differen mobile phone sandards yield cusomers. The variable YEAR (and is square) is included o capure world-wide economies of scale and learning. I is he calendar year minus 1991 divided by seven (o normalize he variable ono he uni inerval). 5. Resuls In Table 3, 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. Below, we will discuss he resuls in wo sages. In he firs subsecion, we discuss all he oher variables bu hose measuring nework effecs. These are discussed in a separae subsecion. In boh subsecions, we discuss all hree models. In he final subsecion, we discuss robusness issues Resuls on Learning, Demographics, and Marke and Technology For he generic model, we find ha ime in he marke has no effec on he learning coefficien. In conras, YEAR has a posiive impac on he share of populaion of poenial adopers, bu YEAR2 (squared years) no impac. We find ha gdp per capia also has a nonlinear effec: he linear erm has a posiive, he squared erm a negaive effec on he proporion of populaion ha are poenial adopers. Wihin our sample, he linear effec dominaes. Urban populaion decreases he share of populaion ha 14

16 are poenial adopers; he share of over 65-year olds, he age-dependency raio, and he geographical size of a counry have no effec. NMT (1 s generaion) mobile phones do no affec he share of poenial analog phone adopers, nor does he GSM (2 nd generaion) sandard affec he number of digial mobile phone adopers. The number of echnologies in use has a negaive effec on he share of poenial adopers. Calling pary pays-conracs and he abiliy o buy prepaid phones have no effec. We find ha he number of licenses has no effec on he proporion of poenial adopers, bu more highly concenraed markes have a smaller proporion of populaion belonging o he se of poenial adopers. Having spli licenses has no effec. [TABLE 3 HERE] For 1G (Column (2)), we find ha TIME has a posiive, bu decreasing impac on he proporion of non-adopers ha adop, i.e., on he learning effec. The value of g(.) goes from 63% in he year of digial phone inroducion o 100% in he 7 h year. We find ha he effec of ime (YEAR) on he proporion of poenial adopers is nonlinear. Gdp per capia has a posiive impac, and is square no impac. Urban populaion and he proporion of pensioners affec he share of poenial adopers posiively, whereas he age-dependency-raio has no effec. Counries wih a large geographical area experience slower analog diffusion. Turning o he echnology variables, we find ha he number of digial and analog sandards play no role in he diffusion of analog phones, nor does he number of licenses or having (geographically) spli licenses. Offering prepaid phones slows down diffusion, as does having a calling-pary-pays sysem. Boh of hese findings are surprising, and could reflec he fac ha hese are more ofen in use in digial neworks. The negaive coefficiens would hen reflec subsiuion effecs beween 15

17 generaions. Somewha surprisingly, diffusion is faser in more concenraed markes. This could be due o operaor-specific (in)direc nework effecs ouweighing any negaive effecs of concenraion on compeiion. NMT analog phones diffusion speed does no differ from ha of oher sandards. On digial diffusion (column (3)), we find ha he ime ha a populaion has had access o digial echnology has no effec on he share of poenial adopers who adop. 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 bu nonlinear impac, as does gdpcap. 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 wha we found for analog phones, he more urban he populaion, he smaller he share of poenial adopers. The share of over 65-year olds and he age-dependency raio have no effec on he populaion share of poenial adopers; he geographical surface increases 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 (see Table 4), prepaid carried a negaive bu insignifican, cpp a posiive and significan coefficien. An increase in he number of digial or analog echnologies has no impac on he populaion share of poenial adopers. Geographical spliing of digial licenses has again no effec on digial diffusion. More licenses leads o a higher populaion share of adopers, whereas (condiional on he number of firms), concenraion has no effec Resuls on Nework Effecs 16

18 In he generic model, we find evidence for nework exernaliies wihin mobile phones. In conras, any nework exernaliies beween fixed line phones and mobile phones is ouweighed by subsiuion effecs (corroboraing GV s resul): he posiive second order erm ouweighs he negaive linear effec only very close o he sample maximum for fixed line peneraion. Generally, he (unrepored) linear and nonlinear esimaion resuls are in line wih each oher (regarding significan coefficiens): he one noable excepion is he coefficien of he squared gdp per capia variable (significan and negaive in nonlinear esimaion, insignifican and negaive in linear regression). In he analog model, we find ha he squared 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. Wihin analog echnology, we find evidence of nework effecs, as he analog peneraion rae and is square have posiive effecs on he proporion of poenial adopers. Noice however ha he absolue value of he analog peneraion coefficiens are much smaller han ha of he squared digial peneraion rae. Subsiuion effecs dominae beween analog and fixed line elephony once he fixed line peneraion rae exceeds 0.11 (one hird of he sample mean). We find posiive nework exernaliies wihin digial echnology. The coefficiens on analog peneraion rae and is square sugges a nonlinear relaionship. The higher he analog peneraion rae, he larger he absolue value of he effec on digial diffusion. Posiive beween generaion-nework effecs exis once he analog peneraion rae exceeds (55% of sample mean for analog peneraion rae). For land lines, subsiuion effecs dominae. 17

19 Our resuls show ha he esimaed nework exernaliies are very differen when one allows for generaional differences. Boh in he linear and he nonlinear esimaions, he esimaed nework effec is significanly smaller han ha esimaed for he digial mobile phones wihin generaion. Some of he variables obain coefficiens of opposie sign for he wo generaions (e.g. surface); for ohers, he absolue values are very differen (e.g. pen_fixed and is square). [TABLE 4 HERE] 5.3. Robusness In his subsecion, we repor on various robusness ess. The firs issue concerns funcional form specificaion. As discussed above, we use ighly parameerized funcional forms due o our limied sample size. The linear model ha we have esimaed provides a useful check on he resuls. In unrepored regressions, we find ha our nonlinear resuls are confirmed. The second issue has o do wih unobservables or possible omied variables, and endogeneiy of some of our explanaory variables. Despie he fac ha we have several conrol variables, i is sill possible ha here are counry-specific unobservables ha are correlaed wih phone usage, hereby affecing our nework exernaliy resuls. Also, i may be possible ha such counry-specific unobservables are correlaed wih some of our conrol variables, such as hhi3 and he calling-pary pays dummy. We believe i is much less likely ha our conrol variables are correlaed wih he period-specific deviaions from counry-level means of he error erm. To verify ha our resuls are no affeced by correlaion beween some of he explanaory variables and he error erm, we re-ran he nonlinear model excluding he prime suspecs for endogeneiy (licence, hhi3, ccp, spli, ech_a, ech_d) and found 18

20 ha our resuls are essenially unchanged. To check he effecs of unobservables, we re-ran he linear model using a wihin esimaor (i.e., including counry fixed-effecs). Again, he resuls are in line wih hose we obain wihou he conrol for counryspecific unobservables. Unforunaely, here is no simple way o esimae he nonlinear model wih counry fixed effecs. Given he large number of counries (80), we would severely lose on degrees of freedom if we insered counry dummies ino eiher g(.) or f(.); furhermore, i is no clear a priori in which one we should inser he counry dummies. 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 a posiive effec on he diffusion of he new one, a leas once he old generaion peneraion rae is high enough. We hus find ha an inermediae echnology such as 1G (i was known by early 80 s ha 1G was going o be replaced by digial 2G) can have a posiive impac on he diffusion speed of a superior (new) echnology, and hus subsiuion effecs are dominaed by nework effecs. 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 19

21 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 (hrough he number of firms) all influence he diffusion speed of mobile phones. Calling-pary-pays sysem and use of he prepaid-echnology enhance diffusion of digial phones, as does an increase in he number of licenses. 20

22 References Arhur, W. B, 1989, Compeing echnologies, increasing reurns and lock-in by hisorical evens, Economic Journal, 99, Brynjolfsson, E., and Kemerer, C, 1998, Nework exernaliies in microcompuer sofware: an economic analysis of he spreadshee marke,. MIT CISR wp. No Caselli, F., and Coleman, J., 2001, Cross-counry echnology diffusion: he case of compuers, American Economic Review, Papers and Proceedings, 91, 2, Choi, J.-P., 1997, Herd behavior, he penguin effec and he suppression of informaional diffusion: an analysis of informaional exernaliies and payoff inerdependency, Rand Joirnal of Economics, 28(3), Choi, J.-P. and Thum, M.,1998, Marke srucure and he iming of echnology adopon wih nework exernaliies, European Economic Review, 42(2), David, P., 1985, Clio and he economics of QWERTY,, American Economic Review Papers and Proceedings,75, Dekimpe, M. G., Parke, P. M., and Sarvary, M., 1998, Saged esimaion of inernaional diffusion models: An applicaion o global elephone adopion, Technology Forecasing and Social Change, 57, Farrell, J., and Saloner, G., 1985, Sandardisaion, compaibiliy and innovaion, RAND Journal of Economics, 16, Gandal, N., 1994, Hedonic price indexes for spreadshees and an empirical es of he nework exernaliies hypoheses, RAND Journal of Economics, 25, 1, Gandal, N., 2000, Compaibiliy, Sandardizaion, & Nework Effecs: Some Policy Implicaions, Oxford Review of Economic Policy, 18, Geroski, P., 2000, Models of Technology Diffusion, Research Policy, 29, 4-5, 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, Gruber, H., and F. Verboven, 2001b, The diffusion of mobile elecommunicaions services in he European Union, European Economic Review, 45, 3, Karshenas, M., and P. Soneman, 1995, Technological Diffusion, Handbook of he Economics of Technological Change, P. Soneman (ed.), Edwar Elgar. 21

23 Kaz, M., and Shapiro, C., 1985, Nework exernaliies, compeiion and compaibiliy, American Economic Review, 75, Kaz., J. E., and Aspden, P., 1998, Theories, daa, and poenial impacs of mobile communicaions: A longiudinal analysis of US naional surveys, Technology Forecasing and Social Change, 57, Liebowiz, S., and Margolis, S., 1990, The fable of he keys, Journal of Law and Economics, Saloner, G., and Shepard, A., 1995, Adopion of echnologies wih nework effecs: an empirical examinaion of he adopion of auomaed eller machines, RAND Journal of Economics, 13, 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, Zeelmeyer, F., and Soneman, P., 1995, Tesing alernaive models of new produc diffusion, Economics of Innovaion and New Technology, 2,

24 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 j in year. Subscribers = number of subscribers using sandard j in year (in millions). 23

25 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. 24

26 Table 3 Non-Linear Model Resuls Variable Generic Analog Digial Learning coeff. g(.) Consan ( 0.270) * (0.025) (0.226) itime (0.063) 0.016*** (0.004) (0.077) itime (0.003) *** (0.0001) (0.006) Populaion Proporion f(.) Demographic and Economic Variables Consan *** (1.289) *** (1.795) *** (1.705) Gdpcap ** (18.699) *** (39.523) *** (4.809) Gdpcap ** (41.800) ( ) *** (5.190) Urb *** (1.098) * (5.532) *** (1.689) Pop (5.633) ** (10.548) (6.597) Agedep (1.455) (8.743) 5.168*** (1.677) Surface (0.685) *** (0.953) 3.266*** (1.270) Technological and Cos Variables GSM (0.400) (0.849) NMT * - (0.176) (0.538) Tech_M *** - - ( 0.345) Tech_A (2.463) (0.764) Tech_D (1.700) (0.351) Cpp (0.767) * (2.479) 4.128*** (0.482) Prepaid (0.116) *** (0.370) a (0.278) License (0.234) (1.806) 3.708*** (1.460) Spli ( 0.364) (0.550) (1.004) HHI a (0.515) 2.060* (1.234) (0.819) Year 4.488*** (1.796) *** (2.830) *** (4.303) Year (0.991) *** (2.248) *** (2.608) Nework Variables Pen_M 8.057* - - (4.960) Pen_M (12.384) Pen_A *** (21.452) ** (4.738) Pen_A * ( ) *** (3.650) Pen_D ( ) *** (6.004) Pen_D *** ( ) (11.317) Pen_fixed *** (3.957) ** (23.268) * (1.992) Pen_fixed *** (3.782) ** (30.877) *** (0.044) R LogL Noes: repored numbers are coefficien and (s.e.). itime is generaion specific. ***, **, *, and a refer o significan coefficiens a 1, 5, 10, and 11% level. S.e. s heeroskedasiciy and auocorrelaion (1 s order) robus. 25

27 Diffusion of Mobile Phones 250 # Users, in millions Analog Phones Digial Phones Year Figure 1 26

28 Peneraion Raios of Differen Phone Technologies in 1998 # Counries Analog Digial Main 5 Peneraion Raio (%) Figure 2 1 Karshenas and Soneman (1995) acually raise he possibiliy of sudying echnology diffusion generaion by generaion, aking ino accoun inerdependencies. 2 Earlier sudies ha measure nework effecs empirically include Saloner and Shepard (1995), Gandal (1994) and Brynjolfsson and Kemerer (1998). 3 The world-wide diffusion curves are similar in shape 4 A referee has correcly poined ou ha here exis provider level nework exernaliies hrough price differeniaion depending on wheher he call akes place wihin he same provider s nework or no. 5 Of course, he differences beween 1G and 2G, and 2G and 3G are no idenical. 6 See hp:// 7 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. 8 The linear model we use regresses he growh rae of he sock of adopers on he same explanaory variables we use in he nonlinear model. The only excepion is ha insead of using peneraion raios, we use he numbers of adopers, as ha is beer in line wih a linearizaion of equaion (1). 9 Our observaions are annual. In oher words, we measure he change in he sock of adopers beween 31 s December in years and -1. The approach ha GV akes presumes ha discree (annual) observaions can be used o esimae a coninuous ime model. Our approach allows an easier incorporaion of measures of nework exernaliies. 27

29 10 We experimened wih using a more general polynomial form, bu LR-ess indicaed ha we could no rejec he shown formulaion agains more general ones. None of he omied variables (ineracion erms of linear and squared peneraion raes) ever obained a significan coefficien. 11 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. 12 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. 28