Information Economics and Policy 23 (2011) 118–126

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Information Economics and Policy

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Does spectrum auctioning harm consumers? Lessons from 3G licensing

Minsoo Park a,1, Sang-Woo Lee b, Yong-Jae Choi c,⇑a School of Economics, Chung-Ang University, 72-1 Nae-Ri, Daedoek-Myeon, Answeong-Si, Gyeonggi-Do 456-756, Republic of Koreab Graduate School of Information, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul 120-749, Republic of Koreac Department of Economics, Hankuk University of Foreign Studies, 89 Wangsan, Mohyun, Yongin, Kyunggi-do 449-791, Republic of Korea

a r t i c l e i n f o a b s t r a c t

Article history:Received 5 February 2010Received in revised form 30 September 2010Accepted 20 October 2010Available online 29 December 2010

JEL classification:L11L96

Keywords:Spectrum auctionsLicensing feesConsumer priceInvestmentMarket concentration

0167-6245/$ - see front matter � 2011 Elsevier B.Vdoi:10.1016/j.infoecopol.2010.10.002

⇑ Corresponding author.E-mail addresses: mpark@cau.ac.kr (M. Park), le

(S.-W. Lee), yjchoi@hufs.ac.kr (Y.-J. Choi).1 First author.

Although the auctioning spectrum is generally considered to be highly successful, manycountries still rely on beauty contests to assign spectrums. This is often attributed to thenegative perceptions about the potential problems that auctions may cause, such as highlicensing fees, high consumer prices, a lower incentive to invest in infrastructure, and con-cerns about market concentration.

To address these negative perceptions, this paper estimates the effects of the auctionsand the licensing fees for the 3G spectrum on consumer prices, the timing of a new servicelaunch, and the market structure using data from the mobile markets of 21 OECD countries.Although our study uses a relatively small sample and a simple methodology, the resultsare meaningful since it examines a single service (3G) in OECD countries. Some of thesecountries have adopted auctions while others have used the traditional beauty contestapproach. This combination provides a natural experiment to evaluate the impact of auc-tions on the mobile telecommunications market.

The estimation results show no evidence to support claims of negative effects of spec-trum auctions in the mobile communications market. This study calls for more positiveaction toward spectrum auctions in many countries who seek to improve the efficiencyand transparency of spectrum assignment.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction quest. The governments’ only limitations were that the

The radio spectrum is an essential component of mobilecommunications services. Since the introduction of mobileservices, mobile communications operators have fought forgreater access to the frequency spectrum at lower costs(Calhoun, 1988). As the government allocates the radiospectrum in a specific bandwidth, its decisions sometimesdetermine the winners and losers in the mobile communi-cations market.

In the early stage of radio communications, the demandfor spectrum was low; thus, the governments grantedspectrum licenses to whichever company made the first re-

. All rights reserved.

esw726@yonsei.ac.kr

companies must use the spectrum for a specific purposewithout interfering with other users. As mobile telecom-munication services have captured an increasing numberof customers, the demand for spectrum has increased.Therefore, the first-come-first-served basis is no longeruseful for allocating this limited resource.

As an alternative to the first-come-first-served basis,governments turned to a ‘‘beauty contest’’ approach. Withthe ‘‘beauty contest’’ approach, firms submitted businessplans to a government committee that granted the licensesto the firms that were best qualified under the govern-ment’s published criteria. Based on the idea that the spec-trum is a public resource ‘‘owned’’ by governments, itseems natural that they would award spectrum access tothe applicant who best serves the public interest. However,it has proven difficult to specify fair criteria and to objec-tively evaluate business plans; this problem can result in

M. Park et al. / Information Economics and Policy 23 (2011) 118–126 119

time-consuming and non-transparent processes that canlead to political controversy and corruption (Binmore andKlemperer, 2002).

Another alternative to the first-come-first-served ap-proach basis is the ‘‘auction’’ approach. The merits of theauction approach have been advocated by many econo-mists; they claim that this approach is most likely to assignresources to those who can most efficiently use them. Theidea of auctioning the radio spectrum initially came fromHerzel (1951) and was later echoed by Coase (1959), butthe idea was considered unrealistic at that time. However,since New Zealand began using a second-price, sealed-bidauction, an increasing number of countries have been rely-ing on auctions for their spectrum assignments. Since themid-1990s, the United States has introduced auctions forspectrum assignments, and overall the auction programhas been highly successful (Cramton, 2001; Kwerel andRosston, 2004).

The 2000–2001 European auctions of ‘‘third generation’’(3G) mobile telecommunication (or UMTS) licenses are an-other milestone in spectrum auctions. The United Kingdomwas the first in the world to auction the 3G spectrum. Inthe UK, nine new entrants have bid highly against theincumbents, creating record-breaking revenues of 39 bil-lion euros ($34 billion), and the auction in the UK is widelyjudged to have been a success (Klemperer, 2002a). Not sur-prisingly, the UK’s example has been copied across theworld. Many European countries, including the Nether-lands, Italy, Austria, Germany, Switzerland, Belgium, Den-mark, and Greece, emulated the UK’s auction when theyassigned spectrum resources.1 Even countries that had orig-inally chosen to use the beauty contest approach haveturned to the auction approach.2 The argument that bureau-crats are the best people to decide which companies wouldbest serve the customers’ needs has come into doubt.

Despite the increasing use of the auctions as a licensingmethod, it has been adopted by few Asian countries. Inmany developing countries, governments still make deci-sions, such as which companies are awarded the spec-trums and how much the companies should pay. Theiravoidance of auctions is partly based on the aversion to-ward ‘‘selling’’ public resources to the highest bidder. Thissentiment is still dominant regarding the spectrum forbroadcasting, which is granted without fees in most coun-tries. But it is also based on fears about the possible ad-verse effects of auctions, which has both anecdotal andacademic support.3

The repercussions of spectrum auctions have long beendebated, but the arguments are primarily based on theoryand are based on very little empirical research. Since thepractice of auctioning the 3G spectrum has only occurredsince 2000–2001, very little data has been generated. Theinitial service date of 3G varies across various countriesand different companies, but it was not started until

1 The auctions raised over $100 billion combined in Europe, althoughthere was an enormous variation between countries (Klemperer, 2002a).

2 For example, Hong Kong switched from a beauty contest to a ‘‘hybrid’’system that requires network operators to open part of their capacity toother companies (Klemperer, 2000).

3 We return to this point in the following section.

2003. With 3G service now provided in most OECD coun-tries, and some countries adopting auctions while othersuse the traditional beauty contest approach,4 we have nat-ural situations in which to evaluate the impact of auctionson the telecommunications market.

This paper investigates the effects of spectrum auctions;in particular, it focuses on whether the auctions haveharmed consumers, which auction opponents claim by cit-ing the experiences on 3G spectrum assignments (forexample, excessive auction prices in UK and delay of initialservice dates for a couple of years by many licensees) andrelated market data of OECD countries. Although severalstudies have analyzed the impacts of license fees and thespectrum allocation methods in terms of both economictheory and empirical method (Kwerel, 2000; Bauer,2003),5 no study has systematically tested the hypothesesabout spectrum auctions using comparable data. This is thefirst paper to access empirically the effects of 3G licensingpolicy on the consumer market.

In this paper, we will first describe the pros and cons ofspectrum auctions and clarify the working hypotheses. InSection 3, we review previous related literature. Empiricalmodels and data are introduced in Section 4, and the re-sults are provided in Section 5. Section 6 presents the con-clusions of this study.

2. Pros and cons on spectrum auctions

2.1. Pros

One of main advantages of spectrum auctions is effi-ciency, since the auctions assign the spectrum to thosecompanies who can use it most efficiently (Coase, 1959;Cai, 2000; Cramton et al., 1987). In auctions, firms thatcan extract the biggest profits from the spectrum by usingit most efficiently are generally the highest bidders andwinners. Still, in most auctions, regulators decide the termsand uses of the spectrum and impose the rights and obliga-tions of licensees. Therefore, the use of auctions alone isnot enough to guarantee that market forces will bringthe maximal efficiency to the use of the spectrum. But, atleast, spectrum auctions are widely recognized as moreefficient than beauty contests for assigning exclusive rightsto spectrum resources (Morris, 2005). In a beauty contest, acommittee generally sets the criteria, such as financialcapability, network reliability, R&D investment, the speedof network rollout, pricing, quality, technology, and com-petitiveness. The candidates’ business plans are then eval-uated according to these criteria. If there were noirregularities in the information between the committeeand candidates, the committee would be able to identifythe best applicants and assign licenses to them. However,in practice, applicants have no incentives to discloseproprietary information. Therefore, applicants in a beauty

4 For example, Finland and Spain have adopted a beauty contestapproach for their third generation (3G) spectrum allocation. The UK,Germany, and other countries use auctions to grant 3G licenses. On theother hand, France and Italy have allocated licenses through a mixedsystem, integrating the beauty contest and an option to increase the bid at asuccessive stage.

5 These previous studies will be reviewed in Section 3.

6 For instance, one of the main goals of auctioning digital dividends, thespectrum left unused after the analog TV switch-off, is raising funds for thedigital TV transition.

7 This was noted by an anonymous referee.8 Athey and Levin (2001), Hong and Shum (2003), Guerre et al. (2000),

and many others.

120 M. Park et al. / Information Economics and Policy 23 (2011) 118–126

contest may present unrealistic business plans, whichcause the committee’s decision-making process to be verydifficult. In contrast, auctions allow the possibility ofassessing the business capability of competing firms dur-ing the bidding process, and bidders must reveal their pri-vate information (Binmore and Klemperer, 2002).

Moreover, with auctions, governments do not have tospecify and evaluate criteria for choosing the best candidatefor spectrum use, which is a ‘‘very time-consuming and opa-que process that leads to political and legal controversy, andthe perception, if not the reality, of favoritism and corrup-tion’’ (Binmore and Klemperer, 2002). In a beauty contest,the set of criteria considered by the selecting committeemay not be fair to every applicant. If this is the case, the com-position of the selecting committee affects the result, anddifferent juries in the selecting committee may allocate li-censes to different operators. This creates opportunitiesfor legal challenges because losers may feel discriminatedagainst if they do not clearly understand the basis on whichthe licensees were actually chosen.

A third advantage of auctions is that the competition isnot wasteful (Cramton, 2001). The competition in auctionsprovides a good way to raise revenue to support public fi-nances. The increased auction revenues can be used to off-set distortionary taxation.

2.2. Cons

When the UK began the world’s first 3G auction inMarch of 2000, the head of the United Nations telecommu-nications agency argued that these auctions would drivethe telecom industry into a crisis, and a Finnish ministercalled these auctions the biggest industrial political failuresince the Second World War (Klemperer, 2002b). After thefirst three 3G auctions in the UK, Netherlands, andGermany ended with unexpectedly high auction prices,some called it a disaster, while some called it a success.

Such auctions are criticized by many people who arguethat auctions increase license fees and that these fees willbe passed to the consumer through price (Sutton, 1998;Garrard, 1998). Some even believe that firms can ‘‘overbid,’’or bid more than the real value of the spectrum in highlycompetitive circumstances. For instance, in the hearingson spectrum auctions held in 1986 before the US House ofRepresentatives, there were arguments that firms wouldhave higher costs since they pay for their licenses insteadof getting them for free, and that these costs would bepassed onto their customers (US House, 1986, p. 43).

In fact, the license fees determined by auctions have gen-erally been higher than those in beauty contests. This mighthappen because bidders do indeed bid aggressively. But en-forced overbidding is not theoretically possible, since bid-ders can always exit if the auction prices are higher thantheir valuation. Higher fees may occur because the spec-trum allocated by auctions happened to be more valuableor beauty contest prices are too low. The impact of licensefees on consumer price is also controversial. In theory, anupfront license fee is a sunk cost that cannot affect pricing.But, as Gruber (2001) argues, higher license fees may lowerthe number of firms sustained by the market. Furthermore,high license fees could signal post-entry collusion, which

into turn raises the consumer price. According to an inter-view with Business Week, Klemperer argued that althoughauction fees will not affect the prices of mobile services, pol-itics might (Peterson, 2000) if telecom companies succeedin persuading the government to increase prices.

There is also the possibility that the auctions forspectrum allocation could reduce investment in the mobileservice infrastructure (Wruck, 1994). According to thisargument, although the bids in the auctions are voluntary,they induce high financial costs. Thus, the auctions’ win-ners would have to reduce their investment in mobile ser-vices or delay their service launch, both of which wouldharm consumers. However, some argue that since the auc-tions’ fees have been paid in full, they cannot be recoupedby cutting investments, which means that the auctions’fees make no difference to the investor’s profitability(Klemperer, 2002b; Kwerel, 2000).

It is also argued that auctions are unfavorable to smallor new companies and thus increase market concentration.According to this argument, auctions are a ‘‘money game’’in which winners are chosen by the amount of their bids;thus, big companies that can finance the fees relativelyeasily are more likely to win. Incumbent operators alsohave a higher chance of bidding more because they wouldotherwise face new competitors and lose their currentprofits. Incumbents add a possible loss to the valuation ofthe spectrum, so their bid could be bigger than the pro-jected future profits derived by the use of the spectrum.For example, telecom operators providing 2G mobile ser-vices were forced to overbid for 3G licenses to protect theirexisting 2G businesses. The final markets could also be-come more concentrated due to the incentive of the regu-latory authority to extract more revenues from auctions.Raising license rent is sometimes considered to be a goalof auctions (McAfee and McMillan, 1996). The revenue isneeded for reducing the budget deficit or funding publicprojects.6 However, auction rules adopted to increase the li-cense revenue can reduce social welfare by harming marketcompetitiveness (Hazlett and Munoz, 2009a).7

3. Previous empirical studies

There have been an increasing number of studies empiri-cally investigating the issues concerning auctions.8 Some ofthese studies have attempted to test the efficiency of spectrumauctions. However, when questioning whether spectrum auc-tions are efficient, the answer may vary depending on what isbeing optimized, what else is assumed to be constant, andthe policies against which they are being compared (Morris,2005). Bajari and Fox (2005, 2007) empirically studied biddingin the 1995–1996 C Block auctions of the US mobile phoneservice licenses and found that the auctions resulted in aninefficient outcome. According to this study, the packages inthe C Block were too small to be efficient because of demand

9 How we set the price variable will be explained later with introductionof data that we used.

10 Fees are measured in USD; for the countries of which fees were not

M. Park et al. / Information Economics and Policy 23 (2011) 118–126 121

reduction and intimidating collusion. They concluded that sev-eral large licenses or a nationwide license would have yielded alarger surplus. Yeo (2008) recently offered somewhat differentfigures from spectrum auctions in the US. By estimating the dis-tribution of bidder valuation, she found that the bidders hadpaid much less than their valuation.

A few studies have empirically tested how spectrumauctions affect the prices of wireless communications ser-vices. Using data on cellular prices and ownership from1985 to 1998 in the 30 largest US markets, Kwerel (2000)empirically supported the theoretical argument that aspectrum license does not increase the prices of wirelessservices. He showed that the changes in the price beforeand after the license award were indifferent between themarkets with fees and those without fees. Furthermore,the firm who values the spectrum the most generally paysonly slightly more than the amount that the second-highest bidder is willing to pay (Kwerel, 2000).

Bauer’s empirical study using 18 mobile voice serviceproviders from OECD countries tested the impact of licensefees on the prices of mobile communication services(Bauer, 2003). The empirical results did not find anystatistically significant evidence for lasting effects. Thatis, although the parameter estimate shows a positive rela-tionship between the license fees and the prices for theresidential market and a negative relationship for the busi-ness market, the parameters are not statistically signifi-cant. This implies that license fees do not influence prices.

Hazlett and Munoz (2009a,b) analyzed quarterly dataon the prices and output of 28 countries; they found thatprices were negatively affected by the amount of the spec-trum and were positively affected by HHI. Based on theseresults, they suggested allocation of more of the spectrum.Though it was not highlighted, one of their estimation re-sults stated that prices were higher in the countries thatheld spectrum auctions. However, they did not isolatethe effects of the licensing method. In contrast, we com-pare the effects of 3G spectrum allocation in the countrieswhere 3G services have already been implemented.

Gruber (2007) analyzed other issues, such as marketconcentration and lags in services, by looking at the Euro-pean 3G spectrum licensing. His regression results showedthat the number of idle licenses was positively correlatedwith the lags in services, but that they were not affectedby the adoption of auctions or the fees. Gruber (2007) alsoargued that the market structure for 3G became worsethan 2G because the number of 3G licenses in servicewas smaller than the number of 2G licenses in service inthe countries that adopted auctions. However, he com-pared the number in 2004, when many operators were stillpreparing for a service launch. Furthermore, the compari-son of the 3G and 2G markets is not an appropriate methodfor measuring the effects on the market structure sinceconsumers do not recognize the 2G and 3G platforms tobe truly distinct from each other.

recorded in terms of USD, the fees were converted to USD using theexchange rates that were current as of the time of the spectrumassignment. Alternative measures, such as a fee per population or fee perMHz, were used in the estimations, but they did not yield qualitativedifferences in the results.

11 For example, see Fader and Schmittlein (1993) and Guadagni and Little(1983).

4. Empirical models and data

Through simple regression analyses, we empiricallyexamine whether the methods of spectrum assignment

influence the telecommunication service market. Amongthe issues raised in the debate on spectrum auctions, weexamine the impact on consumer pricing of services,investment, and market structure.

In Eq. (1), price is regressed based on firm-level data inOECD countries; it determines whether the size of the li-cense fee and/or auctioning spectrum increases the serviceprices.

Priceci ¼ a1 þ b1Feeci þ b2Auctionc þ b3GDPPCc

þ b4HHIc þ b5Shareci þ b6Firm Spectrumci

þ b7Country Spectrumc þ �ci ð1Þ

In the equation, the dependent variable Priceci denotesprice of firm i’s service in country c.9 Explanatory variablesinclude the license fee paid by firm i (Feeci)10 and a dummyvariable that is 1 if country c assigned a spectrum by auc-tions (Auctionc). Different countries use different methodsof assignment, and firms often pay different license feesfor the same services. If the coefficient estimates of thesevariables are significantly positive, it can be argued thatthe prices rise as the spectrum is auctioned and license feesare increased. Other than the fees and the method of spec-trum assignment, the price of telecommunication servicecan be determined by the size and structure of the market.To account for these factors, we use the market share of firmi (Shareci), the Herfindahl–Hirschman Index (HHIc), and theGDP per capita (GDPPCc) of country c as independent vari-ables. The market share represents the quantity of serviceprovided by a firm. If there are economies of scale, the pricewill decrease as more subscribers use the network; thus,negative coefficients on the share and the GDP per capitaare expected. However, in another aspect, a high marketshare may strengthen brand loyalty11 and therefore enablesprices to increase. In a telecommunications market with net-work effects, lock-in effects will keep the customers fromswitching despite the high price. Due to the mixed effects,it is hard to predict the sign of the coefficient on the marketshare. The amount of the spectrum assigned is also animportant factor that affects the service price. As the num-ber of subscribers exceeds the channel limit of a given spec-trum, operators must try to accommodate them withadditional investments, such as cell division and installmentof base stations. Therefore, the marginal cost of providingservice decreases in the spectrum (Hazlett and Munoz,2009a). In an oligopolistic market, the firm’s price is notdetermined only by its own marginal cost, but it is alsodetermined by its competitors’ costs. To capture the effectsof spectrum, we include two variables: Firm Spectrumci

and Country spectrumc. The last term eci is an idiosyncraticerror.

122 M. Park et al. / Information Economics and Policy 23 (2011) 118–126

An econometric issue in Eq. (1) is the possible endoge-neity of dependent variables, especially the auction dum-my and the market share. Auctions may have beenavoided by the countries who are concerned about highprices. However, this study focuses on the prices in 2006,while the auctions primarily took place between 2000and 2002. Despite the persistence of prices, the 2006 pricesare not likely to affect the decision on the assignmentmethod. Prices can reversely affect shares as a firm offeringhigher prices tends to have lower market share with otherthings the same. We address this problem by using themarket shares from 2004. Since 2004 market share is a pre-determined variable, reverse causality does not happen aslong as the current price is not affected by a long-lastingunobservable factor.

It is difficult to construct internationally comparable pric-ing data since each country has a different set of service plansand different pricing schemes. As a proxy for price, we userevenue per minute (RPM) for each operator of 21 OECD coun-tries in 2006 from Merrill Lynch (2007). The RPM data in-cludes the revenue from both incoming and outgoing calls.Another problem with an international comparison is thatsome countries use a ‘‘calling-party-pay (CPP)’’ system, whileothers use a ‘‘receiving-party-pay (RPP)’’ or ‘‘both-pay’’ sys-tem. To get more precisely comparable prices, we adjustedMerrill Lynch’s RPM by the ratio of incoming calls and the mo-bile terminal charges collected by Ovum (2008). More specif-ically, interconnection charges per minute on incoming callsfrom fixed or other mobile operators are deducted from Mer-rill Lynch’s RPM to isolate RPM from outgoing calls. The shareof incoming calls is assumed 40% based on the traffic data ofKorean mobile market. The market share of each firm andthe HHI of each country as of 2006 are also from Merrill Lynch(2007). Data on the method of spectrum assignment andlicensing fees are collected from various sources, such asGSM Europe (2001), ITU (2001a, 2001b), Whalley and Curwen(2006), and OECD (2009). If there were any discrepancies be-tween the sources, we double-checked the regulators’ web-sites and the news reports and adjusted accordingly. Themain sources for the spectrum information are the EuropeanRadiocommunications Office (2007, 2008) for Europeancountries and the websites of regulators, operators, and rele-vant third parties for non-European countries (for example,Lemay-Yates Associates Inc., 2007, for Canada). Our measureincludes only spectrum for 2G (GSM, CDMA, PDC in Japan, andiDEN in Canada) and 3G (the family of standards for IMT-2000) mobile telecommunications, which serve most of sub-scribers. Our country level spectrum is compared and foundconsistent with Hazlett and Munoz (2009b).

A second empirical question is whether the high licensefees from the auctions cause financial difficulty for thewinners and thus delay their investment and servicelaunch. To investigate this question, Eq. (2) is estimated.

Lagci ¼ a2 þ c1Feeci þ c2Auctionc þ c3GDPPCc þ c4HHIc

þ c5Shareci þ c6MNCci þ c7Firm Spectrumci

þ c8Country Spectrumc þ �ci ð2Þ

The dependent variable Lagci is measured as the timelag of a firm i from the date of the license grant to that ofthe 3G service launch. If a company’s investment is

hindered by the burden of the license fees, the company re-quires more time to start its business. In that case, we willget a positive value for c1. Different assignment methodsmay yield different business environments regardless of li-cense fees. For instance, regulators tend to levy morerequirements on the licensees in beauty contests, whileoperators acquire more spectrum usage rights in auctions.Therefore, auctions may expedite the service launch. Thedate of the new service launch is also affected by the sizeand the competition of the market; therefore, some controlvariables in Eq. (2) are included. As the market size andshare increase, the expected profits from investment willalso increase. Thus, the signs of the coefficients on theGDP per capita, HHI, and the market share are expectedto be negative. Capital investment and spectrum are sub-stitutes in the firm’s production function (Reed, 1992;Hazlett and Munoz, 2009a). This implies that the invest-ment time is shortened if a firm has a larger amount of spec-trum. MNC in Eq. (2) is used to represent whether theoperator is affiliated with a multi-national company. Thenetwork deployment in a certain country is often deter-mined by the global plans of multi-national companies,which may have better financial capabilities for investment.

The last empirical issue is the relationship between auc-tions and market concentration. As previously described, asimple comparison of the number of firms providing 3Gservice is not an appropriate measure of the market struc-ture. Instead, we calculate the change in HHI before andafter the 3G spectrum allocation for each OECD country;then we will compare this information in the countriesusing auctions and beauty contests. More specifically, thethree-year averages of HHIs from Merrill Lynch (2007) be-fore and after the spectrum allocation are calculated andthe percentage change is computed. Note that, in mostcountries, the HHI declined and DHHI is the negative ofthe rate of change. The regression Eq. (3) is estimated usingcountry level data.

DHHIc ¼ a3 þ d1Feec þ d2Auctionc þ d3GDPPCc þ d4NOc

þ d5HHIðbeforeÞc þ d6D Spectrumc þ �c ð3Þ

The market structure is determined by various factors,such as fixed costs, difficulties in learning and catch-upprocesses, product differentiation, market size, and so on.Due to the lack of data, we have only included the variablesrepresenting the market size, the license fees, and theassignment method. The change rate of HHI, DHHI, also de-pends on the level of initial competition. Therefore, we in-clude the number of operators (NOc) and HHI prior to theallocation (HHI(before)c) of each country. Spectrum as-signed additionally within these periods (DSpectrum) alsocan change the market structure. We calculate DSpectrumas a share of the newly assigned 3G spectrum comparedwith the existing 2G spectrum. Summary statistics of thevariables are given in Table 1.

Our observations are grouped by the spectrum alloca-tion method so they can be compared with each other. Atthe firm level, the spectrum prices in the countries withauctions dropped more than the spectrum prices in coun-tries with beauty contest although they are not statisticallydifferent. It is not surprising that the most significantdifference between the two groups lies in the license fees,

Table 1Summary statistics by spectrum allocation method.

Auctions Beauty contest

Mean SD Min Max Mean SD Min Max

Firm levelRPM ($) .21 .08 .07 .40 .22 .09 .08 .38DRPM (%) .095 .080 �.094 .273 .075 .079 �.056 .247Lag (mo.) 27.7 15.3 9 55 26.5 16.7 6 68Fee (mil $)a 2026 3074 11 9400 280 389 0 1100Share .308 .136 .043 .627 .366 .151 .165 .676Spectrum (MHz) 78.1 21.8 34.6 150.2 85.6 21.4 20 117

Country levelHHI 3475 695 2268 5032 3917 527 3579 5196DHHIa (%) .161 .135 .17 .374 .065 .096 �.113 .164NO 3.92 0.95 2 5 3.75 1.49 2 7GDPPC (bil $) 690 676 85 2213 914 1114 118 3331Spectrum (MHz) 251.2 79.5 100 380 224.7 71.5 148 331DSpectrum .59 .17 .29 .97 .81 .36 .46 1.60

a In the 10% significant level t-test rejects the hypothesis that the difference between the means of the two groups is zero.

Table 2Regression results for consumer prices.

Variable (1) (2)RPM Log(RPM)

Fee 0.00443 0.0431(0.00545) (0.0368)

Auction �0.0119 0.0227(0.0244) (0.129)

Share 0.0491 0.0232(0.0865) (0.113)

HHI �1.12e�05 0.264(2.72e�05) (0.549)

GDPPC �0.00603** �0.629**

(0.00202) (0.200)Firm spectrum 0.000381 0.118

(0.000598) (0.203)Country spectrum �7.71e�05 0.145

(0.000182) (0.233)Constant 0.385** �2.975

(0.122) (5.101)Observations 59 59R-squared 0.193 0.193

Note: Standard errors in parentheses. In the second column, explanatoryvariables are logged except for the auction dummy.⁄p < 0.05.

** p < 0.01.

12 We included an interaction term Fee � Auction to see the difference inslope between auction countries and non-auction countries. Although theresults are not reported here, the coefficient on Fee in Eq. (1) becamenegative (�0.106) and the coefficient on the interaction term turned outpositive (0.112). Effects of fees on prices for the auction countries are thesum of these two coefficients. The hypothesis that the sum is zero is notrejected by F-test, thus our conclusion that high auction payments do notraise prices is still held. Negative coefficient for the non-auction countriescan be due to economies of scale. Under the command-and-control regimewhere government levies license fees, bigger company tends to pay higherfee. Big companies may offer lower prices by taking advantage of largereconomies of scale as far as market competition is maintained.

13 Another regression was performed with an average rate of change inprices after the 3G spectrum assignment as a regress and instead of level ofprices, but it did not give qualitatively different results. The results are notreported in Table 2 but are available upon request.

M. Park et al. / Information Economics and Policy 23 (2011) 118–126 123

since high auction receipts were observed in some Euro-pean countries. One interesting observation in Table 1 isthat the HHIs in the auction countries decreased signifi-cantly more than those in the non-auction countries.

5. Estimation results

5.1. License fees and consumer price

As shown on Table 1, the average license fee per opera-tor for the 3G spectrum granted by auctions is 2.026 mil-lion dollars, which is higher than that of beauty contests.The high license fees in the auctioned spectrum are mainlyattributable to the first three countries that adopted

auctions: the UK, the Netherlands, and Germany. Excludingthese countries, the average fee is to 359 million dollars,which is not statistically different from the average feefrom the beauty contests. As countries have learned fromprevious auctions and the forecast of the profitability of3G has become discouraging, auction prices have fallen.

Despite the higher average fees for the auctioned spec-trum, there is no evidence that auctions have led to an in-crease in consumer prices. From the estimation results ofEq. (1) shown in Table 2, we can see that the signs of thecoefficients on Fee and Auction are positive and negative,respectively. However, the estimates are not significantlydifferent from zero; thus, we cannot reject the ‘‘sunk cost’’hypothesis about auctions. In the second column of Table2, the regression with a logarithm specification for depen-dent and independent variables (except for Auction, adummy variable) yielded qualitatively the same results.12

The coefficients of market share and HHI are estimated aspositive, but they are also not statistically different fromzero. The spectrum amount available both by a firm andby all firms in a country do not influence the firm-levelprices. Thus, only the variables representing market size af-fect the price. Consumer prices are lower in the countrieswith a larger GDP per capita.13

Table 3Regression results on lag.

Variable (1) (2)Lag Log(Lag)

Fee �0.0779 �0.0731(1.033) (0.0518)

Auction 5.817 0.0417(4.518) (0.182)

Share �6.071 �0.0979(15.87) (0.159)

HHI 0.00280 �1.201(0.00500) (0.778)

MNC 1.741 0.0296(4.199) (0.153)

GDPPC 0.0426 �0.0450(0.371) (0.281)

Firm spectrum 0.0378 0.211(0.112) (0.288)

Country spectrum �0.113** �1.400**

(0.0334) (0.327)Constant 39.01 19.57**

(22.35) (7.209)Observations 59 59R-squared 0.304 0.341

Note: Standard errors in parentheses. In the second column, explanatoryvariables are logged except for the auctions and MNC dummies.⁄p < 0.05.

** p < 0.01.

Table 4Regression results on the change rate of HHI.

Variable (1) (2)DHHI Log(DHHI)

Fee 0.000976 0.224*

(0.00189) (0.0825)Auction 0.0637 0.431

(0.0371) (0.318)GDPPC �0.00213 �0.625

(0.00365) (0.597)No. of operators 0.0918** 2.506**

(0.0234) (0.692)HHIbefore 0.000112** 5.149**

(2.05e�05) (0.762)DSpectrum 0.115 1.575*

(0.0754) (0.504)Constant �0.795** �46.36**

(0.174) (6.255)Observations 19 17R-squared 0.808 0.846

Note: Standard errors in parentheses. In the second column, explanatoryvariables are logged except for the auction dummy.* p < 0.05.

** p < 0.01.

124 M. Park et al. / Information Economics and Policy 23 (2011) 118–126

5.2. Overbidding and delay of services

Some 3G licensees declared bankruptcy or sold theirbusinesses in countries including Germany (MobilCom,Group 3G), Italy (IPSE2000), and Switzerland (3G Mobile),in which auctions were implemented. Some argue thatthese situations were a result of overbidding. In principle,a firm’s decision to enter an auction is based on its expec-tation of future profits, and the firm can always opt to leavethe auction. Firms can overestimate their future earnings,with mistakes being made regardless of the assignmentmethod. For instance, LG Telecom of Korea, Smart Telecomof Ireland, and Orange Sweden all returned their licensesthat were obtained through beauty contests (see Table 3).

Some believe that the burden of fees may have forcedthe licensees to deter investments in the 3G service. Theestimation results of Eq. (2) offer evidence against thisargument. The coefficient estimates are positive, but theyare not significant. In fact, the time spent launching the3G service seems to be quite random, since it is affectedby neither the market size nor the market concentration.The only relevant variable is the amount of spectrum inthe country overall. It has a negative and significant esti-mate for its coefficient, while the firm spectrum coefficienthas no impact. This result can be explained by the spec-trum’s role in the market competition. The marginal costat an early stage of service would not be influenced bythe spectrum, since no operator reaches the limit of itscapacity. If the overall amount of spectrum in a countryis large relative to the demand, operators have to competefor the limited demand; thus they have an incentive tostart services quickly and preoccupy the customers. Bycontrast, when the amount of spectrum is relatively small,operator would not be in such a hurry to start business.

This may be one reason why country spectrum affectsthe time for the service launch.

5.3. Market concentration

In most countries, the 3G market was designed to invitemore, or at least the same number, of operators than thenumber involved in the 2G market. Nineteen out of 37European countries that had awarded 3G licenses choseto issue more licenses for 3G than they did for 2G. In onlyfour countries less 3G licensees were picked than 2Goperators. Some countries, such as the UK, offered somelicenses exclusively for new entrants (Whalley andCurwen, 2006). But the governments’ efforts to increasethe number of 3G firms were not successful. In particular,as shown by Gruber (2007), the number of 3G firms inoperation is less than the number of 2G firms in the coun-tries where auctions occur. However, this does not meanthat the consumer market has become more concentratedin those countries.

It is not easy to investigate a clear link between spec-trum assignment and the final service market structure.A rough estimate can be obtained by determining howthe level of concentration changes after licenses areawarded. As shown in Table 1, the average change rate ofHHI three years before and after the awarding of licenseswas �16.1% in 12 sampled auction-performing countries.Conversely, the average rate was �6.5% in nine countriesthat assigned licenses based on beauty contests. Theregression results also show that auctioning gave rise toa decrease in HHI (larger change in HHI), but it is statisti-cally weak. As shown in Table 4, it can only be verified that‘‘level effects’’ exist. That is, HHI decreased more when thenumber of operators increased or the initial level of HHIwas higher. Spectrum also plays an important role indetermining the market structure as shown by Hazlettand Munoz (2009a,b). In the log–log specification, a greater

M. Park et al. / Information Economics and Policy 23 (2011) 118–126 125

increase in spectrum leads to greater reduction in marketconcentration. These results do not imply that auctionsthemselves encourage more competition, but there is noevidence that auctions increase concentration.

6. Conclusions

For many years, beauty contests were generally used toassign spectrum among competing applicants. However,this method has been criticized for having many problems,such as favoritism, corruption, a lack of objectivity, andtransparency in many countries. To overcome such prob-lems, some pioneering countries turned to an old sugges-tion from an economist and designed a new auction formto assign radio spectrums. Although some problems did oc-cur, such as collusion among bidders and a demand reduc-tion, the results are considered to be highly successfuloverall. Furthermore, those countries that have alreadyadopted spectrum auctions maintain the use of auctionsfor their spectrum assignments. This means that the poten-tial problems with auctions are not as significant as pessi-mists may argue.

However, there are still many countries that have notadopted the use of auctions for spectrum assignment. Thismay be due to the perceptions about the potential prob-lems that auctions may cause, such as high licensing fees,high consumer prices, a lower incentive to invest in infra-structure, and market concentration.

Using data from OECD countries that have assigned 3Gspectrum, this study investigated whether higher licensingfees and adopting auctions as a spectrum assignmentmethod led to higher prices and lower incentives forinvestment. In addition, this study examined whether auc-tions had induced scarce resources to be granted to largerincumbent firms, thus resulting in a more concentratedmarket. The estimation results show no evidence to sup-port these negative effects of spectrum auctions in the mo-bile communications market. Given the higher licensingfees for the firms who were assigned a spectrum by theauctions, it is surprising that prices have been kept aslow as in the countries which used the beauty contestand the market concentration has been reduced evenmore. The results indirectly imply that auctions are a bet-ter method since they are simple and transparent and theycan extract rents, which otherwise would have been givenout to the licensees.

This is the first paper to empirically estimate the effectsof auctions and licensing fees for the 3G spectrum on con-sumer prices, investments, and the market structure usingdata from the mobile markets of 21 OECD countries.Although our study used a relatively small sample and asimple methodology, the results are meaningful since weexamined a single service (3G) in OECD countries. Someof the countries in our sample adopted auctions, while oth-ers used the traditional beauty contest approach, thus pro-viding a natural experiment to evaluate the impact ofauctions on the mobile telecommunications market. Basedon the empirical results, this study calls for more positiveaction toward spectrum auctions in many countries whoseek to improve the efficiency and transparency of spec-trum assignment.

Acknowledgments

We would like to express our appreciation to Dong-MinLim and Il-Joo Lee of the Korea Information Society Devel-opment Institute (KISDI) for their help in collecting data.This Research was supported by the Chung-Ang UniversityResearch Grants in 2011 (M. Park). This research was sup-ported by the Korea Communications Commission, Korea,under the Communications Policy Research Center supportprogram supervised by the National IT Industry PromotionAgency (NIPA; NIPA-2010-(C1091-1001-0005) (S. W. Lee).This work was supported by Hankuk University of ForeignStudies Research Fund of 2010 (Y. J. Choi).

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