Merger simulation in a two sided market

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Merger Simulation in a Two-Sided Market: The Case of the Dutch Daily Newspapers. Lapo Filistrucchi, TILEC and CentER, Tilburg University. Tobias J. Klein, TILEC, CentER and Netspar, Tilburg University. Thomas Michielsen, CentER and TSC, Tilburg University. Media Economics Workshop, Moscow. 28 October 2011

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Merger simulation in a two sided market

  1. 1. Merger Simulation in a Two-Sided Market: The Case of the Dutch Daily Newspapers Lapo Filistrucchi, TILEC and CentER, Tilburg University Tobias J. Klein, TILEC, CentER and Netspar, Tilburg University Thomas Michielsen, CentER and TSC, Tilburg University Media Economics Workshop, Moscow 28 October 2011This paper is based on an empirical study performed for the Dutch competitionauthority (NMa). The views expressed in this paper are not necessarily the ones ofthe NMa.
  2. 2. Newspapers as Two-Sided PlatformsNewspapers‘ publishers sell content to readers and advertisingslots to advertiserstaking into account thatadvertisers care about the number of readersand thatreaders may be affected by the number of ads (or by advertisingconcentration) in the newspaper.In addition, advertisers cannot pass-through to the readers anyincrease in the advertising tariff paid to the publshers becausethere is no direct transaction between them.
  3. 3. Aim of the paperDevelop a structural econometric framework that allows us to simulate the effects of mergers, by- estimating demand for differentiated products on each side of the market.- using the estimated parameters together with a model of the supply-side to recover costs- simulate a merger and find the new equilibrium prices and quantities- calculate the effects of the merger on consumer welfareApply it to the Dutch daily newspaper market.
  4. 4. The literature-1Merger simulation in one-sided markets-Hausman and Leonard (1997), Nevo(2000), Ivaldi and Verboven(2005) and many othersSee also Jaffe and Weyl(2011)Mergers in two-sided markets-Chandra and Collard-Wexler(2009), Lionello(2010)Anderson and MacLaren (2010), Malam(2011)…
  5. 5. The literature-2Merger simulation in two-sided markets:-Fan(2010)-US newspapers, mixed logit for readers, Rysman(2004) for advertisers, no effect of ads on readers-Van Cayseele and Vanormelingen (2010) – Belgian newspapers, nested logit for readers, Rysman(2004) for advertisers, no effect of ads on readers-Jeziorski (2010) – US radio, mixed logit for listeners, log linear demand for advertisers, no price for listeners but negative effect of ads-Song (2010) – German TV magazines, logit on readers side, either logit or similar to Rysman(2004) and GLS(2002) on advertising side, positive effect of advertising on readers
  6. 6. Indirect network effects in the newspaper marketsWell-known and typically found in the empirical literature on media markets that demand for advertising in newspapers depends positively on their circulation.Not generally found that readership demand depends on amount of advertising: -Argentesi and Filistrucchi (2007), Van Cayseele and Varnomelingen (2010) and Fan (2010): no effect of advertising on the number of readers of daily newspapers in Italy, in Belgium and in the US -Kaiser and Wright (2006) and Kaiser and Song (2009) and Song(2010): advertising increases readers demand for magazines in Germany. -Wilbur(2008) and Jeziorski(2010): advertising affects negatively TV viewers and radio listeners in the US
  7. 7. Dutch daily newspapers 1999-2009
  8. 8. Readers demand-IPotential market is total population above 14 yearsEach reader buys at most one newspaper.Reader i utility of reading newspaper j at time t in market m (with i  I m ) uijtm   i ( yit  p n )  x n i  i q a  tn   jRm   ijtm n n jt jt jt n nwhile the utility from buying the outside good is u n iotm   i y     0 Di   0vi   n it n otm n iotmwhere  ijtm and  iotm are type 1 extreme value and i.i.d. n n across readers and newspapers and i         i       Di  vi , with vi ~Pv (v) and Di ~Pv ( D)    i  
  9. 9. Readers demand-IIThus assumptions:
  10. 10. Advertisers demandThe quantity of advertising in a newspaper j at time t isgiven by pa log q a   a log  X a  a   ja  ta   a jt jt jt jt q rjtThus, assumptions:- Price per reader is the price that matters- No cross price or network effects (as in Rysman(2004))- Constant (price per reader) elasticity
  11. 11. Dutch Daily Newspapers DataQuarterly national level HOI data on circulationSubscription prices from … (91% of circulation is subscriptions for the non-free newspapers).From Lexis-Nexis data on newspaper characteristics (number of times certain keywords related to different topics have been mentioned).NOM Print monitor data on reader characteristics (gender, age, wealth, region, percentage bread winners, percentage shopping for groceries).CBS (Statistics Netherlands) on total population over 14 and distribution of age and gender at municipality levelNielsen data on: -number of advertising column millimeters and pages -total number pages -advertising revenues then calculate: -percentage advertising pages -average price (dividing advertising revenues by advertising quantity)
  12. 12. Readers Demand Estimates
  13. 13. Readers demand price elasticities AD1 BAK BND BRA EIN GEL GOO HAR LEI LEW LIM NED NOO NOR NRC NRN PAR PZC REF STE TEL TRO TWE VOLAD1 -2.480988 0.001473 0.015069 0.008825 0.005408 0.011402 0.00281 0.002213 0.007568 0.00842 0.00574 0.013564 0.009762 0.006527 0.105437 0.022052 0.006318 0.010121 0.034882 0.014408 0.178184 0.054173 0.005155 0.091211BAK 0.080895 -1.719475 0 0 0 0.027805 0 0 0 0 0 0.026784 0 0 0.027291 0.007408 0.001911 0 0.138728 0 0.266817 0.057005 0 0.037047BND 0.059596 0 -2.082449 0.013279 0 0 0 0 0 0 0 0.001925 0 0 0.057563 0.012012 0.001155 0.039484 0.005219 0 0.118053 0.019175 0 0.058283BRA 0.029287 0 0.011143 -2.213303 0.006737 0.000873 0 0 0 0 0 0.001911 0 0 0.062226 0.012432 0.001185 0 0.007765 0 0.130034 0.017773 0 0.067573EIN 0.021759 0 0 0.008169 -2.025203 0 0 0 0 0 0.000533 0.001603 0 0 0.064846 0.010327 0.001027 0 0.000187 0 0.113934 0.016702 0 0.058151GEL 0.032996 0.001466 0 0.000762 0 -2.21955 0 0 0 0 0.000963 0.004458 0 0 0.072447 0.01376 0.001398 0 0.011624 0.009951 0.155793 0.031642 0.010752 0.087943GOO 0.045426 0 0 0 0 0 -2.277802 0 0 0 0 0.008927 0 0 0.180162 0.027188 0.037334 0 0.008771 0 0.31571 0.053248 0 0.133757HAR 0.024741 0 0 0 0 0 0 -2.286363 0.001856 0 0 0.004585 0 0.006979 0.155682 0.028141 0.037849 0 0.002224 0 0.318152 0.046812 0 0.148809LEI 0.108459 0 0 0 0 0 0 0.002379 -2.367648 0 0 0.008102 0 0 0.155678 0.027646 0.006225 0 0.017247 0 0.257921 0.053882 0 0.132096LEW 0.042531 0 0 0 0 0 0 0 0 -1.958036 0 0.011644 0.010311 0 0.034636 0.009228 0.001524 0 0.003021 0.002777 0.195376 0.038749 0 0.053602LIM 0.018478 0 0 0 0.000426 0.001071 0 0 0 0 -2.108796 0.000604 0 0 0.051237 0.009004 0.000628 0 6.96E-05 0 0.090915 0.011374 0 0.055586NED 0.176241 0.006339 0.006324 0.007482 0.005176 0.020016 0.007175 0.005328 0.007346 0.02995 0.002439 -3.155627 0.114695 0.011919 0.079783 0.016209 0.008846 0.015728 0.042552 0.126931 0.20772 0.062387 0.025411 0.085863NOO 0.03218 0 0 0 0 0 0 0 0 0.006729 0 0.029098 -2.009618 0 0.055347 0.013059 0.001275 0 0.002357 0.000737 0.136955 0.051422 0 0.072543NOR 0.021051 0 0 0 0 0 0 0.002013 0 0 0 0.002958 0 -2.069238 0.054175 0.010189 0.02717 0 0.001412 0 0.254359 0.028287 0 0.095539NRC 0.179735 0.000847 0.024811 0.031964 0.027471 0.042675 0.018997 0.023736 0.018518 0.011688 0.02713 0.010467 0.028621 0.028634 -3.483968 0.030703 0.064253 0.011579 0.014349 0.032288 0.225115 0.0566 0.016197 0.140167NRN 0.149584 0.000915 0.020602 0.025412 0.017408 0.032252 0.011408 0.017073 0.013086 0.012391 0.018971 0.008462 0.026872 0.021429 0.122174 -2.283077 0.054015 0.008082 0.010477 0.023657 0.176649 0.044188 0.014196 0.118576PAR 0.03235 0.000178 0.001495 0.001828 0.001307 0.002473 0.011824 0.017332 0.002224 0.001545 0.000998 0.003486 0.001981 0.043133 0.192984 0.04077 -2.684122 0.000829 0.002617 0.0034 0.283576 0.048574 0.00137 0.177663PZC 0.083195 0 0.082062 0 0 0 0 0 0 0 0 0.009949 0 0 0.055833 0.009793 0.001331 -1.99269 0.061114 0 0.15267 0.01749 0 0.046935REF 0.273722 0.019828 0.010355 0.018362 0.000365 0.031518 0.004258 0.001561 0.009444 0.004693 0.00017 0.025697 0.005611 0.003436 0.06605 0.01212 0.004012 0.058343 -2.821956 0.075963 0.214497 0.058608 0.024954 0.061041STE 0.047556 0 0 0 0 0.01135 0 0 0 0.001815 0 0.032244 0.000738 0 0.062519 0.011511 0.002192 0 0.031953 -2.250627 0.200059 0.0522 0.017384 0.075619TEL 0.127816 0.003486 0.021412 0.028108 0.02031 0.038617 0.014009 0.020412 0.01291 0.027744 0.020257 0.011467 0.029802 0.056574 0.094729 0.018681 0.03973 0.013323 0.019608 0.043477 -2.471059 0.046002 0.026373 0.099511TRO 0.195305 0.003743 0.01748 0.019309 0.014964 0.03942 0.011875 0.015094 0.013555 0.027655 0.012737 0.01731 0.056237 0.03162 0.119702 0.023485 0.034203 0.007671 0.026926 0.057014 0.231201 -3.29951 0.020427 0.115651TWE 0.020654 0 0 0 0 0.014887 0 0 0 0 0 0.007836 0 0 0.038072 0.008386 0.001072 0 0.012742 0.021104 0.147317 0.022703 -1.895978 0.041247VOL 0.144813 0.001071 0.023398 0.032329 0.022944 0.048248 0.013136 0.021131 0.014635 0.016847 0.027413 0.010491 0.034939 0.047032 0.130548 0.027754 0.055093 0.009066 0.01235 0.036373 0.22025 0.050931 0.016344 -3.023657
  14. 14. Impact of the network effects
  15. 15. Profit Maximization-IProfits areWe cannot obtain first-order conditions in the usual way, since andBut if it were possible to get qtn  M tn ~t n ( ptn , pta ) s and qta  M ta ~t a ( ptn , pta ) s …
  16. 16. Profit Maximization-IISo if we could solve for market-shares as functions of prices on both sides, we could rewrite aswith associated first-order conditions
  17. 17. Profit Maximization-IIIWe thus needwhere ˆ s n ˆ jt s a ˆ jt S an  jk ˆ S na  p a jt jk p n jtDefinewhere
  18. 18. Profit Maximization-IVAlso definewhere
  19. 19. Profit maximization-VBy the implicit function theorem, ˆso that S exists if B is non-singular… ˆIf S exists, then a solution to the first order conditions exists (existence?)
  20. 20. Profit maximization-VIDefine an ownership matrix as in Nevo (2001) * t Ω *  1 if product j belongs to firm r and Ω*  o where jr jrotherwiseAlso definewhereso that the first-order conditions are (unique? maximum?)
  21. 21. Recovering marginal costsFrom the first-order conditionsone can obtain the mark-upsand, by subtracting them from the observed prices,then one obtains the marginal costs(note that for the observed market shares and prices there is a uniquevector of marginal costs solving the f.o.c.s)
  22. 22. Full merger simulation: new equilibriumWe solve (numerically) for the new equilibrium(stability?) Existence, uniqueness…: our strategy We look for restrictions on the demand functions which guarantee existence Weyl and White(2011) restrict the firms behaviour
  23. 23. Estimates of marginal costs
  24. 24. Summary and conclusions• Developed a structural econometric framework to analyze hypothetical mergers in a two sided market.• It allows to simulate quantities and prices of a post-merger equilibrium, the associated welfare changes and, in case, the productive efficiency gains necessary to counterbalance a welfare loss.• We applied it to the Dutch market for daily newspapers and found that an hypothetical merger has some effect on prices and welfare, and that it is important to take the network effect into account. (preliminary, may change with mixed logit)• Upcoming: formal proofs of conditions for existence, uniqueness and stability of equilibria a better specification for the advertising demand a simulation using the insulating tariffs approach of Weyl and White(2011)

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