1. Gergely Csorba (Institute of Economics, CERS-HAS) Zoltán Pápai (Infrapont Economic Consulting) 2013-07-06 CRESSE Conference, Corfu

2. Introduction and motivation Several motivating events in mobile telco markets in last decade: Considerably decreasing voice prices (more than -50% between 2003-2010) Entries first due to 3G spectrum allocation, now 4G – but is it a cause? Number of operators converged to 3 or 4 in every MS till 2012 (except CY) Several regulators consider 4 viable operators necessary (OFT 2013) Mergers with more detailed investigations (H3/Orange in AT 2012) Our goal: implement a policy evaluation methodology to separate effect of entries / mergers from the "normal" price trend On a panel of 27 MS telecom markets in 2003-2010 Analyze only voice prices (because of data constraints) Main message: effects of entries and mergers crucially depends on the number of active operators and the type of entrant Not controlling for these differences might lead to misleading conclusions in ex-post (and also ex-ante) assessment 2

3. Preliminary summary of results Effects on average price compared to the (decreasing) path of counterfactual countries not affected by specific entry type For mergers, no robust price-increasing effects were found Neither for 4-to-3 nor for 5-to-4 mergers 3 2-to-3 entries3-to-4 entries Firm typeMultinat.LocalMulti (H3)Local In 1st year+7%-20%+5%-31% * In 2nd year+36% ***0%-19%-7% After 3rd y+3%-6%-42% ***+10%

4. Estimation methodology Standard quasi-experimental policy evaluation method: compare pre- and post-event price differentials between 1. countries that were affected by the event (treatment group) and 2. countries those that were not (control) s jt -s are the country-specific shocks to be examined In the simplest case, s jt = 0 before the event and 1 thereafter Crucial underlying assumption: events are exogenous country- specific shocks or at least independent from price developments Industry experience (returning spectrum, regulatory favoritism) is that price expectations could not have been very significant in entry decisions We can also check sensitivity of the results by not including same period effects (no significant changes) 4

5. Separation of effects and hypotheses We proceed step-by-step and check how it changes the results: 1. Simple changes in operator number 2. Entries and mergers – do they have symmetric effects? 3. Conditional on operator number before the event: theory and regulatory assessments suggest larger effects with fewer firms 4. Conditional on the type of entrant Multinational entrants: Big4 firms are usually suspected to weaken competition, but Hutchison has more of a "maverick" reputation Local entrants: could these firms generate more fierce competition? 5. Short-run and long-run effects Separated effects for 1st and 2nd year to account for adjustments Average effects from 3rd year till the end to compare steady states Note: all effects measured should be interpreted as average difference from price trend of similar non-affected countries 5

6. Assessed entries and mergers in 2003-2010 No events in 12 countries (1 with 2 ops, 9 w 3 ops, 3 w 4 ops) We do not include those events for which no price data (Romania 2007) Differentiate between entries of multinational and local firms 3-4 and 4-5 multinational entries are always of Hutchison's Differences in events' type and timing allow us to separate treatment and control groups for most effects to be studies 6 2004200520062007200820092010 2-3 entryBigSmallBigSmall 3-4 entryBig 2 SmallSmall 4-5 entryBigSmall 4-3 merger1 5-4 merger121

7. Data, controls and treatments 2003-2010 price data from DG InfoSoc's yearly report (by Teligen) From the year of joining the EU (so only for 15 in 2003)  195 observations Prices are effective in August of each year Best available prices for two leading operators for three predefined baskets OECD2002 baskets: low (25 calls+30 SMS), medium (75+35), high (150+42) We use average prices for each basket + mean of basket averages Controls from Eurostat & InfoSoc (only reds will be significant) Demand: GDP per capita, population General price level: exchange rate, inflation, VAT Mobile service costs: population density, mobile termination rate (MTR) Mobile market structure: penetration, TOP2 shares, presence of MVNO Effective entry time: once they started commercial activity We allow 3 months of adjustment period, so only events before May are assumed to effect prices in August – sensitivity checks can be run on this 7

8. Results from simplified specifications Estimation results only on log of average price, w country clustered robust standard errors, all controls + country and time FEs These results already show that separation of different event types can be crucial for ex-post evaluations 8 (1)(2)(3) Deltaop0.03 (0.06) Entry-0.05 (0.07) Merger-0.15 ** (0.05) 2-3 entry0.01 (0.11) 3-4 entry-0.15 (0.09) 4-5 entry0.09 (0.07) 4.3 merger -0.22** (0.10) 5-4 merger-0.13 (0.09) Within R20.810.82

9. Results from final specification On log of average price with controls+FEs, similar results on basket prices Different dynamics from entry of multinational and local firms Local mavericks do not seem to have a LR effect, multinationals (H3) do No price-increasing effect for mergers 9 In 1st yearIn 2nd yearFrom 3rd year 2-3 big entry 0.07 (0.18)0.36*** (0.12) 0.03 (0.13) 2-3 small entry -0.20 (0.20)-0.00 (0.16) -0.06 (0.18) 3-4 big entry 0.05 (0.10)-0.19 (0.21) -0.42*** (0.09) 3-4 small entry -0.31* (0.17)-0.07 (0.15) 0.10 (0.13) 4-5 big entry -0.30*** (0.04)-0.16 (0.12) 0.49*** (0.09) 4-5 small entry -0.10 (0.07)- - 4-3 merger 0.03 (0.09)-0.01 (0.09) 0.17 (0.11) 5-4 merger -0.15 (0.11)-0.22 (0.22)-0.44*** (0.09) Within R20.87

10. Summary and discussion Our results indicate different price effects for 2-3 and 3-4 entries, and also different dynamic patterns for entrant types No price increases for mergers, but note fewer events + all investigated So ex post experience does not support unambiguously the hypothesis that more firms (a 4th) has beneficial effect on mobile voice prices Cross-country comparisons might not be the best way, but it is mostly the only possible one with classical diff-in-diff method No differently affected local mobile telecom markets within a country Panel benefits: less fear for omitted variables + real changes analyzed Note that (basket) prices observed here are imperfect proxies However, no other public time series data (ARPM only for 2 years) No public time series data are available for data prices either Changes in regulatory approach also matter, but hard to quantify them 10

11. csorba.gergely@krtk.mta.hu zoltan.papai@infrapont.hu