Oracle/PeopleSoft Global Competition Review Law Centre Lunch Talk

1. Oracle/PeopleSoftGlobal Competition Review Law Centre Lunch Talk William Bishop, Lexecon Ltd 4th February 2005

2. Introduction • Oracle made a hostile bid for PeopleSoft in June 2003 • Resulted in longest-ever ECMR review (>12 months) • Case raises many interesting issues in economics, law, politics? • Lexecon Ltd advised Oracle throughout EC process (Lexecon Inc advised PeopleSoft)

3. Timeline • Oracle announces offer9 June 2003 • US: 2nd request 30 June 2003 • EC: notification 14 October 2003 • EC: Phase II starts 17 November 2003 • US: DOJ challenge 26 February 2004 • EC: SO / oral hearing 12 March – 1 April 2004 • EC: clock stopped for new bid data 14 April 2004 • US: trial in San Francisco June - July 2004 • US: SF court ruling 9 September 2004 • EC: clock restarts 7 October 2004 • EC: decision 26 October 2004 • Merger completed 7 January 2005

4. Products and competitors • Overlap area: enterprise application software (EAS); particularly human resources (HR) and financials (FMS) • Merging parties are #2 and #3 in EAS worldwide; SAP is #1 (stronger in Europe than US) • EAS overall is fragmented – more concentrated in the “high end” (largest customers, highest functionality products) • Oracle is also a leader in databases – complementary to EAS • Oracle intended to discontinue PeopleSoft products eventually

5. The main economic issues • “Product” market definition: is there a “high end” market? Who competes in it? • (DOJ/EC disagreed on geographic market: EC found global market; DOJ decided on N. America) • Unilateral (“non-coordinated”) competitive effects analysis • Coordinated effects and (briefly) vertical/conglomerate issues were also considered and rejected

6. “Product” market definition • Commission’s view: “High function HR/FMS purchased by large customers with complex functional needs…” • Defined by proxies: (1) customers >10,000 employees or €1bn revenue; (2) deals >€1m net license value • Relied heavily on defining market by reference to customer groups – nb EAS is sold by individual negotiation • Very difficult to find clear conceptual criteria or practical proxies • Key development in EC case was conclusion on who is in the market – decision accepted a significant “fringe” (i.e. not 3-to-2)

7. Unilateral effects • Concern: reduction in competition without coordination (i.e. merged entity finds it rational to raise prices without expecting similar reaction from non-merging rivals) • Typically an issue in differentiated product markets, when merging parties are close substitutes, and non-merging rivals are less close • Empirical questions: how closely do parties compete? How closely do rivals constrain them? • Need to look at bidding data • SO reached adverse conclusions, but without much data

8. Econometric analysis • PeopleSoft had submitted a very simple (and easy to criticise) analysis, claiming discount is higher when number of bidders is higher • Oracle collected detailed information on 600+ bids; we carried out extensive econometric analysis (50+ regressions) • 2 key questions: • Does number of rival bidders affect Oracle discounts? • Does identity of rival bidders (esp. PeopleSoft) affect Oracle discounts? • We found no systematic evidence of either – Commission agreed

9. Some thoughts on the economic issues • A classic “gap case” (apparently played a role in finalising new ECMR) • Unilateral effects in bid markets: what is the threshold for a SIEC? And what does theoretical “merger simulation” contribute? • Market definition for bid markets with heterogeneous customers and a continuum of characteristics – what is a “relevant product market” in this setting? Does it matter? • Disconnect between geographic and product market approaches? • Loss of choice; effect on PeopleSoft installed base: are they relevant?

10. Why bidding analysis? • Contracts for EAS software awarded after a process of negotiation (i.e. competition takes place for the contract) • Customers normally negotiate with several supplies and there are often several rounds of negotiation • Key factor is how does the merger affect customers’ alternatives – are the merging parties’ products particularly close and how close are the remaining suppliers? • Commission recognised that sales were made via a bidding process

11. Bidding analysis in mergers (1/2) • Aim is to identity who are the credible bidders for a particular type of contract and assess if the merging parties are particularly important competitive constraints on one another • The Commission claimed just Oracle/PS/SAP credible bidders for high-end HR & FMS, and PS an important competitive constraint on Oracle • Market definition typically difficult in bidding markets, in particular because of price discrimination resulting from negotiation, so markets may be defined in terms of customer types • Who competes for different types of contracts can be informative, but customers may not invite all potential suppliers to bid for contracts

12. Bidding analysis in mergers (2/2) • Estimate the effects of the merger using the variation in the number and identity of bidders as a ‘natural experiment’ • Merger reduces the number of credible bidders  examine how # of bidders affects the discounts offered the merging parties and compare coefficients on # of bidder dummies • Merger removes particular competitor  examine how the identity of bidders affects the discounts offered by the merging parties, and compare coefficients on combinations of bidders including and excluding the acquired party

13. Bidding analyses done in Oracle/PeopleSoft • Europe • SO: PeopleSoft regression (Lex Inc) • September: Oracle bidding analysis (Lex Ltd) • October: EU Decision (Commission analysis) • US trial • McAfee (DoJ) • Hausman (Oracle) • Pre-trial – CapAnalysis (Oracle)

14. Lexecon Ltd analyses – Response to the SO • Reviewed PeopleSoft regression analysis. SO claimed this analysis showed that “the removal of one competitor appears to reduce the rebate with [sic] 15% on average” • Problems with PeopleSoft analysis • Result depends on 8 obs with anomalous margins and 3 obs for 3-rival bids which have high discounts • Explained just 8% of variation in discount and failed standard diagnostic tests  excludes explanatory variables and possible omitted variable bias • If the 8 anomalous obs excluded and dummies for the # of rivals included, then only the dummy for 3 rivals is positive and significant

15. Lexecon Ltd analyses – Additional analysis • Collected new dataset on Oracle’s sales opps to large enterprises • Econometric analysis: • Effect of # of bidders on Oracle’s discounts, and • Effect of the identity of bidders on Oracle’s discounts • Control for characteristics of the sales opportunity • Two measures of the discount used, total and discretionary • Estimate regressions using 11 samples of the data – proxies for different possible market definitions

16. Lexecon Ltd analyses – Results • Data showed that suppliers other than Oracle/PS/SAP competed to supply large enterprises, but in just a limited number of occasions • No consistent evidence that a higher # of bidders associated with higher discounts, or discounts were higher when there were 3 rather than 2 bidders • No consistent evidence that discounts were higher when Oracle competed against PS – just isolated examples which were either due to the presence of JDE or reflected just a very small number of sales opps. • Commission obtained same results

17. Issues and problems of any bidding analysis • Omitted variables – common problem with cross-sectional data. If the omitted variable is correlated with any of the explanatory variables then the estimated coefficients are biased and inefficient • Customers may not negotiate with all credible suppliers, so does comparing the outcome with and without the acquired supplier really capture the effect of the merger?