Economists are all quite familiar with the “holdup problem,” i.e. one contracting partner exploiting the other after asset specific investments have been made.  One classic law school textbook example is Alaska Packers v. Domenico in which the Alaska Packers’ Association hired Domenico for the salmon season for $50 plus 2 cents per salmon caught, but after leaving the dock and arriving in Alaskan waters for the short salmon season, the workers demanded an increase in their pay.  The defendant agreed, but upon return to San Francisco, refused to pay.  The seaman sued and lost on the theory that the exchange did not involve fresh consideration.  This, Judge Posner has argued, was the right economic result on the grounds that it discourages holdup.  Many of our readers will also be familiar with the famous Fisher Body / GM example of vertical integration solving the holdup problem, and the subsequent debate between Benjamin Klein and Ronald Coase over that particular example.

Now comes another example of the holdup problem at work.  In fact, it is difficult to imagine a better example.  Apparently, half way through a flight from India to Birmingham, England, an airline took advantage of the asset specific investments made by its passengers to alter the terms of the deal:

Passengers aboard two chartered jetliners from India to Britain were hit up for about $200 each, in cash, to continue their trip this week in what one flier compared to a hostage situation.  The charter company, Austria-based Comtel Air, and the Spanish company that owns the planes pointed fingers at each other over the situation Thursday. But Lal Dadrah, a passenger on one of the flights who recorded the crew passing the hat, called the situation “a complete, utter sham.”

Comtel Air passengers on a Tuesday flight to Birmingham, England, from the Indian city of Amritsar were hit up for 130 pounds — about $200 each — during a layover in Vienna. They were allowed off the aircraft to take the money from teller machines, a process that took about seven hours. There were varying accounts of what the money was to pay for, ranging from fuel to fees.

The NY Times story provides a few more details:

Britain’s Channel 4 news broadcast video showing a Comtel cabin crew member telling passengers: “We need some money to pay the fuel, to pay the airport, to pay everything we need. If you want to go to Birmingham, you have to pay.”

Some passengers said they were sent off the plane to cash machines in Vienna to raise the money.

“We all got together, took our money out of purses — 130 pounds ($205),” said Reena Rindi, who was aboard with her daughter. “Children under two went free, my little one went free because she’s under two. If we didn’t have the money, they were making us go one by one outside, in Vienna, to get the cash out.”

The economics don’t stop there.  There is potential for an agency problem as well:

Bhupinder Kandra, the airline’s majority shareholder, told the Associated Press from Vienna that travel agents had taken the passengers’ money before the planes left but had not passed it on to the airline.  “This is not my problem,” he said. “The problem is with the agents.”

A great example for the classroom.

 

Filed under: contracts

Herb Hovenkamp has posted his new casebook on Innovation and Competition Policy to SSRN, where one can download the chapters individually.  This is a very nice development for students; and the book seems perfectly fit for a course on Innovation and Competition Policy — for which it was designed — but also appropriate for a variety of similarly-themed seminar courses.

Professor Hovenkamp describes the aims of the book here:

This is not an “IP/antitrust” casebook.  There are already excellent books in that field.  Only about half of the principal cases printed in this book are antitrust decisions.  I use this book to present issues of innovation and competition policy to students in a broader context, examining not only antitrust but also the intellectual property laws and including shorter examination of several other topics, such as telecommunications, net neutrality, and competition issues raised by the DMCA.  Brief attention is also given to the industrial organization literature on innovation.

This casebook begins with a chapter on patent scope and its implications for innovation, with brief coverage of the Schumpeter-Arrow literature and the problem of sequential innovation.  Then it looks in some detail at the problem of complementary relationships, addressed in antitrust mainly through the law of tying arrangements.  After that is a chapter on remedies issues, followed by chapters on the patent system, copyright, practices that restrain innovation, and intellectual property misuse.  Another chapter covers exclusionary practices and another a wide variety of collaborative arrangements, including pooling, standard setting, blanket licenses, and the like.  The final chapter focuses on vertical restraints and the post-sale (exhaustion) doctrine.

I hope to keep this book up to date on a regular basis and welcome any suggestions for revision or inclusion.  My overall goal, however, is to hold the book somewhere in the range of its current length.

 

Filed under: antitrust

Today’s Wall Street Journal has a long article-debate on privacy.  The strongest pro-privacy is Christopher Soghoian of the Open Society Institute.  He confuses commercial privacy with government privacy:

“The dirty secret of the Web is that the “free” content and services that consumers enjoy come with a hidden price: their own private data. Many of the major online advertising companies are not interested in the data that we knowingly and willingly share. Instead, these parasitic firms covertly track our web-browsing activities, search behavior and geolocation information. Once collected, this mountain of data is analyzed to build digital dossiers on millions of consumers, in some cases identifying us by name, gender, age as well as the medical conditions and political issues we have researched online.”

When asked “Why is that a problem” he replies

“Many of the dangers posed by digital dossiers do not occur regularly, but are incredibly destructive to people’s lives when they do. An unlucky few will be stalked, fired, surveilled, arrested, deported or even tortured, all as a result of the data kept about them by companies and governments. Much more common are the harms of identity theft or public embarrassment. Even when companies follow best practices—and few do—it is impossible to be completely secure.”

Note that “parasitic firms” are collecting the data which is then used for arrest, deportation, and torture.  A bit of a disconnect. Identity theft is a problem, but the risk is decreasing and the costs are almost always low.  Moreover, identity thieves are crooks, not firms.

What is particularly interesting about the article is the survey data reported.  It demonstrates peoples’ confusion about the issues.  92% of the adults surveyed  “Think that there should be a law that requires websites and advertising companies to delete all stored information about an individual” but between 32% and 47% would like websites to provide information of some sort (ads: 32%, discounts: 47%, or news: 40%) “tailored to their interests.”  But of course these numbers are totally inconsistent.  If websites cannot keep any information about an individual, then they cannot provide tailored information since there will be nothing on which to base the tailoring.  The relevant questions are tradeoff questions, but the reported survey does not address these.

Filed under: advertising, privacy

Source.

Filed under: behavioral economics

Here’s the link to the conference, and the program, which covers all angles of social media and the law.  Predictably, my interest here is competition policy — I’ll be on the 10:45 panel discussing those issues along with: Michael Altschul (CTIA), Nicolas Economides (NYU), Adam Thierer (GMU), and Moderator Steve Wildman (MSU).

The link to the webcast is here.  If you’re there, please come and say hello after the panel.

Filed under: antitrust, technology

In my last post, I discussed Edelman & Lockwood’s (E&L’s) attempt to catch search engines in the act of biasing their results—as well as their failure to actually do so.  In this post, I present my own results from replicating their study.  Unlike E&L, I find that Bing is consistently more biased than Google, for reasons discussed further below, although neither engine references its own content as frequently as E&L suggest.

I ran searches for E&L’s original 32 non-random queries using three different search engines—Google, Bing, and Blekko—between June 23 and July 5 of this year.  This replication is useful, as search technology has changed dramatically since E&L recorded their results in August 2010.  Bing now powers Yahoo, and Blekko has had more time to mature and enhance its results.  Blekko serves as a helpful “control” engine in my study, as it is totally independent of Google and Microsoft, and so has no incentive to refer to Google or Microsoft content unless it is actually relevant to users.  In addition, because Blekko’s model is significantly different than Google and Microsoft’s, if results on all three engines agree that specific content is highly relevant to the user query, it lends significant credibility to the notion that the content places well on the merits rather than being attributable to bias or other factors.

How Do Search Engines Rank Their Own Content?

Focusing solely upon the first position, Google refers to its own products or services when no other search engine does in 21.9% of queries; in another 21.9% of queries, both Google and at least one other search engine rival (i.e. Bing or Blekko) refer to the same Google content with their first links.

But restricting focus upon the first position is too narrow.  Assuming that all instances in which Google or Bing rank their own content first and rivals do not amounts to bias would be a mistake; such a restrictive definition would include cases in which all three search engines rank the same content prominently—agreeing that it is highly relevant—although not all in the first position. 

The entire first page of results provides a more informative comparison.  I find that Google and at least one other engine return Google content on the first page of results in 7% of the queries.  Google refers to its own content on the first page of results without agreement from either rival search engine in only 7.9% of the queries.  Meanwhile, Bing and at least one other engine refer to Microsoft content in 3.2% of the queries.  Bing references Microsoft content without agreement from either Google or Blekko in 13.2% of the queries:

This evidence indicates that Google’s ranking of its own content differs significantly from its rivals in only 7.9% of queries, and that when Google ranks its own content prominently it is generally perceived as relevant.  Further, these results suggest that Bing’s organic search results are significantly more biased in favor of Microsoft content than Google’s search results are in favor of Google’s content.

Examining Search Engine “Bias” on Google

The following table presents the percentages of queries for which Google’s ranking of its own content differs significantly from its rivals’ ranking of that same content.

Note that percentages below 50 in this table indicate that rival search engines generally see the referenced Google content as relevant and independently believe that it should be ranked similarly.

So when Google ranks its own content highly, at least one rival engine typically agrees with this ranking; for example, when Google places its own content in its Top 3 results, at least one rival agrees with this ranking in over 70% of queries.  Bing especially agrees with Google’s rankings of Google content within its Top 3 and 5 results, failing to include Google content that Google ranks similarly in only a little more than a third of queries.

Examining Search Engine “Bias” on Bing

Bing refers to Microsoft content in its search results far more frequently than its rivals reference the same Microsoft content.  For example, Bing’s top result references Microsoft content for 5 queries, while neither Google nor Blekko ever rank Microsoft content in the first position:

This table illustrates the significant discrepancies between Bing’s treatment of its own Microsoft content relative to Google and Blekko.  Neither rival engine refers to Microsoft content Bing ranks within its Top 3 results; Google and Blekko do not include any Microsoft content Bing refers to on the first page of results in nearly 80% of queries.

Moreover, Bing frequently ranks Microsoft content highly even when rival engines do not refer to the same content at all in the first page of results.  For example, of the 5 queries for which Bing ranks Microsoft content in its top result, Google refers to only one of these 5 within its first page of results, while Blekko refers to none.  Even when comparing results across each engine’s full page of results, Google and Blekko only agree with Bing’s referral of Microsoft content in 20.4% of queries.

Although there are not enough Bing data to test results in the first position in E&L’s sample, Microsoft content appears as results on the first page of a Bing search about 7 times more often than Microsoft content appears on the first page of rival engines.  Also, Google is much more likely to refer to Microsoft content than Blekko, though both refer to significantly less Microsoft content than Bing.

A Closer Look at Google v. Bing

On E&L’s own terms, Bing results are more biased than Google results; rivals are more likely to agree with Google’s algorithmic assessment (than with Bing’s) that its own content is relevant to user queries.  Bing refers to Microsoft content other engines do not rank at all more often than Google refers its own content without any agreement from rivals.  Figures 1 and 2 display the same data presented above in order to facilitate direct comparisons between Google and Bing.

As Figures 1 and 2 illustrate, Bing search results for these 32 queries are more frequently “biased” in favor of its own content than are Google’s.  The bias is greatest for the Top 1 and Top 3 search results.

My study finds that Bing exhibits far more “bias” than E&L identify in their earlier analysis.  For example, in E&L’s study, Bing does not refer to Microsoft content at all in its Top 1 or Top 3 results; moreover, Bing refers to Microsoft content within its entire first page 11 times, while Google and Yahoo refer to Microsoft content 8 and 9 times, respectively.  Most likely, the significant increase in Bing’s “bias” differential is largely a function of Bing’s introduction of localized and personalized search results and represents serious competitive efforts on Bing’s behalf.

Again, it’s important to stress E&L’s limited and non-random sample, and to emphasize the danger of making strong inferences about the general nature or magnitude of search bias based upon these data alone.  However, the data indicate that Google’s own-content bias is relatively small even in a sample collected precisely to focus upon the queries most likely to generate it.  In fact—as I’ll discuss in my next post—own-content bias occurs even less often in a more representative sample of queries, strongly suggesting that such bias does not raise the competitive concerns attributed to it.

Filed under: antitrust, business, economics, google, Internet search, law and economics, monopolization, technology Tagged: antitrust, Bias, Bing, Blekko, google, microsoft, search, Web search engine, Yahoo

Last week I linked to my new study on “search bias.”  At the time I noted I would have a few blog posts in the coming days discussing the study.  This is the first of those posts.

A lot of the frenzy around Google turns on “search bias,” that is, instances when Google references its own links or its own content (such as Google Maps or YouTube) in its search results pages.  Some search engine critics condemn such references as inherently suspect and almost by their very nature harmful to consumers.  Yet these allegations suffer from several crucial shortcomings.  As I’ve noted (see, e.g., here and here), these naked assertions of discrimination are insufficient to state a cognizable antitrust claim, divorced as they are from consumer welfare analysis.  Indeed, such “discrimination” (some would call it “vertical integration”) has a well-recognized propensity to yield either pro-competitive or competitively neutral outcomes, rather than concrete consumer welfare losses.  Moreover, because search engines exist in an incredibly dynamic environment, marked by constant innovation and fierce competition, we would expect different engines, utilizing different algorithms and appealing to different consumer preferences, to emerge.  So when search engines engage in product differentiation of this sort, there is no reason to be immediately suspicious of these business decisions.

No reason to be immediately suspicious – but there could, conceivably, be a problem.  If there is, we would want to see empirical evidence of it—of both the existence of bias, as well as the consumer harm emanating from it.  But one of the most notable features of this debate is the striking lack of empirical data.  Surprisingly little research has been done in this area, despite frequent assertions that own-content bias is commonly practiced and poses a significant threat to consumers (see, e.g., here).

My paper is an attempt to rectify this.  In the paper, I investigate the available data to determine whether and to what extent own-content bias actually occurs, by analyzing and replicating a study by Ben Edelman and Ben Lockwood (E&L) and conducting my own study of a larger, randomized set of search queries.

In this post I discuss my analysis and critique of E&L; in future posts I’ll present my own replication of their study, as well as the results of my larger study of 1,000 random search queries.  Finally, I’ll analyze whether any of these findings support anticompetitive foreclosure theories or are otherwise sufficient to warrant antitrust intervention.

E&L “investigate . . . [w]hether search engines’ algorithmic results favor their own services, and if so, which search engines do most, to what extent, and in what substantive areas.”  Their approach is to measure the difference in how frequently search engines refer to their own content relative to how often their rivals do so.

One note at the outset:  While this approach provides useful descriptive facts about the differences between how search engines link to their own content, it does little to inform antitrust analysis because Edelman and Lockwood begin with the rather odd claim that competition among differentiated search engines for consumers is a puzzle that creates an air of suspicion around the practice—in fact, they claim that “it is hard to see why results would vary . . . across search engines.”  This assertion, of course, is simply absurd.  Indeed, Danny Sullivan provides a nice critique of this claim:

It’s not hard to see why search engine result differ at all.  Search engines each use their own “algorithm” to cull through the pages they’ve collected from across the web, to decide which pages to rank first . . . . Google has a different algorithm than Bing.  In short, Google will have a different opinion than Bing.  Opinions in the search world, as with the real world, don’t always agree.

Moreover, this assertion completely discounts both the vigorous competitive product differentiation that occurs in nearly all modern product markets as well as the obvious selection effects at work in own-content bias (Google users likely prefer Google content).  This combination detaches E&L’s analysis from the consumer welfare perspective, and thus antitrust policy relevance, despite their claims to the contrary (and the fact that their results actually exhibit very little bias).

Several methodological issues undermine the policy relevance of E&L’s analysis.  First, they hand select 32 search queries and execute searches on Google, Bing, Yahoo, AOL and Ask.  This hand-selected non-random sample of 32 search queries cannot generate reliable inferences regarding the frequency of bias—a critical ingredient to understanding its potential competitive effects.  Indeed, E&L acknowledge their queries are chosen precisely because they are likely to return results including Google content (e.g., email, images, maps, video, etc.).

E&L analyze the top three organic search results for each query on each engine.  They find that 19% of all results across all five search engines refer to content affiliated with one of them.  They focus upon the first three organic results and report that Google refers to its own content in the first (“top”) position about twice as often as Yahoo and Bing refer to Google content in this position.  Additionally, they note that Yahoo is more biased than Google when evaluating the first page rather than only the first organic search result.

E&L also offer a strained attempt to deal with the possibility of competitive product differentiation among search engines.  They examine differences among search engines’ references to their own content by “compar[ing] the frequency with which a search engine links to its own pages, relative to the frequency with which other search engines link to that search engine’s pages.”  However, their evidence undermines claims that Google’s own-content bias is significant and systematic relative to its rivals’.  In fact, almost zero evidence of statistically significant own-content bias by Google emerges.

E&L find, in general, Google is no more likely to refer to its own content than other search engines are to refer to that same content, and across the vast majority of their results, E&L find Google search results are not statistically more likely to refer to Google content than rivals’ search results.

The same data can be examined to test the likelihood that a search engine will refer to content affiliated with a rival search engine.  Rather than exhibiting bias in favor of an engine’s own content, a “biased” search engine might conceivably be less likely to refer to content affiliated with its rivals.  The table below reports the likelihood (in odds ratios) that a search engine’s content appears in a rival engine’s results.

The first two columns of the table demonstrate that both Google and Yahoo content are referred to in the first search result less frequently in rivals’ search results than in their own.  Although Bing does not have enough data for robust analysis of results in the first position in E&L’s original analysis, the next three columns in Table 1 illustrate that all three engines’ (Google, Yahoo, and Bing) content appears less often on the first page of rivals’ search results than on their own search engine.  However, only Yahoo’s results differ significantly from 1.  As between Google and Bing, the results are notably similar.

E&L also make a limited attempt to consider the possibility that favorable placement of a search engine’s own content is a response to user preferences rather than anticompetitive motives.  Using click-through data, they find, unsurprisingly, that the first search result tends to receive the most clicks (72%, on average).  They then identify one search term for which they believe bias plays an important role in driving user traffic.  For the search query “email,” Google ranks its own Gmail first and Yahoo Mail second; however, E&L also find that Gmail receives only 29% of clicks while Yahoo Mail receives 54%.  E&L claim that this finding strongly indicates that Google is engaging in conduct that harms users and undermines their search experience.

However, from a competition analysis perspective, that inference is not sound.  Indeed, the fact that the second-listed Yahoo Mail link received the majority of clicks demonstrates precisely that Yahoo was not competitively foreclosed from access to users.  Taken collectively, E&L are not able to muster evidence of potential competitive foreclosure.

While it’s important to have an evidence-based discussion surrounding search engine results and their competitive implications, it’s also critical to recognize that bias alone is not evidence of competitive harm.  Indeed, any identified bias must be evaluated in the appropriate antitrust economic context of competition and consumers, rather than individual competitors and websites.  E&L’s analysis provides a useful starting point for describing how search engines differ in their referrals to their own content.  But, taken at face value, their results actually demonstrate little or no evidence of bias—let alone that the little bias they do find is causing any consumer harm.

As I’ll discuss in coming posts, evidence gathered since E&L conducted their study further suggests their claims that bias is prevalent, inherently harmful, and sufficient to warrant antitrust intervention are overstated and misguided.

Filed under: antitrust, business, economics, google, Internet search, law and economics, monopolization, technology Tagged: antitrust, Bing, google, search, search bias, Search Engines, search neutrality, Web search engine, Yahoo

Many antitrust law professors are fond of using arson — e.g., a firm burning down the rival’s factory — as the paradigmatic example of exclusionary conduct that might raise rivals’ costs without plausible efficiency justifications.  Here is a modern example with law school hypothetical written all over it involving a Domino’s Pizza manager burning down a competing Papa John’s franchise:

Two managers of a Domino’s Pizza restaurant in Florida were facing arson charges Saturday after allegedly burning down a competing Papa John’s franchise in an attempt to increase sales, the Ocala Star-Banner reported.

The Oct. 20 fire at the Papa John’s in Lake City, Fla. — about 60 miles west of Jacksonville — was ruled to be a case of arson shortly after it was set alight. The blaze completely gutted the business, Lake City Police Department spokesman Capt. John Blanchard.

An investigation resulted in the arrest of 23-year-old Sean Everett Davidson on Thursday and 22-year-old Bryan David Sullivan on Friday. Both were charged with arson. Both men confessed to the crime and were booked into county jail.

Sullivan said he set the fire so the Papa John’s location would go out of business and sales at his Domino’s location would increase, according to police.

HT: a loyal TOTM reader.

 

Filed under: antitrust, exclusionary conduct

Every year around this time—around week 10 of college football season—we are reminded of the inequity of the Bowl Championship Series (BCS) system. Instead of permitting an open playoff system to determine the college football champion, as is done by most other NCAA sports including Division II football since 1973, and more famously, NCAA basketball, the BCS uses a computer algorithm and polls to decide the contestants according to, among other things, regular season performance, the teams’ conferences (BCS-approved or not), and strength of schedule. In particular, six of the ten BCS playoff slots are set aside for teams from BCS conferences.

Read the full piece here.