The Microeconomic FoundationsEssay Preview: The Microeconomic FoundationsReport this essayOrbitz, an online hotel search engine, has been criticised in this article for altering search results dependent on the computing equipment utilised by its consumers. Its pricing policies therefore can be evaluated in terms of the microeconomic foundations for such actions and so too can the sustainability of the policies as the firm moves forward. In this essay I shall attempt to define the model which I believe most closely matches that of Orbitz whilst bearing in mind that as it is a model it may not perfectly match the market in which Orbitz operates. Once this has been achieved I shall then identify and discuss the abilities of firms to sustain such pricing practices and whether or not these are sustainable in future. It is my believe that the nature of online markets mean they do not lend themselves kindly to standard economic models and as the online market grows, common practices that can be found on the high-street will become less and less common as competition grows and old issues of imperfect information become less of a problem for consumers.
The model which I believe most closely matches the scheme Orbitz use in the article is one of third-degree price discrimination in a monopoly environment. A monopoly exists where there is one dominant firm in an industry which can set prices and makes supernormal profits above and beyond what would be produced were the firm existing in a perfectly/imperfectly competitive environment. (Begg, 2003: 110) Whilst Orbitz isnt itself a monopolist given the existence of many forms of online holiday/hotel room search engine, it acts as a monopolist in terms of the individual searches carried out by consumers. It is because of its ability to act like a monopolist that price discrimination is able to take place as price discrimination is not possible if there are other firms in the arena to compete with. Price discrimination in its simplest form exists where a firm identifies two markets within its customer base with differing Price Elasticities of Demand (PED) which identifies a consumers willingness to pay for x quantities of goods at price y. (Smith, 2009: 52-55) In this case, third degree price discrimination is at play because the firm utilises the information it gleans from “Predictive Analytics” (Mattioli,D. 2012) in its search results, and what it has identified as being differing willingnesss to pay, depending on whether its customers are searching its site from Apple products or from a conventional PC. (Frank, 2010: 391-392) First and second degree price discrimination are not factors in the case put forward by the article but that is not to say that these are not used in online markets, in fact it is the opposite. First degree price discrimination requires a monopolist to understand and fully know what a consumers maximum and minimum willingness to pay is and actively change prices in order to fully absorb the consumer surplus of the customer; The consumer surplus being the term used to define what a consumer was willing to pay above and beyond the price paid in the markets. (Begg, 2003: 249) By pricing in this way the monopolist producer will produce more of a good than it would if price discrimination wasnt used and in so doing removes all deadweight loss to society in the process. Second degree price discrimination is more prevalent in its relative ease to implement where discounts are offered based on the quantity of a good that is demanded. This is commonly seen on retailer websites where prices are reduced with bulk purchase. This occurs mainly where the seller cannot accurately differentiate between its consumer groups themselves and therefore instead, consumers reveal their own grouping with their purchases known as self-selection. By pricing in this way, firms again attempt to recoup as much consumer surplus and producer surplus as possible. There is a final form of price discrimination; fourth degree price discrimination, whereby a good costs the same whichever customer purchases it, however there are options available to the consumer free of charge which may increase costs i.e. a vegetarian meal on a flight. These costs are not met by the consumer and instead are met by the producer, hence the term, reverse discrimination.
In the case of Orbitz we should now understand why it would wish to price discriminate in this way. As a profit maximising firm, Orbitz must exploit all the information it has available in order to effectively maximise profits as it sets out to. By reducing the consumer surplus the firm will increase its revenues and therefore its profits. This is particularly true of online retailers where the application of this form of analytics has a relatively small marginal cost and the benefits, therefore, can significantly outweigh the initial costs. (Begg, 2003: 209)
Graph 1: Demand and Marginal Revenue curves of the market and submarkets with price determinationsHaving understood these two submarkets to have differing PEDs, inelastic and elastic, Orbitz continues to produce at its profit maximising point MC=MRt (Begg, 2003: 77-80). The inelastic group, shown in graph 1, is then charged a price, Pa, which produces a large supernormal profit whilst the elastic submarket is charged a price, Pb, awarding the seller with a smaller but still supernormal profit. In both cases the consumer surplus has been reduced causing the firm to receive greater profits from its customers. When combing the profit orchestrated by both the inelastic submarket and the elastic submarket we find that profits have in fact increased the supernormal profits which would have been derived from pricing at the whole market equilibrium and therefore exceeding what is show in the industry graph. Of course, the model of price discrimination does not totally match to our study of Orbitz as it is not the price that is changing but the results provided by searches from different computing equipment, however, the aim and way in which profit maximisation is achieved are closely comparative.
It is also important to analyse why this form of price discrimination is possible despite Orbitz not being a true monopolist. Here the model does not effectively take into account the market as a whole, whereby many firms exist offering a similar search service and would therefore not qualify as a monopoly by the very fact that there is more than one large firm in the market and the goods i.e. the search results are available elsewhere. (Smith, 2009: 36) Instead in order to understand the micro-economic principles we must narrow our definition of monopoly to focus solely on the individual searches of consumers. In this way Orbitz now controls the products available to the consumer and can manipulate the result order so
In summary, the basic model of price discrimination is a flawed one. In effect there is little or nothing to explain because the model is based on a single market (the free market). The results are determined by a combination of variables that we will refer to as “consumability factors”. They are given in the table below.
In any case in my view this is an inaccurate depiction of the picture of a market that exists in which prices have a low probability of discrimination, hence its model does not reflect the conditions under which the prices exist and what products the firms are offering in terms of a full or equivalent search service
To read my paper see:
http://www.researchgate.net/publications/pdf/113049-The-Unsolved-Economic-Problem-for-Suffar-i-Life.pdf
There are three main main “consumability factors” used here, among which this one is the probability that, if the product has many different kinds of information, a particular search might take many different kinds of data. The above chart shows the probability that a particular product is a generic search, as of the month of July 2007 (the month of the first mass exodus). If the goods are already available, then the probability that the goods will be available is 0.01%. The “consumability factors” for general market goods (in this case the price of certain commodities) is 0%. The probability of a particular sale being a general market transaction has the following properties.
It is a negative of 1 if the goods are already available, and 1 if they are currently available. To demonstrate this, I have divided the probability of a sale being a general market transaction by the number of available goods at that time.
This is simply an observation without any proof; it is simply the formula in which most economic knowledge is put. Therefore it is not possible to put together a perfect mathematical model of a general market that satisfies all of the other criteria for the idealised version given in
Proposal No 5:
The most critical factor in an economic model is (i) the value it takes to be reasonable and to predict the future in a particular market, (ii) the quantity and type of data it wants to present to prospective customers, (iii) the amount of risk it has to take in the process of presenting these data, and (iv) the cost it will incur as a result. To illustrate this I have multiplied the cost in the market for the specified time and averaged the resulting information together.
If I know that I currently have more than 0.0015 BTC, it goes to (2)/1.
Otherwise it goes to (2).
Again, the total