- Srishti Mittal
Module #2: Microeconomics of Product Markets
|1.||A recent study has found that an increase in the price of beer would reduce the amount of marijuana consumed. Is the cross-elasticity of demand between the two products positive or negative? Are these products substitutes or complements? What might be the logic behind this relationship? Illustrate your answers using graphs.
Ans. In economics, the cross elasticity of demand or cross-price elasticity of demand measures the responsiveness of the demand for a good to a change in the price of another good. It is measured as the percentage change in demand for the first good that occurs in response to a percentage change in price of the second good. For example, if, in response to a 10% increase in the price of fuel, the demand of new cars that are fuel inefficient decreased by 20%, the cross elasticity of demand would be: . A negative cross elasticity denotes two products that are complements, while a positive cross elasticity denotes two substitute products. These two key relationships may go against one’s intuition, but the reason behind them is fairly simple: assume products A and B are complements, meaning that an increase in the demand for A is caused by an increase in the quantity demanded for B. Therefore, if the price of product B decreases, then the demand curve for product A shifts to the right, increasing A’s demand, resulting in a negative value for the cross elasticity of demand. The exact opposite reasoning holds for substitutes.
|2.||In the last decade or so there has been a dramatic expansion of small retail convenience stores (such as 7-11s, Kwik shops, and Gas’N Shops), although their prices are generally higher than those in large supermarkets (such as Walmart and Target). Explain the success of the convenience stores in economic terms.
Ans. As Americans have become more time-constrained, convenience stores (C-stores) have become a beacon in the retail sector. Part grocery store, part food-service outlet and frequently part gas station, C-stores – whether local mini-marts or interstate drive-through – offer something for everyone. And consumers are taking notice. An increasing number are turning to C-stores for their purchases, growing the C-store business to a projected $52.8 billion in 20101. Moreover, the industry appears to be poised for continued growth. In the next five years, C-store revenues in the United States are projected to increase by an average annual rate of 3.9 percent and reach $63.9 billion by 20152. But the C-store business is complex, fast moving and rife with challenges such as shifting consumer preferences; intensifying competition from mass retailers; volatile gas prices; multiple legal and regulatory compliance obligations; around-the-clock staffing and operations; and growing consolidation of stores and chains. Consumer demands and tastes change frequently, calling for innovation across an extensive range of products and services. New products and operational changes can be expensive to implement and – if new products, processes or systems are implemented poorly – can undermine the very profitability and growth that they are intended to deliver. C-stores must also be able to maintain strong communication between employees at the point of sale (POS) and upper management personnel, who must disseminate insights and implement best practices throughout the network of stores. It’s a tall order, and not all C-stores fare equally well in the face of these challenges. In the following sections, we draw upon our experience with C-stores to highlight a number of trends affecting the C-store industry as well as ideas for management personnel to consider as they evaluate what’s right for their organization. We touch on both big-picture trends, such as M&A activities, and trends in technical areas that may get overlooked as executives grapple with strategic issues. 1 IBIS World industry report, May 8, 2010. (This statistic excludes gas stations with convenience stores.) 2 IBIS World industry report, May 8, 2010. (This statistic excludes gas stations with convenience stores.) Mapping the road to success Since its inception in 1927, the C-store industry has succeeded based on its chameleon-like ability to adapt to changing customer preferences and an evolving business environment. Now more than ever, C-store executives recognize that both survival and prosperity depend on this ability. The challenge is to execute change quickly and consistently across large numbers – in many cases hundreds – of individual stores. This is no easy task, given that each store has a unique marketplace and that, particularly with franchises, each store is a business in its own right. Executing change successfully requires strong discipline and close attention to detail. It also requires a roadmap that gives directions for achieving consistency across all stores while catering to consumer preferences specific to local markets. This roadmap is a formalized set of guidelines and best practices delineating how to run a C-store at the level of the individual store unit. Effectively a how-to guide, the roadmap serves as a means of standardizing operations for an organization comprising multiple geographically dispersed units that deliver essentially the same product or service nationwide – while meeting changing customer expectations and addressing issues within local markets. An effective roadmap will help the organization achieve operational efficiencies and consistency of service, from store formats to merchandising to regulatory compliance and training of employees. But the roadmap should be flexible enough to accommodate customization at the local level. A one-size-fits-all approach can lead to underperformance if individual stores are not able to adapt their product mix, marketing programs or employment strategies to reflect the expectations and idiosyncrasies of their local markets. An operational roadmap can help C-store businesses:
|3.||Use the concept of economies and diseconomies of scale to explain the shape of a firm’s long-run ATC curve. What is the concept of minimum efficient scale? What bearing can the shape of the long-run ATC curve have on the structure of an industry?
Technical economies of scale:
Marketing economies of scale and monopsony power: A large firm can spread its advertising and marketing budget over a large output and it can purchase its inputs in bulk at negotiated discounted prices if it has monopsony (buying) power in the market. A good example would be the ability of the electricity generators to negotiate lower prices when negotiating coal and gas supply contracts. The big food retailers have monopsony power when purchasing supplies from farmers.
Managerial economies of scale: This is a form of division of labor. Large-scale manufacturers employ specialists to supervise production systems and oversee human resources.
Financial economies of scale: Larger firms are usually rated by the financial markets to be more ‘credit worthy’ and have access to credit facilities, with favorable rates of borrowing. In contrast, smaller firms often face higher rates of interest on overdrafts and loans. Businesses quoted on the stock market can normally raise fresh money (i.e. extra financial capital) more cheaply through the issue of equities. They are also likely to pay a lower rate of interest on new company bonds issued through the capital markets.
|4.||Learning to use software programs takes time. So once consumers have learned to use a particular software package, it is easier to sell them software upgrades than to convince them to switch to new software. What implications does this have for expected rates of return on R&D spending for software from developing upgrades versus from developing imitative products?
Ans. Big technology companies are spending about $9.70 on research and development for every $100 they take in, with the spending being especially heavy in areas like virtualization and Internet-delivered software.
In total, the industry’s top R&D spenders poured almost $51 billion into product development in 2007, according to a survey by CIOZone. But the spending was concentrated at the top, with Microsoft, IBM and Intel accounting for 38% of all research outlays.
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Microsoft’s spending topped $7 billion as the company poured hundreds of millions of new development dollars into its money-losing online services business. It also expanded its R&D operation in some foreign countries, including Israel, where newly hired engineers will focus on communications software.
Microsoft spent 12.8% of its 2007 revenue on R&D, well above the average of the companies on our list but not surprising given the many product areas in which it competes. Besides its core business of software, Microsoft is trying to improve its position in digital music players and Internet search. It trails the leaders in those markets, Apple and Google, by wide margins.
|1.||How can time be incorporated into the theory of consumer behavior? Explain the following comment: “Want to make millions of dollars? Devise a product that saves Americans lots of time”
Ans. The use and expenditure of time is inextricably linked to consumer behavior. As Jacoby et. al (1977) point out “Acquisition and consumption of both products and information regarding products are not cross-sectional events of short and unvarying duration.” It has even been proposed that time may be the most important variable in consumer behavior (Nicosia and Mayer 1976). Our state of knowledge, however, finds us caught in an interesting anomaly. On the one side, the time dimension of consumer behavior is viewed as just beginning to emerge as a major variable of study, as three articles reviewed here indicate. On the other side, however, time has been implicitly and explicitly incorporated into consumer behavior theory and marketing strategies for quite some time. This article first reviews the history of the incorporation of time into consumer behavior theory and marketing strategies and then reviews the three articles concerned with “Perception of Time and Consumer Behavior”.
|2.||Many apartment-complex owners are installing water meters for each apartment and billing the occupants according to the amount of water they use. This is in contrast to the former procedure of having a central meter for the entire complex and dividing up the water expense as part of the rent. Where individual meters have been in stalled, water usage has declined 10 to 40 percent. Explain that drop, referring to price and marginal utility.
Ans. When the water was on a central meter there were, what are called “free riders” in economics (people that “free ride” off of a public or collective benefit), these people use greater amounts of water because their benefit (utility) is increased because the costs are shared and distributed among those that use less water (people have the incentive not to limit water use but to maximize). When the water became individually metered, people could no longer receive the extra utility because they now pay for every bit that they use. Therefore the price increased for heavy water users and utility went down. In order to maintain the budget curve they would have to reduce water usage (because of the price increase), which means that they do not get the same marginal utility but it decreases or in other words the marginal utility curve flattens out. That is why usage dropped.
McConnell, Brue. (2008). Microeconomics: Principles, Problems, and Policies, 17th Ed., New York: McGraw-Hill.