Discussion on “China Goes for (All of) the Gold: Economists predict whether the host country will rule the Beijing Olympics” (Slate, 2008) and “China’s Winning Ways: Did economists correctly predict who would win at the Beijing Olympics?” (Newsweek, 2008), both by Daniel Gross.
In Daniel Gross’s first article “China Goes For (All of) The Gold”, Daniel Gross analyzes PricewaterhouseCoopers and Andrew Bernard’s (Tuck School of Business) methods for projecting total number of Olympic medals, and more specifically, gold medals won by certain countries in the 2008 Beijing Games.
These methods, however, do not look at variables directly affecting athletic performance. Instead, PricewaterhouseCoopers and Bernard project Olympic success based on population, income level, economic status, past performance, home country advantage, and prior membership of a Soviet/Communist bloc. Based on these criteria, PwC believed that much of the statistics played in China’s favor, and thus projected an overall medal increase of about 40%, from 63 to 88, and into the world leader for the 2008 Olympics. PwC also projected that countries such as Mexico and Brazil would see noticeable success, while America, Germany, Russia, and France would suffer a setback. Bernard, on the other hand, saw “the rich getting richer,” placing the US in first, Russia in second, and China in third.
After outlining methods and projections for both PwC and Andrew Bernard, Daniel Gross then discusses his skepticism of both methods. He believed that both projection models overestimated the US decline in worldwide standing, home country advantage, and the loss of market share of Western powers. Gross believed that neither PricewaterhouseCoopers nor Andrew Bernard could accurately project Olympic success without considering more intangible variables, such as the cultural value of sport in different countries. Also, the Olympic results hinge on many other things that can happen during the games, unrelated to economics, like sudden injury to an athlete, or other unpredictable events during the games. Ultimately, Daniel Gross argued that Olympic success is determined by a nation’s ability to recruit, develop, and retain the best athletes available to them, not economic factors.
In his follow up article, “China’s Winning Ways”, Gross discusses the mix of successes and failures of the PwC and Bernard economic projections. One of the biggest successes was Bernard’s ability to predict the continued success of the US by correctly predicting 105 medals won, most in the world. PwC, on the other hand, severely underestimated the performance of the Americans. The biggest failure in the projections involved the underestimation of home country advantage and the performance of China, who finished second in the medal count, and had the most gold medals.
In his conclusion, Gross determines that although a purely economic model would have helped predict China’s success, the variability of cultures in different counties regarding the Olympics make it hard to find a consistent model. For the most part, “the rich generally stayed rich.”
The two articles, mention a multitude of determinants that could affect a country’s medal count in the 2008 Olympics. These include “the basic assumptions that population (more potential competitors) and income levels (more resources to develop competitors) are crucial determinants of Olympic success” as well as “past performance, having been a member of the Soviet/Communist bloc, and/or home field advantage” (Gross 1). While computing the effects of the first group of determinants is easily quantifiable, creating models, such as regressions, to calculate the effects of the later groups proves more difficult. These determinants are more qualitative in nature, and thus make estimations difficult.
Furthermore, the magnitude of the effects of the determinants proves to be rather subjective. While both of the economists performing these tests, PwC and Bernard agree upon the determinants, they do not necessarily concur on how much of an effect these factors have in influencing a country’s medal count. For example, PwC places a greater emphasis on home advantage, while Bernard stresses the importance of a country’s economic prosperity in winning medals. The magnitudes placed upon the determinants thus prove to be highly subjective. This creates a lack of uniformity amongst estimation models and their results.
Finally, the determinants used by the economists disregard the human element in competition. While factors such as past performance and national wealth provide neat medal estimations, they do not include the key determinant of human error. A defining feature of the Olympics is the crushing upset when the projected victor loses his or her competition due to an injury, mistake, or the like. The models do not include this crucial factor in their estimations. Similarly, they do not account for surprise victories where a previously unnoticed athlete shockingly wins his or her event due to adrenaline, unrealized skill, etc. Because of this disregard, the qualitative nature of determinants, and the subjectivity of the determinants magnitudes, forming accurate estimations proves problematic.
The regression idea that the economists used to estimate Olympic Medals in Beijing is akin to how companies conduct demand estimation. Demand estimation is exactly what it sounds like; it is the estimation of the demand of a good or service through the use of various techniques, but mainly statistical regression.
One real world business application of demand estimation is the estimating the demand before the launch of a new product. Take for example, the launch of the iPad. Apple’s industry shifting tablet debuted in 2010, and became almost immediately unavailable at both Apple stores and retail outlets. Apple, and analysts as well, failed to correctly estimate demand for their tablet and thus vastly under-supplied retailers. The following table shows the availability of products at one of Apple’s main retailers Best Buy; it is important to note that the iPad was launched in mid April, and was still sold out in June.
As illustrated in the table (see link above), iPad demand was sufficiently under estimated. The iPad was continuously out of stock in retail stores, and Apple lost a great opportunity to maximize their profits. Even with the iPad 2, demand was originally underestimated by 40% according to analysts from Morgan Stanley. Apple could maximize their profits and make even more money by making better demand estimations. Apple could do this by taking in trends and factors such as price, income, ease of use, general interest, and the market for tablets to better formulate a regression model for demand estimations.
Posted by Matthew, Beth and Jack (Section 2)