Demand Estimation

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.

Demand estimation is a way by which managers make economic or policy decisions based on estimates of the market’s demand function. The two articles give an account of how economists have tried to predict the number of medals that China would win during the Beijing Olympics in 2008.

The first article, “China Goes for (All of) the Gold: Economists predict whether the host country will rule the Beijing Olympics,” discusses how PwC and Andrew Bernard of Darthmouth’s Tuck School of Business have used different models to forecast the number of medals to be won. Both models are based on assumptions that first, population and income levels are crucial determinants of success in the Olympics, and second, other factors like past performance, past political affiliations, and/or home-field advantage enable countries to perform better. PwC projected that China will increase its medal count by 40% due to the home country effect and the state support for sports. Bernard predicted that China would only make modest gains, as he sees the “rich getting richer.”

The second article, “China’s Winning Ways:  Did economists correctly predict who would win at the Beijing Olympics?” critiques how economists have done in their projections. It reports that both analysts underestimated the strength of the US delegation as well as China’s performance. Overall, it was seen that the combination of nationalism and rapid economic growth had a greater impact on China’s medal count than expected. In addition, it found that despite rapid economic growth in emerging countries and increasing decentralization of the global economy, the rich generally remained rich. The results proved that predicting Olympics outcome is very difficult especially because chance and randomness play a big role as well.

Both articles were very relevant to our classroom topic on demand estimation because it listed out the dependent and independent variables for estimating the demand. In this case, the independent variables were factors such as GDP, economic growth, past performance, past political affiliations, and home-field advantage, while the dependent variable was the number of medals won. The variables can be put in a demand function based on their magnitude of impact and whether it will increase or decrease overall demand.

Even though GDP as well as home-field advantage provides a relatively good estimation of the medals won in 2008 Olympics, there are problems with this approach. The first problem is that the current GDP might not have a direct influence on athletic performances.  But for countries that had rapid economic growth in recent years, like Brazil and India, it will generally take years for GDP to translate into better sports facility and even longer for a culture that produces great athletes. The second major problem is that it overlooks the difference in culture with regard to Olympic sports.  Countries in former Soviet blocs have a special cultural and social structure that can produce better athletes which allows them to win more gold medals than countries that have similar GDP.  The third problem is that the impact of home-field advantage is really difficult to estimate, because it is different for different sports. The home-field advantage in sports like basketball may be great and for sports like shooting it would not be that huge.    

A real-world application of demand estimation would be high school graduates and the chance that they apply to Notre Dame.  There are many independent factors that way in on their decision to apply.  These include high school GPA, SAT and ACT scores, resume strength, legacy parents, home location, and preferences.
           
The first three variables determine if the student has the chance to get in to a school of the quality of Notre Dame.  The university takes only the smartest of high school graduates, and students can prove their worth by these measures.  They will know by these academic standards whether or not they have a chance to get in, or whether it would not be worth the time.  The next three variables have more to do with why students would choose to apply to Notre Dame over similar schools.  

Students whose parents attended Notre Dame are much more likely to want to go to Notre Dame, as they probably grew up fans of the school and feel influenced by their parents’ love of the university.  Where the student lives is also an important variable, as students who live in cities with strong alumni networks are exposed to the university more than ones who do not.  Cities like Chicago, New York, Los Angeles and Denver, which have huge Notre Dame alumni networks, tend to send numerous students across the country to northern Indiana to attend Notre Dame.  Preferences of the student also play a large role.  What this means is that a very academically minded person who is picking between Northwestern and Notre Dame may choose Northwestern, as it is slightly higher ranked in the university rankings.  However a student who values athletics and tradition may choose Notre Dame instead, as it offers one of the greatest football programs in history as well as many campus traditions that have been around for decades.  

Posted by Ava, Michael and Tiandong (Section 1)

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