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.

Summary & Discussion of Relevance

China Goes for (All of) the Gold

In this article, Gross compares different economists’ predictions on China’s and other top 10 countries’ results in the 2008 Beijing Olympics.  This “medal-count guessing game” took place between John Hawksworth of PriceWaterhouseCoopers and Andrew Bernard of Dartmouth’s Tuck School of Business.  The purpose of the forecasting grew from the question of how China’s recent expansion and influence in the competitive, global market would impact their Olympic performance, especially considering their home-field advantage in Beijing.

Hawksworth at PwC projected China to out-do the U.S.  Due to increased globalization, he forecasted that market share would shift from the industrialized West to the changing landscape of wealth and progress in expanding markets (China, Brazil, Indonesia, Mexico, Poland).  Bernard, on the other hand, predicted that wealthy nations would continue to flourish while the emerging world would take “less of a bite.”  However, both Hawksworth’s and Bernard’s shoddy predictions from the 2004 Olympics set the stage for equally miscalculated estimations in 2008.

Several reasons for why their projections were wrong were the fact that it may take decades for economic change to translate into dominant athletic achievement; cultures with athletic prowess, like Russia, can withstand economic downturns; sports weave in and out of culture, geography, and traditions that do not react to economic change; and predictions about the Olympics are difficult due to the nature of the events.

Gross argues that the best way to achieve Olympic success is by attracting, retaining, and developing human capital to its fullest potential, and that no country has done that better than the United States.

China’s Winning Ways

Gross wrote this article in response and as a follow up to his article about economic models and results forecasting written before the Beijing Olympics in 2008.  John Hawksworth of PwC and Andrew Bernard of Dartmouth’s Tuck School of Business compared factors such as home-field advantage, the size and growth of national economies, and former political affiliations.  After comparing the results, Hawksworth and Bernard underestimated both the sustaining power of the U.S. and China’s performance as host country.  Another surprising trend found that the wealthy remained wealthy, despite an economic shift towards a flatter world.

As previously mentioned, several factors that influenced the divergence between model and results include the home-country effect and economic growth.  Two other factors include a post-hosting letdown, where the previous Olympic host country (Greece) plummets in performance in the succeeding Games; and a pre-hosting boost, where the next country in line (Great Britain) begins to raise its own standards in preparation for its hosting duties.

Gross describes the difficulty in estimating results of a scenario like the Olympics with a top-down perspective.  By only basing estimation on past models, chance, randomness, and cultural phenomena cannot be considered.  He gives the example of how a nation’s openness to immigration may only a possible factor if economists consult experts in each sport and are able to pick potential winners.

These articles are closely connected to what we are focusing on in class.  As we finish up discussing the importance of consumer demand and utility, the articles demonstrate how shifts in the cultural, political, economic, and social geographies of certain areas of the world can have a significant (or insignificant) impact on predictability and forecasting.  These articles highlight the role that demand-side determinants play in estimation, as seen in the causes and effects of post-hosting letdowns or pre-hosting boosts.  Lastly, these articles show how differences in perspectives — regression models versus consulting experts in the sports — affect the success or failure of estimation.

Commentary

The determinants of medal-winning in the Olympics were estimated by PricewaterhouseCoopers and Andrew Bernard of Dartmouth by considering the following: the general state of the countries’ economy based on indicators such as purchasing power parity; population; past performance in the Olympics; home-field advantage; state-sponsored sports programs; athletic culture; and human wealth. Past performance is the most concrete way to estimate medal numbers, and the estimations made were generally based on these past numbers. The history of communism and state-sponsored sports programs in countries such as China and Russia seem to have a lot to do with their past and continuing success.

As far as estimating changes in medal count between 2004 and 2008, both analysts believed that China’s booming economy and home-field advantage would lead to more medals. Indeed, China’s medal count rose by 59 percent to 100 medals including 51 gold.

The ability of these economists to predict the medal results with fairly good accuracy is impressive considering the nature of the games and how much chance alone can make the difference between a win and a loss for an athlete. A good run and a bad run for one athlete might mean the difference between a gold medal and no medal. Estimating home field advantage and other intangible influences is also very hard to do from a scientific standpoint. A more comprehensive way to estimate winners and losers might be to look at each sport and competition individually, although that would require much more data and inferences than this general, economy-based approach.

Real World Application: Jeremy Lin

Over the last two weeks, a name that has littered the covers of sports publications and TV shows is Jeremy Lin. Lin, a graduate of Harvard College, has risen from being a hardly-played substitute to a name known by all in under a month. While Lin’s meteoric rise to fame has been something of awe, it has also posed certain difficulties and constraints on jersey manufacturers in respect to demand estimation.

Like all other manufacturers, Jersey manufacturers have certain factors that constrain the amount of output they are able to produce, and most often, this is seen through the functioning capacity of machinery and the availability of resources. Given the limited amount of resources, a manufacturer would have to determine how best to allocate their resources in order to maximize profits. Specific to the Jersey manufacturing industry, this often means that manufactures will use determinants such as player popularity and team popularity to determine which and how many jerseys to produce.

However, when there is a sharp change in what is desired by consumers, Jersey manufacturers will often feel financial woe in terms of unrealized potential profits. In the case of Jeremy Lin, no Jersey manufacturer could have estimated that demand for the Jersey of a Knicks 2nd-string player would increase, but ultimately it did. As a result of Lin’s stellar performance, demand for his Jersey sharply rose, and the small supply of Jerseys was quickly depleted. If manufactures were able to somehow foresee this increase in demand and factor it into their demand estimation when choosing which products to make, they could have garnered a much greater deal of profits.

 In the case of Jeremy Lin and the shortage of his Jerseys, we see how unpredictability can muddle demand estimation and cause problems for producers.

Posted by Ben, Kyle and Kevin (Section 4)

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