University of Utah UNDERGRADUATE RESEARCH JOURNAL
An Econometric Approach to Validate the Economic Impact of the 2002 Winter Olympic Games
Keegan VanLeeuwen (Professor Scott Schaefer)
Department of Finance
Abstract
The Olympics Games are perceived as the most significant sporting contest in the world. With roots dating back to ancient Greece, the Olympics display an abundance of athletic competition, nationalism, and cultural appreciation between almost every country on the planet. The cities hosting the Olympic Games become the center of attention around the globe as they showcase the world’s greatest athletes for several weeks. In February 2002, Salt Lake City, Utah obtained this honor as it hosted the 19th Winter Olympic Games. Recently, controversy has risen around the lack of long-term realized economic benefits from hosting the Summer or Winter Olympics, due to the massive initial investments required. There has been little tangible evidence that suggests the Olympic Games have the ability to produce economic benefits, especially in smaller economic regions like Salt Lake. The following research takes an econometric point of view to find evidence that proves if the Olympics can create long-term economic benefits for host cities. By using difference in difference regressions to compare economic factors of nearby regions to Salt Lake, this analysis can provide tangible evidence to conclude if the 2002 Winter Olympic Games created long-term economic benefits for Salt Lake City in employment, real estate, and taxable spending. The results from this econometric research will have the capability to inform Salt Lake City and/or other Olympic venues of similar economic landscapes if long- term economic benefits are possible from hosting future mega-events like the Olympics Games.
1. LITERATURE REVIEW
Several studies have used the tools of econometrics to analyze economic factors associated with the Olympic Games. Many of these case studies have placed considerable doubt that large events like the Winter Olympics, have the capability to improve economic landscapes for host cities. In Olav R. Spilling’s Mega-event as Strategy for Regional Development: The Case of the
1994 Lillehammer Winter Olympics, he discusses the short and long-term industrial impacts of hosting the Olympic games in a small town. He argues that the applications to host these mega- events by cities and surrounding regions are based off irrational thinking, in that corresponding economic benefits of hosting the games are expected to exceed the costs incurred from planning, construction, and setup. Hosting a mega-event such as the Olympics has implied unreasonable connotations that smaller cities, such as Lillehammer, Salt Lake City, and even Sochi would receive enough attention and investment to power their economies for years to come (Spilling,
1996). Spilling believes it is absurd to think the billions of dollars invested into the games will ever generate back into these smaller regions. Even in 2002, president of the International Olympic
Committee, Jacques Rogge, stated “the scale of the games is a threat to their own quality” and expressed the “need to scale down the games so the host cities are not limited to wealthy metropolises” (Roberts).
Hosting the Olympic Games has a risk-reward relationship, and there are many costs and benefits that are associated with hosting the event. In Going for Gold: The Economics of the
Olympics, Robert A. Baade and Victor A. Matheson walk through the Olympic bid process, and dive even further to understand where costs are allocated and what kinds of benefits can be realized. To begin, hosting the Olympic Games starts with an open bidding process, usually taking place a decade before the event is scheduled to begin. Baade and Matheson argue the bid process is no small undertaking, which it usually involves seven to ten cities investing millions of funds to hopefully impress the Evaluation Commission of the International Olympic Committee. They argue this process can be extremely inefficient, as cities such as Chicago have invested over $70 million on unsuccessful bids. Between 1898 and 2000, nearly 90% of Olympic Games were held in developed countries, due to the fact these areas already possessed civil infrastructure to host such a world event such as highways, airports, and accommodations (Baade, 2016). Most importantly, however, Baade and Matheson argue these developed regions possessed the ability to create any needed additional infrastructure to host the events. Such infrastructure required to impress the commission includes sustainable transportation, hotels/accommodations, and athletic venues. Baade and Matheson also break down costs incurred when hosting the Olympics in which costs come in three various waves. The first wave of expenses is derived from developing the infrastructure to accommodate the large number of tourists and athletes that will be arriving to the host city. These expenses include restorations of hotels, construction of the Olympic Village, improved transportation, and any other accommodations to manage the massive influx of spectators. The second wave of expenses comes from building the sporting infrastructure that will be used in the events themselves, such as stadiums and Olympic regulated venues. The final wave of expenses involves the operations when Games have begun, which include the opening and closing ceremonies, general admissions, as well as millions, sometimes billions, of dollars for sufficient security measures. Baade and Matheson would also like to point out there is much discrepancy over disentangled expenses, cost overruns, and corruption that give incentives to officials to release inaccurate data. Host cities are almost entirely responsible to flip this bill for the Olympics, only sometimes with the exception of outside help. Revenue generated from the
Olympic Games is relatively small compared to the amount of capital required to host. Olympic revenue is typically derived from short-run boosts that include ticket sales, public work projects, television rights, and any increase in economic activity during the preparation for the event. Baade and Matheson demonstrate revenue and economic development predictions are often incredibly overestimated when compared to the actual economic gains produced after the events. For example, Salt Lake 2002 was predicted to have an influx of 35,000 permanent, full-time jobs after the event, but in reality, only 4,000 to 7,000 jobs were actually created, most of which lasted only for the event (Baade, 2016).
Many Olympic studies have used econometrics to predict the medal success for countries participating in the games. Goldman Sachs conducted an OLS experiment to predict medal winnings and economic profits in their Global Economics Commodities and Strategy Research project, The Olympics and Economics 2012. By using panel variables such as GDP, Log[GDP],
Development Indexes, Host dummies, and political dummies, their model predicts medal count per country. The 2012 London Summer Games medal prediction is actually quite remarkable, as the team successfully picked 4 out of the top 5 countries and had a margin of error of just 14 medals or 6% (Goldman, 2012). Although these models were originally created to predict national medal count, many of these same variables can be used to predict economic success, as well provide insights for what variables might have been omitted in their analysis.
The tools of econometrics have been used to look at both short-term and long-term economic impacts of the Olympics. Stephanie Jasmand and Wolfgang Maennig use this strategy to understand the long-run economic success of the 1972 Munich Summer Olympic Games in
Regional Income and Employment Effects of the 1972 Munich Olympic Summer Games. Jasmand and Maennig specifically dive into two economic measurements for the Munich region: income effects and employment effects. In their findings, they produced statistically significant income results, as well as an increase in employment in host city and neighboring areas. However, Jasmand and Maennig argue it is wrong to assume any of these results were directly caused by the Olympic event itself as Munich was already in a state of economic development (Jasmand, 2010). Their findings are also in agreement with other economic studies done for Los Angeles 1984 and Atlanta
1996, showing there is limited proof that the Olympic Games single handedly generate economic development. On the other hand, Andrew Wallman published a dissertation known as, The
Economic Impact of the 2002 Winter Olympic Games in Salt Lake City, in which he uses panel econometrics to create both a dynamic employment model and a prediction model for the economic impact of the games on Salt Lake City. His results confirm there are positive short-term economic benefits to hosting the games compared to if not. He also suggests long-term benefits may be possible. However, Dr. Wallman notes that there are many consequences to hosting the Olympics, and in most cases, these benefits will not always outweigh the enormous initial investments
(Wallman, 2006).
One of the most recent works trying to calculate the economic impac from the 2002
Olympics includes Utah’s Olympic Economic Legacy, published by the Kem C. Gardner Policy
Institute in early 2018 which analyses regional impact from the 2002 Olympic surplus to understand changes in tourism, employment, infrastructure, and intangibles (Natalie). Even though this analysis aims at identifying economic impacts from the 2002 Salt Lake Olympics, its analysis omits the use of counterfactuals, a key characteristic in separating treatment effects in a controlled experiment. In addition, this analysis omits the use of statistical significance to test the validity of the results. To build off of this analysis, I will expand by using similar data and target economic variables in which I will add counterfactuals and hypothesis testing to my models.
After considering the analysis of these reports, it is clear to see the investment required to operate the Olympic Games can leave host cities in economic strain without receiving the implied benefits. Subsequently, Salt Lake City, Utah may be a unique economic environment, as it is one of the only Olympic venues to break-even in the past 30 years. Using the tools of econometrics, this paper will attempt to measure long-term economic benefits from hosting the Olympics, particularly paying attention to Salt Lake City and Park City, UT.
2. SALT LAKE CITY 2002 OLYMPIC STRATEGY
Bidding to host the Winter Olympic Games in Salt Lake City began back in the mid
1960s, with Utah state governor, Calvin Rampton, forming the Olympics Utah Incorporated
(OUI) where they raised pins to bid on the 1972 Olympics (Salt Lake City, 2018). Early bids to the Olympics was a strategy for Salt Lake to promote the rapidly growing ski industry in the
Wasatch front. Bidding for the Winter Olympics in Salt Lake took a more serious turn in the late
1980s. In an interview with University of Utah Professor Matt Burbank, co-author of Olympic
Dream: The Impact of Mega-events on Local Politics, Burbank mentioned one of the most important successes in winning the 2002 Olympic bid was the 1989 referendum. The referendum had two key components. One, it approved a small increase in state sales tax to complete the $59 million Olympic Winter Sports Park in Summit County, Utah, which boasted bobsled/luge courses, Nordic combined tracks, ski jumps, and an off-site speed-skating arena. Two, it calmed down opposition to hosting the games by compromising with local environmentalists and declining major development in the Little and Big Cottonwood canyons. The referendum was unique to Utah, as it allowed early public funding to start construction for Olympic related complexes before IOC and corporate sponsorships would become involved. Not allowing early public funding is one of the reasons why Denver, Colorado has lost several Olympic bids
(Burbank, 2020). Strategically, these venues were completed before 1993 to hopefully scratch the bid for the 1998 Winter Olympic Games. Although Nagano, Japan eventually won the bidding war for the 1998 Olympics, this approach would eventually give Salt Lake the outright bid for the 2002 Olympic Games in June 1995. Between 1995 and 2002 the International Olympic Committee, Salt Lake City Hosting Committee, government, and corporate sponsorship began the construction on both Olympic and civil infrastructure in the Salt Lake valley.
Finally, in February 8-24, 2002, Salt Lake City, Utah become the center of attention around the world as it staged the 2002 Winter Olympic Games. Over the span of three weeks, three billion people tuned in to watch 3,500 athletes from 82 different countries battle in over 70 athletic events around the Wasatch region (Salt Lake, 2002). When analyzing the costs of these Olympic Games, the Salt Lake City Hosting Committee paid nearly 70% of the $1.9 billion, with the other 30% came from the state of Utah, local governments, and the federal government (Baumann, 2010).
Salt Lake has become the anomaly of Olympic host cities, as its final estimated costs of $2.5 billion are some of the lowest relative costs for any Olympics since the 1980s. The past two Winter
Olympics, in 2014 Sochi and 2018 Pyeongchang, for example, both had the highest expenditures of any Winter Olympics to date, with estimated costs upwards of $51 billion and $12.9 billion, respectively. Keep in mind, an Olympic Game has never been completed under the original estimated budget. However, Salt Lake had the lowest cost overrun of any Olympics, even with an estimated excess of $100 million (Flyvbjerg, 2016). Unlike other winter venues, Salt Lake was able to save billions by using existing sport infrastructure, which included 11 world-class ski resorts within an hour’s drive of the city’s center, the University of Utah’s Rice Eccles Stadium, and the Delta Center arena.
Many past Olympic host cities have found extreme sunk-cost fallacies, due to the fact that most of their infrastructure and Olympic regulated facilities can no longer be used and/or appreciated. Recently, this has been seen in Sochi, Russia as its Olympic Park is nearly abandoned with unfinished dorms, empty hotels, stadiums, and memorabilia (Ponic, 2019). Early on, Salt
Lake recognized the need to preserve these hefty ventures, in which they created the Utah Sports
Commission and the Utah Athletic Foundation. These foundations were created to not only maintain the Olympic regulated infrastructure, but to expand Utah’s sporting legacy by finding opportunities to host national and world events including the Dew Tour, World Skiing
Championships, and US Olympic Team training events (Factsheet, 2013). The sustainable development strategy also included to invest in infrastructure that would sponsor economic growth for years to come. One of the largest investments included a $53 million, 10,500 seat indoor arena financed primarily by West Valley City and partially by the International Olympic Committee.
After the games would finish in February 2002, the venue was strategically designed to partner with the existing Delta Center to host concerts and house the semi-pro Utah Grizzlies hockey team.
Salt Lake also partnered with the University of Utah to expand Rice-Eccles Stadium for opening/closing ceremonies and to construct student dormitories on upper campus to act as the
Olympic Village. The designation of the 2002 Olympics also propelled Salt Lake to accelerate several public works projects, in which they could be utilized during the games. An investment of nearly $2 billion was needed to expand the Interstate 15 highway and to construct a commuter, light rail train system for the Utah Transit Authority (Kopytoff, 1997). The total investment required for this transportation project is not fully included in the direct cost estimates for the 2002
Olympic Games, due to the fact that these projects were already scheduled to be built for reasons beyond the Olympics.
3. ECONOMETRIC PROCESS
To analyze the long-term economic impact of the Salt Lake 2002 Winter Olympic Games, similar econometric studies were referenced to design difference in difference regression models to analyze several economic indicators. Difference in difference (DnD) is an OLS regression technique that attempts to mimic an experimental research question by only using observational data. The goal of DnD is to differentiate effects of the treatment group from the control group that would’ve happened in a natural experiment. In essence, the technique is comparing the average results over time between the treatment and control group. Since this is an OLS regression technique, the model can still control for fixed effects and other continues variables. Below represents the specification of difference in difference regression model:
Difference in Difference Regression
푦푖 = 훽0 + 훽1푇푟푒푎푡푚푒푛푡푖 + 훽2푃표푠푡푖 + 훽3푇푟푒푎푡푚푒푛푡 x 푃표푠푡푖 + 휏퐹푖푥푒푑 퐸푓푓푒푐푡푠
Since I am hypothesizing that Salt Lake City’s economic landscape is different after hosting the Olympic games, my regression models must create an environment that suggests what would’ve most likely happened to Salt Lake if the 2002 Olympics were never held. In order to create this type of environment, counterfactual cities/counties must be used to separate the treatment effects. The research will mainly focus on two areas of Utah that were directly affected by the 2002 Winter Olympics: Salt Lake City which resides in Salt Lake County, Utah as well as
Park City, which resides in Summit County, Utah. I have identified several counterfactuals for both Salt Lake County/City and Summit County based off of several parameters of which include distance away from Salt Lake/Park City, proximity to ski resorts, population/size, industry types, and the ability to host ski/snow sport functions. In order to prove the statistical validity of the counterfactuals, correlation tests between the counterfactuals are ran in time periods prior to the
2002 Olympic games. Results of these tests will be provided in the corresponding sections. Below is a table representing the counterfactuals that have been decided for both Salt Lake County/City and Summit County, Utah.
Salt Lake County / City1 Summit County / Park City Utah County / Provo, Utah Eagle County / Vail, Colorado Davis County / Weber County Pitkin County / Aspen, Colorado Ada County / Boise, Idaho Summit County / Various Colorado Ski Towns
1 Note: Depending on the structure of the regression and the availability of the data, some analysis is broken down by city while other analysis is broken down by county.
Denver County / City Routt County / Steamboat Springs, Colorado
The difference in difference models in this research will be less obvious that traditional
DnD models as the interaction between the Treatment and Post variable will be known as the Host
Dummy Variable. The Host Dummy Variable will be equal to 1 for Salt Lake/Summit observed data in periods after the 2002 Olympics and 0 otherwise. The coefficient of this dummy variable in a regression will be able to separate any affects in the treated region’s observed data by differentiating it between the treated and counterfactual region’s observed data in the time period prior to the Olympics as well as the counterfactual observed data in the time period following the
Olympics. In order to measure the economic performance of Salt Lake/Park City before and after the 2002 Winter Olympics, the regressions will use various economic indicators that will suggest how the economy is performing. The economic indicators this thesis will explore are as follows: employment in various sectors/industries, median real estate values, and taxable spending in various categories. In addition, the regression models will also be controlling for time effects which include seasonal patterns found in each month as well as effects from individual years. Also, each model will control for fixed effects between the treated and controlled locations. Data provided for these economic indicators were retrieved from various economic databases in which more detail will be provided in the corresponding sections.
4. EMPLOYMENT ANALYSIS
Employment is a common economic factor that can be used to interpret the economic stability of a certain city/region. Using Bureau of Economic Analysis employment data, the DnD regressions will understand how certain sectors of employment changed in Salt Lake for the years leading up to and after the 2002 Olympics. Specifically, the regressions are designed to look at the following employment sectors: Leisure and Recreation, Accommodation, Financial Activities,
Service Providing, and Construction of Buildings.
Monthly total employment data from each of these categories was gathered for the
following time period of June 1990 to May 2019 in order to have enough comparison data. Due to
the availability of data, and the most reasonable counterfactual specifications, the employment
section is targeting effects in the Salt Lake, UT MSA and using the MSA of Boise, ID and the
MSA of Denver, CO as counterfactuals. The dependent variable in each regression specification
will be the quotient between the MSA’s employment in that particular sector and the total non-
farm employment of the MSA. The quotient is then multiplied by 1,000 for interpretational
purposes. The independent variables of the regression specification begin with the Host Dummy
Variable followed by time and location fixed effects. Each month category, year, and MSA will
be controlled for their unique trends in attempt to single out effects from the Host Dummy Variable.
Below represent the specifications for the employment DnD regression model as well as the
employment DnD regression results:
Employment Model Regression Specification
푦푖 = 1,000 * (Employment Type / Total Non-Farm Employment)
푋1 = Host Dummy
휏푖 = Individual Month Effects
훿푖 = Individual Year Effects
휇푖 = Individual Metropolitan Statistical Area Fixed Effects
Final Regression2
푦푖 = 훽0 + 훽1푋1 + 휏푖 + 훿푖 + 휇푖
VARIABLES (1) (2) (3) (4) (5) Leisure Share Accommodation Financial Share Service Share Construction Share Share
Host Dummy Variable -6.786*** -0.296*** 8.656*** -13.72***
2 Note: Construction employment will have a slightly different regression specification. (0.340) (0.105) (0.358) (1.163) Construction Host Dummy 2.274*** (0.147) Year Fixed Effect (1990 – 2019) x x x x x Month Fixed Effects (Jan – Dec) x x x x x Salt Lake MSA Fixed Effects x x x x x Boise MSA Fixed Effects x x x x Denver MSA Fixed Effects x x x x Constant 88.28*** 15.39*** 54.70*** 782.8*** 7.075*** (0.489) (0.209) (0.644) (1.177) (0.316) Observations 706 706 1,059 1,059 1,059 R-squared 0.858 0.710 0.948 0.911 0.774 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
4.1. Leisure Employment
Leisure employment of the art, entertainment, and recreation sector is defined by the
North American Industry Classification System (NAICS) as establishments that are involved in
producing, promoting, or participating in live performances, events, or exhibits intended for
public viewing; establishments that preserve and exhibit objects and sites of historical, cultural,
or educational interest; and establishments that operate facilities or provide services that enable
patrons to participate in recreational activities or pursue amusement, hobby, and leisure-time
interests. Leisure employment is included in the overall service super sector (Industries). The
majority of employment regarded to hosting the Olympic Games would fall under this category.
The leisure regression specification
follows the main employment
regression specification with Boise
MSA as the sole counterfactual.
Counterfactual validity proved to be
vital in this regression, as Boise MSA
had the only positive correlation (0.28)
of leisure employment trends before the Figure 1: Leisure Employment Olympics. Denver MSA on the other hand had a negative correlation, therefore, did not seem fit to be represented as a counterfactual. The dependent variable (Leisure Share) consists of the total
Leisure employment per MSA, divided by the total non-farm population per MSA, and then multiplied by 1,000. After controlling for time and location fixed effects, regression (1) suggests the Salt Lake MSA saw a statistically significant decrease of 6.78 leisure employees per 1,000 non-farm employees after hosting the 2002 Winter Olympics. The interpretation of this coefficient may not seem very surprising, as it could suggest this type of leisure employment peaked heavily during the 2002 Winter Olympics (see Figure 1). However, the results of this regression show the Olympics may not have boosted Salt Lake MSA’s leisure employment following the games, in which the pattern of leisure employment growth in Salt Lake follows very consistently with its counterfactual in the years after the Olympics. The constant of this regression suggests 88.23 per 1,000 workers are employed in the leisure sector on average in
Boise throughout the period which is accurate compared to BEA averages. The R-Squared of this regression is also 0.858, representing 85.8% of the variation in the dependent variable is explained by the independent variables.
4.2. Accommodation Employment
A boost in accommodation employment is vital for the Olympic Games to host spectators from all over the world for several weeks. However, there has been little previous research to suggest how long this accommodation employment boost will last. The NAICS defines industries in the accommodation subsector as those who provide lodging or short-term accommodations for travelers, vacationers, and others. Some provide lodging only; while others provide meals, laundry services, and recreational facilities, as well as lodging. Lodging establishments are classified in this subsector even if the provision of complementary services generates more revenue (Industries).
The following regression model aims to understand if the 2002 Olympics created a lasting effect for accommodation employees in Salt Lake in the years following the Olympics, possibly due to a higher number of travelers headed to Salt Lake after the games. The accommodation employment specification is identical to the leisure employment specification, except for the counterfactuals. Due to a high correlation (0.65) before the games, the accommodation model will use Denver MSA as the sole counterfactual for the DnD regression.
The dependent variable (Accommodation Share) consists of the total accommodation employment per MSA, divided by the total non-farm population per MSA multiplied by 1,000.
The accommodation regression model (2) delivers a similar result as the leisure employment model in that the treatment coefficient is negative. Keeping time and location constant, the employment model predicted a decrease of 0.296 accommodation employees per
1,000 non-farm employees in Salt Lake
for the years following the 2002
Olympics. Although very small, the
result is statistically significant, and does
not show any large, positive changes to
accommodation employment resulted
from hosting the 2002 Olympics. As
Figure 2: Accomodation Employment seen in Figure 2, there is no visible changes of trends in accommodation employment sectors between Denver and Salt Lake. This model does not suggest there is evidence that the accommodation industry increased their employment in Salt Lake in the years following the 2002 Olympics. The constant of this regression implies 15.39 per 1,000 workers are employed in the accommodation sector on average in Denver throughout the period which is accurate compared to BEA averages. The R-
Squared of this regression is also 0.710, representing 71% of the variation in the dependent variable is explained by the independent variables.
4.3. Financial Activities Employment
A change in financial activities is not uncommon when looking at the data of many past
Olympic host cities, in most cases due to an economic slump brought forth from the games. The
NAICS divides the financial activities industry into two groups: Finance/Insurance and Real
Estate. The finance and insurance sector comprise establishments primarily engaged in financial transactions (transactions involving the creation, liquidation, or change in ownership of financial assets) and/or in facilitating financial transactions. The real estate and rental and leasing sector comprise establishments primarily engaged in renting, leasing, or otherwise allowing the use of tangible or intangible assets, and establishments providing related services (Industries).
The financial employment model is attempting to interpret if substantial Salt Lake employment in investing, real estate, or insurance increased/decreased due to the 2002 Olympics.
Due to a high correlations of financial employment trends before the games, the financial model will use Denver MSA (0.99) and Boise MSA (0.69) as the counterfactuals for the DnD regression.
The dependent variable (Finance Share) consists of the total financial activities’ employment per
MSA, divided by the total non-farm population per MSA multiplied by 1,000. The regression in model (3) suggests the finance industry in the
Salt Lake metro area saw an increase of 8.66 employees per 1,000 non-farm employees due to the Olympics, keeping all else constant. The significance of this growth in the finance sector is important as it Figure 3: Finance Employment implies in the years directly after the
Olympics, the finance sector saw large employment growth that it otherwise was not projected to have. The constant of this regression suggests 54.7 per 1,000 workers are employed in the finance sector on average in Boise throughout the period which is accurate compared to BEA averages.
The R-Squared of this regression is also 0.948, representing 94.8% of the variation in the dependent variable is explained by the independent variables.
4.4. Service Employment
All of the employment sectors analyzed above are incorporated into the overall service providing super-sector. The NAICS defines the service providing industry as non-good producing sectors, which include trade, transportation, utilities, information, financial activities, professional/business services, education, health, leisure, etc. Much of the employment provided to the Olympic Games would reside in a service-like industry (Industries).
The regression model (4) analyzes the entire service providing industry to understand if the overall sector of Olympic-related employment was altered in the Salt Lake MSA due the 2002
Winter Olympics. Due to positive correlations of service employment trends prior to the Olympics, both Boise MSA and Denver MSA will be used as counterfactuals. The dependent variable
(Service Share) consists of the total employment in service providing activities per MSA, divided by the total non-farm population per MSA multiplied by 1,000. The regression results of
employment model (4) suggest the
service sector share of employees in
Salt Lake decreased 13.72 per 1,000
non-farm employees in the years
following the Olympic Games,
keeping all else constant. The results
of model (4) may be similar to the
Figure 4: Service Employment leisure employment regression results as the Salt Lake service market has not recouped since the pre-Olympic boom in the service industry. As a result, the model coveys that the Olympics did not create a long-term boost in the service industry compared to similar cities that did not host. The constant of this regression suggests 787 per 1,000 workers are employed in the finance sector on average in Boise throughout the period which is accurate compared to BEA averages. The R-Squared of this regression is also
0.911, representing 91.1% of the variation in the dependent variable is explained by the independent variables.
4.5. Construction of Buildings Employment:
During the preparation for the 2002 Olympic Games, Salt Lake was involved in several large construction projects including the E-Center, Olympic Oval Ice-Skating Arena, improving the interstate system, and expanding the University of Utah’s Rice Eccles Stadium and upper- campus dormitories. The NAICS defines the Construction of Buildings subsector as establishments who are primarily responsible for the construction of buildings. The work performed may include new work, additions, alterations, or maintenance and repairs (Industries).
The construction employment regression follows a slightly different model than previous employment specifications. Rather than searching for impacts after the 2002 games, the construction model is focused on the time period of construction itself, which began in June 1995 when the Olympic bid was awarded and ended in February 2002 when the games were officially held. Therefore, the Construction Host Dummy is equal to 1 in the construction period for Salt
Lake MSA observations and equal to 0 otherwise. Counterfactuals for this model include both
Boise MSA and Denver MSA with correlations of (0.71) and (0.88) respectively between trends in construction employment outside the timeline of construction. The dependent variable
(Construction Share) consists of the total construction employment per MSA, divided by the total non-farm population per MSA multiplied by 1,000.
With a statistically significant coefficient of 2.274, the construction model (5) suggests that
Salt Lake’s construction employment increased 2.274 employees per 1,000 non-farm employees between 1995 and 2002 compared the counterfactuals, keeping everything else constant. When analyzing Figure 5, it is clear to see similar employment trends prior to 1995, then a large boost in construction employment specifically in Salt Lake between 1995 and 2002, and then a divergent back to Figure 5: Construction Employment similar trends in employment after 2002. In addition, the R-Squared of the model is suggesting
77.4% of the variation in the dependent variable is explained by the independent variables.
5. REAL ESTATE ANALYSIS
Real estate factors can be powerful economic indicators hinting at how successful certain markets are performing. Whether it’s physical characteristics, proximity to particular industries, or even status of neighborhoods, factors in real estate can give clues on how and why economics are performing the way they are. Even mega-events such as the super bowl have displayed evidence that host cities have seen increases to real estate values (Lambert, 2018). The real estate regression model will attempt to calculate if the 2002 Olympics influenced any changes to the values of homes in Salt Lake County and Summit County. Indirectly, this model is evaluating the increased level of demand for houses in both Salt Lake and Summit counties after hosting the
Olympics. The model will be using Zillow data Median Value per Square Foot (MVPS) to represent home value for each observation. The Host Dummy Variable is identical to the employment model as it is equal to 1 for observational data in host counties for time periods following the 2002 Olympic Games and equal to 0 otherwise. In addition, the model will control for number of Housing Units to regulate any supply changes to the market which would obviously affect median price. A housing unit is defined by the Census Bureau as a house, an apartment, a mobile home, a group of rooms, a single room occupied as a separate living quarter or vacant units intended for occupancy. The regression specification is also using counterfactuals to create a controlled environment that simulates if the 2002 Olympics never occurred. The model will be using various Utah, Idaho, and Colorado counties due to the fact all had nearly identical annual growth rates in housing of 2.5% in the 1990s in addition to similar economic traits of the host counties. Finally, the model will control for month types, individual years, and locations in attempt to focus the effect on the Host Dummy Variable. Due to data availability, the timeline for this regression is focused between June 1996 and May 2019. Real Estate Model Regression Specification
푦푖 = Median Value Per Square Foot
푋1 = Host Dummy Variable
푋2 = Number of Housing Units Final Regression
휏푖 = Individual Month Effects 푦푖 = 훽0 + 훽1푋1푖 + 훽2푋2푖 + 휏푖 + 훿푖 + 휇푖
훿푖 = Individual Year Effects
휇푖 = Individual County Fixed Effects 5.1. Salt Lake County Regression
The Salt Lake County real estate regression is attempting to measure the impact to median home values in Salt Lake County due to the 2002 Olympics by using the following counterfactuals:
Denver County (Denver), Ada County (Boise), Utah County (Provo), and Weber County (Ogden)3.
During this timeframe, we can assume these areas were in a metropolitan growing phase, which would explain positive coefficients for Housing Units on median homes values in all four regressions. In addition, separate regressions were also executed which exclude Utah and Weber
Counties as counterfactuals. Delivering separate results that exclude these counties could give more accurate interpretations as Utah and Weber County may have also received second-hand benefits from the 2002 Olympics as they can be classified as larger suburbs of Salt Lake County.
Salt Lake County Real Estate Regressions VARIABLES (1) (2) (3) (4) MVPS Log(MVPS) MVPS Log(MVPS)
Host Dummy Variable -7.553*** -0.0653*** -35.28*** -0.186*** (1.606) (0.0100) (2.450) (0.0160) Housing Units 0.000764*** 3.51e-06*** 0.00240*** 9.53e-06*** (4.41e-05) (2.29e-07) (7.91e-05) (5.54e-07) Month Fixed Effects (Jan – Dec) x x x x Year Fixed Effect (1996 – 2019) x x x x Salt Lake County Fixed Effects x x x x Ada County Fixed Effects x x x x Denver County Fixed Effects x x x x Utah County Fixed Effects x x Weber County Fixed Effects x x
Constant -26.81*** 3.839*** -192.0*** 3.198*** (5.409) (0.0306) (9.210) (0.0625) Observations 1,400 1,400 840 840 R-squared 0.940 0.951 0.960 0.961 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 1: Salt Lake County Real Estate Regressions Model (1) regresses Median Value per Square Foot in Salt Lake County on all counterfactuals. The coefficient interprets the MVPS in Salt Lake County decreased by $7.55 in the years after hosting the 2002 Olympics keeping all else constant. Model (2) regresses the logged value of MVPS and interprets the median value per square foot in Salt Lake County housing decreased 6.53% compared to the counterfactuals who did not host the
Olympics, keeping all else constant.
Model (2) also computed a slightly larger R-Squared which deems a larger portion of the dependent variable could be explained by the independent variables. Model (3) drops Utah and Figure 6: Metro Median Home Value per Square Foot
Weber County as counterfactuals, and in return deems a higher R-Squared. However, the interpretation of the Host Dummy coefficient decreases, representing the MVPS in Salt Lake
County decreased by $35.28 in the years following the 2002 Olympics keeping all else constant.
Model (4) also regresses the logged value of MVPS on the new counterfactual set which deems the highest R-Squared of the models. It interprets median values of housing in Salt Lake County has decreased by 18.6% per square foot compared the counterfactuals since the 2002 Olympics keeping all else constant. All coefficients are statistically significant. The models convey median housing prices per square foot in Salt Lake County have actually decreased since the 2002 Olympics compared to counterfactual counties who did not host the Olympics. These results propose real estate growth in metropolitan areas may not be affected from hosting mega-events.
5.2. Summit County Regression
The Summit County real estate regression model is attempting to measure the impact to values of homes in Summit County, Utah in the years after hosting the 2002 Olympics. The model will use following counterfactuals of similar size and interest: Pitkin County (Aspen, CO), Eagle
County (Vail, CO), and Routt County (Steamboat Springs, CO). These counties will be used to separate any treatment effects that Park City received from hosting Olympics events in 2002. The regression specification is identical to the Salt Lake County model of which includes a Host
Dummy Variable, a control for Housing Units, and controls for months, years, and county fixed effects. Due to the higher stature of living conditions as well as the high demand for exclusive real estate in these areas, it makes sense that larger supplies of housing units in these areas could decrease home values as seen in the negative coefficients for Housing Units.
Summit County Real Estate Regressions Results VARIABLES (1) (2) MVPS Log(MVPS)
Host Dummy Variable -35.28*** -0.155*** (3.623) (0.00977) Housing Units - Supply -0.0169*** -9.39e-06*** (0.000870) (1.22e-06) Month Fixed Effects (Jan – Dec) x x Year Fixed Effect (1996 – 2019) x x Summit County Fixed Effects x x Eagle County Fixed Effects x x Pitkin County Fixed Effects x x Routt County Fixed Effects x x
Constant 487.0*** 5.118*** (18.87) (0.0287) Observations 1,120 1,120 R-squared 0.953 0.986 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Model (1) regresses Median Value per Square Foot using all counterfactuals. The model interprets a statistically significant decrease in the median home values in Summit County of
$35.28 per square foot in the years following the 2002 Olympics, keeping all else constant. Model (2) reflects a slightly larger R-Squared and regresses the logged value of MVPS on the independent variables which interprets a statistically significant 15.5% decrease in median home values per square foot in Figure 7: Ski Town Median Home Values per Square Foot
Summit County following the 2002 Olympics keeping all else constant. After analyzing Figure 7, it is clear to see median home values per square foot in Colorado counties grew significantly larger in this time period. Even larger than our Olympic host county of interest. The analysis presented above may suggest hosting Olympic events does not promise an increase in home values, even in areas where the culture surrounding the Winter Olympics lives year-round.
6. TAXABLE SPENDING ANALYSIS
Hosting the Olympics can bring quite a bit of “buzz” to a particular region, but it is unclear if that “buzz” translates into investments. Total taxable spending is an economic indicator representing the amount of income spent into a geographical area that can be legally taxed, predominantly through sale and property taxes. Taxable spending can measure if spending/investments in an area have risen over time. The following regression models will use taxable spending data from the Utah and Colorado Department of Revenues to indicate if total spending and investments grew inside both Salt Lake and Summit County due to the 2002
Olympics, as well as use this indicator to understand if the construction industry had significant spending prior to the 2002 Olympics. The regression model’s dependent variable will consist of each county’s observed Taxable Spending, whether Total or Construction, in distinct time periods.
Each regression model will consist of a Treatment Dummy Variable equaling to 1 for host counties in time periods that reflect the interest of the experiment, and 0 if otherwise. In addition, panel data for Population will contain for any spending that can be correlated to population growth. The coefficient for Population is expected to positive, as a larger growth in population will require more spending on new homes, schools, etc. Finally, the models will control for individual quarter/month, year, and county fixed effects.
Taxable Spending Regression Specification
푦푖 = Taxable Spending Amount
푋1 = Treatment Dummy Variable
푋2 = Population of Area Final Regression
휏푖 = Individual Month Effects 푦푖 = 훽0 + 훽1푋1푖 + 훽2푋2푖 + 휏푖 + 훿푖 + 휇푖
훿푖 = Individual Year Effects
휇푖 = Individual County Fixed Effects
Total Taxable Spending Regressions VARIABLES (1) (2) Log(Total Taxable Spending) for Log(Total Taxable Spending) for Salt Lake County Summit County
Host Dummy Variable 0.104*** 0.233*** (0.0200) (0.0361) Log(County Population) 0.852** 1.109*** (0.378) (0.257) Year Fixed Effect (1999 – 2009) x x Quarter Fixed Effects (1,2,3,4) x x Salt Lake County Fixed Effects x Denver County Fixed Effects x Summit County FE x Pitkin County FE x Eagle County FE x Summit County (CO) FE x Constant 10.34** 7.862***
(4.970) (2.725) Observations 84 168 R-squared 0.985 0.966 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
6.1. Salt Lake County Total Taxable Spending
The Salt Lake County total taxable spending regression model is attempting to measure
any substantial changes to spending in Salt Lake County in the years directly after the 2002
Olympics by using Denver County, CO as its counterfactual. Salt Lake County received the most
“buzz” from hosting the 2002 Olympics in which this regression will suggest if that “buzz”
eventually became monetized by
controlling for a similar economic
landscape that never hosted a world mega-
event in this period. Utah DOP data was
filed quarterly from 1978-2019 while
Colorado DOP data was filed monthly
from 1999-2009. Therefore, the model Figure 8: Total Taxable Spending Salt Lake will regress Total Taxable Spending
between the treated Salt Lake County and control Denver County between 1999 and 2009.
Looking at model (1), the Host Dummy Variable coefficient suggests total taxable spending
in Salt Lake County increased by 10.4% from hosting the 2002 Olympics keeping all else constant,
including population growth. Model (1) exhibits an R-Squared of 0.985. Looking at Figure 8, the
increased growth in taxable spending can be seen as the two lines representing total taxable
spending in Salt Lake and Denver begin to diverge directly after the 2002 Olympics. The
regression results suggest Salt Lake County exhibited larger amounts of spending and investment
inside their economy up until 2009 from hosting the 2002 Olympic Games. These results can advocate the financial return of hosting the 2002 Olympics may be larger than previously anticipated due to the realization of government revenue from the taxable spending.
6.2. Summit County Total Taxable Spending
The Summit County total taxable spending regression model is attempting to measure any substantial changes to spending in Summit County, UT in the years directly after the 2002
Olympics by using Colorado county counterfactuals of Pitkin, Eagle, and Summit (CO). Summit
County, Utah would most likely receive the most amount of “buzz” from any specific snow sport related function of the 2002 Olympics as many of the competition facilities and cultural events were located in Summit County, UT. The model’s specification is identical with the Salt Lake
County Taxable Spending model.
Model (2) for taxable spending in Summit County suggests total taxable spending in Summit County increased 23.3% from hosting the
Olympics keeping all else constant, including population growth. Looking Figure 9: Total Taxable Spending Summit, Utah at Figure 9, it is clear to see the peak of taxable spending in Summit, Utah begins to converge with the peak the larger county of Eagle directly after the 2002 Olympics. The peaks represent winter periods as most of the taxable spending related to these counties occur during the snow/ski season.
It can be interpreted that the 2002 Olympics may have created a boost in total taxable spending up until at least 2009 inside the economies of Summit, Utah compared to if the Olympics were never held in Utah. As a result, the local governments of Summit, Utah have been able to collect more tax revenue and been giving a larger financial return from hosting than previously thought.
6.3. Olympic Bid Construction Spending
The pre-Olympic and post-Olympic bid construction spending model is unique, as it is
attempting to measure total construction related boosts in both Summit County and Salt Lake
County that otherwise wouldn’t have happened if Salt Lake wasn’t in the bidding race for the 2002
Olympics. As mentioned previously, the 1989 referendum that allowed early public funding for
the construction of venues prior to winning the Olympic bid was a necessary challenge in order
for Salt Lake to eventually secure the 2002 Olympics. Construction for the Olympic Park in Park
City, UT began in 1991 and concluded just prior to June 1995 when the bid for the 2002 Olympics
was awarded. Construction for Salt Lake venues did not officially begin until after 1995. Panel
observational data was collected quarterly from 1978-2019 by the Utah Department of Revenue.
The model will use a new specification in that the Pre-Olympic Bid Dummy will differentiate
construction spending between 1989 and 1995 in Summit County, Utah compared to other areas
in the state. In addition, the model will also use the Post-Olympic Bid Dummy to differentiate
construction related spending between 1995-2002 in Salt Lake compared to other areas in the state.
All models will contain for construction spending correlated to growth in population by including
the logged value of observed population data for each geographical area in addition to time and
location fixed effects.
Pre-Olympic & Post Olympic Bid Construction Spending
VARIABLE (1) (2) (3) (4) Taxable Log(Taxable Taxable Log(Taxable Construction Construction Construction Construction Spending Spending) Spending Spending)
Pre-Olympic Bid Pre-Olympic Bid Post-Olympic Bid Post-Olympic Bid 1978-1995 1978-1995 1978-2002 1978-2002
Pre-Olympic Dummy (1989-1995) 5,116,000*** -0.166 (837,588) (0.122) Post-Olympic Dummy (1995-2002) 18,920,000*** 0.126* (2,041,000) (0.0669) Log(County Population) -28,980,000*** 3.110*** -22,840,000** 2.620*** (2,795,000) (0.246) (10,240,000) (0.666) Year Fixed Effects (1999-2009) x x x x Quarter Fixed Effects (1,2,3,4) x x x x Summit County FE x x Salt Lake County FE x x x x Utah County FE x x x x Davis County FE x x x x Constant 339,900,000*** -22.38*** 268,000,000** -16.45** (32,730,000) (2.891) (121,200,000) (7.897) Observations 384 384 288 288 R-squared 0.889 0.957 0.946 0.945 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
6.3.1 Pre-Olympic Bid
The Pre-Olympic bid model is attempting to distinguish any construction related spending
in Summit County between 1989 and 1995 otherwise would not have occurred if Salt Lake was
not in the Olympic bid war. The major
construction project during this period was
the Olympic Park outside of Kimball
Junction. Due to data availability,
counterfactuals for this regression are
Davis, Utah, and Salt Lake Counties. Figure
10 represents in the construction spending
growth rates for the corresponding period Figure 10: Pre-Olympic Bid Construction Spending
and counties.
Model (1) is regressing construction taxable spending on the Pre-Olympic Host Dummy,
as well as controlling for population growth, time, and location fixed effects. The results of model
(1) suggest construction related spending in Summit County, Utah increased by over $5.1 million in the early 1990s, keeping all else constant. Model (2) takes an identical approach except it regresses the logged change of construction related spending. Model (2) exhibits a statistically insignificant 16.6% decrease in construction related spending keeping all else constant. Model (1) and model (2) calculated R-Squared of 0.889 and 0.957, respectively. After analyzing Figure 10, it is clear to see that prior to 1990, construction related spending growth rates in Summit County were very volatile which may have discounted the statistical significance of model (2). However, after the construction of the Olympic Park in 1993, it is clear to see growth rates in construction related spending in Summit County, Utah began to stabilize up until the 2002 Olympics. As a result, entering into the bid war for the 2002 Olympics could be the reason for more stable tax revenues collected by the local government in Summit County, UT.
6.3.2 Post-Olympic Bid
The Post-Olympic Bid model is attempting calculate the growth in construction spending between 1995 and 2002 related to the Olympics coming to Salt Lake County, that otherwise would not have occurred. As mentioned previously, the main construction projects during this period were the E-Center, Olympic Oval Ice-Skating Arena, and expanding several University of Utah facilities. Salt Lake County is using Davis and Utah Counties as counterfactuals. Figure 11 represents the growth rates in construction spending for each of the counties. Model (3) suggests construction
spending in Salt Lake increased $18.92
million during the Olympic construction
timeline keeping all else constant. Model
(4) suggests total construction related
spending in Salt Lake increased 12.6% due
to the Olympics, keeping all else constant.
Figure 11: Post-Olympic Bid Construction Spending Each of the coefficients deliver statistically
significant results and with R-Squareds above 0.94. After the analysis, it can be assumed the 2002
Olympics increased construction taxable spending in Salt Lake County between 1995-2002
compared to other areas of the state that did not host. Obviously, this increase in spending would
result in more tax revenue for the government that would have not been collected otherwise.
7. FINAL INTERPRETATIONS
The results found in this analysis of the 2002 Olympics are very similar to past research
papers about Olympic economic impact which suggest the games bring in marginal to zero long-
term impact in most economic sectors. However, the last section of this thesis finally gives a hint
of evidence for long-term economic growth from the Olympics, especially in taxable spending.
Employment industries related to the logistics of hosting the Olympic Games increased
significantly such as the construction sector which boomed in preparation for the games and the
financial activities sector in which their employee proportion to total non-farm employment
significantly grew in the periods since the 2002 games. Olympic-related employment such as
leisure and accommodation both saw relatively large short-term spikes during the Olympics.
However, the regressions suggest no service-related employment industry saw a long-term increase, in fact, all service-related employment shares of the non-farm working population decreased since the games. Median real estate values per square foot in Salt Lake and Summit
Counties both saw significant decreases in the years following the Olympics compared to counterfactuals who did not host a world mega-event like the Olympics. The lack of realized growth in real estate values may suggest the Olympic Games may not have an impact in boosting demand or increasing a city’s reputation as once suggested. However, total taxable spending in both Salt Lake and Summit Counties in the direct years following the 2002 Olympics increased dramatically. The growth in taxable spending proposes that the 2002 Olympics could have sparked a trend that increased investments/spending into the host economies for years to come. As a result, the financial return on public funds to initially host the 2002 Olympics may be larger than previously calculated. Finally, in the time periods around the Olympic Bid to host the games, both
Summit and Salt Lake Counties saw distinct changes to their construction related economy which not only boosted cash flow in each related location, but it also increased tax revenue for the local governments. To conclude, Olympic officials and host committees need to be more realistic with their expectations on how much economic return will come from hosting the Olympic Games.
8. DISCUSSION ABOUT FUTURE OLYMPIC BIDS
On December 14, 2018 the US Olympic Committee selected Salt Lake City to bid for the
2030 Winter Olympic Games on behalf of the United States. Salt Lake City comes as the most economically viable region to host the 2030 Olympics due to the fact no major new venues would need to be constructed, unlike other cities, including Denver, Colorado and Sapporo, Japan. The estimated costs for Salt Lake to host another Olympics is $1.4 billion, the lowest of any estimated costs for an Olympic event in the past 40 years. All past Olympic venues would be utilized, and
Utah Olympic officials have also emphasized the importance of providing the “an all new venue experience” for Salt Lake. Some of this innovated planning includes proposed outdoor hockey games as well as the inclusion of more social events (Roche, 2018). The argument to host the
Winter Olympics in Salt Lake City again is fairly easy to render since the existing facilities have been well maintained and upgraded. As a result, all new funds would be used for new facilities that could enhance the experience at the Olympics. However, the International Olympic
Committee has strived on an unsustainable model to bring new glamorous stadiums, venues, and infrastructure to each Olympic Game. The culture behind hosting the games has been overshadowed and awarded to who can build the newest, most glamourous sports park. Hopefully the IOC changes its decision process in the upcoming years, because as of recent, the committee has been irresponsibly awarding the Olympic Games to cities with the highest inflated price tag in hopes for the most quality/commitment. Moving forward, if Salt Lake chose to host the Olympic
Games again, it could be done in a cost-efficient manner that reuses sights and venues, but also in a way that focuses the games on sports and culture without promising unreasonable economics benefits.
9. Appendix
Full Employment Regression Results
(1) (2) (3) (5) (6) VARIABLES Leisure Share Construction Share Finance Share Service Share Accommodations Share
Host Dummy -6.786*** 8.656*** -13.72*** -0.296*** (0.340) (0.358) (1.163) (0.105) Construction Host 2.274*** (0.147) 1991.Year -2.270*** 0.0302 2.213** 3.184* -0.318 (0.512) (0.291) (0.973) (1.685) (0.293) 1992.Year -1.198* 0.609** 2.004** 2.043 -0.850*** (0.624) (0.276) (0.829) (1.921) (0.276) 1993.Year -1.517** 1.323*** 2.992*** -1.834 -0.990*** (0.639) (0.299) (0.674) (2.392) (0.275) 1994.Year -1.583*** 3.114*** 4.026*** -4.829* -1.874*** (0.527) (0.352) (0.608) (2.882) (0.231) 1995.Year -2.307*** 2.837*** 2.771*** -3.915 -1.769*** (0.545) (0.322) (0.590) (2.728) (0.241) 1996.Year -3.081*** 2.548*** 4.035*** -4.038 -2.324*** (0.478) (0.274) (0.584) (2.673) (0.212) 1997.Year -4.253*** 2.511*** 3.295*** -4.037 -2.233*** (0.478) (0.291) (0.639) (2.595) (0.209) 1998.Year -5.042*** 2.386*** 4.221*** -2.973 -2.381*** (0.516) (0.309) (0.681) (2.128) (0.212) 1999.Year -6.081*** 2.835*** 4.228*** -0.0664 -2.513*** (0.479) (0.311) (0.661) (1.758) (0.213) 2000.Year -5.385*** 3.042*** 2.211*** 1.917 -2.868*** (0.665) (0.321) (0.731) (1.752) (0.217) 2001.Year -3.462*** 3.343*** 2.689*** 6.629*** -2.762*** (0.665) (0.331) (0.993) (1.816) (0.208) 2002.Year 2.654** 3.315*** 1.611** 20.81*** -2.573*** (1.081) (0.326) (0.775) (2.171) (0.208) 2003.Year 2.270*** 2.991*** 3.398*** 27.48*** -3.111*** (0.670) (0.308) (0.838) (1.795) (0.192) 2004.Year 1.387** 3.213*** 2.413*** 27.81*** -3.332*** (0.685) (0.304) (0.812) (1.652) (0.197) 2005.Year -0.130 4.396*** 2.364*** 25.41*** -3.124*** (0.686) (0.281) (0.798) (1.638) (0.199) 2006.Year -0.878 5.819*** 2.798*** 20.20*** -2.902*** (0.643) (0.329) (0.688) (1.660) (0.195) 2007.Year 1.189** 5.206*** 2.034*** 24.06*** -2.707*** (0.562) (0.301) (0.639) (1.435) (0.220) 2008.Year 1.445*** 3.269*** 0.398 35.72*** -2.569*** (0.494) (0.280) (0.597) (1.993) (0.221) 2009.Year 2.150*** 1.048*** 0.581 53.09*** -2.912*** (0.518) (0.276) (0.604) (2.819) (0.222) 2010.Year 1.690*** 0.0749 -0.886 58.51*** -2.814*** (0.556) (0.264) (0.576) (2.880) (0.221) 2011.Year 2.609*** 0.201 -1.167* 58.76*** -2.898*** (0.556) (0.265) (0.603) (2.832) (0.220) 2012.Year 3.692*** 0.409 -1.908*** 57.79*** -3.188*** (0.518) (0.268) (0.622) (2.643) (0.230) 2013.Year 5.661*** 1.202*** -1.536** 57.16*** -3.041*** (0.542) (0.302) (0.620) (2.334) (0.214) 2014.Year 6.999*** 2.000*** -1.408** 56.42*** -3.087*** (0.605) (0.298) (0.689) (2.483) (0.214) 2015.Year 7.302*** 2.356*** -0.824 55.88*** -3.294*** (0.606) (0.268) (0.671) (2.438) (0.204) 2016.Year 8.558*** 2.960*** -0.187 55.22*** -3.376*** (0.791) (0.274) (0.663) (2.264) (0.224) 2017.Year 9.099*** 3.503*** 0.0628 53.02*** -3.206*** (0.861) (0.311) (0.649) (2.075) (0.220) 2018.Year 10.42*** 4.232*** 0.0419 50.15*** -3.336*** (0.890) (0.353) (0.696) (1.858) (0.201) 2019.Year 12.29*** 4.633*** -0.378 50.34*** -3.281*** (0.952) (0.444) (1.071) (2.640) (0.290) 2.month2 0.761 -0.0512 -0.254 0.701 0.103 (0.484) (0.156) (0.401) (1.644) (0.160) 3.month2 1.415*** -0.0202 -0.494 0.113 0.129 (0.430) (0.156) (0.402) (1.630) (0.155) 4.month2 1.518*** 0.0649 -0.730* -0.549 -0.0844 (0.416) (0.150) (0.394) (1.612) (0.134) 5.month2 0.935** 0.239 -0.968** -1.502 -0.660*** (0.401) (0.150) (0.391) (1.599) (0.126) 6.month2 1.848*** 0.482*** -1.035*** -3.112* -0.443*** (0.423) (0.149) (0.392) (1.618) (0.136) 7.month2 2.138*** 0.745*** -0.493 -5.006*** -0.221 (0.404) (0.150) (0.401) (1.641) (0.141) 8.month2 2.322*** 0.801*** -0.613 -4.995*** -0.182 (0.397) (0.156) (0.400) (1.655) (0.137) 9.month2 0.785** 0.622*** -1.104*** -3.467** -0.494*** (0.390) (0.155) (0.401) (1.634) (0.129) 10.month2 -1.248*** 0.597*** -1.095*** -2.728* -0.732*** (0.403) (0.155) (0.406) (1.634) (0.133) 11.month2 -1.114*** 0.507*** -1.221*** -1.035 -0.647*** (0.395) (0.153) (0.409) (1.655) (0.123) 12.month2 0.104 0.437*** -0.896** 0.905 -0.258* (0.389) (0.156) (0.416) (1.670) (0.134) Denver FE -1.453*** 22.88*** 60.71*** (0.0734) (0.202) (0.935) Boise FE - OMIT
Salt Lake FE -3.644*** 1.395*** 16.34*** 40.77*** -0.119 (0.226) (0.126) (0.355) (0.981) (0.0905)
Constant 88.28*** 7.075*** 54.70*** 782.8*** 15.39*** (0.489) (0.316) (0.644) (1.711) (0.209)
Observations 706 1,059 1,059 1,059 706 R-squared 0.858 0.774 0.948 0.911 0.710 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Salt Lake Real Estate Regressions
(1) (2) (3) (4) (5) VARIABLES MVPS Log(MVPS) Log(MVPS) MVPS MVPS
Host Dummy -7.553*** -0.0653*** -0.186*** -35.28*** -13.12*** (1.606) (0.0100) (0.0160) (2.450) (1.873) Housing Units 0.000764*** 3.51e-06*** 9.53e-06*** 0.00240*** 0.00176*** (4.41e-05) (2.29e-07) (5.54e-07) (7.91e-05) (7.89e-05) 1997.year 1.719 0.0356** 0.00962 -6.891** -3.125 (2.180) (0.0152) (0.0213) (3.227) (2.982) 1998.year 2.873 0.0662*** 0.0164 -14.25*** -7.488*** (2.178) (0.0144) (0.0200) (3.105) (2.805) 1999.year 3.136 0.0843*** 0.0136 -22.06*** -15.03*** (2.002) (0.0124) (0.0184) (2.969) (3.009) 2000.year 6.490*** 0.127*** 0.0312 -27.15*** -20.58*** (1.913) (0.0123) (0.0198) (3.134) (3.348) 2001.year 9.422*** 0.161*** 0.0474** -29.89*** -25.09*** (1.975) (0.0136) (0.0225) (3.155) (3.472) 2002.year 9.099*** 0.169*** 0.0734*** -28.24*** -26.76*** (1.945) (0.0135) (0.0195) (2.874) (3.207) 2003.year 6.580*** 0.160*** 0.0471** -36.87*** -33.58*** (2.053) (0.0138) (0.0200) (2.856) (3.369) 2004.year 5.306** 0.163*** 0.0206 -46.29*** -41.67*** (2.197) (0.0143) (0.0222) (3.047) (3.716) 2005.year 6.376*** 0.188*** 0.0228 -53.09*** -45.53*** (2.424) (0.0155) (0.0254) (3.466) (4.267) 2006.year 16.07*** 0.280*** 0.0885*** -52.42*** -37.04*** (2.630) (0.0171) (0.0280) (3.857) (5.239) 2007.year 27.91*** 0.368*** 0.113*** -52.76*** -28.63*** (2.715) (0.0162) (0.0308) (4.341) (4.881) 2008.year 28.62*** 0.374*** 0.0660* -62.57*** -32.61*** (3.187) (0.0187) (0.0361) (5.149) (5.216) 2009.year 16.57*** 0.296*** -0.0397 -80.34*** -51.50*** (3.074) (0.0176) (0.0343) (4.817) (5.419) 2010.year 6.720** 0.228*** -0.120*** -92.92*** -67.82*** (2.939) (0.0168) (0.0322) (4.481) (5.451) 2011.year -4.273 0.148*** -0.213*** -106.6*** -83.06*** (2.965) (0.0171) (0.0329) (4.594) (5.479) 2012.year -6.264** 0.137*** -0.227*** -110.8*** -87.70*** (2.871) (0.0161) (0.0346) (4.808) (5.579) 2013.year 2.948 0.214*** -0.153*** -105.3*** -78.85*** (2.903) (0.0155) (0.0356) (4.970) (5.781) 2014.year 10.52*** 0.274*** -0.116*** -104.8*** -76.07*** (3.014) (0.0156) (0.0374) (5.269) (6.039) 2015.year 17.31*** 0.319*** -0.0831** -103.0*** -75.92*** (3.272) (0.0168) (0.0405) (5.795) (6.321) 2016.year 28.63*** 0.390*** -0.0485 -101.3*** -71.98*** (3.478) (0.0174) (0.0429) (6.130) (6.689) 2017.year 41.93*** 0.466*** -0.0147 -98.75*** -65.93*** (3.686) (0.0179) (0.0457) (6.562) (7.116) 2018.year 58.66*** 0.552*** 0.0142 -96.47*** -54.99*** (4.276) (0.0207) (0.0514) (7.263) (7.786) 2019.year 72.87*** 0.622*** 0.0307 -96.62*** -45.80*** (4.844) (0.0228) (0.0584) (7.565) (8.141) 2.month 0.666 0.00448 0.00714 1.178 0.850 (1.349) (0.00836) (0.0111) (1.629) (1.422) 3.month 1.100 0.00720 0.0103 1.656 1.350 (1.342) (0.00832) (0.0110) (1.615) (1.379) 4.month 1.709 0.0115 0.0148 2.285 1.878 (1.326) (0.00823) (0.0109) (1.607) (1.356) 5.month 2.134 0.0143* 0.0177 2.729* 2.253* (1.315) (0.00817) (0.0108) (1.596) (1.330) 6.month 2.592** 0.0173** 0.0208* 3.201** 2.774** (1.308) (0.00811) (0.0107) (1.579) (1.302) 7.month 3.000** 0.0199** 0.0234** 3.618** 3.128** (1.307) (0.00811) (0.0108) (1.585) (1.310) 8.month 3.526*** 0.0235*** 0.0275** 4.187*** 3.592*** (1.318) (0.00826) (0.0109) (1.618) (1.333) 9.month 4.082*** 0.0273*** 0.0312*** 4.738*** 4.092*** (1.321) (0.00827) (0.0109) (1.621) (1.362) 10.month 4.517*** 0.0298*** 0.0344*** 5.260*** 4.570*** (1.340) (0.00839) (0.0111) (1.645) (1.414) 11.month 5.021*** 0.0333*** 0.0380*** 5.796*** 5.113*** (1.355) (0.00847) (0.0112) (1.668) (1.467) 12.month 5.665*** 0.0375*** 0.0427*** 6.492*** 5.700*** (1.375) (0.00857) (0.0114) (1.696) (1.525) Denver FE -45.50*** -0.0490 -0.834*** -258.7*** (5.786) (0.0322) (0.0734) (10.34) Ogden FE 75.40*** 0.471*** (2.623) (0.0129) Provo FE 46.34*** 0.351*** (0.961) (0.00501) Salt Lake FE -94.13*** -0.245*** -1.361*** -401.0*** -290.0*** (8.037) (0.0421) (0.104) (14.73) (14.90) Boise FE - OMIT Constant -26.81*** 3.839*** 3.198*** -192.0*** -115.4*** (5.409) (0.0306) (0.0625) (9.210) (8.742)
Observations 1,400 1,400 840 840 560 R-squared 0.940 0.951 0.961 0.960 0.981 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Summit County Real Estate Regressions
(1) (2) VARIABLES MVPS Log(MVPS)
Host Dummy -35.28*** -0.155*** (3.623) (0.00977) Housing Units -0.0169*** -9.39e-06*** (0.000870) (1.22e-06) 1997.year 19.80* 0.0867*** (11.61) (0.00755) 1998.year 41.77*** 0.178*** (11.32) (0.00830) 1999.year 73.93*** 0.305*** (10.75) (0.00838) 2000.year 104.7*** 0.415*** (10.29) (0.00587) 2001.year 132.8*** 0.468*** (10.06) (0.00826) 2002.year 157.9*** 0.539*** (10.50) (0.00841) 2003.year 176.5*** 0.582*** (10.72) (0.0107) 2004.year 195.8*** 0.620*** (10.91) (0.00702) 2005.year 241.6*** 0.732*** (11.53) (0.0111) 2006.year 332.1*** 0.949*** (13.73) (0.0130) 2007.year 411.4*** 1.110*** (16.49) (0.0137) 2008.year 410.0*** 1.096*** (14.87) (0.0153) 2009.year 358.8*** 0.957*** (13.53) (0.0126) 2010.year 329.8*** 0.866*** (13.34) (0.0112) 2011.year 306.6*** 0.786*** (13.31) (0.0113) 2012.year 305.6*** 0.784*** (13.35) (0.0107) 2013.year 322.8*** 0.831*** (13.43) (0.0117) 2014.year 346.5*** 0.901*** (13.54) (0.0103) 2015.year 379.9*** 0.982*** (14.07) (0.0105) 2016.year 399.4*** 1.040*** (13.93) (0.0126) 2017.year 418.0*** 1.085*** (14.18) (0.0156) 2018.year 459.3*** 1.163*** (15.50) (0.0137) 2019.year 500.8*** 1.245*** (17.43) (0.0129) 2.month 1.710 0.00602 (5.261) (0.00870) 3.month 2.960 0.0102 (5.212) (0.00844) 4.month 4.184 0.0144* (5.186) (0.00812) 5.month 5.246 0.0182** (5.151) (0.00801) 6.month 6.236 0.0220*** (5.125) (0.00798) 7.month 7.267 0.0257*** (5.118) (0.00801) 8.month 8.646* 0.0301*** (5.150) (0.00816) 9.month 9.787* 0.0339*** (5.164) (0.00828) 10.month 11.12** 0.0381*** (5.203) (0.00847) 11.month 12.39** 0.0421*** (5.278) (0.00876) 12.month 13.65** 0.0460*** (5.378) (0.00912) Pitkin FE -18.19 0.475*** (14.78) (0.0187) Route FE -285.9*** -0.347*** (12.28) (0.0163) Summit, UT FE -84.32*** -0.0215** (4.749) (0.0104) Eagle FE - OMIT Constant 487.0*** 5.118*** (18.87) (0.0287)
Observations 1,120 1,120 R-squared 0.953 0.986 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Total Taxable Spending Regression Table Salt Lake Summit Summit (1) (2) (2) VARIABLES Log(TTS) Log(TTS) TTS (Total Taxable Sales) host 0.104*** 0.233*** 4.068e+07*** (0.0200) (0.0361) (9.758e+06) 2000.year 0.0234 0.0303 -1.067e+07 (0.0324) (0.0400) (1.007e+07) 2001.year 0.00632 -0.0512 -2.980e+07** (0.0393) (0.0475) (1.186e+07) 2002.year -0.0834** -0.0832* -4.025e+07*** (0.0397) (0.0481) (1.209e+07) 2003.year -0.119*** -0.0965* -4.772e+07*** (0.0370) (0.0499) (1.290e+07) 2004.year -0.0586 -0.0117 -3.443e+07** (0.0391) (0.0544) (1.408e+07) 2005.year 0.00157 0.0667 -1.697e+07 (0.0427) (0.0572) (1.452e+07) 2006.year 0.0751 0.134** -3.760e+06 (0.0498) (0.0610) (1.527e+07) 2007.year 0.136** 0.191*** 1.162e+07 (0.0545) (0.0642) (1.607e+07) 2008.year 0.108* 0.136* -2.893e+06 (0.0633) (0.0742) (1.739e+07) 2009.year -0.0580 -0.0823 -6.481e+07*** (0.0687) (0.0747) (1.837e+07) 2.qtr 0.0626*** -0.695*** -1.591e+08*** (0.0121) (0.0156) (6.079e+06) 3.qtr 0.0855*** -0.414*** -1.099e+08*** (0.0118) (0.0160) (5.817e+06) 4.qtr 0.0921*** -0.412*** -1.046e+08*** (0.0132) (0.0174) (5.915e+06) log(Population) 0.852** 1.109*** 4.521e+08*** (0.378) (0.257) (7.302e+07) Salt Lake County FE 0.00197 (0.189) Denver Count FE - OMIT Pitkin County FE 0.605** 3.762e+08*** (0.292) (8.171e+07) Summit County (UT) FE 0.0474 8.654e+07*** (0.0968) (2.678e+07) Summit County (CO) FE 0.360** 1.970e+08*** (0.159) (4.327e+07) Eagle County FE - OMIT
Constant 10.34** 7.862*** -4.460e+09*** (4.970) (2.725) (7.734e+08)
Observations 84 168 168 R-squared 0.985 0.966 0.940 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Pre & Post Olympic Bid Construction Tables Pre-Bid Pre-Bid Post-Bid Post-Bid (1) (2) (1) (2) VARIABLES Sales Log(sales) Log(sales) Sales
Construction Host (Summit 5.166e+06*** -0.166 0.126* 1.892e+07*** 1989-1995) (837,588) (0.122) (0.0669) (2.041e+06) Log(County population) -2.898e+07*** 3.110*** 2.620*** -2.284e+07** (2.795e+06) (0.246) (0.666) (1.024e+07) County Population
1979.year 1.368e+06 -0.332 -0.124 1.617e+06 (1.444e+06) (0.230) (0.125) (1.232e+06) 1980.year 3.354e+06** -0.0604 -0.208 3.112e+06** (1.370e+06) (0.151) (0.127) (1.371e+06) 1981.year 4.085e+06*** -0.232* -0.242** 4.165e+06*** (1.338e+06) (0.139) (0.120) (1.531e+06) 1982.year 4.604e+06*** -0.177 -0.351** 4.077e+06** (1.508e+06) (0.155) (0.145) (1.849e+06) 1983.year 7.472e+06*** -0.0871 -0.104 7.391e+06*** (1.354e+06) (0.125) (0.148) (1.993e+06) 1984.year 9.124e+06*** -0.00715 -0.0224 9.034e+06*** (1.402e+06) (0.132) (0.154) (2.214e+06) 1985.year 1.142e+07*** 0.217* 0.171 1.159e+07*** (1.639e+06) (0.128) (0.160) (2.599e+06) 1986.year 1.090e+07*** -0.0773 -0.0146 1.052e+07*** (1.648e+06) (0.151) (0.193) (2.694e+06) 1987.year 9.815e+06*** -0.457*** -0.236 8.802e+06*** (1.613e+06) (0.148) (0.182) (2.704e+06) 1988.year 9.402e+06*** -0.444*** -0.342* 7.760e+06*** (1.710e+06) (0.145) (0.188) (2.759e+06) 1989.year 9.581e+06*** -0.377** -0.398* 7.846e+06*** (1.874e+06) (0.180) (0.208) (3.012e+06) 1990.year 9.802e+06*** -0.501*** -0.396** 8.691e+06*** (1.763e+06) (0.126) (0.197) (3.052e+06) 1991.year 1.170e+07*** -0.664*** -0.434** 1.026e+07*** (1.707e+06) (0.157) (0.216) (3.300e+06) 1992.year 1.361e+07*** -0.707*** -0.474** 1.191e+07*** (1.748e+06) (0.159) (0.233) (3.698e+06) 1993.year 1.556e+07*** -0.567*** -0.513** 1.323e+07*** (1.789e+06) (0.170) (0.254) (4.004e+06) 1994.year 1.769e+07*** -0.531*** -0.554** 1.497e+07*** (1.876e+06) (0.169) (0.278) (4.501e+06) 1995.year 2.171e+07*** -0.447*** -0.409 1.532e+07*** (2.125e+06) (0.171) (0.306) (4.831e+06) 1996.year 2.447e+07*** -0.471*** -0.340 1.429e+07*** (2.303e+06) (0.169) (0.330) (5.150e+06) 1997.year 2.773e+07*** -0.446*** -0.279 1.794e+07*** (2.950e+06) (0.171) (0.343) (5.331e+06) 1998.year 3.218e+07*** -0.342** -0.137 2.316e+07*** (3.650e+06) (0.174) (0.354) (5.957e+06) 1999.year 3.478e+07*** -0.220 -0.0535 2.597e+07*** (3.743e+06) (0.180) (0.366) (6.240e+06) 2000.year 3.487e+07*** -0.273 -0.128 2.568e+07*** (3.744e+06) (0.188) (0.379) (6.431e+06) 2001.year 3.288e+07*** -0.493** -0.324 2.257e+07*** (3.071e+06) (0.196) (0.387) (6.281e+06) Apr-June 2.684e+06*** 0.344*** 0.326*** 3.537e+06*** (674,125) (0.0544) (0.0406) (600,474) July-Sept 3.653e+06*** 0.477*** 0.385*** 4.395e+06*** (719,642) (0.0578) (0.0403) (645,282) Oct-Dec 2.828e+06*** 0.410*** 0.317*** 3.428e+06*** (691,471) (0.0518) (0.0402) (595,920) Salt Lake FE 6.575e+07*** -2.507*** -1.865** 5.213e+07*** (4.261e+06) (0.344) (0.935) (1.442e+07) Summit FE -7.576e+07*** 4.874*** (7.005e+06) (0.538) Utah County FE 1.190e+07*** -1.141*** -0.954*** 9.554e+06** (1.132e+06) (0.105) (0.264) (3.913e+06) Davis County FE - OMIT
Constant 3.399e+08*** -22.38*** -16.45** 2.680e+08** (3.273e+07) (2.891) (7.897) (1.212e+08)
Observations 384 384 288 288 R-squared 0.889 0.957 0.945 0.946 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
10. References Baade, Robert A., and Victor A. Matheson. “Going for the Gold: The Economics of the Olympics.” Journal ofEconomic Perspectives, vol. 30, no. 2, 2016, pp. 201–218. Baumann, Robert, May 2010. The Labor Market Effects of the Salt Lake City Winter Olympics. 2nd ed., vol. 10, College of The Holy Cross, 0AD, pp. 1–15. Burbank, Matthew. “Discussion about Olympic Impacts.” 22 Jan. 2020. Goldman Sachs. July 2012. The Olympics and Economics 2012., pp. 1–38, Global Economics Commodities and Strategy Research. Factsheet Legacies of the Games. International Olympic Committee 9–10, Updated December 2013 Flyvbjerg, Bent. Stewart, Allison. Budzier, Alexander. The Oxford Olympics Study 2016: Cost and Cost Overrun at the Games. Oxford. University of Oxford. Said Business School. July, 2016 “How The 2002 Olympics Sparked Salt Lake City's Economic Revival.” Free Enterprise, Staff. 21 Mar. 2019 “Industries at a Glance.” U.S. Bureau of Labor Statistics, U.S. Bureau of Labor Statistics, 2017. Jasmand, Stephanie and Maennig, Wolfgang, 2008 Regional Income and Employment Effects of the 1972 Munich Summer Olympic Games (January, 12 2010). Regional Studies, Vol. 42, No. 7. Kopytoff, Verne. “The 2002 Olympics Are Transforming Salt Lake City.” The New York Times, 7 Nov. 1997. Lambert, Lance. “Hut, Hut, Price Hike! Does Hosting a Super Bowl Have a Super Impact on Home Sales? Realtor.com. 18 Jan, 2018. Roberts, Selena. 2002. “Olympics: Notebook; I.O.C.’s Rogge Steps Into The Cold.” New York Times, February 4. Roche, Lisa Riley. “Another Olympics in Utah? Salt Lake City Selected as Possible 2030 U.S. Bid. December 15, 2018 Ponic, Jason. “Abandoned Olympic Venues.” HowTheyPlay, 23 Mar. 2019 Salt Lake 2002 Olympic Winter Games Global Television Report, Olympic Television Research Centre. Sports Marketing Surveys Ltd. IOC. 2002 “Salt Lake City History Minute - Utah's First Olympic Bid.” YouTube, Salt Lake City Television. 2018
Shank, Ben. 2012. An Econometric Explanation of National Olympic Success, Wabash College. Economics.
Spilling, Olav. 1996. Mega event as strategy for regional development The case of the 1994 Lillehammer Winter Olympics. Entrepreneurship & Regional Development. 8. 321-344. U.S. Bureau of Economic Analysis, “GDP by Metro,” March 29, 2019. Utah Ski Database, Utah Governor’s Office of Planning and Budget. Demographic and Economic Analysis. p.22. Report. 2006 Wallman, Andrew B. The Economic Impact of the 2002 Olympic Winter Games in Salt Lake City. Boston College. May 5, 2006