Modelling the economic impact of sports events: The case of the Beijing Olympics

Economic Modelling 30 (2013) 235–244

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Modelling the economic impact of sports events: The case of the Beijing Olympics ShiNa Li a,⁎, Adam Blake b, 1, Rhodri Thomas a, 2 a b

International Centre for Research in Events, Tourism and Hospitality (ICRETH), Leeds Metropolitan University, UK School of Tourism, Bournemouth University, UK

a r t i c l e

i n f o

Article history: Accepted 9 September 2012 JEL classification: L83 C68 Keywords: Economic impacts Tourism impacts The Beijing Olympics Computable general equilibrium modelling imperfect competition

a b s t r a c t Major sports events are used increasingly by policy-makers to stimulate economic development. This has resulted in a growth of academic interest in ways of analysing their contribution. Following a review of the literature on the contrasting approaches that have been used, this paper assesses the economic impact of the Beijing Olympics, in particular the tourism impact, using computable general equilibrium (CGE) modelling. To add novelty, it analyses the data under conditions of imperfect competition, an approach that has been used to good effect in other contexts, notably international trade. The findings suggest that staging the Beijing Olympics brought economic benefits to the host economy but that the scale of the impact was not significant compared to the total size of the economy. The welfare impacts of the Beijing Olympics under imperfect competition are shown to be higher than when perfect competition is assumed. This is explained by the pro-competitive effect. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Sports governing bodies, such as the International Olympic Committee (IOC) and the Fédération Internationale de Football Association (FIFA), have started to allocate their championships, in part, on the basis of their potential for promoting economic development in particular contexts (Clark, 2008). In recent years, this has resulted in several major sports events being held in developing countries; the 2008 Olympic games in Beijing, the 2010 FIFA World Cup in South Africa, the 2014 FIFA World Cup and the 2016 Olympic games to be held in Brazil are all contemporary examples. Governments eager to host these events justify their actions on the basis that gross domestic product (GDP) will increase and, as a consequence, their populations will eventually be better off (Dwyer et al., 2004). The efficacy of such claims is unclear and can only be judged following rigorous empirical assessment, probably on a caseby-case basis. Various methods have been applied to assessing the economic effects of large sports events. These include input–output (I–O) modelling (see Humphreys and Plummer, 1995; Jang et al., 1999), computable general equilibrium (CGE) modelling (see Blake, 2005; Bohlmann and Van Heerden, 2005; Giesecke and Madden, 2007; Li and Blake, 2008; ⁎ Corresponding author at: 226 Brontë Building, Headingley Campus, Leeds Metropolitan University, Leeds LS6 3QW, UK. Tel.: +44 1138123483; fax: +44 1138121111. E-mail addresses: [email protected] (S. Li), [email protected] (A. Blake), [email protected] (R. Thomas). 1 Mailing address: Fern Barrow, Talbot Campus, Bournemouth University, Poole, Dorset, BH12 5BB, UK. 2 Mailing address: 214 Brontë Building, Headingley Campus, Leeds Metropolitan University, Leeds LS6 3QW, UK. 0264-9993/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.econmod.2012.09.013

Li et al., 2011; Madden, 2002, 2006; New South Wales Treasury, 1997) and econometric modelling (see Baade et al., 2008; Hotchkiss, et al., 2003; Kasimati and Dawson, 2009). Although I–O modelling was the most commonly used method in event impact analysis, it has fallen out of favour because of the limitations of the assumptions used (Dwyer et al., 2000). CGE modelling makes more realistic assumptions, which helps explain why it is now considered a more appropriate approach than the I–O modelling to assessing the economic impact of events (Dwyer et al., 2004). Macro-econometric modelling is less complex and requires fewer data than CGE modelling but fails to capture the interrelationships of different industries in an economy (Russo, 2009). Perhaps not surprisingly, CGE modelling is gaining currency as a powerful means of assessing the economic effects of hosting major events but its application to date remains very limited. Previous studies of the economic impact of sports events using CGE modelling have all applied the assumption of the perfectly competitive markets. As this is an idealised market structure, it may be useful analytically but is ultimately unrealistic. The economic impacts of events estimated using perfectly competitive market assumptions can, therefore, only be valid in the context of an ideal economy and may differ significantly from the real economy. Most markets are imperfectly competitive, in particular service industries which have a variety of products, high mark‐up and restrictions to entry (Blake et al., 2006). Most industries affected by the staging of large sports events are services related to tourism such as restaurants, hotels, media, communication and entertainment. It seems appropriate, therefore, to assume imperfect competition when evaluating the economic effects of sports events. CGE modelling with imperfect competition has been widely used in the field of international trade, such as trade policy analysis, trade integration and liberalisation,

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agricultural trade modelling, antidumping duties, but not in the context of this kind of study. Indeed, this paper is the first to assess the economic impact of a major sports event using CGE modelling under conditions of an imperfect market structure, which is designed to provide more rigorous results. This paper adds to the literature by incorporating imperfect competition into the standard CGE models and assessing the economic welfare effects of events. By evaluating the tourism impacts of the Beijing Olympics, the paper also adds weight to the relatively sparse literature on this topic. This study will focus mainly on the contribution of increased tourism expenditure. The economic effects of sports events are generated by different types of event-related investment and expenditure, such as monies spent on operations, increased tourism expenditure, investment in event venues and related infrastructure, and exports and foreign investment legacies after the event finishes. Additional tourism expenditure brought by holding a major event is considered as one of the most significant contributors to the total effects of a sports event (Blake, 2005). The Olympics, as one of the largest of this genre, can bring positive tourism impacts to host economies (Kasimati and Dawson, 2009; Madden, 2002, 2006; New South Wales Treasury, 1997). There has been wide recognition of the highly desirable economic impacts that major events can generate in attracting visitors to a region to participate or observe a sports event. Tourists' awareness of a host city can be enhanced when the wide media coverage of an event changes a negative image to positive or enhances a positive image of the host country and its city (Hall, 1989). McManus (1999) pointed out that a perceived positive image of a host city can encourage both international and national tourists to visit the city. When more tourists arrive at the host city and spend more money on hotels, accommodation, transport and other services, the “new” money will flow into the host city and generate economic impact at both the macroeconomic and industry levels. When calculating the economic impact of the Beijing Olympics for this paper, the focus will be on the host city, Beijing, rather than the host country, China. This is appropriate because of the size and growth rate of the Chinese economy. It is conceivable that the impact of the Beijing Olympics could be highly significant at the level of the city but insignificant at the level of the economy as a whole (Dwyer et al., 2006). Although this research is conducted after the Beijing Olympics, it runs ex-ante simulations as there are insufficient data available for a full ex-post assessment. As is discussed later, to undertake a rigorous ex-post analysis of long-term impacts requires data that are unavailable. The remainder of the paper is structured as follows. In Section 2, CGE models are introduced. This is followed by an explanation of how imperfect competition has been incorporated into CGE modelling and the introduction of tourism impacts in the model, which can be applied to examining the impacts of any large sports events. The third section describes the specific model developed to evaluate the economic impacts of the Beijing 2008 Olympics. Section 4 reports the findings of the study and conclusions are drawn in Section 5. 2. The model 2.1. CGE models with imperfect competition A perfectly competitive market is usually characterised as a market with infinite buyers and sellers trading homogeneous products and entering or exiting the market freely. Imperfect competition includes three main types: monopoly, oligopoly and monopolistic competition. The majority of studies using CGE modelling with imperfect competition can be found in the international trade field. Some have used conjectural variation models by incorporating oligopoly (see Daughety, 1985; Dixit, 1987; Hoffmann, 2002; Pfaffermayr, 1999). Others have employed monopolistic models (Harrison et al., 1997; Neary, 2000; Swaminathan and Hertel, 1997). There are different models of

monopolistic competition. For example, the Dixit–Stiglitz (D–S) model introduced product diversity and scale economies into the market equilibrium (Dixit and Stiglitz, 1977); “Spence–Dixit–Stiglitz” preferences use different variants (Spence, 1976). Lancaster (1979) assumed that consumers prefer different “variety” of goods, which means that variety rather than quantity could increase their utilities. D–S is more feasible (Neary, 2000) which has been developed and widely applied to international trade (Harris, 1984; Harrison et al., 1995). Harris (1984) assumed internal scale economies at the individual firm level. His model was built under two conditions: profit maximisation when marginal cost equals marginal price, and free entry until price equals average cost. Harrison et al. (1995) built an increasing returns to scale model by assuming product differentiation at the firm level, constant marginal costs and given fixed costs for firms. Studies that have employed CGE modelling with imperfect competition have applied anti-competitive and pro-competitive effects to discuss the findings (see Bye, et al., 2009; Conway and Dhar, 1991; Dee and Hanslow, 2001; Devarajan and Rodrik, 1991; Hsu, 2008; Konan and Assche, 2004; Sheldon, 1996). Pro-competition effects occur when new entrants to the market drive down levels of mark-up on marginal costs leading to an increase in efficiency. This is in contrast to anti-competition where decreases in the level of competition cause welfare loss to the economy (Marrewijk, 2007; Walker, 2006). One of the important characteristics of monopolistically competitive markets is that firms can enter and exit the market freely. The change in number of firms leads to the change in the mark-up. If demand increases, then more firms will enter the market which increases the number of firms and decreases the mark-up they apply. This implies that the level of competition increases, which in turn precipitates increasing efficiency. On the contrary, when demand decreases, more firms will exit the market, which reduces the number of firms and increases the mark‐up. The decrease in the level of competition causes a decrease in efficiency. The effects brought by the Olympic Games in the presence of imperfect competition, therefore, depend on the pattern of demand changes that occur, as demand will increase in some industries and fall in others. If, for example, demand increases mainly in industries that are highly concentrated and falls in industries that are already highly competitive, the overall effect would be that of pro-competitive changes but anti-competitive changes would result if the opposite was true.

2.2. Incorporating imperfect competition to the model This section will explain how imperfect competition has been incorporated into the standard CGE models for assessing the economic effects of a sports event and an approach to modelling tourism impacts. To start with, a standard static model with perfect competition is built. 3 This standard model is adapted from Lofgren et al.'s (2002) model. 4 The main technologies applied to the model are the Leontief, the Cobb–Douglas (C–D), the constant elasticity of substitution (CES) and the constant elasticity of transformation (CET) technology. The software employed to build the CGE models is the General Algebraic Modelling System (GAMS) with a subsystem of GAMS, called Mathematical Programming System for General Equilibrium Analysis (MPSGE), which is applied in this research.

3 The estimation of the economic impacts of large sporting events normally covers a long run period, including pre-, during and post-event stages and so one approach might be to employ a dynamic model. However, the purpose of this paper was to explore the influence of imperfect competition on the estimation, and there was a danger of diverting the focus to a discussion of dynamics. Therefore, in this paper we incorporate imperfect competition to a static model and leave the dynamic issue to further research. 4 See Lofgren et al. (2002) for detailed discussion of the structure of this standard model.

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It is assumed that firms compete in a Dixit–Stiglitz type of imperfectly competitive setting (as in Harris, 1984; Harrison et al., 1997). In the Dixit–Stiglitz setting of perfect competition model, all firms in the same industry produce exactly the same products. To model the feature of heterogeneous goods in an imperfectly competitive market, it is assumed that each firm produces one variety of one type of good, which is different from other varieties supplied by other firms in the same industry. For example, tour operators supply tour packages and each package can be seen as one variety, which is not exactly the same as another tour package (or another variety) in terms of different destinations, or hotels or transport and so on. In this case, each tour package can be seen as a variety of the product. In the model, it is assumed that each firm only produces one variety. Internal economic scale at the firm level is formed due to the product diversity, but constant returns to scale is assumed at the industry level. Mark-up can be obtained from both domestic and exported goods. A dummy sector named entrepreneur is specified in the model. This new sector collects mark-ups and pays for fixed costs. It is assumed that free entry and exit are allowed which means that the number of firms in each industry is endogenous. A key challenge in introducing imperfect competition into CGE models is the way to model mark-up, which is the difference between the market price and the marginal cost. There are different ways to introduce mark-up into the standard model with perfect competition. In the model built for this paper, the mark-up is regarded as endowment of the representative entrepreneur, who demands fixed costs. In other words, the mark-up is used to pay fixed costs. Two types of mark-ups are identified: the mark-up of domestic goods and the mark‐up of exported goods. The number of firms in the market determines the level of the mark-up — the more firms competing in the market, the less the total mark-up, and vice versa. The share of production of each firm is assumed to be the same. The general mark-up rate formula is 5: 1 1 1

− Mark
uprate ¼ ð1 þ ΩÞ N
ε′
σ

N ε′ σ Ω

! þ

1 … σ

ð1Þ

is the number of firms is the price elasticity of demand for the output is the elasticity of substitution between inputs is conjectural variation, “an arbitrary ‘conjecture’ about how firm 2 responds to firm 1's choice of output” (Varian, 1992: 302). It is assumed to be zero in the model, which indicates that each firm does not expect that other firms will respond to its choice of output.

2.3. Incorporating tourism impacts to the model Existing studies, such as Wattanakuljarus and Coxhead (2008) and Li et al. (2011), have explained the incorporation of tourism into CGE modelling. 6 The same approach is applied in this research to modelling tourism impacts. Increased tourism expenditure (or increased tourism demand) is considered a tourism demand shock in the model. International tourism expenditure is modelled as tourism exports to international tourists arriving at an event host destination. Demand for exported tourism products is a downward sloping function of price for exported tourism products. Eq. (2) is the foreign tourism demand equation (ftde) which is determined by shift parameter

5

For details of how this formula is induced, please see Blake et al. (1999). For details of introducing tourism to CGE models, please see Wattanakuljarus and Coxhead (2008) and Li et al. (2011). 6

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for foreign tourism demand (TourShift) and price elasticity of foreign tourism demand (TourElas):  f tde ¼ TourShif t 

er ptour

TourElas−1

…

ð2Þ

where er ptour

is the foreign exchange rate is the price for tourism-related exported goods.

The parameter in modelling tourism demand shock is TourShift. To calculate this parameter, we need to obtain the information for the base case i.e. the base scenario without a large-scale event occurring, as well as an increase in tourism earnings contributed by holding a large-scale event. Assume the total tourism exports — foreign exchange earnings of international tourism without the effects of a large-scale event is $5000 million and holding this event will bring an extra of $100 million to the international tourism receipts. The ratio of the demand shock to the base scenario would be 100/ 5000 = 0.02 and therefore, TourShift is 1.02 (=1 + 0.02). 3. An empirical case: the Beijing Olympics 3.1. The Beijing model and the data The Beijing model is a single-region static model. Beijing trades with foreign countries as well as the rest of China. The Beijing 2002 input– output (I–O) table is the major data source for building the Beijing CGE models.7 The 2002 prices used in the tables have been updated to 2005 prices. 8 It includes 130 industries, total exports, and rural and urban households. Rural and urban households are aggregated into one representative household. Government is the Beijing Municipal Government. The main elasticities applied in the model are Armington elasticity, the transformation elasticity and the substitution elasticity between factors which are adapted from the Global Trade Analysis Project (Hertel, 1997) database, since no data for China and Beijing have been found.9 The Armington assumption is used in the model, which specifies that domestic goods and imported goods cannot be substituted perfectly by each other (Francois and Reinert, 1997). The Beijing table contains two types of exports: exports to foreign countries and to the rest of China. In order to assess the impact of increased tourism expenditures brought by holding the Beijing Olympics, exports to foreign countries are disaggregated into tourism exports and non-tourism exports to foreign countries. Exports to the rest of China are separated into tourism exports and 7 The limitation of the 2002 tables is fully recognised. Due to the data limitation, the best dataset which can be fully accessed for this research is the 2002 I–O tables. Two adjustments have been made to reduce this limitation. First, the original tables with 2002 prices were updated to 2005 prices through considering the GDP expansion, i.e. multiplying the increased rate of GDP of 2005 to 2002. The reason for updating to 2005 prices is that the main data for modelling tourism impacts, such as foreign exchange earnings of international tourism and its composition, is available for 2005. The data for “new” money brought by the Olympics which is model input is available at 2005 prices. Second, when the model results are generated from the CGE model, they are updated again from 2005 to 2008 prices, the year when the Beijing Olympics were held. 8 The main model input – the Olympic tourism demand shock – was predicted at 2005 prices and thus all data sources and model output are in 2005 prices. The Olympic tourism demand is adapted from Li and Blake (2009), which will be explained in the “Model simulations” section. 9 The GTAP database is a global database and available to the public. Adopting or adapting the elasticity of existing work and other modelling exercises is the general practice for most of the CGE models in mainstream economics. These elasticities can be generated by econometric analysis. However, there is a lack of data for running these econometric analyses and even a lack of econometric analyses even when some data are available. Most CGE studies use secondary data from other studies for elasticity use. In order to reduce the limitation of this practice, this paper runs sensitivity analysis by changing the key elasticities to test the reliability and confidence of the model.

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non-tourism exports to the rest of China. The disaggregations are supported by the data of composition of tourism expenditure in Beijing (Appendix A). Values of foreign exchange earnings for each item (Appendix A) are assigned to tourism exports of the corresponding industries. Non-tourism exports are obtained by subtracting tourism exports from the total exports. The same approach is applied to the separation of exports to the rest of China into tourism exports to the rest of China and non-tourism exports to the rest of China. For the purpose of presenting the results at the industry level, the 130 industries are aggregated into thirteen industries 10: primary industry, secondary industry, ten tourism-related industries and other tertiary industries. Tourists are defined as “persons travelling to and staying in places outside their usual environment for not more than one consecutive year for leisure, business and other purposes not related to the exercise of an activity remunerated from within the place visited” (UN/WTO, 1994:5). In this paper, tourism related industries are defined as the industries which directly supply products or offer services to tourists. These tourism products and services include air, railway and highway transport, accommodation, food and beverage, shopping, post and telecommunications, tickets of tourism spots, culture and entertainment and other. The ten tourism-related industries remain separated as this paper focuses on the impact of increased tourism receipts brought by the Beijing Olympics and the majority of tourist receipts flow into these industries. The equations of the mark-up of non-tourism exports and domestic goods (Appendix B) are developed from the general mark-up function (Formula 1). The number of firms in industry j (Nj) is a key parameter in estimating the mark-up. Nj for the Beijing model is generated according to concentration ratios for firms by industry. The concentration ratio reflects the market share (or market power) of the largest businesses, which could also show the market structure. If concentration ratios for the top M businesses are very low, 11 it means that top M of the largest businesses accounts for a small share in the market and there are a number of small or medium firms competing in the market. This indicates that the market is close to a perfectly competitive market. Conversely, if concentration ratios are very high, then the market structure is close to monopoly. As the data for Beijing and China are not available, we calculate concentration ratios using data collected from Datastream. 12 Datastream classifies forty industries for each country and each industry contains financial data for listed companies. Static data for sales (revenue) for each listed company in China in 2005 were obtained from Datastream. 13 The percentage of sales of the two largest listed companies over the total sales of all listed companies in each industry is calculated. Datastream also classifies travel and leisure as one of the forty industries in its database. For the purpose of this research, listed firms in travel and leisure are further classified into seven different tourism-related industries, including airlines, railways, tourism, accommodation, road transportation, catering and recreational services. 10 It should be noted that industries in the I–O table are not aggregated before running the model but after generating the results. The original model output (generated in GAMS/MPSGE) shows results for all 130 industries for Beijing. It is inconvenient to show the results for all 130 industries. Therefore, the 130 industries are aggregated for the purpose of presenting the results more clearly. 11 M can be assigned to different number of firms, such as 2, 5, 10 or 20 firms. 12 Collecting data for research on China's issues is important but difficult given that many data are neither existing nor available to the public. It seems even more difficult when tourism-related data are required. Therefore, the best data the authors can adopt and adapt to estimate concentration ratios for tourism-related industries in China are data of listed companies obtained from Datastream. 13 Datastream provides both market and accounting data of listed firms in different countries. Listed companies normally consist of the largest firms in each industry. Therefore, the concentration ratio calculated using Datastream data should be a close approximation when the real data is not available for China and Beijing. Datastream includes data at the national level but data at the city level is not available. Therefore the concentration ratios calculated for the China industries are applied to the Beijing industries. This could be a close approximation as the top two firms in many industries are registered in Beijing.

Concentration ratios for the seven tourism-related industries are also calculated. There is a negative correlation between concentration ratio (R) and the number of firms (Nj): if the concentration ratio (R) in industry j is high which means the competition level is low, then the number of firms (Nj) is small; while if R is low indicating a high competition level, then Nj is large. There are different forms of mathematical formulas to show a negative correlation between two values. A simple formula between R and Nj is assumed in this research: Nj ¼ α  ð1=RÞ…

ð3Þ

where α is a scale parameter. We assign a value of 10 to α in the central scenario and 15 and 20 to other two sensitivity analyses. 14 In order to evaluate the impacts of different formats of formulas on the model results, another two formulas which show a negative relationship between R and Nj are also employed: Nj ¼ 30−10R…

ð4Þ

20 Nj ¼ pffiffiffiffi : R

ð5Þ

The major types of functions employed in the Beijing models are the Leontief function, Cobb–Douglas function, the CES function and the CET function. Nesting structures are used to illustrate the basic structure of the model — different elasticity parameters and the relation of the inputs and outputs of functions. The Beijing model can be illustrated in a four-level nesting structure (Fig. 1). From top to bottom, the first level displays that the total exports are a CET function of exports to foreign countries and to the rest of China. The second level shows that production of domestic goods is a CET function of exports and total supply. Total supply is a CES function of imported goods and domestic goods. The third level includes two forms of functions: domestic goods are Leontief functions of value-added and intermediate inputs; the total imports are Leontief functions of imports from foreign countries and from the rest of China. In the fourth level, value-added is a CES function of inputs of factors; intermediate inputs are Leontief functions of each individual intermediate good. 3.2. Model simulations There are normally three steps involved in estimating the economic impact of an event using CGE models (Li et al., 2011). The first is to estimate the “new” money injected in the host economy. In this study, “new” money is the increased tourism expenditure brought about by holding the Beijing Olympics. The second step is to build the CGE model. This paper has built a Beijing CGE model under both perfect and imperfect competition market structures. The third step is to shock the model with the “new” money estimated in the first step and analyse the results — the economic impact in terms of such indicators as GDP, employment, welfare or industry impacts. The focus of this paper is on the second and third steps while the first step will be briefly discussed. In the first step, the estimation of the “new” money is based on the statistics of the Olympic committee, government reports, tourism bureaus and previous studies. For example, New South Wales Treasury (1997) and Madden (2002 and 2006) obtained data from the Sydney 14 The scale parameter α is assigned by considering two factors. First, α cannot be too small. If α is too small, then N is small which leads to a large mark-up rate. The base market prices can be calculated in the model by subtracting mark-up rate from 1. Therefore, large mark-up rate which is greater than 1 will generate negative base market prices. Second, α cannot be too large. If α is too large, then N is large which may go beyond the realistic number of firms in Beijing.

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To foreign countries

239

To the rest of China

Total exports

Total supply

Domestic goods

Intermediate inputs

Input(1)

Input(2)

Imported goods

From foreign countries

Value-added

…

Input(n)

Factor(1)

Input(2)

…

From rest of China

Input(n)

Fig. 1. The nesting structure of the Beijing CGE model.

Organising Committee for the Olympic Games and the Olympic Coordination Committee to estimate the “new” money brought by the Sydney Olympics. Blake (2005) used the data provided by the London Olympic Committee when estimating the “new” money brought by the London Olympics. Increased tourism expenditure precipitated by the Beijing Olympics is injected into the economy of the host city before, during and after the Olympic Games. It is considered a tourism demand shock to the Beijing model. As estimating Beijing Olympicrelated investment and expenditure is not the focus of this paper, the data for “new” money injected into the economy is adapted from the estimates predicted by Li and Blake (2009). Li and Blake (2009) has built a framework in evaluating the Olympic expenditure and investment, which consist of operation expenditure by the Olympic Committee, Olympic-related national and international tourism expenditure, investment in Olympic-related infrastructure, investment in Olympic venues and related facilities, and exports and foreign investment legacies. This framework has been applied to estimating the “new” money injected into China and Beijing due to holding the 2008 Beijing Olympics. The estimation is based on the data published by the Beijing Olympic Committee, Beijing and China Bureau of Statistics, Beijing and China Tourism Administration, and Research Office of Beijing Municipal Government. In previous studies, the impact of holding a mega event is conceptualised in three stages: pre-event (from the year of being told that the bid has been successful to the year the event is held), the year the event is held and the post-event stage. These three stages normally last between ten and twelve years (Li and Blake, 2009; Madden, 2006). Tourism expenditure brought to Beijing due to the Olympics is divided into four categories: Olympic international visitors and tourism legacies, national visitors and tourism legacies. The estimation was for each year during three stages: pre-Games (2004–2007), Games' year (2008) and post-Games (2009–2013). Li and Blake (2009) have explained the difference between Olympic visitors and Olympic tourism legacies. Visitors include tourists whose purpose of visiting the host city is to watch the Olympics or to participate in the Olympics, such as athletes, media visitors, sponsors and Olympic family. Visitors arrive at a host city during the event. Tourism legacies include tourists arriving at the host city before or after the Olympics and their visits are indirectly related to holding the Olympics. For example, extensive media coverage during the Olympics may build a good image of tourism destination for the host city, which may generate tourism legacies. Olympic visitors visited Beijing during the Games' year and tourism legacies generated in pre- and post-Games stages. The total tourism expenditure generated by holding the Beijing Olympics was estimated as $4948 million in the central scenario by Li and Blake (2009). Different predictions of tourism

spending can affect the economic impact and thus low and high scenarios are designed to show the differences. The low scenario is one-tenth of the central scenario and the high scenario is ten times that of the central scenario. 15 To introduce a tourism demand shock to the models, Eq. (2) discussed earlier will be used. The two key parameters in Eq. (2) are price elasticity of foreign tourism demand (TourElas) and shift parameter for foreign tourism demand (TourShift). The value of TourElas is taken as − 1.37 according to recent research conducted by Song and Fei (2006). TourShift can be obtained by calculating the percentage of the increased tourism receipts brought by holding the Beijing Olympics over total tourism exports. The increased tourism demand brought by holding the Beijing Olympics shocks the models through multiplying each industry in tourism exports by TourShift. 16 Sensitivity analysis was used to test the reliability and confidence of the CGE models. The main assumptions and parameters in the model, such as elasticities, may introduce uncertainties into the model. Previously, sensitivity tests 1 and 2 had been introduced when α (in Formula 3) was assigned 15 and 20 respectively. Another five sensitivity tests are conducted. In the analysis of the tourism impacts, a key elasticity in the model is the price elasticity of international tourism demand. In order to test the degree of effects that this elasticity might bring to the results, the base elasticity (−1.37) was halved to be − 0.685 and doubled to be − 2.74 in sensitivity tests 3 and 4. Sensitivity analyses 5 to 7 were applied to three groups of main elasticities: Armington elasticities, output transformation elasticities, and factor substitution elasticities, used in the model by doubling one elasticity and maintaining the other two unchanged. For example, Armington elasticities were doubled while output transformation elasticities and factor substitution elasticities were kept the same as the base case. Each group of elasticities included 130 elasticities for the 130 industries in the Beijing 2002 I–O table.

15 A large scale (ten times) is used in the high scenario in order to clearly show the influence of different predictions of model inputs to the model results. 16 For example, to calculate the TourShift for international visitors, the total tourism exports are foreign exchange earnings of international tourism, which are $3620 million. This is the base case — the base scenario without the Beijing Olympics occurring. The increased international visitor expenditure brought by holding the Beijing Olympics was estimated as $713 million in 2008, which is tourism demand shock. The ratio of the demand shock to the base scenario would be 713/ 3620 = 0.2. In order to shock the model, TourShift in the above function is assigned the value of 1 + 0.2 = 1.2. The TourShift 1.2 multiplies tourism exports to foreign countries in each industry, which would generate increased tourism demand (demand shock) for the corresponding industry.

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Table 1 The impact of international and national visitors and tourism legacies in Beijing (the central scenario, imperfect competition — IC model). Central scenario

Calculation

International visitors

International tourism legacies

National visitors

National tourism legacies

Change in EV ($, million) Change in tourism demand ($, million) Change in real tourism consumption ($, million) Percentage change in price of tourism consumption (%) Change in tourism expenditure ($, million) Change in EV per change in tourism demand Change in EV per change in real tourism consumption Change in EV per change in tourism expenditure

A B C D E A/B A/C A/E

119 713 693 0.337 708 0.167 0.172 0.169

284 1694 1636 0.804 1678 0.168 0.174 0.169

59 449 449 0.0003 449 0.132 0.132 0.132

276 2092 2090 0.003 2090 0.132 0.132 0.132

4. Findings 4.1. The economic effects at the macro-economic level The equivalent variation (EV) is defined as “the income that must be given to an agent, at some fixed set of prices, to make them as well-off as they would be under some policy change” (Hanslow, 2001: 23) or in this paper under demand shocks to an economy by holding a large-scale event. EV normally employs the money metric utility function and is the most used indicator when measuring welfare in CGE modelling in the literature (see Ahmed, 2008; Fane and Ahammad, 2003; Margaret and Mabugu, 2008). In this paper, EV is employed to measure the economic welfare impact of holding a large-scale event. As the model assumes that real government consumption is unchangeable, the calculation of EV only captures changes in private utility (household welfare). Following other economic impact evaluations, two scenarios are assumed in this paper: the base scenario, which assumes that the event would not happen, and the event scenario. The latter causes a demand shock with extra investment and expenditure brought by the event. EV in this paper captures the amount of income that would have to be given to (or taken away from) the host economy in the base scenario in order to leave the economy as well off as the economy would be in the event scenario. Table 1 shows the main results of the impact of the additional spending of visitors and tourism legacies generated due to holding the Beijing Olympics under the model with imperfect competition. The second column shows the calculation for each row. For example, row A/B is obtained by dividing values in row A by row B. It can be seen that international ($284 million) and national ($276 million) tourism legacies contribute much more than international ($119 million) and national ($59 million) visitors' expenditure respectively to the change in EV which measures the household welfare gains (row A) in 2008 prices. In general, results from row A to row E for the columns of both international and national tourism legacies are larger than those of visitors respectively. It might be because Table 1 reports on the total tourism legacies for ten years (2004–2013), while it reports on visitors for only one year (2008). The findings reveal the importance of encouraging tourism legacies and prolonging the period of legacies when holding sports events. In each column, the changes in real tourism consumption (row C) are less than the changes in international tourism demand (row B), which is the demand shock brought by holding the Beijing Olympics. This occurs because there is an increase in price of foreign tourism consumption (row D), which offsets international tourism demand. The change in tourism expenditure (row E) is the combination of the changes in real tourism consumption (row C) and the price of tourism consumption. Every unit change in tourism demand (row A/B) leads to a change in welfare by 0.17 and 0.13 generated by international and national tourism. Each unit of tourism expenditure generates around 0.17 and 0.13 of the welfare value for international and national tourism (row A/D). It implies that international tourism contributes

more to the economic welfare than national tourism. This might be because the composition of tourism expenditure by international and national tourists is different (please see Appendix A) — international tourists spend on more expensive tourism products, such as air transports and three to five star hotels while national tourists may spend on shopping, food and beverage, which are relatively cheaper tourism products. When the results generated from the model with imperfect competition (Table 1) are compared with perfect competition (Table 2) for both international and national tourism effects, larger changes in welfare (row A) and in real tourism consumption (row C) can be observed in the imperfect competition model. The findings can be explained by pro-competitive and anti-competitive effects, which have a significant influence on the economic impacts generated in the imperfect competition model. Pro-competitive effects probably play a dominant role in the Beijing model with imperfect competition. As discussed in the previous paragraph, pro-competitive effects happen when demand increases in industries which are highly concentrated. The Beijing Olympics would increase demand for tourism services and products provided by tourism-related industries and these industries are highly concentrated. When tourism spending flows into tourism related industries and more firms enter the market, the level of competition increases, which generates pro-competitive effects. However, demand would decrease in other industries, such as primary and manufacturing and these industries are highly competitive. This process can generate larger welfare gains. The concentration ratios R (%) for most of the tourism-related industries such as communication, travel and leisure, transport, accommodation, catering and recreation are above 50%, which indicates that these industries are highly concentrated. On the other hand, the ratios for most of the other non-tourism industries are below 50%, which indicates that these industries are highly competitive. In three scenarios (low, central and high), when the tourism expenditure brought by holding the Olympics varies, it leads to a large change in economic welfare (see Appendix C). Higher tourism demand can bring higher real tourism consumption, which leads to larger welfare gains. It implies that it is important to formulate policies to encourage tourism demand during the Olympics. The prediction of increased tourism expenditure in the ex-ante estimation studies can greatly affect the quality of the findings. If the expenditure is over-estimated, changes in welfare brought by holding an event are likely to be over-estimated. Seven sensitivity analyses have been designed to evaluate the robustness and reliability of the model results. In the first two sensitivity tests, when the parameter αin Formula 3 is assigned 15 and 20, changes in EV and EV per expenditure in the two tests are very close to the base case (α = 10). In another two tests, when halving and doubling price elasticity of tourism demand, change in EV per change in tourism expenditure remains almost the same. The final tests are to double Armington, output transformation, and factor substitution elasticities respectively while the other two remain unchanged, and the model does not change output qualitatively.

S. Li et al. / Economic Modelling 30 (2013) 235–244

241

Table 2 The impact of international and national visitors and tourism legacies (the central scenario, perfect competition — PC model). Central scenario

Calculation

International visitors

International tourism legacies

National visitors

National tourism legacies

Change in EV ($, million) Change in tourism demand ($, million) Change in real tourism consumption ($, million) Percentage change in price of tourism consumption (%) Change in tourism expenditure ($, million) Change in EV per change in tourism demand Change in EV per change in real tourism consumption Change in EV per change in tourism expenditure

A B C D E A/B A/C A/E

109 713 682 0.526 705 0.153 0.16 0.154

261 1694 1604 1.254 1670 0.154 0.163 0.156

42 449 445 0.014 448 0.095 0.095 0.095

199 2092 2073 0.065 2085 0.095 0.095 0.095

It has been discussed that a simple negative relationship between the concentration ratio (R) and the number of firms (Nj) is assumed in Section 3.1 (see Formula 3). To further examine the reliability of the model, anther two formulas are also employed (see Formulas 4 and 5). Findings were generated when the two functions were applied and these findings do not seem to differ largely from those generated when Function 3 was employed. 4.2. The economic effects at the industry level Table 3 shows the total industry impact of Olympic tourism (international and national) generated from the model with imperfect competition. The holding of the Beijing Olympics provided a stimulus to tourism-related industries through the increase in tourism demand. In general, tourism-related industries in Beijing experience an increase in tourism exports to foreign countries and to the rest of China. Tourism related industries were projected to experience the highest percentage change of output, which was due to high demand for tourism products and services. The largest decrease in the percentage change of output can be observed in water transport, which probably indicates that most tourists might not choose water transport to travel to Beijing and thus water transport was largely crowded out. Increased tourism demand bids up prices in both tourism and non-tourism industries. The percentage change in price index increased in all industries between 1.040 and 1.302%. It can be seen that air transport experienced the largest increase in tourism exports to foreign countries, which was $735.7 million. This is probably because international tourists spent most money on air transport considering that China is a long-haul destination to countries for Americans, Europeans and Australians. Other industries were crowded out by the boom of tourism-related industries, which has also been observed by previous studies, for example, Adams and Parmenter (1995) and Blake et al. (2008). Primary, secondary and other tertiary industries

experienced a decrease in the percentage change of output. The largest increase in tourism exports to the rest of China can be seen in secondary industries ($733.8 million). It might be because secondary industries supply intermediate inputs, such as construction, manufacturing and food processing to tourism-related industries and the boom in tourism demand increases the demand for intermediate inputs. Table 4 presents the percentage changes in output between PC and IC market structures. The differences of the impacts at the industry level between the two market structures are probably attributed to two factors: the effects of distortions of imperfect competition, and anti- and pro-competition effects. If the effects of distortions of imperfect competition and anti-competition played a more important role, the impact generated in the imperfect competition model would be smaller than the perfect competition. If the impacts of distortions of imperfect competition were offset by the impacts of pro-competition then the impact generated in the imperfect competition model could be larger than the perfect competition. The percentage change of output in most tourism related industries was higher under the imperfect competition assumption due to pro-competition effects, which is similar to the discussion of the effects at the macro-economy level. Although procompetition effects exist in railway transport, the percentage change in output in this sector was larger under perfect competition, which can be explained by the dominant role of the effects of distortions of imperfect competition. The concentration ratio for railway transport is 0.988, which reflects the fact that railway transport in China is a national monopoly. An increase in tourism demand on railway transport is unlikely to increase the number of railway firms and thus pro-competition effects in this industry were small while effects of distortions of imperfect competition were large. Primary, secondary and other tertiary industries were crowded out with a decrease in percentage change of output. A smaller decrease can be observed in these non-tourism industries under imperfect competition, which may be caused by the combined effects of

Table 3 The industry impact of Olympic tourism (the central scenario, IC model). Central scenario

Change in tourism exports to foreign countries (million, USD)

Change in tourism exports to the rest of China (million, USD)

Percentage change of price index (%)

Percentage change of output (%)

Primary industry Secondary industry Railway transport Road transport Water transport Air transport Communication Accommodation Catering Tourism Residential services Recreation Other tertiary industries

15.0 429.7 26.2 104.8 0.4 735.7 84.0 412.0 219.025 148.0 5.1 109.5 91.7

16.0 733.8 93.0 246.1 0.3 167.4 31.3 470.9 542.1 177.5 4.7 156.5 98.2

1.206 1.020 1.194 1.227 1.118 1.302 1.292 1.256 1.195 1.085 1.223 1.040 1.247

−0.093 −0.168 1.45 0.859 −0.364 5.085 −0.089 3.003 2.289 10.092 0.041 3.441 −0.012

242

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Table 4 Comparison of the industry impact of the Olympic tourism. Central scenario

Primary industry Secondary industry Railway transport Road transport Water transport Air transport Communication Accommodation Catering Tourism Residential services Recreation Other tertiary

Percentage change of output (%) PC model

IC model

−0.230 −0.237 1.553 0.831 −0.303 4.801 −0.176 2.855 2.197 9.602 −0.003 3.359 −0.015

−0.093 −0.168 1.45 0.859 −0.364 5.085 −0.089 3.003 2.289 10.092 0.041 3.441 −0.012

distortions of imperfect competition and anti-competition. When there is a decrease in demand in non-tourism industries, most of which are highly competitive, the number of firms may decrease which generates anti-competitive effects.

5. Conclusion This paper has applied CGE modelling with imperfect competition to assess the economic impact of the 2008 Beijing Olympics. The findings demonstrate that holding the Beijing Olympics brought benefits to the local people but that the scale of the impact was not significant compared to the total size of the economy. The Beijing Olympics were estimated in this paper to have brought $119 million and $59 million of welfare gains from Olympic international and national visitors in 2008 respectively. The real gross domestic product (GDP) in Beijing was $178 billion for 2008 (Beijing Municipal Bureau of Statistics, 2009); the Olympic impact only accounts for 0.1% of the total GDP in Beijing. Understandably, the larger the economy of the host city (and country), the smaller the economic impact from holding a large event as a percentage of the economy as a whole. This research has implications for other city authorities who intend to host major sports events for the purposes of economic development. As the findings suggest, an increase in economic welfare could be generated in the period after the event if careful planning is undertaken prior to and during its staging to building an image of the city as a tourist destination. Indeed, tourism legacy impacts are considered to be one of the most important reasons why countries and/or cities take an interest in hosting major events, especially developing countries (Cornelissen, 2004). Those planning the 2012 London Olympics, the 2014 Football World Cup and the 2016 Olympics in Brazil, would, therefore, do well to consider tourism legacies well in advance of these events. In addition, seeking to obtain direct economic benefits should not be the only reason for developing countries to hold a mega event. There are potentially other socio-cultural justifications that may be equally powerful ranging from promoting participation in sport to changing the image of particular places. The paper has revealed that the welfare impacts of the Beijing Olympics under imperfect competition structure are higher than when perfect competition is assumed. This can be explained by the pro-competitive effect. Holding the Beijing Olympics attracted additional tourism expenditure on products and services supplied by tourism-related industries. This increased the number of firms entering these industries which led to a decrease in mark-up. This pro-competitive process happened in Beijing because tourism-related industries were highly concentrated. The findings imply, therefore, that the economic impact of a sports

event is affected by the market power of different industries in a host city. It should be noted that this study only considered one of the main types of Olympic expenditure — event tourism expenditure. Other Olympic investment and expenditure were specifically excluded. Future studies might examine the influence of imperfect competition on estimating the economic impact brought by other demand shocks, such as investment in constructing the event venues and related infrastructure, operational expenditure by the event organising committee and legacies of event stadiums. The method developed for evaluating tourism expenditure in this paper could also be used to examine other types of demand shocks. Further simulations would also be useful. For example, simulations using different policy scenarios and other elasticities in the sensitivity analysis would make valuable additional contributions to knowledge in this area. Finally, this research was conducted ex-ante using static modelling with imperfect competition. Future studies may develop this work to incorporate imperfect competition into a dynamic model to assess the economic impact of sports events. This could be undertaken ex-post and the results could be compared with the estimates contained in this paper. The lessons learned from such an exercise could then be used to inform the deliberations of municipal policy-makers. Acknowledgment The authors would like to acknowledge Professor David Paton and Professor Weimin Liu at the University of Nottingham for providing valuable comments on an earlier draft of this paper. We remain responsible for any errors. Appendix A. Composition of tourism expenditure in Beijing in 2005

Tourism foreign exchange earnings

Earnings created by tourists from the rest of China

Item

Value ($ million)

Percentage (%)

Value ($ million)

Percentage (%)

Total Long-distance transportation expenses – Air – Railway – Highway Local transportation expenses Accommodation Food and beverage Shopping Post and telecommunications Tickets of tourism spots Culture and entertainment Other

3620.0 1277.9

100.0 35.3

15,869.7 2031.3

100.0 12.8

1118.6 39.8 119.5 39.8

30.9 1.1 3.3 1.1

– – – 904.6

– – – 5.7

626.3 333.0 720.4 123.1

17.3 9.2 19.9 3.4

2729.6 3142.2 3824.6 174.6

17.2 19.8 24.1 1.1

170.1 166.5

4.7 4.6

1444.1 492.0

9.1 3.1

162.9

4.5

1126.7

7.1

Source: Beijing Bureau of Statistics (2006), Tables 15–2.

The above table displays values and percentage of tourism expenditure on different tourism products and services. It can be observed that foreign tourists spend most of their money on long-distance transportation while domestic tourists from the rest of China spend most of their money on shopping. Tourism foreign exchange earnings account for a part of total exports to foreign countries and earnings created by tourists from the rest of China are a part of total exports to the rest of China. For the purpose of the research, tourism exports to foreign countries and the rest of China are separated from the total exports respectively based on the above table.

S. Li et al. / Economic Modelling 30 (2013) 235–244

Appendix B. The equations of the mark-up of non-tourism exported goods and domestic goods The equation of the mark-up of non-tourism exported goods is calibrated to be:

!
1
1 1

: þ

−
εj
subddj subddj

  1 MARKUPNX j ¼ 1 þ omegaj   Nj

 ExpendSj  þ

1 1− subdxj

!

1 1 − þ subdxj subddj

#

1 subddj

where MARKUPNXj is mark-up of non-tourism exported goods in sector j MARKUPDj is mark-up of domestic goods in sector j omegaj is a conjectural variation term (a prediction of supplier for other suppliers' actions after it changes the quantity of production); this parameter is assumed to be zero Nj is the number of firms in sector j εj is the elasticity of demand for exports in sector j ExpendSj is the share of expenditure for domestic produced good in total production in sector j subdxj is the elasticity of substitution between demand for domestic and for exported good in sector j subddj is the elasticity of substitution between domestic goods in sector j. Tourism exports cannot use the mark-up rate of non-tourism exported goods. Non-tourism goods sell to foreign customers abroad while tourism exports, such as accommodation and catering sell domestically to foreign visitors arriving at Beijing. Therefore, tourism exports use the mark-up rate for domestic goods. Appendix C. Comparisons of the international tourism impact between the Beijing models with perfect and imperfect competition in three scenarios EV ($, million)

Low scenario Central scenario High scenario

gains can be observed under the IC structure, which is attributed to pro-competition effects. Increased tourism expenditure, which is model input, can qualitatively affect changes in welfare, which is model output — larger model input results in larger model output.

Appendix D. Supplementary data

The equations of the mark-up of non-tourism exported goods and domestic goods are generated based on existing literature. It is assumed that each sector only produces one variety of goods. See Blake et al. (1999) for the details of the underlying theory regarding these equations. The equation of the mark-up rate of domestic goods is calibrated to be:   1 MARKUPDj ¼ 1 þ omegaj  Nj "

243

National tourism

International tourism

(PC)

(IC)

(PC)

(IC)

48 242 1254

67 335 1676

73 373 2019

81 405 2085

In this table, PC refers to perfect competition and IC is imperfect competition. The increased tourism expenditure generated by the Beijing Olympics would increase household welfare (EV). In the model simulation, the increased tourism expenditure is assumed to be one-tenth in the low scenario and ten times in the high scenario based on the central scenario. The findings are compared in the three scenarios under PC and IC market structures. Larger welfare

Supplementary data to this article can be found online at http:// dx.doi.org/10.1016/j.econmod.2012.09.013.

References Adams, P., Parmenter, B., 1995. An applied general equilibrium analysis of the economic effects of tourism in a quite small, quite open economy. Applied Economics 27, 985–994. Ahmed, S., 2008. Potential for trade between developing and least developed countries: a CGE analysis. Trade and Development Review 1 (2), 122–143. Baade, R., Baumann, R., Matheson, V., 2008. Selling the game: estimating the economic impact of professional sports through taxable sales. Southern Economic Journal 74, 794–810. Beijing Bureau of Statistics, 2006. Beijing Statistics Yearbook — 2006. China Statistics Press, Beijing. Beijing Municipal Bureau of Statistic, 2009. Beijing Statistics Yearbook—2009. China Statistics Press, Beijing. Blake, A., 2005. The economic impact of the London 2012 Olympics. TTRI Discussion Paper No. 2005/5. University of Nottingham. This report is also formed as part of the UK Government's Olympic Games Impact Study submitted to the International Olympic Committee. Blake, A., Rayner, A., Reed, G., 1999. A computable general equilibrium analysis of agricultural liberalisation: the Uruguay Round and Common Agricultural Policy Reform. Journal of Agricultural Economics 50 (3), 400–424. Blake, A., Gillham, J., Sinclair, T., 2006. CGE tourism analysis and policy modelling. In: Dwyer, L., Forsyth, P. (Eds.), International Handbook on the Economics of Tourism Cheltenham. Edward Elgar, pp. 301–315. Blake, A., Arbache, J., Sinclair, M., Teles, V., 2008. Tourism and poverty relief. Annals of Tourism Research 35 (1), 107–126. Bohlmann, H.R., Van Heerden, J.C., 2005. The impact of hosting a major sport event on the South African economy. Journal of Tourism 26, 594–603. Bye, B., Fahn, T., Heggedal, T., 2009. Welfare and growth impacts of innovation policies in a small, open economy; an applied general equilibrium analysis. Economic Modelling 26 (5), 1075–1088. Clark, G., 2008. Local Development Benefits from Staging Global Events, Organisation for Economic Co-operation and Development (OECD), Paris. Conway, P., Dhar, S., 1991. The economic effects of widespread application of antidumping duties to import pricing. Working Paper 782. Country Economics Department, the World Bank. Cornelissen, S., 2004. It's Africa's turn!… SA bids for the 2006 and 2010 FIFA finals. Third World Quarterly 25 (7), 1293–1309. Daughety, A.F., 1985. Reconsidering Cournot: the Cournot equilibrium is consistent. Rand Journal of Economic 16, 368–379. Dee, P., Hanslow, K., 2001. Multilateral liberalization of services trade. In: Stern, R. (Ed.), Services in the International Economy. The University of Michigan Press, Michigan. Devarajan, S., Rodrik, D., 1991. Pro-competitive effects of trade reform: results from a CGE model of Cameroon. European Economic Review 35 (5), 1157–1184. Dixit, A.K., 1987. Tariffs and subsidies under oligopoly: the case of the US automobile industry. In: Kierzkowski, H. (Ed.), Protection and Competition in International Trade. Blackwell, Oxford, pp. 113–127. Dixit, A.K., Stiglitz, J.E., 1977. Monopolistic competition and optimum product diversity. American Economic Review 67 (3), 297–308. Dwyer, L., Forsyth, P., Madden, J., Spurr, R., 2000. Economic impacts of inbound tourism under different assumptions regarding the macroeconomy. Current Issues in Tourism 3 (4), 325–363. Dwyer, L., Forsyth, P., Spurr, R., 2004. Evaluating tourism's economic effects: new and old approaches. Tourism Management 25, 307–317. Dwyer, L., Forsyth, P., Spurr, R., 2006. Economic evaluation of special events. In: Dwyer, L., Forsyth, P. (Eds.), International Handbook on the Economics of Tourism. Edward Elgar, Cheltenham, pp. 316–355. Fane, G., Ahammad, H., 2003. Alternative ways of measuring and decomposing equivalent variation. Economic Modelling 21, 175–189. Francois, J.F., Reinert, K.A., 1997. Applied Methods for Trade Policy Analysis: A Handbook. Cambridge University Press, Cambridge. Giesecke, J., Madden, J., 2007. The Sydney Olympics, seven years on: an ex-post dynamic CGE assessment. CoPS General Paper No. G-168. Centre of Policy Studies, Monash University, Melbourne. Hall, C., 1989. The destination and analysis of hallmark tourist events. GeoJournal 19 (3), 263–268. Hanslow, K., 2001. A general welfare decomposition for CGE models. GTAP Technical Paper, No. 19. Harris, R., 1984. Applied general equilibrium analysis of small open economies with scale economies and imperfect competition. American Economic Review 74, 1016–1032.

244

S. Li et al. / Economic Modelling 30 (2013) 235–244

Harrison, G.W., Rutherford, T.F., Tarr, D.G., 1995. Quantifying the Uruguay round. In: Martin, W., Winters, L.A. (Eds.), The Uruguay Round and Developing Economies: World Bank Discussion Paper 307, Washington, DC. Harrison, G.W., Rutherford, T.F., Tarr, D.G., 1997. Quantifying the Uruguay round. The Economic Journal 107, 1405–1430. Hertel, T.W. (Ed.), 1997. Global Trade Analysis: Modelling and Applications. Cambridge University Press, New York. Hoffmann, A.N., 2002. Imperfect competition in computable general equilibrium models — a primer. Economic Modelling 20, 119–139. Hotchkiss, J., Moore, R., Zobay, S., 2003. Impact of the 1996 Summer Olympic Games on employment and wages in Georgia. Southern Economic Journal 69 (3), 691–704. Hsu, P., 2008. Service oligopolies and economic-wide performance in Taiwan: a CGE analysis. Working Paper 880. http://www.ecomod.org/files/papers/880.pdf (accessed 20 September 2011). Humphreys, J.M., Plummer, M.K., 1995. The Economic Impact of Hosting the 1996 Summer Olympics. The University of Georgia and IRE Advisors, Georgia. Jang, J., Lee, J., Ahn, H., 1999. The economic impact of the 2002 Korea–Japan World Cup, Seoul. Available at http://ref.daum.net/item/1149750 (accessed on 6 September, 2010). Kasimati, E., Dawson, P., 2009. Assessing the impact of the 2004 Olympic Games on the Greek economy: a small macroeconometric model. Economic Modelling 26, 139–146. Konan, D., Assche, A., 2004. Regulation, market structure and service trade liberalization: a CGE analysis. Working Paper. Global Trade Analysis Project, Purdue University. Lancaster, K.J., 1979. Variety, Equity and Efficiency. Columbia University Press, New York. Li, S.N., Blake, A., 2008. Modelling competition levels in the Chinese economy: the economic impact of the Beijing 2008 Olympic Games. Presented at the Eleventh Annual Conference on Global Economic Analysis (Purdue University, US), Helsinki. Li, S.N., Blake, A., 2009. Estimating Olympic related investment and expenditure. International Journal of Tourism Research 11, 337–356. Li, S.N., Blake, A., Cooper, C., 2011. Modelling the economic impact of international tourism on the Chinese economy: a CGE analysis of the Beijing 2008 Olympics. Tourism Economics 17 (2), 279–303. Lofgren, H., Harris, R., Robinson, S., 2002. A Standard Computable General Equilibrium (CGE) Model in GAMS. International Food Policy Research Institute, Washington. Madden, A.R., 2002. The economic consequences of the Sydney Olympics: the CREA/Arthur Andersen study. Current Issues in Tourism 5, 7–21. Madden, A.R., 2006. Economic and fiscal impacts of mega sports events: a general equilibrium assessment. Public Finance and Management 6, 346–394. Margaret, C., Mabugu, R., 2008. Evaluating the impact of land redistribution: a CGE microsimulation application to Zimbabwe. Journal of African Economics 17 (4), 527–549. Marrewijk, J.G.M., 2007. Competitive advantage. In: Marrewijk, C.V., Ottens, D., Schueller, S. (Eds.), International Economics: Theory, Application, and Policy. Oxford University Press, Oxford. Part II. B. McManus, C., 1999. Marking the most of mega events. New Zealand Management 46 (2), 30–35. Neary, J.P., 2000. Monopolistic Competition and International Trade Theory. Presented to a Conference on the Monopolistic Competition Revolution after Twenty-Five Years, University of Groningen, 30–31 October 2000, Groningen. New South Wales Treasury, 1997. Economic impact of the Sydney Olympic Games. Research & Information Paper, Office of Financial Management, New South Wales. Pfaffermayr, M., 1999. Conjectural-variation models and supergames with price competition in a differentiated product oligopoly. Journal of Economics 70, 309–326.

Russo, B., 2009. Innovation and the long-run elasticity of total taxable income. Southern Economic Journal 75 (3), 798–828. Sheldon, I., 1996. Incorporating industrial organization into agricultural trade modelling. In: Martimort, D. (Ed.), Agricultural Markets (Contributions to Economic Analysis, Volume 234). Emerald Group Publishing Limited, pp. 313–330. Song, H., Fei, B., 2006. Modelling and forecasting international tourist arrivals to mainland China. China Tourism Research 3 (1), 1–12. Spence, A.M., 1976. Product selection, fixed costs and monopolistic competition. Review of Economic Studies 43, 217–236. Swaminathan, P., Hertel, T.W., 1997. Introducing monopolistic competition into the GTAP Model. GTAP Technical Paper No. 6. Centre for Global Trade Analysis, Purdue University, Purdue. United Nations (UN) Department for Economic and Social Information and Policy Analysis, World Tourism Organization (WTO), 1994. Recommendations on Tourism Statistics, United Nations, New York. Varian, H.R., 1992. Microeconomic Analysis. W.W. Norton & Company, New York. Walker, M., 2006. Competition Law, Anti-competitive Behaviour, and Merger Analysis: Economic Foundations. Office of the General Counsel, Asia Development Bank. Wattanakuljarus, A., Coxhead, I., 2008. Is tourism-based development good for the poor? A general equilibrium analysis for Thailand. Journal of Policy Modelling 30, 929–955. Dr. ShiNa Li is Senior Lecturer in Events Management at Leeds Metropolitan University. Before she joined Leeds Met, ShiNa worked as a university teacher in Tourism at the University of Nottingham, where she obtained her PhD. Her thesis topic is “The economic impact of mega-events: a CGE approach to the Beijing Olympic Games”. Her research interest includes economic and social impact evaluation, tourism economics, computable general equilibrium modelling, mega-events and developing countries. Adam Blake is Professor of Economics and Deputy Director of International Centre for Tourism & Hospitality Research. His expertise includes CGE modelling, tourism economics, poverty and climate change mitigation. One of his most recent projects included a detailed study on the economic impact of London 2012, which formed part of the UK Government's Olympic Games Impact Study. He has carried out research and consultancy work for key UK government departments, including Culture, Media & Sport, Revenue & Customs, regional development agencies and the Treasury. He has worked with the European Commission, as well as a number of overseas governments. Rhodri Thomas is Professor of Tourism and Events Policy and Head of the International Centre for Research in Events, Tourism and Hospitality (ICRETH) at Leeds Metropolitan University. His research interests include the use of events and festivals to achieve public policy goals. He is currently working on or has recently completed projects funded by the Economic and Social Research Council (ESRC), the Organisation for Economic Cooperation and Development (OECD) and various government agencies.