Planning of Mega Events: Experiences and Lessons

Abstract

As the Olympic Games have turned gradually into a mega event, the International Olympic Committee (IOC) have tried to professionalise the planning process related to the Games. As a part of this endeavour the IOC has launched the OGGI project (Olympic Games Global Impact Study), which aims to safeguard three dimensions of sustainability: environmental, social and economic values. Host cities are asked to monitor developments along these dimensions by means of social indicator analyses. In this article the author welcomes the monitoring efforts but raises critical questions regarding the strong focus on social indicator analysis. Using examples from past mega events, he argues theoretically and empirically for the use of qualitative data in impact studies.

Keywords:

• Mega events
• impacts
• social indicators
• methodology

Notes

 1. For an overview of rationales for social indicators, see Land (1975). Esping-Andersen (2000, 2003) gives good illustrations of informed use of social indictor analysis in welfare monitoring.

 2. For more detailed references, see Fischer (2003); Flyvbjerg (2001); Hajer (2003) (general perspectives), and Innes (2000); Innes & Booher (2000) (specific comments on community indicator systems).

 3. At the follow-up conference on the Brundtland Commission it was clearly stated that “the scientific community has an absolute duty to give the best possible information and a clear explanation of the quality of the scientific knowledge, including uncertainties” (Ministry of Environment, 1999, referred to in Teigland, 2000, p. 185). The intention here is to discuss some of these uncertainties.

 4. This section is based on the following unpublished papers from the IOC Department: ‘Olympic Games Global Impact (OGGI) Study’, ‘Environmental Sphere—Context Indicators’, and ‘OGGI Beijing—List of Indicators’, together with Van Griethuysen & Hug (2001), Van Griethuysen (2001), and Hug (2001).

 5. In the American boomtown debate some of the researchers were accused of being biased in their analyses due to the fact that they had been sponsored by some of the construction companies (see Pacific Sociological Review, 25 (3), 1982).

 6. In his doctoral thesis, Teigland (2000) arrives at conclusions quite similar to those referred to above. His report Impact Assessment as Policy and Learning Instrument has the telling subtitle Why Effect Predictions Fail (and also: How Relevance and Reliability can be Improved). His conclusion deserves to be read in full: “A large number of impact assessment reports have been written world-wide during the past three decades. The reports probably contain several hundred thousand predictions of how environmental and social systems are expected to respond to different types of projects and programs. However, few have attempted to check if IA-predictions have been relevant and reliable. Recent audits show, though, that most predictions have been vague and often fail” (p. iii, italics added). Teigland's advice has been to invite more pluralism and a wider multiplicity regarding data. Since there exist many realities, alternative impact theories should be included that are capable of understanding the main participants and the decision-making processes upon which their decisions and actions were made. A broader variety of methodological tools are also required.

 7. In the debate on the ‘science war’ (see Flyvbjerg, 2001), some commentators have warned against social science developing into ‘fashionable nonsense’. Berteaux (1982) uses this type of critique of scientism within the social sciences as an argument for strengthening the sociology of biography.

 8. Even though a tradition exists that has tried to make economics a positive science (Levitas, 1986) and even though it is accepted there are some ‘kind-of-nomotetic’ co-variations within this science, the ever-present disagreement among economists as to the understanding of economic processes should be proof enough for a subsuming economy as a social science (cf. the definition of an economist as “an expert who will know tomorrow why the things he predicted yesterday did not happen today” (quoted from Flyvbjerg, 2001, p. 44).

 9. In the author's research on the social impacts of the Olympic Games in Lillehammer, the analysis was based on quantitative as well as qualitative data, and both sets of data covered a period of five years

10. cf. Teigland (2000) in his doctoral thesis on Impact Assessment (IA): “It is an important reality that the relevance of IA-predictions depends not only on the quality of the scientific theory, upon which the predictions are built, but upon the theories that the proponents, opponents and decision-makers use to build their thinking and actions” (p. 185).

11. Campbell (1975) adds to the uncertainty of statistics the imprecision of language: “All scientific knowing is indirect, presumptive, obliquely and incompletely corroborated at best. The language of science is subjective, provincial, approximate, and metaphoric, never the language of reality itself. The best we can hope for are well-edited approximations” (p. 179).

12. This problem is naturally even greater when we have to do with ex ante analyses. Flyvbjerg et al. (2003) note that “ … in terms of risk, most appraisals of megaprojects assume, or pretend to assume, that infrastructure policies and projects exist in a predictable Newtonian world of cause and effect where things go according to plan. In reality, the world of megaproject preparation and implementation is a highly risky one where things happen only with a certain probability and rarely turn out as originally intended” (p. 6)

13. The increase in unemployment was caused by a general/national economic decline combined with the fact that local entrepreneurs could not compete with large national construction companies.

14. Teigland (2000) draws this practical conclusion from the recognition of multifarious interests: “It may therefore be ethically correct to give more weight to the viewpoints of the interest groups involved than to assessments made by scientists of the advantages and risks from development actions” (p. 3).

15. It has jokingly, but acutely, been observed that “an economist is an expert who will know tomorrow why the things he predicted yesterday did not happen today” (The Economist, 25 February 1995, referred to in Flyvbjerg, 2001, p. 44).

16. In addition, the author had longitudinal survey data, informant interviews and a multitude of relevant statistics.