Economic growth is intrinsically linked to social welfare and services, especially in developing countries where adopting an international perspective is crucial for progress. Sports, and particularly football, have become a significant economic sector in developed nations, demonstrating a mutual influence between economic growth and sports consumption. The growth of the sports industry, fueled by increased sports consumption and events, contributes significantly to economic activities. Football, with its global reach and high investment, stands as a prime example of this interrelation. This study focuses on the relationship between the total value of football clubs in G7 countries and their economic growth, employing wavelet analysis to examine the correlation across various time scales. The study aims to unveil this relationship, determine the influence of football clubs on economic growth, conduct a comparative analysis among G7 countries, and propose the most suitable model for analyzing this dynamic relationship.

Existing literature explores the economic impact of football through various lenses, including linear regression models to examine transfers, ticketing, and club stocks; economic modeling to analyze visitor and television revenue; and empirical analysis to examine the sports industry's influence on economic growth. Regional input-output models have been used to study the contribution of spectators, stadium services, and player expenses. However, a gap remains in comprehensively examining club values in conjunction with industrial production growth and subsequent economic expansion. This study aims to bridge this gap by providing novel insights into the relationship between club values and economic growth.

This study employs wavelet analysis, a methodology well-suited for investigating relationships between non-stationary time series data. The researchers utilized monthly data from two sources: the OECD database for industrial production (as a proxy for GDP) and transfermarkt for club values. Data for six G7 countries (US, UK, Germany, France, Italy, and Japan) were collected from November 2010 to December 2021. The wavelet analysis includes three techniques: the continuous wavelet spectrum, cross-wavelet spectrum, and wavelet coherence analysis. These techniques allow for an investigation of the relationship between industrial production and club values across different time scales (short, medium, and long term), without the information loss that can occur with traditional smoothing techniques. The wavelet coherence approach developed by Goupillaud et al. (1984) and formalized by Torrence and Webster (1999) is employed to quantify the co-movement between variables. Monte Carlo simulations (1000 replications) are used to assess significance levels using the method proposed by Grinsted et al. (2004) with modifications.

The continuous wavelet spectrum analysis reveals that industrial production in the analyzed countries fluctuated significantly around 2020, particularly in short and medium-term cycles. In contrast, football club values showed less significant fluctuations. The cross-wavelet analysis indicates a substantial relationship between club values and industrial production only after 2019, across all countries. This relationship is more significant in longer time and frequency periods. The wavelet coherence analysis reveals frequent co-movements between club values and industrial production in short-term cycles across all countries. Long-term co-movements are less consistently observed and confined to narrower areas. The analysis of each country (US, UK, Germany, France, Italy, and Japan) showed variations in the intensity and duration of co-movements, but a clear tendency towards short-term correlations was prevalent. Specifically, the United States primarily shows shorter-term correlations. Italy, the United Kingdom, and Germany all show significant co-movements in shorter cycles with some appearance in longer cycles. France exhibits mostly shorter-term co-movements. Japan has co-movements in both short and longer cycles.

The findings suggest a potential short-term correlation between the value of football clubs and economic growth, particularly evident after the onset of the COVID-19 pandemic in 2019. This short-term correlation implies that economic shocks can affect football club valuations, and, conversely, football industry performance may have limited impact on long-term economic trends. This contrasts with the literature which generally assumes a much stronger positive relationship between the sports industry and broader economic development. The resilience of football club values during the pandemic, despite the significant decline in industrial production, highlights the relative stability and inherent value of these institutions. This resilience suggests a certain degree of decoupling from broader economic downturns in the short term. Policymakers should consider the short-term economic impact of the football industry when formulating economic strategies.

This study contributes significantly to the understanding of the relationship between football clubs and economic growth by employing a novel wavelet analysis approach. The findings highlight a prominent short-term correlation between club values and economic indicators, specifically after 2019. The resilience of football clubs during the COVID-19 pandemic is also noteworthy. Future research could expand the analysis to include a broader range of countries and incorporate additional economic variables to further refine the understanding of this complex relationship. The use of panel data models could enhance the analytical power and generalizability of the findings.

This study's limitations include the use of industrial production as a proxy for GDP due to the unavailability of monthly GDP data, the limited time period (November 2010 to December 2021) due to data availability constraints on club values, and the exclusion of Canada for the same reason. The wavelet methodology's reliance on time series data limits the number of countries included; using panel data models could provide a more comprehensive analysis in future research.