The relation between football clubs and economic growth: the case of developed countries

The study situates sports within broader economic growth and development, noting that sports function as both a socio-cultural phenomenon and an economic sector. Prior work links economic growth with sectors such as tourism, media, sponsorship, and sports, with football playing a prominent role due to large events and expanding markets. The paper argues that sports consumption can stimulate economic growth, while economic growth can reinforce sports industries. Against this backdrop, football has become a significant investment for countries aiming to expand domestic sports markets and international connections, with infrastructure and professional club performance tied to economic outcomes. The research gap identified is a need to analyze football club values alongside industrial production growth to better understand dynamic relationships over time and frequency, especially for developed nations. The study aims to: (i) reveal the relationship between total club values of G7 countries and their economic growth, (ii) determine the influence of football clubs on economic growth and factors contributing to growth from Nov 2010 to Dec 2021, (iii) compare G7 countries, and (iv) employ wavelet analysis to assess the time–frequency dynamics and propose the most suitable model.

The literature positions sports as an economically significant sector with measurable impacts on growth through events, media, sponsorship, tourism, infrastructure, and volunteering. While some studies (e.g., Kobierecki and Pierzgalski, 2022) find no significant macroeconomic effect from mega-events, others document revenue growth in professional leagues and clubs, increased media rights values, and sector resilience (Gratton, 1998; Gratton and Solberg, 2007; Downward and Dawson, 2000; Boroncelli and Lago, 2006; Bradbury, 2019). Research methods previously used include linear regressions for transfers and ticketing, economic models of visitor and TV revenue, empirical analyses linking sports industry revenue to growth, and input–output models capturing spectator spending and facilities’ impacts (Dobson and Goddard, 2001; Roberts et al., 2016; Rohde and Breuer, 2016; He, 2018). The literature also notes sport’s social roles and policy relevance, as well as the expansion of the sports market since the 1980s through mega-events and media. The paper builds on this by examining club market values (a micro/industry indicator) jointly with industrial production (a macro proxy) to understand dynamic co-movements across horizons, a perspective underexplored in prior work.

Data: Monthly data from November 2010 to December 2021 were compiled for G7 countries, excluding Canada due to data availability. Industrial production indices (base year 2015) were sourced from the OECD database as a monthly proxy for economic growth (covering mining, manufacturing, electricity, gas, steam, and air-conditioning). Football club values were sourced monthly from Transfermarkt, aggregating first-tier leagues: USA (Soccer league/MLS), Japan (J1 League), Germany (Bundesliga), France (Ligue 1), Italy (Serie A). For the UK, to align with the national industrial production coverage, total club values were derived by aggregating Premier League, Scottish Premiership, SSE Airtricity League Premier Division, and Cymru Premier. Club values were log-transformed to capture relative changes over time. Wavelet approach: The study applies the wavelet methodology to analyze time–frequency relations using continuous wavelet transform (CWT), cross wavelet transform (XWT), and wavelet coherence (WTC). This framework handles non-stationary series without smoothing, preserving data information. Formally, following Torrence and Webster (1999), with X and Y denoting the two series, the cross wavelet spectrum is W^x_ny = W^x_n(s) * [W^y_n(s)]*, and the squared wavelet coherence is R^2_n = S[(s^{-1}W^x(s))*(s^{-1}W^y(s))]^2 / {S[(s^{-1}W^x(s))^2] · S[(s^{-1}W^y(s))^2]}, where n is time index, s is scale, * denotes complex conjugate, and S is a time-and-scale smoothing operator. Coherence values range from 0 to 1, with higher values indicating stronger localized correlation. Significance testing uses Monte Carlo simulations with 1000 replications, adapting the routine of Grinsted et al. (2004). Results are interpreted within the cone of influence (COI) to avoid edge-effect regions. The analysis evaluates power spectra (CWT), regions of common high power (XWT), and localized correlation/co-movement (WTC) across short, medium, and long cycles, with country-by-country assessment.

- Continuous wavelet spectra showed that industrial production for each country displayed substantial significant power, with marked short- and medium-term fluctuations around 2020, coinciding with COVID-19 restrictions. In contrast, club values exhibited limited significant fluctuations, with only thin or sparse significant areas; the USA showed no significant areas within the COI for clubs, while Italy showed three notable areas post-2018; the UK, France, and Germany displayed thin, narrow significant regions. - Cross wavelet analysis indicated that the relationship between club values and industrial production emerged only from 2019 onward across all countries, with significance broadening in both frequency (towards longer cycles) and time after 2020. Prior to 2019, no significant co-movement was detected at any time–frequency region. - Wavelet coherence (co-movement) patterns by country: • USA: Co-movements concentrated in short cycles across periods with thin significant regions; the only comparatively thicker area occurred ~2019–2020 within the 0.25–0.50 frequency bands; no medium- or long-term co-movements within the COI. • Italy: Small short- and medium-term co-movements around ~2012 and 2016–2018; thicker co-movement spanning short to medium cycles around 2019; longer-cycle co-movement initiated mid-2017 to the end of the sample but largely outside the COI after mid-2018. • United Kingdom: Three short-cycle regions ~2012–2015, then a gap until mid-2017; thicker co-movement mid-2018 to 2020 expanding from 0 to 0.75 cycle bands; thin longer-cycle co-movement from mid-2017 to end, outside the COI after 2018. • France: Medium-term co-movements early in the sample (to ~2014); after 2014, co-movements were confined to short cycles (three regions 2014–2020); no higher-cycle (long-term) co-movement as seen in Italy/UK. • Japan: Co-movements in both short and longer cycles; five regions from 0 to 0.5 frequency bands across the sample, plus one around 0.75 (2014–2015); longer-cycle co-movement from 2017 to end, outside COI after 2019 for the 1.75–2.5 band. • Germany: Multiple co-movement regions 2014–2020; pre-2018 relations in short cycles (0–0.5 bands, six regions); largest co-movement 2018–2020 across 0.25–1 frequency bands. - Overall: Co-movements predominantly occur in shorter cycles across countries; longer-cycle relations are limited or absent or fall outside the COI. The emergence and strengthening of relations post-2019 align with COVID-19 disruptions and subsequent adjustments.

The findings address the central question of whether and how football club values and economic growth (proxied by industrial production) are dynamically related in developed countries. The time–frequency evidence shows a predominantly short-term co-movement structure, with significant relations emerging mainly after 2019. This suggests that shocks and policy responses associated with COVID-19 catalyzed a tighter coupling between macroeconomic conditions and the football sector’s valuations, especially over short horizons. The limited and sporadic long-term co-movements imply that structural linkages are weaker, or masked by edge effects, while short-term demand, liquidity, and sentiment channels likely drive the observed synchrony. Despite sharp declines in industrial production during COVID-19, football clubs’ market values proved comparatively resilient, recovering over time. This resilience indicates sports institutions’ capacity to absorb real-economy shocks and potentially serve, in the short run, as partial stabilizers through continued media/sponsorship revenues and brand equity. Policy-wise, both sports and economic authorities should account for these short-run interdependencies when designing crisis responses, investment in facilities, and athlete development infrastructure, recognizing sport’s potential to support employment and local activity, even amid macroeconomic volatility.

The study contributes a novel time–frequency perspective on the relationship between football club values and economic growth in developed countries by combining monthly club value data with industrial production and applying wavelet methods. It documents that significant relations largely materialized after 2019 and are mostly short-term across countries, with limited long-term coherence within interpretable regions. Methodologically, the wavelet framework captures evolving, localized dynamics without smoothing, offering richer insights than aggregate, static approaches. Future research could expand the country set via panel frameworks, incorporate alternative or higher-frequency macro indicators, and explore mechanisms (e.g., media rights cycles, transfer windows, policy interventions) that mediate short-run co-movements. Overall, the results underscore that football and economic growth connect in ways that vary across time horizons, with pronounced short-term joint movements post-2019.

- Economic growth measured via industrial production index due to unavailability of monthly GDP data. - Sample period constrained to November 2010–December 2021 by the availability of monthly club value data. - Exclusion of Canada from the G7 comparison due to lack of club value data. - Wavelet methodology is inherently time-series based, limiting the number of countries analyzed simultaneously; broader cross-country analysis would benefit from panel data models in future work.