Opposition in Japan to the Olympics during the COVID-19 pandemic

The study investigates how Japanese citizens evaluated hosting the Tokyo 2020 Olympic and Paralympic Games during the COVID-19 pandemic. Following the event’s postponement and continued public health emergency, the research asks which perceptions most shaped support or opposition to hosting the Games. It hypothesizes that perceptions of high costs, heightened infection risks, and burdens on medical institutions would increase opposition, whereas perceptions of effective countermeasures, expected economic benefits, and positive views toward athletes would increase support. Understanding these attitudes is important for governments and event organizers to plan mega-events under crisis conditions and to design effective public communication strategies.

The literature identifies multiple legacies and effects of hosting the Olympics: (1) Strategic development of urban functions and legacy planning, including sustainability and accessibility, with examples from Beijing 2008 improving air quality and urban services; (2) Economic effects such as increased tourism before and long after the event, export stimulus via global attention, and sponsorship-driven brand value; (3) City branding benefits, where hosts re-image and elevate national and city brands (e.g., Turin 2006, Beijing 2008). Prior work also notes the Olympics’ costs and risks, debates about net economic benefits, and the importance of media in shaping resident attitudes toward bids and events. For Tokyo 2020, motivations included recovery from the 2011 disasters and projecting technological leadership, but COVID-19 introduced unprecedented uncertainties, heightened health risk concerns, and logistical challenges. The study positions its hypotheses within this literature by focusing on citizen perceptions under a global health crisis, a relatively unexplored context.

Design: Cross-sectional online survey conducted before the Tokyo 2020 Olympics with N=800 Japanese residents aged 20s–50s. Key sample characteristics (Table 1): 357 men/443 women; age groups evenly distributed (200 each in 20s, 30s, 40s, 50s); 458 married, 342 unmarried; children: 431 none, 148 one, 171 two, 50 three+; income varied across categories; regions covered nationwide with no strong regional concentration. Measures: Outcome was a binary indicator of opposition to hosting the Olympics (1=oppose). Controls included demographic and psychological variables: Female, Age (20s–50s), Married, number of Children, Income (6 categories), Tokyo residence, ExcessiveInformation (aversion to perceived over-information by media), and Prediction (expected time until the end of COVID-19, coded 1–12, with 12 indicating ‘never end’). Explanatory variables were factor-mention dummies derived from respondents’ open-ended reasons for their attitudes. Factor extraction: Open-ended textual reasons were morphologically analyzed using MeCab and dependency-parsed with CaboCha. Seven thematic factors were identified, each coded as a mention dummy if respondent text included related terms: F1_Cost (cost/budget/tax/money/investment), F2_Infection (infection/pandemic/corona/virus/COVID-19), F3_Movement (movement/traffic/foreigner/tourist/sightseeing), F4_MedicalFacility (medical institution/staff/medical collapse/life/treatment), F5_Economy (economy/economic effect/revitalization/economic blow/loss), F6_Countermeasures (PCR/inspection/quarantine/vaccine/countermeasures), F7_Athlete (athlete/sport/training/practice/effort). Frequencies ranged from 29 (F7) to 357 (F2) mentions (Table 2). Statistical analysis: Logistic regression was used with opposition as the dependent variable. Three models were estimated: Model 1 (all variables), Model 2 (stepwise variable selection), Model 3 (all variables using balanced data to address class imbalance: the original data had 642 oppose vs. 158 support; Model 3 used a balanced subset totaling 300 observations). Model fit was assessed via AIC and McFadden’s pseudo-R2 (adjusted). Correlations among factor dummies and outcome were also examined (Table 4). Hypotheses were evaluated based on the best-fitting model (Model 3 with McFadden’s pseudo-R2=0.314).

• Opposition prevalence: 80.3% opposed hosting, aligning with external polls (~78% oppose). By age: 20s 82.0%, 30s 82.5%, 40s 76.5%, 50s 80.0%. No notable regional differences.
• Correlations: Opposition most correlated with F2_Infection (r≈0.300, p<0.001), then F3_Movement (r≈0.120, p<0.001), and F4_MedicalFacility (r≈0.103, p<0.01). Economic factor negatively correlated with opposition (r≈-0.180, p<0.001).
• Logistic regression (Model 3, best fit: AIC=323.307; McFadden’s pseudo-R2=0.314; adjusted=0.218): • Strongest predictor of opposition: F4_MedicalFacility (OR=20.728, p<0.05), indicating concerns about medical system collapse sharply increased opposition. • Infection-related factors increased opposition: F2_Infection (OR=7.658, p<0.001) and F3_Movement (OR=9.373, p<0.01). • Cost concerns were not significant: F1_Cost (ns), rejecting H1. • Factors reducing opposition (supportive of hosting): F5_Economy (OR=0.120, p<0.05), F6_Countermeasures (OR=0.172, p<0.05), F7_Athlete (OR=0.306, p<0.05). • Controls: Female (OR=2.799, p<0.001) more likely to oppose; more Children associated with less opposition (OR=0.825, p<0.05). ExcessiveInformation (media over-information aversion) associated with less opposition (OR=0.696, p<0.05). Prediction of a longer pandemic duration associated with more opposition (OR=1.173, p<0.01). Age, marital status, income, and Tokyo residence were not significant in the final balanced model.
• Hypotheses: Supported for infection spread/movement (H2), medical burden (H3), economy (H4), countermeasures (H5), and athletes (H6); not supported for cost (H1).

Findings show that, during a prolonged public health crisis, citizens’ opposition to hosting a mega-event is primarily driven by perceived medical system risks rather than financial cost. Concerns about medical collapse had the largest effect on opposing attitudes, exceeding direct infection mentions, underscoring the salience of healthcare capacity anxiety. Infection spread via movement/traffic and general infection risk were also strong determinants of opposition. Conversely, perceiving credible COVID-19 countermeasures, anticipating economic benefits, and positive sentiments toward athletes mitigated opposition. Cost perceptions were not significant, possibly because sunk-cost considerations and mixed views offset purely negative cost perceptions. Psychological factors further contextualize attitudes: those who perceive media coverage as excessive tended to support hosting, whereas those expecting the pandemic to persist longer opposed the event. Demographically, women were more likely to oppose; households with more children were more supportive, reflecting a desire to provide a unique experience for children. The results emphasize the need for organizers to address health-system capacity transparently and to communicate effective countermeasures alongside realistic economic narratives. These insights are pertinent for strategic planning and public engagement in future large-scale events under crisis conditions.

This study clarifies which citizen perceptions most influenced support or opposition to hosting the Tokyo 2020 Olympics during COVID-19. Opposition was widespread and most strongly linked to concerns about medical system strain, followed by infection-related mobility concerns, while perceived economic benefits, credible countermeasures, and positive views of athletes reduced opposition. The cost factor did not significantly shape attitudes. Practically, organizers and governments should not underestimate citizen attitudes; they should co-produce communication strategies with residents, design simple, transparent indicators, and proactively share information on hospital capacity and health system resilience to reduce anxiety. These findings contribute to the literature on mega-events under crisis by providing citizen-centered evidence to inform risk communication and planning. Future research should broaden contexts beyond COVID-19, include wider age ranges, and conduct cross-national comparisons to enhance generalizability.

• Context specificity: Focused on the COVID-19 pandemic and Tokyo 2020; other external shocks were not examined.
• Sample age range: Limited to respondents in their 20s–50s; older and younger populations were not included.
• Generalizability: Conducted in Japan; findings may not generalize to other countries or cultural contexts.
• Class imbalance handling: Balanced subsample used for one model; while improving fit, it reduces sample size and may affect precision.