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Assessment of Olympic performance in relation to economic, demographic, geographic, and social factors

Quantile and Tobit approaches

Bibliographic Data

ID15067143
AuthorsWang Shasha (0000-0002-4243-3168, Xi’an Physical Education University, Xian, China, corresponding author), Babar Nawaz Abbasi (0000-0002-2298-339X, Zhengzhou University), Ayesha Sohail (0000-0002-3718-067X, Xi'an Jiaotong University)
Year2023
Volume36
Issue1
Publication date2023-03-31
Open AccessYes
TypeARTICLE
VenueEconomic Research-Ekonomska Istraživanja (JOURNAL)
Journal identifiersISSN: 1331-677X • E-ISSN: 1848-9664
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/1331677x.2022.2080735
OpenAlexW4281695956
LanguageEN
Citations received4
References cited36

The sports industry's global gain is worth drawing attention to in the face of economics. Scholars argued that a country's success in sports is directly related to its economic, demographic, geographic, and social factors. Therefore, investigating the relationship between these factors and sports prizes could be a garment in formulating policies for promoting sports success. Thus, this study used cross-sectional data analysis to investigate such claims based on the recent Olympic Games of Rio 2016 from 207 countries with more than 11, 000 athletes on 306 events in 28 different sports by employing Quantile Regression and Tobit Regression models. The findings revealed that inflation rate in moderate performed countries; economically active population in low, moderate, high, and very high performed countries; and countries' income classification in low, moderate, high, and very high performed countries are influencing the countries medal ranking performance in the Olympic Games. Furthermore, countries with high temperatures are not likely to do well in the games. However, the size of a country's GDP level, corruption ranking level, the number of athletes, and the topographical nature of a country have no impact on the countries medal ranking performance in the Olympic Games

Demographic economics · Econometrics · Economic geography · Economics · Geography · Quantile · Quantile regression · Regional science · Tobit model · Computer Science · Sports Analytics and Performance · Sports Performance and Training

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Citations per year2
Citation span2024 - 2025 (2)
Highly citedNo

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