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on Tourism Economics |
| By: | Toshiyuki MATSUURA; Masahiro ENDOH; Hisamitsu SAITO |
| Abstract: | In a rapid aging society such as Japan, the promotion of inbound tourism is expected to be a catalyst for revitalizing local economies. This study reexamines the so-called tourism-led growth hypothesis by investigating whether tourism promotes regional development using data at the commuting zone level in Japan. We construct the commuting zone-level data for the number of domestic and international overnight visitors by aggregating the individual data of hotels and accommodations obtained from Overnight Travel Statistics Survey (Japan Tourism Agency). To assess the causal impact, a shift-share instrumental variable was used to address the simultaneity of regional economic indicators and the number of overnight visitors. Our results indicate that the increase in inbound tourists has a positive effect on some economic indicators, such as an increase in taxable income per capita, an increase in the population of young people, and an increase in commercial land prices in regions with small population sizes that are highly dependent on tourism and in large cities with international airports. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:eti:rdpsjp:25032 |
| By: | Zarrilli Joaquín; Porto Natalia; De la Vega Pablo; García Carolina Inés |
| Abstract: | Economies around the world are simultaneously undergoing two profound changes: the 'green' (sustainability-focused) transition and the 'automation’ (digital-focused) transition. This dual or ‘twin’ transition has significant implications for the tourism industry, which is a crucial source of employment for many countries. This paper explores the potential for transitions to green and digital jobs in the tourism industry. We analyse household survey data for the seven largest economies of Latin America (Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, and Uruguay) for the period 2011-2024. Our main results show that the tourism sector in Latin America has high potential to reallocate workers from brown to green jobs, thereby reducing the adjustment costs of decarbonization. This capacity is particularly pronounced in Mexico and Ecuador, and is especially strong among younger cohorts, men, and workers with lower levels of formal education. |
| JEL: | E20 Q50 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:aep:anales:4846 |
| By: | Mousavi, Ebrahim; Zare, Hassan; Moula, Ahmad |
| Abstract: | Sentiment analysis in tourism platforms plays a vital role in understanding customer feedback, enhancing service quality, and supporting strategic economic decision-making across tourism markets. Challenges such as imbalanced sentiment classes, domain-specific language, and noisy data reduce the economic efficiency and analytical value of conventional approaches. This paper introduces a novel hybrid framework that combines lexicon-based sentiment and emotion analysis with an economically optimized weighted kNearest Neighbors (kNN) classifier. The framework incorporates advanced data augmentation techniques and comprehensive feature engineering, including n-gram TF-IDF extraction and metric learning—to improve minority sentiment class recognition and increase the economic robustness of predictive analytics. A modified co-optimization layer jointly tunes augmentation parameters, feature extraction methods, and classifier hyperparameters to maximize minority-class F1-scores while minimizing computational and economic costs. Experimental evaluations on real-world tourism review datasets demonstrate significant improvements in classification performance compared to baseline models such as SVM, Random Forest, and CNN, highlighting the framework’s economic value in large-scale tourism data processing. Additionally, a real-time business intelligence dashboard is developed for economic monitoring and dynamic visualization of sentiment trends and minorityclass heatmaps, enabling tourism stakeholders to make informed economic and managerial decisions and strategically respond to customer sentiments. The findings confirm a predominance of positive sentiments across tourism services while identifying economically critical areas requiring improvement. Future work will explore multilingual sentiment analysis and aspect-based models to enhance granularity, scalability, and economic impact. This research contributes an effective, interpretable, and economically oriented solution for advanced sentiment analysis in tourism platforms. |
| Keywords: | Sentiment Analysis; Tourism Economic Platforms; Data Augmentation; kNN; Optimization |
| JEL: | A1 A10 O1 O10 R1 R10 R15 |
| Date: | 2025–06–13 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127063 |
| By: | Fossati Roman; Romero Agustina; Rachinger Heiko; Porto Natalia |
| Abstract: | This paper provides new empirical evidence on the relationship between tourism specialization and quality of life across Argentinean jurisdictions from 2009 to 2019. We construct multidimensional indicators for both tourism specialization—capturing two supply-side and one demand-side dimension—and quality of life, assessed across nine domains including economy, education, environment, and technology. To address complexity and unobserved heterogeneity, we apply principal component analysis and fixed effects panel data models. Our findings reveal a positive and heterogeneous association between tourism specialization and quality of life. Notably, the economic dimension is most strongly linked to tourism, particularly on the supply side, while health and environmental dimensions show the weakest associations. From a policy perspective, the evidence suggests that promoting tourism can play a significant role in fostering stronger economic outcomes, and that policymakers should prioritize supply-side instruments to support this goal. |
| JEL: | C01 C52 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:aep:anales:4802 |