IDEAS home Printed from https://ideas.repec.org/a/spr/apjors/v1y2017i1d10.1007_s41685-017-0038-0.html
   My bibliography  Save this article

A spatial analysis on the determinants of tourism performance in Japanese Prefectures

Author

Listed:
  • João Romão

    (University of Algarve)

  • Hisamitsu Saito

    (Hokkaido University)

Abstract

Assuming tourism as a place-oriented activity where tourist flows often cross regional borders, local and global indicators of spatial autocorrelation can be useful tools in order to identify and to explain different patterns of regional tourism dynamics and their determinants. These techniques recently became widely used in applied economic studies, as a result of their useful insights to understand spatial phenomena and benefiting from the existence of geo-referenced data and adequate software tools. This tendency is also observed in the tourism sector in the last few years. In this work, an exploratory spatial analysis and a spatial econometric model are applied to the case of Japanese Prefectures, leading to the identification of the specific spatial aspects prevailing in Japanese regional tourism dynamics. Spatial heterogeneity and agglomeration processes are identified, with a view on policy and managerial recommendations, offering a contribution to explore potential synergies arising from inter-regional cooperation in crucial aspects of tourism development. The results reveal the existence of such spatial effects, reflecting the importance for tourism of central areas of Japan, while revealing that competition effects among Japanese Prefectures prevail over positive regional spinoffs identified in other countries. It was also possible to observe that regions where tourism plays a more prominent role in terms of its importance within regional employment do not present a relatively high performance in terms of economic impact and benefits. The results suggest that a more balanced regional economic structure and higher levels of education of the work force contribute for improvements in tourism value added. Finally, the important role of foreign tourism boosting regional tourism performance is revealed.

Suggested Citation

  • João Romão & Hisamitsu Saito, 2017. "A spatial analysis on the determinants of tourism performance in Japanese Prefectures," Asia-Pacific Journal of Regional Science, Springer, vol. 1(1), pages 243-264, April.
    • Handle: RePEc:spr:apjors:v:1:y:2017:i:1:d:10.1007_s41685-017-0038-0
    DOI: 10.1007/s41685-017-0038-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s41685-017-0038-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s41685-017-0038-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hjalager, Anne-Mette, 2010. "A review of innovation research in tourism," Tourism Management, Elsevier, vol. 31(1), pages 1-12.
    2. Francesco Capone & Rafael Boix, 2008. "Sources of growth and competitiveness of local tourist production systems: an application to Italy (1991–2001)," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(1), pages 209-224, March.
    3. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    4. James Lesage & Manfred Fischer, 2008. "Spatial Growth Regressions: Model Specification, Estimation and Interpretation," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(3), pages 275-304.
    5. Roberto Patuelli & Maurizio Mussoni & Guido Candela, 2016. "The Effects of World Heritage Sites on Domestic Tourism: A Spatial Interaction Model for Italy," Advances in Spatial Science, in: Roberto Patuelli & Giuseppe Arbia (ed.), Spatial Econometric Interaction Modelling, chapter 0, pages 281-315, Springer.
    6. Baltagi, Badi H. & Heun Song, Seuck & Cheol Jung, Byoung & Koh, Won, 2007. "Testing for serial correlation, spatial autocorrelation and random effects using panel data," Journal of Econometrics, Elsevier, vol. 140(1), pages 5-51, September.
    7. Marrocu, Emanuela & Paci, Raffaele, 2013. "Different tourists to different destinations. Evidence from spatial interaction models," Tourism Management, Elsevier, vol. 39(C), pages 71-83.
    8. Tamara de la Mata & Carlos Llano-Verduras, 2012. "Spatial pattern and domestic tourism: An econometric analysis using inter-regional monetary flows by type of journey," Papers in Regional Science, Wiley Blackwell, vol. 91(2), pages 437-470, June.
    9. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    10. Roberta Capello & Andrea Caragliu & Peter Nijkamp, 2011. "Territorial Capital And Regional Growth: Increasing Returns In Knowledge Use," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 102(4), pages 385-405, September.
    11. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    12. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    13. Manfred M. Fischer & Peter Nijkamp (ed.), 2014. "Handbook of Regional Science," Springer Books, Springer, edition 127, number 978-3-642-23430-9, April.
    14. Irani Arraiz & David M. Drukker & Harry H. Kelejian & Ingmar R. Prucha, 2010. "A Spatial Cliff‐Ord‐Type Model With Heteroskedastic Innovations: Small And Large Sample Results," Journal of Regional Science, Wiley Blackwell, vol. 50(2), pages 592-614, May.
    15. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    16. Raffaele Paci & Emanuela Marrocu, 2014. "Tourism and regional growth in Europe," Papers in Regional Science, Wiley Blackwell, vol. 93, pages 25-50, November.
    17. Ma, Tao & Hong, Tao & Zhang, Haozhe, 2015. "Tourism spatial spillover effects and urban economic growth," Journal of Business Research, Elsevier, vol. 68(1), pages 74-80.
    18. Kozak, Metin, 1999. "Destination competitiveness measurement: analysis of effective factors and indicators," ERSA conference papers ersa99pa289, European Regional Science Association.
    19. David M. Drukker & Peter Egger & Ingmar R. Prucha, 2013. "On Two-Step Estimation of a Spatial Autoregressive Model with Autoregressive Disturbances and Endogenous Regressors," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 686-733, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Taotao Deng & Yukun Hu, 2019. "Modelling China’s outbound tourist flow to the ‘Silk Road’: A spatial econometric approach," Tourism Economics, , vol. 25(8), pages 1167-1181, December.
    2. Ogechi Adeola & Olaniyi Evans, 2020. "ICT, infrastructure, and tourism development in Africa," Tourism Economics, , vol. 26(1), pages 97-114, February.
    3. Dimitrios TSIOTAS & Thomas KRABOKOUKIS & Serafeim POLYZOS, 2020. "Detecting Interregional Patterns In Tourism Seasonality Of Greece: A Principal Components Analysis Approach," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(2), pages 91-112, June.
    4. Chen, Liang & Guo, Yirong, 2023. "Revisiting resources extraction perspective in determining the tourism industry: Globalisation and human capital for next-11 economies," Resources Policy, Elsevier, vol. 85(PA).
    5. Dimitrios Tsiotas & Thomas Krabokoukis & Serafeim Polyzos, 2021. "Detecting Tourism Typologies of Regional Destinations Based on Their Spatio-Temporal and Socioeconomic Performance: A Correlation-Based Complex Network Approach for the Case of Greece," Tourism and Hospitality, MDPI, vol. 2(1), pages 1-27, February.
    6. Martin Thomas Falk & Eva Hagsten & Xiang Lin, 2022. "Domestic tourism demand in the North and the South of Europe in the Covid-19 summer of 2020," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(2), pages 537-553, October.
    7. João Romão & Peter Nijkamp, 2017. "Special issue on innovation and ecology: regional science perspectives on spatial systems," Asia-Pacific Journal of Regional Science, Springer, vol. 1(1), pages 49-52, April.
    8. Comerio, Niccolò & Pacicco, Fausto & Serati, Massimiliano, 2020. "An analysis of sub-national tourism in Japan: Tourist and economic spillovers and their determinants," Annals of Tourism Research, Elsevier, vol. 85(C).
    9. Marta Lisiak-Zielińska & Agnieszka Ziernicka-Wojtaszek, 2020. "Spatial Variation in Tourism and Investment Potential in the Context of Sustainable Development—A Case Study of Staszowski County," Sustainability, MDPI, vol. 13(1), pages 1-20, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. João Romão & João Guerreiro & Paulo M. M. Rodrigues, 2017. "Territory and Sustainable Tourism Development: a Space-Time Analysis on European Regions," REGION, European Regional Science Association, vol. 4, pages 1-17.
    2. Ariane Amin & Johanna Choumert, 2015. "Development and biodiversity conservation in Sub-Saharan Africa: A spatial analysis," Economics Bulletin, AccessEcon, vol. 35(1), pages 729-744.
    3. Álvarez, Inmaculada C. & Barbero, Javier & Zofío, José L., 2017. "A Panel Data Toolbox for MATLAB," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i06).
    4. Marques, André M., 2022. "Is income inequality good or bad for growth? Further empirical evidence using data for all Brazilian cities," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 360-376.
    5. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    6. Paolo Postiglione & Alfredo Cartone & Domenica Panzera, 2020. "Economic Convergence in EU NUTS 3 Regions: A Spatial Econometric Perspective," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    7. João Fernando Guerreiro Romão, 2016. "Regional Tourism Dynamics in Japan: An Exploratory Spatial Analysis," CEFAGE-UE Working Papers 2016_02, University of Evora, CEFAGE-UE (Portugal).
    8. Bernardina Algieri & Antonio à lvarez, 2023. "Assessing the ability of regions to attract foreign tourists: The case of Italy," Tourism Economics, , vol. 29(3), pages 788-811, May.
    9. Ana Catarina Vieira & Luis Delfim Santos, 2017. "Tourism and regional development: a spatial econometric model for Portugal at municipal level," FEP Working Papers 589, Universidade do Porto, Faculdade de Economia do Porto.
    10. Harald Badinger & Peter Egger, 2015. "Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(1), pages 11-35, March.
    11. Piras, Gianfranco & Prucha, Ingmar R., 2014. "On the finite sample properties of pre-test estimators of spatial models," Regional Science and Urban Economics, Elsevier, vol. 46(C), pages 103-115.
    12. Luis Delfim Santos & Ana Catarina Vieira, 2020. "Tourism and regional development: a spatial econometric model for Portugal at municipal level," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 19(3), pages 285-299, September.
    13. Taşpınar, Süleyman & Doğan, Osman & Bera, Anil K., 2017. "GMM gradient tests for spatial dynamic panel data models," Regional Science and Urban Economics, Elsevier, vol. 65(C), pages 65-88.
    14. Doğan, Osman & Taşpınar, Süleyman, 2014. "Spatial autoregressive models with unknown heteroskedasticity: A comparison of Bayesian and robust GMM approach," Regional Science and Urban Economics, Elsevier, vol. 45(C), pages 1-21.
    15. Herrera Gómez, Marcos, 2017. "Fundamentos de Econometría Espacial Aplicada [Fundamentals of Applied Spatial Econometrics]," MPRA Paper 80871, University Library of Munich, Germany.
    16. Martin Thomas Falk & Eva Hagsten & Xiang Lin, 2022. "Domestic tourism demand in the North and the South of Europe in the Covid-19 summer of 2020," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(2), pages 537-553, October.
    17. Demidova, Olga, 2021. "Methods of spatial econometrics and evaluation of government programs effectiveness," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 107-134.
    18. David Boto‐García & Antonio Alvarez & José Baños, 2021. "Modelling heterogeneous preferences for nature‐based tourism trips," Papers in Regional Science, Wiley Blackwell, vol. 100(6), pages 1625-1653, December.
    19. Baltagi, Badi H. & Yang, Zhenlin, 2013. "Heteroskedasticity and non-normality robust LM tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 725-739.
    20. Harald Badinger & Peter Egger, 2013. "Estimation and testing of higher-order spatial autoregressive panel data error component models," Journal of Geographical Systems, Springer, vol. 15(4), pages 453-489, October.

    More about this item

    Keywords

    Tourism performance; Spatial econometrics; Regional analysis; Specialization;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:apjors:v:1:y:2017:i:1:d:10.1007_s41685-017-0038-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.