IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v186y2020ics0165176519304331.html
   My bibliography  Save this article

Unconditional quantile regression analysis of UK inbound tourist expenditures

Author

Listed:
  • Sharma, Abhijit
  • Woodward, Richard
  • Grillini, Stefano

Abstract

Using International Passenger Survey (2017) data, this paper employs unconditional quantile regression (UQR) to analyse the determinants of tourist expenditure amongst inbound tourists to the United Kingdom. UQR allows us to estimate heterogeneous effects at any quantile of the distribution of the dependent variable. It overcomes the econometric limitations of ordinary least squares and quantile regression based estimates typically used to investigate tourism expenditures. However, our results reveal that the effects of our explanatory variables change across the distribution of tourist expenditure. This has important implications for those tasked with devising policies to enhance the UK’s tourist flows and expenditures.

Suggested Citation

  • Sharma, Abhijit & Woodward, Richard & Grillini, Stefano, 2020. "Unconditional quantile regression analysis of UK inbound tourist expenditures," Economics Letters, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:ecolet:v:186:y:2020:i:c:s0165176519304331
    DOI: 10.1016/j.econlet.2019.108857
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176519304331
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2019.108857?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. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    2. Glick, Reuven & Rose, Andrew K., 2002. "Does a currency union affect trade? The time-series evidence," European Economic Review, Elsevier, vol. 46(6), pages 1125-1151, June.
    3. Hossain, Ishrat & Saqib, Najam U. & Haq, Munshi Masudul, 2018. "Scale heterogeneity in discrete choice experiment: An application of generalized mixed logit model in air travel choice," Economics Letters, Elsevier, vol. 172(C), pages 85-88.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Morley, Clive & Rosselló, Jaume & Santana-Gallego, Maria, 2014. "Gravity models for tourism demand: theory and use," Annals of Tourism Research, Elsevier, vol. 48(C), pages 1-10.
    6. Christer Thrane, 2014. "Modelling Micro-Level Tourism Expenditure: Recommendations on the Choice of Independent Variables, Functional Form and Estimation Technique," Tourism Economics, , vol. 20(1), pages 51-60, February.
    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. Luojia Wang & Kerui Du & Bin Fang & Rob Law, 2023. "Escape from air pollution: How does air quality in the place of residence shape tourism consumption?," Tourism Economics, , vol. 29(4), pages 1074-1099, June.
    2. Azam, Mehtabul, 2022. "Microeconomic Determinants of Domestic Tourism Expenditure in India," IZA Discussion Papers 15245, Institute of Labor Economics (IZA).
    3. Moslem Ansarinasab & Sayed Saghaian, 2023. "Outbound, Inbound and Domestic Tourism in the Post-COVID-19 Era in OECD Countries," Sustainability, MDPI, vol. 15(12), pages 1-19, June.

    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. Rudkin, Simon & Sharma, Abhijit, 2017. "Enhancing understanding of tourist spending using unconditional quantile regression," Annals of Tourism Research, Elsevier, vol. 66(C), pages 188-191.
    2. Korom, Philipp, 2016. "Inherited advantage: The importance of inheritance for private wealth accumulation in Europe," MPIfG Discussion Paper 16/11, Max Planck Institute for the Study of Societies.
    3. Huong Thu Le & Ha Trong Nguyen, 2018. "The evolution of the gender test score gap through seventh grade: new insights from Australia using unconditional quantile regression and decomposition," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 7(1), pages 1-42, December.
    4. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    5. Vincenzo Carrieri & Francesco Principe & Michele Raitano, 2018. "What makes you ‘super-rich’? New evidence from an analysis of football players’ wages," Oxford Economic Papers, Oxford University Press, vol. 70(4), pages 950-973.
    6. Jean-Marc Fournier & Isabell Koske, 2012. "The determinants of earnings inequality: evidence from quantile regressions," OECD Journal: Economic Studies, OECD Publishing, vol. 2012(1), pages 7-36.
    7. Ramos, Raul & Sanromá, Esteban & Simón, Hipólito, 2022. "Collective bargaining levels, employment and wage inequality in Spain," Journal of Policy Modeling, Elsevier, vol. 44(2), pages 375-395.
    8. Wang, Wen & Lien, Donald, 2018. "Union membership, union coverage and wage dispersion of rural migrants: Evidence from Suzhou industrial sector," China Economic Review, Elsevier, vol. 49(C), pages 96-113.
    9. Akwasi Ampofo, 2021. "Oil at work: natural resource effects on household well-being in Ghana," Empirical Economics, Springer, vol. 60(2), pages 1013-1058, February.
    10. Collischon Matthias, 2019. "Is There a Glass Ceiling over Germany?," German Economic Review, De Gruyter, vol. 20(4), pages 329-359, December.
    11. Sonja C. Kassenboehmer & Mathias G. Sinning, 2014. "Distributional Changes in the Gender Wage Gap," ILR Review, Cornell University, ILR School, vol. 67(2), pages 335-361, April.
    12. Deshpande, Ashwini & Goel, Deepti & Khanna, Shantanu, 2018. "Bad Karma or Discrimination? Male–Female Wage Gaps Among Salaried Workers in India," World Development, Elsevier, vol. 102(C), pages 331-344.
    13. Möller Joachim & Umkehrer Matthias, 2015. "Are there Long-Term Earnings Scars from Youth Unemployment in Germany?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(4-5), pages 474-498, August.
    14. Domenico Depalo & Raffaela Giordano & Evangelia Papapetrou, 2015. "Public–private wage differentials in euro-area countries: evidence from quantile decomposition analysis," Empirical Economics, Springer, vol. 49(3), pages 985-1015, November.
    15. Daniel D. Schnitzlein, 2016. "A New Look at Intergenerational Mobility in Germany Compared to the U.S," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 62(4), pages 650-667, December.
    16. Balestra, Simone & Backes-Gellner, Uschi, 2017. "Heterogeneous returns to education over the wage distribution: Who profits the most?," Labour Economics, Elsevier, vol. 44(C), pages 89-105.
    17. Bos, Iwan & Davies, Stephen & Harrington, Joseph E. & Ormosi, Peter L., 2018. "Does enforcement deter cartels? A tale of two tails," International Journal of Industrial Organization, Elsevier, vol. 59(C), pages 372-405.
    18. Graham, Bryan S. & Hahn, Jinyong & Poirier, Alexandre & Powell, James L., 2018. "A quantile correlated random coefficients panel data model," Journal of Econometrics, Elsevier, vol. 206(2), pages 305-335.
    19. Valentine Fays & Benoît Mahy & François Rycx, 2023. "Wage differences according to workers' origin: The role of working more upstream in GVCs," LABOUR, CEIS, vol. 37(2), pages 319-342, June.
    20. Gregory, Terry & Zierahn, Ulrich, 2022. "When the minimum wage really bites hard: The negative spillover effect on high-skilled workers," Journal of Public Economics, Elsevier, vol. 206(C).

    More about this item

    Keywords

    Tourist expenditures; Unconditional quantile regressions; United Kingdom;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • Z30 - Other Special Topics - - Tourism Economics - - - General

    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:eee:ecolet:v:186:y:2020:i:c:s0165176519304331. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.