{"type":"script","doc_desc":{"producers":[{"name":"Reyes Retana","abbr":"MRR","affiliation":"World Bank","role":"Junior Data Scientist"}],"version":"1","prod_date":"2023-08-30"},"project_desc":{"title_statement":{"idno":"RR_EUE_2023_PRWP-10552_v01","title":"Reproducibility package for Greener Is Not Always Pricier: Ecolabeling and Price Premium in the Tourism Industry"},"authoring_entity":[{"name":"Ridwan Bolaji Bello","email":"rbello@worldbank.org ","affiliation":"World Bank Group"},{"name":"Olanrewaju Kassim","affiliation":"World Bank Group","email":"okassim@worldbank.org "},{"name":"Sodiq Oladayo Bello","affiliation":"University of Ilorin Nigeria","email":"sodiqoladayobello@gmail.com "}],"output":[{"type":"Working Paper","title":"Greener Is Not Always Pricier: Ecolabeling and Price Premium in the Tourism Industry","authors":"Ridwan Bolaji Bello, Olanrewaju Kassim, Sodiq Oladayo Bello","description":"Policy Research Working Paper (PRWP) 10552","doi":"https:\/\/doi.org\/10.1596\/1813-9450-10552"}],"datasets":[{"name":"Hotel room pricing and attributes ","note":"This data was constructed by the authors by scraping data from Booking.com. The authors searched for accommodation options for two adult guests for a one-night stay exactly one month away from the date of the search. Once the browser returned the search results, the authors used Python to extract information on all the hotel listings in the search results. Performing this data collection procedure for Amsterdam, Barcelona, Brussels, Copenhagen, Lisbon, London, Paris, Stockholm, Venice, and Vienna, the data amounts to 6,262 hotels. These cities represent important international tourism markets and are among the top 100 travel destination cities in the world. This refers to hotel_data in the code.","access_type":"The data from booking.com is publicly available but cannot be reshared. Repository and details provided in the README file."},{"name":"Tourism Competitiveness","note":"The tourism competitiveness data comes from the Top 100 City Destination Index 2021, an index freely\nand publicly downloadable","access_type":"Public - Published with package","uri":"https:\/\/www.euromonitor.com\/","license_uri":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode","license":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License"}],"software":[{"name":"Stata","version":"17"},{"name":"Python","version":"3.11"}],"scripts":[{"file_name":"RR_EUE_2023_PRWP-10552_prg_v01.zip","zip_package":"RR_EUE_2023_PRWP-10552_prg_v01.zip","title":"Reproducibility package (data and code) for Greener Is Not Always Pricier: Ecolabeling and Price Premium in the Tourism Industry","date":"2023-08","software":"Stata, Python","description":"The reproducibility package contains a Stata do file, which performs all necessary calculations to produce the results in the paper using the data present. Also, it has a Python script used to construct some of the figures in the study. ","instructions":"See README in reproducibility package","source_code_repo":"Reproducible Research Repository (World Bank)","notes":"Computational reproducibility verified by Development Impact (DIME) Analytics team, World Bank.","dependencies":"Stata: outreg2 coefplot\nPython: os, pandas, seaborn, matplotlib.pyplot, numpy"}],"production_date":"2023-08","abstract":"Voluntary ecolabeling programs have gained popularity in the tourism industry as initiatives for promoting ecofriendly practices among tourism firms. Yet, for these programs to appeal to firms, it is crucial that they generate positive market benefits for ecolabeled firms. This paper studies the effect of a sustainable tourism label on prices of hotel firms. It uses hotel listing data collected from Booking.com and covering more than 6,000 hotels across 10 popular European cities. The finding show that the presence of the ecolabel is associated with a price premium of approximately 10 percent in the full sample. However, point estimates of this premium vary across cities, from a low of 1 percent in Venice to a high of 22 percent in London. As a novel contribution, the paper shows that the ecolabel delivers a quantitatively and statistically significant price premium only in cities where tourism (destination) competitiveness is high and ecolabel attainment is low. The paper discusses the implications of these findings for firms and policy makers in the industry.","geographic_units":[{"name":"Europe","code":"EUE","type":"reg"}],"keywords":[{"name":"Ecolabel"},{"name":"Sustainable tourism"},{"name":"Hotel"},{"name":"Hedonic pricing model"},{"name":"City"},{"name":"Tourism "}],"topics":[{"id":"D40","name":"Market Structure, Pricing, and Design","parent_id":"D4","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel"},{"id":"L83","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","name":"Sports \u2022 Gambling \u2022 Restaurants \u2022 Recreation \u2022 Tourism","parent_id":"L8"},{"id":"Q50","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel","name":"Environmental Economics","parent_id":"Q5"}],"language":[{"name":"English","code":"EN"}],"data_statement":"There are two datasets used for this package, one is public and available in the package. The data from booking.com is publicly available but cannot be reshared. The code used to scrape this data is available in the README file. ","methods":[{"name":"Ordinary Least Squares regression (OLS) "}],"repository_uri":[{"name":"Reproducible Research Repository (World Bank)","uri":"https:\/\/reproducibility.worldbank.org"}],"reproduction_instructions":"A README file with detailed instructions is part of the reproducibility package.","technology_environment":"\u2022 Computer 1:\n\u2013 OS: Windows 10 Enterprise, version 21H2\n\u2013 Processor: Intel(R) Core(TM) i7-8665U CPU @ 1.90GHz 2.11 GHz\n\u2013 Memory available: 15.8 GB\n\u2013 Software version: Stata 16.1, Python 3.7\n\u2022 Computer 2:\n\u2013 OS: Windows 11 Home, Versions 22H2\n\u2013 Processor: Intel(R) Core(TM) i5-9300H CPU @ 2.40GHz 2.40 GHz\n\u2013 Memory available: 7.85 GB\n\u2013 Software version: Stata 17.01 1 \n*Only the Stata part of the code was run\nin this computer\n\u2022 Computer 3:\n\u2013 OS: MacOS Ventura 13.4\n\u2013 Processor: Dual-Core Intel Core i3, 1.1 GHz\n\u2013 Memory available: 8 GB 3733 MHz LPDDR4X\n\u2013 Software version: Stata 16.1, Python 3.11.","disclaimer":"The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development\/the World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.","license":[{"name":"MIT License","uri":"https:\/\/opensource.org\/license\/mit\/"}],"contacts":[{"email":"rbello@worldbank.org","affiliation":"World Bank Group","name":"Ridwan Bolaji Bello"},{"name":"Olanrewaju Kassim","email":"okassim@worldbank.org","affiliation":"World Bank Group"},{"email":"reproducibility@worldbank.org","name":"Reproducibility WBG","affiliation":"World Bank Group"}]},"tags":[{"tag":"DOI"},{"tag":"Open code"},{"tag":"Restricted data"}],"schematype":"script"}