using web-sourced data to estimate demand for climate ...€¦ · using web sourced data to...
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Bryan [email protected]
Slide: 1/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Using Web-Sourced Data to Estimate Demand for Climate Amenities
Bryan Parthum
Camp Resources
Asheville, NCTuesday, August 13th, 2019
Using Web-Sourced DataData
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Bryan [email protected]
Slide: 2/17Using Web Sourced Data to Estimate Demand for Climate Amenities
“Web scraping, or scraping, is a computer software technique of extracting information from the internet, usually transforming unstructured data on the web into structured data that can be stored and analyzed in a central database.”
- distilnetworks.com
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Bryan [email protected]
Slide: 3/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Bots (1,000 RA’s)
• Crawlers• From a given starting point, navigate (and record) every forward url on the page
(RA’s: create list of relevant literature)
• Scrapers• Collect information from a page
(RA’s: summarize literature and main findings)
• Interactive• Everything from navigating a page to put information into cells, to having a
‘conversation’ with a human. (RA’s: write the first draft of your paper - without co-authorship)
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Bryan [email protected]
Slide: 4/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Rules
• The web is public domain (kind of, for now)
• Legal precedent is mixed • (eBay v. Bidder’s Edge; Intel v. Hamidi; AP v. Meltwater; LinkedIn v. hiQ)
• Intellectual property, proper citing, etc. etc. still apply• If you’re curious about IRB approval, then you should get IRB approval
• Site owners (or providers) can shut down excess traffic
• Bots create lots of traffic (~43% of internet traffic!?)• Keep this in mind when designing a bot. Space out timing of requests, etc.
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Bryan [email protected]
Slide: 5/17Using Web Sourced Data to Estimate Demand for Climate Amenities
• Baylis et al. Weather impacts expressed sentiment. 2018. PloS One.
• Kramer, Guillory, Hancock. Experimental evidence of massive-scale emotional contagion through social networks. 2014. PNAS.
Application: API (application programming interface)
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Bryan [email protected]
Slide: 6/17Using Web Sourced Data to Estimate Demand for Climate Amenities
• Zheng et al. Air pollution lowers Chinese urbanites’ expressed happiness on social media. 2019. Nature Human Behavior.
Application: API (application programming interface)
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Bryan [email protected]
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Application: API (application programming interface)
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Bryan [email protected]
Slide: 8/17Using Web Sourced Data to Estimate Demand for Climate Amenities
#CampResources2019
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Bryan [email protected]
Slide: 9/17Using Web Sourced Data to Estimate Demand for Climate Amenities
• Phadera, L. Unfortunate Moms and Unfortunate Children: Impact of the Nepali Civil War on Women's Stature and Intergenerational Health. 2019. World Bank Group. (scraper built by Mateus Souza)
Application: Scraping
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Bryan [email protected]
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Application: Scraping
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Bryan [email protected]
Slide: 11/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Application: Scraping
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Bryan [email protected]
Slide: 12/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Application: Active Scraping
• Myers et al. Decomposing the Wedge: Evidence from a Home Weatherization Program.
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Bryan [email protected]
Slide: 13/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Application: Active Scraping
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Bryan [email protected]
Slide: 14/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Application: Interactive
• Christensen and Timmins. The Welfare Effect of Racial Discrimination in the US Rental Market for Housing.
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Bryan [email protected]
Slide: 15/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Application: Interactive
• Christensen and Timmins. The Welfare Effect of Racial Discrimination in the US Rental Market for Housing.
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Bryan [email protected]
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Application: Interactive
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Bryan [email protected]
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Application: Interactive
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Bryan [email protected]
Slide: 18/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Application: Interactive
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Bryan [email protected]
Slide: 19/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Application: Tracking
• Christensen and Osman. The Demand for Mobility in a Transit-Constrained City: A Field Experiment with Uber in Cairo.
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Bryan [email protected]
Slide: 20/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Application: Tracking
• Christensen and Osman. The Demand for Mobility in a Transit-Constrained City: A Field Experiment with Uber in Cairo.
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Bryan [email protected]
Slide: 21/17Using Web Sourced Data to Estimate Demand for Climate Amenities
• Parthum and Christensen. The Price of Powder: Evidence on the Demand for Snow from Short Term Property Rentals. Scraping done by Airdna.co
Application: Dynamic Scraping
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Bryan [email protected]
Slide: 22/17Using Web Sourced Data to Estimate Demand for Climate Amenities
• Parthum and Christensen. The Price of Powder: Evidence on the Demand for Snow from Short Term Property Rentals.
Application: Dynamic Scraping
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Bryan [email protected]
Slide: 23/17Using Web Sourced Data to Estimate Demand for Climate Amenities
• Parthum and Christensen. The Price of Powder: Evidence on the Demand for Snow from Short Term Property Rentals.
Application: Dynamic Scraping
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Bryan [email protected]
Slide: 24/17Using Web Sourced Data to Estimate Demand for Climate Amenities
• Parthum and Christensen. The Price of Powder: Evidence on the Demand for Snow from Short Term Property Rentals.
Application: Dynamic Scraping
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Bryan [email protected]
Slide: 25/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Application: Dynamic Scraping
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Bryan [email protected]
Slide: 26/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Application: Dynamic Scraping
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Bryan [email protected]
Slide: 27/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Application: Dynamic Scraping
Resort Snowpack (daily)Price of Short Term Rental (daily)
Den
sity
Cou
nt
Snowpack (in.)Price ($USD)
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Bryan [email protected]
Slide: 28/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Resources• Introduction to Python
• Python• Great resources for complete beginners. Experience with a keyboard
and mouse is helpful.
• Text editors• Spyder
• This one gives you the familiar comfort of a Stata or Rstudioenvironment, which is nice for getting started. (“where’s my .do file?”)
• Sublime• CS kids seem to like this one
• Some places where you can find help for building your own bot• Hackernoon – Beautifulsoup to JSON• Codeburst – Beautifulsoup to .csv
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Bryan [email protected]
Slide: 29/17Using Web Sourced Data to Estimate Demand for Climate Amenities
References• Baylis et al. Weather impacts expressed sentiment. 2018. PloS One.
• Kramer, Guillory, Hancock. Experimental evidence of massive-scale emotional contagion through social networks. 2014. PNAS.
• Zheng et al. Air pollution lowers Chinese urbanites’ expressed happiness on social media. 2019. Nature Human Behavior.
• Phadera, L. Unfortunate Moms and Unfortunate Children: Impact of the Nepali Civil War on Women's Stature and Intergenerational Health. 2019. World Bank Group. (scraper built by Mateus Souza)
• Myers, Erica, Peter Christensen, Paul Francisco, Mateus Souza. Decomposing the Wedge: Evidence from a Home Weatherization Program.
• Christensen and Timmins. The Welfare Effect of Racial Discrimination in the US Rental Market for Housing.
• Christensen and Osman. The Demand for Mobility in a Transit-Constrained City: A Field Experiment with Uber in Cairo.
• Parthum and Christensen. The Price of Powder: Evidence on the Demand for Snow from Short Term Property Rentals.
• Big Data in Environmental Economics and Policy Research Group (BDEEP)
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Bryan [email protected]
Slide: 30/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Bryan Parthum Peter Christensen
Camp Resources
Asheville, NCTuesday, August 13th, 2019
The Price of Powder: Evidence on the demand for snow from short term property rentals
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Bryan [email protected]
Slide: 31/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Motivation: Wintertime recreation is greatly threatened by climate change
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Bryan [email protected]
Slide: 32/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Large Spatial and Temporal Variation
60%10%
Lost Snowpack (%)
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Bryan [email protected]
Slide: 33/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Research Questions
What is the willingness to pay (accept) for snowpack in ski resort markets?
Positive demand for snow We estimate an average marginal utility of snowpack of 0.8. At the means, this implies a WTP of $5 for one inch of resort snowpack.
Markets are heterogeneous We find marginal utilities are spatially heterogeneous, and vary at the state-level between 0.07 and 2.4.
What are the welfare implications of climate change for these consumers?
Climate change is bad We estimate current consumer surplus to be $32 billion per ski season from snowpack alone. Damages under two future scenarios (RCP4.5 and RCP8.5) are estimated to be between $7 billion to $14 billion per ski season, or 30% to 60% of current levels.
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Bryan [email protected]
Slide: 34/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Marginal Willingness to Pay for snow• Morey (1981/84) kicks off the ski literature estimating share equations for Colorado resorts.
Elasticities for annual snowfall are mixed in sign. • Englin and Moeltner (2003/04) use survey data from the Tahoe region in California, include
demand estimates relating to weekly snowfall, and estimate elasticities around 0.2.
Climate change and recreational demand• Loomis and Crespi (1999), Mendelsohn and Markowski (1999) both estimate reductions in
number of trips (52% and 39%) to estimate effects of climate change on skiing. Explicitly omit marginal values of snowpack and simply estimate effect of shorter seasons on number of trips (~$1.4b per year).
• Dundas and von Haefen (2019) discuss the importance of incorporating substitution into recreation demand models (recreational fishing)
Methods• von Haefen and Domanski (2017): Estimating recreation demand with large choice sets• Barry, Levinsohn, and Pakes (1995): Market share inversion to estimate own and cross
elasticities
Previous Work
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Bryan [email protected]
Slide: 35/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Data
Period• August 2014 – May 2017• Daily observations
Ski resorts• 247 ski resorts across 30 states• Daily snowpack, snowfall,
temperature
Short term property rentals• Price paid, date of stay and
reservation• 44k properties, ~7 million
observations
Ski tourist information• National Ski Area Association• Trip costs (lift ticket, flights, etc.)• Some skier demographics
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Bryan [email protected]
Slide: 36/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Random Utility
𝐷 = destination state 𝑘𝛿 = vector of new snowfall bins;
controls for temperature (cubic); holiday week (0,1)
Г = vector of prop characteristics (beds, baths, etc.)ѱ = state and season FE𝛾 = day-of-week (Sun.-Sat.) FE
𝑖 = individual j = alternative 𝑟 = resort; 𝑠 = season 𝑤 = weekday 𝑚 = month-of-sample
𝑉𝑖𝑗 = 𝜆 𝑡𝑟𝑖𝑝 𝑐𝑜𝑠𝑡𝑖𝑗 + 𝛽𝑘 ln 𝑠𝑛𝑜𝑤𝑝𝑎𝑐𝑘 𝑟𝑗 ⋅ 𝐷𝑘
+ 𝛿′𝑟𝑡 + Г′𝑖 + ѱ𝑠 + 𝛾𝑤 + 𝜖𝑖𝑗
𝑡𝑟𝑖𝑝 𝑐𝑜𝑠𝑡𝑖𝑡 = 𝑟𝑒𝑛𝑡𝑎𝑙 + 𝑙𝑖𝑓𝑡 𝑡𝑖𝑐𝑘𝑒𝑡 + ($0.33 ∗ 𝑚𝑖𝑙𝑒𝑠 𝑡𝑜 𝑚𝑒𝑡𝑟𝑜)
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Bryan [email protected]
Slide: 37/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Choice Sets
= Observed choice 𝑉𝑖𝑗
= Unobserved alternative 𝑉𝑖(−𝑗)
Substitution across space Substitution across space and time
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Bryan [email protected]
Slide: 38/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Results
Fixed Parameter Logit (speedglm)
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Bryan [email protected]
Slide: 39/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Marginal Utility of Snowpack
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Bryan [email protected]
Slide: 40/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Marginal Willingness to Pay for 1 inch of Snowpack
MW
TP0.75
0.15
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Bryan [email protected]
Slide: 41/17Using Web Sourced Data to Estimate Demand for Climate Amenities
Climate Data
• Climate Change
• Spatially downscaled estimates from USBR
• Snow-water equivalent (snowpack)
• 29 climate models
• Historical: 1950-2015
• Mid-century: 2040-2060
• Late-century: 2070-2099
• RCP4.5 and RCP8.5
• Ski tourist statistics
• National Ski Area Association
• Skier place of residence/destination
• Visitation
• Trip costs
Skier Demographics
Estimating Damages
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Bryan [email protected]
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Damages Under future Climate Scenarios
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Bryan [email protected]
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Damages Under future Climate Scenarios
• $7.3 to $14.6 billion per season
• 30%-60% of current surplus
• Accounting for changes in visitation
increases damages by 25%-50%
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Bryan [email protected]
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Market Share Transformation (BLP, 1995)
• Pr 𝑗 𝑥, 𝜃 = 𝑠𝑗𝑡 =exp 𝛿𝑗𝑡
1+Σ exp 𝛿𝑘𝑡
• 𝛿𝑗𝑡 = 𝜆𝑝𝑗𝑡 + 𝑋𝑗𝑡′ 𝛽 + 𝜉𝑖𝑗
• 𝜉𝑖𝑗 = 𝑙𝑜𝑔𝑠𝑗𝑡 − log 𝑠0𝑡 − (−𝛼𝑝𝑗𝑡 + 𝑋𝑗𝑡′ 𝛽)
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Bryan [email protected]
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Market Share Transformation (BLP, 1995)
𝑉𝑖𝑗𝑡 = 𝜆𝑝𝑗𝑡 + 𝑋𝑗𝑡′ 𝛽 + 𝜉𝑖𝑗
−
𝑙=1
𝑑
𝜋1𝑙𝐷𝑖𝑙 + 𝜎1𝜐𝑖1 𝑝𝑗𝑡
+
𝑘=1
𝐾
𝑙=1
𝑑
𝜋(𝑘+1)𝑙𝐷𝑖𝑙 + 𝜎(𝑘+1)𝜐𝑖(𝑘+1) 𝑋𝑗𝑘′ + 𝜖𝑖𝑗𝑡
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Bryan [email protected]
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Results of BLP
Estimated using BLPestimatoR in R
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Bryan [email protected]
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Elasticities of Snowpack
𝜂𝑗𝑘𝑡 =𝜕𝑠𝑗𝑡
𝜕𝑥𝑘𝑡
𝑥𝑘𝑡
𝑠𝑗𝑡൝𝛽𝑥𝑗𝑡 1 − 𝑠𝑗𝑡 if 𝑗 = 𝑘
𝛽𝑥𝑘𝑡𝑠𝑘𝑡 if 𝑗 ≠ 𝑘
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Bryan [email protected]
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Own and Cross-snowpack Elasticities (state)
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Bryan [email protected]
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Thank You
www.BryanParthum.comEconTwitter: @bryanparthum