Abstract
Credible empirical estimation of the economic impacts of climate change is dependent on data structure (e.g., cross sectional, panel) and the functional relationship between weather data and behavioral outcomes. We show here how these modeling decisions lead to significantly different results when estimating the effects of weather and simulating the potential welfare impacts of climate change on outdoor recreation. Using participation data from 1.6 million households in the United States from 2004 to 2009, we estimate the impact of temperature and precipitation on participation decisions for marine shoreline recreational fishing. Results from linear models suggest temperature positively impacts participation and, by implication, climate change is likely to improve welfare associated with outdoor recreation in all regions of our study area. Conversely, nonlinear specifications suggest more days with extreme heat reduce participation and lead to significant declines in welfare under future climate scenarios. Differences in the treatment of how weather enters recreation participation decisions change both the sign and magnitude of welfare effects by nearly $1 billion annually. Differences in data structure, however, only affect the magnitude of welfare impacts but not the sign. Disaggregation of welfare estimates suggests warmer baseline climates are more susceptible to these choices. Our results demonstrate the critical nature of modeling decisions about data structure and the use of weather data to assess the future impacts of climate change, especially with nonmarket goods where value is related to environmental quality such as outdoor recreation.
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Notes
We have data from 2004 to 2009 that is disaggregated into two month intervals (36 waves), where wave 1 is January/February, wave 2 is March/April, wave 3 is May/June, wave 4 is July/August, wave 5 is September/October and wave 6 is November/December.
We only identify non-participants by county from the MRIP data. To disaggregate non-participants to the six-digit phone exchange, we exploit the random-digit-dial format of the survey to allocate these observations following Dundas et al. (2018).
We calculate operation costs using national averages for fleet fuel economy from the U.S. Department of Transportation and automobile per-mile operation costs including tires, depreciation and maintenance from AAA, and state-level gas prices from the U.S. Energy Information Administration. Opportunity cost of travel time is defined using zip code-level median household income from the U.S. Census Bureau to calculate a wage rate and dividing that by 1/3. Since the average party size per trip is 2.73, we divide shared costs (tolls, gas, mileage) across all individuals in each party. See Dundas et al. (2018) and Dundas and von Haefen (2020) for additional information.
The interested reader is referred to Dundas and von Haefen (2020) for a full exposition of the modeling framework and specifics on the site choice model.
In this first stage, we allow travel costs to vary by region. The travel cost coefficients (reported in table 1 of Dundas and von Haefen (2020)) are more negative in the Gulf region, suggesting recreators there are more sensitive to travel costs.
We do not observe choices for the same households over time but do observe behavior from different households in the same spatial unit over time.
Based on two meta-analyses of recreation studies, $30 best approximates the value of a coastal shoreline fishing trip (Moeltner and Rosenberger 2014; Johnston and Moeltner 2014).
These simulations are complex exercises that require extensive computing power. 200 draws were chosen as a reasonable number of iterations given available computing resources. For example, expanding our simulations to 2,000 draws would require > 100 h of computing time for each scenario.
The interested reader is referred to Dundas and von Haefen (2020) for further details.
Regional variation in predicted future temperature from GCM output is not likely a driver of our regional heterogeneity in our welfare results. The average predicted temperature changes for each region are relatively similar: the Gulf ranged from 2.7°F (2049) to 7.8°F (2099), the Southeast from 2.9°F (2049) to 8.4°F (2099), and both New England and the Mid-Atlantic from 3.3°F (2049) to 9.4°F (2099).
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We acknowledge support of this research from the USDA National Institute of Food and Agriculture Hatch Multi-state project W-4133.
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Data and replication code are available at Figshare: https://doi.org/10.6084/m9.figshare.13368818.v1
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Dundas, S.J., von Haefen, R.H. The importance of data structure and nonlinearities in estimating climate impacts on outdoor recreation. Nat Hazards 107, 2053–2075 (2021). https://doi.org/10.1007/s11069-020-04484-w
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DOI: https://doi.org/10.1007/s11069-020-04484-w