Journal
ENVIRONMENTAL & RESOURCE ECONOMICS
Volume 76, Issue 4, Pages 1161-1185Publisher
SPRINGER
DOI: 10.1007/s10640-020-00489-y
Keywords
COVID-19; Google mobility data; Latent class regression; Recreation demand model; Non-market valuation
Categories
Funding
- NERC [NE/P011217/1] Funding Source: UKRI
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The COVID-19 outbreak resulted in unprecedented restrictions on citizen's freedom of movement as governments moved to institute lockdowns designed to reduce the spread of the virus. While most out-of-home leisure activities were prohibited, in England the lockdown rules allowed for restricted use of outdoor greenspace for the purposes of exercise and recreation. In this paper, we use data recorded by Google from location-enabled mobile devices coupled with a detailed recreation demand model to explore the welfare impacts of those constraints on leisure activities. Our analyses reveals evidence of large-scale substitution of leisure time towards recreation in available greenspaces. Indeed, despite the restrictions the economic value of greenspace to the citizens of England fell by only 150 pound million over lockdown. Examining the outcomes of counterfactual policies we find that the imposition of stricter lockdown rules would have reduced welfare from greenspace by 1.14 pound billion. In contrast, more relaxed lockdown rules would have delivered an aggregate increase in the economic value of greenspace equal to 1.47 pound billion.
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