Temperature and temperament: Evidence from Twitter
Temperature and temperament: Evidence from Twitter
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DOI：
10.1016/j.jpubeco.2020.104161
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发表时间：
2020-04-01
影响因子：
9.8
通讯作者：
Baylis, Patrick
中科院分区：
文献类型：
--
作者：
Baylis, Patrick
人们如何评价他们的气候？这篇论文展示了一种使用社交媒体数据估计非市场商品偏好的新方法。我将10多亿条Twitter更新与自然语言处理算法结合起来，构建了一个丰富的小组数据集，其中包含了美国和世界上其他六个英语国家的情绪表达。在美国，我发现无论是高温还是寒冷，人们表达的情绪都出现了持续的、在统计上显著下降的现象。为了更好地理解偏好如何适应，我记录了地区和季节性反应的异质性。我用一套验证练习来完成美国的分析，以了解这些影响的程度，并用两种方法来估计气候便利设施的支付意愿。最后，我记录了6个非美国国家中4个国家的温度与表达情绪之间的类似关系。我考察过的国家。(C)2020爱思唯尔B.V.保留所有权利。
How do people value their climate? This paper demonstrates a new approach to estimating preferences for non-market goods using social media data. I combine more than a billion Twitter updates with natural language processing algorithms to construct a rich panel dataset of expressed sentiment for the United States and six other English-speaking countries around the world. In the U.S., I find consistent and statistically significant declines in expressed sentiment from both hot and cold temperatures. To better understand how preferences may adapt, I document heterogeneity in both regional and seasonal responses. I complete the U.S. analysis with a suite of validation exercises to understand the magnitude of these effects and two methods to estimate willingness-to-pay for climate amenities. Finally, I document similar relationships between temperature and expressed sentiment for four out of the six non-US. countries I examine. (C) 2020 Elsevier B.V. All rights reserved.