在经济学中气象数据通常是工具变量的很好来源,例如近些年很热门的逆温数据!比如JDE2020的这篇:
空气污染对体重的影响——来自中国的证据The effect of air pollution on body weight and obesity: Evidence from China
Olivier Deschenes a, Huixia Wang b, Si Wang c, Peng Zhang d,*
a Department of Economics, University of California, Santa Barbara, IZA, and NBER, USA
b School of Economics and Trade, Hunan University, China
c Center for Economics, Finance, and Management Studies, Hunan University, China
d School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, and Shenzhen Finance Institute, China
提供了第一个评估空气污染对体重和肥胖的因果影响的研究。利用《中国健康与营养调查》(China Health and Nutrition Survey),发现空气污染对体重有显著的积极影响,该调查涵盖了1989年至2015年期间13741名成年人的详细纵向健康和社会经济信息。具体来说,过去12个月PM2.5平均浓度每增加1(1.54%),体重指数就会增加0.27%,超重率和肥胖率也会分别增加0.82和0.27个百分点。我们还发现,这些影响可以部分解释为各种行为渠道,包括身体活动减少,步行上班或上学的时间减少,睡眠减少,脂肪摄入增加。
4.2. Air pollution
Our data on air pollution are from the satellite-based Aerosol Optical Depth (AOD) retrievals. In particular, we obtain the AOD data from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) from NASA. The data are available at a 50*60-km grid level for each month since 1980. We calculate the concentration of PM2.5 following the formula provided by Buchard et al. (2016). We then aggregate from grid to county for each month13 and further average to the 12-month exposure window. This dataset has been used in previous studies (Chen et al., 2017; Fu et al., 2017), and validated with ground-based pollution data in China (Chen et al., 2017). We do not use ground-based pollution data mainly because they are only available after 2000 and cover only a few cities.
4.2 空气污染
我们的大气污染数据来自卫星气溶胶光学深度(AOD)反演(检索)。特别是,我们从NASA的现代回顾性研究和应用版本2 (MERRA-2)中获得了AOD数据。自1980年以来,每月可获得50*60公里网格水平的数据。我们采用Buchard et al.(2016)提供的公式计算PM2.5浓度。然后我们将每个月从网格汇总到县,再进一步平均到12个月的暴露窗口。之前的研究使用了该数据集(Chen et al., 2017;Fu et al., 2017),并利用中国的地面污染数据进行验证(Chen et al., 2017)。我们不使用基于地面的污染数据,主要是因为这些数据只在2000年后才能得到,而且只覆盖了少数几个城市。
4.3. Thermal inversions
We also obtain the thermal inversions data from MERRA-2. The data report air temperature for each 50*60-km grid for 42 atmospheric layers, ranging from 110 m to 36,000 m. The data are available at 6-h periods from 1980 onwards. We aggregate all data from grid to county using the same method used for the air pollution data. We determine the existence of a thermal inversion if the temperature in the second layer (320 m) is higher than that of the first layer (110 m) for each 6-h period, and then aggregate the number of inversions to the 12-month exposure window.
4.3 逆温
我们还获得了MERRA-2的热反演(逆温)数据。该数据报告了42个大气层的50*60公里网格的气温,范围从110米到36000米。这些数据从1980年起每6小时提供一次。我们使用与空气污染数据相同的方法来汇总从网格到县的所有数据。如果第二层(320m)的温度高于第一层(110 m)的温度,则我们确定存在热反演(逆温),然后将反演(逆温)的数量合计到12个月的暴露窗口。