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首页> 外文期刊>Journal of Development Economics >Which night lights data should we use in economics, and where?
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Which night lights data should we use in economics, and where?

机译：我们应该在经济学中使用哪个夜晚的灯光数据？

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Popular DMSP night lights data are flawed by blurring, top-coding, and lack of calibration. Yet newer and better VIIRS data are rarely used in economics. We compare these two data sources for predicting GDP, especially at the second subnational level, for Indonesia, China and South Africa. The DMSP data are a poor proxy for GDP outside of cities. The gap in predictive performance between DMSP data and VIIRS data is especially apparent at lower levels of the spatial hierarchy, such as for counties, and for lower density areas. The city lights-GDP relationship is twice as noisy with DMSP data than with VIIRS data. Spatial inequality is considerably understated with DMSP data, especially for the urban sector and in higher density areas. A Pareto adjustment to correct for top-coding in DMSP data has a modest effect but still understates spatial inequality and misses key features of economic activity in big cities.
机译：流行的DMSP夜灯数据存在模糊、顶部编码和缺乏校准的缺陷。然而，更新和更好的VIIRS数据很少用于经济学。我们比较了这两个数据源，以预测印度尼西亚、中国和南非的GDP，尤其是第二个国家以下级别的GDP。DMSP数据不能很好地代表城市以外的GDP。DMSP数据和VIIRS数据之间的预测性能差距在较低的空间层次上尤其明显，例如县和较低密度区域。DMSP数据与VIIRS数据相比，城市照明与GDP的关系噪音是VIIRS数据的两倍。DMSP数据大大低估了空间不平等，尤其是在城市部门和高密度地区。对DMSP数据中的顶层编码进行帕累托调整的效果有限，但仍然低估了空间不平等性，并忽略了大城市经济活动的关键特征。

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