000			00595nab|a22002177a|4500
999			_c62584 _d62576
001			62584
003			MX-TxCIM
005			20200921230240.0
008			200910s2020||||xxu|||p|op||||00||0|eng|d
022			_a1750-6816
022			_a1750-6824 (Online)
024	8		_ahttps://doi.org/10.1093/reep/rez023
040			_aMX-TxCIM
041			_aeng
100	0		_aMeha Jain _93814
245	1	4	_aThe benefits and pitfalls of using satellite data for causal inference
260			_aUSA : _bOxford University Press, _c2020.
500			_aPeer review
520			_aThere has been growing interest in using satellite data in environmental economics research. This is because satellite data are available for any region across the globe, provide frequent data over time, are becoming available at lower cost, and are becoming easier to process. While satellite data have the potential to be a powerful resource, these data have their own sources of biases and error, which could lead to biased inference, even if analyses are otherwise well-identified. This article discusses the potential benefits and pitfalls of using satellite data for causal inference, focusing on the more technical aspects of using satellite data. In particular, I discuss why it is critical for researchers to understand the error distribution of a given satellite data product and how these errors may result in biased inference. I provide examples of some common types of error, including nonrandom misclassification, saturation effects, atmospheric effects, and cloud cover. If researchers recognize and account for these potential errors and biases, satellite data can be a powerful resource, allowing for large-scale analyses that would otherwise not be possible.
546			_aText in English
650		7	_2AGROVOC _912655 _aSatellite observation
650		7	_2AGROVOC _99002 _aData
650		7	_2AGROVOC _99142 _aResearch
650		7	_2AGROVOC _98619 _aEnvironmental Economics
773	0		_tReview of Environmental Economics and Policy _gv. 14, no. 1, p. 157-169 _dUSA : Oxford University Press, 2020. _x1750-6816
942			_cJA _n0 _2ddc