000			00595nab|a22002177a|4500
999			_c62165 _d62157
001			62165
003			MX-TxCIM
005			20200629214548.0
008			200624s2020||||xxk|||p|op||||00||0|eng|d
022			_a0305-750X
024	8		_ahttps://doi.org/10.1016/j.worlddev.2019.104796
040			_aMX-TxCIM
041			_aeng
100	1		_aDillon, A. _914373
245	1	0	_aGood identification, meet good data
260			_aOxford (United Kingdom) : _bElsevier, _c2020.
500			_aPeer review
520			_aCausal inference lies at the heart of social science, and the 2019 Nobel Prize in Economics highlights the value of randomized variation for identifying causal effects and mechanisms. But causal inference cannot rely on randomized variation alone; it also requires good data. Yet the data-generating process has received less consideration from economists. We provide a simple framework to clarify how research inputs affect data quality and discuss several such inputs, including interviewer selection and training, survey design, and investments in linking across multiple data sources. More investment in research on the data quality production function would considerably improve casual inference generally, and poverty alleviation specifically.
546			_aText in English
650		7	_aResearch _2AGROVOC _99142
650		7	_aData _2AGROVOC _99002
650		7	_aResearch methods _2AGROVOC _99171
700	1		_aKarlan, D. _914374
700	1		_aUdry, C. _914375
700	1		_aZinman, J. _914376
773	0		_gv. 127, art. 104796 _dOxford (United Kingdom) : Elsevier, 2020. _x0305-750X _tWorld Development _w444788
942			_cJA _n0 _2ddc