000			03849cam a22004097 4500
001			w29070
003			NBER
005			20211020103104.0
006			m o d
007			cr cnu||||||||
008			210910s2021 mau fo 000 0 eng d
100	1		_aAiken, Emily.
245	1	0	_aMachine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance / _cEmily Aiken, Suzanne Bellue, Dean Karlan, Christopher R. Udry, Joshua Blumenstock.
260			_aCambridge, Mass. _bNational Bureau of Economic Research _c2021.
300			_a1 online resource: _billustrations (black and white);
490	1		_aNBER working paper series _vno. w29070
500			_aJuly 2021.
520	3		_aThe COVID-19 pandemic has devastated many low- and middle-income countries (LMICs), causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have mobilized targeted social assistance programs. Targeting is a central challenge in the administration of these programs: given available data, how does one rapidly identify the individuals and families with the greatest need? This challenge is particularly acute in the large number of LMICs that lack recent and comprehensive data on household income and wealth. Here we show that non-traditional "big" data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our approach uses traditional survey-based measures of consumption and wealth to train machine learning algorithms that recognize patterns of poverty in non-traditional data; the trained algorithms are then used to prioritize aid to the poorest regions and mobile subscribers. We evaluate this approach by studying Novissi, Togo's flagship emergency cash transfer program, which used these algorithms to determine eligibility for a rural assistance program that disbursed millions of dollars in COVID-19 relief aid. Our analysis compares outcomes - including exclusion errors, total social welfare, and measures of fairness - under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo at the time, the machine learning approach reduces errors of exclusion by 4-21%. Relative to methods that require a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine learning approach increases exclusion errors by 9-35%. These results highlight the potential for new data sources to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.
530			_aHardcopy version available to institutional subscribers
538			_aSystem requirements: Adobe [Acrobat] Reader required for PDF files.
538			_aMode of access: World Wide Web.
588	0		_aPrint version record
690		7	_aC55 - Large Data Sets: Modeling and Analysis _2Journal of Economic Literature class.
690		7	_aI32 - Measurement and Analysis of Poverty _2Journal of Economic Literature class.
690		7	_aI38 - Government Policy • Provision and Effects of Welfare Programs _2Journal of Economic Literature class.
690		7	_aO12 - Microeconomic Analyses of Economic Development _2Journal of Economic Literature class.
690		7	_aO38 - Government Policy _2Journal of Economic Literature class.
700	1		_aBellue, Suzanne.
700	1		_aKarlan, Dean. _927631
700	1		_aUdry, Christopher R.
700	1		_aBlumenstock, Joshua.
710	2		_aNational Bureau of Economic Research.
830		0	_aWorking Paper Series (National Bureau of Economic Research) _vno. w29070.
856	4	0	_uhttps://www.nber.org/papers/w29070
856			_yAcceso en línea al DOI _uhttp://dx.doi.org/10.3386/w29070
942			_2ddc _cW-PAPER
999			_c387759 _d346321