Geographic Targeting: Big Data Are Revolutionizing Poverty Mapping

How to Harness the Power of Data and Inference | 349

roads, and railroads) from satellite imagery or allow the relationship between mobile phone behavior and the phone owner’s income to be estimated. Even in the absence of big data, it is important to explore whether the new models may allow more accurate modeling of household income from traditional proxies better than the traditional regression models used in PMT.

Big data and machine learning offer the hope of improving certain aspects of some targeting methods, but they are not a panacea. Big data–driven targeting may sound revolutionary and accurate. Once translated from tech-speak to targeting lingo, it becomes clearer that what is meant is really referring to a poverty map and/or PMT based on different variables or algorithms. This framing makes it is easier to see that the usual questions must be answered. How easy is it to get the data? Do they measure or proxy welfare? How accurately do the formulae predict? How big are the prediction errors? What costs do the methods generate in the usual domains— administrative costs to the program, negative incentives, transaction costs, or stigma for the potential social protection claimant, with respect to political support to the program? How do all these compare with other options? Excitement must be calibrated based on the answers to these questions, which are being actively investigated with new information accruing rapidly but not yet definitively.

Geographic Targeting: Big Data Are Revolutionizing Poverty Mapping

Unlike the other targeting methods discussed in this chapter, geographic targeting does not try to be household or individual specific. Instead, it groups households together at a greater or lesser level of aggregation and supports a treatment differentiated between the resulting groups. It is often used as the first stage of a two-step process, followed by a different method for selecting households or individuals within a selected location.

The poverty map methodology popularized by Elbers, Lanjouw, and Lanjouw (2003) facilitated a wave of geographic targeting. The Elbers, Lanjouw, and Lanjouw (2003) technique was a breakthrough because it found a practical way to combine census data, which can be disaggregated to the lowest geographical level but do not collect information on household income or consumption, with household surveys, which collect income or consumption but are only representative at very gross geographic levels.4 Econometric models are used to make poverty maps, or small area estimates, for levels of aggregation—district, parish, and municipality—that are much more detailed than the survey’s sampling frame (which is often just rural and urban, a few large agroecological zones, or the largest level of administrative unit, such as state). These poverty maps have