Economists have used remotely sensed information since at least the 1930s, but digital technology advancements have allowed economists to start taking significant advantage of this source of information (Donaldson & Storeygard, 2016). Geospatial data cover wind speed, night light, precipitation, forest cover, crop choice, economic activities, urban development, building type and size, roads, pollution, beach quality, and many other difficult-to-measure statistics. The advantage of using geospatial data is amplified by the fact that they are usually available at a much higher degree of spatial resolution than traditional data, addressing a problem frequently found in MENA countries.

For example, National Aeronautics and Space Administration (NASA) data on nighttime lights offers a good proxy for settlement patterns and wealth (Henderson, Storeygard, and Weil, 2012). In fact, estimating economic activity using night lights can more reliably approximate real GDP since it accounts for informal economic activity, and does not depend on government infrastructure to measure. Nighttime light data can play a key role analyzing economic activity at the subnational level, especially in countries with poor quality or non-existent information.

Geospatial information has also been used to estimate welfare indicators. This non-traditional source of information not only allows new ways to explore topics, but also proxies for wealth and poverty data. Marx, Stoker, and Suri (2015) used satellite data to detect better-maintained or new roofs as indicators of higher quality housing in Nairobi. Kudamatsu, Persson, and Stromber (2012) studied how weather fluctuations affect infant mortality in Africa. Daytime imagery has even been used to estimate and replace bad quality poverty data in developing countries (Xie et al., 2016). As with phone surveys, geospatial data have also proved to be an excellent option in persistent conflict and unsafe zones, or for data on illegal and informal activities not usually reported. For example, remote satellite sensing can capture spectral reflectance of different crops, thereby allowing calculation of land surfaces devoted to poppy cultivation (Lind, Moene, and Willumsen, 2014).

# <u>Data requirements and pitfalls</u>

There are three primary pitfalls related to use of geospatial data. The first is related to cost. Although much of the sensor-captured data on land cover, forest, and airborne pollution are public and free, some disaggregated data provided by public entities, or specific data provided by private firms, are costly. For example, access to the global annual cloud-free nighttime lights database costs $70,140 dollars, and the global nighttime light monthly increments cost $7,665 per year. Depending therefore on a study’s time frame type of information needed, geospatial data costs can escalate quickly.

The second drawback is data complexity and the skills needed for processing. Earth’s land area can be subdivided into hundreds of trillions of one-meter cells, making such high-dimensional data difficult to model even with linear functions (Donaldson & Storeygard, 2016). Also, satellite data can often be highly correlated among nearby units. Whether spatial dependence appears as a dependent or independent variable, statistical inference should be taken with caution and adjustments made to correct potentially biased coefficients.

The third pitfall is related to ethical concerns. While approaches have taken advantage of available data, such as nighttime lights, to measure variables such as welfare in developing countries (Donaldson & Storeygard, 2016; Henderson, Storeygard & Weil, 2012), or building type and size information (Marx, Stoker, and Suri, 2015), using very high-resolution imagery might raise individual privacy and regulatory concerns.

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