are more likely to be rapidly growing middle-income countries such as China, Malaysia, or Vietnam. Countries with little change in _AoL_ or _R_ are more likely to be mature, wealthy countries.

# **III. Literature Review**

The literature on the use of night lights data is mature. The literature was established early. Croft (1973) was the first to propose the utility of night-time lights imagery. Croft (1979) demonstrated pioneering work in the use of digitized night-time lights data. In mid-2014, Qingxu et al (2014) found 189 different journal articles. A search on the term “DMSP-OLS” using Google Scholar produced 2,320 results in mid-February 2015. The following summary is therefore far from exhaustive. Even so, it does introduce the reader to many of the key readings in the literature.

Night-time lights data have been used as a proxy for _GDP_ . Elvidge et al (1997) found a strong, positive correlation between the natural log of _AoL_ and the natural log of GDP measured in US dollars for 21 countries in 1994. The resulting elasticity of _GDP_ to _AoL_ was 1.159. Several research teams generated cross-sectional GDP data by using _SoL_ data at sub-national or gridded levels. These included Sutton and Costanza (2002), Ebener et al (2005), Doll et al (2006), Sutton et al (2007) and Ghosh et al (2010). In general, the aggregation of gridded data to sub-national and national levels produced cross-sections that were well correlated with cross-sections of official GDP estimates, although discrepancies between estimates for individual countries were often 10 percent or higher.

Night-time lights have been used as a proxy for electricity consumption. Night-time lights should be tightly and positively correlated with electricity consumption because most of the observed lighting is produced using electricity.<sup>9</sup> Non-satellite estimates of electricity consumption include errors, due to the difficulty in recording off-grid electricity. This is especially true for the numerous small, private generators prevalent in poor countries with inadequate production and/or electrical grids; this includes off grid renewable energy systems. Several researchers have therefore used the night-time lights data as a proxy for electricity consumption, implicitly assuming a fixed ratio between consumption for lighting and total consumption. In their seminal work, Welch (1980) and Welch and Zupko (1980) found a positive correlation between raw DMSP night-time lights imagery and energy usage in the USA. Elvidge et al (1997) found a strong, positive correlation between the log of _AoL_ and the log of recorded electricity consumption for 21 countries in 1994 with an elasticity of 1.178. Shi et al (2014) also found positive a correlation between night-time _SoL_ and electricity consumption correlations at the provincial and prefectural levels of China.

Night-time lights data have been used as a proxy for population with mixed results. Accurate information about the size and distribution of the human population is not consistently available for all countries. Some countries do not have the capacity to conduct accurate census, others lack the resources to conduct censuses on a regular basis, and some may not be able to cover all of their territory because of conflicts. Direct observation by satellite can overcome all of these problems and is often less expensive than census campaigns. The idea was first tested in 1969 from a Gemini spacecraft, as documented by Tobler (1969). While Tobler found a positive correlation, he also found the relationship varied from region to region. Several different satellites and satellite sensors have been used for this purpose since then, in addition to the DMSP-OLS efforts reviewed here. Welch (1980) and Welch and Zupko (1980) were the first to use night-time lights data to estimate urban populations (and electricity usage) for China and 35 cities in the USA. They found strong, positive correlations despite substantial problems in the quality of the data. Elvidge et al (1997) looked at the correlation between the natural log of _AoL_ and the natural log of total 9 According to IEA/OECD 2006, only 1 percent of all lighting is fuel based rather than electrical.