# **I. Introduction**

The growing availability of free or inexpensive satellite imagery has inspired many researchers to investigate earth observation data in monitoring economic activity around the world. There are clear advantages – one can monitor continuously, see almost everywhere, and see in greater spatial detail than is usually possible for economic variables. The results have been encouraging: several useful correlations between satellite imagery and economic measures have been documented.

One of the most popular earth observation data sets among economists recently has been night-time lights. Researchers have found positive correlations across countries between night-time lights data and several socioeconomic variables. In two excellent survey articles, Ghosh et al (2013) and Qingxu et el (2014) refer to examples including electricity consumption, degree of electrification, carbon dioxide emissions, _GDP_ , _GDP_ per-capita, urban population, total population, and the incidence of poverty.

Many researchers have proposed that night-lights could serve as proxies for some of the variables mentioned above. If so, economists and geographers would have a useful tool for monitoring outcomes on the ground in great detail, especially where good data are not available due to conflict, disasters or weak statistical agencies. Yet, logic suggests night-time lights cannot serve as a good proxy for within-in country monitoring for the _growth rates_ all of these variables. For example, some OECD countries show positive GDP growth while their populations are stagnant or shrinking. Moreover, the growth rates for populations and capital stocks are fairly stable over time while those for GDP and electricity consumption are not.

In this paper, however, we revisit several correlations by using a simple test. Econometric theory suggests that a relationship between variables correlated in levels within a cross-section of countries may be spurious if the variables are not also correlated within countries, over time. Our finding is that growth rates of nighttime lights data are robustly correlated with growth rates only for electricity consumption and constant price manufacturing value-added. Even so, the elasticities and explanatory power are low. Positive correlations were also found for constant price GDP, non-agricultural GDP and capital stocks – but the results were not robust to the addition of control variables. The regressions for population and urban population failed to find positive and/or significant correlations with night-lights data. Improved data from a recently launched night-time lights satellite could help correct or confirm these outcomes. Testing this should be an important next step.

The paper is organized as follows. Section II offers a brief introduction to night-time lights data. Section III provides a review of selected night-time lights literature. In Section IV, we make a careful review of several hypotheses involving economic variables. This is followed by an in-depth examination of the characteristics of night-lights data in Section V. The characteristics of the dependent variables are explored in Section VI. Section VII sets out the testing framework. The results are reported in Section VIII. Section IX concludes with a discussion of key conclusions drawn from the hypothesis testing and some suggestions for further research.

# **II. Introduction to Night-time Lights Data**

Night-time lights data are a beneficial by-product of a meteorological satellite program. The data are collected by the United States Air Force Defense Meteorological Satellite Program (DMSP). DMSP satellites have been circling the earth since the 1970s in a polar orbit that allows observations of every