Night-time light emissions are a popular proxy for growth in circumstances where official data are deemed unreliable. We show that the underlying relationship varies substantially across countries, undermining the imposition of a single slope common in the literature. We propose a two-step method to improve country-specific growth estimates informed by night-light data, making use of a machine-learning algorithm to discern factors driving differences in the luminosity-growth elasticity across countries. The improved performance of this strategy over existing approaches is established in a number of simulation exercises. Applied to African data between 1992 and 2013 we find little evidence of an `African Growth Miracle' undetected by official statistics, as suggested by Young (2012); instead, we observe that countries which recently revised their GDP figures tend to report substantially inflated growth rates over recent years, in line with Jerven (2014)'s hypothesis of purely `statistical growth'.
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Lionel Roger
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