# **1. Introduction**

In recent years, the intensity of night lights as measured from space has increasingly been used to estimate economic activity. The use of night lights as a proxy for economic activity rests on the assumption that as almost all consumption and investment activities in the evening or night require lighting, the intensity of night lights (or its growth over time) can be used as a proxy for the intensity of economic activity (or economic growth). Data on night lights have been used to estimate, among others, subnational income per capita (Ebener and others, 2005), the size of the informal economy in India (Gosh and others, 2010a), the global distribution of economic activity (Gosh and others, 2010b), the global incidence of poverty (Elvidge and others, 2009), and economic growth (Henderson, Storeygard and Weil, 2012).

One of the appealing features of night lights data is their availability on every level. Night lights data are measured on the so-called 30 arc sec level, corresponding to roughly 1 square kilometer at the equator. This means that night lights data can be used to estimate economic activity at levels that are not usually captured in national accounts, such as subnational administrative units (provinces, districts, counties, cities, etc.) or regions not coinciding with national borders (coastal vs inland regions, connected vs unconnected regions, etc.). For instance, Henderson, Storeygard and Weil (henceforth HSW) estimate, counter to intuition, that coastal areas in Sub-Saharan Africa are growing slower than the hinterland.

In this paper, we replicate the analysis by HSW but focus only on countries in Sub-Saharan Africa and use a slightly longer time-series (1992-2012 instead of 1992-2008). In line with HSW, we find a strong and robust link between growth in night lights and growth in GDP, with GDP estimated based on lights closely following GDP as measured from national accounts, for most countries in most years. We apply the results to estimate subnational growth in Kenya and Rwanda, two countries that are increasingly devolving powers to decentralized units. To our knowledge, this is the first time such an approach has been attempted to estimate GDP growth and levels for subnational units for Kenya and Rwanda.

We emphasize the fact that estimating subnational economic activity is more than solely an inquisitive exercise. This is because in the context of decentralized settings, there is a strong policy interest in seeking to see how growth can occur in different parts of countries, so that communities can share in national prosperity and do not get left behind. Second, subnational entities may themselves may want to know and understand how they stack up against their neighbors and competitors, and how much they contribute to national GDP. Third, such information would help private investors who shop around for viable investment destinations to assess more accurately where to undertake investments. Incidentally, countries that have decentralized may consider including subnational GDP as one possible criteria that could determine how funds transferred from the national government are shared among subnational governments. If and when that were to happen, then subnational entities stand to gain or lose depending on their contribution to national GDP. This can have ramifications -- for example, one oft-cited “fact” in the case of Kenya is that Nairobi contributes over 60% of Kenya’s GDP (Kenya Urban Areas Brief 2013). However, our indicative results show that Nairobi’s contribution to national 2013 GDP, though still the highest, stood at 13%. It bears emphasizing that our results come with caveats, in particular, underestimating agriculture’s contribution to county level GDP, as detailed in the paper.

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