In Column (2) we test whether there is a quadratic relation between lights and GDP. In contrast to HSW, we find a modest and weakly significant quadratic relation in our sample, with the association between lights and GDP being weakly concave. This quadratic specification is however not robust to different sample definitions and is in large part driven by Equatorial Guinea. Between 2006 and 2012, lights in Equatorial Guinea grew significantly faster than GDP (see Figure **2** ), introducing a mild quadratic relation in the overall sample. The quadratic relation disappears when Equatorial Guinea is dropped from the sample (21 observations dropped from the total of 966). Given that the quadratic specification is not robust, we proceed with the linear specification in further analysis.

In Column (3) we replace lights observed from space by data on electrical power consumption (in kilowatt hours, obtained from the World Development Indicators). As data on electricity consumption are only available for 22 countries in SSA, the number of observations drops from 966 to 425 (not all 22 countries have data for all 21 years). We find a strong and statistically significant association between electricity consumption and GDP in the reduced sample (elasticity of _0.37_ ). The coefficient is however considerably smaller than the one estimated between night lights and GDP ( _0.58_ ). This may potentially be explained by the relatively high use of generators in SSA, where power outages on the main grid are frequent. WDI data on electricity consumption only encompass output from power plants and hence may underestimate the intensity and volume of night lights in countries with a strong reliance on private generators. Indeed, if we estimate a panel regression of log lights on the log of electricity consumption (with year fixed effects), we find that the within-country variation in electricity consumption explains “only” 64 percent of the variation of night lights as observed from space, hinting at the existence of other sources of lighting as well.

In the fourth column of Table 2 we include both electricity consumption and lighting as observed from space (for the 22 countries with data on electricity consumption). Similar to HSW, we find that both remain statistically significant, indicating that they may not capture the exact same underlying economic activity. The goodness of fit somewhat increases when including both electricity consumption and lights (within R-squared of _0.83_ compared to _0.80_ with lights alone – column (1)). The elasticity between lights and GDP ( _0.36_ ) remains higher than the one between electricity consumption and GDP ( _0.23_ ).

Table 3 explores the relation between night lights and two other types of GDP growth: Annual fluctuations about a country’s growth path (specification (2)) and long-term growth between 1992 and 2012 and 2000 and 2012 (specification (3)). Column (1) in Table 3 adds a country-specific time-trend so that lights now only explain fluctuations of GDP about a country’s longer-term growth path. The size of the coefficients falls to _0.11_ (from _0.57_ ), but is still highly statistically significant, indicating that short-term fluctuations in GDP are picked up to some extent by changes in lights. The second column of Table 3 examines the within-country association between growth in GDP and growth in night lights between 1992 and 2012. In line with HSW, we find a strong and statistically significant association between long-term GDP growth and long-term lights growth. The long difference estimate of � amounts to _0.55,_ similar to the fixed effect value of _0.57._ The R-squared remains high at 0.76, indicating that 76 percent of the within-country variation in GDP between 1992 and 2012 can be explained by the variation in night lights.

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