during 2002–2007 and a modest increase (but still a significant decline) to 3.35 percent during 2008–2014.

However, a reversal pattern is evident on the exports of fuel products and ores and metals. Fuel product export shares from Africa surged from 4 percent only during 1990–1995 to 56.84 percent during 2002–2007 and then dipped to 39.43 percent during 2008–2014. The export rates of ores and metals indicate that the amount of metals and minerals that Africa has exported to China has increased. Though smaller in magnitude during 2002–2007 than in the preceding periods, Africa’s positive trade balance in ores and metals is in line with the production and imports patterns described above (cf. Figures 2a and 2b, and Figures 3a, 3b, and 3c in Appendix I).

In sum, while the overall trade balance between Africa and China had been negative, there was still a positive balance in average trade flows for commodities such as fuel products and ores and metals. Second, this positive balance reflected the patterns of metals and minerals production and imports from Africa and to China, respectively, which have been more distinct since China’s WTO accession in 2001. In the next section, we describe the data we used for the empirical analysis.

# 3. DATA AND VARIABLES

# _A. Data_

We combined several data sources in order to carry out the empirical analysis. The first source is the <mark>National Oceanic and Atmospheric Administration, National Geophysical Data Center</mark> (NOAA-NGDC),<sup>7</sup> which provides satellite data on night-time light intensity, recorded daily between 8.30 p.m. and 10.00 p.m. local time, across countries around the world. We cleaned<sup>8</sup> and used this data to approximate measures of local economic development in Africa, consistent with Mveyange, (2015), Alesina et al., (2015), Gennaioli et al. (2014), and Henderson et al. (2012), among others.

Our second source is the United States Geological Survey (USGS),<sup>9</sup> which provides data on spatial location of mineral deposits and mining activities in Africa (see Table 8 in Appendix II for the list countries). This data enabled us to identify the spatial references of the places where mineral deposits are present and in which mining is active relative to places with no such deposits. As explained below, these differential variations in mineral deposits and mining activities inform the baseline empirical strategy. Further, with the USGS data we were able to identify three interesting sources of exogenous variations. We identified mining sites with different attributes such as those involved in minerals extraction or transformation; those with high- or low-value minerals extracted; and those where the scale of mining operations is large or small. As described later, we

> 7The data are available at: <u>https://www.ngdc.noaa.gov/eog/dmsp/downloadV4composites.html</u>

> 8 Lights data are derived in three different formats: cloud-free, average visible, and stable light composites. Consistent with other studies, we employed the stable light composites, which are free from background noises, summer lights, ephemeral events, and gas flares.

> 9 The data are available at: <u>http://minerals.usgs.gov/minerals/pubs/country/africa.html.</u>

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