basket of goods and services that may be fixed or changed at specified intervals, such as yearly (the Laspeyres formula is generally used); _budget balance as % of GDP_ is cash surplus or deficit revenue (including grants) minus expenses and the net acquisition of non-financial assets. In the 1986 GFS manual, non-financial assets were included under revenue and expenditure in gross terms and _trade_ calculated as the sum of exports (% of GDP) and imports (% of GDP).

In terms of institutional variables we use corruption taken from Laporta _et al._ (2008) and the International Country Risk Guide (ICRG). Higher values in corruption reflect more developed institutions.<sup>11</sup> To take into account the level of innovation of a given country we introduce the proximity to the world technology frontier, defined as the ratio of the GDP per-worker of the country in 1985 to the GDP of the US at the same period.

Finally, we add several SWF characteristics as controls. The following variables are used: Transparency, capitalization taken from the SWI.<sup>12</sup> To investigate the foreign investment bias for SWFs that invest in Africa, we define a dummy variable that takes a value 1 if the country origin of SWF is a member of the Organization of the Islamic Conference (OIC) and 0 if not. More particularly, this dummy variable allows us to analyze if SWFs from muslim countries invest more or less in Africa.<sup>13</sup> Table 4 shows the summary statistics of our variables. The average of the logarithm of the investment share in Africa by SWFs is 0.83, with a standard deviation equal to 1.11. The maximum is 3.93, and the minimum is 0. In terms of transparency, the average is 7.25, with a maximum of 10, and a minimum of 1. The average of SWF capitalization in $ billion is 103.95. However, we observe a high dispersion between SWFs in terms of capitalization, confirmed by the value of the standard deviation, which is equal to 184.04. The average of the rate of return is -0.81, while the average of the risk is 0.22. The average of the dummy variable political fund is 0.61. The maximum of political SWF is 1, while the minimum is 0. In terms of institutional variables, we find that the averages of the level of the corruption is -0.49. The maximum level corruption is 0.5, while the minimum is equal to -1.12. The maximum of the variable property rights takes a value equal to 3, while the minimum is 2. The averages of private credit is 30.03 percent of GDP. The minimum of the level of financial development measured by private credit is 4.40 percent of GDP, while the maximum is 108.63 percent. In terms of correlation we find that the logarithm of the share of SWF investments in Africa is negatively correlated with the Linaburg-Maduell transparency index. This suggests that transparent SWFs are less likely to invest in Africa. However, we find a positive correlation between political fund and political index with the logarithm of the investment share, suggesting that political SWFs are more likely to invest in Africa.

> 11Their dataset is publicly available at `http://mba.tuck.dartmouth.edu/pages/faculty/rafael.laporta/publications.html` and `https://www.prsgroup.com/about-us/our-two-methodologies/icrg.`

> 12The database of SWF transparency measured by the Linaburg-Maduell index and capitalization are publicly available at `http://www.swfinstitute.org/`

> 13The list of members are publicly available at `http://www.oic-oci.org/oicv2/states/`

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