a positive and significant effect on SWF investments. The introduction of country and SWF characteristics captured by _Xj_ and _Yi_ allows us to examine if there exists cultural bias for SWF investments in Africa, and if the drivers of these investments are financial rather than political. Indeed, Chhaoccharia and Laeven (2009) found that cultural bias is more pronounced for SWFs and they tend to invest in countries with similar cultural traits. Most importantly, they showed that religious closeness is positively and significantly related to SWF investments. Knill et _et al._ (2012) find that political relations are important in why SWFs invest. However, for Avendano and Santiso (2009), SWF investments do not exhibit political bias, and their investments are not different from Mutual Fund investments. Our dependent variable is the logarithm of one plus the share of each investment in terms of total capitalization for 26 SWFs in Africa over the period 1985-2013.

In order to estimate the empirical model, we use the following proxies. We first calculate the excess return using the average GDP growth rate over the period 1985-2013 taken from the new Penn-World Table 8.0 of Feenstra _et al._ (2013). The United States is used as a proxy in terms of investment in a risk-free asset, _Rf_ . The expression _Rj_ , which denotes the expected return in African country _j_ , is measured as the average per-worker GDP growth rate.<sup>6</sup> To measure the risk of investments in Africa, we use the standard deviation of the average per-worker GDP growth rate over the period 1985-2013. Our networking variables _Xj_ and _Yi_ are proxied by religion and political fund, respectively. Specifically, Bortolotti _et al._ (2015) use the question 11 of Truman (2008): “Are decisions on specific investments made by the Managers?” to construct the index of political fund. They classify all funds with managerial independence score of less than 1 as “political funds” and all others as “non political funds.” They then define a dummy variable that takes the value of 1 for “political fund” and 0 if not.<sup>7</sup> The religion variable includes the share of Christians and Muslims in a given country using the new database of the PewResearch Center.<sup>8</sup> In terms of control variables, we use the following variables: Private credit and bank costs to take into account the level of financial development and bank efficiency, respectively. Private credit is calculated as the credit provided by the banking sector, including all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net and bank costs are measured by overhead costs to total assets (%), defined as operating expenses of a bank as a share of the value of all assets held, and cost to income ratio (%), defined as operating expenses of a bank as a share of the sum of net-interest revenue and other operating income.<sup>9</sup> We also control our results using school, inflation, macroeconomic policies such as budget balance and trade taken from the World Bank WDI.<sup>10</sup> _School_ is measured by the total enrollment in secondary education, regardless of age, expressed as a percentage of the population of official secondary education age. _Inflation, consumer price index_ , measured by the consumer price index, reflects the annual percentage change in the cost to the average consumer of acquiring a

> 6We also use the average per-capita GDP growth rate to test the robustness of our results.

> 7The database of political SWFs are from Bortolotti _et al._ (2015).

> 8The database are publicly available at `http://www.pewforum.org/2015/04/02/religious-projection-table/2010/number/all/`

> 9The data for financial development depth and bank efficiency come from the Global Financial Development Database (GFDD), publicly available at `http://econ.worldbank.org/` .

> 10 The World Development Indicators are publicly available at `http://www.worldbank.org/` .

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