frontier, and transparency measured by corruption as controls. In column (2), we regress the logarithm of investment share of SWFs on rate of return, risk in Africa, political fund, the share of christians and muslims, and inflation. Our results show that return on investment has a positive and significant effect on SWF investment, suggesting that the rate of return enhances the investment of SWFs. The coefficient associated with risk enters negatively but is not significant. This result suggests that risk decreases the investment of SWFs in Africa. Political fund remains positive and significant at the 1 percent level. The share of christians enters positively and statistically significantly different from zero at 10, while the share of muslims remains negative and significant at the 10 percent level. These findings show that the excess return that indicates the rate of return on an investment relative to the rate of return on the risk-free investment increases SWF investments and that there exists political and cultural biases for these investments in Africa. Budget balance is introduced in column (3), and all coefficients of interest enter significant with the expected signs. Most importantly, the rate of return has a positive and significant effect on SWF investments at the 10 percent level. To take into account the degree of openness we control for trade in column (4). Our main results remain unchanged. It could be that SWFs invest in countries close to the technology frontier in terms of innovation. In addition, we add into our specifications the proximity of the country to the technology frontier (where the leader is the United States). The coefficient associated with the rate of return remains positive and significant. More specifically, the magnitude of the coefficient associated with the rate of return increases and remains positive and significant at the 1 percent level. Also, the networking parameter captured by political motive of the fund remains positive and significant at the 1 percent level. This suggests that SWFs with a political bias are more likely to invest in Africa. We also find that the coefficient associated with the share of christians has a positive and significant effect on SWF investments. Interestingly, the coefficient associated with the proximity to the technology frontier also enters positively and statistically significantly different from zero at the 5 percent level. This shows that the share of SWF investments in Africa increases with the level of technological innovation. In terms of institutional variables, we use the level of corruption. The results are shown in columns (6) and (7). The signs and significance of our coefficients of interest remain unchanged. Our main results remain robust to the introduction of inflation, macroeconomic variables, trade, corruption and the proximity to the technology frontier as controls, and confirm at the same time the results listed above, and our theoretical implications.

# **3 Conclusion and policy implications**

Using available data on SWF in Africa, we observe that 47 percent of SWFs that invest in Africa are from minerals (extractive commodity), while 53 percent are from non-commodity sources. In addition, we find that African funds invest less domestically, in contrast to Asian funds.

In addition, we show using an enriched mean-variance portfolio model that the determinants of SWF investments in Africa are excess return, risk and networks. We test our theoretical predictions empirically, using an econometric specification regressing the logarithm of the in18