deteriorated (due to lower fiscal revenues and lower economic activity). As a result, sovereign bond issuances decreased in Sub-Saharan Africa, as the prospects of oil operations were not favorable and external borrowing rates increased.

The retrenchment of flows into the region from traditional financing partners precipitated the need for other funding options, particularly for infrastructure financing, as bilateral loans and grants from European countries and the United States declined. Sub-Saharan Africa’s frontier economies had measured success in tapping global capital markets—especially international bond markets—during the post–global financial crisis period. Since then, several low-income countries in the region—especially lower-middle-income countries—have been issuing eurobonds at an accelerating pace. For instance, there was a rapid rise in sovereign bond issuance between 2013 and 2015, when more countries in Sub-Saharan Africa had access to international capital markets. Sovereign debt issuance in the region increased from an average of US$6 billion during 2013–15 to US$8.2 billion since 2017 (World Bank 2018a). By 2018, 16 countries had issued bonds, several of them on a regular basis, with issuances of considerable size. Conditions for international bond issuances have been favorable, with high and steady demand from investors.

The shifting structure of global capital flows (and the corresponding changes in the composition of the flows into Sub-Saharan African countries) may be associated with changes in the relative importance of global vis-à-vis domestic factors driving capital inflows. For example, it has been argued that the new structure (and resulting volatility) of capital inflows to developing countries (including Sub-Saharan Africa) during the post–global financial crisis period may have increased their sensitivity (or vulnerability) to global (push) factors (Avdjiev et al. 2017). The empirical literature has established that global (push) and domestic (pull) factors are important drivers of capital flows—see, for instance, Calvo, Leiderman, and Reinhart (1993); Fernandez-Arias and Montiel (1996); and Chuhan, Claessens, and Mamingi (1998).

# **3. Estimation Technique and Data**

To estimate the determinants of gross capital inflows, we use an instrumental variable method for panel data which poses two main challenges. The presence of unobserved period- and country-specific effects is our first challenge. Therefore, we include country and time effects in the regression. The second challenge is that capital flows are likely to be jointly endogenous with shocks to pull factors in domestic markets, therefore, we need to control for the biases due to simultaneous or reverse causality in the regression. Consequently, we control for the endogeneity of domestic growth with instrumental variables such as the commodity terms of trade and lagged economic growth. Our baseline regression equation of capital flows presents the following specification: Ω�� �μ� �η� �α<sup>�</sup> Χ�� �ε�� where the dependent variable Ω�� is the ratio of capital flows to GDP. The dependent variable is proxied by the ratio of gross capital inflows to GDP as well as that of FDI, portfolio investments or other investments to GDP for country _i_ in period _t_ . Furthermore, μ� is a country effect and η� is a time effect. The matrix Χ�� contains information on our pull and/or push factors while α� is its coefficient vector. Finally, ε� captures the residuals.

The database for the empirical analysis comprises annual information on gross capital inflows for 45 SubSaharan African countries from 1980 to 2017. It gathers information for total gross inflows as well as its components, such as foreign direct investment, portfolio investment, and other investment from the International Monetary Fund’s (IMF’s) Balance of Payments Statistics BPM 6.0. Other foreign financing 8