represent fixed effects for district and country _×_ year, respectively. The inclusion of district fixed effects absorbs time-invariant district effects that may influence FDI flows. Country-year fixed effects, on the other hand, enable us to absorb country-specific temporal shocks that may influence the outcome variable. The main parameter of interest is _φ_ which represents the causal impact of access to high-speed Internet connectivity on FDI flows. Standard errors are clustered at the district level to allow within-district correlation among the residuals (Abadie et al., 2022). The inverse hyperbolic sine (IHS) transformation was applied to continuous variables, such as investment size, and the number of FDI projects.

In this setup, causal identification of impact relies on the assumption that the timing of the arrival of (connection to) submarine cables is plausibly exogenous (D’Andrea and Limodio, 2019; Hjort and Poulsen, 2019). In other words, absent the arrival of submarine cables, trends in FDI receipts between connected and unconnected districts would have evolved along a similar pattern (i.e., the so-called "parallel trends" assumption). To assess the validity of the identifying assumption, we estimate an event study to trace the trends in FDI receipts before and after connection to the submarine fiber-optic network between connected and unconnected districts. Meanwhile, recent advances in the literature suggest that the two-way fixed effects (TWFE) estimator is likely to be biased due to the possibility of heterogeneity in the treatment effects across groups (De Chaisemartin and D’Haultfoeuille, 2018, 2020a,b; Goodman-Bacon, 2021; Callaway and Sant’Anna, 2021; Borusyak et al., 2021). This implies that the regular event studies estimates based on the TWFE estimator are plausibly biased. Therefore, we present event study estimates using the "imputation" estimator by Borusyak et al. (2021) which ensures consistency in the presence of the TWFE and also deals with issues of spurious identification and negative weighting (Acemoglu et al., 2022; De Chaisemartin and D’Haultfoeuille, 2020b).

Finally, in the main analysis, we focus exclusively on the "second-generation" SMCs to Africa. This is motivated by the fact that: (i) the "first-generation" SMCs had relatively low bandwidth capacity and hence low internet speeds. Bandwidth cost also remained relatively expensive. On the other hand, the "second-generation" SMCs brought faster Internet speeds at low cost to the continent, and marked the beginning of rapid expansion in Internet connectivity in Africa (Hjort and Poulsen, 2019); (ii) the arrival of the "first-generation" SMCs (1997-2002) predates the start of our FDI data (2004), hence limiting the application of our before-and-after design, and (iii) the staggered arrival of the "second-generation" SMCs also enables causal identification of impact as used in Hjort and Poulsen (2019). Therefore, in our quest to examine the effects of high-speed Internet connectivity on FDI, focusing on "second-generation" SMCs offers the best option. However, including countries that already received the "first-generation" SMCs in the analysis while assigning treatment status to the arrival of the "second-generation" 12