|Storms|8.8059|60.0757*|0.1052***|
|---|---|---|---|
|_annual number of events_|_(56.2382)_|_(33.4079)_|_(0.0392)_|
|Extreme rain|-0.0738|0.0437**|-0.0001|
|_annual number of events_|_(0.0605)_|_(0.0213)_|_(0.0001)_|
|N|20359|13734|23333|
|R<sup>2</sup>|0.38|0.12|0.19|

Estimated with weighted OLS, normalised survey stratification weights applied. Standard errors in parenthesis, clustered on region. Significance levels: *** 0.01, ** 0.05, * 0.10.

2.3. Firms’ exposure to infrastructure disruptions vs. direct exposure to natural hazards Based on Rentschler et al. (2019), this section investigates whether direct exposure to natural hazards influences firms’ experience of infrastructure disruptions. In 13 African cities, no such link is found between exposure to flood hazards and unreliable infrastructure.

Especially developing countries tend to perform poorly in terms of the reliability and efficiency of their public infrastructure. Infrastructure users tend to experience frequent disruptions, such as power outages, water supply interruptions, or frequently occurring problems with transport networks. These disruptions can have a range of causes, ranging from sub-standard and malfunctioning equipment, poor management, lacking investment and maintenance, as well as natural stresses and disasters.

To analyze how natural shocks cause infrastructure disruptions, this analysis combines firm-level microdata and natural hazard exposure data. Enterprise Survey observations are used for 13 major African cities. Flood hazard data is drawn from the highest-quality flood map available in each city. Using the nonrandomized ES locations, each firm’s exposure is extracted using the local flood map. For the analysis, the flood exposure is then reclassified into the binary variable Flood-threatened that takes the value 1 if a firm is exposed to significant flood risk and 0 otherwise.

At the firm-level, there is no link between firms’ flood exposure and experience of infrastructure disruptions. Table 3 displays the result from regressions on power outages, water disruptions, and transport problems in 13 African cities. The Flood-threatened dummy variable has no significant effect on the three types of disruption. The absence of a visible relationship between natural hazard exposure and infrastructure disruptions can be explained by two factors: other stressors and network effects.

Infrastructure disruptions are not only caused by natural hazards. Instead, a wide-range of other factors inhibit the functioning of infrastructure systems. Systems in developing countries are usually less robust and exhibit a wider range of vulnerabilities. Thus, the systems in the African cities analyzed here might be under greater relative pressure from other stressors that hides the influence of natural hazards (Rentschler et al., 2019).

Furthermore, infrastructure disruptions caused by natural hazards are likely to affect the whole system as infrastructure systems are large networks. The effects of a flood destroying power distribution infrastructure will not be limited to those firms located in the flood zone but will be felt by all firms in a city. Consequently, firm location is an insufficient proxy to pick up the true influence of natural disasters on infrastructure.

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