## 2.3. OpenStreetMap network data

The transportation network data and the city outlines used in this analysis are sourced from OpenStreetMap (OSM)<sup>4</sup> . OSM is a free project that creates and continuously updates a crowd-sourced map of the whole world. By providing contributors with extensive manuals on how to provide and edit data, OSM achieves standardized data across different regions. Specifically, OSM classifies roads according to different categories such as motorways or residential streets that allow for the differentiation between roads of varying importance. It should be kept in mind that the road network data used here do not account for local infrastructure measures that were implemented specifically to increase a road’s resilience to flooding but are rather described to provide an overall picture of exposure of the road network to flooding based on spatial overlap.

## 2.4. Enterprise Survey

This work also employs firm-level micro data provided by the World Bank Enterprise Survey (ES). Through asking firms in most developing and emerging economies in the world a set of uniform questions, the dataset provides a unique opportunity to compare business activity in different countries. This analysis is based on ES firm observations for a set of 13 large cities in Africa.

In chapter 3, infrastructure disruptions are analyzed through three dependent variables of disruptions experienced by firms: _Blackouts_ , defined as the average annual hours without electricity supply, _water disruptions_ , defined analogously as the average annual hours of water supply outages, and _transport problems_ , an indicator ranging from zero to four that describes the degree to which transport is perceived as an obstacle to business with higher values indicating a greater obstacle.

As firms’ non-randomized location was available, each firm could be classified according to their flood exposure using the flood maps described above. To get a large sample of flood-exposed firms for chapter 3 a methodology was developed to combine flood maps from several sources into a common flood exposure variable. After each firm’s exposure was determined using its city’s respective flood map, a unified binary variable was defined ( _Flood-threatened_ ): It takes the value one if (a) a firm is located in a zone with flood hazard probability greater than 1 percent in any given year according to combined pluvial and fluvial SSBN flood probability maps with return periods of 10, 50, and 100 years, or if (b) a firm is exposed to flooding greater than 5 centimetres in a locally tailored modelled flood with a 50-year return period, or if (c) it is located in the area of the empirical flood map in Dar es Salaam. The variable takes the value of zero for all other firms in the cities for which flood maps were available. This results in a dataset with 3,094 firms from 13 cities of which 329 firms, or about 11 percent, are in flood-threatened areas.

Chapter 3 uses Enterprise Survey weights that are rescaled within each city to account for the firms outside the city boundaries that were dropped. In the pooled regressions, each city is given equal weight: this results in firms in cities with fewer observations having greater relative weight. Consequently, it prevents imbalance in the results due to the differing number of observations in each city. Note that the Enterprise Survey weights were not designed to be spatially representative at the city-level.

# 3. Firms, flood exposure and infrastructure in 13 cities in Africa

This section explores the determinants of infrastructure disruptions for firms in 13 African cities. Using geocoded Enterprise Survey data and flood maps, it demonstrates that infrastructure disruptions should primarily be conceived as system-wide problems that affect all firms in the system.

> 4 <u>https://www.openstreetmap.org</u>

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