metadata: authors: 0: author: Maruyama Rentschler,Jun Erik
1: author: Braese,Johannes Michael
2: author: Jones,Nicholas K.W.
3: author: Avner,Paolo
count: World
docna: 0: docna: Three Feet Under : The Impact of Floods on
            Urban Jobs, Connectivity, and Infrastructure
docty: Policy Research Working Paper
subtopic: Natural Disasters,Transport Services,Labor Markets,Hydrology,Energy Policies & Economics
teratopic: Social Protections and Labor,Energy,Water Resources,Transport,Environment
abstracts: cdata!: This paper analyses the degree to which
            infrastructure reliability and urban economic activity in
            several African cities is impacted by flooding. It combines
            firm-level micro data, flood maps, and several spatial data
            layers across cities through a harmonized geospatial network
            analysis. The analysis shows that a significant share of
            jobs in cities is directly affected by floods. It further
            details how transport infrastructure is subjected to
            significant flood risk that disproportionally affects main
            roads in many cities. While direct flood effects are
            revealed to be significant, this work further shows how
            knock-on implications for the entire urban economy might be
            even larger. Regardless of the direct flood exposure of
            firms, flooded transport networks mean that disruptions
            propagate across the city and drastically reduce the
            connectivity between firms. Access to hospitals is also
            found to be reduced significantly -- even during relatively
            light flooding events: From a third of locations in Kampala,
            floods mean that people would no longer be able to reach
            hospitals within the "golden hour" -- a rule of
            thumb referring to the window of time that maximizes the
            likelihood of survival after a severe medical incident.
            Overall, this study showcases the use of high-detail
            city-level analyses to better understand the localized
            impacts of natural hazards on urban infrastructure networks.
repnme: repnme: Three Feet Under : The Impact of Floods on
            Urban Jobs, Connectivity, and Infrastructure
display_title: Three Feet Under : The Impact of
            Floods on Urban Jobs, Connectivity, and Infrastructure
listing_relative_url: /research/2019/06/31166804/three-feet-under-impact-floods-urban-jobs-connectivity-infrastructure
topicv3: Water m1326522 137,Transport m1326661 135,Power and Electricity Sector m1327094 515,Flood m1503315 3216,Jobs m1503931 2670
publishtoextweb_dt: 2019-06-19T00:00:00Z
historic_topic: Social Protections and Labor,Energy,Water Resources,Transport,Environment
no_of_pages: 30
author_keys: 0: author_key: 1217979
1: author_key: 1679278
2: author_key: 1125716
3: author_key: 1256219
model_extractions: Policy Research Working Paper 8898 Lifelines: The Resilient Infrastructure Opportunity _Background Paper_ Three Feet Under Urban Jobs, Connectivity, and Infrastructure _Jun Rentschler Johannes Braese Nick Jones Paolo Avner_ Climate Change Group Global Facility for Disaster Reduction and Recovery June 2019
Policy Research Working Paper 8898

# **Abstract**

This paper analyses the degree to which infrastructure reliability and urban economic activity in several African cities is impacted by flooding. It combines firm-level micro data, flood maps, and several spatial data layers across cities through a harmonized geospatial network analysis. The analysis shows that a significant share of jobs in cities is directly affected by floods. It further details how transport infrastructure is subjected to significant flood risk that disproportionally affects main roads in many cities. While direct flood effects are revealed to be significant, this work further shows how knock-on implications for the entire urban economy might be even larger. Regardless of the direct flood exposure of firms, flooded transport networks mean that disruptions propagate across the city and drastically reduce the connectivity between firms. Access to hospitals is also found to be reduced significantly—even during relatively light flooding events: From a third of locations in Kampala, floods mean that people would no longer be able to reach hospitals within the “golden hour”— a rule of thumb referring to the window of time that maximizes the likelihood of survival after a severe medical incident. Overall, this study showcases the use of high-detail citylevel analyses to better understand the localized impacts of natural hazards on urban infrastructure networks.

This paper is a product of the Global Facility for Disaster Reduction and Recovery, Climate Change Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The authors may be contacted at jrentschler@worldbank.org and jbraese@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Three feet under: The impact of floods on urban jobs, connectivity, and infrastructure

## **Jun Rentschler**<sup>**1**</sup> **, Johannes Braese**<sup>**1**</sup> **, Nick Jones**<sup>**1**</sup> **, Paolo Avner**<sup>**1**</sup>

_1Global Facility for Disaster Reduction and Recovery, World Bank, Washington DC, USA_

**Acknowledgements:** This working paper contributes to the World Bank’s 2019 flagship report _Lifelines_ on resilient infrastructure. The authors thank Charles Fox, who provided invaluable support and developed the GostNets network analysis tool used in Section 4. The study has also benefited from helpful comments, feedback, and inputs by Andrii Berdnyk, Marga Cantada, Scott Ferguson, Stephane Hallegatte, Steven Rubinyi, Ben Stewart, Mercedeh Tariverdi, and Joshua Wimpey.

**JEL classification:** R41, O18, Q54

**Keywords:** Resilience; infrastructure; cities; urban flooding
# 1. Introduction

In all parts of the world, cities are providing jobs and improved livelihoods to an ever growing number of people. Through the density of social and economic activity, people living in or moving to cities gain easy access to services, jobs, and markets. Analogously, firms benefit from facilitated access to large and diverse labor markets. The benefits from spatial proximity are made possible through infrastructure systems that increase connectivity and efficiency. Transport infrastructure for example connects people to markets and jobs, and connects firms to suppliers, consumers, and markets. In providing the backbone of most economic activity, infrastructure systems thus play a crucial role for anything from meeting the basic needs of people, to supporting sustainable and resilient livelihoods and enabling ambitious economic endeavors.

However, the functioning of infrastructure and the urban economy can be threatened by natural hazards. Urban flooding in particular is a major cause of disruptions in cities across the world, and especially in developing countries. From Buenos Aires to Dar es Salaam, Amman, Dhaka, and Jakarta – around the world, urban flooding is a frequent and devastating occurrence, and the resulting disruptions extend well beyond just the flood-zone. Of course, urban flooding is directly damaging public assets, affecting people’s homes and assets, and destroying the productive assets of firms. But beyond the direct impacts, floods are also causing knock-on effects on infrastructure networks well beyond the flooded areas. As one road segment is flooded, traffic across the entire city is disrupted, thus affecting people’s access to jobs and the smooth functioning of supply chains. The indirect costs of urban flooding are real and often significant.

In order to better assess this threat, this analysis addresses three topics. First, it analyses how economic activity and road transportation infrastructure is exposed by urban flood risk. Second, the network-level effects of floods on urban connectivity and accessibility are assessed. Finally, it investigates whether flood risk affects infrastructure disruptions at the level of an individual firm. To address these questions, flood maps, employment density maps, infrastructure maps, and geocoded firm-level micro data is used for the 13 cities of Abidjan, Accra, Addis Ababa, Bamako, Cotonou, Douala, Dar es Salaam, Freetown, Kampala, Kigali, Monrovia, Nairobi, and Zanzibar.

The analysis finds that a significant fraction of economic activity is exposed to flood risk – though the level exposure varies significantly across cities. In Kampala, about 10% of all jobs are directly located in flood risk zones. Indeed, large fractions of the road network in these cities are located in areas that are potentially prone to flooding, often with depths that make traversing the roads impossible. In Kampala, hydrological flood models estimate flood depths at several road segments to exceed one meter (or three feet). Moreover, the impacts of flooding are felt not only in directly exposed flood zones, but across the entire city. Connectivity between firms is estimated to be affected significantly with travel times more than doubling for a quarter of all firms. Urban flooding is also estimated to make hospitals inaccessible to a third of all locations within the “golden hour”, i.e. the 60 minutes window following a critical medical incident beyond which survival rates are reduced drastically.

The remainder of this study is structured as follows. Section 2 outlines the types of data that are crucial to conduct city level hazard assessments, including high-resolution flood maps, and open source infrastructure network data. Section 3 presents evidence from a pooled dataset of up to 3,000 firms from 13 major cities in Sub-Saharan Africa, showing that infrastructure disruptions are transmitted across citywide networks and affect firms in- and outside of flood zones. Section 4 applies a geospatial network analysis tool to assess the direct exposure of jobs, road networks, and urban connectivity. Section 5 concludes.

2
# 2. Data

This section provides an overview of the data types and sources that are crucial for assessing the impacts of flooding on the urban economy. For this purpose it briefly discusses flood maps (Section 2.1), employment data (Section 2.2), Open Street Map network data (Section 2.3), and firm-level data from the World Bank’s Enterprise Surveys (Section 2.4).

## 2.1. Flood maps

Flood risk information is typically communicated through flood maps that describe the spatial extent of flooding. For the purpose of this study, three different flood maps were used. This section will briefly present each of them: i) flood maps produced through the SSBN global flood model, ii) flood maps produced by a tailored flood model, and iii) empirical flood maps produced through community mapping. Table 2 in Annex 5.1 provides an overview of the availability of different flood maps for 13 cities in SubSaharan Africa.

### 2.1.1. SSBN-modelled flood maps

The main flood data used in this analysis are city-level flood maps derived from the SSBN global flood map.<sup>1</sup> The underlying flood model has a resolution of 90 meters, and considers floods that are either fluvial (i.e. caused by overflowing bodies of water) or pluvial (i.e. caused by a saturation of the absorption capacity of the ground or drainage systems). In this analysis, fluvial and pluvial maps were combined to assess the full extent of potential flooding.

SSBN flood maps are available for several return periods, that denote the average repeat interval of an event with a certain intensity. Here, return periods of 10, 50, and 100 years are considered. Flood model estimates provide the extent of flooding as well as the expected flood depth in each grid cell for each return period. By using a common methodology in all cities, SSBN flood maps are useful for analyses comparing the effects of flood risk in different regions. The availability of different return periods and the assignment of different flood depths allows for the differentiation between flood risk of varying severity along two dimensions. However, these flood maps can be impervious to local built infrastructure.

> 1 <u>http://www.fathom.global/</u>

3
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Figure 3 Flood model comparison using low- and high-resolution digital terrain data (STRM and Photogrammetry) Uncertainty has two main sources. (1) The quality of the DTM, which in this case is considered good; and (2) the rainfall estimate, which itself is dependent on the overall rainfall for a given return period and the losses to apply relating to canopy capture and drainage system storage, and infiltration or other losses. The flood extents and flow paths are therefore considered reasonably representative, however, with no reliable calibration data, the exact return period attributed to the flooding is likely to be less certain. Production of the flood maps for three return <u>periods took approximately 8 days’ work, including pre and post model data processing and model development.</u>

# 2.1.1. Community mapping

A third type of flood map is not based on a model, but instead uses local knowledge to identify historical areas of flooding. Such an empirical flood map is available for Dar es Salaam. Here, the community mapping initiative Ramani Huria<sup>3</sup> provides crowd-sourced information on the areas that had been flooded in recent flood events. By relying on actual observations, community mapping is better able to reflect nonmodelled factors such as the quality of local drainage systems, thus increasing accuracy. However, the Ramani Huria map only provides flood extents and does not offer information on return periods or flood depths. Thus, it does not distinguish different levels of flood risk.

> 3 <u>http://ramanihuria.org/</u>

6
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## 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>

9
# 3.1. Indicators of infrastructure quality

This section analyzes the quality of infrastructure services perceived by firms and the role of flood exposure in explaining service disruptions. It aims to answer the following question: what is the impact for firms of being directly exposed to floods compared to the being indirectly impacted? In other words, do firms which are directly exposed to floods have significantly worse outcomes than firms situated outside of flood zones?

While infrastructure disruptions indeed physically occur with increased frequency and intensity in certain hotspots, infrastructure systems are networks that transmit the adverse effects of disruptions across wide areas. The level of localization however varies across different types of infrastructure. For instance, in the case of electricity, a disruption in one location (e.g. a tree falling on transmission lines during a storm) will affect whole segments of the distribution network.

**Electricity outages:** The spatial distribution of electricity outages is assessed using ES data on firm-level power outages. For each firm, the number of hours without electricity in an average month is calculated. In addition, firms are asked to assess what percentage of their sales they are losing due to power outages. It is found that firms in cities with longer outages forego higher sales, on average. Still, some variation exists.

**Insufficient water supply:** While electricity outages are usually felt with the highest immediacy, disrupted water infrastructure similarly affects firms in many cities in our samples. Based on the Enterprise surveys we calculate the share of firms in each city that experienced insufficient water supply in the year before the survey was carried out. These disruptions of firms relying on water as an input for production or the provision of services are likely to impose significant economic costs.

**Transportation disruptions:** Turning to transportation infrastructure, our analysis shows that the firms suffer from significant disruptions in all 13 cities in our sample. The ES relies on a subjective measure to assess transportation infrastructure disruptions experienced by firms. Firms assess transport as an obstacle to their operations on a scale from 0 (“no obstacle”) to 4 (“very severe obstacle”). A comparison of the average level of transport disruptions is pictured in Figure 6.

Figure 6: Average perception of transport as an obstacle to business across cities. Higher values indicate perception as a greater obstacle <!-- Start of picture text --> 2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Abidjan<br>Monrovia Douala<br>Bamako Kigali<br>Free Town Cotonou Accra<br>Mean perception of greater obstacles) Zanzibar Nairobi Kampala<br>transport as an obstacle (higher values indicate Dar es Salaam<br>Addis Ababa<br><!-- End of picture text --> The perception of transport disruptions seems relatively uniform across cities. Infrastructure disruptions affect everyone 10
# 3.2. Does flood exposure explain infrastructure disruptions in cities?

The two-dimensional relationship between flood exposure and each of the three disruption variables are described in the graphs in Figure 7. The relationship between firms’ exposure to floods and water outages experienced is positive, as could be expected. Considering water, firms in flood-prone areas experience higher annual hours without water supply. For transportation, the descriptive results are as expected also as expected. The share of flood-threatened firms is lower for those with minor transport problems and, on average, increases to a higher share of flood-threatened firms for those perceiving transport to be a major obstacle to their business operations. Overall, no clear relationship between flood exposure and infrastructure disruptions is visible. Counterintuitively, it appears as if firms in flood-threatened areas experience lower electricity supply disruptions than their counterparts. But because electricity is mainly transmitted either through above ground power lines or locally produced by generators, there is no reason a-priori why electricity disruptions would affect firms located in flood-prone areas more.

Figure 7: Average yearly duration of power and water supply outages and perception of transport as an obstacle by flood-threatened and non-threatened firms in 13 African cities <!-- Start of picture text --> 600 450<br>400<br>500<br>350<br>400 300<br>250<br>300<br>200<br>200 150<br>100<br>100<br>50<br>0 0<br>Perception of transport as an obstacle to business<br>100%<br>6.8% 9.6% 7.4% 10.8% 12.4%<br>80% Not flood-<br>threatened<br>60%<br>Flood-threatened<br>40%<br>20%<br>0%<br>zone<br>duration (h) Firms in flood disruption (h) Firms in flood zone<br>Firms outside flood zone Average annual water supply<br>Average annual power outage Firms outside flood zone<br>by flood exposure<br>Decomposition of firms<br>No Obstacle Minor Obstacle Moderate Obstacle Major Obstacle Very Severe Obstacle<br><!-- End of picture text --> These descriptive statistics, while mostly showing expected relationships, are quite weak, with low differences in disruptions depending on the location of firms. Also and more importantly these graphical representations cannot provide clear evidence of the association between disruptions and flooding. This is because they are susceptible to distortions caused by firm characteristics correlated with both flood 11
exposure and infrastructure disruptions. In order to overcome such distortions, regressions can be used that control for industry- and city-level fixed effects and several other firm characteristics. Table 1 summarizes the results of three weighted regressions with such controls that include standard errors clustered at the city-level. Overall, one main message emerges: system-wide disruptions are more important than individual firm-level characteristics. We interpret this as the ripple effect of floods dominating the direct effects, i.e., whether a firm is directly affected by a flood only marginally contributes to the costs that floods it already faces through system wide network disruptions.

Table 1: Results from weighted OLS regression, city-level normalized survey stratification weights applied. Standard errors in italics, clustered on cities. Abidjan city-level fixed effects omitted to allow estimation of model. Significance levels: *** 0.01, ** 0.05, * 0.10.

|Dependent:|Blackouts|Water disruptions|Transportproblems|
|---|---|---|---|
|Firm age|0.217|8.145**|-0.001|
|_years of official registration_|_(1.46)_|_(2.89)_|_(0.00)_|
|Employment|-0.912*|0.494|0.001|
|_number of full time employees_|_(0.50)_|_(0.90)_|_(0.00)_|
|State ownership|3.128|-0.656|0.015|
|_in %_|_(5.81)_|_(7.00)_|_(0.02)_|
|Export orientation|-0.214|4.232|-0.000|
|_% of total sales_|_(1.45)_|_(3.69)_|_(0.00)_|
|Generator ownership|69.452|126.011|0.054|
|_Dummy_|_(50.81)_|_(289.08)_|_(0.15)_|
|Distance to city centre|-0.001|0.008|-0.000|
|_At the ward-level, in meter_|_(0.00)_|_(0.01)_|_(0.00)_|
|Flood-threatened|28.583|54.883|0.150|
|_Dummy_|_(70.41)_|_(262.29)_|_(0.14)_|
|ISIC Sector Fixed Effects|YES|YES|YES|
|Accra|519.455***|212.819|-0.875***|
|_City-level fixed effect_|_(53.64)_|_(170.65)_|_(0.05)_|
|Addis Ababa|185.962***|-189.789*|-1.629***|
|_City-level fixed effect_|_(56.07)_|_(87.65)_|_(0.07)_|
|Bamako|-140.272*|-539.146***|-0.131|
|_City-level fixed effect_|_(68.80)_|_(101.07)_|_(0.08)_|
|Cotonou|941.516***|-530.813***|-0.871***|
|_City-level fixed effect_|_(64.91)_|_(81.03)_|_(0.06)_|
|Dar es Salaam|710.536***|-83.131|-0.568***|
|_City-level fixed effect_|_(57.76)_|_(89.47)_|_(0.04)_|
|Douala|338.875***|-334.603***|-0.817***|
|_City-level fixed effect_|_(62.92)_|_(64.68)_|_(0.06)_|
|Free Town|705.323*|-1568.848|-0.631|
|_City-level fixed effect_|_(332.75)_|_(1738.63)_|_(0.49)_|
|Kampala|362.801***|-588.947***|-1.097***|
|_City-level fixed effect_|_(41.15)_|_(158.44)_|_(0.04)_|
|Kigali|-62.963|-392.183***|-1.045***|
|_City-level fixed effect_|_(43.32)_|_(79.57)_|_(0.05)_|
|Monrovia|282.523***|3.248|-0.500***|

12
|_City-level fixed effect_|_(44.93)_|_(133.91)_|_(0.09)_|
|---|---|---|---|
|Nairobi|72.405|-280.767**|-0.950***|
|_City-level fixed effect_|_(40.63)_|_(102.24)_|_(0.06)_|
|Zanzibar|362.305***|-380.033*|-0.920***|
|_City-level fixed effect_|_(65.27)_|_(180.00)_|_(0.08) _|
|N|2217|828|2600|
|R<sup>2</sup>|0.185|0.115|0.124|

Out of the other firm characteristics, few significantly impact infrastructure disruptions. Considering blackouts, we see that larger firms on average experience a lower duration of electricity outages in a year. This is true even when controlling for the ownership of a generator. Water disruptions seem to correlate strongly with a firm’s age: older firms face significantly worse water supply throughout the year. Transport problems are not significantly associated with any variable at the firm level. There is also no evidence that the quality of infrastructure services is spatially clustered within a city: a firm’s distance to the city center, as measured from the center of this firm’s ward, is not a good determinant of the disruptions it experiences.

In this multivariate analysis, no relationship between flood exposure and any of the disruption variables persists. The negative association between flood exposure and power supply disruptions observed in the descriptive statistics vanishes. While the coefficients point in the directions we could expect to be true for all three types of infrastructure, i.e. associating flood exposure with greater disruptions, the relationships are clearly insignificant in all three cases. This suggests that location in a flood zone is not an important determinant of infrastructure disruptions.

The infrastructure disruptions of a given firm, however, might not only be influenced by whether the firm location itself is in an area that is likely to be flooded. If, for example, the firm is located on a small hill that is surrounded by flood exposed areas, a flood will likely cut-off its access to transport infrastructure as well. As a proxy for this proximity to a flood zone, a variable is introduced that indicates whether any other firm in the spatial unit of the analyzed firm is flood-threatened (see Table 3 in the appendix). It should be noted that due to the definition of administrative boundaries and data availability the size of the spatial units varies considerably between cities. This new variable, however, is clearly insignificant in all three cases, again indicating that flood exposure, be it direct or indirect, does not have a large impact on infrastructure disruptions.

The only group of factors that is able to explain a large fraction of the variance in the infrastructure disruptions is the city-level fixed effects. These are highly significant and of large size in most of the cities for all three types of disruptions. This leads to a simple conclusion: it is not firm characteristics that explain infrastructure disruptions, it is the quality of the overall system. The interconnectedness of firms on the power, water, and transport grids cause disruptions to ripple throughout the system and rarely affect only individual firms. The quality of an infrastructure system is primarily determined at the city-level and then affects all firms within this city to a much larger degree then the characteristics of each individual firm. The same point is made by a related set of regressions described in Table 4 in the appendix. Here, the citylevel fixed effects are replaced by a variable that, for each firm, describes the mean disruption level of all other firms in the city. Again, firm-level characteristics are insignificant while, in the case of power outages and transport disruptions, the overall city-wide disruption level is the strongest predictor of an individual firm’s disruption level. As infrastructure is determined by interconnected systems, infrastructure disruptions are experienced by everyone connected to these systems.

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### **Robustness analyses using different classifications of flood threats**

The relationship between infrastructure disruptions and flood exposure is subjected to two robustness tests. The first robustness test consists in testing whether the relationship between disruptions and firms’ location in flood prone-areas is stronger when we focus on the _heavily flood-threatened firms_ . Results show that there is no change in the relationship between firm characteristics and disruption variables using this. The second robustness check consists in creating a continuous variable of flood severity instead of the binary variable to indicate whether a firm is located in a flood-prone area. Here again no significant effect of flood exposure on power, water, or transport infrastructure disruptions can be observed (see appendix A.4).

# 4. The influence of flooding on the urban economy

This section analyses flood hazards in relation to the distribution of employment and the transportation network in Kampala, Bamako, Dar, and Kigali. It assesses the direct exposure of jobs (Section 4.1) and transport infrastructure (Section 4.2) to flood risk, and shows how flooding can reduce connectivity across the whole city (Section 4.3).

## 4.1. The flood exposure of jobs

In order to assess to what extent economic activity in a city is impacted by potential flooding, this section overlays employment density maps with flood maps. Figure 8 shows employment density and the extent of a modelled flood with a return period of 50 years in Kampala. The area of analysis for which the flood map was constructed, here referred to as Inner Kampala, is outlined with a black line.

Figure 8: Employment density and 50-year flood zones in Kampala 14
The modelled flood zone takes a ring-like shape around the inner city of Kampala. In Inner Kampala, 12% of all jobs are directly located in flood zones with a return period of 50 years. Out of these jobs, a considerable fraction is affected by significant flood depths: In a 50 year flood, 10% of all jobs in Inner Kampala would be affected by flooding with a water depth of more than 15 centimeters. In Kampala, floods with 10-, 50-, or 100-year return periods have relatively similar effects on employment as shown in Figure 9. Generally, most jobs are only affected by relatively shallow depths as evident in the fast decline of the fraction flooded with increasing flood depth.

Figure 9: Fraction of all jobs in Kampala that are directly located in a flood zone and the associated flood depth <!-- Start of picture text --> 14%<br>10-year return period<br>12% 50-year return period<br>100-year return period<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>0 0.15 0.3 0.45 0.6 0.75 0.9 1.05<br>Flooding depth ( x meters or higher)<br>Fraction of jobs located in flood zone<br><!-- End of picture text --> The exposure of jobs to flooding varies significantly across cities. Figure 10 compares the fraction of jobs located in flood zones in Bamako, Dar es Salaam, Kampala, and Kigali. It should be noted that the point estimates for the percentage of jobs affected by a certain flood depend on the defined area of analysis: Assuming that cities are more likely to be located in flood zones close to rivers or the sea, extending the area of analysis could reduce the area of flooding and consequently the relative share of urban jobs located in flood zones. For the sake of consistency, the area of analysis corresponds to the city boundaries defined by OSM.

Valuable comparisons between cities can be drawn from the rate with which exposure increases as flood events become more extreme. In Kampala and Kigali, the share of flood exposed jobs stays relatively flat as flood events intensify. Most firms affected by a catastrophic event (e.g. with a 100-year return period) are also likely to be affected by less intense and more frequent events. In these it is more the flood depth rather than the flood extent that increases for higher intensity events. This could mean the residents and firms in flood zones are sensitized to the risk of flooding due to frequent exposure. On the contrary, in Bamako and Dar es Salaam the extent of flooding increases significantly for higher return periods. Especially in Bamako, an extreme event has a drastically wider reach than lower intensity events. This implies that a majority of people exposed to a low-probability event will not be exposed to more frequent smaller events. As a result, they may be less prepared and have a lower capacity to respond and recover. A likely explanation for these different dynamics lies in the topography of the respective city areas.

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Figure 10: Fraction of jobs in Bamako, Dar es Salaam, Kampala, and Kigali directly located in flood zones for 10, 50, and 100 year return periods <!-- Start of picture text --> 35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Bamako Dar es Salaam Kampala Kigali<br>50-year return period<br>100-year return period<br>Fraction of jobs affected 10-year return period<br><!-- End of picture text -->

# 4.2. Road transportation networks and flooding

In addition to the direct effect of firm locations being submerged in water, floods also indirectly affect many businesses. In fact, indirect effects of floods may be significantly larger than the immediate direct impacts. By slowing down traffic or closing off roads, flood zones disrupt transport networks that firms depend on. In the case of a flood, employees and customers are unable to reach businesses, input materials are not delivered to production sites, and finished products cannot be transported to their destination on time. Such impacts present significant disruptions to the operations of businesses of all sizes and can cause large economic damages, even for firms that are not located in flood zones.

The ‘networked’ nature of infrastructure becomes apparent when overlaying the urban road map with flood risk zones. For this purpose, we use the same flood data as in the previous section and combine it with data on roads from the OSM project. Figure 11 shows how flooding is affecting the road network in the area of this analysis in Kampala 16
<!-- Start of picture text --> i<br>A AT Road Network and Flooding<br>= ‘ in Inner Kampala<br>Lp \\ \) 4 AS —— MainMain Roads Roads in Flood Zone<br>yy Ne 1 ‘ IS =. Secondary(50yr Return and Period)Other Roads<br>KeZ; j waDORRlane> Y EY \ J Rp<s~e\ 4 SecondaryFlood Zone and(50yr Other Return RoadsPeriod) in<br>. Oi \s } 3<br>Y\ os a “J mR a a (©) Area of Analysis<br>CK Fim : } Uy, . ‘ ’ i MH Bodies of Water<br>bra a MR kted= f AHZa a) Se hi yo —— Waterways<br>\ |<br>)<br>; ~3<br>a 0 1 2 3 km<br>Ua<br><!-- End of picture text -->
be accounted for as two streets in OSM which impacts the calculated length of roads in the city or flood zone.

Figure 12: Fraction of roads affected by flooding in Kampala by road type and flood depth <!-- Start of picture text --> 12%<br>Motorway and primary<br>Secondary<br>10% Tertiary<br>Residential and others<br>8%<br>6%<br>4%<br>2%<br>0%<br>0 0.15 0.3 0.45 0.6 0.75 0.9 1.05<br>Flooding of over [m]<br>Fraction of roads affected<br><!-- End of picture text --> Just like in Kampala, main roads are disproportionally affected by flooding in Bamako and Kigali. Figure 13 compares the fraction of different types of roads flooded by more than 15cm in an event with a 50-year return period in the four cities. The high stress on main roads indicates significant indirect effects from transport disruptions to a flooded economy. At the same time, it suggests that flood protection measures can prove to be efficient investments. Concentrating flood mitigating efforts on main roads could already provide significant relief to the transportation network.

Figure 13: Fraction of roads in Bamako, Dar es Salaam, Kampala, and Kigali affected by flood depth of over 15cm in an event with 50-year return period <!-- Start of picture text --> 12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Bamako Dar es Salaam Kampala Kigali<br>Motorway and primary Tertiary<br>Secondary<br>Fraction of roads affected<br>Residential and others<br><!-- End of picture text --> 18
<!-- Start of picture text --> a. Travel times between firms in Inner Kampala b. Journey time increase due to flooding<br>Pa) Pa]<br>c= 2<br>Cc Cc<br>oO oO<br>xe) eo)<br>> ><br>.e) 1S)<br>Cco CcoO<br>a a]<br>oOoO / o OO<br>5 15 25 35 45 (0) 50 100 150 200 250 300 350 400<br>Minutes Time travel increase (%)<br>No flood —— 10-year flood —— 50-year flood<br><!-- End of picture text -->
Figure 15. Increase in inter-firm connectivity for urban floods with 10 and 50-year periods

|||**10-year return period**<br>**(% of all firms)**|**50-year return period**<br>**(% of all firms)**|
|---|---|---|---|
||**Less than 25%**|0.3%|0.0%|
|**Increase in inter-firm**|**25% - 50%**|52.8%|42.3%|
|**travel times due to**|**50% - 75%**|9.8%|14.7%|
|**flooding in Kampala**|**75% - 100%**|2.9%|3.9%|
||**More than 100%**|26.7%|27.4%|

Connectivity between firms in Kampala declines significantly due to flooding, according to our model. Considering the population of other firms to represent customers and suppliers, reduced inter-firm connectivity is detrimental to economic health of businesses in Kampala. The increase in travel times between pairs of firms ranges from 54% (10-year flood) to 69% (100-year flood) on average. Interestingly, travel time disruption in a 10-year flood event comes close in severity to the impacts of a 100-year flood event. While larger floods cause extensive property damage, more frequent ‘nuisance floods’ also impose major impacts on firm connectivity, posing a constraint to private sector growth. In our modelled 50-year flood scenario, most journey times increase by some 25%-75%, but for the ten percent of most affected firm pairs, journey times more than double.

We now turn to households’ accessibility to important services (in this case hospitals) and how it it is altered by flood events. Accessibility to hospitals also declines significantly due to flooding. Within Inner Kampala, mean transport times to a hospital are just over 17 minutes. In our model of the 50-year flood event, this journey time rises to 30 minutes. Due to impassable roads and the need to take more circuitous routes in a flood event, for 17% of city locations the mean journey time at least doubles. Moreover, 7% of trips from city locations to hospitals become impassable.

A common rule of thumb in emergency response states that the survival rate for life threatening health issues drops significantly after the “golden hour”, i.e. 60 minutes after the incident (Campbell, 2017). Absent flooding, some 98% of city locations have mean journey times to hospitals below 30 minutes. Assuming that ambulances are based at hospitals, a one-way travel time of less than 30 minutes implies a round-trip of less than the 60-minute threshold. However, journey times exceed this level for 30% of city locations in a 10-year flood, and 44% of locations in a 50-year flood. The additional travel times to reach critical facilities may impose severe consequences on residents seeking urgent medical care. As the above estimated travel times do not account for the potentially significant impact of congestion (both in the flood and no-flood scenarios), the true disruptive impact of floods for accessibility is likely to be even larger in practice.

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