# Growth Under Rising Pressure: Weather Shocks in Sub-Saharan African Cities

Robert Reinhardt

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Robert Reinhardt. Growth Under Rising Pressure: Weather Shocks in Sub-Saharan African Cities. 2026. halshs-05510186

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CES

Centre d'Économie de la Sorbonne
UMR 8174

Growth Under Rising Pressure: Weather Shocks
in Sub-Saharan African Cities

Robert REINHARDT

2026.04
# Growth Under Rising Pressure: Weather Shocks in Sub-Saharan African Cities

Robert Reinhardt

Université Paris 1 Panthéon - Sorbonne, CNRS, Centre d'Économie de la Sorbonne,
Paris, France

**Abstract:** Sub-Saharan African cities are among the fastest growing in the world but face significant climatic risks. This study investigates how the four most important weather shocks (floods, heat waves, drought, and storms) have shaped the physical expansion of 5,721 cities in the region between 2000 and 2019. Using high-resolution remote sensing data combined with a panel of weather shocks observed over time, we find that floods, especially, reduce urban growth by 3-9%, most notably in western Africa. The effects are substantially amplified when floods follow heat waves, a common co-occurring combination. Droughts, when considered in the surrounding areas of cities, are associated with a 3% growth in urban areas. However, inverse effects are observed when treatment history is taken into account. Storms appear to accelerate the growth of wealthier towns, although the evidence is limited. Heatwaves alone show no clear effect. Our findings emphasize the need for integrated flood adaptation policies that take common co-occurring hazards equally into account. Furthermore, we emphasize the importance of considering both the historical context and the spatial dimension of the shock in empirical work.

**Keywords:** Climate Change, Resilience, Urban Growth, Development

**JEL Codes:** P25, Q54, O44

## 1 Introduction

African cities are expected to triple in population by 2050, compared to their size in 2018, and are estimated to house 22 percent of the world population by mid-century (UNDESA, 2019). These growing cities are major drivers of economic growth and have improved many socioeconomic outcomes, such as education and financial inclusion (OECD, United Nations Economic Commission for Africa and African Development

<footnote>* Contact: robert.reinhardt@etu.univ-paris1.fr. For the great guidance on this article, I thank Lisa Chauvet. Likewise, I thank my thesis committee for their feedback. Lastly, I thank colleagues and seminar participants at the 14th European Meeting of the Urban Economics Association, the IPSWD at Columbia University and internal research seminars at Paris 1 and the LEO at the University of Orléans for valuable comments and discussions.</footnote>
Bank, 2022). However, rapid expansion of cities, particularly those near the coasts, is likely to expose a growing number of residents and businesses to the impacts of climate change (Dodman et al., 2022; Rentschler, Avner et al., 2023; Tuholske et al., 2021). Along with urbanization, one finds the sealing of the ground that might exacerbate flooding and heat exposure (Rentschler, Avner et al., 2023; Founda and Santamouris, 2017). However, the effects of climate change are more complex than occasional exposure to a specific extreme event. By 2030, the likelihood of urban areas facing floods and droughts will have doubled compared to risk levels in 2000 (Güneralp, Güneralp and Liu, 2015) and some cities will experience exposure to events beyond floods and droughts (Šakić Trogrlić et al., 2024). When multiple such events occur in a short time frame, often referred to as "compound events", their joint effects might be more severe than their individual effects (Zscheischler, Westra et al., 2018). The increasing likelihood of co-occurrence of different extreme events and the rapid urban growth in Sub-Saharan Africa make it crucial to understand how these events shape urban growth.

This article studies the effect of four types of climate-driven weather shocks on physical urban expansion in Sub-Saharan African cities between 2000 and 2019. Floods, heat waves, storms and droughts are chosen as they are likely to change in the upcoming decades due to global warming (IPCC, 2021, Chapter 12). In contrast to previous studies, the four hazards are introduced simultaneously. This allows for investigating whether the effects found in the literature are driven by a specific hazard class or instead by the presence of another unaccounted-for hazard. Understanding how cities respond to extreme events is critical for several reasons. Although many studies have studied the relationship between climate change and urbanization (see, e.g. Henderson, Storeygard and Deichmann, 2017; Helbling and Meierrieks, 2023), very few have focused on the effects beyond a specific hazard (mainly flood or drought) on urban growth. Given the increasing likelihood of co-occurring extreme events due to climate change, this understanding is particularly important for anticipating urban growth patterns. This understanding is particularly important for anticipating urban growth patterns under extreme climate scenarios where co-occurrence becomes more likely. Extreme events may drive population movements (Hoffmann et al., 2020), intensifying pressure on growing cities but serving as an adaptive response to climate shocks. Yet, if these shocks exacerbate pre-existing vulnerabilities in cities, they might reduce welfare and induce additional humanitarian and economic pressure on cities. Lastly, many studies focus on the evolution of the population over time. The quality of this data is often limited since it relies on interpolated estimates. Using novel high-resolution building footprint data allows researchers to capture a clearer picture of extensions of urban areas.

To answer the question of how urban growth is changed after extreme events, we rely on a temporal panel model to compare the growth of 5 721 urban agglomerations in 44 Sub-Saharan countries between 2000 and 2019. After selecting urban agglomerations, as defined by OECD and SWAC (2020), the data is combined with high-resolution data on the growth of a measure called "impervious surfaces". This data, coming at a 30
m resolution, provides information on the annual evolution of human-modified areas within cities. Impervious surfaces capture, beyond building evolution, also evolution in infrastructure in general. It is especially important in climate impact studies, as it is correlated with a higher risk of heat waves and urban flooding. Data on extreme events is drawn from Reinhardt (2025) and includes information on floods, heat waves, droughts and wind storms. The data is homogenized and converted into an annual count of shocks for each hazard that occurs in a city. However, the spatial scope of the events varies. Droughts have large-scale effects, while floods tend to be local. Thus, we repeat part of our analysis for a city-catchment area, which accounts for the surrounding regions connected to the city. Then, while annual counts enable us to investigate whether an increasing number of shocks affects growth, we also examine the effects of a more "climatic" definition of compound events. Namely, we investigate whether our results change when accounting for different combinations of shocks that occur one after another within a year (e.g., a flood following a drought). Finally, in the article, we also provide insights on how much of the coefficients are driven by the history of exposure over the past eight years. Therefore, we rely on an event study estimator by Imai and Kim (2019).

Our main results paint a clear picture: experiencing floods, especially those induced by extreme precipitation, reduces urban expansion by around 3-9% in poorer and more rural cities. These effects are largely centered around Western Africa. In addition, these effects are amplified when floods follow heat waves, which is a common type of "compound event". Rather than highlighting opportunities for sustainable growth, the findings are linked to undesirable side effects. We also document a contemporaneous increase in informal population, people returning to high-risk flood zones, as well as a slight increase in the likelihood of violent urban unrest. Heat waves might have a small, negligible negative effect as they create potential disamenities in the city and reduce labor productivity (Lai et al., 2023). They might thus slow down urban expansion. Although some of our results suggest this, the effects are weak and negative. We find that most of the negative effects are caused by interactions with flood events. The effects of droughts have already been extensively studied, with numerous papers finding positive impacts on urban expansion (see, e.g. Chlouba, Mukim and Zaveri, 2023). The mechanisms proposed in the literature focus on droughts as a factor that incentivizes rural-urban migration. Although little effect is observed when droughts are studied purely at the city level, accounting for the effects in catchment areas of cities, we document an increase in urban expansion of around 3% (or 0.12 percentage points). In partial contrast to the literature (e.g. Van der Borght et al., 2025), however, we find that these effects originate mainly in medium-sized rural cities. These effects become negative once the history of droughts is taken into account, suggesting that treatment history matters. Finally, storms, although they show strongly positive effects, should be regarded with caution, since only small fractions of African cities are exposed to this hazard. However, relatively wealthy cities experience an increase in urban expansion after damaging windstorms, highlighting that they can induce population movements. Thus, combining the results, we find that shocks, especially droughts, tend to reveal
urbanization as an adaptive response to surrounding areas. However, the shocks studied also increase vulnerabilities within cities. From a policy perspective, floods have a major influence on urban expansion in Sub-Saharan Africa. They add pressure to already poorer cities and exacerbate living conditions. Finally, from an empirical perspective, we emphasize that in addition to the history of hazard exposure and the spatial dimension of the shock, researchers should consider sequences of different hazard events. This approach might help us better understand whether the hazard alone or a combination of hazards drives the results.

This article contributes to several streams of literature. First, the article contributes to the literature on the effects of shocks on urban growth and recovery from shocks. This topic has been extensively studied in urban economics in the context of war (Davis and Weinstein, 2002; Bosker et al., 2007), human-caused events such as the Boston Fire of 1872 (Hornbeck and Keniston, 2017) or in the aftermath of particularly costly extreme events such as Hurricane Katrina in 2005 (Vigdor, 2008). The paper contributes to this discussion by examining how extreme events influence urban growth and expansion. Instead of focusing solely on a single extreme event, such as a fire or a hurricane, we extend this literature by studying, in a high-resolution setting, how cumulative rather than single shocks might influence growth behavior.

Second, it contributes to the literature on the urban growth-climate change nexus. In recent years, an increasing number of studies have shifted their attention from individual cities or events to the effects of climate change on urban growth. An entire chapter of the Intergovernmental Panel on Climate Change is dedicated to this topic (Dodman et al., 2022). Many notable examples in economics study the effects of specific hazards or joint hazards such as temperature and precipitation (Castells-Quintana, Krause and McDermott, 2021; Helbling and Meierrieks, 2023; Colmer, 2021), droughts (Henderson, Storeygard and Deichmann, 2017; Chlouba, Mukim and Zaveri, 2023; Van der Borgh et al., 2025) or floods (Gandhi et al., 2022; Kocornik-Mina et al., 2020; Magontier and Martinez, 2023). Many of these studies are at the city level. The closest related to this study are the works of Magontier and Martinez (2023) and Kocornik-Mina et al. (2020) for floods and Van der Borgh et al. (2025) for droughts. Magontier and Martinez (2023) focus on the effects that extreme floods, caused by rainfall shocks, have on urban development in Spain. Applying an event-study approach, they document that floods have led to the vertical expansion of cities. This expansion has generally contributed positively to structural change and increased amenities in affected cities. Kocornik-Mina et al. (2020), in contrast, focus on a more global sample of cities and document heterogeneity depending on different elevation levels within cities. Affected areas do not appear to adapt by shifting economic activity away from risky areas, except in areas that are relatively recent. In contrast to Magontier and Martinez (2023), our study focuses on a broad scale of developing countries, where the effects might be fundamentally different. Contrary to Kocornik-Mina et al. (2020), we actively avoid relying on nightlight data, as older data, such as DMSP, tends to be biased in post-disaster studies (Gibson et al., 2024). Instead, we use physical urban expansion. This metric, changing more slowly, is more robust against the large fluctuations of
nightlight. The article by Van der Borgh et al. (2025) is the closest related to this study, as it examines the effects of urban expansion between 1984 and 2015 following severe drought events. They document significant urban expansion in metropoles of affected countries, where, especially in high-flood-risk areas, build-up appears to increase. While relying on the same input to define urban areas as this study, we extend their work in three ways: we account for other hazards than only droughts, we study the temporal dimension accounting for the history of droughts in a city and we do not exclude small cities as they might serve as an important piece in the impact dimension (if displaced persons tend to settle first in nearby towns due to lower displacement costs). In contrast to existing literature, we document the importance of accounting for the co-occurrence of other hazards, either by including them simultaneously in the model or by testing for specific interactions. Furthermore, our article discusses the spatial scale of hazard events and emphasizes the importance of going beyond continental heterogeneity analysis, which might overlook within-continent hazard variation due to variation in atmospheric conditions.

Finally, we attempt to take a more holistic approach by including multiple types of hazards in the analysis. Although the importance of analyzing connected and compound events is well-documented (Zscheischler, Westra et al., 2018), an emerging body of research examines their impact on particular cities (Šakić Trogrlić et al., 2024) or specific zones, including coastal and valley regions (Dodman et al., 2022). Some also focus on specific hazard combinations in a specific country (Ghanbari et al., 2023). Despite the simplified model used here and its failure to account for the full complexity of compound events, the paper makes a contribution by studying, in its extension, the effects of combining sequences of hazards. In summary, previous literature has primarily focused on general shocks or how specific weather shocks drive urban expansion, and has documented heterogeneous growth responses. Little attention is given to moving beyond single hazards and accounting for the co-occurrence of other hazards. This article aims to fill the gap by providing a broader understanding of how different weather shocks impact urban expansion and whether their temporal and spatial dynamics, as well as their interactions, matter.

The paper is structured as follows: first, the data (Section 2) and the empirical framework (Section 3) are presented. Next, the results, their heterogeneity and robustness are discussed in section 4.1. Section 4.2 adds extensions to the core model by accounting for compound events, different spatial aggregation and finally accounting for treatment history. The results are placed in literature and the question is asked if the effects found improve or deteriorate the condition of the city in section 4.3. Finally, the paper is concluded.
<figure><img src="image_3.png" /><figcaption>Figure 1: Illustration of Data Generation: Nigeria</figcaption></figure>

**Note:** Panel A displays Nigeria with its urban agglomerations, as defined by OECD and SWAC (2020), alongside the added hazard grid from Reinhardt (2025). Panel B focuses on a single agglomeration—Ibadan in Oyo State—showing its overlap with the 10x10 km grid and the computation of weighted zonal statistics (in this case, for the 5-day maximum Wet Bulb Globe Temperature in March 2010). Finally, Panel C presents the raw data alongside the assigned data for the outcome variable—physical urban growth. Red and yellow areas are areas added after 2000. The grey background is the urban extent of the city based on Africapolis.
## 2 Data

This article relies on a large variety of data sources. In the following, the main variables are described and the way in which the panel set is constructed is explained. A general description of how data is generated is shown in Figure 1.

**Urban Areas** At first, a definition of an urban area is needed. In this study, we rely on urban agglomerations as defined by the Africapolis by OECD and SWAC (2020), similar to the article by Van der Borght et al. (2025). This dataset applies spatial criteria (area where buildings are less than 200m apart) and population criteria (at least 10,000 people) to define an "urban agglomeration". For the Sub-Saharan sample around 5 721 agglomerations are included. In contrast to Van der Borght et al. (2025), we do not apply exclusion criteria to the sample. The study aims to include smaller, rapidly growing towns that can serve as initial destinations for displaced populations from nearby areas. We aim to thereby obtain a more complete picture of urban development in Sub-Saharan Africa. In the robustness part, we test how changing the sample of cities changes the results. Figure 1 shows, in the first column of panel A, the agglomerations included in Nigeria, and in panel B, the shape of a representative agglomeration. Throughout the article, we refer to these urban agglomerations as "cities", while acknowledging that the differences in size between them may be relevant. A possible alternative could have been the use of the Global Human Settlement Layer (GHSL) (Pesaresi, 2023). However, we prefer to rely on Africapolis, given that it relies on a standardized definition of urban areas for the African context, whereas GHSL is designed to be globally applicable, thus losing some scope for African cities.

### 2.1 The Dependent Variable

**Physical Urban Expansion** To measure the physical expansion of urban areas, we rely on the evolution of impervious surfaces at a resolution of 30 m from the GISA dataset (Ren et al., 2025). This data comes as a raster where each pixel is indicated as built or not built, as well as the year when it was built. Impervious surfaces (e.g streets, buildings, and parking lots) are human-made structures that prevent water from being absorbed into the ground. They are indicative of physical development. More precisely, it measures the horizontal physical expansion of a city, allowing cities to grow in space rather than height. If poorly managed, they are also strong predictors of future vulnerability to hazards such as flooding (Feng, Zhang and Bourke, 2021) and extreme heat (Huang et al., 2023). Beyond their environmental significance, the expansion of impervious surfaces can also be interpreted through an economic lens. Urban growth may result from productive economic activity that leads to urban expansion or from booming, unregulated informal settlements, often triggered by displacement and a lack of affordable housing. Thus, in this study, urbanization itself is considered neutral. If it leads to economic growth, it would be considered positive. However, if it is accompanied by increased informality and more people living in vulnerable areas, urbanization would be negative. Although not the key focus of this study, we assess
whether the effects found are rather positive or negative in the "Discussion" section of
this paper.

In the literature, one usually finds either nightlight or the WSFevo dataset<fnref n="1" /> (German Aerospace Center (DLR), 2024; Marconcini et al., 2020) to study urban expansion. We include WSFevo as a robustness check due to its widespread use. However, the selected GISA dataset offers two key advantages: longer temporal coverage (2000-2019) and more climate-relevant growth (since it includes infrastructure). Nightlight data is not reliable for hazard studies. Changes in luminosity reflect not only physical urban growth, but also economic activity and access to electricity. Furthermore, the type of satellite sensor used in nightlight studies might distort the impacts (Gibson et al., 2024). Using 2020 urban agglomeration boundaries from Africapolis, we create bounding boxes around each city to measure annual impervious surface area from 2000 to 2019. As shown in panel C of Figure 1 for Ibadan, Nigeria, we use the 2020 functional boundary as the maximum extent of the city. We then calculate the number of built-up pixels each year, with brighter orange indicating later expansion. Each 30×30m pixel equals 0.0009 km<fnref n="2" />, allowing conversion to a total human-modified area in each city.

## 2.2 The Independent Variables

The hazard data is predominantly drawn from Reinhardt (2025), a dataset that combines pre-existing datasets from 2000 to 2019 into a unified global grid. The data is reported on a 10x10 km grid for each month, primarily in terms of hazard intensities. For this study, data on floods, droughts, heat waves and extreme wind/tropical cyclones are extracted. An illustration for the heat wave metric, Wet Bulb Global Temperature, for the case of Nigeria can be seen in Panel A of Figure 1. As the outcome variable is annual and most cities are substantially smaller than the grid cells (averaging about one-fourth their size), some transformation is needed. This transformation is necessary to align the data with an annual panel at the city level. All described hazards are characterized by monthly continuous intensity functions<fnref n="2" />. First, a monthly area-weighted average or maximum of the hazard intensity is calculated at the city level to account for potential overlap across multiple 10×10 km grid cells. An illustration of this process can be seen in panel B of Figure 1. This value is then converted into a binary shock indicator (shock / no shock) using hazard-specific thresholds. The annual city-level shock is defined as the number of months the city transitions from non-treated to treated status<fnref n="3" />. A mathematical representation of the procedure is shown in Appendix Supplementary Appendix C. The following paragraphs briefly describe the intensity measure for each

<footnote n="1">¹See, for instance, Van der Borgh et al. (2025), Chlouba, Mukim and Zaveri (2023) and Rentschler, Avner et al. (2023) for applications.</footnote>

<footnote n="2">²With the exception of flood, which has a categorical value range</footnote>

<footnote n="3">³That means if a flood occurred from February-March and July-August, two events will be recorded. Certainly, the duration of an event might also matter, especially for floods and droughts. Accounting for the duration is assessed in the robustness section of this paper</footnote>
of the four weather shocks<fnref n="4" />. They also explain the threshold used to define a shock for each hazard. The statistics and original data sources are presented in Table 7.

**Floods** Floods are well studied in the context of urbanization (see, e.g. Kocornik-Mina et al., 2020; Rentschler, Avner et al., 2023; Magontier and Martinez, 2023). If older, less productive assets, such as machinery or buildings, are destroyed in catchment areas and replaced with newer, more productive ones, floods could contribute to economic growth in parts. In literature, this concept is sometimes referred to as "constructive destruction" (Hallegatte and Dumas, 2009; Hsiang and Jina, 2014). However, floods within cities can cause economic disruption (Kocornik-Mina et al., 2020), evasion of flooded areas, especially near the coast (Bukvic and Barnett, 2023), or zoning restrictions that reduce urban growth. We expect a reduction in urban expansion, as floods are notoriously destructive and could reduce incentives to grow. Although often used in the literature, rainfall itself is not a sufficient predictor of floods, as floods can originate from upstream river basins. We use the Dartmouth Flood Observatory (DFO) Index, which reflects both flood magnitude and likelihood. Since floods are common across much of Africa, we define "extreme" events as those with a return period of approximately 1 in 50 years (DFO Index > 1.5). For robustness, all recorded flood events are also included. Furthermore, in the robustness section, we distinguish by type of flood, assuming that a monthly 2-standard deviation from historical means in precipitation might be indicative of a *pluvial* flood. Similarly, a two-standard deviation from historical means over a 3-day period of maximum river discharge could be indicative of a *riverine* flood<fnref n="5" />. Finally, since not all parts of an urban area are equally impacted by a flood, we add information on flood risk from from Rentschler, Salhab and Jafino (2022), originally derived from Fathom Global, and coastal flood risk from Vousdoukas et al. (2018) and Vousdoukas (2023). Figure 3 visualizes on a province level the average total exposure of cities to floods in Africa for the period 2000 to 2019. Most floods are centered around Eastern Africa, Madagascar and parts of Nigeria.

**Heat wave** Heat waves, though less clear than floods, could influence urban growth by reducing manual labor productivity (Zhao et al., 2021). In addition, they create heat-related discomfort and risk in densely constructed urban areas (Huang et al., 2023), reducing incentives to move into cities. Thus, we expect heat waves to have a minor or no clear effect on urban growth. For heat waves, the Wet-Bulb Global Temperature (WBGT) is chosen as a metric, in particular the average WBGT over a 5-day window in a given month. Then the 5-day monthly maxima are taken. Using a fixed threshold does not account sufficiently for geographic variation. Some parts of Western Africa experience very high WBGT values almost every year. Thus, in addition, we compute a standardized score based on historical daily WBGT temperatures from 1970 to 1999

<footnote n="4">⁴We refrain in this article from calling the observed shocks "climate" shocks, albeit they are heavily influenced by climatic change. Our aim is to emphasize their short-term, high-frequency impacts consistent with the framework of climate-driven weather shocks (Kolstad and Moore, 2020)</footnote>

<footnote n="5">⁵The data is drawn from the Global Flood Awareness System (Joint Research Center and Copernicus Emergency Management Service, 2019) on daily river discharge in m³ per second. They are standardized on monthly means for the covered period (2000-2020).</footnote>
to calculate heat wave anomalies. A heat wave month is a month in which a 2 standard
deviation anomaly occurred. Furthermore, wet-bulb temperatures must have exceeded
at least 33 °C, a threshold that is considered extreme for the human body. Figure 3
shows that despite accounting for these extreme values, Western Africa is still the
region where most heat wave events are recorded.

**Drought** Prolonged drought, as a slow-onset event, can be damaging for agricultural production (e.g. Santini et al., 2022) and therefore lead to food insecurity. They could accelerate urbanization (see e.g. Van der Borgh et al., 2025; Chlouba, Mukim and Zaveri, 2023) through rural-urban migration, especially if droughts occur in catchment areas of urban areas. We expect that drought may lead to increased urban expansion through the indirect, non-observed channel of rural-urban migration. For drought, the paper relies on the Standardized Precipitation Evapotranspiration Index (SPEI) for a 3-month (SPEI-3) and 12-month (SPEI-12) period. Having the monthly intensities on a city level, an "extreme drought" is defined as a situation where both SPEI-03 and SPEI-12 are below a value of -1.5 in the same month, commonly referred to as severely dry environments. As droughts in some regions are fairly common, both conditions must be met. A similar approach has been used in the past to define "compound droughts" (see e.g. Wu et al., 2022). Given that droughts are fairly large-scale events, city-level treatment is likely to also capture similar drought conditions in the catchment area of a city. Figure 3 shows that most parts of Subs-Saharan Africa have seen at least a single drought over the period covered<fnref n="6" />. In our sample, this hazard appears to be one of the most common, as becomes clear in the descriptive statistics discussed below.

**Storm** Damaging winds, as a rapid-onset type of event, tend to damage the infrastructure and cause economic losses (Hsiang and Jina, 2014; Heinen, Khadan and Strobl, 2019; Elliott et al., 2019). They might lead to unplanned expansion of cities in response to rebuilding destroyed housing. They could also lead to increased urban growth in cities away from the coast, as many storms occur near the coast. We therefore expect that windstorms have a positive effect on urban expansion. In this study, extreme wind and wind damage can be caused by wind speed anomalies or cyclones. Thus, the wind intensity combines the highest wind speed in a month recorded by tropical cyclones or 10m wind gusts. To be considered "extreme", a wind speed of at least 25 meters per second is required. This threshold is used, for example, by the European Severe Storm Laboratory (ESSL) (European Severe Storms Laboratory, 2014). However, Figure 3 shows that storms exceeding this threshold are relatively rare in Sub-Saharan Africa. They are mainly concentrated in Madagascar and a few provinces in southern Africa. Although the sample itself might be relatively small, we have included storm as a fourth category nonetheless. This decision was made in light of the expected increase in the intensity of future storms.

Other Controls

As main controls, similar to Van der Borgh et al. (2025), we use country-level GDP per

<footnote n="6">⁶One might wonder why countries such as Mali and Niger see no drought events. In such hyper-arid and consistently dry climates, it is more challenging to trigger a "compound" drought, as defined here. This is more common in regions with more rainfall variability</footnote>
capita and share of employment in agriculture over total employment from the World
Bank (through Azevedo, 2011⁷). GDP per capita and employment in agriculture aim
to capture important time-varying factors that could affect the response to extreme
events. Poorer or more agriculturally dependent countries might be more vulnerable
to these weather shocks (Meierrieks, 2021; Cattaneo et al., 2019). Thus, we control for
time-varying country characteristics. Furthermore, we include a population estimates
from the global gridded population dataset (GlobPOP) by Liu, Cao et al. (2024). Urban
expansion that excludes population estimates overlooks a significant aspect of what
drives urbanization. We refrain from defining urbanization by relying exclusively on
population estimates, given their interpolated nature. However, the GlobPop data
is documented to outperform other existing population estimated datasets and is
therefore used in some parts of that article. We expect that population trends follow
broadly urban expansion. Finally, wealth estimates are taken from Lee and Braithwaite
(2022) and estimate a time-invariant index of the absolute wealth of a location. This
follows a similar reasoning as accounting for GDP per capita on a country-level and
aims to allow us to distinguish between poorer and richer cities in relative terms within
each country. All other data is described in the respective sections.

## 2.3 Descriptive Statistics and Limitation of the Data

The descriptive statistics of this article are shown in Table 8. Originally, the sample for Sub-Saharan Africa included 5 821 cities, of which 5 721 are included. The excluded cities show growth rates in urban expansion of above 150%. They were manually validated as outliers and therefore excluded<fnref n="8" />. The final sample covers 5 721 cities for the period 2000 until 2019. Out of this sample, 59 can be considered metropolitan areas<fnref n="9" /> as population and economic centers of a country. The average city has only about seven square kilometers in impervious area. Two things can be taken from this: many of the our sample are small towns. Furthermore, with a standard deviation of 42 square km, there is a large heterogeneity in urban size in our sample. Columns (2) to (5) divide the sample into African subregions<fnref n="10" />. Most small cities are found in eastern and central Africa. Across the entire sample and all regions, cities exhibit annual growth rates of approximately 3%. These rates are computed as first log-differences. The by far most common hazards are droughts. An average city sees around 0.1 drought events a year or roughly one drought every ten years. Similarly, floods are also common. Heat waves, as discussed earlier, are most commonly found in western Africa. In terms of control variables, most cities lie in countries where non-negligible shares of the population are employed in agriculture. In general, the descriptive statistics suggest significant variation between subregions. Thus, we test heterogeneity by subregion.

<footnote n="7">⁷For Eritrea, missing GDP per capita data after 2012 was supplemented using data from the IMF World Economic Outlook (accessed May 5, 2025).</footnote>

<footnote n="8">⁸Growth rates of 100% are rare in our sample, but they can occur when relatively small urban agglomerations expand rapidly. Because these areas start from a low base, even modest absolute increases in urban extent can result in very high growth rates.</footnote>

<footnote n="9">⁹This definition is provided by OECD and SWAC (2020).</footnote>

<footnote n="10">¹⁰The division is illustrated in Appendix Figure S1.</footnote>
Furthermore, when we count the number of cities by country, Nigeria represents one-fifth of the cities in our sample (see the lower panel in Figure S1). This proportion mirrors the demographics of the real world, where approximately one in five people in Sub-Saharan Africa lives in Nigeria. However, this motivates robustness checks of sample dependence, which will be included in the robustness section of this article. A large part of cities are at high flood risk. The included towns of central Africa show, on average, the lowest wealth but the highest share of agricultural employment at the country level. In terms of shock computation, most cities overlap between two and three 10x10km cells, so city-level exposure values are based on averages of weighted statistics of two 10x10km cells by Reinhardt (2025).

There are some notable data limitations. The measure of urban expansion, impervious surfaces, measures horizontal urban expansion rather than vertical expansion. So, if the city cores grow in height, this growth will not be captured. Given the focus on many smaller urban areas, horizontal expansion is likely the main driver of urban growth, in contrast to studies that focus on the effects in already densely populated regions (Magontier and Martinez, 2023). The hazard data is also limited. Converting hazard intensities into binary dummies may oversimplify the complexity of hazard events. There are two main concerns with this method: first, having a single dummy assumes homogeneous treatment effects and excludes the possibility of nonlinearity. Given that the thresholds are set to rather extreme values, the binary indicator may fail to capture the variation in the hazard intensity just below the threshold. This could potentially lead to underestimating weaker yet still impactful events. This study focuses on fairly high hazard values, making this threshold choice both interpretable ("one additional event") and consistent with parts of the existing literature<fnref n="11" />. For example, flood events are frequently operationalized as binary indicators, as seen in Kocornik-Mina et al. (2020). A second concern relates to how "extreme" is defined. According to the Intergovernmental Panel on Climate Change (IPCC) (Seneviratne et al., 2012), all four hazard categories considered in this study are influenced by global warming. Consequently, these hazards may become more or less extreme. However, since the thresholds for defining extreme events are set retrospectively, they are based on a relatively short 20-year period. It is unlikely that substantial shifts in the underlying distributions of each hazard would be detectable within this time frame.

# 3 Empirical Framework

This study attempts to assess the effects of the four major extreme weather hazards on horizontal physical urban extension in Sub-Saharan Africa. We focus particularly on changes in urban expansion growth rates in the years following extreme events. In contrast to previous work, we account for the presence of other climatic shocks by

<footnote n="11">¹¹This concerns particularly articles relying on event studies that often reformulate treatment intensities into binary dummies to fit into treatment models</footnote>
explicitly including them in the regression model.

## 3.1 Specification

The main specification of this article is a panel regression model of the following form:

$$ \Delta \log(Y_{icrt}) = \alpha + \sum_{k=1}^{4} \beta_k \cdot \text{Haz}_{icrt-1}^{(k)} + \gamma X_{ct-1} + \lambda_t + \lambda_i + \lambda_{r \times t} + \epsilon_{it} \quad (1) $$

The main outcome variable in this model is $\Delta \log(Y_{icrt})$, which measures the logged first-difference of the area in $m^2$ of a city that is covered with impervious surfaces, so human-modified land. Cities (i) are located in country c that again is located in sub-region r (Western, Eastern, Central or Southern Africa) and recorded in year t. The $\beta_k \cdot \text{Haz}_{icrt-1}^{(k)}$ represents the dot product between the regression coefficients $\beta_k$ for each hazard type k (flood, heat wave, drought, storm) and the corresponding components of the hazard vector $\text{Haz}_{icrt-1}$, which includes all hazard variables lagged by one year. The four hazards, as described in the previous section, measure the number of events of this type in a year<fnref n="12" />. Hazards are therefore considered cumulative and additive, based on the total number of events per year. They are lagged under the assumption that shocks require time to influence urban growth. As controls X, similar to Van der Borgh et al. (2025), the share of agricultural employment over total employment and GDP per capita in a country are included as lagged terms. They account for important factors that vary in time and could influence the response to urban growth. $\lambda_t$ control for common global shocks such as commodity price shocks, pandemics, but also common trends in weather (see Kolstad and Moore, 2020). $\lambda_i$ control for city-specific time-invariant effects such as distance from the coastline, average elevation, accessibility by road, and remoteness. Finally, we include region-year fixed effects ($\lambda_{r \times t}$) to capture region-specific trends, particularly those driven by large-scale climate teleconnections (e.g., El Niño Southern Oscillation, Madden Julian Oscillation), which tend to affect entire sub-regions rather than individual localities. These trends might heavily influence the occurrence of hazards and serve as omitted variables. The countries by subregions are shown in Table S1. The standard error is clustered at the city-level<fnref n="13" />.

## 3.2 Identification Strategy

The baseline coefficients ($\beta_k$) measure the short-term response in the year following an event to changes in physical urban growth resulting from an additional hazard event k. Identification comes from the temporal random variation in the hazard occurrence

<footnote n="12">¹²The number of times a hazard switches from non-treated to treated</footnote>

<footnote n="13">¹³In literature, often clustering at larger levels is found (see for a discussion by Ortiz-Bobea, 2021 section 4.6). Given that part of the underlying mechanism, such as rural-urban migration, will be at the province level, this clustering might introduce correlation in the residuals. Clustering at the city level assumes errors are independent across cities, which may not fully capture this dependence. This is assessed in the baseline.</footnote>
between and within cities. Following Kolstad and Moore (2020), the use of this variation in panel models describes a short-run response. In this short-run scenario, we expect little to no adjustments in beliefs or adjustments in investment, in contrast to longer-run effects of climate variation ("shift in distribution"). Given the calibration of the shock to extreme values, however, we expect that shocks nonetheless will create non-negligible effects. Although not actively discussed, the main underlying mechanisms are likely rural-urban migration, policy responses, or urban zoning in response to shocks that have been studied in previous literature (for some discussions see Cattaneo et al., 2019; Ferreira, 2024). However, there are several concerns with the proposed methodology, notably, violation of the Stable Unit Treatment Value Assumption (SUTVA), omitted variable bias, reverse causality, spatial and temporal autocorrelation of the error term, and selection into treatment.

First and foremost, there is concern about violating the Stable Unit Treatment Value Assumption. This assumption states that the treatment assigned to a specific hazard is independent of the treatment status of other units. In the context of extreme events, this is often not the case. Potential reasons might be spatial spillovers, treatment anticipation or non-uniformly distributed treatment definitions. In this article, we consider anticipation to be limited. Although Early Warnings For All and other initiatives have substantially advanced access to information in recent years, their coverage remains unevenly distributed across our sample. Many countries still have only limited access to this information. As a result, anticipation might not be a major concern. Regarding treatment uniformity, we standardize treatments by event counts using mostly country-specific thresholds. This reduces concerns and heterogeneity of the treatment definition. However, spatial spillovers remain a valid threat to the SUTVA. Spillovers from nearby areas are possible (Chlouba, Mukim and Zaveri, 2023), thus we include a test if treatments in catchment areas have more explanatory power. This will at least reduce concerns to some extent.

The omitted variable bias is present if time-varying factors that influence horizontal urban expansion are not captured in the model. Fixed effects help control for many omitted variables in the climate context. However, if shock variables come from the same distribution as climatic variables, omitted variable bias may still occur if key climatic variables are excluded (Hogan and Schlenker, 2024). Urban studies often examine the effects of rainfall and temperature on growth. However, air moisture, linked to heat waves and urban disamenities, may also be important. Our use of pre-composed intensity measures (e.g., heat waves or floods) helps reduce such bias, though some residual environmental factors remain. For example, powerful local weather systems, known as mesoscale convective systems, can lead to floods, storms, and hailstorms, an impact that is omitted in this article. From a socio-economic point of view, other predictors of urban growth at city level are excluded given that they might represent "bad controls", which means mediating the effect of the shocks (Dell, Jones and Olken, 2014). It is not possible to fully account for all omitted variables, but we believe that with the set of fixed effects, the chosen controls as well as the inclusion of all events at once large parts of the omitted variable bias is limited.
Then, the impact of shocks on urban growth may be nonlinear. The current model assumes constant marginal effects ($\beta_k$), treating each additional event as having the same impact — e.g. from zero to one heat wave is equal to three to four. This may oversimplify reality: if marginal effects vary with the number of events, unaccounted non-linearities remain in the error term as omitted variables. Ideally, we would employ a non-linear specification similar to that used by Burke, Hsiang and Miguel (2015) by directly including intensities. However, this approach is more challenging in our case, as our model includes multiple main independent variables that may interact with each other when used to predict extreme events. Adding a non-linear function for rainfall extremes alone might be easy to implement. However, adding wet-bulb temperature, our measure for heat waves in a non-linear way at the same time introduces significant complexity for interpretation. The relationship between these variables and their polynomials complicates isolating the impact of rainfall and wet-bulb temperature on observed effects. The chosen proxy (number of events per year) still retains at least an idea of the quantity of shocks, but allows a better identification of what shock drives the results. To at least partially address this limitation, our robustness checks include quadratic polynomial specifications and incorporate annual raw intensity measures. Reverse causality is unlikely, as hazard shocks are lagged by one period. However, urban growth today may increase the likelihood of future heat waves or flood (see e.g. Feng, Zhang and Bourke, 2021; Tuholske et al., 2021). Increasing the risk of an event and causing an event are not the same thing. Floods and heat waves depend on multiple factors beyond urbanized land, including city characteristics, geography, and climate. However, this introduces temporal autocorrelation of the error term. To address this, we apply spatial heteroskedastic-autocorrelated corrected (HAC) standard errors as a robustness check.

In addition, shock exposure varies spatially: droughts expand over large areas, while floods are often localized. Thus, our data likely captures urban flooding well but may underrepresent drought exposure, introducing spatial measurement error. To address this and account for the larger spatial footprint of some shocks, one of our extended heterogeneity analyses expands the definition of a shock from the city to its surrounding catchment area using geographic buffers.

A final concern is selection into treatment. In the baseline model, some cities in the control group are never exposed to any of the four shocks, which may have fundamentally shaped their growth trajectories. To assess whether this influences our baseline results, we apply the method proposed by Imai and Kim (2019), which constructs control groups with similar treatment histories and adjusts them based on relevant covariates. This approach enables us to assess the potential impact of selection bias on our findings.

# 4 Results

In the following sections, we first present our baseline estimates, followed by analyses of heterogeneity and robustness. We then introduce three key extensions. First, we
disentangle the number of hazard events from the co-occurrence of other events, commonly referred to as "compound events". Second, extend the spatial scope beyond city boundaries to examine intensive margin effects by exploiting variation in hazard exposure across surrounding catchment areas. Third, we incorporate temporal dynamics through an event study that accounts for the history of treatment, allowing us to explore longer-term effects and address potential selection bias.

<table><caption>Table 1: Toy Equation</caption><thead><tr><th></th><th colspan="4">Growth of Impervious Surfaces</th></tr><tr><th></th><th>(1)<br>Flood</th><th>(2)<br>Heatwave</th><th>(3)<br>Drought</th><th>(4)<br>Storm</th></tr></thead><tbody><tr><td>Σ Events in t-1</td><td>-0.0029***<br>[-0.004,-0.002]</td><td>-0.0016***<br>[-0.002,-0.001]</td><td>0.0011**<br>[0.000,0.002]</td><td>0.0038*<br>[0.000,0.008]</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.223</td><td>0.223</td><td>0.223</td><td>0.223</td></tr><tr><td>Obs.</td><td>107,796</td><td>107,796</td><td>107,796</td><td>107,796</td></tr><tr><td>Average Urban Growth</td><td>.03</td><td>.03</td><td>.03</td><td>.03</td></tr><tr><td>Controls?</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>City-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region-Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Note: *** 0.01,** 0.05, * 0.1 p-val. Confidence intervals are at a 90% level. This table shows the results of Equation 1 excluding region-year FE and only adding hazards individually. Controls included are country employment in agriculture (in %) and the country's total GDP per capita. Regions are defined as subregions of Africa and are shown in Figure S1.

Before presenting the results of Equation 1, Table 1 illustrates the common approach in the literature, which involves including hazards individually. This toy regression is similar to Equation 1 but includes only one hazard at a time and excludes region-year fixed effects. This shall allow us to illustrate the raw correlations in a two-way fixed-effects setting. Each flood leads to a reduction in urban growth by around 0.3pp (or 10% relative to the mean growth rate), heat waves by 0.2pp (or 7% to mean growth), while droughts increase them by 0.1pp (+3% to mean growth) and storms of 0.4pp. (+13% to means). In the next sections, we are presenting how these individual coefficients change once accounting for the mean level of the other hazards and will see that especially the heat wave becomes insignificant with droughts and storms losing magnitude and significance.

## 4.1 Main Results

We find that flood events significantly reduce the rate of urban expansion. Our estimates suggest reductions in urban growth of approximately 0.24 to 0.28 percentage points (pp) for each additional flood experienced, representing a reduction of around 8 to 9% relative to the city's average rate of growth of 3% (with a 3 to 9% reduction in the confidence intervals). This effect is robust to all tested specifications and is the only
hazard that exhibits a clear pattern. Table 2 reports results from Equation 1, which examine the short-term effects of weather shocks on the intensive margin of additional shocks on urban expansion. We gradually increase the restrictiveness of our model from column (1) to (4). Column (1) shows a simple two-way fixed effects model, often found in the literature. Floods and heat waves significantly reduce urban growth at the 1% level, while droughts and storms appear to increase it.

However, these positive effects do not hold when we account for spatial and temporal autocorrelation in column (2), using a 100 km spatial and 2-year temporal clustering window<fnref n="14" />. This suggests the importance of spatial dependencies, particularly for drought and storm events. We study this insight in more detail in *subsubsection 4.2.2*. In column (3), we present our preferred specification, which is equivalent to Equation 1. We introduce region-year fixed effects and use clustering of standard errors at the city level. Here, the flood effect remains negative and significant, while the effects of droughts and storms become weaker. In contrast to Van der Borgh et al. (2025) the effect of droughts is with 0.08pp on a 10% level substantially smaller than in literature, likely due to the inclusion of smaller cities in the sample. Finally, clustering at the country level in column (4) renders all coefficients but flood insignificant. This emphasizes that substantial heterogeneity exists within countries.

<table><caption>Table 2: Baseline Regression</caption><thead><tr><th></th><th colspan="4">Growth of Impervious Surfaces</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Σ Flood Events in t-1</td><td>-0.0028***<br>[-0.004,-0.002]</td><td>-0.0028***<br>[-0.004,-0.001]</td><td>-0.0024***<br>[-0.003,-0.001]</td><td>-0.0024**<br>[-0.004,-0.000]</td></tr><tr><td>Σ Heatwave Events in t-1</td><td>-0.0015***<br>[-0.002,-0.001]</td><td>-0.0015**<br>[-0.003,-0.000]</td><td>-0.0008<br>[-0.002,0.000]</td><td>-0.0008<br>[-0.002,0.001]</td></tr><tr><td>Σ Drought Events in t-1</td><td>0.0011**<br>[0.000,0.002]</td><td>0.0011<br>[-0.000,0.002]</td><td>0.0008*<br>[0.000,0.002]</td><td>0.0008<br>[-0.001,0.003]</td></tr><tr><td>Σ Storm Events in t-1</td><td>0.0038*<br>[0.000,0.008]</td><td>0.0038<br>[-0.000,0.008]</td><td>0.0036*<br>[0.000,0.007]</td><td>0.0036<br>[-0.001,0.008]</td></tr><tr><td>Adjusted R²</td><td>0.223</td><td></td><td>0.233</td><td>0.233</td></tr><tr><td>Obs.</td><td>107,796</td><td>107,797</td><td>107,796</td><td>107,796</td></tr><tr><td>Average Urban Growth</td><td>.03</td><td>.03</td><td>.03</td><td>.03</td></tr><tr><td>Controls?</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>City-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region-Year FE</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Clustered at</td><td>City</td><td>HAC</td><td>City</td><td>Country</td></tr></tbody></table>

**Note:** *** 0.01,** 0.05, * 0.1 p-val. Confidence intervals are at a 90% level. This table shows the baseline results of Equation 1. Controls included are country employment in agriculture (in %) and the country's total GDP per capita. Column (2) applies spatial HAC errors following Colella et al. (2019) with a distance cut-off of 100km and with 2 temporal lags. Regions are defined as subregions of Africa and are shown in Figure S1.

<footnote n="14">¹⁴Unfortunately, this clustering does not allow for more than two fixed effects, which is why it is applied to the two-way fixed effects model rather than the one with included regional fixed-effects.</footnote>
### 4.1.1 Heterogeneity

In the following paragraphs, we investigate whether the results vary when considering stratified samples from the entire city sample. All results are still based on Equation 1, but are divided into subsamples.

<table><caption>Table 3: Heterogeneity by Region</caption><thead><tr><th></th><th>(1)<br>Southern Africa</th><th>(2)<br>Eastern Africa</th><th>(3)<br>Central Africa</th><th>(4)<br>Western Africa</th></tr></thead><tbody><tr><td>Σ Flood Events in t-1</td><td>-0.0014<br>[-0.003,0.000]</td><td>0.0009<br>[-0.001,0.003]</td><td>-0.0017<br>[-0.007,0.004]</td><td>-0.0046***<br>[-0.006,-0.003]</td></tr><tr><td>Σ Heatwave Events in t-1</td><td>-0.0032<br>[-0.007,0.000]</td><td>0.0040**<br>[0.001,0.007]</td><td>-0.0028<br>[-0.006,0.000]</td><td>-0.0010<br>[-0.002,0.000]</td></tr><tr><td>Σ Drought Events in t-1</td><td>-0.0003<br>[-0.001,0.001]</td><td>0.0044***<br>[0.003,0.006]</td><td>0.0000<br>[-0.003,0.003]</td><td>-0.0015*<br>[-0.003,-0.000]</td></tr><tr><td>Σ Storm Events in t-1</td><td>0.0042<br>[-0.001,0.009]</td><td>0.0015<br>[-0.002,0.005]</td><td>0.0000<br>[0.000,0.000]</td><td>0.0000<br>[0.000,0.000]</td></tr><tr><td>Obs.</td><td>19,551</td><td>28,275</td><td>16,716</td><td>43,254</td></tr><tr><td>Average Urban Growth</td><td>.02</td><td>.03</td><td>.03</td><td>.03</td></tr><tr><td>Controls?</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>City-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region-Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

**Note:** *** 0.01, ** 0.05, * 0.1 p-val. Confidence intervals are at a 90% level. This table shows the baseline results of Equation 1. All samples include controls (a country's employment in agriculture (in %) and the country's total GDP per capita) and have city and year fixed effects. Regions are defined as subregions of Africa and are shown in Figure S1 and countries are listed in Table S1. The exposure by country is shown in Figure 3.

**By Geographic Region** Following the Intergovernmental Panel on Climate Change (Seneviratne et al., 2012), the different regions of Africa are differently exposed to extreme events, which motivates the division of the country into broader regions. The negative effects of floods are found to be strongly driven by Western African cities, as shown in Table 3. In these cities, each additional shock reduces growth by 0.4 pp. Droughts accelerate urban growth especially in Eastern Africa, where they increase urban growth by approximately 14% relative to the means. However, in western Africa, they tend to significantly reduce urban growth at the 10% level. All models include fixed effects for city and year. Two important takeaways can be concluded from Table 3: first, there is strong heterogeneity in Sub-Saharan Africa, likely because weather shocks can be spatially centered around a specific region, and second, the direction of the effects may also depend on the region. In Western, Southern and Central Africa floods, heat waves and droughts are generally harmful to urban expansion, albeit mostly not significant, while the opposite is true in Eastern Africa. This reveals that the underlying mechanisms that might drive or hinder urban expansions are different. Regions may differ in their regional governance, their ability to respond to migration in the face of shocks, or their differences in urban absorptive capacity. Cities in East Africa might
have potentially better absorptive capacity, or rural farmers tend to respond faster
with rural-urban migration. In other parts of Africa, these channels might be less
pronounced, or other factors such as rising conflict might be different responses to
weather shocks keeping people from moving into cities. In Appendix Supplement-
ary Appendix D we additionally assess if global climate phenomena that influence
regional climate events might influence urban expansion. Specifically, we focus on
the El Niño Southern Oscillation, which might amplify rainfall in some regions of
Africa and increase dry conditions in others. Thus, as an additional test, the effects are
analyzed if the year of the event is an El Niño year or not. The model and the data are
described in the respective section. The findings show that effects are most pronounced
in neutral years, where communities or policymakers might anticipate less extreme
events. There is regional variation in responses to events, depending on the year in the
El Niño cycle. However, more temporally disaggregated studies are needed to draw
robust conclusions from these results.

**By Urban Poverty** The effects on the response of urban areas could depend on the ability of residents to cope with shock, especially in the context of poor households (Hallegatte, Vogt-Schilb et al., 2017; Cattaneo et al., 2019). Richer cities might have more capabilities to adjust to shocks, whereas poorer cities might respond rather than anticipate a weather shock. To this end, we use data on poverty and wealth measures at the village level by Lee and Braithwaite (2022) and compute the time-invariant wealth average of a city. The authors provide the International Wealth Index, which we use for this study. Based on the time-invariant measure at the city level, we construct four quantiles within each country, which show the least (richest) and most (poorest) deprived cities within each country in our sample. The results are shown in Figure 4a. Flood has negative effects throughout the entire wealth distribution. A likely explanation is that flooding might lead to displacement and destruction within the city, independently of the city's wealth. These factors can cause the effects of growth to be negative. Droughts increase urban expansion only in the poorest cities; for all others, they appear to have no significant impact. A conjectured explanation is that rural-urban migrants might first settle in nearby agglomerations, as the cost of settling is potentially lower than migrating into richer, larger cities. Storms show a clear pattern, where richer cities seem to expand more after severe storms. This could be because storms are destructive and manifest more frequently near coastlines, where wealthier cities are often located. These factors prompt infrastructure investment after such events and lead to real estate redevelopment, which might drive urban expansion.

**By Remoteness** Usually, cities that are located in proximity to the main city of a country, the "metropole", might be better connected through trade and transportation networks. Households in remote cities might be poorer (Provenzano, 2024) and thus be more severely affected if help takes longer to arrive or their remoteness makes providing disaster aid less politically interesting (Hamza, Eriksson and Staupe-Delgado, 2021). Thus, we computed the distance of each city to the metropole of the country and
built quantiles of distance within each country. The only clear pattern is observed
in Figure 4b for floods, where the strongest negative effects are found for the most
remote cities. Combined with the previous result, it becomes clear that the largest
loss in growth in response to floods is paid by remote, poorer cities. No effects are
found for heat waves. For storms, it is mostly cities close to metropolitan areas that
experience increased growth. This is again in line with previous results, which show
that the richest cities tend to see the largest growth effects. For droughts, the results are
more nuanced, where only cities at a medium distance see the positive effects. Overall,
it seems that remoteness plays a role especially for flood events, where more remote
cities see larger losses in growth.

**By City Rank** Van der Borgh et al. (2025) find that droughts seem to especially increase growth in large economic centers. To test whether, in addition to wealth, the relative size of a city matters, we stratify our sample by creating a ranking of the top 3, 5, 10, and all other cities within a country<fnref n="15" />. One might imagine that large cities offer more opportunities for work and greater flexibility in relocating internally after an event. The results are shown in Figure 4c. The results for floods are consistent with the previous sections. For heat waves, we observe weak effects that indicate the largest cities in a country also experience the largest relative expansion after heat wave events. For drought, the contrary is the case. If large cities are affected compared in comparison to less affected ones, the effects of a drought reduce urban expansion. Storm has a sample too small to be evaluated here.

**By Industry Type** Lastly, we also test the proposed finding by Henderson, Storeygard and Deichmann (2017) that conjectures that negative shocks of soil moisture contributed especially in manufacturing and export-oriented towns to increased urbanization. According to their argument, this is especially due to the possibility of manufacturing cities to absorb incoming labor from rural-urban migration. However, in all non-manufacturing towns, hardly any changes are observed. Similarly, Colmer (2021) finds that for India the possibility for firms to absorb incoming labor substantially reduced economic losses after rainfall and temperature shocks. In our context, manufacturing cities are defined as cities where more than 20% of total carbon emissions are derived from industrial production and processes<fnref n="16" />. This data is taken from the European Commission - JRC and International Energy Agency (IEA) (2023) and computed as sectoral averages by city-year. For floods and heat waves, we find that cities that are classified as non-manufacturing see a minimal reduction in urban growth after each additional event in Figure 4d. Droughts, similar to Henderson, Storeygard and Deichmann (2017), have a positive effect especially in manufacturing cities. This is surprising in light of the results shown above. Previously, we found that especially

<footnote n="15">¹⁵The ranking is build as average urban extent over the 20-year period within the country.</footnote>

<footnote n="16">¹⁶The threshold has been set after assessing this metric for a random sample of 10 manufacturing cities taken from the replication package of Henderson, Storeygard and Deichmann (2017). The share of industrial CO₂ fluctuated between 18% and 60%. The 20% is used as a lower bound estimate.</footnote>
small cities experience expansions after droughts, while large cities observe the oppos-
ite. Manufacturing cities tend to be larger cities also, so one would expect negative,
not positive, effects. This is likely driven by the change in sample. In the city-rank
analysis, around 130 cities fall into the top 3 tier, while in this figure, one of every five
cities fits the criteria. An additional robustness test, excluded here, revealed that the
positive effects are driven by medium-sized cities with ranks between 10 and 50 that
are sufficiently industrialized but not the primary cities of a country. For storms, we
observe large confidence intervals, given the limited sample size. In contrast to Hende-
rson, Storeygard and Deichmann (2017), however, we include a larger sample of cities,
covering a shorter but more recent period, and focus on a more recent classification of
cities by manufacturing status. Nonetheless, despite different magnitudes, the direction
is comparable. We do not test for the underlying mechanism that might explain why
manufacturing cities see stronger growth responses. In the Indian context, short-term
weather shocks (Colmer, 2021) and long-term climate shocks (Liu, Shamdasani and
Taraz, 2023) caused by temperature anomalies have led to labor adjustments and rural-
urban migration in response. Here, it has been found that short-term weather shocks
have led to reallocation from agricultural to non-agricultural employment, which is
not observed in the long run. Similarly, as noted by Colmer (2021), similar effects may
also be observed in an African context for other short-term weather shocks. A possible
explanation might therefore be that drought induces more rural-urban migration, while
floods cause temporary displacement (Cattaneo et al., 2019). If migrants choose a
destination, they might relocate to cities with larger labor market opportunities and
thus settle in cities with more non-agricultural markets, thus accelerating growth in
manufacturing cities. Floods or heat waves might, especially in rural towns in Africa,
trap the population due to losses of income, thus temporarily reducing urban growth
in cities that do not strongly rely on manufacturing services. However, this finding
is only indicative evidence. Further studies using firm-level data are needed to draw
definitive conclusions.

In general, the results of this section reveal a large heterogeneity in the dimension of
the impact of hazards on urban expansion in Africa. Western Africa experiences the
most significant overall negative impacts, particularly from floods. These floods reduce
urban expansion, especially for poorer, remote cities that tend to depend more on
non-manufacturing industries. Droughts tend to drive urban expansion in the poorest
and medium-distance cities. This is likely due to displacement-driven rural-urban
migration. In general, this section highlights the importance of considering spatial and
economic context in examining the effects of urban expansion. Papers in the literature
often pool cities only by continents. However, there may be significant heterogeneity
in the impact response due to the characteristics of the city and region. This should be
taken into account.
### 4.1.2 Robustness Checks

In order to ensure that our results are not driven by specifications or sample selection, a set of robustness checks is conducted.

<table><caption>Table 4: Robustness Tests</caption><thead><tr><th rowspan="2"></th><th colspan="2">Alt. Outcomes</th><th colspan="2">Alt. Growth Specifications</th><th>Alt. Structural Form</th></tr><tr><th>(1)<br>WSFevo</th><th>(2)<br>Radar Signal</th><th>(3)<br>log(Δ Increment+1)</th><th>(4)<br>2-year Growth</th><th>(5)<br>Standard</th></tr></thead><tbody><tr><td>Σ Flood Events in t-1</td><td>-0.002<sup>**</sup><br>[-0.003,-0.000]</td><td>-0.003<sup>***</sup><br>[-0.003,-0.002]</td><td>-0.083<sup>***</sup><br>[-0.121,-0.045]</td><td>-0.007<sup>***</sup><br>[-0.011,-0.003]</td><td>-0.002<sup>***</sup><br>[-0.003,-0.001]</td></tr><tr><td>Σ Heatwave Events in t-1</td><td>-0.001<br>[-0.001,0.000]</td><td>-0.000<sup>**</sup><br>[-0.001,-0.000]</td><td>-0.028<br>[-0.066,0.010]</td><td>0.006<sup>*</sup><br>[0.000,0.011]</td><td>-0.001<sup>*</sup><br>[-0.002,-0.000]</td></tr><tr><td>Σ Drought Events in t-1</td><td>0.000<br>[-0.001,0.001]</td><td>0.008<sup>***</sup><br>[0.008,0.009]</td><td>0.004<br>[-0.026,0.034]</td><td>0.003<br>[-0.000,0.006]</td><td>0.001<br>[-0.000,0.002]</td></tr><tr><td>Σ Storm Events in t-1</td><td>-0.001<br>[-0.004,0.002]</td><td>-0.004<sup>***</sup><br>[-0.007,-0.002]</td><td>0.117<sup>**</sup><br>[0.021,0.213]</td><td>-0.005<br>[-0.014,0.005]</td><td>0.004<sup>*</sup><br>[0.000,0.007]</td></tr><tr><td>Build-Up (in km<sup>2</sup>) in t-1</td><td></td><td></td><td></td><td></td><td>-0.000<sup>***</sup><br>[-0.001,-0.000]</td></tr><tr><td>Obs.</td><td>84,977</td><td>100,986</td><td>108,907</td><td>96,520</td><td>107,796</td></tr><tr><td>Average Urban Growth</td><td>.03</td><td>.03</td><td>9.4</td><td>.07</td><td>.03</td></tr></tbody></table>

**Note:** *** 0.01, ** 0.05, * 0.1 p-val. Confidence intervals are at a 90% level. This table shows the baseline results of Equation 1. All samples include controls (a country's employment in agriculture (in %) and the country's total GDP per capita) and have city and year fixed effects. Regions are defined as subregions of Africa and are shown in Figure S1. Column (1) includes data from the German Aerospace Center (DLR) (2024) and Marconcini et al. (2020) measuring build-up growth until 2015. Column (2) utilizes low-resolution backscattering radar signals from unified time series from Tao et al. (2023) as an alternative measure of construction growth. Columns (3) and (4) change the outcome transformation, and Column (5) adds a lagged term into the model.

**Definition of Outcome** In the literature, one of the most common annual data on urban expansion is the World Settlement Footprint (WSF) Evolution (WSFevo) from 1985 to 2015 provided by Marconcini et al. (2020) (see e.g. studies by Chlouba, Mukim and Zaveri (2023) and Rentschler, Avner et al. (2023)). The method of obtaining WS-Fevo slightly varies from the GISA data used in this study. Column (1) of Table 4 if the results hold using the WSFevo data instead. The results are comparable to the baseline, except for droughts, which become insignificant. WSFevo and the used GISA data have the shortcoming that they always increase, which might overlook urban destruction. To this end, column (2) measures the urban growth in a variable that can regress: low-resolution radar backscattering. In this method, radar signals are sent and received from satellites, and the stronger the signal received, the greater the potential build-up. For this purpose, we use a homogenized time series of Tao et al. (2023). After computing growth rates based on average city-year signal<fnref n="17" />, Column (2) of Table 4 shows that effects that allow destruction in urban land do not fundamentally change the baseline results. In contrast, it makes them even stronger. In general, it appears that

<footnote n="17">¹⁷Note that backscatter might also increase due to high-rise urban build-up, a phenomenon known as "double bouncing." It can also increase through high surface backscatter from flooded areas. Most cities in Africa have a fairly low share of high-rise buildings. In addition, annual backscatter statistics help smooth out noise flooded areas. Therefore, this is likely a minor concern in this robustness test.</footnote>
the measure of urban expansion is robust against other measures.

**Definition of Growth** Two closely related papers by Chlouba, Mukim and Zaveri (2023) and Van der Borgh et al. (2025) use different definitions of growth. To avoid the definition of growth driving the results, the two definitions of growth are tested. Column (3) of Table 4 tests the effects using an increment plus a constant factor, and Column (4) relies on longer-term growth, assuming that extreme events, in the case of Van der Borgh et al. (2025) droughts, take place to affect urbanization. The change does not fundamentally change the result.

**Growth Regression Framework** To align with the growth literature following, e.g., Mankiw, Romer and Weil (1992), Column (5) of Table 4 includes a lagged level of the dependent variable. This specification yields results that are statistically more significant, suggesting that the baseline results are robust to this specification change. The model tested might be subject to the so-called "Nickell bias" (Nickell, 1981) referring to concerns that in short panel models when lagged dependent variables are included as regressors in a fixed-effects model. Here, the demeaning due to individual fixed effects induces a correlation between the transformed lagged dependent variables and the transformed error term, which biases the estimates and renders them inconsistent. To avoid drawing incorrect conclusions, we also apply a bias-corrected model by (Breitung, Kripfganz and Hayakawa, 2022), which yields comparable results.

**Sample Dependence** Most articles in the literature apply some sort of threshold to only include a fraction of the sample. We test in Table S2 whether the results change if we winsorize the dependent variable by applying a 5% threshold in Column (1). The results become weaker for floods and stronger for storms, but the direction of the result remains the same. Next, we progressively excluded cities from columns (2) to (4) that had populations below a certain threshold by the end of 2019. The results for floods are persistent around 0.2-0.3pp excluding cities with urban population of less than 5 000, 10 000¹⁸ and 50 000 people. As only around 20% of the cities in the sample have more than 50 000 people in 2019, this test mainly excludes large parts of the sample. Including only small cities with less than 50 000 people or less in 2019 we have similar effects in magnitude, but droughts have a more significant positive effect of increasing urban expansion by 0.1pp. This shows that part of the positive baseline effects of droughts are driven by small cities, which was already discussed in Figure 4c. In general, the main claim is that floods, especially, are causing a reduction in urban expansion. This claim is strongly robust against sample dependence and outliers in growth. To also test if one country drives the results, we perform a leave-one-out exercise as shown in Figure 5. The results for floods remain significant regardless of which country is

<footnote n="18">¹⁸Note that one criteria for cities to be included in Africapolis (OECD and SWAC (2020)) is that more than 10 000 people live in a city. The data used here to compute urban population is different to the one of Africapolis, thus we might find still urban agglomerations with less than 10 000 people. Some cities may also have lost population over the years</footnote>
dropped. For heat waves, it seems that Nigeria and the Ivory Coast drive the results more significantly. For drought, excluding Nigeria, the results are more positive and significant. For storm, since the largest treated sample is in South Africa, excluding it makes the storm insignificant. This reveals that the results for floods are stable across countries. For heat waves and droughts, Nigeria partially drives the results, which, given that it accounts for 20% of the sample, is not entirely unlikely.

**Measure of Shock** The main specification of the article sums all events of the four hazards in a year over an entire year. Two critiques should be tested: first, an earlier shock in the year might have a greater impact on urban growth. Second, the intensity (duration or physical intensity) might also matter. To address the first concern, in column (3) of Table S3, we compute a time-decay weight<fnref n="19" /> that gives a stronger weight to earlier occurring events. The events become more significant and marginally larger, especially for floods, heat waves and droughts. The weighting of the shock has an influence, but the direction of the results remains. To address concerns about intensity, column (5) includes, instead of the number of events, the number of months in a year that have conditions exceeding a threshold. This captures instead of the frequency of new onsets, the duration of onsets. Only the effects for floods are significant and smaller. For other hazards that are insignificant in terms of duration, the impacts may be more centered around the onset rather than the duration of an event. Lastly, instead of including event counts, we add the raw intensity measures aggregated at the yearly level, which may oversimplify the effects. For floods, we include the maximum DFO-Index in a city within a year, for heat wave the highest recorded 5-day block maximum in terms of Wet Bulb Temperature, for drought the lowest recorded SPEI-3 and for storm the highest daily wind gusts in meters per second<fnref n="20" />. The effects are around 0.09pp smaller as in the baseline in Column (1). Heat waves become significant, probably because not accounting for historical deviations a large fraction of the sample sees already fairly high levels of temperature (most of Western Africa). An increase of one unit in SPEI-03 reduces urban growth, while the storm is still significant. Although effects on floods and storms remain, the direction of effects for droughts and heat waves is reversed. A likely explanation is either that intensities affect urban expansion in a non-linear manner or that measurement errors occur. This is given because the Min-Max values of the intensities can be fairly noisy. We must be careful in interpreting these intensities as causal for two reasons. First, we underestimate the actual exposure, given that we only consider the maximum or minimum values over a year. This means that we only capture the most extreme events, underpredicting weaker, yet still impactful events. Additionally, especially for heat waves and droughts, we do not account for historical deviations (for droughts for longer-term droughts). Overall, this section has shown

$$ \frac{13 - (\text{month})}{12} $$

<footnote n="19">¹⁹The weights are computed by month of the event as:</footnote>

<footnote n="20">²⁰Note that in the conversion for the events, heat waves also include deviations from historic means of months and droughts also include SPEI-12 beside SPEI-03</footnote>
that the results are fairly robust to variations in the definition and weighting of the
shock variable.

**Non-linearities** Our model might have an omitted variable bias if non-linearities are not accounted for. Building on the results from the previous robustness test, we also add squared terms of all variables into all the described models. Column (2) of Table S3 tests adding squared terms to our main specification. Floods, become are sitll signific- ant but now only on a 10% level, no other coefficient is significant. Column (4) applies the same to time-decay-weighted shocks. Here, we find non-linearity in floods where having more than a single event tends to reverse the growth effects. No other hazard is significant. The same results are found when using the number of months squared in Column (6). In Column (8), accounting for squared non-linearities in intensities turns the flood entirely insignificant. However, for raw intensities, heat waves and storms change the direction of the results, accounting for their squared terms. This highlights that for flood intensity, the linear and non-linear terms might offset each other (due to strong correlation), making both insignificant, or there is no relationship across intensity levels<fnref n="21" />. Heat waves and storms seem to have non-linear dependencies. The key takeaway from this robustness analysis is that our baseline measure, annual event counts, is relatively robust to alternative shock definitions and the inclusion of non-linear terms. However, we find some indication that the economic impacts of floods are both nonlinear and temporally sensitive, particularly when considering earlier events in a given year. This illustrates that aggregating shocks into an annual, equal-weighted count of floods may overlook important variations related to the timing of events. Additionally, this approach may overlook variations in the frequency of events within a given year. Given that our empirical strategy relies on a panel model at the yearly level and we assume that shocks take some time to influence urban expansion, we continue to use annual event counts in our main specification. However, we acknowledge that this approach may overlook some time-sensitive non-linear dynamics, especially for floods.

**Significance of Floods** What is striking in the previous results is the robustness of flood events. Thus, additional robustness tests for flood events are performed in Table S4 to test more or less restrictive flood definitions. Neither including more common floods nor excluding cities with little or no flood risk changes the results. To avoid assigning floods to non-treated cities, we furthermore build more restrictive measures of flood. These measures consider whether flood records co-occurred with extreme rainfall shocks (pluvial floods) or with extreme river streamflow discharges (riverine floods). Both effects are significant and negative, but pluvial floods have notably stronger impacts.

<footnote n="21">²¹A likely explanation can be found looking at the range of considered intensities. The values range from 0 (no flood) to 2 (extreme flood). Most flooded years have an intensity of one. This causes the squared term to be collinear to the flood event and being dropped. This might explain at least in part the insignificant results.</footnote>
## 4.2 Beyond the Baseline: Three Extensions

The previous section focused on the baseline equation in Equation 1. The following sections introduce three extensions, notably the definition of a shock, before turning to a discussion and interpretation of the results. First, the shock is decomposed into cumulative hazard shocks and interconnected shocks, referred to as "compound events." Second, the analysis examines whether the results change when shifting from an intensive margin at the city level to an extensive margin at a broader spatial scale. Third, the temporal dynamics of the proposed shock are explored.

### 4.2.1 Accounting for the Shock Complexity

Following the work of, for instance, Zscheischler, Martius et al. (2020), accounting only for four distinct hazards might oversimplify the true nature of the effect of shocks. This part will study what happens if shocks of different types co-occur, e.g. a flood following a heat wave. These combinations might exacerbate the impact of shocks and thus lead to significantly different results. We categorize shocks into single-hazard and multi-hazard events, based on whether one or more hazards occurred within a month or a given time window. We define "compound" events as occurrences where multiple hazard types (e.g. flood and storm) are observed within a single month or a 2-month span. Furthermore, we also test for the two-month window the order in which both event classes took place. More information on the creation of the shock dummy and the updated regression model can be found in Supplementary Appendix E. Then the baseline model is re-run now including these compound shocks. The results can be seen in Table 5. Columns (1) and (2) show the results when the baseline model's shocks omit months with compounded shocks in the same month (column (1)) or within a 2-month period (column (2)). Excluding compound events, the coefficients are about the same magnitude as in the baseline. Next, adding compound dummies in columns (3) and (4), the effects remain stable. The compound interactions of flood-storm and heat-storm are the most significant and both positive. The combination of droughts and floods shows a significant reduction of urban expansion by 0.7pp, almost triple the effect than for floods alone. This effect is only found for compound events within the same month. Flood-storm and heat-storm pairs need to be interpreted with caution given that only around 10 cities have seen these hazard pairs<fnref n="22" />. Finally, in Column (5), accounting for the order of the sequence, again, the effects of individual hazards remain. The most significant effects are observed for floods that follow heat waves. Each additional sequence of that order occurring in a year reduces urban expansion by 0.7pp. This reduction amounts to around 23% reduction from the mean urban growth rate. These types of compound events are the most common found interaction and have been shown in literature to exist (Sauter et al., 2023). While we would have expected to also find significant effects for droughts before floods, notably of dry soil increasing the

<footnote n="22">²²See Appendix S2</footnote>
<table><caption>Table 5: Extension: Accounting for Compound Events</caption><thead><tr><th></th><th colspan="2">No Compound Events (CE)</th><th colspan="2">Not ordered</th><th>Ordered</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Σ Flood Events ex. CE</td><td>-0.002***<br>[-0.003,-0.001]</td><td>-0.002***<br>[-0.003,-0.001]</td><td>-0.002***<br>[-0.003,-0.001]</td><td>-0.002***<br>[-0.003,-0.001]</td><td>-0.002***<br>[-0.003,-0.001]</td></tr><tr><td>Σ Heat Events ex. CE</td><td>-0.001*<br>[-0.002,-0.000]</td><td>-0.001*<br>[-0.002,-0.000]</td><td>-0.001*<br>[-0.002,-0.000]</td><td>-0.001*<br>[-0.002,-0.000]</td><td>-0.001*<br>[-0.002,-0.000]</td></tr><tr><td>Σ Drought Events ex. CE</td><td>0.001<br>[-0.000,0.002]</td><td>0.001<br>[-0.000,0.002]</td><td>0.001<br>[-0.000,0.002]</td><td>0.001<br>[-0.000,0.002]</td><td>0.001<br>[-0.000,0.002]</td></tr><tr><td>Σ Storm Events ex. CE</td><td>0.003<br>[-0.000,0.007]</td><td>0.003<br>[-0.000,0.007]</td><td>0.004<br>[-0.000,0.007]</td><td>0.003<br>[-0.001,0.007]</td><td>0.003<br>[-0.001,0.007]</td></tr><tr><td colspan="6"><em>Multivariate Compound Events</em></td></tr><tr><td>Flood × Heat</td><td></td><td></td><td>-0.002<br>[-0.006,0.002]</td><td>-0.004*<br>[-0.007,-0.000]</td><td></td></tr><tr><td>Flood × Drought</td><td></td><td></td><td>-0.007*<br>[-0.014,-0.000]</td><td>0.001<br>[-0.003,0.006]</td><td></td></tr><tr><td>Flood × Storm</td><td></td><td></td><td>0.006*<br>[0.000,0.011]</td><td>0.006**<br>[0.002,0.010]</td><td></td></tr><tr><td>Heat × Drought</td><td></td><td></td><td>0.004<br>[-0.002,0.010]</td><td>0.002<br>[-0.003,0.008]</td><td></td></tr><tr><td>Drought × Storm</td><td></td><td></td><td>0.001<br>[-0.001,0.003]</td><td>0.002<br>[-0.001,0.004]</td><td></td></tr><tr><td>Heat × Storm</td><td></td><td></td><td>0.015***<br>[0.013,0.017]</td><td>0.015***<br>[0.013,0.017]</td><td></td></tr><tr><td>Flood → Heatwave</td><td></td><td></td><td></td><td></td><td>0.004<br>[-0.000,0.009]</td></tr><tr><td>Heatwave → Drought</td><td></td><td></td><td></td><td></td><td>0.001<br>[-0.006,0.008]</td></tr><tr><td>Flood → Storm</td><td></td><td></td><td></td><td></td><td>0.010**<br>[0.003,0.018]</td></tr><tr><td>Storm → Drought</td><td></td><td></td><td></td><td></td><td>0.003<br>[-0.005,0.012]</td></tr><tr><td>Drought → Flood</td><td></td><td></td><td></td><td></td><td>0.002<br>[-0.002,0.006]</td></tr><tr><td>Drought → Heatwave</td><td></td><td></td><td></td><td></td><td>0.001<br>[-0.005,0.007]</td></tr><tr><td>Drought → Storm</td><td></td><td></td><td></td><td></td><td>-0.001<br>[-0.006,0.003]</td></tr><tr><td>Flood → Drought</td><td></td><td></td><td></td><td></td><td>-0.009*<br>[-0.017,-0.000]</td></tr><tr><td>Heatwave → Storm</td><td></td><td></td><td></td><td></td><td>0.006<br>[-0.005,0.016]</td></tr><tr><td>Heatwave → Flood</td><td></td><td></td><td></td><td></td><td>-0.007***<br>[-0.009,-0.005]</td></tr><tr><td>Storm → Flood</td><td></td><td></td><td></td><td></td><td>0.006**<br>[0.002,0.010]</td></tr><tr><td>Obs.</td><td>107,796</td><td>107,796</td><td>107,796</td><td>107,796</td><td>107,796</td></tr><tr><td>City-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region-Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Clustered at</td><td>City</td><td>City</td><td>City</td><td>City</td><td>City</td></tr><tr><td>Window to Define CE</td><td>1-month</td><td>2-month</td><td>1-month</td><td>2-month</td><td>2-month</td></tr></tbody></table>

Note: *** 0.01,** 0.05, * 0.1 p-val. The table illustrates the impact of compound events on physical urban expansion, based on an adjusted Equation 1. More information on the creation of the variables is given in Supplementary Appendix E.
flooding impact, the effects are insignificant. In contrast, floods before droughts show a weak yet still significant negative effect. Seemingly paradoxical, floods can precede droughts for two reasons: first, floods are local events while droughts are global events. We might have a city experiencing a flood, and in the following month the catchment of a city experiencing a drought. Second, a flood does not exclude the possibility of a drought given that rapid flash floods might run-off without soaking into the soil. Lastly, again with a small sample, storm and floods and floods and storms likely capture tropical storms that bring storm surges in coastal areas. Here effects are positive and large with 1pp increase in expansion. The main takeaway from this extension is that, while the general direction of the results holds, compound pairs, especially in the case of heat wave-flood interactions, may show a significantly larger response than either floods or heat waves alone.

### 4.2.2 Accounting for Spatial Scope

The second extension is related to the spatial aggregation of the shock. In Equation 1, shocks are computed as an intensive margin ("What is the homogeneous short-term effect of an additional event of type k?"). This is especially useful for indirectly examining local drivers of urban expansion, such as reconstruction, zoning policies, or city-specific responses to local events (floods and heat waves). For events, notably droughts, covering larger spatial environments, it may be worth studying catchment area dynamics rather than cities themselves, as done by Van der Borgh et al., 2025 (country-level shocks) or Chlouba, Mukim and Zaveri, 2023 (shocks in neighboring 50 km cells). To account for this dynamic, the analysis of this article is repeated by computing the extensive margin ("What fraction of the surrounding land around a city is affected?"). For this purpose, buffers of 50, 100, and 150 km are drawn around each city and intersected with a 10x10 km grid. As the shock is now studied not categorically but in the affected area, the shocks are also recomputed at the city level. More information on how buffers and shocks are defined is provided in Appendix Supplementary Appendix F. Buffers have the limitation that they do not account for the transportation infrastructure within. Thus, as robustness, we also include daily commuting zone data by Data for Good at Meta (2023).

The models presented in Table 9 follow Equation 5. The model used is similar to the original, but it estimates what would happen if every spatial unit (cell) within the catchment of a city were to experience an additional event. Thus, it now measures the average aggregate effect of a shock with widespread exposure. We control for the population living within that buffer to determine the exposure of the population within that buffer. Column (1) shows that with the new definition of the shock that defines shocks at the city level, suddenly all coefficients become significant, similar to the model without region-year fixed effects in Table 2. Floods are most significant, but, more importantly, droughts are also significant with 0.1pp, increasing average urban growth by around 3.6%. Columns (2) to (4) show that the effects become stronger for floods, droughts and storm. As robustness, considering commuting zones instead, all
but droughts are significant. It remains unclear why droughts are insignificant. However, a conjectured hypothesis is that, given that commuting zones might cross country borders, underlying migration mechanisms through droughts might predominantly take place within countries. One needs to be careful in the coefficient interpretation, as the average level of shocks slightly vary one needs to be careful to directly compare shocks in different buffers. Although the average shock for each hazard remains relatively stable across buffer sizes, the coefficients controlling for population in each buffer increase in magnitude. This pattern suggests that larger coefficients are likely due to the broader spatial impact of the shock rather than differences in shock means across buffers. Thus, part of the observed impact on urban expansion in the baseline area may be driven by dynamics occurring in the city's broader catchment area. These dynamics may involve hidden mechanisms such as rural-urban migration or the avoidance of flooded cities as a destination.

### 4.2.3 Accounting for Temporal Dynamics

Third, and finally, all the models studied so far have studied the short-term impact of events on urban growth. However, researchers might be interested in the longer-term dynamics of shocks. In addition, it might be worthwhile to consider the history of treatment of a city to avoid selection bias of the results. To this end, we apply a recent estimator by Imai and Kim (2019). This estimator follows a three-step procedure. First, for each treated city by a hazard, a set of cities is created that share a similar treatment history over a specified pre-treatment period. In our case, for each treated city, we match five comparable cities with similar treatment histories over the last 8 years. Second, the comparable cities are matched to have a better balance between the treated city and the control set. In our case, we use the last two years of all other shocks than the one under consideration. Additionally, the total urban size in square km in 1990 and the average urban growth over the last four years are used to create a better control group. Finally, a difference-in-difference estimate is computed for post-treatment periods. More details are specified in Supplementary Appendix G. The results of this exercise are shown in Figure 2. Accounting for the treatment history, floods not only lead in the year after, but over the full studied five post-treatment years to a negative effect on urban expansion. The negative effects increase from 0.2pp in the treatment year up to 0.6pp reduction three years after the event, all significant at a 10% level. Heat waves show a positive, yet barely significant effect. Surprisingly, accounting for the treatment history of droughts, experiencing a drought decreases urban expansion by around 0.3pp in the year after a drought event up to two years. Then it turns insignificant. This is in contrast to the baseline results, where we document positive effects. That highlights that, while in the short run, droughts might have some positive effects. Especially for this hazard type, dynamic treatment matters, which simple growth models might miss. Thus, cumulative drought shocks over time must be taken into account. Lastly, for storms, no significant effects are found. The results are robust against different matching methods
<figure><img src="image_4.png" /><figcaption>Figure 2: Temporal Dynamics of the Shocks</figcaption></figure>

Note: The four panels show the average treatment effect of the treated (ATT) following the methodology by Imai and Kim (2019). The estimates use covariate propensity score matching to build valid counterfactuals and are matched on the eight previous treatment periods. Confidence intervals shown represent the 10% confidence intervals.

and placebo test to determine if pre-treatments exist. The key takeaway from this analysis is that the found effects for floods indeed are significant and might persist even longer than shown in the baseline. For droughts, it might be important to consider the dynamic treatments of these events. Taking into account the history of drought, the effects become negative.

## 4.3 Interpretation and Discussion

This section evaluates whether the changes in urban expansion have meaningful implications in the real world. Floods cause damage and potential loss of life. However, they may also prolong urban expansion. This creates an opportunity for communities to adapt by implementing non-structural measures, such as stricter zoning laws or incentivizing relocation. To understand the internal impacts of these events, we provide descriptive insights in Table 6 on how urban expansion dynamics are connected to population dynamics. More information on the data sources used and their transformations is shown in Supplementary Appendix H. The baseline models in Table 2 demonstrate a robust decline in short-term urban growth rates following floods. However, this appears to coincide with an increase in population, approximately doubling the growth rate in Column (1). Increasing urban population in the context of climate have already been documented in literature (Castells-Quintana, Krause and McDermott, 2021). The combination of slower build-up growth but strong population growth appears to increase population density by up to 30 persons more per square km in Column (2). This effect is only found for floods. Furthermore, we observe a 64% increase to the means in population growth (or 3.2pp) in strongly informal settlements and almost 80% (or 4pp) increase in weakly informal areas of cities<fnref n="23" />. To our knowledge, this is a novel insight to the limited literature on how hazards influence informal growth. For a Colombian context, it was shown that, it was found that informal settlements reduce

<footnote n="23">²³The data is taken from the Million Neighborhoods project (Bettencourt and Marchio, 2023) and measures informal areas as gradient within neighborhoods that are badly connected to urban infrastructure.</footnote>
in growth after extreme events (Camacho, Aryal and Rajabifard, 2025). Surprisingly, Column (5) shows that population growth in previously flooded areas (or at least areas at high risk) almost doubles in the year following a flood. This might suggest people returning after a temporary displacement potentially, either due to lower housing costs or lack of regulation or enforcement of zoning laws or the housing market in these areas. This is in line with literature finding long-term growth of build-up in flood zones (Rentschler, Avner et al., 2023). This effect is also, albeit weaker, found for droughts, where each additional drought event increases population in flood-risk zones by 22% to the mean growth (0.9pp). This result is also found with a different magnitude by Van der Borgh et al. (2025). Finally, weather shocks might also influence violence and social unrest (Castells-Quintana, Lopez-Uribe and McDermott, 2022; Eberle, Rohner and Thoenig, 2025). Column (6) tests the effects on urban unrest and violent protests. Here, instead of growth, we focus on the log of conflict events reported in the ACLED database (Raleigh, Kishi and Linke, 2023). Floods and droughts significantly increase the number of violent unrests that occur within urban boundaries in the year after an event. This is in line with findings by Castells-Quintana, Lopez-Uribe and McDermott (2022). There is indicative evidence that reduced urban growth does not necessarily translate into improvements in urban welfare after extreme events. Quite the contrary is true: especially floods, but also partially droughts, lead to increased population in informal neighborhoods and high flood-risk areas. They also increase the propensity for urban social unrest.

<table><caption>Table 6: Within-City Impacts</caption><thead><tr><th rowspan="2"></th><th colspan="2">Population</th><th colspan="3">Population Growth in ...</th><th>Other</th></tr><tr><th>(1)<br>Population<br>Growth</th><th>(2)<br>Population<br>Density</th><th>(3)<br>Informal<br>Settlements (strong)</th><th>(4)<br>Informal<br>Settlements (weak)</th><th>(5)<br>High-Risk<br>Flood Areas</th><th>(6)<br>Log(Conflict<br>Count)</th></tr></thead><tbody><tr><td>Σ Flood Events in t-1</td><td>0.042***<br>[0.035,0.049]</td><td>27.287***<br>[16.050,38.524]</td><td>0.032***<br>[0.024,0.041]</td><td>0.040***<br>[0.033,0.048]</td><td>0.042***<br>[0.035,0.050]</td><td>0.058*<br>[0.009,0.107]</td></tr><tr><td>Σ Heatwave Events in t-1</td><td>0.004<br>[-0.001,0.008]</td><td>-0.976<br>[-12.753,10.801]</td><td>0.008*<br>[0.001,0.015]</td><td>0.003<br>[-0.002,0.008]</td><td>0.003<br>[-0.002,0.008]</td><td>-0.051<br>[-0.108,0.005]</td></tr><tr><td>Σ Drought Events in t-1</td><td>0.006<br>[-0.001,0.012]</td><td>4.967<br>[-2.400,12.333]</td><td>-0.000<br>[-0.009,0.009]</td><td>0.005<br>[-0.001,0.012]</td><td>0.009**<br>[0.002,0.016]</td><td>0.047**<br>[0.011,0.084]</td></tr><tr><td>Σ Storm Events in t-1</td><td>-0.031<br>[-0.094,0.033]</td><td>12.562<br>[-23.472,48.597]</td><td>-0.060<br>[-0.170,0.051]</td><td>-0.027<br>[-0.085,0.032]</td><td>0.009<br>[-0.030,0.048]</td><td>-0.191<br>[-0.415,0.034]</td></tr><tr><td>Obs.</td><td>96,885</td><td>108,736</td><td>64,701</td><td>96,402</td><td>94,366</td><td>10,979</td></tr><tr><td>Average Growth of Y</td><td>.04</td><td>899.41</td><td>.05</td><td>.05</td><td>.04</td><td>1.1</td></tr></tbody></table>

Note: *** 0.01,** 0.05, * 0.1 p-val. Confidence intervals are at a 90% level. This table presents the baseline results of Equation 1 for different outcomes at the city level. Columns (1) and (2) show population growth and density based on estimates by Liu, Cao et al. (2024). Columns (3) and (4) estimate population within informal settlements with complexity index ¿10 (column (3)) and ¿6 (column (4)) from Bettencourt and Marchio (2023). Column (5) shows population in high-risk flood zone with inundation depths >50cm in 100-year floods following Rentschler, Salhab and Jafino (2022). Column (6) uses conflict data from the ACLED database (Raleigh, Kishi and Linke, 2023). Population data is interpolated as described in Supplementary Appendix H.

Combining all these dimensions allows us to place this article in the literature. Floods significantly reduce urban expansion, especially in more remote rural towns. This is similar to the findings on nightlights after flood impacts by Gandhi et al. (2022).
Magontier and Martinez (2023) find for Spanish cities a vertical expansion in build-up, which is not tested in this paper. They also see increases in population in the aftermath of flood events, similar to our findings. Yet additionally, we find that they are amplified when co-occurring with rainfall extremes and following heat wave events. This is especially a concern for Western Africa. Heat waves themselves have very mixed effects, and it is not clear if they contribute or not to urban expansion. We are not aware of studies studying the impact of heat waves onto urban expansion as most studies focus on the inverse direction. Part of the results regarding droughts align with previous literature. Similarly to Chlouba, Mukim and Zaveri (2023) and Van der Borgh et al. (2025), we find coefficients that indicate that droughts accelerate urban expansion, especially taking into account catchment areas. There are, however, two major differences: first, as we account also for small cities, most positive growth effects are recorded in small cities, not in the primary cities of the country. This may be due to differences in sample sizes and varying definitions of the drought shock. Second, adjusting for the history of droughts, the effects become negative, highlighting that the longer-term effects of droughts might not be as positive, which has also been found by Nordfors (2024).

# 5 Conclusion

This paper aims to answer the question what the effects of four of the most important weather shocks in Sub-Saharan Africa are on urban expansion. In contrast to previous research, this study adopts a dedicated multi-hazard perspective, given the increased likelihood of co-occurring events in future decades. Using high-resolution data on urban development in combination with city-wide hazard exposure data for flood, heat waves, droughts and storms, we reveal a complex pattern of the impact of extreme events across space, time and hazard type. Each additional flood reduces urban growth in the range of 3-9% relative to the average growth rate, especially in poorer, more remote towns, leading to increased population pressure in informal settlements and, surprisingly, parts of cities with high flooding risk. These effects are notably found in Western Africa. A novel finding is that these effects are fundamentally amplified when floods follow heat wave events, combinations that have been studied in atmospheric science before (Sauter et al., 2023). Similarly to previous studies, we find that drought increases urban expansion, but contrary to previous work (Van der Borgh et al., 2025) mainly in smaller cities. For drought impacts, spatial scale matters. Although droughts may be harmful to the nearby agricultural land in urban areas, the more catchment areas affected, the stronger the effects on urban expansion. We discuss, however, that these findings need to be regarded with care, as we reveal that accounting for the history droughts in cities the effects turn negative. This highlights that while in the short term some positive effects might be seen, accounting for longer-term effects, droughts tend to be more harmful to urban expansion. For heat wave, as expected, barely any significant effects are found, if so they tend to be rather negative. Extreme wind storms are geographically more centered leading to small treatment samples and
large coefficients. Interpreted carefully, they seem to especially contribute to urban expansion in richer larger cities in southern Africa.

The paper is grounded in several assumptions whose implications should be considered when interpreting the results. A main limitation is the way how hazards are included. Most models assume a linear parametric short-term impact of shocks onto outcomes, which following the literature by Hogan and Schlenker (2024) might overlook important non-linearities. While tested for simple quadratic terms, the usage of intensity thresholds to define extreme events might omit this non-linearity. This is a significant limitation, as also points marginally below a threshold might be important and interact non-linearly with the model. Another limitation is that we only indirectly consider mechanisms such as rural-urban migration and within-city dynamics. A more rigorous micro-founded analysis is needed to substantiate the points made. These limitations provide several directions for future research, particularly non-linear modelling of events and spatial mechanisms such as migration.

The findings of this study have strong policy implications. Although adaptation action in Sub-Saharan Africa is currently focused on metropolitan areas, where a large fraction of the population is clustered, the findings suggest that small flood-prone cities should be more in focus for climate action. These cities tend to bear a large part of the burden of extreme events, with increasing informality and higher conflict potential. Furthermore, the analysis highlights that policymakers should refrain from conducting research exclusively on a whole-continent scale or along a single hazard dimension. The underpinning climate dynamics, such as teleconnections, might cause fundamentally different effects across subregions. Likewise, weather shocks do not occur in isolation. Their impact also depends on the past treatment history of a hazard and their co-occurrence with other events, which may interact in non-linear ways. Ignoring such dynamics might mislead policy guidance.

This study is far from exhaustive. Beyond just adding non-linear models and spatial mechanisms, more research is needed to better study the individual and firm response on a large scale to extreme events, especially those that are expected to see a substantial rise and interaction with others in the years to come. Future research should adopt a more inclusive approach that incorporates multiple types of hazards simultaneously. It should also connect with high-frequency microlevel estimates of response and damage reported to get a better overall picture of the complex heterogeneous response to climatic shock. Given the current climatic predictions that the 2.5-degree global warming goal is likely to be missed, it is more important than ever to start implementing adaptation policies in regions and urban agglomerations with less resilient infrastructure to safeguard human lives.
References

Azevedo, J. P. (2011). WBOPENDATA: Stata Module to Access World Bank Databases. Version S457234. Boston College Department of Economics.

Baker, A., B. Callaway, S. Cunningham, A. Goodman-Bacon and P. H. C. Sant'Anna (2025). *Difference-in-Differences Designs: A Practitioner's Guide*. Version 1. DOI: 10.48550/ARXIV.2503.13323. Pre-published.

Bettencourt, L. M. A. and N. Marchio (2023). *Million Neighborhoods Africa Database*. University of Chicago's Mansueto Institute for Urban Innovation. URL: https://www.millionneighborhoods.africa. (Accessed on 20/04/2025).

Bosker, M., S. Brakman, H. Garretsen and M. Schramm (2007). 'Looking for Multiple Equilibria When Geography Matters: German City Growth and the WWII Shock'. *Journal of Urban Economics* 61.1, pp. 152-169. DOI: 10.1016/j.jue.2006.07.001.

Brakenbridge, G. and Dartmouth Flood Observatory (2023). *Global Active Archive of Large Flood Events*. URL: https://floodobservatory.colorado.edu/Archives/index.html. (Accessed on 27/03/2023).

Breitung, J., S. Kripfganz and K. Hayakawa (2022). 'Bias-Corrected Method of Moments Estimators for Dynamic Panel Data Models'. *Econometrics and Statistics* 24, pp. 116-132. DOI: 10.1016/j.ecosta.2021.07.001.

Bukvic, A. and S. Barnett (2023). 'Drivers of Flood-Induced Relocation among Coastal Urban Residents: Insight from the US East Coast'. *Journal of Environmental Management* 325, p. 116429. DOI: 10.1016/j.jenvman.2022.116429.

Burke, M., S. Hsiang and E. Miguel (2015). 'Global Non-Linear Effect of Temperature on Economic Production'. *Nature* 527.7577, pp. 235-239. DOI: 10.1038/nature15725.

Camacho, R., J. Aryal and A. Rajabifard (2025). 'How Do Disasters Disrupt the Spatial Growth of Informal Settlements? A Multi-Temporal Remote Sensing Approach – The Case Study of Mocoa, Colombia'. *Habitat International* 156, p. 103272. DOI: 10.1016/j.habitatint.2024.103272.

Castells-Quintana, D., M. Krause and T. K. J. McDermott (2021). 'The Urbanising Force of Global Warming: The Role of Climate Change in the Spatial Distribution of Population'. *Journal of Economic Geography* 21.4, pp. 531-556. DOI: 10.1093/jeg/lbaa030.

Castells-Quintana, D., M. D. P. Lopez-Uribe and T. K. McDermott (2022). 'Population Displacement and Urban Conflict: Global Evidence from More than 3300 Flood Events'. *Journal of Development Economics* 158, p. 102922. DOI: 10.1016/j.jdeveco.2022.102922.

Cattaneo, C., M. Beine, C. J. Fröhlich, D. Kniveton, I. Martinez-Zarzoso, M. Mastrorillo, K. Millock, E. Piguet and B. Schraven (2019). 'Human Migration in the Era of Climate Change'. *Review of Environmental Economics and Policy* 13.2, pp. 189-206. DOI: 10.1093/reep/rez008.

Chlouba, V., M. Mukim and E. Zaveri (2023). *After Big Droughts Come Big Cities: Does Drought Drive Urbanization?* 10408. World Bank, Washington, DC. URL: https://openknowledge.worldbank.org/entities/publication/53e73155-a428-4d0f-a22e-4242d3ed9f20. (Accessed on 26/08/2024).

Climate Hazards Center (UCSB) (2023). *CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations*. URL: https://chc.ucsb.edu/data/chirps/. (Accessed on 29/06/2023).

Colella, F., R. Lalive, S. O. Sakalli and M. Thoenig (2019). 'Inference with Arbitrary Clustering'. *SSRN Electronic Journal*. DOI: 10.2139/ssrn.3449578.

Colmer, J. (2021). 'Temperature, Labor Reallocation, and Industrial Production: Evidence from India'. *American Economic Journal: Applied Economics* 13.4, pp. 101-124. DOI: 10.1257/app.20190249.
Copernicus Climate Change Service (2024). ERA5-Land Post-Processed Daily Statistics from 1950 to Present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e9c9c792. (Accessed on 18/10/2024).

Crameri, F., G. E. Shephard and P. J. Heron (2020). 'The Misuse of Colour in Science Communication'. *Nature Communications* 11.1, p. 5444. DOI: 10.1038/s41467-020-19160-7.

Data for Good at Meta (2023). *Facebook Commuting Zones*. URL: https://dataforgood.facebook.com/dfg/tools/commuting-zones. (Accessed on 28/04/2023).

Davis, D. R. and D. E. Weinstein (2002). 'Bones, Bombs, and Break Points: The Geography of Economic Activity'. *American Economic Review* 92.5, pp. 1269-1289. DOI: 10.1257/000282802762024502.

Dell, M., B. F. Jones and B. A. Olken (2014). 'What Do We Learn from the Weather? The New Climate-Economy Literature'. *Journal of Economic Literature* 52.3, pp. 740-798. DOI: 10.1257/jel.52.3.740.

Dodman, D., B. Hayward, M. Pelling, V. Castan Broto, W. Chow, E. Chu, R. Dawson, L. Khirfan, T. McPhearson, A. Prakash, Y. Zheng and G. Ziervogel (2022). 'Cities, Settlements and Key Infrastructure'. *Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change*. Ed. by H.-O. Pörtner, D. Roberts, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegria, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem and B. Rama. Cambridge, UK and New York, USA: Cambridge University Press, pp. 907-1040. URL: https://doi.org/10.1017/9781009325844.008.

Done, J. M., M. Ge, G. J. Holland, I. Dima-West, S. Phibbs, G. R. Saville and Y. Wang (2020). 'Modelling Global Tropical Cyclone Wind Footprints'. *Natural Hazards and Earth System Sciences* 20.2, pp. 567-580. DOI: 10.5194/nhess-20-567-2020.

Eberle, U. J., D. Rohner and M. Thoenig (2025). 'Heat and Hate: Climate Security and Farmer-Herder Conflicts in Africa'. *Review of Economics and Statistics*, pp. 1-47. DOI: 10.1162/rest_a_01546.

Elliott, R. J. R., Y. Liu, E. Strobl and M. Tong (2019). 'Estimating the Direct and Indirect Impact of Typhoons on Plant Performance: Evidence from Chinese Manufacturers'. *Journal of Environmental Economics and Management* 98, p. 102252. DOI: 10.1016/J.JEEM.2019.102252.

European Commission - JRC and International Energy Agency (IEA) (2023). EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database - Version 8.0. European Commission, JRC (Datasets). URL: https://edgar.jrc.ec.europa.eu/report_2023. (Accessed on 15/04/2024).

European Severe Storms Laboratory (2014). *ESWD Event Reporting Criteria*. European Severe Weather Database. URL: https://www.eswd.eu/docs/ESWD_criteria_en.pdf. (Accessed on 08/05/2024).

Feng, B., Y. Zhang and R. Bourke (2021). 'Urbanization Impacts on Flood Risks Based on Urban Growth Data and Coupled Flood Models'. *Natural Hazards* 106.1, pp. 613-627. DOI: 10.1007/s11069-020-04480-0.

Ferreira, S. (2024). 'Extreme Weather Events and Climate Change: Economic Impacts and Adaptation Policies'. *Annual Review of Resource Economics* 16.1, pp. 207-231. DOI: 10.1146/annurev-resource-101623-095314.

Founda, D. and M. Santamouris (2017). 'Synergies between Urban Heat Island and Heat Waves in Athens (Greece), during an Extremely Hot Summer (2012)'. *Scientific Reports* 7.1, p. 10973. DOI: 10.1038/s41598-017-11407-6.

Gandhi, S., M. Kahn, R. Kochhar, S. Lall and V. Tandel (2022). *Adapting to Flood Risk: Evidence from a Panel of Global Cities*. w30137. Cambridge, MA: National Bureau of Economic Research, w30137. DOI: 10.3386/w30137.
Gebrechorkos, S., J. Peng, E. Dyer, D. G. Miralles, S. M. Vicente-Serrano, C. Funk, H. Beck, D. Asfaw, M. Singer and S. Dadson (2023). *Hydro-JULES: Global High-Resolution Drought Datasets from 1981-2022*. application/xml. NERC EDS Centre for Environmental Data Analysis. DOI: 10.5285/AC43DA11867243A1BB414E1637802DEC. (Accessed on 01/10/2024).

German Aerospace Center (DLR) (2024). *World Settlement Footprint (WSF) Evolution - Landsat-5/-7 - Global*. URL: https://download.geoservice.dlr.de/WSF_EVO/#details. (Accessed on 15/08/2024).

Ghanbari, M., M. Arabi, M. Georgescu and A. M. Broadbent (2023). 'The Role of Climate Change and Urban Development on Compound Dry-Hot Extremes across US Cities'. *Nature Communications* 14.1, p. 3509. DOI: 10.1038/s41467-023-39205-x.

Gibson, J., Y. Jiang, X. Zhang and G. Boe-Gibson (2024). 'Are Disaster Impact Estimates Distorted by Errors in Popular Night-Time Lights Data?' *Economics of Disasters and Climate Change* 8.3, pp. 391-416. DOI: 10.1007/s41885-024-00152-6.

Güneralp, B., İ. Güneralp and Y. Liu (2015). 'Changing Global Patterns of Urban Exposure to Flood and Drought Hazards'. *Global Environmental Change* 31, pp. 217-225. DOI: 10.1016/j.gloenvcha.2015.01.002.

Hallegatte, S. and P. Dumas (2009). 'Can Natural Disasters Have Positive Consequences? Investigating the Role of Embodied Technical Change'. *Ecological Economics* 68.3, pp. 777-786. DOI: 10.1016/j.ecolecon.2008.06.011.

Hallegatte, S., A. Vogt-Schilb, M. Bangalore and J. Rozenberg (2017). *Unbreakable: Building the Resilience of the Poor in the Face of Natural Disasters*. Climate Change and Development. World Bank, Washington, DC. URL: http://documents.worldbank.org/curated/en/512241480487839624.

Hamza, M., K. Eriksson and R. Staupe-Delgado (2021). 'Locating Potential Sources of Capacity and Vulnerability in Geographically Remote Areas: Reflections Based on Three Case Studies'. *International Journal of Disaster Risk Reduction* 63, p. 102433. DOI: 10.1016/j.ijdrr.2021.102433.

Heinen, A., J. Khadan and E. Strobl (2019). 'The Price Impact of Extreme Weather in Developing Countries'. *The Economic Journal* 129.619, pp. 1327-1342. DOI: 10.1111/ecoj.12581.

Helbling, M. and D. Meierrieks (2023). 'Global Warming and Urbanization'. *Journal of Population Economics* 36.3, pp. 1187-1223. DOI: 10.1007/s00148-022-00924-y.

Henderson, V., A. Storeygard and U. Deichmann (2017). 'Has Climate Change Driven Urbanization in Africa?' *Journal of Development Economics* 124, pp. 60-82. DOI: 10.1016/j.jdeveco.2016.09.001.

Hoffmann, R., A. Dimitrova, R. Muttarak, J. Crespo Cuaresma and J. Peisker (2020). 'A Meta-Analysis of Country-Level Studies on Environmental Change and Migration'. *Nature Climate Change* 10.10, pp. 904-912. DOI: 10.1038/s41558-020-0898-6.

Hogan, D. and W. Schlenker (2024). 'Empirical Approaches to Climate Change Impact Quantification'. *Handbook of the Economics of Climate Change*. Vol. 1. Elsevier, pp. 53-111. ISBN: 978-0-443-31324-0. DOI: 10.1016/bs.hesecc.2024.10.006.

Hornbeck, R. and D. Keniston (2017). 'Creative Destruction: Barriers to Urban Growth and the Great Boston Fire of 1872'. *American Economic Review* 107.6, pp. 1365-1398. DOI: 10.1257/aer.20141707.

Hsiang, S. and A. Jina (2014). *The Causal Effect of Environmental Catastrophe on Long-Run Economic Growth: Evidence From 6,700 Cyclones*. w20352. Cambridge, MA: National Bureau of Economic Research, w20352. DOI: 10.3386/w20352.

Huang, W. T. K., P. Masselot, E. Bou-Zeid, S. Fatichi, A. Paschalis, T. Sun, A. Gasparrini and G. Manoli (2023). 'Economic Valuation of Temperature-Related Mortality Attributed to Urban Heat Islands in European Cities'. *Nature Communications* 14.1, p. 7438. DOI: 10.1038/s41467-023-43135-z.
Imai, K. and I. S. Kim (2019). 'When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?' *American Journal of Political Science* 63.2, pp. 467–490. DOI: 10.1111/ajps.12417.

IPCC (2021). *Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change*. Ed. by V. Masson-Delmotte, P. Zhai, A. Pirani, S. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. Matthews, T. Maycock, T. Waterfield, O. Yelekci, R. Yu and B. Zhou. 1st ed. Cambridge, United Kingdom and New York, NY: Cambridge University Press. ISBN: 978-1-009-15789-6. DOI: 10.1017/9781009157896.

Joint Research Center and Copernicus Emergency Management Service (2019). *River Discharge Historical Data from the Global Flood Awareness System*. Early Warning Data Store (EWDS). DOI: 10.24381/CDS.A4FDD6B9. (Accessed on 24/01/2025).

Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond and C. J. Neumann (2010). 'The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data'. *Bulletin of the American Meteorological Society* 91.3, pp. 363–376. DOI: 10.1175/2009BAMS2755.1.

Kocornik-Mina, A., T. K. J. McDermott, G. Michaels and F. Rauch (2020). 'Flooded Cities'. *American Economic Journal: Applied Economics* 12.2, pp. 35–66. DOI: 10.1257/app.20170066.

Kolstad, C. D. and F. C. Moore (2020). 'Estimating the Economic Impacts of Climate Change Using Weather Observations'. *Review of Environmental Economics and Policy* 14.1, pp. 1–24. DOI: 10.1093/reep/rez024.

Lai, W., Y. Qiu, Q. Tang, C. Xi and P. Zhang (2023). 'The Effects of Temperature on Labor Productivity'. *Annual Review of Resource Economics* 15.1, pp. 213–232. DOI: 10.1146/annurev-resource-101222-125630.

Lee, K. and J. Braithwaite (2022). 'High-Resolution Poverty Maps in Sub-Saharan Africa'. *World Development* 159, p. 106028. DOI: 10.1016/j.worlddev.2022.106028.

Liu, L., Y. Wang and Y. Xu (2024). 'A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data'. *American Journal of Political Science*, ajps.12723. DOI: 10.1111/ajps.12723.

Liu, L., X. Cao, S. Li and N. Jie (2024). 'A 31-Year (1990–2020) Global Gridded Population Dataset Generated by Cluster Analysis and Statistical Learning'. *Scientific Data* 11.1, p. 124. DOI: 10.1038/s41597-024-02913-0.

Liu, M., Y. Shamdasani and V. Taraz (2023). 'Climate Change and Labor Reallocation: Evidence from Six Decades of the Indian Census'. *American Economic Journal: Economic Policy* 15.2, pp. 395–423. DOI: 10.1257/pol.20210129.

Magontier, P. and R. Martinez (2023). 'Floods \& Urban Density'. *SSRN Electronic Journal*. DOI: 10.2139/ssrn.4388712.

Mankiw, N. G., D. Romer and D. N. Weil (1992). 'A Contribution to the Empirics of Economic Growth'. *The Quarterly Journal of Economics* 107.2, pp. 407–437. DOI: 10.2307/2118477.

Marconcini, M., A. Metz-Marconcini, S. Üreyen, D. Palacios-Lopez, W. Hanke, F. Bachofer, J. Zeidler, T. Esch, N. Gorelick, A. Kakarla, M. Paganini and E. Strano (2020). 'Outlining Where Humans Live, the World Settlement Footprint 2015'. *Scientific Data* 7.1, p. 242. DOI: 10.1038/s41597-020-00580-5.

Meierrieks, D. (2021). 'Weather Shocks, Climate Change and Human Health'. *World Development* 138, p. 105228. DOI: 10.1016/j.worlddev.2020.105228.

Mistry, M. N. (2020). 'A High Spatiotemporal Resolution Global Gridded Dataset of Historical Human Discomfort Indices'. *Atmosphere* 11.8, p. 835. DOI: 10.3390/atmos11080835.

Nickell, S. (1981). 'Biases in Dynamic Models with Fixed Effects'. *Econometrica* 49.6, p. 1417. DOI: 10.2307/1911408.
NOAA Climate Prediction Center (2025). *Historical El Nino / La Nina Episodes (1950-Present)*. Cold & Warm Episodes by Season. URL: https://origin.cpc.ncep.noaa.gov/products/analysis-monitoring/ensostuff/ONI_v5.php. (Accessed on 07/04/2025).

Nordfors, N. (2024). *Droughts and the Growth of Cities*. URL: https://nicklasnordfors.github.io/assets/pdf/droughts_cities.pdf. Pre-published.

OECD and SWAC (2020). *Africapolis*. URL: www.africapolis.org. (Accessed on 24/12/2023).

OECD, United Nations Economic Commission for Africa and African Development Bank (2022). *Africa's Urbanisation Dynamics 2022: The Economic Power of Africa's Cities*. West African Studies. OECD. ISBN: 978-92-64-91580-0. DOI: 10.1787/3834ed5b-en.

Ortiz-Bobea, A. (2021). 'The Empirical Analysis of Climate Change Impacts and Adaptation in Agriculture'. *Handbook of Agricultural Economics*. Vol. 5. Elsevier, pp. 3981-4073. ISBN: 978-0-323-91501-4. DOI: 10.1016/bs.hesagr.2021.10.002.

Pesaresi, M. (2023). *GHS-BUILT-S R2023A - GHS Built-up Surface Grid, Derived from Sentinel2 Composite and Landsat, Multitemporal (1975-2030)*. European Commission, Joint Research Centre (JRC). DOI: 10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA. (Accessed on 15/08/2024).

Prein, A. F. and G. Holland (2018). 'Global Estimates of Damaging Hail Hazard'. *Weather and Climate Extremes* 22, pp. 10–23. DOI: 10.1016/j.wace.2018.10.004.

Provenzano, S. (2024). 'Accountability Failure in Isolated Areas: The Cost of Remoteness from the Capital City'. *Journal of Development Economics* 167, p. 103214. DOI: 10.1016/j.jdeveco.2023.103214.

Raleigh, C., R. Kishi and A. Linke (2023). 'Political Instability Patterns Are Obscured by Conflict Dataset Scope Conditions, Sources, and Coding Choices'. *Humanities and Social Sciences Communications* 10.1, p. 74. DOI: 10.1057/s41599-023-01559-4.

Reinhardt, R. (2025). *The Platform for Economic Analysis of Climate Hazards (PEACH): Gridded Hazards from 2000-2019*. URL: https://shs.hal.science/halshs-05314666v1. Pre-published.

Ren, H., X. Huang, J. Yang and G. Zhou (2025). 'Improving 30-Meter Global Impervious Surface Area (GISA) Mapping: New Method and Dataset'. *ISPRS Journal of Photogrammetry and Remote Sensing* 220, pp. 354–376. DOI: 10.1016/j.isprsjprs.2024.12.023.

Rentschler, J. (2024). *Global Flood Exposure: Gridded Exposure Headcounts by Country — Data Catalog*. URL: https://datacatalog.worldbank.org/search/dataset/0062763/Global-Flood-Exposure-Gridded-exposure-headcounts-by-country. (Accessed on 18/10/2024).

Rentschler, J., P. Avner, M. Marconcini, R. Su, E. Strano, M. Vousdoukas and S. Hallegatte (2023). 'Global Evidence of Rapid Urban Growth in Flood Zones since 1985'. *Nature* 622.7981, pp. 87–92. DOI: 10.1038/s41586-023-06468-9.

Rentschler, J., M. Salhab and B. A. Jafino (2022). 'Flood Exposure and Poverty in 188 Countries'. *Nature Communications* 13.1, p. 3527. DOI: 10.1038/s41467-022-30727-4.

Runfola, D., A. Anderson, H. Baier, M. Crittenden, E. Dowker, S. Fuhrig, S. Goodman, G. Grimsley, R. Layko, G. Melville, M. Mulder, R. Oberman, J. Panganiban, A. Peck, L. Seitz, S. Shea, H. Slevin, R. Youngerman and L. Hobbs (2020). 'geoBoundaries: A Global Database of Political Administrative Boundaries'. *PLOS ONE* 15.4. Ed. by W. Tang, e0231866. DOI: 10.1371/journal.pone.0231866.

Šakić Trogrlić, R., H. E. Thompson, E. Y. Menteşe, E. Hussain, J. C. Gill, F. E. Taylor, E. Mwangi, E. Öner, V. G. Bukachi and B. D. Malamud (2024). 'Multi-Hazard Interrelationships and Risk Scenarios in Urban Areas: A Case of Nairobi and Istanbul'. *Earth's Future* 12.9, e2023EF004413. DOI: 10.1029/2023EF004413.

Santini, M., S. Noce, M. Antonelli and L. Caporaso (2022). 'Complex Drought Patterns Robustly Explain Global Yield Loss for Major Crops'. *Scientific Reports* 12.1, p. 5792. DOI: 10.1038/s41598-022-09611-0.
Sauter, C., J. L. Catto, H. J. Fowler, S. Westra and C. J. White (2023). 'Compounding Heatwave-Extreme Rainfall Events Driven by Fronts, High Moisture, and Atmospheric Instability'. *Journal of Geophysical Research: Atmospheres* 128.21, e2023JD038761. DOI: 10.1029/2023JD038761.

Seneviratne, S., N. Nicholls, D. Easterling, C. Goodess, S. Kanae, J. Kossin, Y. Luo, J. Marengo, K. McInnes, M. Rahimi, M. Reichstein, A. Sorteberg, C. Vera and X. Zhang (2012). 'Changes in Climate Extremes and Their Impacts on the Natural Physical Environmen'. *Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation*. Ed. by C. Field, V. Barros, T. Stocker, D. Qin, D. Dokken, K. Ebi, M. Mastrandrea, K. Mach, G.-K. Plattner, S. Allen, M. Tignor and P. Midgley. Vol. Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press, pp. 109–230.

Sundberg, R. and E. Melander (2013). 'Introducing the UCDP Georeferenced Event Dataset'. *Journal of Peace Research* 50.4, pp. 523–532. DOI: 10.1177/0022343313484347.

Tao, S., Z. Ao, J.-P. Wigneron, S. Saatchi, P. Ciais, J. Chave, T. Le Toan, P.-L. Frison, X. Hu, C. Chen, L. Fan, M. Wang, J. Zhu, X. Zhao, X. Li, X. Liu, Y. Su, T. Hu, Q. Guo, Z. Wang, Z. Tang, Y. Y. Liu and J. Fang (2023). 'A Global Long-Term, High-Resolution Satellite Radar Backscatter Data Record (1992–2022+): Merging C-band ERS/ASCAT and Ku-band QSCAT'. *Earth System Science Data* 15.4, pp. 1577–1596. DOI: 10.5194/essd-15-1577-2023.

Tuholske, C., K. Caylor, C. Funk, A. Verdin, S. Sweeney, K. Grace, P. Peterson and T. Evans (2021). 'Global Urban Population Exposure to Extreme Heat'. *Proceedings of the National Academy of Sciences* 118.41, e2024792118. DOI: 10.1073/pnas.2024792118.

Tziokas, N., C. Zhang, G. C. Drolias and P. M. Atkinson (2023). 'Downscaling Satellite Night-Time Lights Imagery to Support within-City Applications Using a Spatially Non-Stationary Model'. *International Journal of Applied Earth Observation and Geoinformation* 122, p. 103395. DOI: 10.1016/j.jag.2023.103395.

UNDESA (2019). *World Urbanization Prospects: The 2018 Revision Produced by the Population Division of the UN Department of Economic and Social Affairs*. New York: United Nations.

UNHCR (2024). *Informal Settlements. Emergency Handbook*. URL: https://emergency.unhcr.org/emergency-assistance/shelter-camp-and-settlement/settlements/informal-settlements. (Accessed on 11/05/2025).

UNSD (2023). *Standard Country or Area Codes for Statistical Use (M49)*. URL: https://unstats.un.org/unsd/methodology/m49/. (Accessed on 03/05/2023).

Van der Borgh, R., O. A. Ishizawa, J. Thuret and J. Muñoz (2025). *Dry Spells, Urban Swells: Analyzing the Drought-Induced Expansion of Cities in Sub-Saharan Africa*. 11099. World Bank, Washington, DC: World Bank. URL: http://hdl.handle.net/10986/43051.

Vigdor, J. (2008). 'The Economic Aftermath of Hurricane Katrina'. *Journal of Economic Perspectives* 22.4, pp. 135–154. DOI: 10.1257/jep.22.4.135.

Vousdoukas, M. (2023). *Global Coastal Flood Maps*. Zenodo. DOI: 10.5281/ZENODO.8057902. (Accessed on 02/04/2025).

Vousdoukas, M. I., L. Mentaschi, E. Voukouvalas, M. Verlaan, S. Jevrejeva, L. P. Jackson and L. Feyen (2018). 'Global Probabilistic Projections of Extreme Sea Levels Show Intensification of Coastal Flood Hazard'. *Nature Communications* 9.1, p. 2360. DOI: 10.1038/s41467-018-04692-w.

WorldPop (2020). *Global 100m Population Total Adjusted to Match the Corresponding UNPD Estimate*. University of Southampton. DOI: 10.5258/SOTON/WP00660. (Accessed on 21/11/2024).

Wu, J., H. Yao, X. Chen, G. Wang, X. Bai and D. Zhang (2022). 'A Framework for Assessing Compound Drought Events from a Drought Propagation Perspective'. *Journal of Hydrology* 604, p. 127228. DOI: 10.1016/j.jhydrol.2021.127228.
Zhao, M., J. K. W. Lee, T. Kjellstrom and W. Cai (2021). 'Assessment of the Economic Impact of Heat-Related Labor Productivity Loss: A Systematic Review'. *Climatic Change* 167.1–2, p. 22.
DOI: 10.1007/s10584-021-03160-7.

Zscheischler, J., O. Martius, S. Westra, E. Bevacqua, C. Raymond, R. M. Horton, B. Van Den Hurk, A. AghaKouchak, A. Jézéquel, M. D. Mahecha, D. Maraun, A. M. Ramos, N. N. Ridder, W. Thiery and E. Vignotto (2020). 'A Typology of Compound Weather and Climate Events'. *Nature Reviews Earth & Environment* 1.7, pp. 333–347. DOI: 10.1038/s43017-020-0060-z.

Zscheischler, J., S. Westra, B. J. J. M. Van Den Hurk, S. I. Seneviratne, P. J. Ward, A. Pitman, A. AghaKouchak, D. N. Bresch, M. Leonard, T. Wahl and X. Zhang (2018). 'Future Climate Risk from Compound Events'. *Nature Climate Change* 8.6, pp. 469–477. DOI: 10.1038/s41558-018-0156-3.
# 6 Tables & Figures

<figure><img src="image_5.png" /><figcaption>Figure 3: Spatial Distribution of the Hazards</figcaption></figure>

**Note:** The four figures show on a province-level (ADM 1) the average number of total shocks cities have seen between 2000 and 2019 across Sub-Saharan Africa. The data is derived from Reinhardt (2025) and provinces are defined as in *GeoBoundaries* (Runfola et al. (2020)).
<figure><img src="image_6.png" /><figcaption>Figure 4: Heterogeneity Analysis - Part 1/2</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 4: Heterogeneity Analysis - Part 2/2</figcaption></figure>

**Note:** The four figures show the results of using Equation 1 on stratified samples. Figures show the 95% CIs with p-values *<0.1,**<0.05, ***<0.01. Panel (a) uses stratification by the International Wealth Index (IWI) taken from Lee and Braithwaite (2022). Panel (b) computes the distance of cities to the country's metropole (usually the capital) as defined in OECD and SWAC (2020). Panel (c) ranks the cities by build-up area and creates ranks to determine primary cities in a country. Panel (d) uses carbon emission data from the European Commission - JRC and International Energy Agency (IEA) (2023) to define manufacturing cities as cities with >20% industrial carbon emission over total city emissions.
<figure><img src="image_8.png" /><figcaption>Figure 5: Robustness: Leave-one-out-exercise</figcaption></figure>

**Note:** The figure shows the results of sequentially dropping one country at a time and examining whether the significance level changes for each of the four hazards. The shown confidence intervals are at a 10% level and all regressions follow Equation 1 with city, year and region-year fixed effects. Areas in light grey are insignificant at a 10% level.
<table><caption>Table 7: Thresholds for Cut-off Intensity (Reinhardt, 2025)</caption><thead><tr><th>Hazard</th><th>Measure</th><th>Onset Type</th><th>Share of sample above threshold</th><th>Expected Effect on Urbanisation</th></tr></thead><tbody><tr><td>Heat wave (WBGT)</td><td>5-day MA</td><td>Slow</td><td>2.8%</td><td>Mixed</td></tr><tr><td>Drought</td><td>SPEI-3 & SPEI-6</td><td>Slow</td><td>1.8%</td><td>Positive</td></tr><tr><td>Tropical Storm</td><td>m/s</td><td>Rapid</td><td>&lt;1%</td><td>Positive</td></tr><tr><td>Wind gusts</td><td>m/s</td><td>Rapid</td><td>&lt;1%</td><td>Positive</td></tr><tr><td>Flood</td><td>DFO Index</td><td>Rapid</td><td>1.3%</td><td>Negative</td></tr></tbody></table>

**Note:** WBGT: Wet Bulb Globe Temperature, MA: Moving Average, SPEI: Standardized Precipitation Evapotranspiration Index, DFO: Dartmouth Flood Observatory. Onset type explains the time it needs for these events to show substantial effects relative to the other included hazards. Original input sources based on: Mistry, 2020; Gebrechorkos et al., 2023; Done et al., 2020; Knapp et al., 2010; Copernicus Climate Change Service, 2024; Brakenbridge and Dartmouth Flood Observatory, 2023; Prein and Holland, 2018.

<table><caption>Table 8: Descriptive Statistics</caption><thead><tr><th></th><th>(1)<br>Full sample</th><th>(2)<br>Southern Africa</th><th>(3)<br>Eastern Africa</th><th>(4)<br>Central Africa</th><th>(5)<br>Western Africa</th></tr></thead><tbody><tr><td><strong>Outcomes</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Impervious Area (km²)</td><td>7.07<br>(41.87)</td><td>14.86<br>(76.29)</td><td>4.89<br>(28.98)</td><td>4.34<br>(16.50)</td><td>6.03<br>(32.40)</td></tr><tr><td>City growth (%)</td><td>0.03<br>(0.06)</td><td>0.02<br>(0.04)</td><td>0.03<br>(0.06)</td><td>0.03<br>(0.08)</td><td>0.03<br>(0.06)</td></tr><tr><td><strong>Treatments</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Flood</td><td>0.09<br>(0.33)</td><td>0.10<br>(0.32)</td><td>0.09<br>(0.32)</td><td>0.02<br>(0.15)</td><td>0.12<br>(0.38)</td></tr><tr><td>Storm</td><td>0.00<br>(0.07)</td><td>0.01<br>(0.15)</td><td>0.00<br>(0.05)</td><td>0.00<br>(0.00)</td><td>0.00<br>(0.00)</td></tr><tr><td>Heatwave</td><td>0.11<br>(0.40)</td><td>0.02<br>(0.17)</td><td>0.02<br>(0.19)</td><td>0.06<br>(0.31)</td><td>0.22<br>(0.55)</td></tr><tr><td>Drought</td><td>0.11<br>(0.36)</td><td>0.14<br>(0.41)</td><td>0.12<br>(0.36)</td><td>0.13<br>(0.38)</td><td>0.09<br>(0.32)</td></tr><tr><td><strong>Other Variables</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>High-Risk Flood</td><td>14.46</td><td>9.48</td><td>15.10</td><td>16.02</td><td>15.68</td></tr><tr><td>Areas (% of Urban Area)</td><td>(15.81)</td><td>(9.73)</td><td>(18.05)</td><td>(16.04)</td><td>(15.93)</td></tr><tr><td>Population</td><td>903.57</td><td>835.28</td><td>793.99</td><td>795.96</td><td>1047.19</td></tr><tr><td>Density (per km²)</td><td>(960.33)</td><td>(748.30)</td><td>(894.08)</td><td>(1096.72)</td><td>(1011.92)</td></tr><tr><td>Absolute Wealth (IWI)</td><td>45.74<br>(16.56)</td><td>60.81<br>(19.42)</td><td>38.42<br>(13.16)</td><td>31.93<br>(14.34)</td><td>49.03<br>(9.90)</td></tr><tr><td>Employment in Agriculture (%) - country</td><td>52.00<br>(18.22)</td><td>39.70<br>(25.11)</td><td>63.14<br>(15.49)</td><td>64.36<br>(10.93)</td><td>45.50<br>(9.39)</td></tr><tr><td>Total GDP per capita - country</td><td>1575.89<br>(1799.22)</td><td>3749.99<br>(2803.33)</td><td>672.26<br>(396.85)</td><td>795.61<br>(1525.07)</td><td>1485.13<br>(774.10)</td></tr><tr><td>Nb. of 10x10km</td><td>2.50</td><td>2.94</td><td>2.91</td><td>2.18</td><td>2.16</td></tr><tr><td>Cells overlap</td><td>(3.30)</td><td>(3.02)</td><td>(4.79)</td><td>(2.02)</td><td>(2.46)</td></tr><tr><td>Nb. Cities²</td><td>5 721</td><td>1 029</td><td>1 577</td><td>894</td><td>2 280</td></tr><tr><td>Observations</td><td>107796</td><td>19551</td><td>28260</td><td>16716</td><td>43254</td></tr></tbody></table>

**Note:** The means and standard deviations are shown in brackets.² shows the number of cities. One might note that the total number of cities does not sum up to the total sample. This is due to some cities being omitted because of a lack of data availability or unrealistically high growth rates. This table shows the descriptive statistics of an average city in this study. Standard deviations are shown in brackets. Division into Subregions follows the UN Statistical Division. Hazard Exposure indicates the average number of events that occur within any given year.
<table><caption>Table 9: Extension: Accounting for Spatial Scope</caption><thead><tr><th></th><th>(1)<br>0km</th><th>(2)<br>50km</th><th>(3)<br>100km</th><th>(4)<br>150km</th><th>(5)<br>Commuting Zone</th></tr></thead><tbody><tr><td>Σ Flood Events in t-1</td><td>-0.0025***<br>[-0.003,-0.002]</td><td>-0.0030***<br>[-0.004,-0.002]</td><td>-0.0035***<br>[-0.005,-0.002]</td><td>-0.0038***<br>[-0.005,-0.003]</td><td>-0.0046***<br>[-0.006,-0.003]</td></tr><tr><td>Σ Heatwave Events in t-1</td><td>-0.0019***<br>[-0.003,-0.001]</td><td>-0.0021***<br>[-0.003,-0.001]</td><td>-0.0019***<br>[-0.003,-0.001]</td><td>-0.0017**<br>[-0.003,-0.001]</td><td>-0.0022***<br>[-0.003,-0.001]</td></tr><tr><td>Σ Drought Events in t-1</td><td>0.0011**<br>[0.000,0.002]</td><td>0.0012**<br>[0.000,0.002]</td><td>0.0016**<br>[0.000,0.003]</td><td>0.0022**<br>[0.001,0.004]</td><td>0.0010<br>[-0.000,0.002]</td></tr><tr><td>Σ Storm Events in t-1</td><td>0.0025***<br>[0.001,0.004]</td><td>0.0043***<br>[0.003,0.006]</td><td>0.0051***<br>[0.003,0.007]</td><td>0.0055***<br>[0.003,0.008]</td><td>0.0041***<br>[0.003,0.006]</td></tr><tr><td>Obs.</td><td>107,777</td><td>107,777</td><td>107,777</td><td>107,777</td><td>99,158</td></tr><tr><td>Average Urban Growth</td><td>.03</td><td>.03</td><td>.03</td><td>.03</td><td>.03</td></tr><tr><td><strong>Average Shock by Buffer</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Flood</td><td>.066</td><td>.066</td><td>.066</td><td>.066</td><td>.063</td></tr><tr><td>Heatwave</td><td>.213</td><td>.215</td><td>.217</td><td>.217</td><td>.222</td></tr><tr><td>Drought</td><td>.117</td><td>.116</td><td>.116</td><td>.117</td><td>.114</td></tr><tr><td>Storm</td><td>.016</td><td>.016</td><td>.016</td><td>.016</td><td>.017</td></tr><tr><td>Controls?</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>City-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region-Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Clustered at</td><td>City</td><td>City</td><td>City</td><td>City</td><td>City</td></tr></tbody></table>

Note: *** 0.01,** 0.05,* 0.1 p-val. This table shows the results of an adjusted version of the baseline model. The description of the shocks and the model are discussed in Supplementary Appendix F. Shocks measure here the average fraction of the buffer affected by a shock. Data from commuting zones are based on Data for Good at Meta (2023).
Supplementary Appendix A Additional Figures

<figure><img src="image_9.png" /><figcaption>Figure S1: More Information on the Sample</figcaption></figure>

Note: The left figure shows how this paper classifies regions following the UN Statistical Division. The right plots the number of cities by country ISO-3 code. The black dots represent each an urban agglomeration included from OECD and SWAC (2020).
Supplementary Appendix B Additional Tables

<table><caption>Table S1: Classification of Countries by Region</caption><thead><tr><th>Region</th><th>Countries (ISO3)</th></tr></thead><tbody><tr><td>Southern Africa</td><td>Angola (AGO), Botswana (BWA), Eswatini (SWZ), Lesotho (LSO), Malawi (MWI), Mozambique (MOZ), Namibia (NAM), South Africa (ZAF), Zambia (ZMB), Zimbabwe (ZWE)</td></tr><tr><td>Eastern Africa</td><td>Djibouti (DJI), Eritrea (ERI), Ethiopia (ETH), Kenya (KEN), Madagascar (MDG), Rwanda (RWA), Somalia (SOM), South Sudan (SSD), Sudan (SDN), Tanzania (TZA), Uganda (UGA)</td></tr><tr><td>Central Africa</td><td>Burundi (BDI), Cameroon (CMR), Central African Republic (CAF), Chad (TCD), Republic of the Congo (COG), Democratic Republic of the Congo (COD), Equatorial Guinea (GNQ), Gabon (GAB)</td></tr><tr><td>Western Africa</td><td>Benin (BEN), Burkina Faso (BFA), Côte d'Ivoire (CIV), The Gambia (GMB), Ghana (GHA), Guinea-Bissau (GNB), Guinea (GIN), Liberia (LBR), Mauritania (MRT), Mali (MLI), Niger (NER), Nigeria (NGA), Senegal (SEN), Sierra Leone (SLE), Togo (TGO)</td></tr></tbody></table>

Note: The table shows how the different countries are classified into regions. It follows the classification of the UN (UNSD, 2023)

<table><caption>Table S2: Robustness: Sample Dependence</caption><thead><tr><th rowspan="2"></th><th colspan="1">Winsor Urban Growth</th><th colspan="4">Exclude cities that have population (2019) of...</th></tr><tr><th>(1)<br>Both Tails (5%)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Σ Flood Events in t-1</td><td>-0.001***<br>[-0.001,-0.000]</td><td>-0.002***<br>[-0.003,-0.001]</td><td>-0.002***<br>[-0.003,-0.001]</td><td>-0.003***<br>[-0.004,-0.002]</td><td>-0.002***<br>[-0.003,-0.001]</td></tr><tr><td>Σ Heatwave Events in t-1</td><td>-0.000<br>[-0.001,0.000]</td><td>-0.001<br>[-0.002,0.000]</td><td>-0.001<br>[-0.002,0.000]</td><td>-0.000<br>[-0.004,0.003]</td><td>-0.001**<br>[-0.002,-0.000]</td></tr><tr><td>Σ Drought Events in t-1</td><td>0.000<br>[-0.000,0.001]</td><td>0.000<br>[-0.000,0.001]</td><td>0.000<br>[-0.001,0.001]</td><td>-0.001<br>[-0.002,0.000]</td><td>0.001**<br>[0.000,0.002]</td></tr><tr><td>Σ Storm Events in t-1</td><td>0.003***<br>[0.001,0.005]</td><td>0.002*<br>[0.000,0.004]</td><td>0.003**<br>[0.001,0.005]</td><td>0.012<br>[-0.002,0.026]</td><td>0.003<br>[-0.001,0.007]</td></tr><tr><td>Obs.</td><td>107,796</td><td>102,190</td><td>84,426</td><td>21,885</td><td>85,911</td></tr><tr><td>Average Urban Growth</td><td>.02</td><td>.03</td><td>.03</td><td>.03</td><td>.03</td></tr></tbody></table>

Note: *** 0.01,** 0.05, * 0.1 p-val. This table shows the baseline specification from Equation 1. All models include city, year and region-year fixed effects and standard errors are clustered at the city-level. Population counts are based on data from Liu, Cao et al. (2024).
<table><caption>Table S3: Robustness: Treatment Definition + Non-Linearities</caption><thead><tr><th rowspan="2"></th><th colspan="2">Events</th><th colspan="2">Time-Decay Events</th><th colspan="2">Number of Months</th><th colspan="2">Intensity</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Flood in t-1</td><td>-0.0024***<br>[-0.003,-0.001]</td><td>-0.0025*<br>[-0.005,-0.000]</td><td>-0.0036***<br>[-0.005,-0.002]</td><td>-0.0099***<br>[-0.014,-0.006]</td><td>-0.0008**<br>[-0.001,-0.000]</td><td>-0.0022**<br>[-0.004,-0.001]</td><td>-0.0015***<br>[-0.002,-0.001]</td><td>-0.0010<br>[-0.002,0.000]</td></tr><tr><td>Heatwave in t-1</td><td>-0.0008<br>[-0.002,0.000]</td><td>-0.0009<br>[-0.003,0.001]</td><td>-0.0029***<br>[-0.004,-0.001]</td><td>-0.0011<br>[-0.005,0.003]</td><td>-0.0001<br>[-0.001,0.000]</td><td>-0.0002<br>[-0.001,0.001]</td><td>0.0008***<br>[0.000,0.001]</td><td>-0.0025*<br>[-0.005,-0.000]</td></tr><tr><td>Drought in t-1</td><td>0.0008*<br>[0.000,0.002]</td><td>-0.0004<br>[-0.002,0.001]</td><td>0.0018**<br>[0.000,0.003]</td><td>0.0003<br>[-0.003,0.004]</td><td>0.0004<br>[-0.000,0.001]</td><td>0.0001<br>[-0.001,0.001]</td><td>-0.0016***<br>[-0.002,-0.001]</td><td>-0.0007<br>[-0.002,0.001]</td></tr><tr><td>Storm in t-1</td><td>0.0036*<br>[0.000,0.007]</td><td>-0.0040<br>[-0.012,0.004]</td><td>0.0026<br>[-0.004,0.009]</td><td>-0.0092<br>[-0.033,0.014]</td><td>0.0013<br>[-0.002,0.004]</td><td>-0.0002<br>[-0.005,0.004]</td><td>0.0006***<br>[0.000,0.001]</td><td>0.0015***<br>[0.001,0.002]</td></tr><tr><td>Flood in t-1 (sq.)</td><td></td><td>0.0001<br>[-0.001,0.002]</td><td></td><td>0.0075***<br>[0.004,0.011]</td><td></td><td>0.0005<br>[-0.000,0.001]</td><td></td><td>-0.0002<br>[-0.001,0.000]</td></tr><tr><td>Heatwave in t-1 (sq.)</td><td></td><td>0.0000<br>[-0.001,0.001]</td><td></td><td>-0.0014<br>[-0.004,0.001]</td><td></td><td>0.0000<br>[-0.000,0.000]</td><td></td><td>0.0001**<br>[0.000,0.000]</td></tr><tr><td>Drought in t-1 (sq.)</td><td></td><td>0.0008<br>[-0.000,0.002]</td><td></td><td>0.0015<br>[-0.002,0.005]</td><td></td><td>0.0001<br>[-0.000,0.000]</td><td></td><td>-0.0004<br>[-0.001,0.000]</td></tr><tr><td>Storm in t-1 (sq.)</td><td></td><td>0.0046<br>[-0.001,0.010]</td><td></td><td>0.0122<br>[-0.016,0.040]</td><td></td><td>0.0006<br>[-0.001,0.002]</td><td></td><td>-0.0000**<br>[-0.000,-0.000]</td></tr><tr><td>Obs.</td><td>107,796</td><td>107,796</td><td>107,796</td><td>107,796</td><td>107,796</td><td>106,732</td><td>106,732</td><td>106,732</td></tr><tr><td>Average Urban Growth</td><td>.03</td><td>.03</td><td>.03</td><td>.03</td><td>.03</td><td>.03</td><td>.03</td><td>.03</td></tr><tr><td>Controls?</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>City-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year-FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region-Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

**Note:** *** 0.01,** 0.05, * 0.1 p-val. This table shows the baseline specification from Equation 1 but with a changed treatment definition. Note that there are fewer observations in Column (1) due to restricting the sample to make it comparable between (1) and (6). Column (1) shows the sample-restricted baseline. Column (3) applies a time-decay weight for the shock. (5) show the number of months in a year in which an event is present. Column (5) uses annual raw intensities. All even columns add quadratic terms. All models include city, year and region-year fixed effects and standard errors are clustered at the city-level.

<table><caption>Table S4: Robustness: Flood Measure</caption><thead><tr><th rowspan="2"></th><th colspan="2">Flood Robustness</th><th colspan="4">Has at least one flood of type...</th></tr><tr><th>(1)<br>Include common flood</th><th>(2)<br>Has > 10% flood risk</th><th>(3)<br>Any</th><th>(4)<br>Pluvial</th><th>(5)<br>Riverine</th><th>(6)<br>Both</th></tr></thead><tbody><tr><td>Has Flood</td><td>-0.0018***<br>[-0.003,-0.001]</td><td>-0.0017**<br>[-0.003,-0.000]</td><td>-0.0024***<br>[-0.003,-0.001]</td><td>-0.0137***<br>[-0.018,-0.010]</td><td>-0.0027***<br>[-0.004,-0.002]</td><td>-0.0026<br>[-0.006,0.001]</td></tr><tr><td>Obs.</td><td>107,796</td><td>107,796</td><td>107,796</td><td>100,749</td><td>103,757</td><td>107,796</td></tr></tbody></table>

**Note:** *** 0.01,** 0.05, * 0.1 p-val. This table shows the baseline specification from Equation 1. Column (1) changes the treatment definition from floods being above DFO Index 2 to DFO Index 1 so account for weaker floods. Column (2) adopts a more restrictive definition, where floods only count if at least 10% of an urban area is at high flooding risk. Column (3) uses the definition of the baseline with the change that it is binary (has a flood or not), independent of the number. (4) defines a pluvial flood if the month in which flood occurs has a rainfall anomaly of 2 standard deviations based on data from Climate Hazards Center (UCSB) (2023). (5) defines a flood if in same month a 3-day streamflow anomaly is detected with at least 2 standard deviations above historical means based on Joint Research Center and Copernicus Emergency Management Service (2019). (6) is defined if both conditions hold. Missings are higher in (4)-(6) as years where other flood type occurred are omitted. All models include city, year and region-year fixed effects, controls and also heat wave, drought and storm events and standard errors are clustered at the city-level.
## Supplementary Appendix C Mathematical Construction of the Shock

This section explains mathematically how the 10x10 km grid of Reinhardt (2025) is adjusted on a monthly level to align with city boundaries, enabling the computation of a yearly city panel dataset.

At first, the 10x10km grid overlaps with the city polygons and overlapping areas are computed for each cell. Then, a city-specific weighted mean for each hazard is computed based on the following equation:

$$ \bar{x}_{imt}^{(k)} = \frac{\sum_{g} A_{ig} \cdot x_{gmt}^{(k)}}{\sum_{g} A_{ig}} $$

where $\bar{x}_{imt}^{(k)}$ is the weighted average of hazard intensity for hazard k in city i, month m and year t. It is computed as summing, across all all 10x10km grid cells g, the product of two components: (1) the area overlap between city i and cell g denoted as $A_{ig}$ ("What part of a grid cell overlaps with city boundaries?" ) and (2) the cell specific intensity value for the hazard ($x_{gmt}^{(k)}$). This is then divided by the total overlap area of all grids within a city. This intensity can be the 5-day maximum wet-bulb temperature (heat wave), the SPEI-03 and SPEI-12 (drought) or the maximum windspeed recorded (storm). This is divided by the total number of overlapping area. For floods, the maximum recorded DFO Index is used.

Next, the monthly intensities are converted into binary dummies by applying the predefined thresholds.

$$ haz_{imt}^{(k)} = \begin{cases} 1 & \text{if } \bar{x}_{imt}^{(k)} > \tau^{(k)} \\ 0 & \text{otherwise} \end{cases} $$

where $\tau^{(k)}$ are the defined thresholds and the weighted average needs to be above the threshold. Every hazard has a specific defined $\tau$ described in section 2. Then, the annual city-level event number is defined as the number of events that switch from 0 to 1.

$$ haz_{it}^{(k)} = \sum_{m \in M \setminus \{\min(M)\}} 1(haz_{it,m-1}^{(k)} = 0 \land haz_{it,m}^{(k)} = 1) $$

where m is the month of the year as part of M, the total number of months, but excluding the first month of the panel (January 2000) as the previous month could not be determined. This will give us the total number of "unique" events for hazard k in city i in year t. The final vector Haz contains four elements ($haz_{it}^{(k)}$), one for each hazard. Especially for flood, this approach might overlook events. If for instance flood 1 lasted from February to March and another flood occured in March to April, this would be considered as a single event. To avoid this, for flood, the total number of events is compared to the unique counts of flood-specific identifiers of floods that intersect with a city.
# Supplementary Appendix D Additional Heterogeneity Tests

By Geographic Region and ENSO period This additional analysis tests if the coefficients presented in Table 3 vary across timing in the El Niño - Southern Oscillation (ENSO). It is included that, depending on the cycle, within the ENSO, extremely wet or extremely dry conditions can be expected. The effect of ENSO varies by broader regions, which is why it fits well in the geographic heterogeneity analysis. We suspect that during ENSO-neutral years, extreme events (like floods or droughts) may have stronger or different effects. This is because they are less anticipated, and thus may catch governments or communities more by surprise than when expectation and preparedness are higher. To define ENSO we take the Oceanic Niño Index (ONI) from the NOAA Climate Prediction Center (2025) that allows to define months as part of neutral, El Niño or La Niña months. El Niño or La Niña years are defined if at least four consecutive months of a year fulfill conditions according to the ONI to be classified as El Niño or La Niña month. The dummies are added as interaction terms with the shocks in a stratified region of the following form:

$$ \Delta \log(Y_{icrt}) = \alpha_r + \sum_{k=1}^{4} \beta_{k,r} \text{Haz}_{icr,t-1}^{(k)} + \sum_{k=1}^{4} \delta_{k,r} \text{Haz}_{icr,t-1}^{(k)} \cdot \text{Niño}_{t-1} + \sum_{k=1}^{4} \theta_{k,r} \text{Haz}_{icr,t-1}^{(k)} \cdot \text{Niña}_{t-1} + \gamma_r X_{c,t-1} + \lambda_{t,r} + \lambda_{i,r} + \varepsilon_{icrt} $$

The results are shown in Table S5. In the sample analyzed, urban expansion in Eastern Africa does not appear to be significantly influenced by climate hazards during El Niño or La Niña years. Instead, observed effects are primarily associated with hazards occurring during ENSO-neutral years. For Southern Africa, floods have a significant impact on urban growth, though the direction varies by ENSO phase: floods during neutral years reduce urban expansion, whereas during La Niña years, they are estimated to increase urban growth. Droughts in this region reduce urban expansion, but this effect is significant only during non-neutral ENSO phases. In Central Africa, droughts during El Niño or La Niña years are associated with reduced urban growth, effectively offsetting any positive effects observed during neutral years. For Western Africa, the negative association of floods and urban expansion of Table 3 is likely driven by floods occurring during neutral ENSO years. Interestingly, during ENSO years, floods may be associated with increased urban growth, suggesting a potential context-specific or adaptive response. Heat waves also show negative effects on urban expansion but only in ENSO years. Lastly, droughts also have negative effects in neutral years, but they also show positive effects. A possible explanation for the stronger responses in general during neutral years is the lower expectation of extreme weather events during such periods. As a result, early warnings might show larger uncertainties. Unlike El Niño or La Niña phases, which are often accompanied by specific forecasts and preemptive responses, neutral years may result in less preparedness, thereby amplifying the strong responses of particularly droughts and floods.
<table><caption>Table S5: Heterogeneity by Region and ENSO Cycle</caption><thead><tr><th></th><th>(1)<br>Southern Africa</th><th>(2)<br>Eastern Africa</th><th>(3)<br>Central Africa</th><th>(4)<br>Western Africa</th></tr></thead><tbody><tr><td colspan="5"><em>In neutral years</em></td></tr><tr><td>Σ Flood Events in t-1</td><td>-0.0046**<br>[-0.008,-0.001]</td><td>0.0000<br>[-0.003,0.003]</td><td>-0.0010<br>[-0.013,0.011]</td><td>-0.0078***<br>[-0.010,-0.006]</td></tr><tr><td>Σ Heatwave Events in t-1</td><td>-0.0043**<br>[-0.007,-0.001]</td><td>0.0040**<br>[0.001,0.007]</td><td>-0.0020<br>[-0.006,0.002]</td><td>0.0003<br>[-0.001,0.002]</td></tr><tr><td>Σ Drought Events in t-1</td><td>0.0017<br>[-0.000,0.004]</td><td>0.0036***<br>[0.002,0.006]</td><td>0.0081*<br>[0.000,0.016]</td><td>-0.0052***<br>[-0.007,-0.004]</td></tr><tr><td>Σ Storm Events in t-1</td><td>0.0046<br>[-0.001,0.010]</td><td>0.0033<br>[-0.000,0.007]</td><td>0.0000<br>[0.000,0.000]</td><td>0.0000<br>[0.000,0.000]</td></tr><tr><td colspan="5"><em>In El-Niño Years..</em></td></tr><tr><td>Σ Flood Events in t-1</td><td>0.0045<br>[-0.001,0.010]</td><td>0.0028<br>[-0.001,0.007]</td><td>-0.0097<br>[-0.023,0.003]</td><td>0.0041***<br>[0.002,0.007]</td></tr><tr><td>Σ Heatwave Events in t-1</td><td>0.0202<br>[-0.019,0.060]</td><td>-0.0010<br>[-0.006,0.004]</td><td>-0.0033<br>[-0.008,0.001]</td><td>-0.0020**<br>[-0.004,-0.001]</td></tr><tr><td>Σ Drought Events in t-1</td><td>-0.0052***<br>[-0.008,-0.002]</td><td>0.0025<br>[-0.000,0.005]</td><td>-0.0094*<br>[-0.018,-0.001]</td><td>0.0038***<br>[0.002,0.006]</td></tr><tr><td>Σ Storm Events in t-1</td><td>0.0014<br>[-0.002,0.005]</td><td>-0.0031<br>[-0.008,0.001]</td><td>0.0000<br>[0.000,0.000]</td><td>0.0000<br>[0.000,0.000]</td></tr><tr><td colspan="5"><em>In La-Niña Years...</em></td></tr><tr><td>Σ Flood Events in t-1</td><td>0.0069***<br>[0.003,0.011]</td><td>0.0007<br>[-0.003,0.005]</td><td>0.0049<br>[-0.011,0.020]</td><td>0.0049***<br>[0.002,0.008]</td></tr><tr><td>Σ Heatwave Events in t-1</td><td>0.0062<br>[-0.006,0.018]</td><td>0.0064<br>[-0.023,0.036]</td><td>-0.0012<br>[-0.023,0.020]</td><td>-0.0049***<br>[-0.008,-0.002]</td></tr><tr><td>Σ Drought Events in t-1</td><td>-0.0023*<br>[-0.005,-0.000]</td><td>-0.0004<br>[-0.004,0.003]</td><td>-0.0120**<br>[-0.020,-0.004]</td><td>0.0096***<br>[0.005,0.014]</td></tr><tr><td>Σ Storm Events in t-1</td><td>-0.0036<br>[-0.011,0.004]</td><td>-0.0020<br>[-0.010,0.006]</td><td>0.0000<br>[0.000,0.000]</td><td>0.0000<br>[0.000,0.000]</td></tr><tr><td>Obs.</td><td>19,551</td><td>28,275</td><td>16,716</td><td>43,254</td></tr><tr><td>Average Urban Growth</td><td>.02</td><td>.03</td><td>.03</td><td>.03</td></tr><tr><td>Controls?</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>City-FF</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year-FF</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region-Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Note: *** 0.01,** 0.05, * 0.1 p-val. Confidence intervals are at a 90% level. This table shows the baseline results of Equation 1. All samples include controls (a country's employment in agriculture (in %) and the countries total GDP per capita) and have city and year fixed effects. Regions are defined as subregions of Africa and are shown in Figure S1. El Niño and La Niña are defined if at least 4 month of a year fulfill these conditions taken from NOAA Climate Prediction Center (2025).
# Supplementary Appendix E Constructing of the Compound Shocks

In this part of the paper the previously individually regarded shock metrics are disentangled into two components: number of events of a hazard type $k_j$ where no other hazard than $k_j$ occurred in temporal and spatial proximity and those where another hazard type $k_{l \neq j}$ occurred. Furthermore, two increasing constraining restrictions are included: first, just account for a temporal window in which $k_l$ needs to take place and second, the order of sequence in which $k_j$ and $k_l$ take place. They will be presented as unordered and ordered sequences, respectively.

## Mathematical Intuition Behind the Unordered Sequence

First, we construct a compound event in an unordered sequence as happening in the same month or within a given time frame. To this end we construct monthly moving windows that define if within that period both type of hazards are present or not. Having computed on the city-level the presence of a hazard, we obtain the following (note $e$ is an event of a hazard type and element of the earlier presented vector $haz$):

$$e_{im}^{(fl)}, e_{im}^{(hw)}, e_{im}^{(dr)}, e_{im}^{(st)} \in \{0,1\}$$

So for all of the four hazards (flood, heat wave, drought and storm) we know if the month exceeded a threshold or not. Note here as we currently study the effects over the years the subscript $t$ is omitted as the windows, which will be introduced next, move across years. Next for a rolling-window of a specified length and for each hazard, we define the rolling maximum:

$$\tilde{e}_{i,m}^{(j)}(w) = \max_{h \in W_{w}(m)} e_{i,h}^{(j)}, \quad W_{w}(m) = \{m - \lfloor(w - 1)/2\rfloor, \dots, m + \lfloor(w - 1)/2\rfloor\}$$

Which gives us $\tilde{e}_{i,m}^{(j)}(w)$ as the rolling-maximum of hazard $k$ with window length $w$ in city $i$ and month $m$. It is defined as maximum value over the month-block $h$ which is part of a new set of aggregated month-sets $W$. This set $W$ just defines the month-blocks to consider when computing the shock. In our sample we define $w \in \{1,2\}$, so 1- or 2-month windows. Having computed the block-maxima for each of the four hazard types, we build a raw compound hazard indicator for each unordered pair $(j,l)$, whereby $j \neq l$ between both types:

$$cp_{i,m}^{(j,l)}(w) = 1 \left( e_{i,m}^{(j)} = 1 \land \tilde{e}_{i,m}^{(l)}(w) = 1 \lor e_{i,m}^{(l)} = 1 \land \tilde{e}_{i,m}^{(j)}(w) = 1 \right)$$

This will give us $cp_{i,m}^{(j,l)}(w)$ as one of six possible combinations of $j$ and $l$. The criteria requests that either $j$ is 1 and falls into a rolling maxima of $l$ or the other way round. So
for the example of flood and heat waves we essentially ask:

$$ cp_{i,m}^{(j,l)}(w) = 1 \text{ iff } \begin{cases} \text{flood in } m \text{ AND heat-wave somewhere in window } w, \\ \text{OR} \\ \text{heat-wave in } m \text{ AND flood somewhere in window } w. \end{cases} $$

To define compound events, we want to avoid double-counting consecutive months. For example, if a heat wave and flood combination extend over two months, we want to define it as a single event rather than two separate events. Thus, we define a final monthly equation that only keeps the first month of the spell:

$$ haz_{i,m}^{(j,l)}(w) = 1 (cp_{i,m}^{(j,l)}(w) = 1 \land cp_{i,m-1}^{(j,l)}(w) = 0) $$

So here we only keep the first occurrence of a specific sequence. In a final step for each hazard combination ($haz_{i,m}^{(j,l)}(w)$) with a set window $w$ we compute the number of events per year. To avoid double accounting for the same type of shock, we subtract the number of compound events from the total number of each event involved. For instance, there were two flood events recorded and one heat wave-flood pair. The included number for flood will be one, given that presumably the other flood overlapped with the heat wave event. This model is not perfect as there might be some risk that using the "take-the-first-month" spell condition, might overlook other newly occuring events. This means that we likely underestimate the number of events and the measure is likely a lower bound estimate.

## Empirical Estimation of the Unordered Sequence

To only account for the presence of a second hazard type independent of the order between hazard type $k_j$ and $k_i$ the baseline equation Equation 1 is thereby adjusted to accommodate this division:

$$ \Delta \log(Y_{icrt}) = \alpha + \sum_{k=1}^{4} \beta_k \cdot \widetilde{Haz}_{icrt-1}^{(k)} + \sum_{c=5}^{11} \theta_c \cdot CP_{icrt-1}^{(c,w)} + \gamma X_{ct-1} + \lambda_t + \lambda_i + \lambda_{r \times t} + \epsilon_{it} \quad (2) $$

where $\widetilde{Haz}_{icrt-1}^{(k)}$ is similar to Equation 1 but excludes from the count of hazard events, events where within a window $w$ another hazard type has been recorded, thus the tilde. It is therefore on average lower than our baseline variable. New is the part of $CP_{icrt-1}^{(c,w)}$ that is a variable that counts the number of compound combinations of type $c$ in a given window $w$. $c$ is one of six potential unordered pairs (e.g., flood-heat wave), and the window is set in the model to either one or two months. The interpretation of $\theta$ is the marginal short-term effect of having one additional compound shock of type $c$ in the last year on the growth rate of urban expansion in percentage points.
## Mathematical Intuition Behind the Ordered Sequence

As before here we have defined the four distinct hazard types per month-event:

$$e_{im}^{(fl)}, e_{im}^{(hw)}, e_{im}^{(dr)}, e_{im}^{(st)} \in \{0,1\}$$

Every month our algorithm investigates if none, one, or more than one hazard occurred. Formally, we build a hazard set S for each month-city pair that accounts for the combinations:

$$S_{im} = \{k \in \{\text{FL, HW, ST, DR}\} : e_{im}^{(k)} = 1\}$$

Which is for instance for a month with flood and heat wave ("FL;HW") in a month under consideration (m). If nothing is reported then $S_{im} = \emptyset$. Next, we define a forward-looking window (w), in our case set to two months. Essentially, if the considered month is m, we look forward into the months m+1 and m+2.

For the same city i consider any pair of months (m,n) with $m < n \le m + w$. Thus, m is the current months and n is a later month inside the forward looking window w. For example, if the current month is March (m=3), the window extends to May (m+w=5), and n would be April (4). Then, we build ordered pairs as

$$k_1 \to k_2, \quad k_1 \in S_{im}, k_2 \in S_{in}, k_1 \neq k_2$$

Note here that $k_1$ is defined at month m and $k_2$ is defined at month n. As a next step, to account for unique sequences in a given year, we introduce a city-specific memory set that checks if the hazard sequence within a given window is already recorded or not. We name this memory set $P_i$ and check if either $k_1 \to k_2$ or $k_2 \to k_1$ already exists within the set. If the sequence or its reverse is already recorded, we ignore it. Otherwise, we add it to $P_i$ at the later month n (as we presume this is where the sequence is fully established and shows the most significant effects). This condition filters out repeated sequences or order reversals that might take place due to fading out events (an area sees a reduction of flooded areas while experiencing still drought conditions).

We enforce a 12-month cool down period. This means if a compound sequence is recorded at a particular months, the same sequence (in either order) cannot be recorded again within the next 12 month. This reduces concerns of double-counting the same compound combination. Formally, a sequence is then defined as:

$$ \text{Seq}_{in} = \begin{cases} \exists m \text{ s.t. } n - w \le m < n, & (3) \\ k_1 \to k_2 & k_1 \in S_{im}, k_2 \in S_{in}, k_1 \neq k_2, \\ (k_1 \to k_2) \notin P_i(n-1), (k_2 \to k_1) \notin P_i(n-1) & \end{cases} $$

Essentially Seq_in records a hazard-sequence $k_1 \to k_2$ if neither $k_1 \to k_2$ nor $k_2 \to k_1$ has occurred in the previous 12-month window. If these conditions are satisfied, the new sequence is added to $P_i$ (at month n). We look at $P_i(n-1)$ because the condition set must use a memory set before month n is updated, otherwise if we use $P_i$ at time n, the new sequence would already be added to $P_i$ and thus disqualify itself.

Once the sequence variable (Seq_in) is defined, we obtain unique counts by city i and
hazard-sequence $k_1 \rightarrow k_2^{24}$. As the variable is currently on a monthly level, we aggregate each unique sequence into a city-year panel. In short, for each city we identify hazard $k_1$ (e.g. flood) occurring in a given month, then look forward within a window (w) to check if another hazard $k_2$ (e.g. heat wave) occurs. If so, we form an ordered pair $k_1 \rightarrow k_2$. If this pair (or its reverse $k_2 \rightarrow k_1$) occured in the past 12 months, we exclude it; otherwise, it is labelled as a new compound sequence$^{25}$.

## Empirical Estimation of the Ordered Sequence

The derived empirical estimation is fairly similar to the previous one:

$$ \Delta \log(Y_{icrt}) = \alpha + \sum_{k=1}^{4} \beta_k \cdot \widetilde{\text{Haz}}_{icrt-1}^{(k)} + \sum_{j=5}^{J} \theta_j \cdot \text{CPseq}_{icrt-1}^{(j)} + \gamma X_{ct-1} + \lambda_t + \lambda_i + \lambda_{r \times t} + \epsilon_{it} \quad (4) $$

Here $\text{CPseq}_{icrt-1}^{(j)}$ is a vector of the j-th ordered pair of the vector $\text{CPseq}$ defined for a fixed 2-month window. All else remains the same.

## How does the number of combinations change between models?

The change in definition between a month, a two-month unordered setup, and an ordered sequence with a 12-month cooling period will change the number of existing combinations. The hazard-pair counts are shown in Figure S2. One can see that for all hazards, the number of events increases, allowing for a larger time window. The anti-diagonal is empty as we exclude the possibility of within-hazard compound events$^{26}$. The most common combination of hazards is heat waves and floods, and heat waves and droughts, followed by floods and droughts. Storms, given the spatial concentration in some parts of Eastern and Southern Africa, see substantially fewer compound events. It is not surprising to see that heat wave-flood pairs are the most common type given that they might be connected through convective storms$^{27}$. For an ordered 2-month period, we see that the by far most common sequence in Sub-Saharan Africa is heat waves followed by flood events. The next most common are droughts followed by floods and droughts followed by heat waves. Note that the number of events is not identical to the unordered 2-month period due to slightly different computations of the sequence mentioned above.

<footnote>$^{24}$For simplicity reasons, here only $k_1$ to $k_2$ pairs are shown. In practice, the designed algorithm in Python also allows for more than just one ordered pair to enter the sequence. In practice thus we capture more than just one bivariate pair.</footnote>

<footnote>$^{25}$Tested but not included is using other thresholds of the "exclusion criteria" of 12 months. Allowing for shorter windows increases the noise and risk of double accounting, but includes more event combinations. Broadly comparable results have been documented for a 10 month and 14 month window.</footnote>

<footnote>$^{26}$Within-month within-hazard compound events would require higher temporal resolution, which we do not have. Otherwise, within the year, the adjusted $Haz_{icrt-1}^{(k)}$ accounts for the number of events of that type in a given year.</footnote>

<footnote>$^{27}$A possible example for this hazard pair could be in the Sahel zone. In May and June, before the northward shift of the Intertropical Convergence Zone (ITCZ) and the onset of the West African Monsoon (WAM), the region experiences intense heat, potentially sufficient to be considered heat waves. When the monsoon arrives in July, the combination of moist air from the Gulf of Guinea and the super-heated land surface leads to strong atmospheric instability. This can trigger intense convective storms, sometimes resulting in floods.</footnote>
<figure><img src="image_10.png" /><figcaption>Figure S2: Compound Sequence: Number of Hazard Pairs</figcaption></figure>

Note: The figure shows the number of unordered (1-month and 2-month) and ordered hazard pairs. The creation of these pairs follows the methods described above. Note that for the ordered pairs, the y-axis indicates the first, the x-axis indicates the second event.

# Supplementary Appendix F Constructing Spatial Shock

In an extension of the baseline model of Equation 1 we include the exposure to flood, storm, drought and heat waves also in nearby rural land. To this end, we employ two approaches to assess weather shocks in nearby land: (1) a spatial buffer analysis around each urban agglomeration and (2) an analysis based on the commuting zones of each major city. The following sections describe the methodology used to compute weather shocks under each approach.

## *Creation of the Buffers*

First, the spatial buffer approach draws a 50km, 100km, and 150km catchment area around the centroid of each urban agglomeration. The different buffers allow one to assess the sensitivity of the results to different magnitudes of buffer. An illustration of these buffers can be seen in Figure S3 (a) and (a1) for the example of Kinshasha. As the treatment variables of drought, heat wave, storm, and flood are taken from Reinhardt (2025), the same data is used to compute the number of shocks within these buffers. The 10x10km grid of Reinhardt (2025) is overlapped with the buffers and intersections are computed. If buffers exceed national boundaries, they are clipped to only include areas within the same country. In Figure S3 (2) one can see that for Kinshasha only the catchment area within the Democratic Republic of Congo is included, while Congo-Brazzaville in the Republic of the Congo is excluded. This is done under the assumption that if internal migration happens, migrating into a border country is costly, and in this case excluded by construction. Having defined the buffers, a *continuous* shock variable is calculated for each buffer. The computation is slightly different from that of the baseline model. We modify the treatment definition to more closely reflect the spatial distribution of the shocks. Instead of relying solely on the urban agglomeration, we construct spatial buffers (e.g., 50km) around urban cores and intersect these with
<figure><img src="image_11.png" /><figcaption>Figure S3: Illustration of Creation of the Spatial Shocks</figcaption></figure>

**Note:** The figure shows in Panel (a) the buffer-approach to compute shocks. In (1) a 50,100 and 150km buffer is drawn around each city (here in Kinshasha, Democratic Republic of Congo). In (2) the buffers are overlayed with the 10x10km grids based on Reinhardt (2025) and city-cells are excluded and in (3) additionally cells with less than 10 people are excluded. In panel (b) the commuting-zone approach is shown. First, it shows the number of cities and the commuting zones included in the sample. In (1) the commuting-zone for "Kinshasha" is plotted, in (2) the grid as mentioned above is added and finally in (3) urban areas and uninhabitated cells are excluded. The remaining cells that are not a city and have more than 10 people living there are used to compute the number of shocks.
the 10x10km grid. Here, each grid cell is directly converted to a binary shock indicator using the same threshold as before. These are then summed annually<fnref n="28" />. The new shock is the average within each buffer<fnref n="29" />. For cities within these buffers, the same method is applied to all grid cells intersecting the urban shapefile, ensuring comparability. As a result, the new shock variable becomes continuous (average area of a buffer affected), reflecting the intensity of shocks within the buffer area. An increase in this measure can be interpreted as, for example, a 100% increase in the number of shocked grid cells within the buffer. This differs from the baseline, where the focus is on the number of events derived from average intensities within an urban area. The baseline does not focus on the average number of shocks in cells with which the city (or its buffers) intersects. Given the description above, a problem arises if the number of cells used to compute the buffer might vary (e.g. Kinshasa has only half of the cells given that the other half falls into another country). To account for this, additionally, the total population within each buffer is computed using data from GlobPop (Liu, Cao et al. (2024)).

Static buffers have two main limitations. First, they do not account for country-specific contexts such as the size of a country. For instance, a 150 km radius covers a significant portion of a small country like Burundi, whereas it covers only a minor part of a much larger country like Niger. Second, buffers ignore terrain and infrastructure, which are important to assess given that they are strongly related to the costs of displacement and mobility. A 150 km journey through the hilly and densely forested Kivu region in the Northeastern Democratic Republic of Congo is more time-consuming and potentially expensive than an equivalent distance in the well-connected catchment area of Cape Town, South Africa. To account for this, an alternative buffer scheme is used: commuting zones provided by Data for Good at Meta (2023). The commute zones are based on daily movement maps from home locations to potential work locations of individuals who have activated the location services of used Meta apps (such as Facebook). By tracking weekday movements, the project attempts to build commute zones to nearby population centers (nodes) and isolate them by drawing Voronai shapes around these catchment areas. These data were taken for all African countries considered in this article and intersected with urban agglomerations. The 416

$$ \operatorname{hexp}_{gmt}^{(k)} = \begin{cases} 1 & \text{if } x_{gmt}^{(k)} > \tau^{(k)} \\ 0 & \text{otherwise} \end{cases} $$

so computing for each month and cell g if thresholds are exceeded. Then sum them as

$$ \operatorname{hexp}_{gt}^{(k)} = \sum_{m \in M \setminus \{\min(M)\}} \left( \operatorname{hexp}_{gt,m-1}^{(k)} = 0 \land \operatorname{hexp}_{gt,m}^{(k)} = 1 \right) $$

This gives the total number of events for type k in cell g

$$ \operatorname{hexp}_{ict}^{(k,b)} = \frac{1}{G_i^{(b)}} \sum_{g \in G_i^{(b)}} \operatorname{hexp}_{gt}^{(k)} $$

, where G is the total number of grid cells in buffer b to city i. The value of G might vary between cities due to the overlap of the boundaries.

<footnote n="28">²⁸Following the notation of Supplementary Appendix C, we compute</footnote>

<footnote n="29">²⁹In the notation of Supplementary Appendix C this would correspond to</footnote>
included commuting zones that cover 5 185 cities of the 5 821 cities in the full sample are shown in Figure S3 panel (b).

The commuting zones are named after the main destinations of weekday movements. I identify the main commuting node in each commuting zone using fuzzy matching on the name and based on a distance-matching criterion. This is important, as many, especially smaller urban agglomerations, fall within the commuting zones of larger cities. For example, the small agglomeration of Bankana, 120km south-east of Kinshasa, in the Democratic Republic of Congo, falls into the Kinshasa commuting zone. This means that our final sample will have in parts multiple cities in the same commuting zone with one city being the "population hub". Similarly to the buffer approach explained above, the 10x10km grids as in the main approach are overlapped with each commuting zone and 10km grid cells that contain urban agglomerations and population less than 10 people are excluded (see lower row (2) and (3)). Attentive readers may notice that the commuting zone of Kinshasa in Figure S3 extends across the river into the Republic of Congo, including parts of Brazzaville. This is intentional, as the analysis is based on functional urban catchment zones rather than strict administrative boundaries. If shocks drive migration to larger urban areas, some internal migrants in the Republic of the Congo may relocate to Brazzaville instead of Kinshasa, both of which are captured in the sample. Similarly to the buffer approach, based on the same threshold and methodology as in the main paper, flood, drought, storm, and heat wave events are computed for each cell. Finally, the resulting yearly events are then aggregated on a city level as the maximum number of events and the average number of events in the catchment area, e.g the average number of flood events recorded in 2015 in the Kinshasa catchment areas (excluding areas with high urban presence and absence of local population) for all cities that fall into this catchment area.

## Computation of Model

To estimate the effects of a 100% shock in the catchment area (buffer or commuting zone) a slightly modified version of the baseline model is used:

$$ \Delta \log(Y_{icrt}) = \alpha + \sum_{k=1}^{4} \beta_k \cdot \text{Hexp}_{icrt-1}^{(k,b)} + \gamma X_{ct-1} + \kappa \log(\text{Pop}_{icrt-1}^{(b)}) + \delta_t + \lambda_i + \lambda_{r \times t} + \epsilon_{it} \quad (5) $$

The model is comparable to the original specification, with three key exceptions. First, instead of the vector Haz, which contained categorical indicators for each of the four types of hazards, we now use the vector Hexp, which measures continuous hazard exposure for each hazard. Second, this exposure vector is defined at a specific buffer size b, chosen from one of the following: 0 km, 50 km, 100 km, 150 km, or the commuting zone. The interpretation of each coefficient β also changes: rather than capturing the effect of an additional discrete shock, each β now reflects the effect of an additional shock affecting the full buffer. Lastly, we introduce a new control variable: the lagged log of population within buffer b. This accounts for the size of the potentially affected population and improves comparability across buffers that may contain a different
number of grid cells due to exclusions near borders. Including lagged population along with hazard exposure risks that effects of weather shocks on urbanisation are mediated through population. This risks to underestimate the effects if population endogenously respond to the hazards, which likely in the case of weather shocks. Including it nonetheless here serves three purposes: first, it reduces risk of ommitted variable bias. Second, it improves comparability of effects across catchments with varying baseline population levels. Third and lastly, in includes indirectly a key mechanism that may drive urban expansion: rural-urban migration. For these reasons, we add population directly in the same lag structure into the model.

# Supplementary Appendix G Constructing the Event-Study

## Overview

This section provides more context on the use of the event-study estimator of panel matching suggested by Imai and Kim (2019). This estimator is especially useful in cases with non-random treatment assignment and repeated treatments over time. Following Baker et al. (2025), there are currently only very few causal difference-in-difference-like estimators that allow treatment to switch from treated to nontreated, an assumption that is crucial in the case of extreme events. In addition, the estimator proposed by Imai and Kim (2019) is relatively flexible, as it allows conditioning on both time-varying and time-invariant covariates, unlike most other estimators, with the exception of the one by Liu, Wang and Xu (2024). Furthermore, standard two-way fixed effects models rely on strong identification assumptions, notably strict exogeneity: no time-varying confounders (i.e., no omitted variable bias), no carry-over effects from past treatments, and no feedback from past outcomes to current treatment. The method of Imai and Kim (2019) instead relies on the assumption of sequential ignorability, which is less restrictive. This assumption allows for carry-over effects from past treatments and outcomes onto the current outcome, but assumes that the current treatment is not influenced by past covariates. Although sequential ignorability is generally easier to defend—especially in the context of heat waves—there remains a small risk of violation. This risk arises if, for example, urban expansion increases the likelihood of heat waves. However, we argue that while heat wave risk and heat wave occurrence may be correlated, the latter depends on additional stochastic factors. These include, among others, precipitation, local weather variability, and local adaptation efforts. Therefore, the increase in risk does not directly translate to an increase in heat wave events. Based on this reasoning, we consider the sequential ignorability assumption plausible in our setting and proceed using the estimator by Imai and Kim (2019).

## The Setup

The method proposed follows a three-step procedure. First, a matched set is created for each transition of the shocks. Note here that floods, heat waves, droughts and storms are considered binary, given that the panel match estimator does not allow
<figure><img src="image_12.png" /><figcaption>Figure S4: Event-Study: Selecting Best Matching Method</figcaption></figure>

**Note:** The plots compare the difference in covariate balancing for the three tested matching methods. The closer coefficients are to the zero line the better on average the matching performance. Outcome is the standard deviation for the average difference between treated units and average matched units.

for categorical treatments. Thus, for every city, a set of matched cities with similar treatment histories for an event is iteratively created. For example, if Nairobi was flooded in 2008 and 2015, the algorithm searches for other cities (in our case, we set the size of the matching group to 5) that were flooded in 2008 but not in 2015. Second, once the set of cities with similar treatment histories has been selected, a more comparable control city is created by refining the matched set using matching methods. In our case, we tested for Mahalanobis matching, covariate balance propensity score matching and propensity score weighting. To build a better fitting sample, decisions need to be made on which covariates the matched sample should be refined. Given that the outcome is urban expansion, the covariates include the last two years of all other hazards except the one in question. This assumes that their past treatment might influence urban expansion. Furthermore, we use the initial urban area in 1990 to get a better approximation on the growth level the cities have seen in the past. Finally, we take the last four years before a treatment in terms of urban expansion to get cities with similar historical growth rates. However, it is not trivial to decide which matching procedure to use. We assess the best-fitting model by comparing the average difference of variables used for matching between the treated unit and the weighted average of the control units over all matched sets. This assessment follows the author's guidelines. The results are shown in Figure S4. The general standard deviation was the smallest for covariate balancing, making it our preferred specification for this study. Having selected the pretreatment window for the treatment history, the matching method and covariates to match on, in the third step, the average treatment effect of treated (ATT) is computed between the treated unit and the refined set. Standard errors are computed
<figure><img src="image_13.png" /><figcaption>Figure S5: Event-Study: All Models</figcaption></figure>

**Note:** This figure shows the results of using panel matching by Imai and Kim (2019) on the four different hazard on physical urban expansion. Treatment histories are fitted for an eight-year window and standard errors are bootstrapped 100 times. The confidence intervals shown represent a 10% level. The first row shows Mahalanobis matching, the second Covariate Balance Propensity Score matching and the last row shows propensity score weighting.

using block bootstrapping with 100 replications.

## Additional Results

The results of all different methods are shown in Figure S5. For flood, all models show a similar pattern. Heat waves show only significant positive effects when using propensity score weighting. A reason might be that propensity score weighting does not exclude poor matches, but might be more model-dependent than Mahalanobis or Covariate Balance Propensity Score matching. For drought and storm, again, similar results are found. We also test, yet not shown, using different pre-treatment lags and different lag structures in the covariates.

## Robustness

To test whether the results are driven by strong pre-trends, a placebo test is conducted to assess whether parallel trends hold. The results are shown in Figure S6 and appear to hold for all but heat waves, where one period is significant. Thus, the mild and mostly insignificant effects of heat waves should be interpreted with caution.
<figure><img src="image_14.png" /><figcaption>Figure S6: Event Study: Placebo Tests</figcaption></figure>

**Note:** The four panels show the placebo test on the pre-treatment sample using the same covariate setup as in the main event-study. Estimates are based on the tests proposed by Imai and Kim (2019). Confidence intervals shown represent the 10% level. The model tested here is the Covariate Balance Propensity Score Model.
# Supplementary Appendix H Data Sources and Their Transformation for the Within-City Analysis

This section describes the data used and transformations applied to estimate the results of Table 6.

## Data Sources

**Population** As mentioned in the main part of the paper, the data are based on a dataset by Liu, Cao et al. (2024). This data provides annual population counts and density estimates at a resolution of 1km. Most annual data for population counts is interpolated from census data and fine-tuned with remote sensing extensions (e.g. building footprints, nightlight, etc.). The data by Liu, Cao et al. (2024) seems to perform equally well or slightly better than comparable annual population datasets.

**Informal Settlements** Acquiring data on informal settlements is challenging, since the criteria that define them differ. They can generally be defined by high land tenure insecurity, lack of access to roads and urban infrastructure, little to no amenities (such as hospitals, canalization system) or areas with high social marginalization of vulnerable groups (UNHCR, 2024). Here, we rely on the Million Neighborhoods project (Bettencourt and Marchio, 2023) to focus on the lack of access to street networks as a measure of "informality". The data used to define these areas is recent, thus the assumption is made that areas with lack of access today will have likely had little to no access in the part. The data provided come in a range zero up to 67 in our sample, where neighborhoods with values above 10 are classified as "off-network" and above six have a very low connectivity. Thus, we use 10 as a strong "informal" metric and 6 as a "weak" informal metric. Within each of these areas, the population is computed using a downscaled estimate for population from Liu, Cao et al. (2024), described below. Instead of relying on urban expansion by impervious surfaces here, we focus on population given that impervious surfaces might underestimate the extent of informal activity (knowing that they also capture increase in infrastructure beyond just buildings.)

**Flood Risk** While already briefly presented in the Data Section, flood risk data is drawn from Rentschler (2024) and coastal flooding risk from Vousdoukas et al. (2018) and Vousdoukas (2023), both have an ultra-high resolution of less than 300m. High-flood risk zones are defined as areas that would be inundated by more than 50 cm in the event of a 100-year flood.

**Conflict** There exist two common databases for conflict: the ACLED databases (Raleigh, Kishi and Linke, 2023) and the Uppsala Conflict Data Program (UCDP) (Sundberg and Melander, 2013). We choose the former and account for conflict only if it took place in the form of violent conflicts or riots within a city's urban boundary. However, the dependent outcome variable is also tested for using a binary dummy (whether it has conflict or not) and using the UCDP dataset. The results are comparable.
## Downscaling of Population Data

Attentive readers might have noticed that the population data presented comes at a 1km resolution while flood risk as well as neighborhood data might be in a substantially smaller geographic area. To compute population estimates, we apply a simple downscaling method<fnref n="30" />. In order to downscale the data, we use building footprints on a 100-m resolution from the Global Human Settlement Layer dataset (Pesaresi, 2023). To account only for population locations, only the residential part of building footprints is used. The motivation is that the population will be allocated where the residential build-up is recorded. Although this approach is rather simplistic, GHSL has been used in other literature also as one of several factors besides interpolated population count and Landsat images (see e.g. Tziokas et al., 2023). As the GHSL data is only available in 5-year periods, we always use the last available year. After bilinear resampling and coarsening, downscaling weights are computed based on the normalized weight of the 100m area to the total sum of build-up within the 1km grid. To reduce noise from individual data points, a Gaussian smoothing filter is applied. We apply here for the following model

$$G(x, y) = \frac{1}{2\pi\sigma^2} \exp\left(-\frac{x^2 + y^2}{2\sigma^2}\right)$$

, where G() is the Gaussian function at each lon-lat pair (x,y), a σ value of 1. The Gaussian filter spreads cell-specific estimates over local neighbors, reducing the possibility of extreme outliers. The sigma of one is a rather small amount of smoothing as we want to retain a sufficiently high variation in population. Applying the Gaussian smoothing might lead to a slight underestimation of population serving rather as a lower bound estimate.

To compute area-specific statistics, we rely on an auxiliary 300m grid within each city that defines if an area is informal or not or if it is at high flood risk or not (see panel (c) and (d) of S7 for a visualization). Once downscaling is completed, zonal statistics are then computed with the sum of population within this auxiliary grid. Given the centroid overlapping approach implemented in the rasterstats package in Python, the sum values might be slightly overestimated for population, which is why the means are also computed for robustness. An illustration of the procedure is shown in S7. To benchmark the estimates, population estimates are validated for three points in time in three countries with unconstrained interpolated population data of 100 m adjusted with UN estimates of WorldPop (2020). The results are shown in Figure S7. In comparison with the World Pop data, the downscaled data slightly overestimates the population of cells. The R² varies between 0.46 and 0.69. Although that indicates not a perfect prediction in comparison with other interpolated data sets, given that large fractions of the overestimation is due to outliers that are reduced as in the main specification, the outcome is going to be logged, overall, the matching quality is sufficiently good.

<footnote n="30">³⁰We are aware that annual population estimates exist at higher resolution, but using them for all of Africa exceeded the scope of this paper. However, we validate our estimates against them on a random selection of cities.</footnote>
<figure><img src="image_15.png" /><figcaption>Figure S7: Downscaling of Population data and Robustness Tests</figcaption></figure>

**Note:** The figure visualizes the data generating process. The upper panel shows the data. Panel (a) presents the original population count data at a 1 km resolution. (b) provides the downscaled version using the produre described in the text. (c) visualizes high flood risk zones (dark blue where distance to flood zone is <300m) and (d) shows the used informality complexity measure by are. The lower panel shows for three countries and years the validation of a linear regression of the 100m World Pop data from WorldPop (2020) with the 100m downscaled version based on GHSL residential building footprint. Color palettes follow Crameri, Shephard and Heron (2020).