metadata: authors: 0: author: Ben Bih,Karima
1: author: Desjonqueres,Chloe Genevieve Helene
2: author: Jafino,Bramka Arga
3: author: Blanc,Elodie
4: author: Masson,Solene Adeline Johanne
count: Mozambique,Nigeria,World
docna: 0: docna: Impacts of Disasters in Conflict Settings :
            Evidence from Mozambique and Nigeria
docty: Policy Research Working Paper
theme: Forced Displacement,Disaster Preparedness,Disaster Response and Recovery,Social Development and Protection,Conflict Prevention,Environment and Natural Resource Management,Fragility, Conflict and Violence,Climate change,Urban and Rural Development,Adaptation,Disaster Risk Management
abstracts: cdata!: This paper estimates the differentiated
            economic impact of natural hazard-related disasters (the
            specific disasters and climate shocks studied here being
            floods) when they occur in conflict versus non-conflict
            affected areas. Existing literature shows that disasters and
            climate shocks can cause significant distress to countries
            and people on an institutional and household level. However,
            assumptions are made that their impact tends to be larger in
            conflict-affected areas, with little evidence available on
            the differentiated extent of these damages. This paper
            investigates whether, and to what extent, the presence of
            conflicts has amplified the impacts of floods on economic
            activity and people, and hampered recovery. The paper
            applies a &#x201c;top-down&#x201d; approach to
            estimating the differential impacts of disasters and climate
            shocks between conflict and non-conflict affected areas
            using satellite-derived imagery of nightlight radiance as a
            proxy for economic activity, along with geospatial
            footprints of floods. The analysis considers two case
            studies: the 2019 tropical cyclones Idai and Kenneth and
            subsequent floods in Mozambique, and the July 2022 floods in
            Nigeria. Using difference-in-difference estimations, the
            analysis finds that there are significant differences in
            disaster and climate shock impacts and recovery between
            conflict and non-conflict affected areas. Particularly,
            there is a greater decline in economic activity and a longer
            recovery time in conflict affected areas, as proxied by the
            greater change in the intensity of nightlight radiance.
display_title: Impacts of Disasters in Conflict
            Settings : Evidence from Mozambique and Nigeria
listing_relative_url: /research/2024/12/34433856/impacts-disasters-conflict-settings-evidence-mozambique-nigeria
topicv3: Climate Risk Management m1326584 573,Fragility, Conflict and Violence m1326586 2432,Disaster Risk Management m1326682 820,Climate Change m1326829 567,Poverty, Nutrition and Food Security
            m1327659 1162
publishtoextweb_dt: 2024-12-09T00:00:00Z
dois: 10.1596/1813-9450-10995
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 45
author_keys: 0: author_key: 1493943
1: author_key: 1844432
2: author_key: 1706123
3: author_key: 2346952
4: author_key: 2346948
model_extractions: Policy Research Working Paper 10995 Impacts of Disasters in Conflict Settings Evidence from Mozambique and Nigeria _Karima Ben Bih Chloe Desjonqueres Bramka Jarafino Elodie Blanc Solene Masson_ Urban, Disaster Risk Management, Resilience and Land Global Department December 2024
Policy Research Working Paper 10995

# **Abstract**

This paper estimates the differentiated economic impact of natural hazard-related disasters (the specific disasters and climate shocks studied here being floods) when they occur in conflict versus non-conflict affected areas. Existing literature shows that disasters and climate shocks can cause significant distress to countries and people on an institutional and household level. However, assumptions are made that their impact tends to be larger in conflict-affected areas, with little evidence available on the differentiated extent of these damages. This paper investigates whether, and to what extent, the presence of conflicts has amplified the impacts of floods on economic activity and people, and hampered recovery. The paper applies a “top-down” approach to estimating the differential impacts of disasters and climate shocks between conflict and non-conflict affected areas using satellite-derived imagery of nightlight radiance as a proxy for economic activity, along with geospatial footprints of floods. The analysis considers two case studies: the 2019 tropical cyclones Idai and Kenneth and subsequent floods in Mozambique, and the July 2022 floods in Nigeria. Using difference-in-difference estimations, the analysis finds that there are significant differences in disaster and climate shock impacts and recovery between conflict and non-conflict affected areas. Particularly, there is a greater decline in economic activity and a longer recovery time in conflict affected areas, as proxied by the greater change in the intensity of nightlight radiance.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The authors may be contacted at kbenbih@worldbank.org.

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

Novembre 20, 2024 Karima Ben Bih, World Bank Chloe Desjonqueres, World Bank Bramka Jarafino, World Bank Elodie Blanc, Motu Economic and Public Policy Research Center Solene Masson, World Bank VIIRS; Floods; Nigeria; Mozambique; Conflicts influence on Disaster Impacts and Recovery; GDP.

The authors are grateful to Stephane Hallegatte, Oscar Ishizawa, and Jun Rentschler for their thoughtful comments, suggestions, and guidance.
# Introduction

The aim of this study is to examine the differential impact of disasters and climate shocks on populations in conflict-affected regions, specifically investigating the repercussions of flooding in conflict versus non-conflict areas. Using remote sensing technology, we attempt to overcome the challenge of data scarcity in conflict-affected countries, allowing us to account for short-term impacts of recent disaster and climate shock events. Despite the inherent limitations of using nightlight intensity as an economic activity indicator, it provides an empirical foundation for the analysis and enough observations for an ex-post quasi-experimental impact evaluation. We employ a difference-in-difference econometric approach, using satellite imagery of nightlight radiance alongside geospatial data on flood and conflict events. This methodological framework is applied to assess the aftermath of the March-April 2019 Floods in Mozambique following Cyclones Kenneth and Idai, as well as the 2022 floods spanning July to October in Nigeria.

Results show significant disparities in the effects of disasters and climate shocks between conflictaffected and non-conflict-affected areas. Specifically, we observe a more pronounced decline in economic activities in conflict-affected regions.

attention to the interconnectedness of conflict and disasters and climate shocks, outlining the methodology and empirical strategy derived to estimate such ex-post impact. In the second section, we present the results and supporting data derived from the study, including the case studies on Mozambique and Nigeria. Finally, we discuss limitations as well as broader implications before concluding.

# Context: Flood impact and conflict affected population (Literature)

## 1.

Quantitative economic analyses have frequently used nightlight radiance as proxy for economic activity (Chen & Nordhaus, 2011; Henderson et al., 2012). These have also been used to estimate the impacts of weather variability and disasters and climate shocks (Bertinelli & Strobl, 2013; Elliott et al., 2015; Felbermayr et al., 2022; Heger & Neumayer, 2019; Miranda Montero et al., 2017) and, more specifically, floods (Kocornik-Mina et al., 2020) _._ Most analyses using nightlight data usually demonstrate a negative impact of disaster and climate shocks on nightlights but with effects resorbing within the year following the event (Bertinelli & Strobl, 2013; Elliott et al., 2015; Gillespie et al., 2014). Schippers & Botzen (2023) find that for a severe disaster such as Hurricane Katrina, the effect can be longer lasting.

However, there is a debate about the accuracy of nightlights as a proxy for economic activity. Critics argue that nightlight intensity may not capture economic activity accurately in all contexts, such as highly rural areas, where changes in lighting efficiency could affect the amount of light observed without necessarily reflecting changes in economic activity. Possibly other cultural and social factors or government policies on lighting could also influence the amount of nightlight observed.

2
Despite these concerns, nightlights have several advantages as a data source. They are globally available, providing coverage even in regions where economic data might be scarce or unreliable. Nightlights also have a standard spatial resolution and time intervals, which allows for consistent comparisons over time and across different geographic areas. When processed and interpreted correctly, taking into account the potential limitations and biases, nightlight data can indeed serve as a useful proxy for the intensity of economic activity (Gibson et al., 2021).

2. Relationship between disaster and conflict-affected population traction over the past decade (Siddiqi, 2018), specifically focusing on co-location and causation debates associated with climate-related hazards, violent and armed conflict, and insecurity (Gemenne et al., 2014; Gleditsch, 2012). Often, previous studies have focused on the impacts of disasters on conflicts – whether they exacerbate existing conflicts, ignite new ones, or in some cases halt ongoing conflicts (Nel & Regharts, 2008; Schleussner et al., 2016; Slettebak, 2012; Ghimire et al., 2015; Nardulli et al., 2015). Due to such uncertain impacts of disasters and disaster recovery effort on conflicts, other studies explore how disaster risk reduction and recovery measures should be done differently in conflict contexts (Brzoska, 2018; Peters et al., 2019; World Bank, 2016).

attention has been given to understanding and quantifying the influences of conflicts on disaster impacts – the additional economic impacts of disasters should they take place in conflict areas and its effect on conflict-affected population – as well as the causal pathways and mechanisms behind such additional impacts. The absence of comprehensive economic data and ground truth data to validate disaster impacts, coupled with the complexity of defining conflict-affected populations are among the scientific challenges prohibiting analyzing the influence of conflicts on disaster impacts. This paper seeks to address this gap and support further quantitative analyses on the additional impact on households’ welfare and nations’ economic growth in countries experiencing these compounded crises.

# Empirical strategy

# Data

density, and administrative boundaries, to econometrically analyze the specific effects of flood events in Mozambique as well as Nigeria's conflict and non-conflict affected regions.

## _Nightlights data_

Furthermore, we utilize composite images of nighttime radiance data captured by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the NASA-NOAA Suomi satellite. These monthly composites are available since 2012 at a resolution of 15 arcseconds by 15 arcseconds (approximately 463 meters at the equator). VIIRS Day Night Bands (DNB) data exclude grid cells affected by lightning, stray light, lunar illumination, and cloud cover (Elvidge et al., 2017). We favor VIIRS data over traditionally used data from the Defense Meteorological Satellite Program (DMSP) 3
and poor suitability as a GDP proxy in rural areas (Gibson et al., 2021).

To address challenges associated with using VIIRS nightlights data as a proxy for economic activity (Skoufias et al., 2021), we apply filters to remove pixels with extreme values (i.e., we restrict the sample to values comprised between the 1<sup>st</sup> and 99<sup>th</sup> percentiles) and account for the number of observations available per pixel.<sup>1</sup> We calculate the average nightlight radiance monthly spanning from 1 to 6 months before and after the occurrence of the 2019 floods in Mozambique and the 2022 floods in Nigeria. Two variables are computed: "avg_rad," representing the nightlight radiance at the flooded pixel level, and "avg_radBuff05" which averages for each flooded pixel the nightlight radiance of the pixel itself and adjacent pixels within a 0.5-kilometer buffer.<sup>2</sup> The latter variable is preferred to ensure maximum observation availability and to capture the impact on indirectly affected grid cells.<sup>3</sup> We also extracted the associated variables "cf_cvg" and "avg_cvgBuff05", which indicate the number of cloud-free observations in the month used to calculate average nightlight radiance.<sup>4</sup>

# _Flood data_

Flood events are determined based on the methodology outlined by DeVries et al. (2020). We use S1 Ground Range Detected scenes from the Synthetic Aperture Radar sensors onboard the Sentinel-1 satellite, part of the European Space Agency's Copernicus program (ESA, 2023). These scenes provide data on Z-scores derived from SAR backscatter time series of single band co-polarization vertical transmit vertical receive (VV) and dual cross-polarization vertical transmit horizontal receive (VH). Since October 2014, this data has been available every 6 days at a 10-meter resolution.

floods from permanent or seasonally occurring surface water, we utilize the historical Landsatderived monthly water probabilities dataset produced by the European Commission’s Joint Research Centre (Pekel et al., 2016). Flood confidence is categorized as high if both VV and VH Z-scores fall below the identified thresholds, and as medium if only one of these polarizations is below the thresholds. We classify floods in areas not designated as permanent open water (with a probability of water greater than 95%) or with a historical inundation probability less than or equal to 25%. For each case study, we preselect a historical reference period based on existing knowledge of past flooding events in the respective area.

##

Event Data Project (ACLED) database (ACLED, 2023), covering the period from January 2012 to December 2023 for Nigeria and from January 2016 to December 2023 in Mozambique. For the purpose of this study, conflict, as defined by the WBG (2024) is, “a state of acute insecurity resulting from the use of lethal force by a group— encompassing state forces, organized non-state entities, or other irregular bodies— driven by a political purpose or motivation. Such force may manifest

> 1 Pixels with no cloud-free observations are excluded.

> 2 A pixel is around 100m2, we tested without buffer, 500m and 1km and chose a 500m buffer to introduce more variation of nightlight intensity within flooded pixels.

> 3 A time series depicting both variables is provided in Figure 16 in the appendices for Nigeria.

> 4 The corresponding time series for is presented in Figure 17 for Nigeria.

4
bilaterally— involving engagements among multiple organized, armed factions, occasionally leading to collateral civilian harm— or unilaterally, wherein a group targets civilians deliberately.” Furthermore, for the most precise depiction of areas severely affected by conflict, fatalities stemming from protests, riots, and strategic development (as per ACLED data) have been excluded, maintaining consistency with the WBG Classification of Fragility and Conflict Situation’s (FCS) objectives and the scope of this study. Our analysis focuses on conflict records categorized as ‘Battles’, ‘Explosions/Remote violence’, and ‘Violence against civilians’. These types of conflicts are selected due to their violent nature.

# _Settlement data_

To determine the urbanization level, we use the Global Human Settlement Layer (GHSL) which combines gridded population data estimated by CIESIN GPW v4.11 GHS-POP R2023 and built-up surface information from Landsat and Sentinel-2 data GHS-BUILT-S R2023 (Schiavina et al., 2023).<sup>5</sup> The settlement data are available at the 1km resolution. We consider the data for the year 2020, which is the closest available to the time period of interest for both countries. In case of Nigeria, we defined ‘urban’ areas as cells defined as high-density cluster,<sup>6</sup> ‘suburban’ as moderate-density cluster,<sup>7</sup> ‘rural’ as rural and low-density clusters<sup>8</sup> and ‘Uninhabited’ as very low density rural and water covered areas (Figure 1).<sup>9</sup>

> 5 - In Google Earth Engine, this Image collection is accessible through <u>https://developers.google.com/earth engine/datasets/catalog/JRC_GHSL_P2023A_GHS_SMOD.</u>

> 6 The ‘urban’ category includes the classes 30: “Urban Centre grid cell”, 23: “Dense Urban Cluster grid cell”.

> 7 The ‘suburban’ category includes the classes 22: “Semi-dense Urban Cluster grid cell” and 21: “Suburban or peri-urban grid cell”.

> 8 The ‘rural’ category includes the classes 13: “Rural cluster grid cell” and 12: “Low Density Rural grid cell”.

> 9 The ‘uninhabited’ category includes the classes 11: “Very low density rural grid cell” and 10: “Water grid cell”.

5
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'PopDens' provides insights into how population density might influence or be influenced by specific flood events. The variables 'cf_cvgBuff05' and 'avg_radBuff05' described above, measure economic activity through cloud observations and nightlight radiance, respectively. Additionally, they are averaged over adjacent pixels to provide context for each location.

‘Treated' and 'Treated_after' distinguish areas affected by conflict before and after the flood in each country : March-April 2019 are months where the flood occurred in Mozambique while July 2022 was considered as the flooding month in this analysis for Nigeria. This is essential for determining the causal inference of conflict impact. 'Settlement' and 'Urban_Suburban' categorize urbanization levels to understand how different types of areas are affected by and respond to both floods and conflict events. Lastly, 'Fatalities' provides a direct measure of the human cost of conflicts.

These variables collectively enable a comprehensive analysis of the effects of floods and conflicts when they co-occur in the same location. They are used to analyze the impacts of floods and conflict on specific aspects of economic activity, as potentially inferred by nightlight changes. Table 1 describes these variables, their units of measurements, and how they will be used in the analysis.

Table 1. Description of variables

|**Variables**|**Description**|**Unit**|
|---|---|---|
|lat|latitude|Decimal coordinates|
|lon|longitude|Decimal coordinates|
|months_EE|Month since the event|Months (positive if after event, negative if before<br>event)|
|flood|Flood variable|=1 or 2 if medium reliability, =3 if high reliability|
|PopDens|HRSL population density|Person/km<sup>2</sup>|
|cf_cvgBuff05|Total<br>number<br>of<br>cloud-free<br>observations that went into each<br>pixel (averaged over the adjacent<br>pixels)||
|avg_radBuff05|Average nightlight radiance values<br>(averaged over the adjacent pixels)|nanoWatts/sr/cm<sup>2</sup>|
|Treated|Dummy<br>variable<br>representing<br>conflict-affected area|=1 if subject to a conflict within the buffer area<br>before the floods, =0 otherwise|
|Treated_after|Dummy<br>variable<br>representing<br>conflict-affected area|=1 if subject to a conflict within the buffer area<br>after the floods, =0 otherwise|
|settlement|Degree of Urbanization|=11 if uninhabited, =12 if rural, =21 if suburban<br>and =23 if urban|
|Urban_Suburban|Urbanization dummy variable|=1 if urban or suburban, =0 otherwise|
|Fatalities|Total number of fatalities associated<br>with conflicts within the buffer area||

# Overall empirical strategy

To differentiate the impact of floods on economic activity prior to the flood between conflict-affected (treatment group), and non-conflict affected (control group) areas, we first restrict the sample to flood-impacted pixels. We then apply the difference-in-differences regression method, a quasiexperimental technique commonly used for ex-post impact evaluations. The underlying concept 7
involves comparing two groups over time. Due to their distinct characteristics, we expect differences in outcomes between the groups. However, the evolution of these differing outcomes over time, while holding group characteristics constant, should follow a similar trend (i.e., the common trend assumption) until an exogenous shock occurs. The presence of this parallel trend is crucial for establishing causal evidence of impact. The difference-in-differences research design is particularly suitable for ‘event’ studies and the quantification of the impact of unexpected shocks on economic outcomes. This method has been extensively employed in the reviewed literature (Card & Krueger, 2000; Galiani et al., 2005). In our case studies, we are using the canonical difference in difference, which means two groups and two time periods (before and after).

The difference-in-difference regression is specified as follows: = T T T 1 + P P 2 + Treatment 3 4 (1) YYwhere ii,tt is the average log of nightlights data for each flooded pixelTTiiββ PPTT PPT TTttββ ii,ttββ + ccPPccT PPT TTPPii,tt _i_ ββat time + εεiitt _t._ The use of remote sensing data allows us to explore immediate to short term impact of the flood over our two groups. ii,tt T T T YY is a dummy variable equal to 1 for flooded pixel _i_ located within the conflict buffer zone _i_ before the flood event, and to 0 for flooded pixel located in a non-conflict affected area before the ii flood eventTT _._ P P is a dummy variable that represents the period after the exogeneous shock.<sup>10</sup> T T T T is the treatment variable, i.e., the variable of interest in a difference-intt PPTT PPT TT difference specification which accounts for the interaction of the treated and Post period variable; iitt TT is the set of additional explanatory variable suspected to impact the level of nightlights radiance; and is an independent and identically distributed error term, clustered at the iitt administrative level 2, to avoid spatial autocorrelationccPPccT PPT TTPP _._ iitt εε One of the challenges in our analysis is the definition of conflict-affected areas. Conflict events in Mozambique are clustered geographically as such that it is readily ascertainable what regions are most impacted by these events, and thus defined as conflict-affected areas for the purpose of this study.

Nigeria, conflict-affected areas in Nigeria are not defined based on number of events alone but are based on the WBG FCS conflict classification. This classification uses publicly available data to annually assess countries, pinpointing those most affected by fragility and conflict. This method differentiates between territories experiencing _Fragility_ and/or _Conflict_ situations. Aligned with this definition, the study employs the following conflict indicators identified by the FCS index to delineate conflict-affected areas in Nigeria at the Local Government Area (LGA) scale:

> 10 This variable takes a value of 1 if _t_ = August 2020 to estimate the effect 1 month after the disaster, _t_ = November 2020 for the effect 3 months after the disaster, etc. and 0 otherwise.

8
- (1) 250, and (b) above 2 deaths per 100,000 population.

- (2) For rapidly declining security situations according to ACLED, (a) an absolute number of conflict deaths above 250, (b) between 1 and 2 deaths per 100,000 population, and (c) the number of casualties has more than doubled in the past year.

Difference in difference methodology relies on different assumptions where the common trend is the most important one. To validate this assumption of common trends before the flood, indicating that the dependent variable for both groups would have continued moving similarly in the absence of the extreme event, we conduct a test by comparing changes in the dependent variable for the treatment and control groups over multiple periods preceding the floods (i.e. estimate the difference-indifference between t-2 and t-1, the t-3 and t-2, etc.) _._ This analysis helps ascertain whether the economic trajectories of the two groups were indeed parallel before the occurrence of the flood events. The regression is specified as:

- events. The regression is specified as: = T T T 1 + P P 2 + Treatment 3 4

- ii,tt ii tt ii,tt ii,tt

- YY TT ββ PPTT PPT TT ββ ββ + ccPPccT PPT TTPP ββ + εε

(2) statistical tests as well as the common trend visually represented for each of our case study.

# Case study selection

The study’s focus on disaster impacts in conflict vs non conflict affected areas limits the pool of country case studies. There are several aspects that determine the choice of the case study countries. First, the selected countries need to have geographically localized conflicts, allowing for a controlled comparison where conflict is the primary differing factor of disaster impacts. Second, there is a requirement that the selected countries were affected by a rapid onset disaster in recent years, to increase the possibility of available data in assessing the disaster’s impacts. If multiple countries are selected, the rapid-onset disaster events had to happen within the same time frame. This criterion ensures that the case studies provide a focused examination of the impact of disasters on economic activities in conflict setting vs non conflict settings, without the confounding effects of different country conditions or timelines of disaster events. Third, the disaster footprints should cover a substantial geographical extent of the selected countries’ area as opposed to localized disasters. This criterion is to ensure that there are both conflict- and non-conflict-affected areas hit by the disaster. Given the criteria above, we selected the 2019 Tropical Cyclones Idai and Kenneth in Mozambique and the July 2022 floods in Nigeria as case studies. Furthermore, the two countries have comparable contexts in terms of conflict characteristics which are crucial for isolating the variable of conflict in comparative analysis.

# Mozambique case study: 2019 Tropical Cyclones Idai and Kenneth

Mozambique was hit by two Tropical Cyclones (TC), Idai (March 4-15) and Kenneth (April 25-28), both of which have been qualified as among the strongest TCs on record in the Southern Hemisphere (Charrua et al., 2021). The northeastern region of the country is characterized by a widespread long9
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‘Uninhabited’ grid cells are removed from the estimation sample. Corresponding summary statistics are presented in Table 4 and Table 5. The size of the restricted sample is now of 427,553 pixels.

Table 3. Number of observations for the whole sample before selection

|||**sett**|**lement**||
|---|---|---|---|---|
||**Uninhabited**|**Rural**|**Urban**|**Total**|
|Total Observations|5,410,141|303,450|41,895|5,755,486|
|Percentage of total|94%|5%|1%||

our main variable of interest, nightlights, exhibits a relatively low average intensity at the pixel level. This observation is aligned with the overall low density of distribution of nightlights, as corroborated by a population density of 16 person per km2. Additionally, the average value of the urban dummy variable, which is close to 0, indicates a prevalence of rural flooded pixels over urban flooded pixels within our sample.

Table 4. Summary statistics for habited flooded pixels (rural and urban categories) sample

||**N**|**Mean**|**SD**|**Min**|**p5**|**Median**|**p95**|**Max**|
|---|---|---|---|---|---|---|---|---|
|_PopDens_|427,553|16.99|75.32|0|0|0|89.89|1367.76|
|cf_cvgBuff05|427,553|11.74|3.33|1|5.34|12|16.49|21|
|avg_radBuff05|427,553|1.02|4.16|0|0.06|0.2|3.75|88.18|
|urban|427,553|0.12|0.32|0|0|0|1|1|

When considering the conflict-affected areas variable, the number of treated pixels – reflecting conflict-associated with each flooded grid cell - will depend on the conflict definition and buffer size considered. Table 5 provides a summary statistic of the number of conflicts and fatalities across different buffer sizes going from 10km to 100km.

Table 5. Summary statistics of variables differing by buffer size

|**Bufer size**|**Variable**|**Mean**|**SD**|**Min**|**p5**|**Median**|**p95**|**Max**|
|---|---|---|---|---|---|---|---|---|
|100km|conficts|16.71|59.46|0|0|5|45|740|
||fatalities|4.12|11.59|0|0|1|14|156|
|90km|conficts|13.98|49.77|0|0|4|38|645|
||fatalities|3.54|10.15|0|0|1|12|154|
|80km|conficts|11.56|41.84|0|0|4|31|577|
||fatalities|3.03|9.19|0|0|1|11|135|
|70km|conficts|9.25|34.58|0|0|2|26|526|
||fatalities|2.48|7.93|0|0|0|10|113|
|60km|conficts|7.15|27.23|0|0|1|25|422|
||fatalities|1.98|6.82|0|0|0|9|102|

11
|50km|conficts|5.69|21.33|0|0|1|22|371|
|---|---|---|---|---|---|---|---|---|
||fatalities<br>|1.64|5.68|0|0|0|9|95|
|40km|confict|4.15|14.36|0|0|0|16|248|
||fatalities<br>|1.25|4.36|0|0|0|9|69|
|30km|conficts|2.92|10.07|0|0|0|13|163|
||fatalities<br>|0.91|3.21|0|0|0|9|44|
|20km|conficts|1.76|6.49|0|0|0|10|89|
||fatalities<br>|0.2|1.19|0|0|0|1|26|
|10km|conficts|0.75|3.37|0|0|0|4|60|
||fatalities|0.2|1.19|0|0|0|1|26|

Number of observations: 427,553 density of conflict events spanning from 2016 to 2023. It is worth noting that some regions exhibit higher density of conflict, namely Cabo Delgado, Sofala and Gaza. It is particularly striking to note the occurrence of over 500 incidents in each of these three states over the studied period, which denotes a concentration of conflicts in these regions. To provide a comprehensive analysis our investigation is expanded to include the prevalence of conflict spanning the designated period (20162023). This is visually depicted in Figures 5, 6, and 7. It is worth noting that the frequency of conflicts across all studied categories remained subdued prior to 2018. However, there was a significant surge in violence beginning in 2018, culminating in a peak in 2020 for incidents of violence against civilians, and in 2021 for battle-related conflicts. Even if we can observe a peak in the number of conflicts in 2018, we would rather use conflicts over a long-term period (meaning a few years before the floods). In doing so, we are dealing with treated areas that have been affected by a conflict over the long term and do not include flooded areas that might have been affected by a conflict a few years before the flood occurred. In the same way, all flooded pixels affected by a conflict after the cutoff date (MarchApril 2019) are not accounted in this analysis. We thus exclude them from our sample in order to avoid reverse causality: the flood reinforcing the number of conflicts.

12
<!-- Start of picture text --> Me<br>q , Conflicts Number (from 2016 to 2023)<br>! [J[J 100156 -- 156212<br>bane (7) 212 - 267<br>1D 267 - 323<br>1 323 - 379<br>GS 379 - 435<br>M- Gl 435 - 491<br>/ HH 491 - 546<br>Hl 546 - 602<br>HB 602 - 658<br><!-- End of picture text --> <!-- Start of picture text --> 800 -<br>600<br>a)<br>ee<br>E<br>400<br>8 ,<br>5<br>2<br>200 -<br>0 fl<br>2016 2018 2020 2022 2024<br>year<br><!-- End of picture text -->
<!-- Start of picture text --> 400<br>300 —<br>200-<br>Pale<br>fe ~,<br>a cn — Explosions/Remote violence<br>100 rol ts oir —— Battles<br>oP dh Oe ‘ “~~~ Protests<br>a sf 4 ~ Riots<br>és Sa 7 se 2 = sone — ‘Strategic developments<br>o- = ae oe ee Violence against civilians<br>2016 2018 2020 2022 2024<br>year<br><!-- End of picture text -->
_pop_HRSL_dens_km2_ to control for variations in population density across studied pixels, which could affect nightlights independently from disaster-induced changes in access to electricity. The second column of Table 6 includes the number of cloud-free observations that went into each pixel averaged over the adjacent pixels. This variable is insignificant, confirming that the nightlight radiance is not impacted by the quality of observations as represented by the number of observations that were used to calculate the monthly average values. The third column includes the urban dummy variable which is positive and significant reassuringly proving that nightlight is stronger in urban areas that in rural areas.<sup>12</sup> The last column includes the number of fatalities. This variable is not significant, indicating that the effect is not associated with the number of deaths related to the conflicts within the buffer zone.

Table 6. Regression results of equation (1) for conflicts within 60km buffer for 1 month post TCs. Dependent variable avg_radBuff05

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES|Period -1 to 1<br>avg_radBuff05|Period -1 to 1<br>avg_radBuff05|Period -1 to 1<br>avg_radBuff05|Period -1 to 1<br>avg_radBuff05|
|tot_treated|0.0291**|0.0297**|0.0197**|0.0175*|
||(0.0113)|(0.0117)|(0.00990)|(0.0100)|
|temp_shock|-0.0366***|-0.0417***|-0.0366***|-0.0366***|
||(0.00538)|(0.00934)|(0.00538)|(0.00538)|
|**did_var**|**-0.0134***|**-0.0149***|**-0.0134***|**-0.0134***|
||**(0.00795)**|**(0.00889)**|**(0.00795)**|**(0.00795)**|
|pop_HRSL_dens_km|||||
|2|0.00635***|0.00635***|0.00511***|0.00511***|
||(0.000848)|(0.000848)|(0.000770)|(0.000770)|
|cf_cvgBuff05||0.00124<br>(0.00199)|||
|Urban|||0.384***|0.384***|
||||(0.0642)|(0.0641)|
|tot_fatalities||||0.00108<br>(0.00102)|
|Constant|0.199***|0.186***|0.176***|0.174***|
||(0.0131)|(0.0261)|(0.0114)|(0.0117)|
|Observations|16,880|16,880|16,880|16,880|
|R-squared|0.773|0.773|0.806|0.806|
|Treatment Obs|9684|9684|9684|9684|
|Control Obs|7196|7196|7196|7196|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 1<sup>st</sup> to 99<sup>th</sup> percentile ( .02 to 21.8 nanoWatts/sr/cm<sup>2</sup> )

> 12 We also estimated this specification including interactions with the urban dummy variable to see if we would obtain different impacts for urban areas, but did not find a significant impact.

15
<!-- Start of picture text --> ish) ' 1<br>oo i i<br>ic<br>a e:<br>ii<br>r<br>=‘ ‘<br>a = !) ABs ' '<br>iH<br>ca I' I'<br>+6 i] 4 t-3 te t-1 1 t2 ted 4 5 +6<br>Floods<br>— Conflicted areas (Treatment) —— Non conflicted areas (Control)<br><!-- End of picture text -->
in recovery rates suggests that in the medium term ( _t+5_ and _t+6_ greater access to resources, institutional capacity, and potentially further financial support, facilitating their recovery process compared to conflict-affected regions.

Table 7. Regression results for conflicts within 60km buffer, preferred specification at different periods post TCs

||(1)<br>Period -1 to<br>1|(2)<br>Period -1 to<br>2|(3)<br>Period -1 to<br>3|(4)<br>Period -1 to<br>4|(5)<br>Period -1 to<br>5|(6)<br>Period -1 to<br>6|
|---|---|---|---|---|---|---|
|VARIABLES|avg_radBuff<br>05|avg_radBuff<br>05|avg_radBuff<br>05|avg_radBuff<br>05|avg_radBuff<br>05|avg_radBuff<br>05|
|tot_treated|0.0291**|0.0250**|0.0228**|0.0220**|0.0276***|0.0337***|
||(0.0113)|(0.00998)|(0.0111)|(0.0109)|(0.0106)|(0.0105)|
|temp_shock|-0.0366***|-0.0683***|-0.0860***|-0.0653***|0.00238|0.0618***|
||(0.00538)|(0.00422)|(0.00373)|(0.00535)|(0.00460)|(0.00503)|
|did_var|-0.0134*|0.000594|0.00802|0.00634|-0.00966*|-0.0142*|
||(0.00795)|(0.00560)|(0.00705)|(0.00699)|(0.00499)|(0.00758)|
|pop_HRSL_dens_k<br>m2|0.00635***|0.00643***|0.00652***|0.00640***|0.00628***|0.00601***|
||(0.000848)|(0.000863)|(0.000896)|(0.000895)|(0.000876)|(0.000830)|
|Constant|0.199***|0.200***|0.200***|0.202***|0.200***|0.201***|
||(0.0131)|(0.0128)|(0.0134)|(0.0134)|(0.0131)|(0.0126)|
|Observations|16,880|16,880|16,880|16,880|16,880|16,880|
|R-squared|0.773|0.773|0.774|0.772|0.773|0.770|
|Treatment Obs|9684|9684|9684|9684|9684|9684|
|Control Obs|7196|7196|7196|7196|7196|7196|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 1<sup>st</sup> to 99<sup>th</sup> percentile (0.02 to 2.7 nanoWatts/sr/cm<sup>2</sup> )

# Robustness checks

We also ran the regression using only time periods prior to the disaster, from 6 to 1 months before the cyclones hit, to ensure that there were no significant pre-disaster nightlight variation discrepancies between the two groups that could have explained our results. The results presented in Table 8 and confirm that the _did_var_ ( _Treated_<sup>_*_</sup> _PostPeriod)_ variable is not significant. This test implies that our findings are robust, and that usual nightlights variations do not drive the results of our difference-indifference specification, confirming causality.

Table 8. Regression results for conflicts within 60km buffer testing common trends using log(nightlights), 1 to 6 months before the Tropical Cyclones

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Period -2 to -1|Period -3 to -2|Period -4 to -3|Period -5 to -4|Period -6 to -5|
||Mozambique|Mozambique|Mozambique|Mozambique|Mozambique|
|VARIABLES|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|

17
|tot_treated|0.0271***|0.0209**|0.0269***|0.0246**|0.0265***|
|---|---|---|---|---|---|
||(0.00997)|(0.00939)|(0.00958)|(0.00995)|(0.00981)|
|temp_shock|0.0799***|-0.0529***|-0.0589***|0.00812**|0.0168***|
||(0.00270)|(0.00394)|(0.00260)|(0.00318)|(0.00514)|
|did_var|-0.00383|0.00280|-0.00476|0.00122|-0.00428|
||(0.00570)|(0.00482)|(0.00411)|(0.00545)|(0.00687)|
|pop_HRSL_dens_km||||||
|2|0.00639***|0.00648***|0.00646***|0.00634***|0.00629***|
||(0.000899)|(0.000926)|(0.000887)|(0.000863)|(0.000860)|
|Constant|0.122***|0.175***|0.234***|0.228***|0.213***|
||(0.0129)|(0.0129)|(0.0128)|(0.0126)|(0.0128)|
|Observations|16,880|16,880|16,880|16,880|16,880|
|R-squared|0.772|0.772|0.775|0.764|0.759|
|Treatment Obs|9684|9684|9684|9684|9684|
|Control Obs|7196|7196|7196|7196|7196|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 1<sup>st</sup> to 99<sup>th</sup> percentile (0.02 to 2.7 nanoWatts/sr/cm<sup>2</sup> )

#

#

According to EM-DAT & CRED / UCLouvain (2023), Nigeria suffered flooding during the period of July 1, 2022 to October 31, 2022. The first row of Figure 9 presents the flooded pixel during each month and over the whole period, The second row shows flooded pixel during each month but not flooded during the other months. During the first couple of months of the flooding season, most flooded pixels are located in the south. During the second part of the period, the floods mainly occur along the Benue and Niger Rivers in the center part of the country. Given the length of the flooding period and the potential for adaptation and population displacement after the first flooding events, we focus on the ‘unexpected’ first floods. Therefore, we select the pixels that were flooded in July but not in August, September or October.

18
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Nonetheless, the spatial distribution is different in Nigeria compared to Mozambique, we surprisingly observe that a peak of conflicts number also occurs from 2018. However, three main types of conflicts occurred from 2018: battles, protests and violence against civilians. To capture the indirect effects of these conflicts and considering the uniform distribution of those conflicts in Nigeria, we implement a buffer zone surrounding each event. Four studies examining geographic accessibility to markets, health care, and education by rural households in West Africa inform our understanding of the average distances of these households to such amenities in Nigeria. They showed that rural households are generally within 5 to 7 kilometers of essential amenities like markets, health care, and schools. These distances ranged from 4.82 to 7.10 kilometers for health care and approximately 6.9 kilometers for schools.<sup>14</sup> In peri-urban areas, households are closer, typically within 1 to 2 kilometers of amenities, with distances ranging from 1 to 1.8 kilometers for primary schools and around 1 kilometer for secondary schools.<sup>15</sup> To represent the extent of the impact of conflicts depending on the degree of urbanization, we consider two types of buffers around each conflict: large and small (see figures 13 and 14). Figure 14 also shows evidence of the uniform spatial distribution of conflicts within Nigeria. As opposed to Mozambique, where we observed a concentrated spatial distribution of conflicts as well as the context in Nigeria regarding conflicts, we cannot derive the same buffer zone size. Regarding these differences in settlement between Mozambique and Nigeria, we decide to specify different buffer zone areas that should better reflect Nigeria’s conflicts and population settlement (see Table 11).

> 14 Adetunji (2020) found that rural traders in Nigeria lived an average of 6.56 kilometers from markets, while Tanou et al. (2021) reported distances of 5.29 to 7.10 kilometers to health care facilities in rural North Benin. Oldenburg et al. (2021) found that households with young children in Northwest Burkina Faso were approximately 4.82 kilometers from health care facilities. Popoola's (2022) research in Nigeria showed that rural villages were, on average, 6.9 kilometers from the nearest primary school.

Adetunji, Musilimu A. (2020). Households Travel Behaviour to Markets in Rural Communities in Ayedaade Local Government Area of Osun State, Nigeria. Journal of Asian Rural Studies, 4, 202. https://doi.org/10.20956/jars.v4i2.2336 Tanou, M., Kishida, T., & Kamiya, Y. (2021). The effects of geographical accessibility to health facilities on antenatal care and delivery services utilization in Benin: A cross-sectional study. Reproductive Health, 18(205). https://doi.org/10.1186/s12978-021-01249-x Oldenburg, C. E., Sié, A., Ouattara, M., Bountogo, M., Boudo, V., Kouanda, I., Lebas, E., Brogdon, J. M., Lin, Y., Nyatigo, F., Arnold, B. F., & Lietman, T. M. (2021). Distance to primary care facilities and healthcare utilization for preschool children in rural northwestern Burkina Faso: Results from a surveillance cohort. BMC Health Services Research, 21(1), 212. https://doi.org/10.1186/s12913-021-06226-5

> 15 In peri-urban areas, households are typically closer to amenities, with distances ranging from 1 to 2 kilometers. Popoola (2002) observed that households in peri-urban settlements near secondary towns or capital cities were around 1.2 to 1.8 kilometers from primary schools, and approximately 1 kilometer from secondary schools.

Popoola, A., Magidimisha-Chipungu, H., & Chipungu, L. (2022). Towards rural inclusion: Improving the governance of service delivery in Nigeria. Cogent Social Sciences, 8(1), 2118793. https://doi.org/10.1080/23311886.2022.2118793 22
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Table 12. Regression results for Nigeria for July floods in Nigeria and large conflict buffers

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||T-1 to t+1|T-1 to t+1|T-1 to t+1|T-1 to t+1|
|VARIABLES|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|
|Treated|-0.0522<sup>***</sup>|-0.0522<sup>***</sup>|-0.000604|0.000655|
||(0.0109)|(0.0109)|(0.0105)|(0.0104)|
|PostPeriod|-0.0322<sup>***</sup>|-0.0392<sup>***</sup>|-0.0322<sup>***</sup>|-0.0322<sup>***</sup>|
||(0.00488)|(0.00784)|(0.00488)|(0.00488)|
|Treated*PostPeriod|-0.0120<sup>**</sup>|-0.0119<sup>**</sup>|-0.0120<sup>**</sup>|-0.0120<sup>**</sup>|
||(0.00547)|(0.00554)|(0.00547)|(0.00547)|
|PopDens|0.000505<sup>***</sup>|0.000505<sup>***</sup>|0.000408<sup>***</sup>|0.000413<sup>***</sup>|
||(8.49e-05)|(8.48e-05)|(7.93e-05)|(7.95e-05)|
|cf_cvgBuff05||0.00148<br>(0.00132)|||
|Urban_Suburban|||0.112<sup>***</sup><br>(0.0156)|0.112<sup>***</sup><br>(0.0157)|
|Fatalities||||-3.88e-05<sup>*</sup><br>(2.16e-05)|
|Constant|0.439<sup>***</sup>|0.433<sup>***</sup>|0.382<sup>***</sup>|0.382<sup>***</sup>|
||(0.00633)|(0.00832)|(0.00826)|(0.00821)|
|Observations|13,330|13,330|13,330|13,330|
|R-squared|0.674|0.674|0.682|0.682|
|Treatment Obs|5342|5342|5342|5342|
|Control Obs|7988|7988|7988|7988|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 0.14 to 16.64.

Table 13. Regression results for July floods in Nigeria and large conflict buffers at different periods post July floods

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||T-1 to t+1|T-1 to t+2|T-1 to t+3|T-1 to t+4|T-1 to t+5|T-1 to t+6|
|VARIABLES|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|
|Treated|-0.0522<sup>***</sup>|-0.0554<sup>***</sup>|-0.0553<sup>***</sup>|-0.0600<sup>***</sup>|-0.0564<sup>***</sup>|-0.0531<sup>***</sup>|
||(0.0109)|(0.0113)|(0.0111)|(0.0119)|(0.0122)|(0.0117)|
|PostPeriod|-0.0322<sup>***</sup>|0.0163<sup>***</sup>|0.0705<sup>***</sup>|0.0633<sup>***</sup>|0.0533<sup>***</sup>|-0.0445<sup>***</sup>|
||(0.00488)|(0.00593)|(0.00689)|(0.00841)|(0.0108)|(0.00963)|
|Treated*PostPeriod|-0.0120<sup>**</sup>|-0.00657|-0.00898|-0.0101|-0.0232<sup>**</sup>|-0.0204<sup>**</sup>|
||(0.00547)|(0.00713)|(0.00680)|(0.00877)|(0.0107)|(0.00957)|
|PopDens|0.000505<sup>***</sup>|0.000485<sup>***</sup>|0.000555<sup>***</sup>|0.000580<sup>***</sup>|0.000634<sup>***</sup>|0.000602<sup>***</sup>|
||(8.49e-05)|(8.18e-05)|(8.59e-05)|(9.22e-05)|(9.82e-05)|(9.32e-05)|
|Constant|0.439<sup>***</sup>|0.441<sup>***</sup>|0.438<sup>***</sup>|0.438<sup>***</sup>|0.434<sup>***</sup>|0.434<sup>***</sup>|
||(0.00633)|(0.00630)|(0.00655)|(0.00705)|(0.00792)|(0.00752)|

26
|Observations|13,330|13,330|13,330|13,330|13,330|13,330|
|---|---|---|---|---|---|---|
|R-squared|0.674|0.669|0.683|0.682|0.678|0.678|
|Treatment Obs|5342|5342|5342|5342|5342|5342|
|Control Obs|7988|7988|7988|7988|7988|7988|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 0.14 to 16.64.

months before and 6 months after the July floods, we first inspect the trend in mean nightlight radiance. Figure 15 shows that both conflict-affected (represented by the red line), and non-conflictaffected (represent by the dashed grey line) pixels follow a similar trajectory until the occurrence of the floods in July. Between t-1 (June 2022) and t+1 (August 2022), nightlight radiance and picks up again afterward, suggesting a temporary power outage due to the flood. However, the decline in radiance is sharper for conflicted areas than non-conflict affected areas. Furthermore, nightlight radiance in the conflict affected area rises more slowly than in the non-conflict area until December 2020, suggesting longer term structural differences between the two groups following the flooding event.

# Robustness checks

common trend. The test consists of re-estimating the difference in difference regressions considering months prior to the flood event of July. The results presented in Table 14 show that the _Treated*PostPeriod_ variable alternate between negative and positive values and is not significant and, confirming the common trend assumption is respected.

27
Table 14. Regression results for July floods in Nigeria and large conflict buffers testing common trends using log(nightlights), 1 to 6 months before the floods

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||t-2 to t-1|T-3 to t-2|T-4 to t-3|T-5 to t-4|T-6 to t-5|
|VARIABLES|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|
|Treated|-0.0597<sup>***</sup>|-0.0575<sup>***</sup>|-0.0579<sup>***</sup>|-0.0677<sup>***</sup>|-0.0733<sup>***</sup>|
||(0.0108)|(0.0120)|(0.0120)|(0.0132)|(0.0137)|
|PostPeriod|-0.0493<sup>***</sup>|-0.0225<sup>***</sup>|0.0524<sup>***</sup>|-0.000375|0.0389<sup>***</sup>|
||(0.00454)|(0.00633)|(0.00625)|(0.00726)|(0.00377)|
|Treated*PostPeriod|0.00693|-0.000900|-0.00428|0.00614|0.00676|
||(0.00468)|(0.00651)|(0.00669)|(0.00729)|(0.00488)|
|PopDens|0.000507<sup>***</sup>|0.000554<sup>***</sup>|0.000568<sup>***</sup>|0.000606<sup>***</sup>|0.000697<sup>***</sup>|
||(8.21e-05)|(8.77e-05)|(9.17e-05)|(9.92e-05)|(0.000112)|
|Constant|0.488<sup>***</sup>|0.508<sup>***</sup>|0.457<sup>***</sup>|0.457<sup>***</sup>|0.413<sup>***</sup>|
||(0.00609)|(0.00685)|(0.00660)|(0.00787)|(0.00771)|
|Observations|13,330|13,330|13,330|13,330|13,330|
|R-squared|0.679|0.682|0.682|0.679|0.681|
|Treatment Obs|5342|5342|5342|5342|5342|
|Control Obs|7988|7988|7988|7988|7988|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 0.14 to 16.64.

# Limitation and Discussion

Assessing the quantitative impact of disasters on populations and economies is particularly challenging in poor and conflict-affected regions, where reliable socio-economic data may be lacking. Our analysis of descriptive statistics has indicated a tendency towards lower nightlight density in rural areas, which could potentially lead to an underestimation of economic activity due to the limitations in spatial resolution. Nightlight radiance in rural settings is typically less intense than in urban centers, and a higher incidence of such readings could result in a skewed sample. Furthermore, sectors like agriculture, which are less dependent on electricity, might not be fully represented by nightlight data. This is particularly true for poorer households in rural areas that may not have access to electricity, rendering them invisible in satellite-based observations. Consequently, vital aspects of a developing economy might be missed when relying exclusively on nightlight data for analysis.

Nonetheless, and despite these limitations, access to advanced earth observation technologies, including regularly updated satellite imagery, presents new avenues for analysis using quasiexperimental approaches. Nightlight satellite data can provide valuable insights into economic activity, primarily enabling the assessment of immediate to short-term impacts. Our case studies highlight the exacerbated effects of floods on nightlight in conflict-affected regions. For instance, we observed a decrease in nightlight radiance following the July 2022 floods in Nigeria, with conflict28
affected areas experiencing up to 1.2% greater negative impacts than non-conflict areas. Similar patterns were observed in Mozambique, where cyclones led to a 1.39% reduction in economic activities in conflict-affected populations compared to their non-conflict-affected counterparts. As a potential next step, a long-term difference in difference regression analysis, as well as the use of observational data that could be more nuanced and contextual, could explore, in addition to the short-term impact identified here, the long-term impact of disasters dynamics in conflict settings.

To fully comprehend these dynamics, it is essential to conduct further research to pinpoint the causal factors that exacerbate vulnerability in conflict-stricken settings. Our findings, however, emphasize the critical need for investment in disaster risk reduction and the prioritization of recovery initiatives in conflict-affected regions. Inhabitants of these areas suffer more significant impacts and face extended recovery periods in the aftermath of disasters, underscoring the necessity for focused interventions to reduce their vulnerabilities and bolster their resilience.

# Conclusion

has been given to understanding how conflicts influence disaster impacts and recovery. By employing a difference-in-difference method alongside geospatial and satellite imagery datasets, this paper seeks to address this literature gap by estimating the impacts of a disaster in conflict settings and contrasting them with impacts in non-conflict settings.

This study represents one of the initial attempts to quantitatively compare the diverging impacts of a disaster between conflict and non-conflict affected regions. Our findings suggest that in the aftermath of the floods of interest, conflict-affected areas in Nigeria and Mozambique experienced the most severe negative impacts following the flood event, up to 1.2% and 1.39%, respectively. These results indicate that conflict-affected areas bore a heavier burden of the floods compared to non-conflict areas. Additionally, the negative impacts in conflict settings continued to be larger than in non-conflict affected regions for the six months following the event, which gives an indication that these floods prolonged the recovery period for conflict-affected populations.

In conclusion, despite existing methodological constraints, our study demonstrates that the impacts of disasters are significantly more pronounced in conflict settings compared to non-conflict areas. In the next phase of our analysis, we aim to supplement the current 'top-down' approach of assessing disaster impacts through satellite imagery with a 'bottom-up' approach using panel household survey data. By combining both approaches, we will gain insights into household-level impacts and 29
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