# Groundwater, Climate Change and Conflict: Empirical Evidence from Africa

Mathieu Couttenier, Remi Jedwab, Lucile Laugerette

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CERGIC Working Papers

No. 0006

February 2025

Groundwater, Climate Change and Conflict:
Empirical Evidence from Africa

Mathieu Couttenier

Remi Jedwab

Lucile Laugerette

<figure><img src="image_2.png" /></figure>

JEL codes: D74, O13, Q54, Q25, Q32, Q34.

Keywords: Groundwater, Climate, Water Scarcity, Resources and Conflict, Africa.
GROUNDWATER, CLIMATE CHANGE AND CONFLICT:

Empirical Evidence from Africa *

Mathieu Couttenier†

Remi Jedwab‡

Lucile Laugerette§

February 19, 2025

Abstract

Groundwater plays a crucial role in supporting livelihoods and economic activities in Africa, particularly in regions affected by climate-induced water scarcity. This study explores the economic implications of groundwater resources during periods of climate shocks and their relationship to conflict dynamics in affected regions. It employs a comprehensive dataset covering the period from 1997 to 2021, encompassing information on conflict events and groundwater availability. The relationship between groundwater depth, climate shocks, and conflict occurrences is analyzed through both cross-sectional and temporal analyses across 10,310 cells (each 0.5×0.5°, or ≈ 55×55 km). We define two measures of local climate shocks: a *temporary* shock based on yearly variation, and a *persistent* shock using a moving average of temperature over multiple years. The findings suggest that areas with a higher proportion of shallow water –i.e., more accessible groundwater– have become increasingly prone to violence over time, particularly in the presence of persistent local climate shocks. Moreover, these persistent shocks have a greater impact on low-intensity and localized conflict events in regions with more accessible groundwater. Further, within-cell inequalities in water access emerge as significant conflict drivers. Lastly, secondary analyses examining the respective contributions of pastoralist activities and state capacity to the observed effects provide insights into the mechanisms. Overall, the findings underscore the need for equitable water management strategies to mitigate conflict and promote sustainable development in Africa.

**Keywords:** Groundwater; Climate; Water Scarcity; Resources and Conflict; Africa

**JEL Codes:** O13; D74; Q54; Q25; Q32; Q34

<footnote>*Corresponding author: Remi Jedwab. We thank various seminar and conference audiences for helpful comments. We thank Eoin McGuirk for sharing data on the location of pastoralists groups in Africa. We thank Mitchell Garlock, Eleanor Greenbaum, Maria Papera and Devika Mukherjee for excellent research assistance. We gratefully acknowledge financial support from the World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely ours. 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 or the governments they represent.</footnote>

<footnote>†Ecole Normale Superieure de Lyon, Center for Economic Research on Governance, Inequality and Conflict; CEPR.</footnote>

<footnote>‡George Washington University, Elliott School of International Affairs.</footnote>

<footnote>§Ecole Normale Superieure de Lyon, Center for Economic Research on Governance, Inequality and Conflict.</footnote>
# 1 Introduction

Rainfall and surface water serve as vital water sources for agriculture, livestock herding, and drinking water. However, in regions where climate shocks have caused aridification due to rising temperatures and decreasing rainfall, groundwater – water stored beneath the Earth’s surface in aquifers – is becoming increasingly important. Although groundwater can be used for irrigation, it remains limited to about 6% of the agricultural area in Africa (You et al., 2011). In sub-Saharan Africa (SSA), groundwater plays a crucial role in supplying drinking water to rural communities and meeting the water needs of transhumant pastoralists and their livestock (UN Water/Africa, 2012; UNOWAS, 2018). Importantly, it is common for communities to sell access to their wells and groundwater resources to others, which can provide revenues for those with easier access to groundwater, especially as water becomes scarce (Krätli and Toulmin, 2020a; Thebaud, 2017).

"If climate change is the shark, water is its teeth" is a common saying in the development community, implying that the impacts of climate change are deeply felt through the water sector (World Bank, 2023). The increasing economic significance of groundwater given continental climate shocks prompts the question of whether its presence in regions specifically affected by climate stress leads to a *reduction* or *escalation* of conflict. Two countervailing effects come into play. In the presence of water scarcity resulting from climate shocks, areas with more accessible groundwater are less prone to violence compared to those with limited groundwater access. Groundwater serves as an alternative water source, reducing reliance on surface water or rainfall. In other words, when fresh surface water or rainfall becomes scarce, communities can turn to groundwater reserves as a substitute. This alternative source helps alleviate the social tensions associated with climate-induced water scarcity. Additionally, in cases where communities sell groundwater, the scarcity of fresh surface water and rainfall may allow them to sell water at a higher price, generating a positive income effect and increasing the opportunity cost of violence (*income effect*). At the same time, the rising economic value of groundwater, like other natural resources, may intensify conflict in these regions (*prize effect*). Water scarcity exacerbates existing tensions and can trigger conflicts over access to this essential resource. Competition for limited water supplies may lead to conflicts at various levels, ranging from local disputes to international tensions. Given imperfect water markets and competition for scarce resources, the prize effect – by which the value of controlling access to groundwater increases – may outweigh the income effect, leading to heightened conflict.

Both effects are investigated using a dataset in which the units of analysis are 10,310 cells of 0.5°×0.5° (≈ 55×55 km at the equator), covering the entire African continent from 1997 to 2021. For every cell×year unit, two types of information are gathered: on conflict events; and on the availability of groundwater. Specifically, the *Armed Conflict Location Events Data* (ACLED) offers comprehensive details regarding the date, location, and type of conflict events. Bonsor and MacDonald (2011) then provide geolocalized data on groundwater levels (pixels of 0.05°×0.05°). For each 0.5°×0.5° cell, one can then compute the share of pixels contained in different categories of water depths (0-7, 7-25, 25-50, 500-100, 100-250 and 250+ meters). As suggested by the literature, the time-invariant share of the cell with groundwater depth between 0 to 50 meters is used as a baseline measure to capture *shallow water* (i.e., groundwater accessibility) at the cell level.
In a preliminary descriptive exercise, we first explore how the spatial relationship between conflict and groundwater evolved over time, considering *global* temperature change, which affected the entire continent during the period. On average, cells with larger shares of shallow water tend to experience higher levels of violence, with this pattern appearing more pronounced in the 2010s as climate change accelerated. From 1997 to 2021, cells with more shallow water are associated with a 5% higher likelihood of experiencing conflict, with an additional +9% increase in the 2010s compared to the 2000s. The relationship between shallow water and conflict varies by conflict type, being four times more pronounced for *low-intensity* (i.e., more localized) conflict events, such as protests and riots, where local community actors are prominent. Moreover, significant correlations are observed for conflicts explicitly linked to water-related issues. Finally, it is shown that spatial inequality in groundwater access (within-cell) is associated with higher levels of conflict in the post-2010 period. To address potential confounding factors and better understand the observed patterns, the second part of the paper focuses on the interaction between local climate shocks and local groundwater availability, exploring how these factors jointly correlate with conflict dynamics.

To investigate these patterns further, we define two measures of *local* climate shocks at the cell level: the first measure captures *temporary* shocks based on yearly deviations, while the second captures *persistent* shocks using a moving average of temperature over multiple years. We estimate how these *local* climate shocks specifically affect conflict in areas with groundwater access. The results reveal interesting patterns: local persistent shocks disproportionately increase conflict likelihood in groundwater-accessible areas for a given temperature increase. For instance, a one-degree Celsius increase in temperature is linked to a 2 to 9% higher likelihood of violence in these areas, depending on shock persistence. Counterfactual simulations further illustrate these effects: in cells without shallow groundwater, the absence of a positive temperature trend during the study period would have reduced conflict probability by 0.5%. However, in cells where shallow water covers over 50% of the area, conflict likelihood would have decreased by 7%. Similarly, in cells without inequality in groundwater access, the probability of conflict would have been 1.6% lower, while for cells in the highest percentile of groundwater inequality, the likelihood of observing conflict would have been 11% lower. These patterns remain consistent after accounting for rainfall, aridity, geology, proximity to rivers, and broader geographic characteristics. They also hold when incorporating spatially refined fixed effects, which allows us to compare similar cells.

Two mechanisms are then investigated. First, a heightened prize effect is likely among livestock herder communities, as water is a critical input for livestock rearing. Additionally, nomadic livestock communities are more mobile, allowing them to reach water-points and wells more easily, and are often more heavily armed to protect against the risk of livestock theft. Stronger effects of climate shocks in groundwater-accessible areas are observed within pastoralist regions, or farming areas near the edges of pastoralist regions, where livestock herders frequently relocate during the dry season. Second, state capacity plays a complex role in conflict dynamics. While areas with stronger state capacity may be better able to manage conflict, they may also see increased fighting or lobbying by groups seeking control over state resources, which could increase conflict in those areas. These two effects appear to offset each other, as state capacity does not appear to
significantly mitigate conflict incidence. Yet, for national-level state capacity, this overall neutrality hides contrasting effects: stronger state presence appears to reduce high-intensity conflicts but is associated with increases in low-intensity conflicts. In areas where the state is stronger, authorities are likely better able of controlling violent conflict, though these areas may attract more lobbying due to their political importance. Conversely, regional capitals – often “weak” in the African context (Fearon and Laitin, 2003; Müller-Crepon et al., 2021) – may exhibit different dynamics.

Overall, the findings emphasize the importance of understanding local dynamics and contextual factors when analyzing the effects of climate shocks on violence. By acknowledging the agency and actions of local actors, policymakers and stakeholders can develop targeted interventions and strategies to address the underlying drivers of conflict in affected regions.

**Contributions.** This research contributes to two strands of the literature: (i) on the effect of climate shocks on development and conflict; and (ii) on the water-conflict nexus.

Given the multiple consequences of changing climatic conditions, this research contributes to literature studying the impact of climate/shocks on economic development (Hodler and Raschky, 2014; Fishman et al., 2019; Damania et al., 2020; Russ, 2020), demographic growth (Flückiger and Ludwig, 2018), inequality (Castells-Quintana and McDermott, 2023), migration (Castells-Quintana et al., 2021), human capital (Fishman et al., 2019; Hyland and Russ, 2019; Blakeslee et al., 2020; Flückiger and Ludwig, 2022), crime (Blakeslee and Fishman, 2018), and cropland expansion (Zaveri et al., 2020) (see Castells-Quintana et al. (2017) for a survey of the literature).

The study also contributes to an extensive literature studying the causes of conflict, focusing on the role of natural resources (Berman et al., 2017; Vanden Eynde, 2018; Shapiro and Vanden Eynde, 2023; Hodler et al., 2023), ethnic divisions (Manotas-Hidalgo et al., 2021; Mueller et al., 2022), and competition between armed groups (Gassebner et al., 2023). The role of climate shocks in the determinants of conflict has received particular interest in recent years (Hsiang et al., 2013). Van Weezel (2020) explores the effects of long-term climate shocks on conflict in Africa, demonstrating a significant correlation between rising temperatures and heightened conflict risk. Breckner and Sunde (2019) reveals a positive effect of temperature extremes on conflict incidence in Africa, with stronger effects observed in regions facing more severe and prolonged temperature extremes, higher population density, lower agricultural productivity, and more pronounced land degradation. Barnett and Adger (2007) argue that climate change is anticipated to diminish nations' capacity to provide opportunities and services vital for livelihood sustainability, as well as limit access to and quality of natural resources, thereby undermining human security which could lead to violent conflicts. The role of natural resources in this climate change-conflict nexus is often studied through agricultural income shocks due to climate shocks (Miguel et al., 2004). Von Uexkull et al. (2016) find that the onset of droughts has minimal impact on the propensity of groups to initiate military confrontations with the state. Nonetheless, among agriculturally dependent and politically marginalized groups, the occurrence of localized drought conditions significantly amplifies the likelihood of sustained violence. Finally, McGuirk and Nunn (2020), Eberle et al. (2020) and Gehring and Schaudt (2024) show how climate shocks heighten tensions between
sedentary farming communities and nomadic livestock communities in sub-Saharan Africa.<fnref n="1" />

The paper contributes to literature on the water-conflict nexus. Recent research investigates the effects of water scarcity on conflict (Vanden Eynde, 2018; Cobbing and Hiller, 2019; Punjabi and Johnson, 2019). Devoto et al. (2012) highlight the escalation of disputes, specifically those related to irrigation water during water shortages. Sekhri (2014) supports this finding, reporting a 25% increase in self-reported disputes over irrigation water during water scarcity episodes. Detges (2016) finds that regions with limited access to improved water sources are more prone to experiencing drought-related communal violence. This latter result is supported by Döring (2020), who investigates the relationship between water scarcity, local water access and communal violence, emphasizing the influence of state presence, adaptation opportunities, and short-range migration as contributing factors to the relationship. Indeed, migration emerges as a coping mechanism in response to water scarcity (Fishman et al., 2017; Jedwab et al., 2023). Couttenier and Soubeyran (2014) find a weak positive link between droughts and civil war in SSA. Almer et al. (2017) find that droughts escalate small-scale social conflict, particularly in SSA areas with low water supply. Unfried et al. (2022) link climate-induced water mass changes, such as increased evapotranspiration and drought-induced reduction in water mass, to social conflict. Finally, Detges (2014) highlights a consistent pattern of pastoralist violence in Northern Kenya, with violence disproportionately occurring in close proximity to wells, as well as areas with more rainfall.<fnref n="2" /> Closest to our research, Decet and Marcucci (2024) examine how surface water access influences local violence in Africa, finding that rainfall scarcity increases conflict in upstream, water-rich areas that control river flow, particularly where water access is crucial for agriculture, ethnic inequalities exist, and governance is weaker.

In summary, the literature showcases the interconnected nature of climate shocks and water scarcity and their multifaceted impacts on economic development, poverty rates, migration, and conflict. Understanding these relationships is crucial for designing effective strategies to address and mitigate the challenges posed by climate shocks and water scarcity. In addition to the existing literature, the present study extends the understanding of the relationship between climate shocks, water scarcity, and conflict in different ways. Firstly, it shows how the impact of climate shocks on conflict is mediated through the availability of groundwater. Second, the effects are identified using: (i) temperature changes both at the continent level and at the local (cell) level; and (ii) both (temporary) climate shocks and (more permanent) climate shocks. Third, the analysis considers both violent and non-violent disputes over water resources. This broader scope provides a comprehensive understanding of the range of conflicts associated with water scarcity and availability. Fourth, the concept of within-cell inequality in groundwater access is introduced and tested. This approach helps capture the nuances and disparities in water availability and its impact on conflict in localized areas. Fifth, mechanisms are investigated via triple interaction analyses, to address mechanisms that may be specific to the African context where transhumant

<footnote n="1">¹To a lesser extent, the paper also contributes to literature on adaptation to climate shocks and strategies for coping with it (among others, Fisher et al. (2010); Cole et al. (2014); Fishman (2018); Taraz (2018); Wouterse et al. (2022); Gehring and Schaudt (2024)). See Castells-Quintana et al. (2018) for an extensive survey of this literature.</footnote>

<footnote n="2">²There is a broader literature on the role of water in economic development (Sekhri, 2014; Zaveri et al., 2016; Zaveri and B. Lobell, 2019; Damania et al., 2019; Zaveri et al., 2020; Desbureaux et al., 2022).</footnote>
pastoralism constitutes a large share of the economy and where states tend to be weak.

The following section describes the data and provides summary statistics. Section 3 contains
the economic strategy, as well as the baseline results and a number of sensitivity exercises. Section
4 investigates different mechanisms and highlight hotspots of violence. The last section concludes.

## 2 Data

This section summarizes the main variables and sources used in the baseline estimations.

### 2.1 Unit of Analysis

The units of analysis are 10,310 cells of size 0.5×0.5° (≈ 55×55 km), covering the entire set of African countries. Most of the data used is available at a more disaggregated level. The data is thus aggregated to generate a dataset at the *cell-year* level during the period 1997-2021.<fnref n="3" />

### 2.2 Data Sources

**Conflict data.** The Armed Conflict Location and Event Dataset (ACLED) contains information on conflict events in all African countries from 1997 to 2021 (https://acleddata.com/).<fnref n="4" /> The date, location, types, and actors are known for each event. Events are compiled from various sources, including press accounts from regional and local news, humanitarian agencies, and research publications. The dataset contains information on 281,311 distinct violent events. Events coded as “part of a region”, “region”, or “country” were excluded, leaving 211,474 localized events. *Low-intensity* violence encompasses protests and riots, while *high-intensity* violence includes battles, violence against civilians, and remote violence. Violence is also classified based on the spatial dimension of the actors. There are *local actors* (protesters, rioters, and identity militias) and *non-local actors* (external forces, political militias, rebel groups, and state forces).

**Groundwater availability.** Unfortunately, granular data on groundwater depth is not available for Africa. One thus must make use of the updated version of Bonsor and MacDonald (2011)'s data, which employs a modeling approach to recreate localized data on groundwater depth in Africa. Their model incorporates high-resolution data on geology (including on rock and aquifer types), rainfall, and rivers. Data is created following three primary rules: (i) depth to groundwater tends to increase with reducing rainfall; (ii) basement rocks play a significant intermediating role in this relationship, particularly in semi-arid regions <fnref n="5" />; (iii) if an area is located within 5 km of a river, the depth to groundwater is considered to be very shallow (less than 7 meters). <fnref n="6" />

<footnote n="3">³Online Appendix Figure A.1 shows the 49 countries, 662 admin 1 regions, and 10,310 cells used in the analysis.</footnote>

<footnote n="4">⁴Downloaded on June 2⁷th 2022.</footnote>

<footnote n="5">⁵According to Bonsor and MacDonald (2011), “experience of groundwater development indicates groundwater-levels within basement rocks to be shallower than in other rock types in areas of lower rainfall [...] because of the relatively limited depth [...] of the impermeable base of the weathered zone within basement terrain.” More generally, “basement rocks form relatively thin, shallow, aquifers across Africa [...] and groundwater-levels remain shallow even in areas of relatively low rainfall.” In contrast, “sedimentary aquifers have much greater thicknesses [...] within Africa [...] and groundwater-levels within the aquifers decrease with rainfall”</footnote>

<footnote n="6">⁶Bonsor and MacDonald (2011) indeed writes that “very shallow groundwater-levels are known to be common within alluvium (often unmapped) close to (within 5 km) perennial rivers.”</footnote>
The model's predictions are validated using external groundwater depth data sourced from international organizations and NGOs, primarily involved in well construction projects, as well as from research on existing aquifers in Africa. The raw map consists of 0.05×0.05° pixels. For each 0.5×0.5° sample cell, the proportion of pixels falling into specific groundwater depth categories (0-7, 7-25, 25-50, 50-100, 100-250, and 250+ meters) can then be estimated.

While imperfect, Bonsor and MacDonald (2011) is to our knowledge the best source of data on groundwater availability for Africa.<fnref n="7" /> While the model's predictions have been validated, one issue is that geology, rainfall, aridity, and rivers were used as inputs to create the data. We will show that results hold when controlling for these (in order to only rely on their “interactions” within the model) or including spatially refined fixed effects capturing local geographical conditions (in order to only compare “neighboring” pixels with different levels of groundwater availability).<fnref n="8" />

The costs associated with digging for and pumping groundwater rise exponentially as groundwater depth increases (MacDonald et al., 2012; Bonsor and MacDonald, 2011; Uhl et al., 2009; Baumann, 2000). Up to ~7 m, standard hand pumps (suction pumps) can be used effectively (Sekhri, 2014). Beyond this depth, lift pumps with reciprocating action become necessary. Manual drilling is usually feasible until 25-50 m. Hand-operated lift pumps can be utilized up to 50 m, with a cost of ~500 USD. For depths ranging from 50 to 100 m, water is not easily accessed by hand pumps, and rotary action pumps are typically employed (MacDonald et al., 2012). Depths exceeding 100 m demand deep wells as well as machine-driven rigs (costing over 100,000 USD) and engine-driven rotary action pumps that require energy sources (diesel or gasoline).<fnref n="9" />

Informed both by anecdotal evidence and the literature, the share of 0-50 m groundwater depth in the cell is the baseline measure capturing *Shallow Water*. Results will however hold with: (i) 0-25 m; and (ii) several categories (0-7 m, 7-25 m, 25-50 m, 50-100 m, 100-250 m).

In addition, within-cell inequality is measured by a fractionalization index $Frac_c$ capturing the degree to which a cell c is split into distinct groups of groundwater depth:

$$ Frac_c = \sum_{d=0}^{250+} (share \ groundwater_{dc} \times (1 - share \ groundwater_{dc})), $$

where, share groundwaterdc is the share of groundwater of depth d in cell c; and d ∈ (0-7, 7-25, 25-50, 50-100, 100-250, 250+ meters). The more spatially unequal the cell is in terms of groundwater levels, the higher the fractionalization index is.

**Other data.** Data on wells was collected from Open Street Map by retrieving the location of structural facilities to access groundwater.<fnref n="10" /> As this information is self-reported, it may suffer from reporting bias, especially in conflict-prone areas. As a result, these measures are not used as the baseline measures. Instead, groundwater availability is the baseline measure.

<footnote n="7">⁷Conversations with Alan MacDonald confirmed that their data is still the best data available. Studies based on complex global groundwater models (e.g., Fan et al., 2013) likely perform less well due to the lack of training data for the continent. The fact that non-African training data is over-represented may increase prediction errors.</footnote>

<footnote n="8">⁸We will consider 667 admin 1-level units (e.g., regions) and 2,466 admin 2-level units (e.g., departments).</footnote>

<footnote n="9">⁹“Ultra deep” hand pump technologies capable of lifting water from depths of 50-75 m are being developed and were thus not available in Africa during the period of study (Bonsor and MacDonald, 2011; MacDonald et al., 2012).</footnote>

<footnote n="10">¹⁰The data was collected using the tag "man_made = water_well" (on December 15th, 2022).</footnote>
Climate data was obtained from the Climatic Research Unit gridded Time Series dataset (CRU-
TS), which covers the period from 1901 to 2022 (Harris et al. (2020); resolution: 0.5×0.5°). Climate
prediction data from the World Bank Climate Change Knowledge Portal was also used (resolution:
1×1°). Köppen maps then categorize 0.5×0.5° cells into major climate types based on temperature
and rainfall patterns (Rubel and Kottek, 2010). Nutrient availability is measured using data from
Berman et al. (2021), who retrieve data from the Harmonized World Soil Database to quantify
soil fertility. Surface geology data was retrieved from United States Geological Survey (2002).
Euclidean distance to rivers and lakes was computed using shapefiles obtained from Natural Earth
(2024) and Jenness et al. (2007), respectively. Next, McGuirk and Nunn (2020) identify transhumant
pastoral societies. To add to this analysis, Eberle et al. (2020) provide data on the location of mixed
settlements populated by both herders and farmers. Fine-grained spatial information on malaria
prevalence comes from Cervellati et al. (2017). Finally, to capture local state capacity, Agneman
et al. (2022) use geocoded survey data on local state presence (resolution: 0.05×0.05°).

**2.3 Summary Statistics and Visualization**

Appendix Table A.1 displays summary statistics for the cumulative cross-sectional variation as well as the panel variation (1997-2021). On average, the proportion of shallow water (0-50 m) is 58%. Half of the cells have a proportion of shallow water that is larger than 95% and 30% have no shallow water. While 45% of cells experienced at least one violent event during the period, this figure conceals significant variation. Cells with more than 95% of shallow water were three times more likely to face conflict compared to those without shallow water (62% vs. 21%). In the panel, the unconditional probability of violence is at 10%. The probability of conflict is 14% for cells with at least 95% of shallow water, compared to 4% for cells without shallow water. Finally, Figure 1 below shows groundwater depth as well as the distribution of conflict events since 1997.

<figure><img src="image_3.png" /><figcaption>Figure 1: Maps of Groundwater Depth and Conflict Events</figcaption></figure>

Notes: Figure (a): Underlying groundwater depth categorization data from the updated version of Bonsor and MacDonald (2011).
Figure (b): Cumulative number of ACLED conflict events over the period 1997-2021.
## 2.4 Validation of the Shallow Water Measure

The following model is estimated to corroborate that cells with more shallow water have more wells:

$$ Wells_c = \alpha_t Shallow\ Water\ Share_c + \gamma_t Official\ record_c + \varepsilon_c \quad (1) $$
where $Wells_c$ measures either the number of wells or the share of each type of wells in cell c. The information contained in the data makes it possible to distinguish between wells using only a rope and a bucket, wells with a manual pump, and wells with a powered pump. A control is also added for whether the reporting of the well's location in Open Street Map comes from an official source such as NGO records. $Shallow\ Water\ Share_c$ measures (0-50 m deep) groundwater availability.

<table><caption>Table 1: Groundwater Depth and Type of Wells, Cross-Section, c. 2022</caption><thead><tr><th rowspan="2">Type of well:</th><th colspan="3">Number of</th><th colspan="3">Share of</th></tr><tr><th>(1)<br>Bucket</th><th>(2)<br>Manual</th><th>(3)<br>Powered</th><th>(4)<br>Bucket</th><th>(5)<br>Manual</th><th>(6)<br>Powered</th></tr></thead><tbody><tr><td>Shallow water share</td><td>0.01<br>(0.07)</td><td>0.81**<br>(0.38)</td><td>0.20<br>(0.16)</td><td>0.76**<br>(0.32)</td><td>1.55**<br>(0.68)</td><td>-0.83<br>(0.77)</td></tr><tr><td>Mean<br>Obs. (Cells)</td><td>0.41<br>10310</td><td>0.76<br>10310</td><td>0.14<br>10310</td><td>0.71<br>10310</td><td>3.13<br>10310</td><td>0.81<br>10310</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes: "Shallow water share" refers to the share of 0-50 meter deep groundwater in the cell (the share ranges from 0 to 1). In columns (1), (2), and (3), the dependent variable is the number of wells by type of pump: rope with a bucket, manual pump, and powered pump, respectively (defined c. 2022). In columns (4), (5), and (6), the dependent variable is the percentage share of each type of pumps in the total number of wells. Note that information on the type of wells is only available for 5% of the cells. Results hold when considering those cells only (results not shown but available upon request). Country fixed effects are added in all regressions. Standard errors clustered at the first administrative level (e.g., regions) in parentheses. * Significant at 10%, ** 5%, *** 1%.

As seen in Table 1 above, there are more manual pumps in shallow water areas (consistent with the various predictions from MacDonald et al. (2012)). Furthermore, there is a higher proportion of bucket-type wells and manual pump wells in such areas. These findings align with the cost considerations associated with different water depths, emphasizing the shift towards powered (lift) pumps and machine-driven rigs for deeper wells. However, given possible reporting biases in the well data, the shallow water measure is used for the baseline analysis.

## 2.5 Spatial Variation in the Shallow Water Measure

Within-country variation in shallow water accounts for 37% of the variation across space, indicating significant sub-national variation in groundwater availability. Similarly, 17% and 12% of total variation comes from within 667 admin 1-level units (e.g., regions) and within 2,466 admin 2-level units (e.g., departments) respectively. Hence, there is substantial variation below the sub-national units. This permits the incorporation of spatially refined fixed effects in the analysis.

## 3 Preliminary Evidence

Over the past decades, climate shocks and variability have intensified across Africa. Against this backdrop of continent-wide changes, we examine the evolving relationship between local conflict
and local groundwater. Theoretically, the effect of *global* climate shocks on the relationship between groundwater and conflict is ambiguous. If conflict increases in groundwater-rich areas over time, one interpretation is that global climate shocks are intensifying societal pressure over groundwater resources. Conversely, if conflict decreases in groundwater-rich areas over time, it may indicate that global climate shocks are enhancing the economic value of groundwater resources, thereby raising the opportunity cost of engaging in conflict in areas with greater groundwater access.

**Econometric specification.** The following equation is estimated for 10,310 cells c located in 49 countries i and considering 25 years t from 1997-2022 (observations = 10,310×25 = 257,750):

$$Y_{cit} = \sum_{t=1998}^{2021} \beta_t \text{Shallow Water}_c \times \mathbb{I}_t + \theta_c + \theta_{it} + \varepsilon_{cit}, \quad (2)$$

where $Y_{cit}$ represents conflict incidence (considering all events). Specifications are also estimated using different types of conflict such as *low-intensity* violence that encompasses protests and riots, and *high-intensity* violence that includes battles, violence against civilians, and remote violence. One can also distinguish between *local actors*, such as protesters, rioters, and identity militias, and *non-local* actors, including external forces, political militias, rebel groups, and state forces. *ShallowWater*$_c$ denotes the share of the cell with 0-50 meters deep groundwater. Its interactions with the year dummies $\mathbb{I}_t$ allow the examination of the evolving relationship between groundwater and conflict as climate change is unfolding in Africa over time. $\theta_c$ and $\theta_{it}$ capture cell fixed effects and country×year fixed effects, respectively, accounting for unobserved factors specific to each cell and each country-year. Eq. (2) is estimated using a linear probability model and standard errors are clustered at the cell level. The coefficients of interest are the $\beta_t$ capturing the relationship between local groundwater and local conflict in year t relative to the initial year 1997.

<figure><img src="image_4.png" /><figcaption>Figure 2: The Relationship between Shallow Groundwater and Conflict Over Time, 1997-2021</figcaption></figure>

Notes: This figure reports the estimated $\hat{\beta}_t$ for each year (relative to the omitted year 1997). The dependent variable is a dummy taking the value 1 if at least one conflict event is observed in the cell during the year. Cell fixed effects and country×year fixed effects are added to the regression. Standard errors clustered at cell level. 90% confidence intervals.

**Baseline results.** Figure 2 above displays the estimated $\hat{\beta}_t$. Compared to the baseline year 1997,
groundwater-rich cells do not show significantly different levels of violence before 2010 (except in 2004, 2006, and 2008). From 2011 onward, an upward trend emerges. By 2021, the magnitude is substantial: conflict likelihood is approximately 15% higher compared to 1997, reflecting a stronger correlation between conflict and groundwater-rich areas over time.

Since 2010, extreme heat has been increasingly prevalent across nearly all regions of the globe, affecting both daytime and nighttime temperatures. Throughout the decade from 2011 to 2020, each year exhibited a frequency of extreme warm days and nights that significantly surpassed the Global Historical Climatology Network Daily Dataset Extremes (GHCNDEX) 1961-1990 average of 10%. These trends indicate a marked acceleration in climate change during the post-2010 period (World Meteorological Organization, 2020), likely explaining the stronger post-2010 results in Figure 2.

Table 2 presents the results from the combination of the multiple $\hat{\beta}_t$ estimates. A significant positive correlation is found for the entire period from 1998 to 2021, with the likelihood of conflict occurring in the cell being 5% higher (col. 1). In column (2), the point estimates are differentiated between the years before and after 2010. A notable acceleration of +9% is observed post-2010. In other words, cells with more shallow water showed a stronger correlation with violence over time, likely due to the more frequent climate shocks affecting the continent. Including fractionalization leaves the point estimates for shallow water largely unchanged (cols. 3-4). Cells with a higher fractionalization index also exhibit a higher likelihood of violence over time (col. 4; +6%).

<table><caption>Table 2: Relationship between Shallow Groundwater and Conflict for Selected Subperiods, 1997-2022</caption><thead><tr><th>Dep. Var. : Dummy for conflict</th><th>(1)<br>Full period</th><th>(2)<br>Diff post/pre 2010</th><th>(3)<br>Full period</th><th>(4)<br>Diff post/pre 2010</th></tr></thead><tbody><tr><td>Average effect:</td><td></td><td></td><td></td><td></td></tr><tr><td>Shallow water</td><td>0.05***<br>(0.01)</td><td>0.09***<br>(0.01)</td><td>0.05***<br>(0.01)</td><td>0.07***<br>(0.01)</td></tr><tr><td>Frac.</td><td></td><td></td><td>0.01<br>(0.01)</td><td>0.06***<br>(0.01)</td></tr><tr><td>Obs.</td><td>257750</td><td>257750</td><td>257750</td><td>257750</td></tr><tr><td>Cell (10,310) FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country (49) × Year (25) FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes: In all columns, the dependent variable is a dummy taking the value 1 if at least one conflict event is observed in the cell during the year, 0 otherwise. Shallow water refers to the share of 0-50 meters deep groundwater. Columns (1) and (3) show the combination of the estimated $\hat{\beta}_t$ for the full period 1998-2022 (estimated relative to the baseline year 1997), while columns (3) and (4) display the difference in the average point estimates in the post-2010 period relative to the pre-2010 period. Fractionalization (Frac.) is an index of within-cell inequality in access to groundwater. The more unequal the cell is in terms of groundwater levels, the higher the index of fractionalization is. SE clustered at cell level. * Significant at 10%, ** 5%, *** 1%.

**Results by type of violence and actors.** Figure 3a below indicates that the increasing association between conflict and shallow water over time is significant for both *low intensity* violence (protests and riots combined) and *high intensity* violence (battles, violence against civilians, and remote violence). To ease the comparison of magnitudes, all the dependent variables in this figure are standardized, expressed as Z-scores.<fnref n="11" /> The standardized correlation for low-intensity violence is

<footnote n="11">¹¹Z-score = ((value - mean in the sample)/standard deviation in the sample).</footnote>
four times larger than for high-intensity violence, suggesting that groundwater availability might have played a greater role in shaping more localized conflicts.

Second, the likelihood of violence appears to increase over time with the presence of shallow water for both *local* actors (protesters, rioters, and identity militias) and *non-local* actors (external forces, political militias, rebel groups, and state forces). The correlations are approximately four times greater for local actors, suggesting that much of the increase in violence in shallow water areas over the past 30 years may possibly be due to local actors (Figure 3a below). It also suggests that climate-related conflicts, fueled by local grievances or tensions within or between local communities may be more strongly influenced by groundwater availability.

<figure><img src="image_5.png" /><figcaption>Figure 3: Groundwater Availability and Type of Conflict Events and Actors</figcaption></figure>

Notes: Results from combinations of the $\hat{\beta}_t$ for the full period (left panel) and differentiating the estimates to show the average for the years after 2010 relative to the average for the years before 2010 (right panel). The dependent variables are standardized. *Low-intensity* = z-score of a dummy if at least one protest and/or riot is observed in the cell during the year. *High intensity* = z-score of a dummy if at least one battle, violence against civilians and/or remote violence event is observed in the cell during the year. *Local actors* = z-score of a dummy if protesters, rioters and/or identity militias are at the origin of at least one event observed in the cell during the year. *Non-local actors* = z-score of a dummy if external forces, political militias, rebel groups and/or state forces are at the origin of at least one event observed in the cell during the year. *Water conflict* = z-score of a dummy if a water-related conflict took place in the cell during the year (keywords used: "water", "wells", "borehole", "reservoir", and "pump"). 90% confidence intervals are reported.

Third, one can make use of the richness of the ACLED data to identify conflicts that are likely water-related. For this analysis, water conflicts are defined as indicated in the “notes” item of the ACLED data (the following keywords are used: “water”, “wells”, “borehole”, “reservoir”, and “pump”). The analysis reveals quantitatively significant correlations between shallow water and water-related violence over time. These findings indicate that, with global climate shocks, cells with shallow water resources are more likely to experience conflict related to water access and disputes over water sources (see Figures 3a-3b as well as Appendix Table A.2). Lastly, it is important to note that the correlation for water-related conflicts is stronger than for high-intensity conflicts but weaker than for all conflicts and low-intensity conflicts. Water-related conflicts include only those where water is explicitly mentioned. However, if water scarcity increases tension in groundwater-rich areas, water-related conflicts may still occur, even if they are not directly classified as such.
Fourth, regardless of the type of violence or actors involved, there has been a noticeable acceleration in groundwater-linked violence after 2010. However, this acceleration has been disproportionately larger for low intensity violence and local actors (see Figure 3b).

Lastly, Figure 4 below displays the results for the fractionalization index, controlling for the direct effects of mean groundwater availability. Cells with higher spatial inequality in groundwater availability are correlated with significantly more low-intensity violence and violence involving local actors and water over time, with a noticeable increase in all types of conflicts post-2010.

<figure><img src="image_6.png" /><figcaption>Figure 4: Groundwater Fractionalization and Type of Conflict Events and Actors</figcaption></figure>

Notes: Results from combinations of the coefficients of the groundwater fractionalization measure for the full period 1997-2022 (left panel) and differentiating the point estimates for the years before 2010 and the years after 2010 (right panel). The mean groundwater availability measure (the 0-50 m deep groundwater share) and their interactions with the year dummies are always included as a control. Dependent variable: *Low-intensity* = z-score of a dummy if at least one protest and/or riot is observed in the cell during the year. *High-intensity* = z-score of a dummy if at least one battle, violence against civilians and/or remote violence event is observed in the cell during the year. *Local actors* = z-score of a dummy if protesters, rioters and/or identity militias are at the origin of at least one event observed in the cell during the year. *Non-local actors* = z-score of a dummy if external forces, political militias, rebel groups and/or state forces are at the origin of at least one event observed in the cell during the year. *Water conflict* = z-score of a dummy if a water-related conflict took place in the cell during the year. 90% confidence intervals are reported.

To assess the consistency of the findings, an extensive sensitivity analysis is conducted using standard exercises commonly found in the literature, as well as robustness checks tailored to the research question. In particular, the baseline results hold if we individually or simultaneously control for geology, rainfall, aridity, and proximity to rivers, or include spatially refined fixed effects.<fnref n="12" /> Results also hold if we include additional controls for economic or physical geography,

<footnote n="12">¹²Results are robust to i) including admin 1 (N = 662)*year fixed effects or admin 2 (2,466)*year fixed effects (Appendix Tables A.3-A.4); (ii) adding five Köppen climate group fixed effects × year fixed effects (Appendix Table A.6) (we rely on the 1st-level classification, which includes five groups: tropical, dry, temperate, continental, and polar, with results holding even when using the third-level classification with 31 fixed effects, not shown); (iii) controlling for historical rainfall (mean 1960-1990; mm) interacted with year dummies (Appendix Table A.5); (iv) adding six surface geology groups × year fixed effects (Appendix Table A.6) (the six geology groups are: Basement, Igneous, Sedimentary, Mobile Belt, Water, and Other, with results holding when using more detailed geology categories.); (v) controlling for logged distance to rivers/lakes interacted with year dummies (data sets from Natural Earth (2024) and Jenness et al. (2007) include both major and minor rivers and lakes, which allows us to mimic the analysis from Bonsor and MacDonald (2011)) (we obtain similar results if we only use data on perennial (i.e., major) rivers from World Bank (2017)); (vi)</footnote>
exclude countries impacted by Boko Haram, or account for spatial autocorrelation.<fnref n="13" />

Finally, related to the question of how to measure groundwater availability, results are robust to alternative measures of groundwater with the percentage of the cell with water depth ranging from 0-7 m, 7-25 m, 25-50 m, 50-100 m and 100-250 m (Appendix Figures A.2a-A.2b). As expected, the correlations are stronger for more shallow depths (only using 0-25 m to define shallow water areas also leads to slightly stronger effects than when using 0-50 m; Appendix Table A.7).

# 4 Main Results - Groundwater and the Effects of Local Climate Shocks

So far, the impacts of climate instability have been assumed to be uniform across cells, even though its effects are diverse across regions in Africa (Trisos et al., 2022). Figure 5 displays the difference between the mean temperature for the period 1997-2022 and the mean temperature for 1961-1990 (in °C) across the 10,310 cells. Since 1997, temperatures have increased by ~0.7°C (33°F). Alarmingly, 11% of cells have experienced a temperature increase of more than one degree.

While the literature has shown that climate shocks generally lead to increased conflict, the key question in this study is whether this relationship is exacerbated or mitigated in groundwater-rich areas. With less surface water due to higher temperatures and increased evaporation rates, the economic value of groundwater could rise, potentially leading to less conflict in groundwater-rich areas (income effect). On the other hand, higher temperatures could intensify pressure on remaining water resources, resulting in more conflict in these areas (prize effect).

**Empirical strategy.** To estimate the effect of shorter or longer local climate shocks, depending on local groundwater availability, we estimate the following panel equation:

$$Y_{cit} = +\beta_1 Temperature_{cit}^{d=k} + \beta_2 ShallowWater_c \times Temperature_{cit}^{d=k} + \theta_c + \theta_{it} + \varepsilon_{cit}, \quad (3)$$

where $Y_{cit}$ represents the incidence of violence in cell c, country i, and year t. $ShallowWater_c$ is the share of groundwater at a depth of 0-50 meters in the cell. $Temperature_{cit}^{d=k}$ is a moving average of temperature over the years $[t-d;t]$, with d taking the values from 0 to 7. Consequently, we define two measures of local temperature shocks: the first relies on yearly variation (a *temporary* shock, d = 0), and the second is a moving average of temperature over multiple years (a more *persistent* shock, with d = 1 to d = 7).<fnref n="14" /> Both effects are interesting in and of themselves. One issue with temporary temperature shocks is that they do not account for adaptation. Longer-term temperature changes are more likely to capture broader ecosystem collapses, which could lead local communities to monetize access to, or fight over, wells in shallow water areas, thereby mitigating or increasing tensions. To account for cell-specific and country × year-specific characteristics, fixed effects $\theta_c$ and $\theta_{it}$ are included. If $\hat{\beta}_1$ is positive, cells experiencing rising temperatures experience more conflict.

<footnote>controlling for latitude and longitude (and their squares) interacted with year dummies, in order to flexibly control for geography; and (vii) simultaneously control for geology, rainfall, aridity, and rivers (Appendix Table A.6).</footnote>

<footnote n="13">¹³Results are robust to (see Appendix Table A.6): (i) controlling for initial (c. 1995) log population density, soil fertility or malaria suitability interacted with the year dummies; (ii) excluding Nigeria, Chad, Cameroon and Niger, four countries where Boko Haram has expanded its area of influence over time; and (iii) allowing the error term to be spatially correlated (Conley, 1999). Standard errors are reported using 500 km (but results hold with other cut-offs).</footnote>

<footnote n="14">¹⁴Of course, we only use previous years for this moving average.</footnote>
<figure><img src="image_7.png" /><figcaption>Figure 5: Average Temperature Anomalies (°C), 1997-2022 vs. 1961-1990</figcaption></figure>

Notes: This figure shows for the 10,310 cells of the main analysis the absolute difference between the mean average temperature during the period 1997-2022 and the mean average temperature during the period 1961-1990 (in °C).

Furthermore, if $\hat{\beta}_2$ is positive (negative), it indicates an additional (a lower) effect of temperature in groundwater-rich areas, suggesting that conflict may further increase (actually decrease) with rising temperatures where groundwater is more easily accessible.

**Baseline results.** Table 3 shows the results. Focusing on local *temporary shocks* (d=0), a one degree Celsius increase in temperature is associated with a ~3.2% higher likelihood of violence (Panel A, col. 1). However, no differential changes are observed in the likelihood of violence based on groundwater availability. When focusing on local *persistent shocks* (d=1 to d=7), cells with greater groundwater availability are disproportionately more prone to violence for a given temperature increase (Panel A, cols. 2-8). For instance, for d=7 (col. 8), a one degree increase in temperature is associated with a ~10% higher likelihood of violence in groundwater-rich areas. Conversely, the coefficient of temperature converges towards 0 as one considers a higher d, indicating more prolonged temperature changes. This suggests that during periods of intensified and more persistent climate shocks, conflict tends to concentrate in areas with greater groundwater access.

Panel B shows that conflict also increases in areas with higher levels of groundwater inequality (*Frac.*), but only when shocks are very persistent (*Frac. x Temperature*), i.e., in situations of more permanent water stress. The coefficient increases from 0 with d=0 to 0.051 with d=7.

These results suggest that conflict increases with temperature across *all* cells in the short run. However, when local shocks become more permanent, temperatures do not significantly affect conflict *except in* groundwater-rich areas and areas with high groundwater inequality. As such,
<table><caption>Table 3: Local Climate Shocks, Groundwater Availability, and Conflict</caption><thead><tr><th>Dep. Var.: Dummy if conflict<br>Moving average for temp. (t-d):</th><th>(1)<br>d=0</th><th>(2)<br>d=1</th><th>(3)<br>d=2</th><th>(4)<br>d=3</th><th>(5)<br>d=4</th><th>(6)<br>d=5</th><th>(7)<br>d=6</th><th>(8)<br>d=7</th></tr></thead><tbody><tr><td><strong>Panel A</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Temperature<sup>d=k</sup></td><td>0.032***<br>(0.005)</td><td>0.036***<br>(0.007)</td><td>0.029***<br>(0.008)</td><td>0.024***<br>(0.009)</td><td>0.018*<br>(0.010)</td><td>0.007<br>(0.010)</td><td>0.003<br>(0.011)</td><td>-0.004<br>(0.011)</td></tr><tr><td>Shallow Water × Temperature<sup>d=k</sup></td><td>0.001<br>(0.006)</td><td>0.007<br>(0.007)</td><td>0.021**<br>(0.009)</td><td>0.037***<br>(0.010)</td><td>0.055***<br>(0.011)</td><td>0.072***<br>(0.011)</td><td>0.084***<br>(0.011)</td><td>0.096***<br>(0.011)</td></tr><tr><td><strong>Panel B</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Temperature<sup>d=k</sup></td><td>0.031***<br>(0.005)</td><td>0.034***<br>(0.007)</td><td>0.025***<br>(0.008)</td><td>0.019**<br>(0.009)</td><td>0.012<br>(0.010)</td><td>0.002<br>(0.011)</td><td>-0.004<br>(0.011)</td><td>-0.012<br>(0.011)</td></tr><tr><td>Shallow Water × Temperature<sup>d=k</sup></td><td>-0.001<br>(0.006)</td><td>0.004<br>(0.008)</td><td>0.015<br>(0.010)</td><td>0.030***<br>(0.011)</td><td>0.047***<br>(0.012)</td><td>0.064***<br>(0.012)</td><td>0.075***<br>(0.012)</td><td>0.086***<br>(0.012)</td></tr><tr><td>Frac. × Temperature<sup>d=k</sup></td><td>0.007<br>(0.011)</td><td>0.015<br>(0.015)</td><td>0.027<br>(0.019)</td><td>0.033<br>(0.021)</td><td>0.035<br>(0.022)</td><td>0.036<br>(0.022)</td><td>0.041*<br>(0.022)</td><td>0.051**<br>(0.022)</td></tr><tr><td>Obs.</td><td>257750</td><td>257750</td><td>257750</td><td>257750</td><td>257750</td><td>257750</td><td>257750</td><td>257750</td></tr><tr><td>Cell (10,310) 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>Country (49) × Year (25) 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>

Notes: In all columns, the dependent variable is a dummy equal to 1 if at least one conflict event is observed in the cell during the year. Shallow water is the share of 0-50 m deep groundwater. "Temperature" is the moving average of temperature, computed as the average over the years t-d to t where d takes the values from 0 to 7. Fractionalization is an index of within-cell inequality in access to groundwater. Cell fixed effects and country × year fixed effects are always included. Standard errors clustered at cell level in parentheses. * Significant at 10%, ** 5%, *** 1%.

when local shocks become more permanent, conflict may (relatively) shift from all affected areas to those with greater access to groundwater and/or higher spatial inequality in groundwater access. These latter areas may therefore become “conflict hotspots”, as will be discussed below. Results by type of violence and actors. For the sake of simplicity, the results for Shallow Water × Temperature are only shown for the intermediary value d = 4 (see Figure 6 above).<fnref n="15" /> To facilitate the comparison of magnitudes, all dependent variables are standardized and converted to Z-scores. When climate shocks become more persistent, low-intensity violence and violence involving local actors disproportionately increase in groundwater-rich areas. Significant positive effects are also observed for water-related conflict (though it is important to note that some conflicts may be indirectly related to water and therefore not fully captured by this variable).

**Sensitivity analysis.** Table 4 above shows the main results for Shallow Water*Temperature and Frac*Temperature when using *d* = 4 and implementing some of the same robustness checks as for the more descriptive analysis, related to how the groundwater data was created by Bonsor and MacDonald (2011). Results hold if we i) include admin 1 (N = 662) × year fixed effects (col. 2) or admin 2 (2,466) × year fixed effects (col. 3); ii) add five Köppen climate group fixed effects × year fixed effects (col. 4);<fnref n="16" /> iii) control for historical rainfall (mean 1960-1990; mm) interacted with year dummies (col. 5);<fnref n="17" /> iv) add six surface geology groups × year fixed effects (col. 6);<fnref n="18" /> v) control for logged distance to rivers/lakes interacted with year dummies (col. 7);<fnref n="19" /> vi) simultaneously control

<footnote n="15">¹⁵Results remain mostly unchanged for higher values of *d*. For example, see Appendix Figure A.3 for *d* = 7.</footnote>

<footnote n="16">¹⁶We rely on the 1st-level classification, which includes five groups: tropical, dry, temperate, continental, and polar. Results hold if we use the third-level classification instead (31 fixed effects) (not shown).</footnote>

<footnote n="17">¹⁷Results do not depend on using unlogged rainfall or logged rainfall (not shown).</footnote>

<footnote n="18">¹⁸The six geology groups are: Basement, Igneous, Sedimentary, Mobile Belt, Water, and Other. Results hold if we use more detailed geology categories (N = 35) for the geology group fixed effects (not shown).</footnote>

<footnote n="19">¹⁹The data sets from Natural Earth (2024) and Jenness et al. (2007) include both major and minor rivers and lakes,</footnote>
<figure><img src="image_8.png" /><figcaption>Figure 6: Local Climate Shocks, Shallow Water, and Types of Conflict and Actors</figcaption></figure>

Notes: Results from $\beta_2$ (ShallowWater x Temperature) considering climate shocks with d=4. The dependent variables are standardized. *Low-intensity* = z-score of a dummy equal to 1 if at least one protest or riot is observed in the cell during the year. *High intensity* = z-score of a dummy equal to 1 if at least one battle, violence against civilians, or remote violence event is observed in the cell during the year. *Local actors* = z-score of a dummy equal to 1 if protesters, rioters, or identity militias are at the origin of at least one event observed in the cell during the year. *Non-local actors* = z-score of a dummy equal to 1 if external forces, political militias, rebel groups or state forces are at the origin of at least one event observed in the cell during the year. *Water conflict* = z-score of a dummy equal to 1 if a water-related conflict took place in the cell during the year. Cell FE and country × year FE are included. 90% confidence intervals are reported.
<table><caption>Table 4: Local Climate Change, Groundwater, and Conflict - Main Robustness Checks</caption><thead><tr><th>Dep. Var. : Dummy for conflict</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th></tr></thead><tbody><tr><td><strong>Panel A</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Temperature<sup>d=k</sup></td><td>0.018*<br>(0.010)</td><td>0.027**<br>(0.012)</td><td>0.050***<br>(0.019)</td><td>0.016<br>(0.010)</td><td>-0.023**<br>(0.011)</td><td>0.024**<br>(0.010)</td><td>0.004<br>(0.010)</td><td>-0.032***<br>(0.011)</td><td>0.009<br>(0.010)</td></tr><tr><td>Shallow Water × Temperature<sup>d=k</sup></td><td>0.055***<br>(0.011)</td><td>0.062***<br>(0.014)</td><td>0.056***<br>(0.015)</td><td>0.056***<br>(0.011)</td><td>0.125***<br>(0.016)</td><td>0.052***<br>(0.011)</td><td>0.058***<br>(0.011)</td><td>0.102***<br>(0.018)</td><td>0.063***<br>(0.011)</td></tr><tr><td>Obs.</td><td>257750</td><td>255250</td><td>214350</td><td>257075</td><td>257325</td><td>257675</td><td>257750</td><td>257250</td><td>257750</td></tr><tr><td><strong>Panel B</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Temperature<sup>d=k</sup></td><td>0.012<br>(0.010)</td><td>0.019<br>(0.012)</td><td>0.040**<br>(0.019)</td><td>0.011<br>(0.010)</td><td>-0.031***<br>(0.011)</td><td>0.018*<br>(0.010)</td><td>0.001<br>(0.010)</td><td>-0.033***<br>(0.011)</td><td>0.003<br>(0.011)</td></tr><tr><td>Shallow Water × Temperature<sup>d=k</sup></td><td>0.047***<br>(0.012)</td><td>0.052***<br>(0.014)</td><td>0.047***<br>(0.016)</td><td>0.048***<br>(0.012)</td><td>0.069***<br>(0.020)</td><td>0.044***<br>(0.012)</td><td>0.054***<br>(0.012)</td><td>0.099***<br>(0.018)</td><td>0.055***<br>(0.012)</td></tr><tr><td>Frac. × Temperature<sup>d=k</sup></td><td>0.035<br>(0.022)</td><td>0.052***<br>(0.019)</td><td>0.054**<br>(0.022)</td><td>0.035<br>(0.022)</td><td>0.038<br>(0.035)</td><td>0.035<br>(0.022)</td><td>0.020<br>(0.021)</td><td>0.011<br>(0.021)</td><td>0.037*<br>(0.022)</td></tr><tr><td>Obs.</td><td>257750</td><td>255250</td><td>214350</td><td>257075</td><td>257325</td><td>257675</td><td>257750</td><td>257250</td><td>257750</td></tr><tr><td>Cell (10,310) 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><td>Yes</td></tr><tr><td>Country (49) x Year (25) FE</td><td>Yes</td><td>-</td><td>-</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Admin 1 (662) FE × Year FE</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Admin 2 (2,466) FE × Year FE</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Köppen climate group (5) FE × Year FE</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Historical rainfall (1960-1990) x Year FE</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>Yes</td><td>-</td></tr><tr><td>Surface geology (6) FE × Year FE</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>Yes</td><td>-</td></tr><tr><td>Log distance to rivers/lakes x Year FE</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>Yes</td><td>-</td></tr><tr><td>(Lat/Long & Lat/Lon sq.) x Year FE</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td></tr></tbody></table>

Notes: The dependent variable is a dummy taking the value 1 if at least a conflict event is observed in the cell during the year, 0 otherwise. Shallow water refers to the share of 0 to 50 m deep groundwater. Fractionalization is an index of within cell inequality in access to groundwater, the more diverse the cell is in terms of groundwater levels, the higher the index of fractionalization is. Col. (1) reports the baseline results. In cols. (2)-(3), we include 662 level 1 region FE and 2,466 level 2 region FE, which we interact with year FE. In col. (4), we account for 5 Köppen climate group FE (tropical, dry, temperate, continental, polar) (interacted with year FE). Results hold if we use the third-level Köppen climate group classification instead (31 FE x year FE). In col. (5), we interact historical rainfall (mean precipitation 1960-1990) with year FE. In col. (6), we include six surface geology category FE (Basement, Igneous, Sedimentary, Mobile Belt, Water, Other) x year FE. Results hold if we use more detailed geology categories for the geology group FE (35 FE x year FE). In col. (7), we include log Euclidean distance to major and minor rivers/lakes x year FE. In col. (8), we simultaneously control for historical rainfall, the geology category variables, and distance to rivers/lakes, all interacted with year FE, in order to rely on their interactions within the model of Bonsor and MacDonald (2011). In col. (9), we take into account geographical coordinates*year FE (we include longitude, longitude squared, latitude, and latitude squared x year FE in order to flexibly account for the role of geography). SE clustered at cell level in parentheses. * Significant at 10%, ** 5%, *** 1%.
for historical rainfall, the geology category variables, and distance to rivers/lakes, all interacted
with year fixed effects, in order to only rely on their “interactions” within the model of Bonsor and
MacDonald (2011); and (vii) control for latitude and longitude (and their squares) interacted with
year dummies (col. 9), in order to flexibly control for geography.

Finally, results are also robust to (Appendix Table A.8): (i) using 0-25 m instead of 0-50 m to define shallow water areas (the Shallow Water*Temperature effect is about one third stronger whereas precision significantly improves for the Frac*Temperature effect); (ii) controlling for initial (c. 1995) log population density, soil fertility or malaria prevalence, interacted with the year dummies; (iii) excluding Nigeria, Chad, Cameroon and Niger, countries where Boko Haram has expanded its area of influence over time; and (iv) allowing the error term to be spatially correlated (Conley, 1999). Standard errors are reported for both 250 km and 500 km.

5 Investigating the Mechanisms

The previous results were already informative regarding the underlying mechanisms, as the effects
were stronger for low-intensity conflicts and local actors. This section goes further by analyzing
whether specific mechanisms driving these results can be identified. Referring to the conceptual
framework in the introduction, with climate instability, the *income effect* (or opportunity cost of
conflict) should reduce conflict in groundwater-rich areas, since access to water becomes more
economically valuable. At the same time, the *prize effect* should increase conflict incidence in
groundwater-rich areas, as water becomes scarcer and more valuable to monetize or capture by
force. The results suggest that the prize effect likely dominates the income effect.

Note that while we suggest two potential mechanisms, this does not rule out the importance of
other factors. First, the effects could vary depending on group or local characteristics. In the African
context, groundwater is rarely used for irrigation. Only 6% of the total cultivated area is irrigated
in Africa compared to 37% in Asia (You et al., 2011). Instead, groundwater is most commonly used
to increase the drinking water supply, whether for humans or livestock. In fact, "75 percent of the
African population uses groundwater as its main source of drinking water" (UN Water/Africa,
2012). For their livestock, transhumant pastoralists disproportionately rely on community- or
government-owned wells along migration routes (UN Water/Africa, 2012; UNOWAS, 2018). As
a result, a prediction is that the effects should be stronger in areas specializing in pastoral activities,
where the prize effect is amplified due to the value of water for livestock herders and their animals.
Additionally, the concentration of conflict in groundwater-rich areas assumes that actors involved
in conflict can easily travel to such areas from other regions. Livestock herders are far more mobile
than farmers, as they can move with their "capital". Furthermore, livestock herder communities
tend to be more militarized, as they are organized and armed to defend against livestock theft.
Thus, one might expect stronger conflict effects, particularly high-intensity conflicts, in regions
focused on pastoral activities. Similarly, more conflict might be expected in areas where livestock
herders travel more frequently during the dry season, which are typically areas where farmers

<footnote>which allows us to mimic the analysis from Bonsor and MacDonald (2011). We obtain similar results if we only use data
on perennial (i.e., major) rivers from World Bank (2017). Bonsor and MacDonald (2011) indeed focus on rivers.</footnote>
grow crops. The movement and presence of herders are often driven by the availability of water, forage, and other environmental conditions. McGuirk and Nunn (2020) show that droughts within the territory of transhumant pastoralists increase conflict in adjacent areas. Below, we thus consider a cell-level measure of transhumant pastoralism, as well as a measure of Euclidean proximity to the “border” between farming and livestock herder communities.

In addition, the likelihood that conflict arises in some regions depends on the state’s ability to control those regions. As Döring (2020) explains, when state presence is weak, communities may be more prone to rely on violence against other groups trying to access their water points or to seize water points from neighboring communities. At the same time, low-intensity violence (such as protests and riots) is more likely where the state is present, as the goal of such violence is to pressure local authorities to address the populations’ needs. To explore this, proximity to national or regional capitals is used to investigate whether local state capacity can mitigate conflict in groundwater-rich areas. Additionally, a broader state capacity index, developed by Agneman et al. (2022), is used to provide a more comprehensive measure of state capacity in the analysis.

## 5.1 Empirical strategy

We assess the importance of these two mechanisms when studying the correlational relationship between shallow groundwater and conflict over time, as well as in the context of estimating the impact of local (temporary and permanent) climate shocks and conflict.

**Shallow groundwater and conflict over time (descriptive relationship).** The following equation is estimated for 10,310 cells c, 49 countries i, and 25 years t:

$$ Y_{cit} = \sum_{t=1998}^{2022} \beta_t \text{Shallow Water}_c \times \text{Mechanism}_c \times \mathbb{I}_t + \sum_{t=1998}^{2022} \alpha_t \text{Shallow Water}_c \times \mathbb{I}_t + \sum_{t=1998}^{2022} \gamma_t \text{Mechanism}_c \times \mathbb{I}_t + \theta_c + \theta_{it} + \varepsilon_{cit}. \quad (4) $$

$Y_{cit}$ represents the incidence of violence. The variable *Shallow Water*<sub>c</sub> denotes the share of 0-50 m deep groundwater. *Mechanism*<sub>c</sub> alternatively denotes: (i) a measure of transhumant pastoralism in cell c; (ii) a measure of the combined presence of herders and farmers in the cell (i.e., of the “border” between the two communities); (iii) measures of the proximity to the national capital or the closest regional capital today, which are computed as the negative of the logged Euclidean distance from the cell’s centroid to the selected capital; and (iv) the index of local state capacity. The year dummies $\mathbb{I}_t$ are interacted with *Shallow Water*<sub>c</sub> and *Mechanism*<sub>c</sub>, thus allowing these variables to have a direct, time-varying effect on conflict. The terms $\theta_c$ and $\theta_{it}$ capture cell fixed effects and country × year fixed effects, respectively. Eq. (4) is estimated using a linear probability model. Standard errors are clustered at the cell level. Lastly, the $\beta_t$ coefficients capture the triple interaction effects of groundwater availability, the selected mechanism, and the post-1997 years.
**Local climate shocks, groundwater, and conflict.** We estimate the following equation:

$$Y_{c,i,t} = \beta_1 \text{Shallow Water}_c \times \text{Mechanism}_c \times \text{Temperature}_{cit}^{d=k} \\ + \beta_2 \text{Shallow Water}_c \times \text{Temperature}_{cit}^{d=k} + \beta_3 \text{Mechanism}_c \times \text{Temperature}_{cit}^{d=k} \\ + \beta_4 \text{Temperature}_{cit}^{d=k} + \theta_c + \theta_{i,t} + \varepsilon_{c,i,t}. \quad (5)$$

$Y_{c,i,t}$ is the incidence of violence and Shallow Water$_c$ is the share of 0-50 m deep groundwater. Mechanism$_c$ is the selected mechanism variable. Temperature$_{cit}^{d=k}$ is the moving average of temperature computed as the average over the years $t-d$ to $t$. Cell ($\theta_c$) and country × time ($\theta_{i,t}$) fixed effects are included. $\beta_1$ is the coefficient of interest for the triple interaction between climate shocks, groundwater access, and the potential mechanism variable.

## 5.2 Results on the Mechanisms

**Pastoralism.** Conflicts between farmers and herders have escalated globally, particularly in the Sahel region, as climatic stress intensifies competition over dwindling resources. Anecdotal evidence suggests that economic threats caused by drought compel nomadic groups to encroach on traditional territories, sparking violent clashes with sedentary farming communities and underscoring the significant role of climate-related factors in fostering conflict (Zakari et al., 2017; McGuirk and Nunn, 2020; Eberle et al., 2020). Additionally, pastoralist groups fight for the control of waterpoints along the livestock migration routes (Marty et al., 2010; Zakari et al., 2017; Krätli and Toulmin, 2020b). Local non-pastoralist communities then fight back when they own and maintain wells, but pastoralists use force and do not pay for the water they take (Thebaud, 2017; Krätli and Toulmin, 2020b). If the wells are owned by local or regional authorities instead, they are blamed for not maintaining the peace (Detges, 2016). Worse, they are often accused of siding with specific ethnic or religious groups or pastoralist factions, which heightens local tensions (Döring, 2020).

One can thus use the measures of transhumant pastoralism from McGuirk and Nunn (2020) and of the combined presence of herders and farmers in the cell from Eberle et al. (2020) (capturing the “border”). Figure 7a below displays the combinations of $\hat{\beta}_1$ estimated from eq. (4) and Figure 7b below displays the $\beta_1$ estimated from eq. (5). The conflict-groundwater relationship is accentuated in pastoral cells. This is partly due to an intensification of low-intensity conflicts. However, high-intensity conflicts are also more likely in such areas, possibly due to their higher degree of militarization to protect against risks of theft. Pastoral groups are indeed larger and less local than farming groups. Remarkably, similar effects are obtained for the measure of proximity to the farmers-herders border (see Figures 7c and 7d below). Conflict increases across all dimensions.

From a policy perspective, this suggests that climate shocks lead to more conflict in groundwater-rich areas, particularly if they are located in pastoral regions. Since pastoral regions tend to be more remote, less densely populated, and pastoral groups tend to be more marginalized in national political processes, there is a risk that these regions become fertile ground for separatist or terrorist groups. This is already evident in the Sahel’s “conflict belt” which includes Mali, Burkina-Faso, Niger, Northern Nigeria, Chad, South Sudan, and Sudan (OECD, 2022).
<figure><img src="image_9.png" /><figcaption>(c) Farmer-Herders Border, Avg Effect, Full Period</figcaption></figure>

<caption>(d) Farmer-Herders Border, Shallow Water*Temp MA</caption>

*Notes*: Left figure: Results from selected combinations of the effects from triple interactions between a measure of transhumant pastoralism (subfigures (a)-(b)) or a measure of farmer-herder presence (subfigures (c)-(d)), the share of shallow water, and the year dummies. Right figure: Results from the effects from triple interactions between a measure of transhumant pastoralism (subfigures (a)-(b)) or a measure of farmer-herder presence (subfigures (c)-(d)), the share of shallow water, and the moving average of temperature (i.e., the measure of local climate shocks; d=4 is selected). Dependent variable: *All conflict* = z-score of a dummy equal to 1 if at least one conflict event is observed in the cell during the year. *Low-intensity* = z-score of a dummy equal to 1 if at least one protest and/or riot is observed in the cell during the year. *High intensity* = z-score of a dummy equal to 1 if at least one battle, violence against civilians and/or remote violence is observed in the cell during the year. 90% confidence intervals are reported.

**State Capacity.** Proximity to a regional or national capital (c. 2010) is used in this analysis. Being located closer to a capital could indicate a stronger military and police presence and better governance, reducing conflict. However, military and police presence could also increase conflict, as fighting groups may be more motivated to control state resources, leading to more conflict in areas with greater state presence. The findings suggest that proximity to a regional capital neither exacerbates nor reduces the link between climate shocks and conflict (see Figures 8a and 8b below). This is not surprising as regional governments tend to be particularly “weak” in the African
context (Fearon and Laitin, 2003; Müller-Crepon et al., 2021). Power is often highly concentrated in the largest or capital city in most countries (Bates, 2014). However, local climate shocks in groundwater-rich areas located closer to regional capitals appears to disproportionately increase the likelihood of high-intensity conflicts or conflicts involving non-local actors (effects nearly significant at 10%; see Figure 8b). It is possible that local climate shocks foster more organized regionalist movements aimed at capturing local resources, including water-related resources.

<figure><img src="image_10.png" /><figcaption>Figure 8: Mechanisms - Climate shocks, Groundwater, and State Presence</figcaption></figure>

*Notes*: Left figure: Results from selected combinations of the effects from triple interactions between proximity to a regional capital (subfig. (a)-(b)) or proximity to a national capital (subfig. (c)-(d)), the share of shallow water, and the year dummies. Right figure: Results from the effects from triple interactions between proximity to a regional capital (subfig. (a)-(b)) or proximity to a national capital (subfig. (c)-(d)), the share of shallow water, and the moving average of temperature (d=4 is selected). Dependent variable: *All conflict* = z-score of a dummy equal to 1 if at least one conflict event is observed in the cell during the year. *Low-intensity* = z-score of a dummy equal to 1 if at least one protest and/or riot is observed in the cell during the year. *High intensity* = z-score of a dummy equal to 1 if at least one battle, violence against civilians and/or remote violence is observed in the cell during the year. 90% confidence intervals are reported.

Interestingly, proximity to a national capital has more ambiguous effects (see Figures 8c and 8d above). Overall, conflict incidence appears unchanged, but the nature of conflict shifts. Closer
to the capital city, there tends to be more low-intensity conflicts and fewer high-intensity conflicts. This may be because central areas dominate the national political process, making protests and riots more effective in swaying the state to allocate resources. Additionally, the state is stronger in more central areas, making it possibly costlier to engage in high-intensity conflicts. This contrasts with what is observed for proximity to regional capitals, where a weaker state and limited military and police resources likely embolden regionalist actors to organize more effectively. Alternatively, one can make use of a broader measure of local state capacity from Agneman et al. (2022). In this context, local state capacity has similar effects to the effects of proximity to the national capital (Appendix Section 4 and Appendix Figures A.4a and A.4b). Indeed, many regional capitals are “weak” whereas local state capacity is more likely to truly capture local state presence.

## 5.3 Past and Future Conflict Hotspots

The results highlight the contribution of climate shocks to the diffusion of conflict in Africa, particularly in “hot” cells with more shallow water. Two related exercises are now considered.

First of all, temperatures have increased by an average of 0.7°C during the period of interest (1997-2021) compared to the period 1961-1990 (Fig. 5). Regarding the relationship between groundwater, temperature, and conflict, what has been the contribution of rising temperature trends to observed conflicts in Africa? One way to answer the question is to use the panel estimates from the local climate shocks analysis to predict the change in conflict probability that was due to the conflict-groundwater-temperature relationship. Col. 7 of Panel B in Table 3 showed the effects of more permanent temperature (d=7) increases on conflict in groundwater-accessible areas. By knowing the shallow water share, Frac., and the absolute temperature change for each cell over the long run, one can estimate the increase in conflict likelihood due to these effects. The left panel of Figure 9 displays for each cell the predicted decrease in the conflict probability that would have been observed in 2021 had the temperature been the same as the median temperature in 1961-1990. The effect is heterogeneous across cells. In cells without shallow water, the absence of a positive trend in temperatures would have led to a 0.5% lower probability of observing conflict. However, in cells where the proportion of shallow water exceeds 50%, the likelihood of conflict would have been reduced by 7%. Interestingly, in cells without water fractionalization, the probability of observing conflict would have been 1.6% lower. Conversely, for cells in the highest percentile of fractionalization, the likelihood of observing conflict would have been reduced by 11%.

The right panel of Figure 9 shows the predicted local increases, hence future conflict hotspots due to the interplay between climate shocks and groundwater. In this case, the temperature data used is from the World Bank's Climate Change Portal Knowledge (CCKP) portal (resolution ~1 km*1 km).<fnref n="20" /> The temperature increases considered are for the CCKP period 2030-2050 relative to the baseline CCKP period 1995-2014.<fnref n="21" /> Thus, the estimated temperature increases are for an average period of ~35 years. Temperatures are now predicted to increase by 1.33 °C on average,

<footnote n="20">²⁰Url: https://climateknowledgeportal.worldbank.org/. Last accessed 05-22-2024.</footnote>

<footnote n="21">²¹The median value for annual mean surface air temperature derived from CMIP6 climate models is used. The data covers the period from 2030 to 2050 under the Shared Socioeconomic Pathway 5-8.5 scenario.</footnote>
or 1.9 times the average increase for the past period.<fnref n="22" /> Conflict hotspots appear in most of Africa, and in the Sahel and Southern Africa in particular. In East Africa, many specific “red” cells can be observed in the Rift Valley area, implying that local governments and international organizations may have to invest in the securitization and development of these areas to avoid future conflict.

<figure><img src="image_11.png" /><figcaption>Figure 9: Climate Shocks, Groundwater, and Past (Left) & Future (Right) Conflict Hotspots</figcaption></figure>

Notes: The left subfigure shows the predicted decrease in the conflict probability that would have been observed in 2021 had the temperature been the same as the median temperature over the period 1961-1990. As such, the direct conflict effect of temperature and the indirect conflict effects of temperature through groundwater accessibility and fractionalization are all considered when predicting the decrease in the conflict probability. The right subfigure shows the predicted increase in conflict likelihood due to the interplay between future climate shocks (temperature difference between 1995-2014 and 2030-2050) and groundwater (in terms of both accessibility and fractionalization). As such, only the indirect conflict effects of temperature through groundwater accessibility and fractionalization are considered.

# 6 Conclusion

Due to climate shocks, water is becoming an increasingly scarce resource, thus making groundwater access a highly relevant economic and policy issue. The study uses a cell-year panel data set covering the period 1997-2021 for Africa to investigate the relationship between groundwater level, climate shocks, and conflict. To do so, the analysis combines data on groundwater depth, from which a measure of the share of shallow water in each cell and an index of within-cell inequality in access to water are extracted, with geolocalized data on conflict events. Two empirical analyses are used to uncover various aspects of the research question.

Firstly, the analysis explores the correlational impact of global climate shocks, which affected the entire continent during the sampling period, on the relationship between groundwater and

<footnote n="22">²²Alternatively, the same method can be used to identify past conflict hotspots when considering the difference in mean temperatures between the 1997-2022 and 1961-1990 periods (see Appendix Figure A.5).</footnote>
conflict. Secondly, we estimate the link between conflict and groundwater in areas experiencing
local climate shocks (both temporary and permanent). The results display a pronounced effect on
low-intensity events such as protests and riots, as well as a significant role of local actors (protesters,
rioters, and identity militia activists) who operate at a local level for objectives often related to
natural resources. The study also highlights the role of inequality in access to groundwater in
driving water-related conflict. Lastly, analyses examining the contributions of pastoralist activities
and state capacity to the observed effects provide insights into the underlying mechanisms.

This study helps to broaden our understanding of the relationship between water access and conflict, and contributes to knowledge of the mechanisms by which climate change may economically impact Africa in the future. The focal point of discussions should revolve around policies that encourage the sustainable use of groundwater, in order to prevent frictions between and within communities. The study also highlights the importance of improving groundwater access, but only in a way that inter-communal inequality is not reinforced.

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Online Appendix

Mathieu Couttenier*

Remi Jedwab†

Lucile Laugerette‡

December 17, 2024

# 1 Spatial Units and Summary Statistics

<figure><img src="image_12.png" /><figcaption>Figure A.1: Countries, Region and Cells Used in the Analysis</figcaption></figure>

Notes: This figure shows the 49 countries, 662 first-level administrative (admin 1) regions, and 10,310 cells used in the analysis (source for the first-level administrative units: GADM 4.1 (release 16 July 2022)).

<footnote>*Ecole Normale Superieure de Lyon, Center for Economic Research on Governance, Inequality and Conflict; CEPR</footnote>

<footnote>†George Washington University, Department of Economics.</footnote>

<footnote>‡Ecole Normale Superieure de Lyon, Center for Economic Research on Governance, Inequality and Conflict.</footnote>
<table><caption>Table A.1: Summary Statistics, Cumulative Sample and Panel Sample</caption><thead><tr><th rowspan="2"></th><th colspan="3">Cumulative 1997-2022</th><th colspan="3">Panel 1997-2022</th></tr><tr><th>(1)<br>Obs</th><th>(2)<br>Avg</th><th>(3)<br>SD</th><th>(1)<br>Obs</th><th>(2)<br>Avg</th><th>(3)<br>SD</th></tr></thead><tbody><tr><td colspan="7"><strong>Incidence of violence:</strong></td></tr><tr><td><strong>Conflict</strong></td><td>10,310</td><td>0.45</td><td>0.50</td><td>257,750</td><td>0.10</td><td>0.30</td></tr><tr><td>if share shallow water = 0</td><td>3,032</td><td>0.21</td><td>0.40</td><td>75,800</td><td>0.04</td><td>0.20</td></tr><tr><td>if share shallow water &gt;= 1% &amp; &lt;=95%</td><td>2,130</td><td>0.41</td><td>0.49</td><td>53,250</td><td>0.09</td><td>0.29</td></tr><tr><td>if share shallow water &gt; 95%</td><td>5,148</td><td>0.62</td><td>0.49</td><td>128,700</td><td>0.14</td><td>0.35</td></tr><tr><td><strong>Low intensity</strong></td><td>10,310</td><td>0.29</td><td>0.46</td><td>257,750</td><td>0.05</td><td>0.22</td></tr><tr><td>if share shallow water = 0</td><td>3,032</td><td>0.12</td><td>0.32</td><td>75,800</td><td>0.02</td><td>0.14</td></tr><tr><td>if share shallow water &gt;= 1% &amp; &lt;=95%</td><td>2,130</td><td>0.28</td><td>0.45</td><td>53,250</td><td>0.05</td><td>0.22</td></tr><tr><td>if share shallow water &gt; 95%</td><td>5,148</td><td>0.40</td><td>0.49</td><td>128,700</td><td>0.07</td><td>0.26</td></tr><tr><td><strong>High intensity</strong></td><td>10,310</td><td>0.38</td><td>0.48</td><td>257,750</td><td>0.07</td><td>0.26</td></tr><tr><td>if share shallow water = 0</td><td>3,032</td><td>0.17</td><td>0.37</td><td>75,800</td><td>0.03</td><td>0.17</td></tr><tr><td>if share shallow water &gt;= 1% &amp; &lt;=95%</td><td>2,130</td><td>0.30</td><td>0.46</td><td>53,250</td><td>0.06</td><td>0.24</td></tr><tr><td>if share shallow water &gt; 95%</td><td>5,148</td><td>0.53</td><td>0.50</td><td>128,700</td><td>0.10</td><td>0.30</td></tr><tr><td><strong>Local actors</strong></td><td>10,310</td><td>0.32</td><td>0.47</td><td>257,750</td><td>0.05</td><td>0.23</td></tr><tr><td>if share shallow water = 0</td><td>3,032</td><td>0.14</td><td>0.35</td><td>75,800</td><td>0.02</td><td>0.15</td></tr><tr><td>if share shallow water &gt;= 1% &amp; &lt;=95%</td><td>2,130</td><td>0.31</td><td>0.46</td><td>53,250</td><td>0.05</td><td>0.22</td></tr><tr><td>if share shallow water &gt; 95%</td><td>5,148</td><td>0.43</td><td>0.50</td><td>128,700</td><td>0.07</td><td>0.26</td></tr><tr><td><strong>Non-local actors</strong></td><td>10,310</td><td>0.37</td><td>0.48</td><td>257,750</td><td>0.07</td><td>0.25</td></tr><tr><td>if share shallow water = 0</td><td>3,032</td><td>0.16</td><td>0.37</td><td>75,800</td><td>0.03</td><td>0.16</td></tr><tr><td>if share shallow water &gt;= 1% &amp; &lt;=95%</td><td>2,130</td><td>0.29</td><td>0.46</td><td>53,250</td><td>0.06</td><td>0.24</td></tr><tr><td>if share shallow water &gt; 95%</td><td>5,148</td><td>0.52</td><td>0.50</td><td>128,700</td><td>0.09</td><td>0.29</td></tr><tr><td><strong>Water related</strong></td><td>10,310</td><td>0.09</td><td>0.28</td><td>257,750</td><td>0.01</td><td>0.08</td></tr><tr><td>if share shallow water = 0</td><td>3,032</td><td>0.05</td><td>0.22</td><td>75,800</td><td>0.00</td><td>0.06</td></tr><tr><td>if share shallow water &gt;= 1% &amp; &lt;=95%</td><td>2,130</td><td>0.12</td><td>0.32</td><td>53,250</td><td>0.01</td><td>0.10</td></tr><tr><td>if share shallow water &gt; 95%</td><td>5,148</td><td>0.10</td><td>0.30</td><td>128,700</td><td>0.01</td><td>0.09</td></tr><tr><td colspan="7"><strong>Variables of interest:</strong></td></tr><tr><td><strong>Share of shallow water</strong></td><td>10,310</td><td>0.58</td><td>0.46</td><td>329,920</td><td>0.58</td><td>0.46</td></tr><tr><td><strong>Fractionalization</strong></td><td>10,310</td><td>0.29</td><td>0.21</td><td>329,920</td><td>0.29</td><td>0.21</td></tr></tbody></table>

Notes: Dummy for conflict = 1 if at least one conflict event is observed in the cell. Dummy for low intensity event = 1 if at least one protest or one riot is observed in the cell. Dummy for high intensity events = 1 if at least one battle, one event of violence against civilian, or one remote violence event is observed in the cell. Local actors is a dummy = 1 if protesters, rioters and/or identity militias are at the origin of at least one event observed in the cell during the year. Non-local actors is a dummy = 1 if external forces, political militias, rebel groups and/or state forces are at the origin of at least one event observed in the cell during the year. Dummy for water-related violence = 1 if at least 1 water related event is observed in the cell. The variable "share of shallow water" refers to the share of 0 to 50 meters deep groundwater in the cell. For each type of conflict, we show the summary statistics depending on the share of shallow water present in the cell. Share of shallow water = 0 means that the cell does not contain 0-50 m deep groundwater. Share of shallow water &gt; 95% means that the cell is more than 95% composed of 0-50 m deep groundwater. Fractionalization is the index capturing the degree to which a cell is diverse in terms of groundwater levels, with the maximal value occurring when all possible groundwater levels (0-7, 7-25, 25-50, 50-100, etc.) can be found in a single cell.
## 2 Sensitivity Analysis: Descriptive Analysis, Global Climate Change

<table><caption>Table A.2: Groundwater and Conflict 1997-2022, Water-Related Conflicts</caption><thead><tr><th>Dep. Var. : Dummy for water related conf.</th><th>(1)<br>Full period</th><th>(2)<br>Diff post/pre 2010</th><th>(3)<br>Full period</th><th>(4)<br>Diff post/pre 2010</th></tr></thead><tbody><tr><td>Average effect:</td><td></td><td></td><td></td><td></td></tr><tr><td>Shallow water</td><td>0.01***<br>(0.00)</td><td>0.02***<br>(0.00)</td><td>0.01***<br>(0.00)</td><td>0.02***<br>(0.00)</td></tr><tr><td>Frac.</td><td></td><td></td><td>0.01***<br>(0.00)</td><td>0.02***<br>(0.00)</td></tr><tr><td>Obs.</td><td>257750</td><td>257750</td><td>257750</td><td>257750</td></tr><tr><td>Cell FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country × year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes: In all columns, the dependent variable is a dummy if at least one water-related conflict event is observed in the cell. Shallow water refers to the share of 0-50 m deep groundwater. Column (1) and (2) show the combination of the estimated $\hat{\beta}_t$ for the full period, while column (3) and (4) display the difference of the point estimates for the years before 2010 and the years after 2010. Fractionalization is an index of within-cell inequality in access to groundwater. SE clustered at cell level in parentheses. * Significant at 10%, ** 5%, *** 1%.

<table><caption>Table A.3: Groundwater and Conflict, with Admin 1-Year Fixed Effects</caption><thead><tr><th>Dep. Var. : Dummy for conflict</th><th>(1)<br>Full period</th><th>(2)<br>Diff post/pre 2010</th><th>(3)<br>Full period</th><th>(4)<br>Diff post/pre 2010</th></tr></thead><tbody><tr><td>Average effect:</td><td></td><td></td><td></td><td></td></tr><tr><td>Shallow water</td><td>0.04***<br>(0.01)</td><td>0.04***<br>(0.01)</td><td>0.03***<br>(0.01)</td><td>0.04***<br>(0.01)</td></tr><tr><td>Frac.</td><td></td><td></td><td>0.02<br>(0.01)</td><td>0.05***<br>(0.01)</td></tr><tr><td>Obs.</td><td>255250</td><td>255250</td><td>255250</td><td>255250</td></tr><tr><td>Cell FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Admin 1 (662) × Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes: In all columns, the dependent variable is a dummy equal to one if at least one conflict event is observed in the cell during the year. Shallow water refers to the share of 0-50 m deep groundwater. Column (1) and (2) show the combination of the estimated $\hat{\beta}_t$ for the full period, while column (3) and (4) display the difference of the point estimates for the years before 2010 and the years after 2010. Fractionalization is an index of within-cell inequality in access to groundwater. Admin 1-year fixed effects are included. The number of observations is lower than before because the Stata command reghdfe drops singleton observations. Standard errors clustered at cell level in parentheses. * Significant at 10%, ** 5%, *** 1%.
<table><caption>Table A.4: Groundwater and Conflict, with Admin 2-Year Fixed Effects</caption><caption>Dep. Var.: Dummy for conflict</caption><thead><tr><th></th><th>(1)<br>Full period</th><th>(2)<br>Diff post/pre 2010</th><th>(3)<br>Full period</th><th>(4)<br>Diff post/pre 2010</th></tr></thead><tbody><tr><td>Average effect:</td><td></td><td></td><td></td><td></td></tr><tr><td>Shallow water</td><td>0.02**<br>(0.01)</td><td>0.03***<br>(0.01)</td><td>0.02**<br>(0.01)</td><td>0.02***<br>(0.01)</td></tr><tr><td>Frac.</td><td></td><td></td><td>0.02*<br>(0.01)</td><td>0.05***<br>(0.01)</td></tr><tr><td>Obs.</td><td>214350</td><td>214350</td><td>214350</td><td>214350</td></tr><tr><td>Cell FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Admin 2 (2,466) × Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes: In all columns, the dependent variable is a dummy equal to one if at least one conflict event is observed in the cell during the year. Shallow water refers to the share of 0-50 m deep groundwater. Column (1) and (2) show the combination of the estimated $\hat{\beta}_t$ for the full period, while column (3) and (4) display the difference of the point estimates for the years before 2010 and the years after 2010. Fractionalization is an index of within cell inequality in access to groundwater. Admin 2-year fixed effects are included. The number of observations is lower than before because the Stata command reghdfe drops singleton observations. Standard errors clustered at cell level in parentheses. * Significant at 10%, ** 5%, *** 1%.

<table><caption>Table A.5: Groundwater and Conflict, Controlling for Historical Rainfall Patterns</caption><caption>Dep. Var.: Dummy for conflict</caption><thead><tr><th></th><th>(1)<br>Full period</th><th>(2)<br>Diff post/pre 2010</th><th>(3)<br>Full period</th><th>(4)<br>Diff post/pre 2010</th></tr></thead><tbody><tr><td>Average effect:</td><td></td><td></td><td></td><td></td></tr><tr><td>Shallow water</td><td>0.04***<br>(0.01)</td><td>0.06***<br>(0.01)</td><td>0.04***<br>(0.01)</td><td>0.05***<br>(0.01)</td></tr><tr><td>Frac.</td><td></td><td></td><td>0.02<br>(0.01)</td><td>0.07***<br>(0.01)</td></tr><tr><td>Obs.</td><td>257325</td><td>257325</td><td>257325</td><td>257325</td></tr><tr><td>Cell FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country × Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Historical rainfall (1960-1990) × Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes: In all columns, the dependent variable is a dummy equal to one if at least one conflict event is observed in the cell during the year. Shallow water refers to the share of 0-50 m deep groundwater. Column (1) and (2) show the combination of the estimated $\hat{\beta}_t$ for the full period, while column (3) and (4) display the difference of the point estimates for the years before 2010 and the years after 2010. Fractionalization is an index of within cell inequality in access to groundwater. Standard errors clustered at cell level in parentheses.
<table><caption>Table A.6: Average Effect - Other Robustness Checks</caption><thead><tr><th>Dep. Var.: Dummy for conflict</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td colspan="11"><strong>Panel A</strong></td></tr><tr><td>Shallow water:</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Average effect</td><td>0.019***<br>(0.006)</td><td>0.048***<br>(0.007)</td><td>0.051***<br>(0.007)</td><td>0.049***<br>(0.008)</td><td>0.050***<br>(0.008)</td><td>0.047***<br>(0.007)</td><td>0.037***<br>(0.009)</td><td>0.047***<br>(0.008)</td><td>0.054***<br>(0.007)</td><td>0.048***<br>(0.012)</td></tr><tr><td>Mean</td><td>0.103</td><td>0.103</td><td>0.103</td><td>0.114</td><td>0.103</td><td>0.103</td><td>0.103</td><td>0.114</td><td>0.103</td><td>0.103</td></tr><tr><td>Obs.</td><td>256750</td><td>257075</td><td>257750</td><td>200650</td><td>257675</td><td>257750</td><td>257250</td><td>200725</td><td>225175</td><td>257750</td></tr><tr><td colspan="11"><strong>Panel B</strong></td></tr><tr><td>Average effect:</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Shallow water</td><td>0.020***<br>(0.006)</td><td>0.045***<br>(0.007)</td><td>0.046***<br>(0.007)</td><td>0.050***<br>(0.008)</td><td>0.047***<br>(0.008)</td><td>0.044***<br>(0.007)</td><td>0.047***<br>(0.008)</td><td>0.052***<br>(0.007)</td><td>0.034***<br>(0.009)</td><td>0.045***<br>(0.012)</td></tr><tr><td>Frac.</td><td>-0.007<br>(0.011)</td><td>0.013<br>(0.011)</td><td>0.014<br>(0.011)</td><td>-0.002<br>(0.013)</td><td>0.015<br>(0.011)</td><td>0.012<br>(0.011)</td><td>-0.002<br>(0.013)</td><td>0.009<br>(0.012)</td><td>0.014<br>(0.011)</td><td>0.014<br>(0.013)</td></tr><tr><td>Mean</td><td>0.103</td><td>0.103</td><td>0.103</td><td>0.114</td><td>0.103</td><td>0.103</td><td>0.114</td><td>0.103</td><td>0.103</td><td>0.103</td></tr><tr><td>Obs.</td><td>256750</td><td>257075</td><td>257750</td><td>200650</td><td>257675</td><td>257750</td><td>200725</td><td>225175</td><td>257250</td><td>257750</td></tr><tr><td>Country x 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><td>Yes</td><td>Yes</td></tr><tr><td>Log pop. density (1995) x Year FE</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Köppen climate group FE (5) x Year FE</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Lat/Long & Lat/Lon sq.) x Year FE</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Soil fertility x Year FE</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Surface geology group FE (6) x Year FE</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Log distance to river/lake x Year FE</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>Yes</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Historical rainfall (1960-1990) x Year FE</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Malaria prevalence</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td></tr><tr><td>Dropping NGA, TCD, NER, CMR</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td></tr><tr><td>Conley SE (500 km)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td></tr></tbody></table>

Notes: In all columns, the dependent variable is a dummy taking the value 1 if at least a conflict event is observed in the cell during the year, 0 otherwise. Shallow water refers to the share of 0 to 50 meters deep groundwater. Fractionalization is an index of within cell inequality in access to groundwater, the more diverse the cell is in terms of groundwater levels, the higher the index of fractionalization is. In col. (1), we control for the logarithm of population density at cell level in 1995 (interacted with year fixed effects). In col. (2), we account for 5 Köppen climate group fixed effects (tropical, dry, temperate, continental, polar) (interacted with year fixed effects). Results hold if we use the third-level Köppen climate group classification instead (31 fixed effects x year fixed effects). In column (3), we take into account geographical coordinates*year fixed effects (we include longitude, longitude squared, latitude, and latitude squared x year fixed effects in order to flexibly account for the role of geography). In col. (4), we control for soil fertility x year fixed effects. In col. (5), we include six surface geology category fixed effects (Basement, Igneous, Sedimentary, Mobile Belt, Water, Other) x year fixed effects. In col. (6), we include log Euclidean distance to major and minor rivers/lakes x year fixed effects. In col. (7), we simultaneously control for historical rainfall, the geology category variables, and distance to rivers/lakes, all interacted with year FE, in order to rely on their interactions within the model of ? . In col. (8) we control for malaria prevalence in the cell x year fixed effects. In col. (9), the regression is ran dropping Nigeria, Chad, Niger and Cameroon to account for countries that are prone to conflict, specifically those affected by Boko Haram. In col. (10), we apply a spatial HAC correction to our standard errors, allowing for both cross-sectional spatial correlation and location-specific serial correlation, following the method developed by Conley. In other columns, standard errors are clustered at the cell level (shown in parentheses). * Significant at 10%, ** significant at 5%, *** significant at 1%.
<figure><img src="image_13.png" /><figcaption>Figure A.2: Effects of Groundwater Availability, By Water Depth</figcaption></figure>

Notes: Results for the cell-level area shares of 0-7 m, 7-25 m, 25-50 m, 50-100 m and 100-250 m deep groundwater when all these shares are simultaneously included (the reference category is 250+ m). Left panel: Average effects for the full period. Right panel: Differentiating the point estimates for years before 2010 and after 2010. The dependent variable is a dummy if at least one conflict event is observed in the cell during the year. 90% confidence intervals are reported.

<table><caption>Table A.7: Groundwater and Conflict, Using 0-25 Meters to Define the Water Share Measure (Instead of 0-50 Meters)</caption><caption>Dep. Var.: Dummy for conflict</caption><thead><tr><th></th><th>(1)<br>Full period</th><th>(2)<br>Diff post/pre 2010</th><th>(3)<br>Full period</th><th>(4)<br>Diff post/pre 2010</th></tr></thead><tbody><tr><td>Average effect:</td><td></td><td></td><td></td><td></td></tr><tr><td>Shallow water (0-25m)</td><td>0.05***<br>(0.01)</td><td>0.13***<br>(0.01)</td><td>0.04***<br>(0.01)</td><td>0.11***<br>(0.01)</td></tr><tr><td>Frac.</td><td></td><td></td><td>0.03**<br>(0.01)</td><td>0.07***<br>(0.01)</td></tr><tr><td>Obs.</td><td>257750</td><td>257750</td><td>257750</td><td>257750</td></tr><tr><td>Cell FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country × Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes: Notes: In all columns, the dependent variable is a dummy equal to one if at least one conflict event is observed in the cell during the year. Shallow water refers to the share of 0-25 m deep groundwater (instead of using 0-50 m). Column (1) and (2) show the combination of the estimated $\hat{\beta}_t$ for the full period, while column (3) and (4) display the difference of the point estimates for the years before 2010 and the years after 2010. Fractionalization is an index of within cell inequality in access to groundwater. Standard errors clustered at cell level in parentheses. * Significant at 10%, ** 5%, *** 1%.
3 Sensitivity Analysis: Main Analysis, Local Climate Change

<figure><img src="image_14.png" /><figcaption>Figure A.3: Local Climate Shocks, Shallow Water, and Types of Conflict and Actors</figcaption></figure>

Notes: Results from $\beta_2$ (ShallowWater x Temperature) considering climate shocks with $d=7$. The dependent variables are standardized. *Low-intensity* = z-score of a dummy equal to 1 if at least one protest or riot is observed in the cell during the year. *High intensity* = z-score of a dummy equal to 1 if at least one battle, violence against civilians, or remote violence event is observed in the cell during the year. *Local actors* = z-score of a dummy equal to 1 if protesters, rioters, or identity militias are at the origin of at least one event observed in the cell during the year. *Non-local actors* = z-score of a dummy equal to 1 if external forces, political militias, rebel groups or state forces are at the origin of at least one event observed in the cell during the year. *Water conflict* = z-score of a dummy equal to 1 if a water-related conflict took place in the cell during the year. Cell FE and country × year FE are included. 90% confidence intervals are reported.
<table><caption>Table A.8: Local Climate Change, Groundwater, and Conflict - Robustness Checks</caption><thead><tr><th>Dep. Var. : Dummy for conflict<br>d=4</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th></tr></thead><tbody><tr><td colspan="12"><strong>Panel A</strong></td></tr><tr><td>Shallow Water × Temperature<sup>d=k</sup></td><td>0.071<sup>***</sup><br>(0.013)</td><td>0.029<sup>***</sup><br>(0.010)</td><td>0.056<sup>***</sup><br>(0.011)</td><td>0.063<sup>***</sup><br>(0.011)</td><td>0.083<sup>***</sup><br>(0.012)</td><td>0.052<sup>***</sup><br>(0.011)</td><td>0.058<sup>***</sup><br>(0.011)</td><td>0.095<sup>***</sup><br>(0.012)</td><td>0.067<sup>***</sup><br>(0.012)</td><td>0.055<sup>***</sup><br>(0.013)</td><td>0.055<sup>***</sup><br>(0.015)</td></tr><tr><td>Temperature<sup>d=k</sup></td><td>0.020<sup>**</sup><br>(0.009)</td><td>-0.041<sup>***</sup><br>(0.010)</td><td>0.016<br>(0.010)</td><td>0.009<br>(0.010)</td><td>-0.057<sup>***</sup><br>(0.013)</td><td>0.024<sup>**</sup><br>(0.010)</td><td>0.004<br>(0.010)</td><td>-0.080<sup>***</sup><br>(0.012)</td><td>0.002<br>(0.011)</td><td>0.018<br>(0.012)</td><td>0.018<br>(0.014)</td></tr><tr><td>Obs.</td><td>257750</td><td>256750</td><td>257075</td><td>257750</td><td>200650</td><td>257675</td><td>257750</td><td>200725</td><td>225175</td><td>257750</td><td>257750</td></tr><tr><td colspan="12"><strong>Panel B</strong></td></tr><tr><td>Shallow Water × Temperature<sup>d=k</sup></td><td>0.065<sup>***</sup><br>(0.014)</td><td>0.034<sup>***</sup><br>(0.011)</td><td>0.048<sup>***</sup><br>(0.012)</td><td>0.055<sup>***</sup><br>(0.012)</td><td>0.095<sup>***</sup><br>(0.013)</td><td>0.044<sup>***</sup><br>(0.012)</td><td>0.054<sup>***</sup><br>(0.012)</td><td>0.107<sup>***</sup><br>(0.013)</td><td>0.059<sup>***</sup><br>(0.013)</td><td>0.047<sup>***</sup><br>(0.014)</td><td>0.047<sup>***</sup><br>(0.016)</td></tr><tr><td>Frac. × Temperature<sup>d=k</sup></td><td>0.035<sup>*</sup><br>(0.021)</td><td>-0.023<br>(0.020)</td><td>0.035<br>(0.022)</td><td>0.037<sup>*</sup><br>(0.022)</td><td>-0.064<sup>***</sup><br>(0.023)</td><td>0.035<br>(0.022)</td><td>0.020<br>(0.021)</td><td>-0.058<sup>**</sup><br>(0.023)</td><td>0.035<br>(0.024)</td><td>0.035<br>(0.024)</td><td>0.035<br>(0.025)</td></tr><tr><td>Temperature<sup>d=k</sup></td><td>0.012<br>(0.010)</td><td>-0.038<sup>***</sup><br>(0.010)</td><td>0.011<br>(0.010)</td><td>0.003<br>(0.011)</td><td>-0.045<sup>***</sup><br>(0.013)</td><td>0.018<sup>*</sup><br>(0.010)</td><td>0.001<br>(0.010)</td><td>-0.070<sup>***</sup><br>(0.013)</td><td>-0.004<br>(0.011)</td><td>0.012<br>(0.012)</td><td>0.012<br>(0.014)</td></tr><tr><td>Obs.</td><td>257750</td><td>256750</td><td>257075</td><td>257750</td><td>200650</td><td>257675</td><td>257750</td><td>200725</td><td>225175</td><td>257750</td><td>257750</td></tr><tr><td>Country (49) x Year (25) 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><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Shallow water (0-25m)</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Log pop. density (1995) x Year FE</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Köppen climate group FE x Year FE</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(Lat/Long & Lat/Lon sq.) x Year FE</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Soil fertility x Year FE</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Surface geology (6) FE x Year FE</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Distance to rivers/lakes x Year FE</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Malaria prevalence x Year FE</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Dropping NGA, TCD, NER, CMR</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td><td>-</td></tr><tr><td>Conley SE (250 km)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td><td>-</td></tr><tr><td>Conley SE (500 km)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Yes</td></tr></tbody></table>

Notes: In all columns, the dependent variable is a dummy taking the value 1 if at least a conflict event is observed in the cell during the year, 0 otherwise. Shallow water refers to the share of 0 to 50 meters deep groundwater. Fractionalization is an index of within cell inequality in access to groundwater, the more diverse the cell is in terms of groundwater levels, the higher the index of fractionalization is. In col. (1), we use the share of 0 to 25 meters deep groundwater instead. In col. (2), we control for the logarithm of population density at cell level in 1995 (interacted with year fixed effects). In col. (3), we account for 5 Köppen climate group fixed effects (tropical, dry, temperate, continental, polar) (interacted with year fixed effects). Results hold if we use the third-level Köppen climate group classification instead (31 fixed effects x year fixed effects). In column (4), we take into account geographical coordinates*year fixed effects (we include longitude, longitude squared, latitude, and latitude squared x year fixed effects in order to flexibly account for the role of geography). In col. (5), we control for soil fertility x year fixed effects. In col. (6), we include six surface geology category fixed effects (Basement, Igneous, Sedimentary, Mobile Belt, Water, Other) x year fixed effects. In col. (7), we include log Euclidean distance to major and minor rivers/lakes x year fixed effects. In col. (8), we control for malaria prevalence in the cell x year fixed effects. In col. (9), the regression is ran dropping Nigeria, Chad, Niger and Cameroon to account for countries that are prone to conflict, specifically those affected by Boko Haram. In cols. (10)-(11), we apply a spatial HAC correction to our SE, allowing for both cross-sectional spatial correlation and location-specific serial correlation, following the method developed by Conley. In other columns, SE are clustered at the cell level.
# 4 Additional Analyses: Mechanisms and Conflict Hotspots

<figure><img src="image_15.png" /><figcaption>Figure A.4: Mechanisms - Climate Change, Groundwater, and Local State Capacity</figcaption></figure>

*Notes*: Left figure: Results from selected combinations of the effects from triple interactions between local state capacity, the share of shallow water, and the year dummies. Right figure: Results from the effects from triple interactions between local state capacity, the share of shallow water, and the moving average of temperature (i.e., the measure of local climate change; d=4 is selected). Dependent variable: *All conflict* = z-score of a dummy equal to 1 if at least one conflict event is observed in the cell during the year. *Low-intensity* = z-score of a dummy equal to 1 if at least one protest and/or riot is observed in the cell during the year. *High intensity* = z-score of a dummy equal to 1 if at least one battle, violence against civilians and/or remote violence is observed in the cell during the year. 90% confidence intervals are reported.

<figure><img src="image_16.png" /><figcaption>Figure A.5: Climate Change, Groundwater, and Past (Left) & Future (Right Fig.) Conflict Hotspots</figcaption></figure>

*Notes*: The left subfigure shows the predicted increase in conflict likelihood due to the interplay between past climate change (between 1997-2022 and 1961-1990) and groundwater (in terms of both accessibility and fractionalization). The right subfigure shows the predicted increase in conflict likelihood due to the interplay between future climate change (between 1995-2014 and 2030-2050) and groundwater (in terms of both accessibility and fractionalization).