# Economic Impact of the Socio-Political Crisis in the Northwest and Southwest Regions of Cameroon: A Forward Difference-in-Differences Approach

Jared Greathouse, Vincent Nossek, Pierre Mandon

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Jared Greathouse, Vincent Nossek, Pierre Mandon. Economic Impact of the Socio-Political Crisis in the Northwest and Southwest Regions of Cameroon: A Forward Difference-in-Differences Approach. 2026. hal-05523002

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Centre d'Études
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Développement International

WORKING PAPER

No. 2026/3

Economic Impact of the Socio-Political
Crisis in the Northwest and Southwest
Regions of Cameroon: A Forward
Difference-in-Differences Approach

Jared Greathouse

Vincent Remi Nossek

Pierre Jean-Claude Mandon
## Cite this working paper

Greathouse J., Nossek V. R., Mandon P. J. C. "Economic Impact of the Socio-Political Crisis in the Northwest and Southwest Regions of Cameroon: A Forward Difference-in-Differences Approach", CERDI Working papers, 2026/3, CERDI.

## The authors

### Jared Greathouse

PHD student of public policy,
Georgia State University, Atlanta, GA, USA.
Email: jgreathouse3@student.gsu.edu

### Vincent Remi Nossek

PhD candidate in Economics,
Université Clermont Auvergne, CNRS, IRD,
CERDI, F-63000 Clermont-Ferrand, France.
Consultant and data scientist,
World Bank Group, Washington, D.C., USA.
Email: vnossek@worldbank.org

### Pierre Jean-Claude Mandon

Economist,
World Bank Group, Washington, D.C., USA.
Research fellow,
Université Clermont Auvergne, CNRS, IRD,
CERDI, F-63000 Clermont-Ferrand, France.
Email: pmandon@worldbank.org
(Corresponding author)

## Acknowledgments

We thank our colleagues from the Yaounde's regional office at the World Bank, especially Francis Ghislain Ngomba Bodi, Elisabeth Rose Keme Etame and Guillemette Jaffrin for their valuable feedback and comments.

Replication files can be found here: [URL](#)

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

Ce travail a bénéficié d'une aide de l'Etat français gérée par l'Agence Nationale de la Recherche au titre du programme "France 2030" portant la référence "ANR-16-IDEX-0001".

This work was supported by the Agence Nationale de la Recherche of the French government through the program "France 2030" "ANR-16-IDEX-0001".

CERDI Working Papers are available online at: [https://tinyurl.com/2xwfzw8s](https://tinyurl.com/2xwfzw8s)

Director of Publication: Simone Bertoli

Editor: Catherine Araujo Bonjean

Publisher: Marie Dussol

ISSN: 2114 - 7957

## Disclaimer

Working papers are not subject to peer review; they constitute research in progress. Responsibility for the contents and opinions expressed in the working papers rests solely with the authors. Comments and suggestions are welcome and should be addressed to the authors.
**Abstract**

The economic consequences of violent conflict pose significant challenges for
development policy, yet measuring these impacts remains methodologically
difficult in data-scarce environments. This paper employs the novel Forward
Difference-in-Differences estimator with satellite-based nighttime light
intensity data to analyze the economic impact of the socio-political crisis in
Cameroon's Northwest and Southwest regions and associated recovery
dynamics. Using quarterly data from 2013-2024 across 105 subnational regions,
we find that the crisis reduced mean nighttime light intensity by 25 percent
and total output by 28 percent in the Northwest region over seven years.
Conversely, we observe that economic activity in the Southwest region
increased by approximately 32 percent over five years, a divergence potentially
attributable to multiple factors including greater connectivity, economic
diversification, a more favorable business environment, and political economy
considerations. Our methodology addresses key limitations of traditional
difference-in-differences and synthetic control methods when applied to
heterogeneous donor pools by selecting optimal subsets of control units
whose pretreatment trajectories most closely match treated units. The results
demonstrate that modern causal inference methods combined with satellite
remote sensing data provide credible estimates of conflict's economic costs
and recovery potential in fragile and conflict-affected states where
conventional data sources are unreliable.

**Keywords**

Cameroon; causal inference; conflict; forward difference-in-differences; nighttime lights; recovery; satellite data.

**JEL codes**

C21, C23, D74, O12, O55.
# 1 Introduction

The economic consequences of violent conflict represent one of the most pressing challenges facing developing nations, with global costs estimated at a 14 percent reduction in economic output (de Groot et al., 2022). Armed violence systematically undermines economic development through multiple channels: disrupting markets, weakening institutions, displacing labor, destroying infrastructure, and constraining long-term growth trajectories. Recent empirical evidence from Sudan and the Democratic Republic of Congo demonstrates how conflict fragments local economies, disrupts agricultural production, and severs critical trade networks (Mohamed and Lucero-Prisno, 2025; Eberhard-Ruiz, 2024). Despite the recognized importance of these effects, precisely measuring the economic toll of conflict remains methodologically challenging, particularly in emerging markets and developing economies (EMDEs) where data limitations and institutional constraints are most acute.

Economists have traditionally employed two primary analytical frameworks to quantify conflict's economic impact. Dynamic stochastic general equilibrium (DSGE) models provide sophisticated macroeconomic simulation tools widely used for national-level forecasting, as demonstrated by World Bank applications studying oil production effects in South Sudan (Mawejje, 2022) and fiscal spending scenarios in Kenya (Handjiski et al., 2016). Alternatively, counterfactual-based causal inference approaches estimate conflict effects by modeling how outcomes would have evolved absent the intervention. The Difference-in-Differences (DID) framework compares outcome evolution between treated and control units under parallel trends assumptions (Roth et al., 2023), proving valuable for studying both short-term and long-term conflict effects (Korovkin and Makarin, 2023; Mueller et al., 2017; Tkalec and Žilić, 2021). The Synthetic Control Method (SCM), originally developed by Abadie and Gardeazabal (2003) to analyze the Basque conflict and later extended by Abadie et al. (2010) for policy evaluation, constructs weighted averages of donor units to approximate pre-treatment trends in treated regions. SCM has become increasingly prevalent in conflict studies (Estévez-Soto and Lecona Esteban, 2025; Barrett, 2022; Calderón et al., 2015; Mayberry, 2023; Kešeljević and Spruk, 2024; Mandon et al., 2025), where quantitative comparative case studies provide valuable insights.

However, these established approaches face significant limitations in EMDE contexts. DSGE models require extensive data, numerous structural assumptions, and specialized expertise that may be unavailable in conflict-affected regions. DID estimators fail when control regions exhibit dissimilar trends relative to treated units or when unobserved heterogeneity varies across time. SCM, while technically more flexible,
ible than DID, performs poorly when pre-treatment periods are short relative to the number of donor units, leading to overfitting and interpolation bias, particularly when donors are insufficiently similar to treated units (Abadie, 2021; Abadie and Vives-i Bastida, 2022; Abadie et al., 2015).

These methodological constraints have motivated researchers to develop more agile and scalable al-
ternatives (Mandon et al., 2025; Saenger et al., 2024). Satellite-based remote sensing data, particularly
nighttime light (NTL) intensity, offer promising proxies for sub-national economic activity with distinct
advantages: high spatial granularity, regular global coverage, and independence from national statistical
systems (Hu and Yao, 2022; Román et al., 2018; Pérez-Sindín et al., 2021; McCallum et al., 2022). Si-
multaneously, recent advances in causal inference methodology enable researchers to handle settings with
short pre-treatment panels and numerous potential controls (Amjad et al., 2018; Kinn, 2018).

This paper adopts the Forward Difference-in-Differences (FDID) estimator developed by Li (2024), which relaxes global parallel trends assumptions by selecting targeted subsets of donor units whose pre-treatment trends most closely match treated units. This greedy model selection algorithm identifies optimal donor subsets for DID estimation. Using open-source software (Greathouse, 2025), we implement FDID to analyze the economic impact of the socio-political crisis in Cameroon's Northwest and Southwest regions. This crisis represents an ongoing conflict that has severely affected both regions since 2017, with differing economic trajectories emerging across the two areas.

Our analysis contributes to the conflict and development literature in three distinct ways. First, we provide high-frequency, sub-national causal estimates of conflict's economic impact in a lower-middle-income country setting. Second, we demonstrate how combining satellite remote sensing data with modern causal inference methods can effectively measure economic shocks as well as eventual subsequent recoveries in data-scarce environments. Specifically, we show how the recently developed Forward Difference-in-Differences method by Li (2024) can be applied to NTL data to assess sub-national and intra-annual economic costs of conflict with greater precision than traditional approaches.

We have two sets of findings, one set corresponding to the crisis impact in the Northwest and another set for the divergent trajectory observed in the Southwest. Firstly, our results suggest that the crisis reduced the mean NTL intensity by 25 percent in the Northwest region in the seven years following the onset of the conflict (from 2017 onwards). For total NTL output, the Northwest region's output was 28 percent lower than what it would have been had the crisis never occurred. For the Southwest region, we observe a contrasting pattern. For both the mean and total NTL intensity, we estimate that the average NTL output was 32 percent higher than the counterfactual in the five years following 2019.
This divergence may be attributable to multiple factors, including the Southwest's greater connectivity to major economic centers, more diversified economic base, more favorable conditions for private sector activity, and political economy dynamics that merit further investigation.

## 2 Background and Institutional Setting

Cameroon's Northwest and Southwest regions have experienced significant socio-political disruption since 2017.<fnref n="1" /> The crisis emerged from tensions between regional populations and national institutions, escalating from civil protests in late 2016 into armed conflict by September 2017.

The immediate catalyst for the crisis was a series of protests by lawyers and teachers in the Northwest and Southwest regions beginning in late 2016. These professional groups raised concerns about the application of institutional frameworks in regional courts and schools. The subsequent government response, including arrests and communication restrictions, contributed to an escalation that transformed civil unrest into armed conflict. By 2017, the situation had evolved into a significant security crisis affecting both regions (Bang and Balgah, 2022).

The socio-economic impacts of this crisis have been substantial across multiple sectors. Agriculture, which remains central to livelihoods in both regions and represents a key pillar of national exports, has been severely disrupted. Population displacement has reduced farming activity, destroyed harvests, and driven up food prices throughout the affected areas. Even farmers who remain in place face limited market access, particularly for cash crops like cocoa that require regular transportation to processing facilities and export terminals (AfricaNews, 2018; Bread for the World, 2024). The education sector has suffered comparable disruption, with school closures and attendance declines significantly affecting human capital accumulation (Human Rights Watch, 2021).

The World Bank's assessment of the crisis reveals its devastating multisectoral impact (World Bank, 2021). The crisis has damaged critical infrastructure, straining service delivery across the affected regions. Economically, the conflict has precipitated a notable contraction in regional economic activity and escalated poverty levels, with national repercussions evident in household welfare indicators, as substantiated by DSGE analyses. Human development outcomes have suffered acutely, with education and health sectors experiencing significant disruptions that have resulted in declining school enrollment rates and

<footnote n="1">¹For comprehensive treatments of the historical background and institutional antecedents of this crisis, see Lee and Schultz (2012), Dippel (2014), and Bang and Balgah (2022). These studies provide detailed analyses of the colonial period, post-independence institutional arrangements, and the evolution of regional governance structures.</footnote>
restricted healthcare access. The report warns that protracted conflict risks entrenching these adversities, significantly complicating recovery efforts and affecting Cameroon's long-term development trajectory.

Importantly, while both regions have been affected by the crisis, their economic trajectories have diverged since 2019. Several factors may help explain this divergence. The Southwest region benefits from greater connectivity to major economic hubs, including proximity to the port city of Douala in the Littoral region. The region also has a more diversified economic base, including a broader range of cash crop production and commercial activities. Additionally, the Southwest's relatively more developed infrastructure and historically more favorable business climate have facilitated a degree of economic resilience and recovery not observed in the Northwest. Political economy factors may also have contributed to these divergent trajectories: notably, the Southwest has attracted the majority of private investment under Cameroon's tax incentive programs for economically distressed zones, reflecting the region's relative attractiveness to investors.<fnref n="2" /> Disentangling these channels remains challenging. The Northwest region, by contrast, faces greater geographic isolation, a more agriculture-dependent economy with less diversification, limited industrial capacity, and infrastructure constraints that have compounded the crisis's economic impact.

Rigorous measurement of the crisis's economic toll is essential for post-conflict recovery planning, humanitarian response, and development policy in fragile settings. Understanding the differential economic trajectories across affected regions can inform both immediate policy responses and longer-term reconstruction efforts.

## 3 Data

We employ a panel of subnational regions spanning quarter 1 of 2013 to quarter 4 of 2024. Each unit is indexed by $i \in \mathcal{N} := \{1, 2, \dots, N\}$, with $N = |\mathcal{N}| = 105$.<fnref n="3" /> The treated units are $\mathcal{N}_1 := \{1, 2\}$, corresponding to the Northwest and Southwest regions of Cameroon. The remaining units, $\mathcal{N}_0 := \mathcal{N} \setminus \mathcal{N}_1$, form the control group ($N_0 = 104$). Time is indexed by $t$. We divide the time series into pre- and post-treatment periods depending on the intervention of interest, whether crisis onset or the period of divergent

<footnote n="2">²According to recent government data, the Southwest hosts over half of the projects benefiting from tax and customs incentives among the three designated economically distressed zones (Far North, Northwest, and Southwest), with investors citing proximity to Douala, efficient road networks, and cross-border trade opportunities as key factors (Business in Cameroon, 2025).</footnote>

<footnote n="3">³In addition to the 7 unaffected regions of Cameroon, we supplement the control group with state- or province-level equivalents from neighboring countries: 37 from Nigeria, 24 from Chad, 12 from the Republic of the Congo, 9 from Gabon, 7 from the Central African Republic, and 7 from Equatorial Guinea.</footnote>
economic trajectories.

For the Northwest region, the treatment represents crisis onset, with the pre-treatment period $T_1 := \{1, 2, \dots, T_0\}$, where $T_1 = 19$ quarters. For the Southwest region, the treatment represents the period beginning in 2019 when economic trajectories between the two regions began to diverge notably. Coinciding with the government's designation of both regions as economically distressed zones eligible for tax incentives, the Southwest attracted a disproportionate share of private investment due to its structural advantages (Business in Cameroon, 2025), and we treat this period as a distinct analytical window. The Southwest region has a pre-treatment period of 28 quarters spanning 2013q1 through 2019q4. For each treated unit, the post-treatment period extends to the end of the time series, resulting in 29 post-treatment periods for the Northwest region and 20 for the Southwest region.

Our primary outcome variable is satellite-based NTL intensity, which serves as a proxy for regional economic activity. These estimates are drawn from NASA's Black Marble project, which provides globally consistent measurements of artificial lighting captured by satellite sensors. These data incorporate corrections for cloud cover, atmospheric effects, terrain characteristics, vegetation interference, lunar illumination cycles, and stray light contamination (Román et al., 2018). Our specific outcome variables are mean luminosity and total luminosity measured at the regional level.

The NTL data offer several advantages for studying economic impacts in conflict-affected regions. First, they provide consistent temporal coverage that is unaffected by conflict-related disruptions to national statistical systems. Second, they capture economic activity at high spatial resolution, enabling analysis of subnational impacts that aggregate statistics might obscure. Third, they are available with minimal reporting lags, allowing for near real-time assessment of economic conditions. Fourth, they are less susceptible to political manipulation or administrative capacity constraints that can affect traditional economic indicators in fragile states.

# 4 Empirical Methodology

## 4.1 Difference-in-Differences and Synthetic Control Methods

DID methods are attractive because of their simplicity and minimal data requirements. The DID estimator requires only one treated group, one control group, and at least one pre-treatment period and one post-treatment period. Following Li (2024), the DID estimator takes the form:
$$y_{1t} = \hat{\alpha}_{N_0 t} + \bar{y}_{N_0 t}, \quad t \in T_1, \qquad (4.1)$$

where $\bar{y}_{N_0 t} := N_0^{-1} \sum_{i \in N_0 t} y_{it}$ represents the control group average in the pre-intervention period. The intercept DID estimates is computed as:

$$\hat{\alpha}_{N_0 t} := T_1^{-1} \sum_{t \in T_1} (y_{1t} - \bar{y}_{N_0 t}). \qquad (4.2)$$

The DID method estimates a single intercept parameter representing the baseline differences in the pre-intervention period. The DID prediction follows:

$$\hat{y}_{1t}^0 = \hat{\alpha}_{N_0 t} + \bar{y}_{N_0 t}. \qquad (4.3)$$

This prediction equals the average of the control group shifted by the pre-treatment baseline difference. The DID approach relies critically on the parallel trends assumption:

$$\hat{y}_{1t}^0 - \bar{y}_{N_0 t} = \hat{\alpha}_{N_0 t} + \varepsilon_t. \qquad (4.4)$$

Equation (4.4) requires that the average difference between the treated group and untreated group would remain constant in the absence of treatment. Since we cannot observe the counterfactual outcome, making parallel trends globally untestable, researchers focus primarily on testing parallel trends in the pre-treatment period.

The SCM offers an alternative to DID under certain conditions (Abadie and Gardeazabal, 2003; Abadie et al., 2010; Abadie, 2021). The SCM solves a similar regression problem to Equation (4.1), but eliminates the intercept term $\hat{\alpha}_{N_0 t}$ and employs a flexible weighting scheme. The weights for the SCM are typically constrained to be non-negative, contrasting with the DID weights which equal $\frac{1}{N_0}$ by construction.

The classical parallel trends assumption underlying DID and the identification assumptions for the SCM rely on different asymptotic regimes, though both setups may be violated for different reasons. DID relies on the plausibility of a global parallel trends assumption, which becomes more credible when $N_0$ is sufficiently large to average out heterogeneity in the control group. For this reason, DID methods are frequently applied when researchers have individual-level or fine-grained outcome measurements.

In contrast, the SCM's theoretical guarantees require fixed-$N$, large-$T$ asymptotics, where researchers need a sufficiently long pre-treatment period to accurately reconstruct the treated unit's trajectory
(Abadie et al., 2010). While our analysis benefits from a reasonable pre-treatment period, we face a very large donor pool. This configuration is problematic because the SCM may overfit to noise, particularly when $N_0 \gg T_1$, as methodologists have documented (Li and Sonnier, 2023; Li and Van den Bulte, 2023; Kinn, 2018). The standard recommendation in this setting involves reducing the donor pool to units similar to the treated unit. However, this approach requires caution, as interpolation biases may become severe when the donor pool is small and consists of dissimilar units (Abadie and L'Hour, 2021; Kellogg et al., 2021).

# 5 Forward Difference-in-Differences

<figure><img src="image_3.png" /><figcaption>Figure 1: Parallel Trends Illustration</figcaption></figure>

To address the limitations of DID and SCM, we adopt the FDID estimator proposed by Li (2024), following the Python implementation by Greathouse (2025). FDID is a model selection procedure that relaxes the global parallel trends assumption by identifying a subset of control units whose pre-treatment outcome trajectories most closely align with those of the treated units.

Figure 1 provides intuition for this approach. The left panel illustrates a setting where the classical parallel trends assumption holds. With three control units, we display each control unit individually, their average, the DID counterfactual prediction, and the treated unit. The treated unit and the average of the control group maintain a constant difference in the pre-treatment period, satisfying the standard parallel trends condition.

The right panel demonstrates a situation where Forward parallel trends hold despite the failure of classical parallel trends. One control unit behaves substantially differently from the others, causing the
average of all controls to diverge from the treated unit. However, a subset of two control units exhibits a consistent relationship with the treated unit prior to treatment. This example illustrates the core insight behind FDID: rather than using the complete set of controls, we isolate and employ only the most relevant ones—those whose pre-treatment outcomes best mirror those of the treated unit.

The selection algorithm proceeds as follows. Let $\mathcal{U} \subseteq \mathcal{N}_{0t}$ represent a candidate subset of control units. FDID aims to identify the subset $\hat{\mathcal{U}}^*$ that provides parallel trends to the treated unit before treatment through a greedy forward-selection procedure. At the first iteration ($k = 1$), we evaluate each control unit individually by estimating Equation (4.1) and computing how well each unit fits the treated unit in the pre-treatment period using the $R^2$ statistic:

$$R^2(\mathcal{U}) = 1 - \frac{\sum_{t \in \mathcal{T}_1} (y_{1t} - \hat{y}_{1t}^{(\mathcal{U})})^2}{\sum_{t \in \mathcal{T}_1} (y_{1t} - \bar{y}_1)^2} \quad (5.1)$$

We then select the best-performing singleton set from $N_0$ one control unit DID models:

$$\hat{\mathcal{U}}_1 := \arg \max_{\mathcal{U} \in \mathcal{C}_1} R^2(\mathcal{U}), \quad (5.2)$$

where

$$\mathcal{C}_1 := \{\{i\} | i \in \mathcal{N}_{0t}\}. \quad (5.3)$$

This unit is then removed from the candidate pool. For intuition, suppose the treated unit's pre-treatment outcomes over time are $\{1, 2, 3\}$, and a candidate control unit has outcomes $\{6, 7, 8\}$. The difference at each time point equals a constant 5. Because the control unit perfectly tracks the treated unit up to this constant shift, the $R^2$ statistic equals 1, indicating perfect parallel trends relative to this control.

At each subsequent step $k \ge 2$, we expand the previously selected set $\hat{\mathcal{U}}_{k-1}$ by adding one additional unit. The candidate class becomes:

$$C_k^+ := \{\hat{\mathcal{U}}_{k-1} \cup \{i\} | i \in \mathcal{N}_{0t} \setminus \hat{\mathcal{U}}_{k-1}\}. \quad (5.4)$$

We then choose the subset that yields the highest pre-treatment $R^2$:

$$\hat{\mathcal{U}}_k := \arg \max_{\mathcal{U} \in \mathcal{C}_k^+} R^2(\mathcal{U}). \quad (5.5)$$
This process continues iteratively, expanding the subset one unit at a time until all control units have been selected into a candidate class. We ultimately select the subset that yields the best overall fit:

$$ \hat{U}^* := \arg \max_{1 \le k \le N_{0t}} R^2(\hat{U}_k). \qquad (5.6) $$

With this selected donor group $\hat{U}^*$, we fit the DID model on the pre-treatment data:

$$ y_{1t} = \hat{\alpha}_{\hat{U}^*} + \bar{y}_{\hat{U}^*t}, \quad t \in \mathcal{T}_1, \qquad (5.7) $$

where the control group mean is

$$ \bar{y}_{\hat{U}^*t} := |\hat{U}^*|^{-1} \sum_{i \in \hat{U}^*} y_{it}, \qquad (5.8) $$

and the intercept is

$$ \hat{\alpha}_{\hat{U}^*} := |\mathcal{T}_1|^{-1} \sum_{t \in \mathcal{T}_1} (y_{1t} - \bar{y}_{\hat{U}^*t}). \qquad (5.9) $$

The FDID predicted counterfactual outcomes are:

$$ \hat{y}_{1t}^0 = \hat{\alpha}_{\hat{U}^*} + \bar{y}_{\hat{U}^*t}, \quad t \in \mathcal{T}_2. \qquad (5.10) $$

The estimated average treatment effect on the treated is:

$$ \widehat{ATT} = T_2^{-1} \sum_{t \in \mathcal{T}_2} (y_{1t} - \hat{y}_{1t}^0). \qquad (5.11) $$

For Equation (5.11) to be identified, we require the selected parallel trends assumption to hold:

$$ y_{1t}^0 - \bar{y}_{\hat{U}^*t} = \hat{\alpha}_{\hat{U}^*} + \varepsilon_t. \qquad (5.12) $$

Compared to Equation (4.4), which must hold across all donor units, this assumption is more flexible. It requires parallel trends relative to only some subset of controls. This flexibility makes FDID particularly useful in settings with short panels and high-dimensional donor pools. As demonstrated in Li (2024), this forward-selection procedure is consistent as $|\mathcal{T}_1| \to \infty$ and performs well even with limited pre-treatment data, including as few as six periods in synthetic studies.
# 6 Results

<table><caption>Table 1: Treatment Effects on NTL by Region, Outcome, and Estimator</caption><thead><tr><th></th><th></th><th>FDID</th><th>DID</th></tr></thead><tbody><tr><td colspan="4" style="text-align: center; font-weight: bold;">Panel A: Northwest</td></tr><tr><td rowspan="6" style="font-weight: bold;">NTL Mean</td><td>ATT</td><td>-0.060</td><td>-0.060</td></tr><tr><td>Percent ATT</td><td>-25.143</td><td>-25.182</td></tr><tr><td>SE</td><td>0.005</td><td>0.0432</td></tr><tr><td>95% CI</td><td>[-0.070, -0.050]</td><td>[-0.145, 0.024]</td></tr><tr><td>R<sup>2</sup></td><td>0.879</td><td>-8.132</td></tr><tr><td>ATT</td><td>-17.245</td><td>-58.162</td></tr><tr><td rowspan="6" style="font-weight: bold;">NTL Sum</td><td>Percent ATT</td><td>-28.550</td><td>-57.405</td></tr><tr><td>SE</td><td>1.4582</td><td>28.9179</td></tr><tr><td>95% CI</td><td>[-20.103, -14.387]</td><td>[-114.840, -1.484]</td></tr><tr><td>R<sup>2</sup></td><td>0.821</td><td>-69.512</td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">Panel B: Southwest</td></tr><tr><td rowspan="6" style="font-weight: bold;">NTL Mean</td><td>ATT</td><td>0.465</td><td>0.301</td></tr><tr><td>Percent ATT</td><td>32.802</td><td>19.046</td></tr><tr><td>SE</td><td>0.0366</td><td>0.0805</td></tr><tr><td>95% CI</td><td>[0.393, 0.537]</td><td>[0.143, 0.459]</td></tr><tr><td>R<sup>2</sup></td><td>0.679</td><td>-0.551</td></tr><tr><td>ATT</td><td>81.798</td><td>-43.787</td></tr><tr><td rowspan="6" style="font-weight: bold;">NTL Sum</td><td>Percent ATT</td><td>32.522</td><td>-11.611</td></tr><tr><td>SE</td><td>6.7065</td><td>35.1394</td></tr><tr><td>95% CI</td><td>[68.653, 94.942]</td><td>[-112.658, 25.085]</td></tr><tr><td>R<sup>2</sup></td><td>0.657</td><td>-8.421</td></tr></tbody></table>

*Notes*: This table reports estimated treatment effects (ATT) on mean and total NTL for two treated regions (Northwest and Southwest), using both FDID and DID estimators. Standard errors (SE) and 95% confidence intervals are shown for each estimator as computed in Li (2024). R² values refer to pre-treatment fit.
We find that the crisis had a substantial negative impact on economic activity in the Northwest region. The FDID estimator indicates that mean and total NTL activity decreased by around 25 and 28 percent respectively, relative to the counterfactual scenario where the crisis did not occur. Following the recommendation of Li (2024), we observe that the forward parallel trends assumption is likely plausible in this context, given that the FDID model fits well the pre-treatment trajectory of the Northwest region. The pre-treatment $R^2$ statistic indicates that the shift of the selected donor pool by the mean pre-treatment difference between the treated unit and donor pool explains over 80 percent of the variation in the pre-treatment trajectory. In contrast, the standard DID model performs poorly: the $R^2$ statistic is consistently negative, meaning that the average of the donor units anti-predicts the treated unit.

For the Southwest region, we observe positive effects on mean and total NTL output from 2019 onwards relative to the counterfactual. The $R^2$ statistics, while not as high as those for the Northwest region, suggest that the FDID method fits the pre-intervention trajectory reasonably well in the pre-intervention period for the Southwest region. For both outcomes of interest, we find that the NTL metrics increased by approximately 32 percent compared to the counterfactual scenario. As with the Northwest region, the DID model is clearly ill-suited for this dataset given the negative pre-treatment $R^2$ statistics.

The divergent economic trajectories between the Northwest and Southwest regions merit careful interpretation. The Northwest region's continued economic decline can be attributed to several compounding factors: its greater geographic isolation from major economic centers, a more agriculture-dependent economy with limited sectoral diversification, more severe infrastructure damage, and sustained population displacement that has constrained labor markets and reduced productive capacity. The region's mountainous terrain and less developed road networks have further impeded economic recovery by limiting market access and increasing transportation costs for agricultural products.

In contrast, the Southwest region demonstrates signs of relative economic resilience beginning in 2019. Multiple factors likely contribute to this divergence. First, the Southwest benefits from significantly greater connectivity to major economic hubs, including its proximity to the Littoral region and the port city of Douala, which facilitates trade and market access. Second, the region possesses a more diversified economic base, including a broader range of commercial and agricultural activities that provide alternative income sources when traditional sectors face disruption. Third, the Southwest's coastal geography and historically more developed infrastructure have created a more favorable environment for private sector activity and economic dynamism. Fourth, the region shares a border with Nigeria, Cameroon's largest trading partner, and has modern road infrastructure supporting cross-border trade. Fifth, political
economy factors—including the concentration of private investment attracted by tax incentive programs in the Southwest—may also play a role, though our data do not allow us to isolate these channels from the structural factors. The Southwest's attractiveness to private investors under Cameroon's economically distressed zone incentive programs appears to reflect these underlying structural advantages. Sixth, the resumption of key economic activities such as cash crop processing and export operations has contributed to the observed recovery in nighttime light intensity.

These divergent patterns provide empirical support for the proposition that while conflict system-
atically depresses economic activity, regional characteristics—both structural and political-economic—
significantly influence recovery trajectories. The findings highlight the importance of connectivity, eco-
nomic diversification, geographic factors, and the relative attractiveness of regions to private investment
in determining how different areas respond to and recover from crisis conditions.

Methodologically, our results underscore the failure of DID when faced with a large and heterogeneous donor pool: the inclusion of many irrelevant units leads to poor pre-treatment fit and negative R² statistics because the average of a sizable number of heterogeneous units is unlikely to resemble the trend of a single treated unit. In contrast, FDID delivers a much more parallel pre-intervention trend, selecting only a subset of control units that better mirror the units of interest in their trends. Since parallel trends is only testable in the pre-treatment period, we can be more confident that FDID's results are closer to the truth than DID, whose parallel pre-trends assumption is consistently violated.

7 Conclusion

This paper demonstrates how modern causal inference methods combined with satellite remote sensing data can provide rigorous estimates of conflict's economic impact in local, data-scarce environments. Using the FDID estimator with NTL intensity data, we find that the socio-political crisis in Cameroon's Northwest and Southwest regions reduced economic activity in the Northwest region by approximately 25-28 percent over seven years, while the Southwest region exhibited a 32 percent increase in economic activity relative to its counterfactual trajectory over five years following 2019.

Our methodological contribution addresses a fundamental challenge in development economics: measuring economic impacts in fragile and conflict-affected states where traditional data sources are unreliable or unavailable. The FDID approach proves particularly valuable in settings with large, heterogeneous donor pools where standard DID methods fail due to violations of the parallel trends assumption. By se-
lecting optimal subsets of control units whose pre-treatment trajectories most closely match treated units, FDID provides more credible causal estimates than traditional approaches. The consistently negative $R^2$ statistics for standard DID models in our analysis underscore the importance of this methodological innovation.

The economic costs we document are substantial and persistent. The Northwest region's 25-28 percent reduction in NTL intensity represents a significant contraction in economic activity that has persisted throughout the crisis period. This finding contributes to a growing literature documenting the severe economic consequences of internal conflicts in developing countries. The magnitude of these effects aligns with previous research on conflict's economic impact while providing higher-frequency, spatially disaggregated estimates that capture subnational variation often obscured by aggregate statistics.

The divergent trajectories between the Northwest and Southwest regions offer important insights for understanding heterogeneous crisis impacts. The Southwest's relative resilience appears attributable to a combination of structural, geographic, and political economy factors, including greater connectivity to economic centers, a more diversified economic base, more favorable conditions for private sector activity, and the region's greater attractiveness to private investors under tax incentive programs. Conversely, the Northwest's continued decline reflects the compounding effects of geographic isolation, economic concentration in vulnerable sectors, limited industrial capacity, and sustained disruption to productive activities. These findings suggest that both pre-existing regional characteristics and private sector responses to investment incentives influence how local economies respond to and recover from crisis conditions.

The research contributes to several important policy debates. First, it provides empirical evidence for the substantial economic costs of internal conflicts, reinforcing arguments for conflict prevention and early intervention. Second, it demonstrates that economic trajectories can diverge significantly across regions affected by the same crisis, highlighting the importance of understanding local structural conditions when designing recovery programs. Third, it illustrates how satellite-based measurements can enable rapid assessment of economic conditions in areas where traditional monitoring is impossible due to security constraints or institutional weakness.

Several limitations warrant acknowledgment. NTL intensity, while strongly correlated with economic activity, captures only certain types of economic behavior and may not fully reflect subsistence agriculture or informal sector activities that remain important in rural areas, which are likely more prevalent in the Northwest. The assumption that the selected control units would have continued to follow parallel trends absent treatment, while more plausible than global parallel trends, remains fundamentally untestable.
Additionally, and more importantly, our analysis focuses on economic impacts without examining broader
social, political, or humanitarian consequences that are critical for understanding the full costs of the
crisis.

Future research should extend this methodology to other conflict settings to establish the generaliz-
ability of our findings. Comparative studies across different types of conflicts, development levels, and
institutional contexts would help identify the conditions under which various causal inference methods
perform best. Integration of additional satellite-based measures, such as vegetation indices or built-up
area indicators, could provide a more comprehensive picture of conflict's multisectoral impacts. Finally,
linking satellite-based estimates to household survey data would enable researchers to better understand
how aggregate economic changes translate into individual welfare outcomes.

The socio-political crisis in Cameroon's Northwest and Southwest regions continues to impose sub-
stantial costs while demonstrating how structural factors shape differential regional outcomes. Our find-
ings suggest that combining modern causal inference methods with satellite remote sensing data offers
a promising approach for monitoring economic conditions and evaluating outcomes in conflict-affected
regions where traditional data sources are inadequate. As conflicts increasingly affect urban areas and
developing countries with growing formal economies, these methodological advances become increasingly
valuable for both researchers and policymakers seeking to understand and address the economic conse-
quences of violent conflict.

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