Climate Variability Modulates the Impact of Price Spikes on Food Insecurity

Jordi Cerdà-Bautista; Vasileios Sitokonstantinou; Homer Durand; Gherardo Varando; Michele Ronco; Gustau Camps-Valls

cs.LGSep 21, 2026

Climate Variability Modulates the Impact of Price Spikes on Food Insecurity

Authors: Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, Homer Durand, Gherardo Varando, Michele Ronco, Gustau Camps-Valls

Organizations: Image Processing Laboratory, Universitat de València, València, Spain. · Artificial Intelligence Group, Wageningen University & Research, Wageningen, The Netherlands. · Joint Research Centre, European Commission, Ispra, Italy.

Abstract

Climate variability influences whether a market disruption escalates into a food crisis, yet broad climate patterns like El Niño, tracked months before they alter hydro-climatic conditions, are still not incorporated as an early-warning component in food-security responses. We address this gap by introducing sensitivity regimes, a stratification of regions by the direction and strength of their vegetation response to the El Niño Southern Oscillation, and using them to estimate how food price spikes affect acute food insecurity across sub-Saharan Africa. Integrating remote sensing, socioeconomic data, and causal machine learning, we find that in regions where ENSO systematically suppresses vegetation, a price spike raises the share of the population at acute risk by 5.4 percentage points in the following month. In regions where vegetation is unaffected by or positively linked to ENSO, the estimated effect is smaller (around 2 percentage points) and statistically insignificant. These results demonstrate that climate context is critical for understanding food security vulnerabilities. Sensitivity regimes can be combined with operational price-spike triggers to stage anticipatory action: the ENSO state flags vulnerable regions months ahead, and a pre-positioned response in those regions to a price spike would avert the largest jump in acute food insecurity.

Figures & tables

Figure 1 : Proposed analytical pipeline. The workflow begins by extracting the dominant ENSO-linked vegetation signal, then stratifies regions by sensitivity regimes, and concludes with the estimation of causal effects averaged within each regime. The time-series plots show examples from Kenya, Mali, and Zimbabwe.

Figure 3 : Geographical distribution of sensitivity regimes based on GPCA stratification. (Left) First PC of the GPCA analysis on ENSO–NDVI, providing a continuous map of the causal influence. (Right) Regions classified as Positive, Negative, or Weak based on the strength and direction of ENSO–NDVI causal linkages.

Model	Regime	CATE (p.p.)	C.I.	p-value
	Negative	6.00	(2.57, 8.73)	0.020
Linear DML	Weak	1.94	(-0.47, 4.99)	0.098
	Positive	2.16	(-3.92, 4.21)	0.784
	Negative	5.35	(2.16, 8.08)	0.020
Causal Forest DML	Weak	1.80	(-0.47, 4.75)	0.137
	Positive	1.70	(-3.70, 4.15)	0.922

Table 1 : Conditional Average Treatment Effect (CATE) estimates for ALPS impacts on rCSI, stratified by sensitivity regimes. Confidence intervals are reported at the 95% level.

Figure 4 : Baseline risk of acute food insecurity under normal food price conditions and added risk when prices spike. Estimations provided by the Causal Forest DML CATE estimates stratified for the sensitivity regimes.

Figure 5 : Feature importance of the nuisance models. Estimations of the treatment (top) and outcome (bottom) for the Causal Forest DML estimator stratified for the sensitivity regimes.

\beta_{p}\;\;\text{from}\;\;\text{NDVI}_{p}(t)=\alpha_{p}+\beta_{p}\widetilde{\Phi}_{1}(t)+\varepsilon_{p,t},

Algorithm 1 GPCA for sensitivity Regime Classification

Appendix figures & tables17 assets

Supplementary material from the paper’s appendix.

Appendix

Domain	Variable	Description	Source
Outcome	rCSI	Reduced Coping Strategy Index crisis prevalence, monthly mean.	WFP

Treatment	ALPS	Alert for Price Spikes averaged for staple food commodities.	WFP Prices

	Mean Temperature (Crop)	Spatial mean of the monthly average temperature over crop-covered areas.	ASAP
	Mean FPAR (Crop)	Spatial mean of the monthly average fraction of absorbed photosynthetically active radiation (FPAR) over crop-covered areas.	ASAP

Appendix

Table 2 : Outcome, treatment, and confounders derived from the HFID and used in the causal analysis. All variables are aggregated at the ADM1-month level. Confounders are grouped into climate, economic, and social domains.

From	To	Justification and references
ENSO	NDVI, Crop Drivers	ENSO alters regional rainfall and temperature patterns, which drive vegetation greenness and define crop growing conditions (Anyamba et al., 2018; Ropelewski and Halpert, 1987; FAO et al., 2023).
ENSO	GDP	Climate shocks associated with ENSO influence aggregate output and growth, particularly in agriculture-dependent economies (Dell et al., 2012; Burke et al., 2015).
NDVI	Crop Drivers	Vegetation condition indices are closely tied to local biophysical crop conditions such as canopy development and water stress (Anyamba et al., 2018; FAO et al., 2023).
Crop Drivers	GDP	Weather- and climate-sensitive crop conditions affect yields and agricultural value added, which is a key component of GDP in many low- and middle-income countries (Dell et al., 2012; Burke et al., 2015; FAO et al., 2023).
Crop Drivers	Conflicts	Agricultural prod. failures can exacerbate income loss, rural hardship, and competition over resources, recognized as structural and proximate drivers of conflict and instability (Burke et al., 2015; FSIN and Global Network Against Food Crises, 2024).
Crop Drivers	IDPs	Agroclimatic shocks contribute to livelihood collapse and displacement, especially in agriculture-dependent settings (FSIN and Global Network Against Food Crises, 2024; IDMC, 2024).

Appendix

Table 3 : Justification of the causal links in the DAG (Part I). For each directed edge we provide a short rationale and example supporting references.

From	To	Justification and references
GDP, Food Prices	rCSI	Household income and employment prospects modulated by GDP growth influence the need to adopt negative coping strategies (FAO et al., 2023; World Bank, 2020).
Conflicts	IDPs	Violent conflict is the leading global driver of internal displacement (IDMC, 2024; FSIN and Global Network Against Food Crises, 2024).
Conflicts	ALPS	Conflict disrupts markets, transport, and production, constraining supply and raising transaction costs (FAO et al., 2023; FSIN and Global Network Against Food Crises, 2024).
Conflicts	rCSI	Conflicts reduce incomes, destroy assets and constrain humanitarian access, all of which raise the prevalence of acute food insecurity and severe coping captured by rCSI (FSIN and Global Network Against Food Crises, 2024; FAO et al., 2023).
IDPs	ALPS	Inflows of displaced populations can increase local demand, strain markets and infrastructure, and contribute to localised food price spikes (FSIN and Global Network Against Food Crises, 2024; IDMC, 2024).
IDPs	rCSI	IDPs face greater exposure to shocks, have constrained access to livelihoods and services, and are consistently among the most food-insecure groups (FSIN and Global Network Against Food Crises, 2024; IDMC, 2024).

Appendix

Table 4 : Justification of the causal links in the DAG (Part II). For each directed edge we provide a short rationale and example supporting references.

Strong-pixel q	Min. Dominance	Min. Coverage	Unchanged	$\kappa$	Pos $\leftrightarrow$ Neg	#Neg	#Weak/#Pos
0.20	0.20	0.10	1.000	1.000	0	158	204/203
0.20	0.20	0.20	1.000	1.000	0	158	204/203
0.10	0.15	0.10	0.993	0.989	0	159	200/206
0.10	0.15	0.20	0.993	0.989	0	159	200/206

Appendix

Table 5 : Sensitivity of ADM1 regime labels to threshold choices (study countries). Agreement metrics compare each configuration to the baseline labeling. Pos

\leftrightarrow

Neg counts direct Positive

\leftrightarrow

Negative sign reversals.

Metric	Value	Notes
# ADM1 units	222	Study countries only
Baseline counts (Neg/Weak/Pos)	71 / 64 / 87
Mean stability	0.941	$\Pr(\widehat{X}_{r}=X_{r}^{\mathrm{base}})$
Share stability $\geq 0.9$	0.794
Share stability $=1.0$	0.622
Mean Pos $\leftrightarrow$ Neg switch prob.	$1.39\times 10^{-4}$	Median = 0, 75th pct = 0

Appendix

Table 6 : Subsampling stability summary (study countries). Stability is the probability of retaining the baseline label when pixels are subsampled. The Pos

\leftrightarrow

Neg switch probability measures direct sign reversals.

GID_0	Country	ADM1	Baseline	Stability	$P(\text{Neg})$	$P(\text{Weak})$	$P(\text{Pos})$
MOZ	Mozambique	Maputo	Negative	0.575	0.575	0.425	0.000
NGA	Nigeria	Sokoto	Weak	0.585	0.000	0.585	0.415
KEN	Kenya	Kericho	Negative	0.585	0.585	0.380	0.035
NGA	Nigeria	Nasarawa	Weak	0.590	0.410	0.590	0.000
NGA	Nigeria	Rivers	Positive	0.600	0.005	0.395	0.600
KEN	Kenya	Turkana	Negative	0.615	0.615	0.385	0.000

Appendix

Table 7 : Lowest-stability ADM1 units in the subsampling test (study countries). Columns show baseline label, stability, and empirical label probabilities under subsampling.

Figure 6 : Kernel density estimates of the propensity scores. The density distributions illustrate the propensity score distributions for the treated and control groups. The vertical dashed lines indicate the lower and upper cutoffs for the propensity-score trimming.

Regime	Before trimming		After trimming
	Control	Treated	Control	Treated
Overall	829	845	483	455

Negative	318	238	227	130
Weak	271	378	158	220
Positive	240	229	98	105

Appendix

Table 8 : Sample sizes before and after propensity-score trimming, overall and by sensitivity regime. We report the number of control (

T{=}0

) and treated (

T{=}1

) observations before trimming and after restricting to the common-support region

0.2\leq\hat{e}(W_{i})\leq 0.8

Confounder	$\mid$ SMD $\mid$ (Before)	$\mid$ SMD $\mid$ (After)
Conflict Stock Displacement	0.080	0.240
Mean Temperature (Crop)	0.115	0.118
Mean Soil Moisture (Crop)	0.202	0.064
Mean FPAR (Crop)	0.232	0.156
ACLED Fatalities	0.199	0.182
Rural Population	0.035	0.085

Appendix

Table 9 : Standardized mean differences (SMD) for confounders before and after propensity-score trimming (Overall sample).

Confounder	$\mid$ SMD $\mid$ (Before)	$\mid$ SMD $\mid$ (After)
Conflict Stock Displacement	0.441	0.274
Mean Temperature (Crop)	0.137	0.118
Mean Soil Moisture (Crop)	0.408	0.333
Mean FPAR (Crop)	0.406	0.325
ACLED Fatalities	0.302	0.165
Rural Population	0.183	0.102

Appendix

Table 10 : Standardized mean differences (SMD) for confounders before and after propensity-score trimming (Negative regime).

Confounder	$\mid$ SMD $\mid$ (Before)	$\mid$ SMD $\mid$ (After)
Conflict Stock Displacement	0.518	0.309
Mean Temperature (Crop)	0.177	0.061
Mean Soil Moisture (Crop)	0.123	0.336
Mean FPAR (Crop)	0.220	0.065
ACLED Fatalities	0.291	0.299
Rural Population	0.034	0.088

Appendix

Table 11 : Standardized mean differences (SMD) for confounders before and after propensity-score trimming (Weak regime).

Confounder	$\mid$ SMD $\mid$ (Before)	$\mid$ SMD $\mid$ (After)
Conflict Stock Displacement	0.248	0.089
Mean Temperature (Crop)	0.212	0.336
Mean Soil Moisture (Crop)	0.028	0.003
Mean FPAR (Crop)	0.213	0.018
ACLED Fatalities	0.086	0.083
Rural Population	0.071	0.049

Appendix

Table 12 : Standardized mean differences (SMD) for confounders before and after propensity-score trimming (Positive regime).

Model family	CV $R^{2}$ for ( $Y\mid X,W$ ) (Regressor)	CV ROC–AUC for ( $T\mid X,W$ ) (Classifier)
Random Forest	0.214	0.613

Gradient Boosting	0.394	0.633

XGBoost	0.352	0.637
In-sample (selected models)	0.786	0.982

Appendix

Table 13 : Nuisance model selection and fit for the CATE experiments. Best cross-validated

R^{2}

and ROC–AUC for each candidate model family when predicting the outcome

Y\mid X,W

and treatment

T\mid X,W

. The final nuisance models used in the CATE estimators are highlighted in bold; the corresponding in-sample metrics are reported in the last row.

Model	Regime	CATE (p.p.)	C.I.	p-value
	Negative	6.065	(3.257, 8.124)	0.020
Diff. in Means	Weak	-0.040	(-3.596, 3.114)	0.922
	Positive	0.423	(-4.807, 5.344)	0.765
	Negative	4.135	(2.051, 5.633)	0.020
Linear Regression	Weak	1.084	(-0.881, 4.204)	0.157
	Positive	3.312	(-1.014, 5.671)	0.216

Appendix

Table 14 : Full CATE estimates for ALPS impacts on rCSI, stratified by sensitivity regimes. Baseline and DML models. Confidence intervals at 95%, and estimations are statistically significant if

p

-values

\leq

0.05.

Method	Regime	Placebo eff.	Placebo $p$	RCC eff.	RCC $p$	RSR eff.	RSR $p$
	Negative	0.008	0.385	0.000	0.885	0.000	0.938
LinearDML	Weak	-0.004	0.807	0.002	0.808	0.002	0.500
	Positive	0.012	0.615	-0.005	0.769	0.002	0.688
	Negative	0.010	0.269	0.002	0.769	0.001	0.750
CausalForestDML	Weak	-0.004	0.731	0.002	0.731	0.002	0.500
	Positive	0.012	0.615	-0.004	0.769	0.003	0.688

Appendix

Table 15 : Robustness diagnostics for regime-specific CATEs (DML estimators). For each estimator and sensitivity regime, we report the Placebo effect, the RCC mean absolute change, the RSR effect, and their respective

p

-values. Tests succeed if

p

-values

\geq

0.05.

Method	Lead $\ell$	N (T/C)	Negative	Weak	Positive
	0	823 (423/400)	0.0514 (0.0255, 0.0795) $p=0.0196$	0.0206 (-0.0030, 0.0558) $p=0.1176$	-0.0070 (-0.0475, 0.0317) $p=0.6471$
	1	938 (455/483)	0.0599 (0.0257, 0.0873) $p=0.0196$	0.0194 (-0.0047, 0.0499) $p=0.0980$	0.0216 (-0.0392, 0.0421) $p=0.7843$
LinearDML	2	845 (430/415)	0.0550 (0.0328, 0.0814) $p=0.0196$	0.0024 (-0.0179, 0.0472) $p=0.4314$	-0.0472 (-0.0772, 0.0049) $p=0.1373$
	3	840 (419/421)	0.0675 (0.0294, 0.0894) $p=0.0196$	0.0151 (-0.0112, 0.0370) $p=0.2745$	-0.0496 (-0.0908, -0.0056) $p=0.0392$
	4	777 (414/363)	0.0638 (0.0412, 0.0923) $p=0.0196$	0.0193 (-0.0145, 0.0679) $p=0.2941$	-0.0738 (-0.1054, -0.0209) $p=0.0196$
	0	823 (423/400)	0.0524 (0.0256, 0.0805) $p=0.0196$	0.0199 (-0.0060, 0.0510) $p=0.1373$	-0.0125 (-0.0465, 0.0398) $p=0.6078$

Appendix

Table 16 : Lead–lag robustness: regime-specific CATEs under outcome leads

\ell\in\{0,\dots,4\}

. Rows report regime-specific CATEs for the Negative/Weak/Positive sensitivity regimes. Each cell shows the point estimate, the 95% confidence interval, and the p-value.

N

reports the post-trimming sample size (treated/control) under the common-support restriction

0.2\leq\hat{e}(W_{i})\leq 0.8

Method	Contrast	$\Delta$ (p.p.)	C.I.	$p$ -value
Linear DML	Neg – Pos	3.844	(0.909, 11.738)	0.039
Linear DML	Neg – Weak	4.061	( $-0.083$ , 6.809)	0.098
Causal Forest DML	Neg – Pos	3.650	( $-0.129$ , 10.678)	0.078
Causal Forest DML	Neg – Weak	3.549	( $-0.446$ , 6.572)	0.137

Appendix

Table 17 : Regime-contrast tests. Bootstrap distributions of pairwise differences between regime-specific CATEs for the DML estimators. Confidence intervals at 95%, and estimations are statistically significant if

p

-values

\leq

0.05.

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Credit Access is Associated with Improved Food Security in the Horn of Africa

Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, José Manuel Veiga López-Peña, Duccio Piovani+2

Image Processing Laboratory, Universitat de València, Spain · Artificial Intelligence Group, Wageningen University & Research, Wageningen, The Netherlands · Food Security Unit, Joint Research Centre (JRC), European Commission, Ispra, Italy · Early Warning & Forecasting Unit, World Food Programme (WFP), United Nations, Rome, Italy · Internal Displacement Monitoring Centre (IDMC), Geneva, Switzerland

The intensification of climate change poses a growing threat to food security, especially in vulnerable communities. This study employs an observational machine-learning framework to estimate the causal association between access to credit and acute food insecurity in Somalia and across the Horn of Africa, drawing on a harmonized dataset spanning key environmental, socioeconomic, and conflict-related factors from 2015 to 2022. Results indicate that greater credit access is associated with a 2% reduction in acute food insecurity at the population level over the study period. Given that, on average, 16% of the population is in crisis, this effect represents a meaningful shift within the at-risk group. We interpret these estimates under explicit identification assumptions and complement them with robustness and refutation tests. The results provide context-specific evidence on how financial access correlates with food security outcomes in data-scarce, crisis-affected settings, and offer a transparent framework for integrating heterogeneous data sources when randomized evaluations are infeasible.

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Abstract

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