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; José María Tárraga; Gustau Camps-Valls

cs.LGSep 21, 2026

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

Authors: Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, José Manuel Veiga López-Peña, Duccio Piovani, José María Tárraga, Gustau Camps-Valls

Organizations: 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

Abstract

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.

Figures & tables

Figure 1 : Study area and data sources. (A) Map of Somalia showing districts with available data, highlighting Baidoa, an important city in the Bay area (shown in the map), as an example. (B) Standardized time series and box plots displaying key environmental and socioeconomic variables for Baidoa, illustrating temporal trends and variability. (C) The annual distribution of IPC phases (percentages) for Baidoa shows shifts in food security over time.

Variable	Source	Spatial Resolution	Temp. Resolution
ENSO	https://climexp.knmi.nl/selectdailyindex.cgi?id=someone@somewhere	Country	Daily
SPI	https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY#bands	$0.05\degree$	Daily
Violent Conflict	https://acleddata.com/#/dashboard	Geolocated Event	Hourly
Local Market Prices	https://fsnau.org/ids/dashboard.php	District	Monthly
Sorghum Production	https://crops.fsnau.org/	District	Seasonal
Drought Displacement	https://unhcr.github.io/dataviz-somalia-prmn/index.html	District	Weekly

Table 1: Variables and sources used in the study, spatial and temporal resolutions.

Figure 3 : Treatment Effect of Credit Access on IPC 3+ populations across different data aggregations: yearly, seasonal, IPC-based, and monthly. Each panel represents a distinct aggregation level, showing ATE, ATT, and ATC estimates across treatment thresholds. The top section of each panel displays the distribution of treated- and control-group samples, while the bottom section shows treatment estimates with confidence intervals. The density plots on the right depict the distribution of ATE, ATT, and ATC values. These results highlight how treatment effects vary depending on data aggregation and treatment definition.

Figure 4 : Sensitivity analysis of confounders in causal effect estimation. The left column illustrates causal diagrams excluding a key confounder, while the right column shows the corresponding ATE estimates.

Figure 5 : Heterogeneity analysis of the causal effect of credit access on IPC 3+ populations under two crisis scenarios using the IPC-based data aggregation. The top panel shows the distribution of treated and control group samples across different treatment thresholds, distinguishing between crisis and non-crisis conditions. The bottom panel presents the estimated Average Treatment Effect (ATE) for each threshold and crisis scenario, along with the distribution of all ATE estimations.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6 : Comparison of food insecurity levels in included vs. excluded districts. IPC 3+ values are normalized by district population. Blue: included districts (n=56). Orange: excluded districts (n=16).

Figure 7 : Kernel density estimates of propensity scores across four data aggregations. Rows correspond to data aggregation (yearly, seasonal, monthly, IPC-based). Columns correspond to treatment thresholds (30th/45th/60th/75th percentiles), with increasing strictness from left to right.

Causal Link	Justification	Reference(s)
ENSO $\rightarrow$ SPI	ENSO modulates rainfall anomalies, impacting drought frequency.	Gitz et al. (2016) FAO (2023)
ENSO $\rightarrow$ Market prices; Sorghum Production	Climatic shocks influence agricultural yields and market prices.	Gitz et al. (2016) FAO (2023)
SPI $\rightarrow$ Market prices; Sorghum Production	Rainfall deficits reduce production and raise prices.	Misselhorn (2005) iDMC (2020)
SPI $\rightarrow$ Remittances	Drought increases remittance reliance as coping.	Misselhorn (2005) FSIN (2020)
Market prices; Sorghum Production $\rightarrow$ Fatalities, Drought IDPs	Resource stress may increase conflict and displacement.	Clapp et al. (2022) FSIN (2020) iDMC (2020)
Fatalities, Drought IDPs, Population, Remittances $\rightarrow$ Credit Access	Humanitarian targeting and household responses are triggered by distress signals.	IPC Manual (2021) FAO (2023) iDMC (2020)

Appendix

Table 2: Justification of causal links in the DAG based on literature.

Confounder removed	Mean $\Delta$ ATE	95% CI of $\Delta$ ATE	p-value
Food prices	-0.002	[-0.003, -0.001]	0.549
Livestock prices	0.001	[0.000, 0.002]	0.561
Sorghum Production	0.001	[0.000, 0.002]	0.527
Drought IDPs	-0.001	[-0.002, -0.001]	0.525

Appendix

Table 3: Average difference in ATE between the full and LOO models across thresholds.

Cause Effect Estimation					Refutation Tests
Cause Effect Estimation					Placebo		RCC		RSR
Th	Method	ATE	CI	p-value	Effect*	p-value	Effect*	p-value	Effect*	p-value
30	LR	-0.038	(-0.068, -0.010)	0.014	0.000	0.008	-0.038	0.988	-0.038	0.947
30	M	-0.009	(-0.041, 0.010)	0.518	0.006	0.563	-0.019	0.052	-0.013	0.630
30	IPS W	-0.024	(-0.087, 0.036)	0.447	0.001	0.125	-0.029	0.030	-0.029	0.481
30	T-L	-0.038	(-0.058, -0.016)	0.019	0.001	0.220	-0.039	0.318	-0.039	0.866

Appendix

Table 4 : Treatment threshold, method, ATE estimates, 95% confidence intervals and p-values. Refutation tests fail if their p-value is less than 0.05. Yearly dataset.

Cause Effect Estimation					Refutation Tests
Cause Effect Estimation					Placebo		RCC		RSR
Th	Method	ATE	CI	p-value	Effect*	p-value	Effect*	p-value	Effect*	p-value
30	LR	-0.036	(-0.059, -0.015)	0.001	0.000	0.980	-0.036	0.992	-0.036	0.986
30	M	-0.033	(-0.056, -0.011)	0.004	0.004	0.920	-0.032	0.802	-0.033	0.982
30	IPS W	-0.032	(-0.075, 0.007)	0.133	0.001	0.420	-0.032	0.787	-0.032	0.938
30	T-L	-0.050	(-0.073, -0.024)	0.001	0.000	0.480	-0.047	0.009	-0.047	0.614

Appendix

Table 5 : Treatment threshold, method, ATE estimates, 95% confidence intervals and p-values. Refutation tests fail if their p-value is less than 0.05. Seasonal dataset.

Cause Effect Estimation					Refutation Tests
Cause Effect Estimation					Placebo		RCC		RSR
Th	Method	ATE	CI	p-value	Effect*	p-value	Effect*	p-value	Effect*	p-value
30	LR	-0.023	(-0.044, -0.004)	0.018	0.000	0.002	-0.023	0.995	-0.023	0.989
30	M	-0.029	(-0.051, -0.008)	0.007	0.002	0.020	-0.030	0.574	-0.028	0.913
30	IPS W	-0.015	(-0.056, 0.027)	0.470	0.000	0.201	-0.021	0.001	-0.021	0.003
30	T-L	-0.041	(-0.066, -0.018)	0.001	-0.000	0.430	-0.041	0.927	-0.041	0.974

Appendix

Table 6 : Treatment threshold, method, ATE estimates, 95% confidence intervals and p-values. Refutation tests fail if their p-value is less than 0.05. IPC-based dataset.

Cause Effect Estimation					Refutation Tests
Cause Effect Estimation					Placebo		RCC		RSR
Th	Method	ATE	CI	p-value	Effect*	p-value	Effect*	p-value	Effect*	p-value
30	LR	-0.024	(-0.043, -0.003)	0.024	0.000	0.980	-0.025	0.996	-0.024	0.988
30	M	-0.026	(-0.049, -0.006)	0.012	0.001	0.980	-0.029	0.465	-0.026	0.876
30	IPS W	-0.012	(-0.050, 0.026)	0.541	-0.000	0.217	-0.017	0.001	-0.017	0.009
30	T-L	-0.021	(-0.038, -0.018)	0.001	-0.000	0.490	-0.021	0.942	-0.021	0.976

Appendix

Table 7 : Treatment threshold, method, ATE estimates, 95% confidence intervals and p-values. Refutation tests fail if their p-value is less than 0.05. Monthly dataset.

Explore similar work

Climate Variability Modulates the Impact of Price Spikes on Food Insecurity
Sep 21, 2026Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, Homer Durand +3
Population-level measures of perceived food access reveal barriers beyond geographic proximity
Sep 14, 2026Teresa Groton, Benjamin rachunokNon-Destructive Frying Oil Oxidation Assessment Proximity Operations
Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings
Sep 14, 2026Steven Ndung'u, Adel Daoud, Ismael Yacoubou Djima +2Humanitarian Reports Sri Lanka

Sep 21, 2026 · cs.LGJ/K move · Enter open · S save

Climate Variability Modulates the Impact of Price Spikes on Food Insecurity

Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, Homer Durand, Gherardo Varando+2

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

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.

Open paper Ask about it PDF

Abstract

Explore similar work

Climate Variability Modulates the Impact of Price Spikes on Food Insecurity

Population-level measures of perceived food access reveal barriers beyond geographic proximity

Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings