Search arXivSearch

arXiv · 2006.10696

Conflict in Africa during COVID-19: social distancing, food vulnerability and welfare response

Abstract

We study the effect of social distancing, food vulnerability, welfare and labour COVID-19 policy responses on riots, violence against civilians and food-related conflicts. Our analysis uses georeferenced data for 24 African countries with monthly local prices and real-time conflict data reported in the Armed Conflict Location and Event Data Project (ACLED) from January 2015 until early May 2020. Lockdowns and recent welfare policies have been implemented in light of COVID-19, but in some contexts also likely in response to ongoing conflicts. To mitigate the potential risk of endogeneity, we use instrumental variables. We exploit the exogeneity of global commodity prices, and three variables that increase the risk of COVID-19 and efficiency in response such as countries colonial heritage, male mortality rate attributed to air pollution and prevalence of diabetes in adults. We find that the probability of experiencing riots, violence against civilians, food-related conflicts and food looting has increased since lockdowns. Food vulnerability has been a contributing factor. A 10% increase in the local price index is associated with an increase of 0.7 percentage points in violence against civilians. Nonetheless, for every additional anti-poverty measure implemented in response to COVID-19 the probability of experiencing violence against civilians, riots and food-related conflicts declines by approximately 0.2 percentage points. These anti-poverty measures also reduce the number of fatalities associated with these conflicts. Overall, our findings reveal that food vulnerability has increased conflict risks, but also offer an optimistic view of the importance of the state in providing an extensive welfare safety net.

Explore related subjects

Keep this discovery

BibTeXRIS

Roxana Gutiérrez-Romero. 2020-06-18. Conflict in Africa during COVID-19: social distancing, food vulnerability and welfare response. https://arxiv.org/abs/2006.10696

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Identification in Linear Quantile Panel Models

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

econ.EM

Experimental Design for Policy Choice

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

econ.EM

Designing Spatial Treatments

Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a unit's response diminishes with distance to a treated site. Our estimand of interest is an ``uncontaminated'' effect equal to the average impact of a single intervention site over all hypothetical sites. We propose a novel design based on a Mat\'{e}rn point process which separates treatments by a distance of at least $r$. A larger choice of $r$ reduces bias by separating interventions but increases variance by reducing their numerosity. We choose $r$ to maximize the rate of convergence of a Horvitz-Thompson estimator and prove that this is minimax rate-optimal. We provide weak conditions under which the estimator is asymptotically normal and propose a variance estimator.

econ.EM