It is widely recognized that national or global shocks can affect household food security in rural areas of low-income countries. However, there is still limited rigorous micro-level evidence regarding how such impacts manifest and are distributed. A primary reason for this gap is data limitations; many studies are unable to adequately control for the seasonal and inter-annual variations in production and consumption that can bias identification of the effects of large, covariate shocks. We use three years of monthly household panel data to characterize the food security effects of COVID-19 in Southern Malawi. These high frequency data begin two years before the pandemic, allowing us to control for seasonality and inter-annual variation in growing conditions and to demonstrate how these confounding factors can lead to spurious estimates. We find that a strong 2020 harvest coincided with the COVID-19 shock and buffered households on average from food insecurity. We show, however, that the strong harvest on average was insufficient to protect the poorest households: the majority (74%) of the most food insecure households pre-pandemic was worse-off in 2020 than they would have been otherwise. We use qualitative methods to explore underlying mechanisms: the pandemic led better-off households to reduce their demand for local labor, resulting in declining earning opportunities for the poorest households. Our findings demonstrate the importance of controlling for concurrent dynamics to generate more accurate estimates about how large covariate shocks are associated with household food security, particularly among the most vulnerable rural households.

For many countries in the Global South traditional poverty estimates are available only infrequently and at coarse spatial resolutions, if at all. This limits decision-makers’ and analysts’ ability to target humanitarian and development interventions and makes it difficult to study relationships between poverty and other natural and human phenomena at finer spatial scales. Advances in Earth observation and machine learning-based methods have proven capable of generating more granular estimates of relative asset wealth indices. They have been less successful in predicting the consumption-based poverty measures most commonly used by decision-makers, those tied to national and international poverty lines. For a study area including four countries in southern and eastern Africa, we pilot a two-step approach that combines Earth observation, accessible machine learning methods, and asset-based structural poverty measurement to address this gap. This structural poverty approach to machine learning-based poverty estimation preserves the interpretability and policy-relevance of consumption-based poverty measures, while allowing us to explain 72- to 78-percent of cluster-level variation in a pooled model and 40- to 54-percent even when predicting out-of-country.

Recent research highlights how the same vulnerabilities that lead to disasters also condition the impact of hazards on violent conflict. Yet it is common practice in the literature to proxy rapid-onset hazards with disaster impacts when studying political violence. This can bias upward estimates of hazard-conflict relationships and obscure heterogeneous effects, with implications for forecasting as well as disaster risk reduction and peace-building activities. To overcome this, we implement an approach that measures and models the separate components of a tropical cyclone event: the hazard, the exposure, and the impacts. We then estimate a set of models that quantify how the incidence and intensity of organized violence respond to hazard exposure. We find little evidence that the average tropical cyclone enhances or diminishes violent conflict at the country level over a two year time horizon. Yet rather than signaling that storms do not matter for political violence, unpacking this average result reveals two countervailing effects within countries. Conflict, and especially one-sided violence against civilians, tends to escalate in regions directly exposed to the tropical cyclone. In contrast, areas outside the path of the storm may experience a decrease in conflict. These results are heterogeneous with tropical cyclone intensity, and conflict escalation is more likely to occur in settings with less effective governments. Our results underscore the importance of ex-ante efforts targeting government capacity and effective disaster risk reduction to moderate the risk of violent conflict in the wake of tropical cyclones.

Resilience offers a useful lens for studying how human well-being and agri-food systems absorb and recover from a range of shocks and stressors, including the COVID-19 pandemic. Looking beyond the direct effects of observable shocks to the mechanisms that shape their impacts can guide our understanding of COVID-19 and leverage findings from the pandemic to better understand resilience to future shocks. We develop a conceptual framework for the multiple paths through which observed shocks interact with systemic mechanisms to influence resilience. We illustrate this framework with reference to the pandemic and policy responses as they unfolded in three rural areas in Malawi, Madagascar, and Kenya. Consistent with this framework, we find multiple pathways through which the pandemic affected household food security and resilience. Our findings highlight that, in some settings, the direct effects—in this case severe illness and mortality from SARS-CoV-2—may impact fewer people than the indirect impacts that arise as behaviors, markets, and policies adjust. We illustrate that although COVID-19 is a new shock, its massive, broad-reaching impacts manifest through familiar stressors and uncertainties that frequently burden poor rural populations in much of the low- and middle-income world.

Development and humanitarian agencies have rapidly embraced the concept of resilience since the 2008 global financial and food price crises. We report the results of a formal scoping review of the literature on development resilience over the ensuing period. The review identifies the theoretical and methodological underpinnings and empirical applications of resilience as the concept has been applied to individual or household well-being in low-and middle-income countries. From 9,558 search records spanning 2008-20, 301 studies met our pre-registered inclusion criteria. Among these, we identify three broad conceptualizations employed – resilience as capacity, as a normative condition, or as return to equilibrium – and explain how the resulting variation in framing leads to marked differences in empirical methods and findings. We study in greater depth a set of 45 studies that met five key criteria for empirical studies of resilience. The larger, more established, qualitative empirical literature yields insights suggestive that the concept of resilience can add value. The quantitative literature is thinner and divided among methods that limit cross-study comparability of findings. Overall, we find that development resilience remains inconsistently theorized and reliant on methods that have not been adequately reconciled to identify which tools are best suited to which questions. Despite much published evidence, most findings concentrate on just a few countries and natural shocks, and rely on cross-sectional data at just one scale of analysis. The result is a dearth of generalizable evidence, especially of rigorous impact evaluations, to guide whether or how agencies might build resilience among target populations.

Strong institutions as well as economic development are generally understood to play critical roles in protecting societies from the adverse impacts of natural hazards, such as tropical cyclones. The independent effect of institutions on reducing these risks, however, has not been confirmed empirically in previous global studies. As a storm's path and intensity influence the severity of the damages and may be spatially correlated with human vulnerabilities, failing to accurately capture the physical exposure in an econometric analysis may result in imprecise and possibly biased estimates of the influence of the independent variables. Here, we develop a novel approach to control for the physical exposure by spatially interacting meteorological and socioeconomic data for over one-thousand tropical cyclone disasters from 1979 to 2016. We find new evidence that higher levels of national government effectiveness are associated with lower tropical cyclone mortality, even when controlling for other socioeconomic conditions such as GDP per capita. Within countries, deaths are higher when strong winds are concentrated over areas of the country with weaker or less inclusive institutions. These results suggest that policies and programs to enhance institutional capacity and governance can support risk reduction from extreme weather events.

This special issue on “Bridging Research and Policy on Climate Change and Conflict” brings together the results of a 2018 workshop organized by the Peace Research Institute, Oslo (PRIO) and the Wilson Center with six papers that address different aspects of the translation of the research on climate change and conflict to policy and practice. Here, we provide an overview of the workshop and papers to highlight key opportunities and challenges to linking the climate-conflict scholarship with pressing issues in diplomacy, development, and security. Multiple methods, especially comparative case studies, should be applied to elucidate the more complex mechanisms of the climate-conflict link. This approach may also enhance engagement with the policymakers who draw on examples and narratives. There is also a need for both predictive models that capture contextual factors and policy interactions as well as decision-support tools, such as integrated assessment models, that can be used to test the implications of different theories and models in the literature. Scholars should engage the policy community to formulate research questions that are more policy relevant, such as the effectiveness of interventions. There is also the need for models and frameworks that help practitioners synthesize the academic results. Practitioners are encouraged to leverage the comparative advantages of academic researchers in new policy and projects to inform data collection and future analysis of effectiveness.

Greater investments in community college workforce education are fostering large-scale partnerships between employers and educators. However, the evaluation work in this area has focused on outcome and productivity metrics, rather than addressing measures of implementation quality, which is critical to scaling any innovation. To deepen understanding of the field, sound metrics need to be assembled of the processes involved in workforce education (e.g., partnering with employers, designing and delivering instruction). This article addresses that gap with the Workforce Education Implementation Evaluation (WEIE) framework. Relying on five case studies of employer-community college collaborations, and drawing on labor market analysis methods, partnership capital and regional ecosystem theory, and the learning sciences, the WEIE framework provides tools to characterize implementation quality. To illustrate how it works, we have applied the framework to two contrasting cases that represent the predominant approaches to engaging employers in workforce education programming: large-scale partnership and employer outreach.