Publication: Predicting Food Crises
Loading...
Files in English
2,398 downloads
Published
2020-09
ISSN
Date
2020-09-24
Author(s)
Editor(s)
Abstract
Globally, more than 130 million people are estimated to be in food crisis. These humanitarian disasters are associated with severe impacts on livelihoods that can reverse years of development gains. The existing outlooks of crisis-affected populations rely on expert assessment of evidence and are limited in their temporal frequency and ability to look beyond several months. This paper presents a statistical forecasting approach to predict the outbreak of food crises with sufficient lead time for preventive action. Different use cases are explored related to possible alternative targeting policies and the levels at which finance is typically unlocked. The results indicate that, particularly at longer forecasting horizons, the statistical predictions compare favorably to expert-based outlooks. The paper concludes that statistical models demonstrate good ability to detect future outbreaks of food crises and that using statistical forecasting approaches may help increase lead time for action.
Link to Data Set
Citation
“Spencer, Phoebe; Kraay, Aart; Wang, Dieter; Andree, Bo, Pieter Johannes. 2020. Predicting Food Crises. Policy Research Working Paper;No. 9412. © World Bank. http://hdl.handle.net/10986/34510 License: CC BY 3.0 IGO.”
Digital Object Identifier
Associated URLs
Associated content
Other publications in this report series
Publication Climate and Social Sustainability in Fragility, Conflict, and Violence Contexts(Washington, DC: World Bank, 2026-01-07)Climate change is widely recognized as a driver of violent conflict, but its broader social effects remain less understood. Ignoring these dimensions risks a vicious cycle where climate policies might undermine socially just adaptation. Evidence is still limited on how climate shocks influence political participation, trust, or migration. This paper helps fill that gap by examining links between climate change, conflict, and social sustainability, with a focus on inclusion, resilience, cohesion, and legitimacy. Using secondary data from 2019–24, the study applies simple correlation-based methods to test three hypotheses on the nature, severity, and composition of these associations. The analysis combines multiple climate impact measures, new conflict classifications, recent social sustainability frameworks, and controls for population and geography. The results reveal strong correlations—not causation—between climate events and contexts of fragility, conflict, and violence. Climate impacts are most pronounced in both national and subnational conflict settings. The study also finds robust links between fragility, conflict, and violence and low levels of social sustainability, reflecting its role as both a driver and consequence of conflict. Some dimensions—such as violent events and insecurity—appear weaker in areas most affected by climate shocks. Two of the hypotheses are supported, and one remains inconclusive.Publication The Macroeconomic Implications of Climate Change Impacts and Adaptation Options(Washington, DC: World Bank, 2025-05-29)Estimating the macroeconomic implications of climate change impacts and adaptation options is a topic of intense research. This paper presents a framework in the World Bank's macrostructural model to assess climate-related damages. This approach has been used in many Country Climate and Development Reports, a World Bank diagnostic that identifies priorities to ensure continued development in spite of climate change and climate policy objectives. The methodology captures a set of impact channels through which climate change affects the economy by (1) connecting a set of biophysical models to the macroeconomic model and (2) exploring a set of development and climate scenarios. The paper summarizes the results for five countries, highlighting the sources and magnitudes of their vulnerability --- with estimated gross domestic product losses in 2050 exceeding 10 percent of gross domestic product in some countries and scenarios, although only a small set of impact channels is included. The paper also presents estimates of the macroeconomic gains from sector-level adaptation interventions, considering their upfront costs and avoided climate impacts and finding significant net gross domestic product gains from adaptation opportunities identified in the Country Climate and Development Reports. Finally, the paper discusses the limits of current modeling approaches, and their complementarity with empirical approaches based on historical data series. The integrated modeling approach proposed in this paper can inform policymakers as they make proactive decisions on climate change adaptation and resilience.Publication Institutional Capacity for Policy Implementation: An Analytical Framework(Washington, DC: World Bank, 2026-01-07)State capacity is an important prerequisite for policy implementation, yet at the country level it is difficult to measure, assess, and reform. This paper proposes a focus on institutional capacity: the ability of public institutions to implement the specific policy mandates for which they are responsible. Based on a review of existing literature, the paper defines the different dimensions that compose institutional capacity and groups them into two cross-cutting categories: organizational dimensions (personnel, financial resources, information systems, and management practices) and governance dimensions (transparency, independence, and accountability). The paper proposes measures for organizational and governance dimensions using existing data, shows intra-institutional variation of these measures within countries, and discusses how new data could be collected for better measurement of these concepts. Finally, the paper illustrates how the framework can be used to diagnose the sources of common problems related to weak policy implementation.Publication South Africa’s Fragmented Cities: The Unequal Burden of Labor Market Frictions(Washington, DC: World Bank, 2026-01-08)Using high-resolution administrative, census, and satellite data, this paper shows that South African cities are characterized by spatial mismatches between where people live and where jobs are located, relative to 20 global peers. Areas within 5 kilometers of commercial centers have 9,300 fewer residents per square kilometer than expected, which is 60 percent below the global median. Poor, dense neighborhoods are most affected. In Johannesburg, a 10-percentile increase in distance from the nearest business hub corresponds to a 3.7-percentile drop in asset wealth (a proxy of household wellbeing) and 4.9-percentile drop in employment. In Cape Town, the declines are 4.0 and 3.7 percentiles, respectively. Employment is 87 percent lower in the poorest decile than the richest in Johannesburg and 61 percent lower in Cape Town. These findings suggest that South Africa’s spatial organization of people and economic activity constrains agglomeration and reinforces inequality. This methodology provides a scalable and standardized data-driven framework to analyze spatial accessibility and agglomeration frictions in complex, data-constrained urban systems.Publication Investment in Emerging and Developing Economies(Washington, DC: World Bank, 2026-01-07)The world faces a pressing challenge to meet key development objectives amid slowing growth and rising macroeconomic and geopolitical risks. With the number of job seekers rising rapidly, infrastructure shortfalls continuing to be large, and climate costs mounting, the case for a significant investment push has never been stronger. Yet the capacity to respond in many emerging markets and developing economies has eroded. Since the global financial crisis, investment growth has slowed to about half its pace in the 2000s, with both public and private investment weakening. Foreign direct investment inflows—a critical source of capital, technology, and managerial know-how—have also fallen sharply and become increasingly concentrated, leaving low-income countries with only a marginal share. The risks of further retrenchment are significant, as trade tensions, policy uncertainty, and elevated debt levels continue to weigh on investment. Reigniting momentum will require ambitious domestic reforms to strengthen institutions, rebuild macro-fiscal stability, and deepen trade and investment integration—the foundations of a supportive business climate. At the same time, international cooperation is indispensable. A renewed commitment to a predictable system of cross-border trade and investment flows, combined with scaled-up financial support and sustained technical assistance, is essential to help emerging markets and developing economies—especially low-income countries and economies in fragile and conflict situations—bridge financing gaps and implement the domestic reforms needed to restore investment as an engine of growth, jobs, and development.
Journal
Journal Volume
Journal Issue
Collections
Related items
Showing items related by metadata.
Publication Stochastic Modeling of Food Insecurity(World Bank, Washington, DC, 2020-09)Recent advances in food insecurity classification have made analytical approaches to predict and inform response to food crises possible. This paper develops a predictive, statistical framework to identify drivers of food insecurity risk with simulation capabilities for scenario analyses, risk assessment and forecasting purposes. It utilizes a panel vector-autoregression to model food insecurity distributions of 15 Sub-Saharan African countries between October 2009 and February 2019. Statistical variable selection methods are employed to identify the most important agronomic, weather, conflict and economic variables. The paper finds that food insecurity dynamics are asymmetric and past-dependent, with low insecurity states more likely to transition to high insecurity states than vice versa. Conflict variables are more relevant for dynamics in highly critical stages, while agronomic and weather variables are more important for less critical states. Food prices are predictive for all cases. A Bayesian extension is introduced to incorporate expert opinions through the use of priors, which lead to significant improvements in model performance.Publication Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks(World Bank, Washington, DC, 2019-02)This paper introduces a Spatial Vector Autoregressive Moving Average (SVARMA) model in which multiple cross-sectional time series are modeled as multivariate, possibly fat-tailed, spatial autoregressive ARMA processes. The estimation requires specifying the cross-sectional spillover channels through spatial weights matrices. the paper explores a kernel method to estimate the network topology based on similarities in the data. It discusses the model and estimation, focusing on a penalized Maximum Likelihood criterion. The empirical performance of the estimator is explored in a simulation study. The model is used to study a spatial time series of pollution and household expenditure data in Indonesia. The analysis finds that the new model improves in terms of implied density, and better neutralizes residual correlations than the VARMA, using fewer parameters. The results suggest that growth in household expenditures precedes pollution reduction, particularly after the expenditures of poorer households increase; that increasing pollution is followed by reduced growth in expenditures, particularly reducing the growth of poorer households; and that there are significant spillovers from bottom-up growth in expenditures. The paper does not find evidence for top-down growth spillovers. Feedback between the identified mechanisms may contribute to pollution-poverty traps and the results imply that pollution damages are economically significant.Publication Machine Learning Guided Outlook of Global Food Insecurity Consistent with Macroeconomic Forecasts(World Bank, Washington, DC, 2022-10)Motivated by the deterioration in global food security conditions, this paper develops a parsimonious machine learning model to derive a multi-year outlook of global severe food insecurity from macro-economic projections. The objective is to provide forecasts that are internally consistent with wider economic assessments, allowing both food security policies and economic development policies to be informed by a cohesive set of expectations. The model is validated on holdout data that explicitly test the ability to forecast new data from history and extrapolate beyond observed intervals. It is then applied to the World Economic Outlook database of April 2022 to project the severely food insecure population across all 144 World Bank lending countries. The analysis estimates that the global severely food insecure population may remain above 1 billion through 2027 unless large-scale interventions are made. The paper also explores counterfactual scenarios, first to investigate additional risks in a downside economic scenario, and second, to investigate whether restoring macroeconomic targets is sufficient to revert food insecurity back to pre-pandemic levels. The paper concludes that the proposed model provides a robust and low-cost approach to maintain reliable long-term projections and produce scenario analyses that can be revised systematically and interpreted within the context of available economic outlooks.Publication Environment and Development(World Bank, Washington, DC, 2019-02)This paper revisits the issue of environment and development raised in the 1992 World Development Report, with new analysis tools and data. The paper discusses inference and interpretation in a machine learning framework. The results suggest that production gradually favors conserving the earth's resources as gross domestic product increases, but increased efficiency alone is not sufficient to offset the effects of growth in scale. Instead, structural change in the economy shapes environmental outcomes across GDP. The analysis finds that average development is associated with an inverted $U$-shape in deforestation, pollution, and carbon intensities. Per capita emissions follow a $J$-curve. Specifically, poverty reduction occurs alongside degrading local environments and higher income growth poses a global burden through carbon. Local economic structure further determines the shape, amplitude, and location of tipping points of the Environmental Kuznets Curve. The models are used to extrapolate environmental output to 2030. The daunting implications of continued development are a reminder that immediate and sustained global efforts are required to mitigate forest loss, improve air quality, and shift the global economy to a 2°pathway.Publication Comparative Analysis of AI-Predicted and Crowdsourced Food Prices in an Economically Volatile Region(Washington, DC: World Bank, 2024-04-23)High-frequency monitoring of food commodity prices is important for assessing and responding to shocks, especially in fragile contexts where timely and targeted interventions for food security are critical. However, national price surveys are typically limited in temporal and spatial granularity. It is cost prohibitive to implement traditional data collection at frequent timescales to unravel spatiotemporal price evolution across market segments and at subnational geographic levels. Recent advancements in data innovation offer promising solutions to address the paucity of commodity price data and guide market intelligence for diverse development stakeholders. The use of artificial intelligence to estimate missing price data and a parallel effort to crowdsource commodity price data are both unlocking cost-effective opportunities to generate actionable price data. Yet, little is known about how the data from these alternative methods relate to independent ground truth data. To evaluate if these data strategies can meet the long-standing demand for real-time intelligence on food affordability, this paper analyzes open-source daily crowdsourced data (104,931 datapoints) from a recently published data set in Nature Journal, relative to complementary ground truth sample. The paper subsequently compares these data to open-source monthly artificial intelligence–generated price data for identical commodities over a 36-month period in northern Nigeria, from 2019 to 2022. The results show that all the data sources share a high degree of comparability, with variation across commodity and market segments. Overall, the findings provide important support for leveraging these new and innovative data approaches to enable data-driven decision-making in near real time.
Users also downloaded
Showing related downloaded files
Publication How to Make Grants a Better Match for Private Sector Development(World Bank, Washington, DC, 2016)Matching grants (MGs) have been implemented by the World Bank for over two decades. They remain a very popular instrument for private sector development interventions, despite often challenging implementation and insufficient evidence of impact. The objective of this study is to synthesize the current knowledge on MGs and to review the experience with this instrument, as designed and implemented by the World Bank from the early 1990s to the present. In doing so, we hope to equip teams in charge of ongoing and planned MG operations with a better understanding of the instrument and to help them choose the design and implementation arrangements that are best fitted to their objectives. The authors look at both the ‘why’ and the ‘how’ of MG programs, focusing on those aiming to foster private sector development and small and medium enterprises (SMEs) competitiveness primarily through the use of business development services (BDS). The authors also look at how success is defined and question the way the outcome of MG programs is often assessed. While 75 percent of projects in the sample were deemed to have some degree of success, the definition of success rarely reflects a measurement of broad and sustainable economic benefits that will justify the subsidization of private enterprises with public funds. We argue that this is linked to a common weakness in identifying a sound economic rationale, such as a specific market failure to be solved, and in subsequently not focusing the design and measurement of the MG on the fund’s additionality beyond the private benefit of recipients. The authors conclude that a robust definition of the economic rationale is a critical prerequisite for the use of MG programs and should include, among others, an identification of a specific market failure, consideration of alternative instruments, cost-benefit analysis, assessment of the potential for additionality and spillovers, and a realistic exit strategy. The authors recommend an equally robust monitoring and evaluation (M&E) system tied directly to the economic rationale, which is essential for real-time assessment of impact, potential course correction, and learning, and could be utilized to gauge additionality and sustainability. Increased attention to these elements could help teams make the most of this potentially powerful instrument for private sector development and competitiveness.Publication Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage(World Bank, Washington, DC, 2021-07)Advances in agricultural data production provide ever-increasing opportunities for pushing the research frontier in agricultural economics and designing better agricultural policy. As new technologies present opportunities to create new and integrated data sources, researchers face trade-offs in survey design that may reduce measurement error or increase coverage. This paper first reviews the econometric and survey methodology literatures that focus on the sources of measurement error and coverage bias in agricultural data collection. Second, it provides examples of how agricultural data structure affects testable empirical models. Finally, it reviews the challenges and opportunities offered by technological innovation to meet old and new data demands and address key empirical questions, focusing on the scalable data innovations of greatest potential impact for empirical methods and research.Publication Digital Africa(Washington, DC: World Bank, 2023-03-13)All African countries need better and more jobs for their growing populations. "Digital Africa: Technological Transformation for Jobs" shows that broader use of productivity-enhancing, digital technologies by enterprises and households is imperative to generate such jobs, including for lower-skilled people. At the same time, it can support not only countries’ short-term objective of postpandemic economic recovery but also their vision of economic transformation with more inclusive growth. These outcomes are not automatic, however. Mobile internet availability has increased throughout the continent in recent years, but Africa’s uptake gap is the highest in the world. Areas with at least 3G mobile internet service now cover 84 percent of Africa’s population, but only 22 percent uses such services. And the average African business lags in the use of smartphones and computers as well as more sophisticated digital technologies that catalyze further productivity gains. Two issues explain the usage gap: affordability of these new technologies and willingness to use them. For the 40 percent of Africans below the extreme poverty line, mobile data plans alone would cost one-third of their incomes—in addition to the price of access devices, apps, and electricity. Data plans for small- and medium-size businesses are also more expensive than in other regions. Moreover, shortcomings in the quality of internet services—and in the supply of attractive, skills-appropriate apps that promote entrepreneurship and raise earnings—dampen people’s willingness to use them. For those countries already using these technologies, the development payoffs are significant. New empirical studies for this report add to the rapidly growing evidence that mobile internet availability directly raises enterprise productivity, increases jobs, and reduces poverty throughout Africa. To realize these and other benefits more widely, Africa’s countries must implement complementary and mutually reinforcing policies to strengthen both consumers’ ability to pay and willingness to use digital technologies. These interventions must prioritize productive use to generate large numbers of inclusive jobs in a region poised to benefit from a massive, youthful workforce—one projected to become the world’s largest by the end of this century.Publication Future of Food(World Bank, Washington, DC, 2019-04)Digital technologies have significant potential to improve efficiency, equity, and environmental sustainability in the food system. A range of digital technologies are already leading to: better informed and engaged consumers and producers, smarter farms, and improved public services. Adoption of digital technologies varies significantly across countries, with lower current adoption rates in low-income countries. Increasing adoption will require addressing supply-side factors, such as rural network coverage and availability of digital applications, and demand-side factors, including skills and knowledge, trust, affordability, and complementary investments. While digital technologies have significant potential they also pose several risks that need to be addressed including: an overconcentration of service provider market power; lack of data privacy; exclusion; and cybersecurity breaches. These risks cut across all segments of the economy, including the food system. In addition, digital technologies should not be viewed as a panacea. Other investments are needed to address the multiple constraints farmers face and to realize the potential benefits of digital technologies.Publication Estimating Food Price Inflation from Partial Surveys(World Bank, Washington, DC, 2021-12)The traditional consumer price index is often produced at an aggregate level, using data from few, highly urbanized, areas. As such, it poorly describes price trends in rural or poverty-stricken areas, where large populations may reside in fragile situations. Traditional price data collection also follows a deliberate sampling and measurement process that is not well suited for monitoring during crisis situations, when price stability may deteriorate rapidly. To gain real-time insights beyond what can be formally measured by traditional methods, this paper develops a machine-learning approach for imputation of ongoing subnational price surveys. The aim is to monitor inflation at the market level, relying only on incomplete and intermittent survey data. The capabilities are highlighted using World Food Programme surveys in 25 fragile and conflict-affected countries where real-time monthly food price data are not publicly available from official sources. The results are made available as a data set that covers more than 1200 markets and 43 food types. The local statistics provide a new granular view on important inflation events, including the World Food Price Crisis of 2007–08 and the surge in global inflation following the 2020 pandemic. The paper finds that imputations often achieve accuracy similar to direct measurement of prices. The estimates may provide new opportunities to investigate local price dynamics in markets where prices are sensitive to localized shocks and traditional data are not available.