Person:
Andree, Bo, Pieter Johannes

Fields of Specialization

Inflation, Food Insecurity, Statistics, Time Series, Machine Learning, Forecasting

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Last updated: June 11, 2025

Biography

Bo Pieter Johannes Andree is a Data Scientist in the Development Data Group, where his work focuses on data-poor regions. He joined the World Bank in 2017 and worked in the Environment and Natural Resources Global Practice and in the Fragility, Conflict & Violence global themes. He worked on environmental issues and crisis analytics. Bo’s research interests include applied forecasting and machine learning. His work has been published in journals such as the Renewable and Sustainable Energy Reviews, Agricultural Systems, and World Development. He serves as a reviewer for the World Bank Economic Review and Nature. In the Data Group, Bo is responsible for maintaining Real Time Food Prices (RTFP), a real-time inflation dataset compiled and updated using a combination of direct measurement and machine learning estimation. Prior to joining the World Bank, Bo worked for the OECD, Joint Research Centre, and the Asian Development Bank. He holds a Ph.D. in econometrics from the Tinbergen Institute.

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Publication Search Results

Now showing 1 - 9 of 9

Altered Destinies: The Long-Term Effects of Rising Prices and Food Insecurity in the Middle East and North Africa
(Washington, DC : World Bank, 2023-04-06) Gatti, Roberta; Lederman, Daniel; Islam, Asif M.; Andree, Bo, Pieter Johannes; Lotfi, Rana; Mousa, Mennatallah Emam; Bennett, Federico; Assem, Hoda
Growth is forecasted to slow down for the Middle East and North Africa region. The war in Ukraine in 2022 exacerbated inflationary pressures as the world recovered from the COVID 19 pandemic induced recession. The response by central banks to raise rates to curb inflation is slowing economic activity, while rising food prices are making it difficult for families to put meals on the table. Inflation, when it stems from food prices, hits the poor harder than the rich, thus compounding food insecurity in MENA that had been rising over decades. The immediate effects of food insecurity can be a devastating loss of life, but even temporary increases in food prices can cause long-term irreversible damages, especially to children. The rise in food prices due to the war in Ukraine may have altered the destinies of hundreds of thousands of children in the region, setting them on paths to limited prosperity. Food insecurity imposes challenges to a region where the state of child nutrition and health were inadequate before the shocks from the COVID-19 pandemic. The report discusses policy options and highlights the need for data to guide effective decision making.
Machine Learning Imputation of High Frequency Price Surveys in Papua New Guinea
(World Bank, Washington, DC, 2023-09-28) Andrée, Bo Pieter Johannes; Pape, Utz Johann; Andree, Bo, Pieter Johannes
Capabilities to track fast-moving economic developments re-main limited in many regions of the developing world. This complicates prioritizing policies aimed at supporting vulnerable populations. To gain insight into the evolution of fluid events in a data scarce context, this paper explores the ability of recent machine-learning advances to produce continuous data in near-real-time by imputing multiple entries in ongoing surveys. The paper attempts to track inflation in fresh produce prices at the local market level in Papua New Guinea, relying only on incomplete and intermittent survey data. This application is made challenging by high intra-month price volatility, low cross-market price correlations, and weak price trends. The modeling approach uses chained equations to produce an ensemble prediction for multiple price quotes simultaneously. The paper runs cross-validation of the prediction strategy under different designs in terms of markets, foods, and time periods covered. The results show that when the survey is well-designed, imputations can achieve accuracy that is attractive when compared to costly–and logistically often infeasible–direct measurement. The methods have wider applicability and could help to fill crucial data gaps in data scarce regions such as the Pacific Islands, especially in conjunction with specifically designed continuous surveys.
Machine Learning Guided Outlook of Global Food Insecurity Consistent with Macroeconomic Forecasts
(World Bank, Washington, DC, 2022-10) Andree, Bo Pieter Johannes; Andree, Bo, Pieter Johannes
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.
Estimating Food Price Inflation from Partial Surveys
(World Bank, Washington, DC, 2021-12) Andree, Bo Pieter Johannes; Andree, Bo, Pieter Johannes
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.
Predicting Food Crises
(World Bank, Washington, DC, 2020-09) Spencer, Phoebe; Kraay, Aart; Wang, Dieter; Andree, Bo, Pieter Johannes
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.
Stochastic Modeling of Food Insecurity
(World Bank, Washington, DC, 2020-09) Wang, Dieter; Andree, Bo Pieter Johannes; Chamorro, Andres Fernando; Girouard Spencer, Phoebe; Andree, Bo, Pieter Johannes
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.
Incidence of COVID-19 and Connections with Air Pollution Exposure: Evidence from the Netherlands
(World Bank, Washington, DC, 2020-04) Andree, Bo Pieter Johannes; Andree, Bo, Pieter Johannes
The fast spread of severe acute respiratory syndrome coronavirus 2 has resulted in the emergence of several hot-spots around the world. Several of these are located in areas associated with high levels of air pollution. This study investigates the relationship between exposure to particulate matter and COVID-19 incidence in 355 municipalities in the Netherlands. The results show that atmospheric particulate matter with diameter less than 2.5 is a highly significant predictor of the number of confirmed COVID-19 cases and related hospital admissions. The estimates suggest that expected COVID-19 cases increase by nearly 100 percent when pollution concentrations increase by 20 percent. The association between air pollution and case incidence is robust in the presence of data on health-related preconditions, proxies for symptom severity, and demographic control variables. The results are obtained with ground-measurements and satellite-derived measures of atmospheric particulate matter as well as COVID-19 data from alternative dates. The findings call for further investigation into the association between air pollution and SARS-CoV-2 infection risk. If particulate matter plays a significant role in COVID-19 incidence, it has strong implications for the mitigation strategies required to prevent spreading.
Environment and Development: Penalized Non-Parametric Inference of Global Trends in Deforestation, Pollution and Carbon
(World Bank, Washington, DC, 2019-02) Andree, Bo Pieter Johannes; Chamorro, Andres; Spencer, Phoebe; Dogo, Harun; Andree, Bo, Pieter Johannes
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.
Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks
(World Bank, Washington, DC, 2019-02) Andree, Bo Pieter Johannes; Spencer, Phoebe; Chamorro, Andres; Wang, Dieter; Azari, Sardar Feredun; Dogo, Harun; Andree, Bo, Pieter Johannes
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.