Corral, Paul

Person:
Corral, Paul

Author Name Variants

Corral, Paul, Corral Rodas, Paul Andres, Corral Rodas, Paul A.

Fields of Specialization

Human Development

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Blog

Last updated:October 6, 2025

Biography

Paul Corral is a senior economist in the Office of the HD Chief Economist at the World Bank. He previously worked as a data scientist with the Poverty and Equity Global Practice, where he focused on small area estimation methods and applications. He has published peer-reviewed articles on agriculture and development for specific African countries and is the author of multiple Stata commands. He holds a PhD in economics from American University and an MSc degree in agricultural economics from the University of Hohenheim.

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Now showing1 - 10 of 22

The Future of Poverty: Projecting the Impact of Climate Change on Global Poverty through 2050
(Washington, DC: World Bank, 2025-07-15) Fajardo-Gonzalez, Johanna; Nguyen, Minh C.; Corral, Paul
Climate change is increasingly acknowledged as a critical issue with far-reaching socioeconomic implications that extend well beyond environmental concerns. Among the most pressing challenges is its impact on global poverty. This paper projects the potential impacts of unmitigated climate change on global poverty rates between 2023 and 2050. Building on a study that provided a detailed analysis of how temperature changes affect economic productivity, this paper integrates those findings with binned data from 217 countries, sourced from the World Bank’s Poverty and Inequality Platform. By simulating poverty rates and the number of poor under two climate change scenarios, the paper uncovers some alarming trends. One of the primary findings is that the number of people living in extreme poverty worldwide could be nearly doubled due to climate change. In all scenarios, Sub-Saharan Africa is projected to bear the brunt, contributing the largest number of poor people, with estimates ranging between 40.5 million and 73.5 million by 2050. Another significant finding is the disproportionate impact of inequality on poverty. Even small increases in inequality can lead to substantial rises in poverty levels. For instance, if every country’s Gini coefficient increases by just 1 percent between 2022 and 2050, an additional 8.8 million people could be pushed below the international poverty line by 2050. In a more extreme scenario, where every country’s Gini coefficient increases by 10 percent between 2022 and 2050, the number of people falling into poverty could rise by an additional 148.8 million relative to the baseline scenario. These findings underscore the urgent need for comprehensive climate policies that not only mitigate environmental impacts but also address socioeconomic vulnerabilities.
Stress Testing Survey to Survey Imputation: Understanding When Poverty Predictions Can Fail
(Washington, DC: World Bank, 2025-08-21) Corral, Paul; Ham, Andres; Lanjouw, Peter; Lucchetti, Leonardo; Stemmler, Henry
Accurate and timely poverty measurement is central to development policy, yet the availability of up-to-date high-quality household survey data remains limited—particularly in countries where poverty is most concentrated. Survey-to-survey imputation has emerged as a practical response to this challenge, allowing practitioners to update poverty estimates using recent surveys that lack direct welfare measures by borrowing information from other comprehensive surveys. A critical review of the method is provided, revisiting its statistical underpinnings and testing its limitations through extensive model-based simulations. Through these simulations, the analysis demonstrates how violations of parameter stability, omitted variable bias, and shifts in survey design can introduce substantial errors—particularly when imputing across time or under economic and structural change. Results show that standard corrections such as re-weighting or covariate standardization may fail to eliminate these biases, especially when imputing across time or under structural change. The performance of alternative model specifications is also evaluated under various methods, including performance under heteroskedastic errors, non-normality. The findings offer practical guidance for practitioners on when survey-to-survey imputation is likely to succeed, when it should be reconsidered, and how to communicate its limitations transparently in the context of poverty monitoring and policy design.
Guidelines to Small Area Estimation for Poverty Mapping
(Washington, DC : World Bank, 2022-06-16) Corral, Paul; Cojocaru, Alexandru; Segovia, Sandra; Molina, Isabel
The eradication of poverty, which was the first of the millennium development goals (MDG) established by the United Nations and followed by the sustainable development goals (SDG), requires knowing where the poor are located. Traditionally, household surveys are considered the best source of information on the living standards of a country’s population. Data from these surveys typically provide a sufficiently accurate direct estimate of household expenditures or income and thus estimates of poverty at the national level and larger international regions. However, when one starts to disaggregate data by local areas or population subgroups, the quality of these direct estimates diminishes. Consequently, national statistical offices (NSOs) cannot provide reliable wellbeing statistical figures at a local level. For example, the module of socioeconomic conditions of the Mexican national survey of household income and expenditure (ENIGH) is designed to produce estimates of poverty and inequality at the national level and for the 32 federate entities (31 states and Mexico City) with disaggregation by rural and urban zones, every two years, but there is a mandate to produce estimates by municipality every five years, and the ENIGH alone cannot provide estimates for all municipalities with adequate precision. This makes monitoring progress toward the sustainable development goals more difficult.
When Aggregation Misleads: Bias in Unit-Level Small Area Estimates of Poverty with Aggregate Data
(Washington, DC: World Bank, 2025-05-01) Corral, Paul
This paper explores why small area poverty estimates from models at the household level that only use aggregate data as covariates, exhibit systematic bias. The analysis demonstrates that this bias stems from the model’s inability to capture the complete between-household variation in welfare, as they rely solely on covariates aggregated at geographic levels. Through model-based simulations, the paper shows that the bias in these models is minimized when the empirical variability of simulated welfare based on the model is closest to the true empirical variance of welfare at the area level. This finding also has implications for bias in unit-level models.
Poverty Mapping in the Age of Machine Learning
(World Bank, Washington, DC, 2023-05-04) Henderson, Heath; Corral, Paul; Segovia, Sandra
Recent years have witnessed considerable methodological advances in poverty mapping, much of which has focused on the application of modern machine-learning approaches to remotely sensed data. Poverty maps produced with these methods generally share a common validation procedure, which assesses model performance by comparing subnational machine-learning-based poverty estimates with survey-based, direct estimates. Although unbiased, survey-based estimates at a granular level can be imprecise measures of true poverty rates, meaning that it is unclear whether the validation procedures used in machine-learning approaches are informative of actual model performance. This paper examines the credibility of existing approaches to model validation by constructing a pseudo-census from the Mexican Intercensal Survey of 2015, which is used to conduct several design-based simulation experiments. The findings show that the validation procedure often used for machine-learning approaches can be misleading in terms of model assessment since it yields incorrect information for choosing what may be the best set of estimates across different methods and scenarios. Using alternative validation methods, the paper shows that machine-learning-based estimates can rival traditional, more data intensive poverty mapping approaches. Further, the closest approximation to existing machine-learning approaches, using publicly available geo-referenced data, performs poorly when evaluated against “true” poverty rates and fails to outperform traditional poverty mapping methods in targeting simulations.
Sustaining Poverty Gains: A Vulnerability Map to Guide the Expansion of Social Registries
(Washington, DC: World Bank, 2024-09-05) Barriga-Cabanillas, Oscar; Bossuroy, Thomas; Corral Rodas, Paul Andres; Rodríguez-Castelán, Carlos; Skoufias, Emmanuel
Poverty maps are a useful tool for targeting social programs on areas with high concentrations of poverty. However, a static focus on poverty ignores its temporal dimension. Thus, current nonpoor households still face substantial welfare volatility and are at risk of becoming poor in the face of shocks. This paper combines the methods of poverty mapping and vulnerability estimation to create highly disaggregated vulnerability maps. The maps include predictions of the share of chronically poor households (poverty-induced vulnerability)—the focus of traditional poverty maps—and the share of households showing a significant probability of falling into poverty (risk-induced vulnerability). As an application of the method, the paper estimates a vulnerability map for Senegal that provides quotas for the expansion of the social registry. Accounting for the poor and the population at risk of poverty implies, in practice, the expansion of coverage into urban and peri-urban areas that tend to experience lower poverty rates. The inclusion of nonpoor households also serves as a first step toward supporting a dynamic social registry.
Counting People Exposed to, Vulnerable to, or at High Risk From Climate Shocks: A Methodology
(World Bank, Washington, DC, 2023-12-04) Doan, Miki Khanh; Hill, Ruth; Hallegatte, Stephane; Corral, Paul; Brunckhorst, Ben; Nguyen, Minh; Freije-Rodriguez, Samuel; Naikal, Esther
Based on global datasets, 4.5 billion people were exposed to extreme weather events (flood, drought, cyclone, or heatwave) in 2019, an increase from 4 billion in 2010. Among exposed people in 2019, 2.3 billion people lived with less than $6.85 per day and about 400 million lived in extreme poverty (on less than $2.15 per day). This paper presents a methodology to estimate the number of people who are at high risk from extreme weather events, defined as the people who are exposed to these events and highly vulnerable to them. Vulnerability is proxied by a set of indicators measuring (1) the physical propensity to experience severe losses (proxied by the lack of access to basic infrastructure services, here water and electricity) and (2) the inability to cope with and recover from losses (proxied by low income, not having education, not having access to financial services and not having access to social protection). Estimates from 75 countries for which data on all indicators are available suggest that, in 2019, 42 percent of the total population (and 70 percent of people exposed) are at high risk from extreme weather shocks, if one indicator is enough to be considered as highly vulnerable. If high vulnerability is defined based on being vulnerable on two dimensions or more, then 12 percent of the total population (and 20 percent of people exposed) are at high risk from extreme weather shocks. The trend between 2010 and 2019 can be explored in a subset of countries covering 60 percent of the world population. In these countries, even though the population exposed to extreme weather events has been increasing, the number of people at high risk has declined. The exception is Sub-Saharan Africa where the number of people at high risk has increased between 2010 and 2019.
Fragility and Conflict: On the Front Lines of the Fight against Poverty
(Washington, DC: World Bank, 2020-02-27) Corral, Paul; Irwin, Alexander; Krishnan, Nandini; Mahler, Daniel Gerszon; Vishwanath, Tara
Fragility and conflict pose a critical threat to the global goal of ending extreme poverty. Between 1990 and 2015, successful development strategies reduced the proportion of the world’s people living in extreme poverty from 36 to 10 percent. But in many fragile and conflict-affected situations (FCS), poverty is stagnating or getting worse. The number of people living in proximity to conflict has nearly doubled worldwide since 2007. In the Middle East and North Africa, one in five people now lives in such conditions. The number of forcibly displaced persons worldwide has also more than doubled in the same period, exceeding 70 million in 2017. If current trends continue, by the end of 2020, the number of extremely poor people living in economies affected by fragility and conflict will exceed the number of poor people in all other settings combined. This book shows why addressing fragility and conflict is vital for poverty goals and charts directions for action. It presents new estimates of welfare in FCS, filling gaps in previous knowledge, and analyzes the multidimensional nature of poverty in these settings. It shows that data deprivation in FCS has prevented an accurate global picture of fragility, poverty, and their interactions, and it explains how innovative new measurement strategies are tackling these challenges. The book discusses the long-term consequences of conflict and introduces a data-driven classification of countries by fragility profile, showing opportunities for tailored policy interventions and the need for monitoring multiple markers of fragility. The book strengthens understanding of what poverty reduction in FCS will require and what it can achieve.
Seventh Ghana Economic Update: Price Surge - Unraveling Inflation’s Toll on Poverty and Food Security
(Washington, DC: World Bank, 2023-07-24) Kwakye, Kwabena Gyan; Corral Rodas, Paul Andres; Elmaleh, David; Sebastian, Ashwini Rekha
Ghana’s economy entered a full-blown crisis in 2022, after having rebounded from the COVID-19 slowdown in 2021. In response to the macroeconomic challenges, the authorities enacted some fiscal adjustment in 2022 but fell short of their consolidation targets; the 2023Q1 fiscal deficit (cash) was within target. Expenditure consolidation and revenue mobilization continued to be hampered by structural constraints. To address these unsustainable domestic and external imbalances, the authorities embarked on a comprehensive debt restructuring operation. Against the backdrop, growth is projected to decelerate further in 2023-24, before picking up in the medium-term. The government has embarked on an ambitious fiscal consolidation plan: however, delivering on it will require addressing long-standing revenue mobilization and budget control weaknesses. In 2023, the authorities intend to finance the fiscal deficit from multilateral (and other official) sources, in the context of the International Monetary Fund (IMF) - supported program, and from the domestic treasury bills (T-bills) market. In addition, leveraging government programs to build up resilience against vulnerability is an imperative and should not be suspended during the crisis. Beefing up the government’s payments through the livelihood empowerment against poverty will be critical. Second, support for food self-sufficiency is needed in Ghana (a goal for many countries now due to the global food crisis), while opening the country to generate more export revenues. The Ghana Tree Crops Diversification Project can serve as a critical puzzle piece of the country’s current challenges. The project will support poverty alleviation while setting the country up to generate more foreign revenues in the medium to long-term.
Pull Your Small Area Estimates Up by the Bootstraps
(Taylor and Francis, 2021-05-08) Molina, Isabel; Corral, Paul; Nguyen, Minh
This paper presents a methodological update to the World Bank's toolkit for small area estimation. The paper reviews the computational procedures of the current methods used by the institution: the traditional ELL approach and the Empirical Best (EB) addition introduced to imitate the original EB procedure of Molina and Rao [Small area estimation of poverty indicators. Canadian J Stat. 2010;38(3):369–385], including heteroskedasticity and survey weights, but using a different bootstrap approach, here referred to as clustered bootstrap. Simulation experiments provide empirical evidence of the shortcomings of the clustered bootstrap approach, which yields biased and noisier point estimates. The document presents an update to the World Bank’s EB implementation by considering the original EB procedures for point and noise estimation, extended for complex designs and heteroscedasticity. Simulation experiments illustrate that the revised methods yield considerably less biased and more efficient estimators than those obtained from the clustered bootstrap approach.