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Corral, Paul

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Last updated: July 24, 2023
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.
Citations 188 Scopus

Publication Search Results

Now showing 1 - 10 of 17
  • Publication
    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.
  • Publication
    Poverty Mapping in the Age of Machine Learning
    (World Bank, Washington, DC, 2023-05-04) 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.
  • Publication
    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.
  • Publication
    Modeling the Revenue and Distributional Effects of Tax Reform in Paraguay: Challenges and Lessons
    (World Bank, Washington, DC, 2021-06) Canavire, Gustavo; Corral, Paul; Farfan, Gabriela; Galeano, Juan Jose; Gayoso, Lyliana; Piontkivsky, Ruslan; Sacco, Flavia
    In 2019, the Government of Paraguay (GOP) embarked on a comprehensive tax reform to simplify and modernize its tax system. At the request of the Ministry of Finance (MOF), and building on long-standing policy dialogue with the country, the World Bank (WB) Poverty team worked quickly to develop a micro-simulation tool to inform policymakers on the potential revenue and distributional effects of the reform as it was being developed. In this note, we summarize the context, approach, challenges, and lessons learned from constructing the Fiscal Simulation Tool and highlight the tool’s long-term value for policy dialogue and decision making.
  • Publication
    Pull Your Small Area Estimates Up by the Bootstraps
    (Taylor and Francis, 2021-05-08) 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.
  • Publication
    A Map of the Poor or a Poor Map?
    (World Bank, Washington, DC, 2021-04) Himelein, Kristen; Corral, Paul; McGee, Kevin; Molina, Isabel
    This paper evaluates the performance of different small area estimation methods using model and design-based simulation experiments. Design-based simulation experiments are carried out using the Mexican Intra Censal survey as a census of roughly 3.9 million households from which 500 samples are drawn using a two-stage selection procedure similar to that of Living Standards Measurement Study surveys. Several unit-level methods are considered as well as a method that combines unit and area level information, which has been proposed as an alternative when the available census data is outdated. The findings show the importance of selecting a proper model and data transformation so that the model assumptions hold. A proper data transformation can lead to a considerable improvement in mean squared errors. The results from design-based validation show that all small area estimation methods represent an improvement, in terms of mean squared errors, over direct estimates. However, methods that model unit level welfare using only area level information suffer from considerable bias. Because the magnitude and direction of the bias are unknown ex ante, methods that rely only on aggregated covariates should be used with caution, but they may be an alternative to traditional area level models when these are not applicable.
  • Publication
    Pull Your Small Area Estimates Up by the Bootstraps
    (World Bank, Washington, DC, 2020-05) Molina, Isabel; Corral, Paul; Nguyen, Minh
    After almost two decades of poverty maps produced by the World Bank and multiple advances in the literature, 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 World Bank: the traditional approach by Elbers, Lanjouw and Lanjouw (2003) and the Empirical Best/Bayes (EB) addition introduced by Van der Weide (2014). The addition extends the EB procedure of Molina and Rao (2010) by considering heteroscedasticity and includes survey weights, but uses a different bootstrap approach, here referred to as clustered bootstrap. Simulation experiments comparing these methods to the original EB approach of Molina and Rao (2010) provide empirical evidence of the shortcomings of the clustered bootstrap approach, which yields biased point estimates. The main contributions of this paper are then two: 1) to adapt the original Monte Carlo simulation procedure of Molina and Rao (2010) for the approximation of the extended EB estimators that include heteroscedasticity and survey weights as in Van der Weide (2014); and 2) to adapt the parametric bootstrap approach for mean squared error (MSE) estimation considered by Molina and Rao (2010), and proposed originally by González-Manteiga et al. (2008), to these extended EB estimators. Simulation experiments illustrate that the revised Monte Carlo simulation method yields estimators that are considerably less biased and more efficient in terms of MSE than those obtained from the clustered bootstrap approach, and that the parametric bootstrap MSE estimators are in line with the true MSEs under realistic scenarios.
  • Publication
    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.
  • Publication
    Poverty, Inequality, and Agriculture in the EU
    (World Bank, Washington, DC, 2018-11) van den Brink, Rogier J. E.; Azevedo, Joao Pedro; Avila, Montserrat; Corral, Paul; Zhao, Hongxi; Mostafavi, Mohammad-Hadi
    Boosting convergence and shared prosperity in the European Union achieved renewed urgency after the global financial crisis of 2008. This paper assesses the role of agriculture and the Common Agricultural Program in achieving this. The paper sheds light on the relationship between poverty and agriculture as part of the process of structural transformation. It positions each member country on the path toward a successful structural transformation. The paper then evaluates at the regional level where the Common Agricultural Program funding tends to go, poverty-wise, within each country. This approach enables making more informed policy recommendations on the current state of the Common Agricultural Program funding, as well as evaluating the role of agriculture as a driver of shared prosperity. The analysis performed throughout the paper uses a combination of data sources at several spatial levels.
  • Publication
    SAE - A Stata Package for Unit Level Small Area Estimation
    (World Bank, Washington, DC, 2018-10) Nguyen, Minh Cong; Corral, Paul; Azevedo, Joao Pedro; Zhao, Qinghua
    This paper presents a new family of Stata functions devoted to small area estimation. Small area methods attempt to solve low representativeness of surveys within areas, or the lack of data for specific areas/sub-populations. This is accomplished by incorporating information from outside sources. Such target data sets are becoming increasingly available and can take the form of a traditional population census, but also large scale administrative records from tax administrations, or geospatial information produced using remote sensing. The strength of these target data sets is their granularity on the subpopulations of interest, however, in many cases they lack the ability to collect analytically relevant variables such as welfare or caloric intake. The family of functions introduced follow a modular design to have the flexibility with which these can be expanded in the future. This can be accomplished by the authors and/or other collaborators from the Stata community. Thus far, a major limitation of such analysis in Stata has been the large size of target data sets. The package introduces new mata functions and a plugin used to circumvent memory limitations that inevitably arise when working with big data. From an estimation perspective, the paper starts by implementing a methodology that has been widely used for the production of several poverty maps.