82 records from EconBiz based on author Name

1. Intergenerational mobility around the world : a new database

Weide, Roy van der; Lakner, Christoph; Mahler, Daniel Gerszon; Narayan, Ambar; Gupta, Rakesh;
2024
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link

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2. New Evidence on Inequality of Opportunity in Sub-Saharan Africa : More Unequal than we Thought

abstract

Unequal access to economic opportunity for individuals with different innate characteristics, such as ethnicity or parents' socioeconomic status, is often seen as both morally undesirable and bad for economic growth. This paper estimates inequality of opportunity, or the share of inequality explained by birth characteristics, across 18 countries in Sub-Saharan Africa. For many countries, this is the first time inequality of opportunity is measured. The paper uses nationally representative household survey data harmonized to allow for cross-country comparisons. Using consumption per capita as the outcome, the findings show that inequality of opportunity in Sub-Saharan Africa is stark and more pronounced than previously estimated. On average, inherited circumstances explain more than half of inequality in the region. Estimates range from 40 to 60 percent in most countries and reach 74 percent in South Africa. The findings show that birthplace, parents' education, and ethnicity tend to be the most significant contributors, but there is large variation in the importance of circumstances across countries. This represents the most comprehensive estimate of inequality of opportunity to date in the poorest and one of the most unequal regions in the world, and it underscores the pressing need for policy makers to intensify their efforts to address inequality of opportunity to foster societies that are more equitable and unlock the full potential for growth in the region

Atamanov, Aziz; Cuevas, P. Facundo; Lebow, Jeremy; Mahler, Daniel Gerszon;
2024
Type: Arbeitspapier; Working Paper;
Availability: Link Link

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3. The roots of inequality : estimating inequality of opportunity from regression trees and forests

Brunori, Paolo; Hufe, Paul; Mahler, Daniel Gerszon;
2023
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link

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4. What Makes Public Sector Data Valuable for Development?

abstract

Data produced by the public sector can have transformational impacts on development outcomes through better targeting of resources, improved service delivery, cost savings, increased accountability, and more. Around the world, the amount of data produced by the public sector is increasing rapidly, but we argue the full potential of data to improve development outcomes has not been realized yet. We outline 12 features needed for data to generate greater value for development and present case studies substantiating these features. We argue that a key reason why the transformational value of data has not yet been realized is that suboptimal data-data not satisfying these 12 features—are being supplied. The features are that the data should be of adequate spatial and temporal coverage (complete, frequent, and timely), should be of high quality (accurate, comparable, and granular), should be easy to use (accessible, understandable, and interoperable), and should be safe to use (impartial, confidential, and appropriate)

Jolliffe, Dean; Kilic, Talip; Mahler, Daniel Gerszon; Veerappan, Malarvizhi; Wollburg, Philip;
2023
Availability: Link

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5. Death and Destitution : The Global Distribution of Welfare Losses from the Covid-19 Pandemic

abstract

The Covid-19 pandemic has brought about massive declines in wellbeing around the world. This paper seeks to quantify and compare two important components of those losses â increased mortality and higher poverty â using years of human life as a common metric. We estimate that almost 20 million life-years were lost to Covid-19 by December 2020. Over the same period and by the most conservative definition, over 120 million additional years were spent in poverty because of the pandemic. The mortality burden, whether estimated in lives or in years of life lost, increases sharply with GDP per capita. The poverty burden, on the contrary, declines with per capita national incomes when a constant absolute poverty line is used, or is uncorrelated with national incomes when a more relative approach is taken to poverty lines. In both cases the poverty burden of the pandemic, relative to the mortality burden, is much higher for poor countries. The distribution of aggregate welfare losses â combining mortality and poverty and expressed in terms of life-years â depends both on the choice of poverty line(s) and on the relative weights placed on mortality and poverty. With a constant absolute poverty line and a relatively low welfare weight on mortality, poorer countries are found to bear a greater welfare loss from the pandemic. When poverty lines are set differently for poor, middle and high-income countries and/or a greater welfare weight is placed on mortality, upper-middle and rich countries suffer the most

Ferreira, Francisco H. G.; Sterck, Olivier; Mahler, Daniel Gerszon; Decerf, Benoit;
2023
Availability: Link Link
Citations: 1 (based on OpenCitations) Cited By

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6. Predicting Income Distributions from Almost Nothing

abstract

This paper develops a method to predict comparable income and consumption distributions for all countries in the world from a simple regression with a handful of country-level variables. To fit the model, the analysis uses more than 2,000 distributions from household surveys covering 168 countries from the World Bank's Poverty and Inequality Platform. More than 1,000 economic, demographic, and remote sensing predictors from multiple databases are used to test the models. A model is selected that balances out-of-sample accuracy, simplicity, and the share of countries for which the method can be applied. The paper finds that a simple model relying on gross domestic product per capita, under-5 mortality rate, life expectancy, and rural population share gives almost the same accuracy as a complex machine learning model using 1,000 indicators jointly. The method allows for easy distributional analysis in countries with extreme data deprivation where survey data are unavailable or severely outdated, several of which are likely among the poorest countries in the world

Mahler, Daniel Gerszon; Lakner, Christoph; Montes, Jose; Nguyen, Minh; Schoch, Marta;
2025
Availability: Link

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7. Nowcasting Global Poverty

abstract

This paper evaluates different methods for nowcasting country-level poverty rates, including methods that apply statistical learning to large-scale country-level data obtained from the World Development Indicators and Google Earth Engine. The methods are evaluated by withholding measured poverty rates and determining how accurately the methods predict the held-out data. A simple approach that scales the last observed welfare distribution by a fraction of real GDP per capita growth performs nearly as well as models using statistical learning on 1,000 plus variables. This GDP-based approach outperforms all models that predict poverty rates directly, even when the last survey is up to five years old. The results indicate that in this context, the additional complexity introduced by applying statistical learning techniques to a large set of variables yields only marginal improvements in accuracy

Castañeda Aguilar, R. Andrés; Mahler, Daniel Gerszon; Newhouse, David Locke;
2022
Availability: Link

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8. How much does reducing inequality matter for global poverty?

abstract

The goals of ending extreme poverty by 2030 and working towards a more equal distribution of incomes are part of the United Nations' Sustainable Development Goals. Using data from 166 countries comprising 97.5 percent of the world’s population, we simulate scenarios for global poverty from 2019 to 2030 under various assumptions about growth and inequality. We use different assumptions about growth incidence curves to model changes in inequality and rely on a machine-learning algorithm called model-based recursive partitioning to model how growth in GDP is passed through to growth as observed in household surveys. When holding within-country inequality unchanged and letting GDP per capita grow according to World Bank forecasts and historically observed growth rates, our simulations suggest that the number of extreme poor (living on less than 1.90 dollars/day) will remain above 600 million in 2030, resulting in a global extreme poverty rate of 7.4 percent. If the Gini index in each country decreases by 1 percent per year, the global poverty rate could reduce to around 6.3 percent in 2030, equivalent to 89 million fewer people living in extreme poverty. Reducing each country’s Gini index by 1 percent per year has a larger impact on global poverty than increasing each country’s annual growth 1 percentage point above forecasts. We also study the impact of COVID-19 on poverty and find that the pandemic may have driven around 60 million people into extreme poverty in 2020. If the pandemic increased the Gini index by 2 percent in all countries, then more than 90 million may have been driven into extreme poverty in 2020

Mahler, Daniel Gerszon; Lakner, Christoph; Negre, Mario; Beer Prydz, Espen;
2022
Availability: Link Link

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9. What makes public sector data valuable for development?

Jolliffe, Dean; Mahler, Daniel Gerszon; Veerappan, Malarvizhi; Kilic, Talip; Wollburg, Philip;
2023
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link

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10. On the front lines of the fight against poverty : fragility and conflict

abstract

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

Corral, Paul; Irwin, Alexander; Krishnan, Nandini; Mahler, Daniel Gerszon; Vishwanath, Tara;
2020
Availability: Link

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