What a Moonshot to End Extreme Poverty Would Cost – Stanford Graduate School of Business

Report on the Cost to End Extreme Poverty and Its Alignment with Sustainable Development Goals (SDGs)

Introduction

One of the most enduring challenges in global development is determining the cost required to end extreme poverty, defined as living on less than $2.15 per person per day. This report highlights recent research efforts that leverage data science and policy learning to estimate the financial resources needed to eradicate extreme poverty worldwide, emphasizing the alignment with the United Nations Sustainable Development Goals (SDGs), particularly SDG 1: No Poverty.

Background and Approach

Paul Niehaus, an economist at the University of California, San Diego, and co-founder of the nonprofit GiveDirectly, proposed a direct cash transfer approach to alleviate extreme poverty. GiveDirectly has distributed $1 billion to 2 million people across seven African countries and the U.S., based on the principle that individuals are best positioned to decide how to improve their lives.

The challenge was to calculate the total cost to raise incomes of all individuals below the poverty line to at least $2.15 per day. However, accurate income data for approximately 700 million people living in extreme poverty is unavailable due to limitations in household surveys and unreliable self-reported income.

Innovative Data Science Collaboration

1. Collaboration between experts from University of California, Berkeley, and Stanford Graduate School of Business led to the development of advanced statistical and optimization methods.
2. Graduate student Roshni Sahoo developed a policy learning model that uses household survey data from 23 countries, representing half of the world’s poorest population, to estimate the cost of targeted cash transfer programs without perfect income information.
3. The model incorporates multiple indicators of living standards, such as housing quality and access to infrastructure, to prioritize aid effectively.

Key Findings

• Reducing the global extreme poverty rate to 1% would cost approximately 0.3% of the global GDP, equivalent to $318 billion annually.
• This cost is significantly lower than alternative proposals such as universal basic income at the poverty line, estimated at $895 billion per year.
• The research demonstrates that targeted cash transfers informed by high-quality data and policy learning can efficiently allocate resources to those most in need.

Implications for Sustainable Development Goals (SDGs)

The research directly supports the achievement of several SDGs:

• SDG 1: No Poverty – By providing a data-driven estimate of the resources needed to eradicate extreme poverty, the study informs policies aimed at ending poverty in all its forms.
• SDG 10: Reduced Inequalities – Targeted cash transfers can reduce income disparities by prioritizing the most vulnerable populations.
• SDG 17: Partnerships for the Goals – The interdisciplinary collaboration across universities and nonprofit organizations exemplifies the partnerships necessary to achieve sustainable development.

Moral and Policy Perspectives

Niehaus emphasizes the integration of ethical considerations with cutting-edge data science, highlighting the moral imperative to act on the findings. Stefan Wager notes that policy learning methods, often used in commercial settings, have significant potential to impact public-interest challenges such as poverty alleviation.

Future Directions

• Expanding the study to include more countries to refine cost estimates globally.
• Analyzing macroeconomic effects of implementing large-scale cash transfer programs.
• Engaging policymakers to translate research findings into actionable international development strategies.

Conclusion

The research provides a clear, evidence-based estimate that ending extreme poverty globally requires an investment of approximately 0.3% of global GDP annually. This figure translates to about 0.3% of an individual’s income, e.g., $135 for a typical American earning $45,000 per year. The findings underscore the feasibility of achieving SDG 1 through targeted, data-informed cash transfers and call for renewed global commitment to poverty eradication.

“Our results here highlight how, given access to high enough quality data, policy learning methods can also help make a difference in public-interest settings.”

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 1: No Poverty
   • The article focuses on ending extreme poverty, defined as living on less than $2.15 per day.
   • It discusses strategies to lift people above the poverty line through direct cash transfers.
2. SDG 10: Reduced Inequalities
   • The article addresses income disparities and the allocation of resources to the poorest individuals.
   • It emphasizes targeted cash transfers to reduce inequality within and among countries.
3. SDG 17: Partnerships for the Goals
   • The research involves collaboration among universities, economists, and nonprofits to develop data-driven solutions.
   • It highlights the use of data science and policy learning methods to support development goals.

2. Specific Targets Under Those SDGs Identified

1. Under SDG 1: No Poverty
   • Target 1.1: Eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.90 a day (updated to $2.15 in the article).
   • Target 1.2: Reduce at least by half the proportion of men, women, and children living in poverty in all its dimensions according to national definitions.
2. Under SDG 10: Reduced Inequalities
   • Target 10.1: Achieve and sustain income growth of the bottom 40% of the population at a rate higher than the national average.
3. Under SDG 17: Partnerships for the Goals
   • Target 17.18: Enhance capacity-building support to developing countries to increase significantly the availability of high-quality, timely and reliable data.

3. Indicators Mentioned or Implied to Measure Progress

1. Proportion of population living below the international poverty line
   • The article uses the threshold of $2.15 per person per day to define extreme poverty, which aligns with the international poverty line indicator.
2. Poverty gap index
   • The article discusses calculating the poverty gap—the amount of cash needed to lift people above the poverty line.
   • This indicator measures the intensity of poverty by quantifying how far below the poverty line people are.
3. Income data and standards of living metrics
   • The article implies the use of household income data, housing quality (e.g., roof material), and access to infrastructure as indicators to assess living standards and target aid effectively.
4. Cost of poverty eradication as a percentage of global GDP
   • The article estimates the cost to reduce extreme poverty to 1% at 0.3% of global GDP, which can be used as a macroeconomic indicator of resource allocation efficiency.

4. Table: SDGs, Targets and Indicators

Source: gsb.stanford.edu