Could poverty interventions in the US be cost-effective for global health?

How does US Poverty compare to poverty in LMIC?

In Matthew Desmond’s book Poverty, by America, he shares the finding that 5.3 million Americans live below the global poverty line. This is $4 a day in the US, equivalent in quality of life to the $1.9 a day used by the World Bank for defining poverty in low-income countries.

18 million live in deep poverty—making under 50% of $13,000, the bare subsistence poverty line. We can safely say that their low income significantly reduces their life expectancy:

The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women.

According to this research, the difference between being in the 4th income quartile ($17k/​year) vs the 3rd ($47k/​year) is roughly 5 years. Following this nonlinear trend, we might guess that moving from the 0-5% range to the 5-10% range would improve life expectancy about 2.5 years.

While charitable interventions that directly pay for interventions are probably not cost-effective compared to those in low-income countries, policy interventions may be. Why? Because the American economy is so large, and our existing (and possible) tax basis is enormous. In 2022, the federal government spent 1.19 trillion on welfare programs, three orders of magnitude higher than Givewell’s budget. State and Local governments spend another trillion on welfare programs and hospitals. Therefore, making existing programs more effective, or expanding their funding through advocacy, could highly cost-effective, on par with direct health interventions in LMIC.

Well, is it? Let’s look at a few napkin math examples.

Example Intervention: Improving an existing program through non-legislative means

  • ~38 million Americans received SNAP (food assistance based on income), and the government estimated in 2021 that 1 in 6 eligible did not receive it (7.6M).

  • Let’s assume half of those are children, and that if they had food security in childhood, they would get a benefit of 3 QALYs due to better health, education, and home stability. That represents ~10 million QALYs. This money is already theirs by law, they just aren’t accessing it.

  • An intervention might be organizing volunteer outreach through schools, churches, or food shelters; or automatic enrollment through employers (eg, Walmart helps its employees sign up for the Earned Income Tax Credit).

  • Organizations like Code for America and New America specifically work on technical and process interventions with government agencies to make benefits easier to access.

  • I’d guess that interventions like these are in roughly the $1M-5M range for cost. If they can increase uptake by just 10%, that’s 1M QALYs. Valuing a QALY at 100k, the expected ROI is 20,000, or $5 per QALY.

Example Intervention: Increasing the coverage for an existing program through legislative means

  • ~15 million Californians (40 percent of the population) are covered by MediCal, effectively free health care for people in poverty (The income limit is $30k for one person and $60k for a family of 4).

  • Suppose that the income limit was raised, such that 1% more Californians were covered under MediCal. Suppose each of them benefited from 2 QALYs due to coverage. I’m guessing the QALY improvement would come from direct and preventative care, but also ability to work and preventing financial catastrophe → 3M QALYs. (Rough guess based on this research.)

  • MediCal is roughly $15k per person per year, so this expansion represents a $2.2B cost (about 1% of the CA budget)

  • Advocacy Path: Suppose EA worked directly with a state congressperson to get this expansion written into law.

    • The goal would be to work with a congressperson on the appropriate committee, to find this money.

    • When hospitals treat the uninsured in an emergency, they often do not get fully reimbursed, and the government covers some of that cost. Preventative healthcare should reduce this expense.

    • One possibility to come up with additional funds would be to pass a tax on the wealthiest to pay for this expansion. Eg, a property tax on homes valued at more than $5M, or a capital gains tax on Retirement portfolios over $5M. Lower wealth limits will introduce more opposition from voters.

    • To achieve this end, EA might do relationship building with relevant legislators, providing research, and possibly campaign donations, to state legislators aligned with its goals. So suppose EA invested 5M in relationship building and legislative support.

      • The reelection campaign budget of a CA state legislator is around 1 million, so contributions in the 50-100k range go a long way.

    • If this had a 50% chance of working, the ROI would be 300,000 or $0.33 per QALY.

  • Ballot Path: Suppose EA worked to get a proposition on the ballot that would pass this funding.

    • Suppose EA funded a CA ballot to expand Medicaid by passing the tax that focuses on the top 1% of earners.

    • On average, getting enough ballot signatures costs about $11M. Advertising, depending on controversiality, might be around $60M.

    • Suppose that this investment of 70M has a 50% chance of succeeding. The ROI would be about 2000, or $46 /​ QALY.

This may not convince you that we should start writing checks today! But to me, this is suggests that compared to other near-term global health work, health and anti-poverty interventions in America is a research area worth looking at. It seems that this was an area of inquiry for OpenPhil 10 years ago, but has not been seriously pursued (with the exception of anti-incarceration work, which I haven’t been able to find a clear impact metric for).

The rest of this series will explore:

  • A framework for assessing tractability and impact, and how to consider neglectedness in prioritization.

  • The pros and cons of choosing poverty advocacy in America as an EA cause area.

  • What sorts of interventions might be researched, with some rough prioritization.

Thanks for reading, and looking forward to any thoughts on this topic!

The next post, on prioritization, is here.