An Independent Evaluation of the Data Behind the Headlines

El, the data analyst behind the YouTube channel House of El, recently published a video essay built around a single, counterintuitive finding: Americans are roughly twice as poor as citizens of Germany, France, and the United Kingdom — despite having higher average incomes. The claim rests on research by Olivier Sterck, an Associate Professor of Economics at the University of Oxford, who has proposed a new way of measuring poverty that dispenses with poverty lines entirely. El's video walks through the methodology, presents the numbers, and draws out implications for American economic stability and global financial architecture.

We verified every data point in the video against Sterck's published research, his interviews with Euronews Business, his VoxDev article, and the underlying SSRN paper. The short version: El's reporting is accurate, well-sourced, and responsibly presented. The longer version requires some additional context about what this metric captures, what it misses, and why the distinction matters.

The Metric: What Sterck Actually Measures

Traditional poverty measurement draws a line — $2.15 per day, $6.85 per day, $30 per day — and counts how many people fall below it. Everyone above the line vanishes from the analysis. As Sterck writes in The Conversation, "nothing magical happens when you cross some arbitrary line." A person earning $100 a day is better off than someone earning $75, who is better off than someone earning $50. The line obscures that gradient entirely.

Sterck's alternative is elegant. He defines individual poverty as the reciprocal of income: if incomes are measured in dollars per day, poverty is measured in days per dollar. Average poverty for a population is simply the average time needed for anyone — of any age, in any circumstance, not limited to workers — to earn one international dollar (a PPP-adjusted unit representing equivalent purchasing power across countries). The result is a metric that treats poverty as a continuous spectrum rather than a binary classification and is inherently sensitive to income distribution.

This is real academic work, published on SSRN under the title "Poverty without Poverty Line" (first posted September 2024, with data spanning 1990–2025 across 218 countries using World Bank Poverty and Inequality Platform data). Sterck holds joint appointments at the University of Oxford and the University of Antwerp, leads the Refugee Economies Programme, and co-authored the underlying work on the World Bank's Prosperity Gap indicator. This is not a blog post repackaged as a study. It is peer-facing research from an economist with a substantial publication record.

The Numbers: Verified

El's headline figures match Sterck's published data precisely.

As of 2025, the average time to earn $1 in international dollars is 63 minutes in the United States, 26 minutes in Germany, 31 minutes in France, and 34 minutes in the United Kingdom. The US figure is roughly double the European average, which is the basis for El's framing that Americans are "twice as poor."

The historical trajectory is equally well-sourced. In 1990, the US figure was 43 minutes — almost identical to France at 42 minutes and better than the UK at 51 minutes. Germany stood at 34 minutes. Over the subsequent 35 years, the US figure rose to 63 minutes (a 47% increase), while all three European comparators declined. The UK saw the sharpest improvement, falling from 51 to 34 minutes.

The income dispersion ratio is confirmed by Sterck's interview with Euronews Business: randomly selecting two individuals from the US population yields an expected income ratio above 4:1, compared with approximately 1.5:1 in Germany, France, and the UK.

The Mississippi comparison also checks out. In Q3 2024, Mississippi's GDP per capita was €49,780 ($53,872), compared with Germany's €51,304 — a gap of roughly €1,500. This is a standard Eurostat/BEA comparison that Sterck uses to illustrate how GDP per capita creates the illusion that American and European living standards are roughly equivalent when the distributional reality diverges sharply.

El's claim that the World Bank has adopted this metric under the name "Prosperity Gap" is substantively correct, though it merits a technical clarification. The World Bank's Global Prosperity Gap, formally adopted in 2023, measures the average factor by which incomes must be multiplied to reach a $25/day prosperity standard. This is a related but distinct formulation from Sterck's "average poverty" (the reciprocal of income). However, Sterck is a co-author on the Kraay et al. (2023) paper that proposed the Prosperity Gap, and in his VoxDev article he explicitly states that "the World Bank has already adopted this metric, under the name 'Prosperity Gap,' as its core indicator for tracking shared prosperity." The two measures belong to the same intellectual family and produce directionally consistent results, even if they are not mathematically identical.

The Mechanism: Why Growth and Poverty Move in Opposite Directions

This is where El's presentation is strongest, and where the underlying research is most illuminating.

Sterck's metric can be decomposed into two components: average income and inequality. When average income rises, average poverty falls — all else being equal. But when inequality rises faster than income, average poverty increases even in a growing economy.

In all four countries El examines, average incomes grew by slightly over 1% per year over the past three decades, according to World Bank PIP data. The divergence lies entirely in distribution. In the United States, average inequality increased by approximately 2.2% per year, outpacing income growth. In Germany, France, and the United Kingdom, inequality remained relatively stable, allowing income growth to translate directly into poverty reduction.

This is the core finding, and it is mathematically clean: the same rate of income growth produced opposite poverty outcomes because of how the gains were distributed. In Europe, growth was broadly shared. In the United States, it was concentrated at the top of the distribution, mechanically increasing the average time required for the rest of the population to earn each dollar.

El correctly notes that the metric briefly improved during the COVID-19 pandemic, when direct transfers, expanded unemployment benefits, and child tax credits temporarily compressed the income distribution. When those programs expired, the trend resumed. The implication — that distributional policy can move this metric, and that the US has chosen not to sustain such policy — is Sterck's own conclusion, not editorial embellishment by El.

What the Metric Does Not Capture

No poverty measure is complete, and Sterck's is no exception. El's video presents the findings straightforwardly without much interrogation of the metric's limitations, so it falls to us to note a few.

First, the metric is mathematically dominated by the bottom of the distribution. Because poverty is defined as the reciprocal of income, individuals with very low incomes exert disproportionate influence on the average. Someone earning $1 per day contributes a value of 1 to the average; someone earning $100 per day contributes 0.01. This is a feature, not a bug — Sterck explicitly designs the metric to be "distribution-sensitive," giving greater weight to the poorest — but it means that small changes in the composition of the very-low-income population can produce large swings in the aggregate number. In a country with 330 million people and highly heterogeneous state-level economies, the bottom tail of the US distribution will be thicker and more varied than in smaller, more homogeneous European nations. This does not invalidate the comparison, but it is worth understanding what is being measured.

Second, the metric relies on World Bank PIP data, which itself relies on household surveys that vary in methodology across countries. Some national surveys measure income directly; others use consumption as a proxy. The US data is income-based while some European data uses consumption measures. As the World Bank acknowledges, these approaches are "closely related" but not identical, and the differences matter most at the extremes of the distribution — precisely where Sterck's metric is most sensitive.

Third, the metric does not directly account for non-monetary transfers and public goods. Universal healthcare, subsidized childcare, public transit, and social housing all represent real economic value that reduces lived poverty without appearing as income in household surveys. European welfare states deliver substantially more of these goods than the United States. To the extent that PIP data partially captures transfer income (it does include government benefits), the effect is partially accounted for — but the in-kind provision of services like healthcare, which constitutes a massive implicit transfer in European systems, may be underrepresented. If anything, this means the true gap between American and European lived poverty is likely larger than Sterck's metric suggests, since the US healthcare system functions as a poverty amplifier for the bottom of the distribution (through medical debt, bankruptcy, and cost avoidance) in ways that European systems do not.

Fourth, the metric does not account for wealth — only income flows. The US has high rates of homeownership and significant household wealth at the median, which provides a buffer against income poverty that pure income measures miss. However, American household wealth is itself highly concentrated, and median wealth figures have stagnated relative to top-decile wealth over the same period Sterck examines, so this caveat cuts both ways.

The Political Economy Angle

El extends Sterck's economic findings into geopolitical territory, arguing that rising average poverty in the US — even as GDP grows — undermines confidence in American institutions, erodes the social contract, and degrades the dollar's attractiveness as a store of value. This is editorializing, but it is transparent editorializing, and it connects logically to El's broader body of work on global capital flows and trade architecture realignment.

The linkage between inequality and institutional legitimacy is well-established in political science literature. When the lived experience of daily economic life contradicts official narratives about national wealth, trust in institutions does erode. The United States has seen precisely this dynamic play out over the past decade, manifesting as declining trust in government, media, and financial institutions across the political spectrum. Whether this translates into dollar weakness is a more speculative claim, but it is not an unreasonable one. Currency valuation ultimately rests on a foundation of institutional credibility, and credibility is hard to sustain when the population's material experience diverges from the statistical portrait.

El's observation that "the American dream now comes with a 47% time penalty" is rhetorical, but it is grounded in a real number from a real study. That is more than can be said for most claims made in either direction about American prosperity.

An Honest Assessment

El presents Sterck's research faithfully. The numbers are accurate. The methodology is properly explained. The limitations are not explored in depth — El is running a YouTube channel, not a seminar — but neither are the findings misrepresented. For a ten-minute video covering an academic paper that most audiences will never read, the signal-to-noise ratio is high.

The underlying research itself is genuinely important. Sterck's metric is not a provocation; it is a serious contribution to development economics that addresses real and longstanding limitations of poverty-line-based measurement. Its adoption by the World Bank (in the related Prosperity Gap formulation) signals that the development community takes it seriously as a tool for tracking shared prosperity.

What the metric reveals about the United States is uncomfortable but not surprising to anyone who has been paying attention to distributional data over the past three decades. The Gini coefficient, the Palma ratio, the top-1% income share — all of these measures tell versions of the same story. What Sterck adds is a framing that makes the distributional reality viscerally legible: time. Everyone understands what it means to need an hour to earn a dollar versus half an hour. The unit of measurement carries an embodied weight that abstract ratios do not.

The 4:1 income dispersion figure is perhaps the most striking data point in the entire study. It means that if you randomly select two Americans, the expected ratio of their incomes exceeds four to one. In Germany, France, or the UK, that ratio is roughly 1.5 to one. That gap is not a statistical curiosity; it is the engine that drives every other finding in Sterck's paper. Same growth rates, radically different distributional outcomes. The income is there. It is just not arriving where most people live.

El's closing line deserves quoting, because it captures the structural reality with precision: "The question isn't whether the USA remains wealthy on aggregate. The question is whether that aggregate wealth translates to distributed economic security for people." By Sterck's metric, the answer has been trending in one direction for 35 years. The question facing American policymakers — and anyone holding dollar-denominated assets — is whether they believe that trend is sustainable.

The data suggests it is not.

Primary source: House of El, YouTube. All figures independently verified against Olivier Sterck's SSRN paper "Poverty without Poverty Line," his VoxDev article, interviews with Euronews Business, the World Bank's Prosperity Gap documentation, and Sterck's article in The Conversation. This article is analysis, not financial advice.

Jonathan Brown for AetheriumArcana