AI Summary. Measured total factor productivity growth is estimated to come almost entirely from higher input utilisation rather than underlying efficiency gains, based on a method that infers unobserved capital and labour utilisation from movements in hours per worker. On this evidence, recent productivity gains reflect "working harder" rather than "working smarter.

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US labor productivity has accelerated, with output per hour growing at a mean annual rate of 2.5% btw 2023–2026Q1, ~1pp above its 2005–2019 pace. The gains have largely been driven by higher input utilisation, which is ‘inherently temporary.”

Does higher input utilisation explain recent measured productivity growth?

Core argument: Since the beginning of 2024, utilisation growth accounts for essentially all measured total factor productivity growth, suggesting the measured productivity gains derive from "working harder" rather than "smarter".

Input utilisation is not observed directly for the entire economy;  it must be inferred. Consider a firm that wants to meet strong demand but has a given capital stock, workforce, and technology. It can ask its existing workers to work longer (which we observe). It can also run its capital longer. Each margin is costly, for example, overtime pay, so firms typically use all of them at once. The observed margin serves as a proxy for the unobserved ones. The method infers changes in industry utilisation from observed movements in hours per worker, scaled by an estimated pass-through of those movements into labour productivity. Industry estimates are aggregated using Domar weights, which reflect each industry’s importance. The stacked bars in Figure 2 show measured TFP growth split into [Fernald's] measure of utilisation growth in light grey and utilisation-adjusted TFP growth in dark grey. In 2023, utilisation growth was a drag on measured TFP growth, and utilisation-adjusted TFP growth soared. Since the beginning of 2024, however, utilisation accounts for essentially all TFP growth. For now, the measured productivity gains appear to derive from “working harder” rather than “smarter.”

Takeaways by Macro Roundup® AI

  1. Since the beginning of 2024, utilisation growth accounts for essentially all measured total factor productivity growth, suggesting the measured productivity gains derive from "working harder" rather than "smarter".
  2. In 2023, utilisation growth was a drag on measured TFP growth while utilisation-adjusted TFP growth soared, according to Fernald's decomposition of measured TFP.
  3. Economy-wide input utilisation is not observed directly and is inferred from movements in hours per worker, scaled by an estimated pass-through into labour productivity, with industry estimates aggregated using Domar weights.

AI Summary. In the US, Europe and Japan, pay growth has decoupled from productivity growth, with productivity pulling ahead. US labor productivity, measured as real GDP per hour worked, is more than 5x its 1947 level while real hourly compensation is only about 3x, and median wage growth lags even further behind.

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In advanced economies, increases in labor productivity growth have decoupled from real wage growth, especially post-1980.

Are wages falling further behind productivity growth in advanced economies?

Core argument: US labour productivity, measured as real GDP per hour worked, is more than five times its 1947 level, while real hourly compensation is only about three times greater.

In the US, Europe and Japan, pay growth has decoupled from productivity growth, with the latter having pulled ahead. US Labour productivity — measured as real GDP per hour worked — is now more than five times greater than in 1947, whereas real hourly compensation is only about three times greater. Median wage growth has lagged even further behind productivity growth, with inequality pushing the average wage higher than the median. (The figures above show average rather than median wages; plotting median wages would show an even greater divergence.)

Takeaways by Macro Roundup® AI

  1. US labour productivity, measured as real GDP per hour worked, is more than five times its 1947 level, while real hourly compensation is only about three times greater.
  2. Pay growth has decoupled from productivity growth in the US, Europe and Japan, with productivity pulling ahead in all three.
  3. Median wage growth has lagged productivity growth by even more than average wage growth, as inequality pushes the average wage above the median.

AI Summary. About 73m US households hold retirement accounts, mostly defined-contribution plans whose balances have risen $6.7tn, and 45m households account for nearly $15tn in annual outlays, close to 3x Germany's economy and 70% of China's GDP. This group's spending has sustained faster growth than comparably wealthy economies.

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Citing the spending of the top 45mm US households, Jason Thomas argues that US economic growth could be characterized less as ‘k-shaped’ than “travelling at two speeds, one roughly the same rate as Europe’s and another that’s racing far ahead.”

Does retirement wealth explain why US spending outpaces peer economies?

Core argument: Balances in US defined-contribution retirement plans have risen by $6.7tn since the start of 2019, a 12.7% annually compounded gain, with roughly 73 million US households holding retirement accounts.

Roughly 73 million US households have retirement accounts (primarily defined-contribution plans like 401ks) that consist primarily of claims on businesses whose revenue growth and market values provide another hedge against inflation. Balances in defined contribution plans have increased by $6.7 trillion (+12.7%, annually compounded) since the start of 2019. US personal savings rates have fallen sharply over the past year (from 5.5% in April 2025 to 3% in May 2026) but that seems entirely rational. If your retirement balance is far above where you expected it to be, why not spend a bit more of your current income than previously intended? If we net across these overlapping cohorts and exclude recent first-time home purchases, we’re left with 45 million households that combine to account for nearly $15 trillion in annual outlays. That’s an extraordinary sum, equal to nearly 3x the size of the entire German economy and 70% of the GDP of China. And this third of the population has not only been insulated from the inflation shock but also exhibits the propensity to spend out of wealth and income to an extent that’s sustained a far higher rate of GDP growth than observed in economies with comparable living standards.

Takeaways by Macro Roundup® AI

  1. Balances in US defined-contribution retirement plans have risen by $6.7tn since the start of 2019, a 12.7% annually compounded gain, with roughly 73 million US households holding retirement accounts.
  2. Netting overlapping cohorts and excluding recent first-time home purchases leaves 45 million US households accounting for nearly $15tn in annual outlays, equal to nearly 3x the German economy and 70% of China's GDP.
  3. The US personal savings rate fell from 5.5% in April 2025 to 3% in May 2026, a decline the article argues is rational when retirement balances sit far above expectations.

AI Summary. Using 5.1m product listings from Sears catalogs, the authors construct a quality-adjusted price index implying much lower goods inflation for 1900–1990 than standard deflators, with real goods consumption growing 39x rather than 10.3x. They estimate faster growth before World War II (5.4% a year, 1900–1939) than after (4.2%, 1946–1980).

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Using 5.1mm Sears catalog listings and LLM-based hedonic estimation, this study finds real goods consumption grew 39-fold from 1900 to 1990, not 10.3-fold as official data imply, with the largest mismeasurement concentrated before WWII.

Does quality-adjusted pricing show faster goods consumption growth before the war?

Core argument: Using a quality-adjusted price index built from 5.1 million Sears catalog listings, the authors estimate real goods consumption grew by a factor of 39 between 1900 and 1990, compared with a factor of 10.3 using standard goods deflators.

Measuring real GDP growth requires distinguishing changes in prices from changes in product quality... systematic quality adjustment. price indexes are unavailable for much of the twentieth century. We construct a new quality-adjusted price index using 5.1 million product listings from Sears catalogs, 1900–1990. The resulting cost-of-living index implies substantially lower goods inflation than conventional deflators between 1900 and 1990, real goods consumption grew by a factor of 39 using our index,compared with a factor of 10.3 using standard goods deflators. Figure 5 shows our estimates of the cost-of-living index for consumer goods in the solid red line. The dashed black line shows average cumulated price changes for the same sample of consumer goods. The gap.is largest before World War II, reversing the conventional view that goods consumption growth was slower before 1945 than in the post-war decades.As Gordon puts it, 'the history of price changes from 1914 to 1947 [is] the black hole where little is known.' This paper sheds light on that black hole. We estimate average annual real goods consumption growth of 5.4% for 1900-1939 and 4.2% for 1946-1980... the era of fastest growth was in the prewar, not postwar, contrary to conventional estimates.

Takeaways by Macro Roundup® AI

  1. Using a quality-adjusted price index built from 5.1 million Sears catalog listings, the authors estimate real goods consumption grew by a factor of 39 between 1900 and 1990, compared with a factor of 10.3 using standard goods deflators.
  2. The authors estimate average annual real goods consumption growth of 5.4% for 1900–1939 and 4.2% for 1946–1980, implying the fastest growth came in the prewar rather than postwar era, contrary to conventional estimates.
  3. The new cost-of-living index implies substantially lower goods inflation than conventional deflators, with the gap largest before World War II, a period Gordon describes as "the black hole where little is known.".

AI Summary. Sub-Saharan Africa's cereal output is nearly 5x its 1960s level, but most gains came from cultivating more land rather than higher yields, and arable land per person has been falling for decades. Food and Agriculture Organisation data show cereal yields flat in recent years, with most African countries recording lower agricultural total factor productivity than a decade earlier.

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Most Sub-Saharan African countries had lower agricultural TFP in 2023 than they had a decade earlier. Btw 2008 and 2019, in the face of a rapidly rising population, small farms’ yields were falling by 3 to 4% per year.

Is Africa's crop growth coming from more land, not higher yields?

Core argument: Sub-Saharan Africa's farmers grow nearly five times as much cereal, such as maize and rice, as in the 1960s, but most of those gains came from cultivating more land rather than raising output per hectare.

In aggregate [sub-Saharan Africa's] farmers are growing more cereals, such as maize (corn) and rice, than ever: nearly five times as much as in the 1960s. But most of those gains came from cultivating more land, which cannot go on for ever. The amount of arable land per person has been falling for decades, and now sits at roughly the global average. That might not matter if farmers were also growing more crops per hectare. But recently gentle growth in agricultural productivity has given way to stagnation, perhaps even decline. Consider figures drawn from national statistics in Africa by the Food and Agriculture Organisation. Cereal yields did not grow between 2020 and 2024, the latest data point. Nor did total factor productivity (TFP), a measure of how efficiently inputs of all kinds (such as labour and machinery) are turned into produce. Most African countries had lower agricultural TFP in 2023 than a decade before.

Takeaways by Macro Roundup® AI

  1. Sub-Saharan Africa's farmers grow nearly five times as much cereal, such as maize and rice, as in the 1960s, but most of those gains came from cultivating more land rather than raising output per hectare.
  2. Cereal yields in Africa did not grow between 2020 and 2024, and neither did total factor productivity, a measure of how efficiently inputs such as labour and machinery are turned into produce, according to Food and Agriculture Organisation figures drawn from national statistics.
  3. Arable land per person in sub-Saharan Africa has been falling for decades and now sits at roughly the global average, while most African countries had lower agricultural total factor productivity in 2023 than a decade before.

AI Summary. As labor force participation falls, a shrinking group of workers generates the income, taxes, and innovation supporting a growing number of non-workers, leaving the economy more fragile because labor shocks are concentrated among fewer participants. Labor scarcity raises the return on automation, robotics, and software, making growth depend on productivity rather than participation.

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Overall LFP is at a 50-year low. Kedrosky warns, “As fewer adults work or seek work, a larger fraction of voters experience the economy mostly as consumers, retirees, or rentiers, not as workers…[which] changes incentives around….taxation and redistribution.”

How does falling labor force participation reshape growth and fragility?

Core argument: Falling labor force participation leaves a shrinking core of workers generating the income, taxes, and innovation that support a growing number of non-workers, and makes the economy more fragile because labor shocks are concentrated among fewer participants.

As fewer adults work or seek work, a larger fraction of voters experience the economy mostly as consumers, retirees, or rentiers, not as workers. That changes political incentives around wages, immigration, AI, taxation, and redistribution. The economy becomes increasingly dependent on a shrinking core. A shrinking group of workers generates the income, taxes, and innovation that support a growing number of non-workers. The economy becomes more fragile because labor shocks are concentrated among fewer participants. Capital must increasingly substitute for labor. Labor scarcity raises the return on automation, AI, robotics, and software. The AI bet becomes as much about compensating for workers' absence as simple replacement. Growth increasingly depends on productivity, on not participation. Any [productivity] miss will be much more consequential than in the past, given that labor no longer picks up the slack.

Takeaways by Macro Roundup® AI

  1. Falling labor force participation leaves a shrinking core of workers generating the income, taxes, and innovation that support a growing number of non-workers, and makes the economy more fragile because labor shocks are concentrated among fewer participants.
  2. As more voters experience the economy as consumers, retirees, or rentiers rather than workers, political incentives shift around wages, immigration, AI, taxation, and redistribution.
  3. Labor scarcity raises the return on automation, AI, robotics, and software, making the AI bet as much about compensating for workers' absence as replacing them, with growth depending on productivity rather than participation and any productivity miss more consequential than in the past.

AI Summary. Europe consumes AI products like ChatGPT without capturing the capital gains, payroll taxes on high-paid engineers, or agglomeration effects, because the tax base accrues where high-value production occurs rather than where products are consumed. The author argues Europe's welfare commitments were implicitly backed by expected future growth that is no longer anticipated.

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Lustig argues that Krugman is wrong to dismiss concerns about slower European growth relative to the US based on welfare measures: “Europe’s social model is not financed out of consumer surplus; it is financed out of taxable income and taxable profits.”

Does consuming AI without producing it erode Europe's tax base?

Core argument: Europe consumes ChatGPT but collects none of the capital gains, payroll taxes on million-dollar AI engineers, or agglomeration effects, because the tax base migrates to where high-value production occurs rather than where products are consumed.

Europe consumes ChatGPT; it does not collect the capital gains, the payroll taxes on million-dollar AI engineers, or the agglomeration effects. The worst possible combination for a welfare state: contented consumers committed to the status quo, eroding public finances. The tax base migrates to where the high-value production takes place, not where the products are ultimately consumed. All this insurance that European states are committed to providing was implicitly backed by the promise of future growth. And that growth is no longer expected to materialize, but the promises remain. The problem is not simply that Europeans chose a different point on the efficiency-equity frontier. Their choices have shifted the frontier itself inward over time, leaving future generations of Europeans with a trade-off that is much worse.

Takeaways by Macro Roundup® AI

  1. Europe consumes ChatGPT but collects none of the capital gains, payroll taxes on million-dollar AI engineers, or agglomeration effects, because the tax base migrates to where high-value production occurs rather than where products are consumed.
  2. Europe's welfare-state insurance was implicitly backed by a promise of future growth that is no longer expected to materialize, while the promises themselves remain in place.
  3. European choices have shifted the efficiency-equity frontier itself inward over time, leaving future generations with a worse trade-off rather than merely a different point on the same frontier.

AI Summary. Productivity growth from 2000-2019 was higher in the US than in western Europe, but almost half the gap disappears when information and communications technology is excluded, even though tech was 9.2% of US output versus 5.4% in the EU. The author estimates this difference in industrial mix accounts for roughly 40% of the overall productivity growth gap.

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The superior productivity growth of the US relative to Europe from 2000–2019 is highly concentrated in the tech sector. Krugman computes the productivity growth differential excluding ICT and finds that it shrinks from .65 to .38 per year.

How much of the US-Europe productivity gap reflects tech sector size?

Core argument: Productivity growth over 2000–2019 was higher in the US than in the EU11, essentially western Europe, but almost half of that gap disappears when the ICT sector is excluded.

The [Draghi] report states that, apart from the ICT [information, communication and technology] sectors in the EU and the US, productivity performance across the rest of the two economies has been similar. Chart 5 shows what I find for productivity growth rates from 2000-2019 in the US and the EU11, basically western Europe. Overall productivity growth was higher in the US than in Europe, but almost half the difference goes away if one excludes ICT, even though the tech sector was only 9.2% of US GDP and 5.4% of EU GDP. It’s worth noting that, within the US, productivity rose much faster in tech than in the rest of the economy. When trying to model the US-EU paradox, I focused on the difference in the share of GDP accounted for by the tech sector. As noted above, the productivity gap outside tech is relatively small. How much of the US-EU paradox can this story, which emphasizes differences in industrial mix, explain? Industrial mix effect = (Share of tech in US GDP – share of tech in EU GDP) * (productivity growth rate in tech – productivity growth rate in non-tech). If I plug in the US-EU difference in ICT share — 3.8 percentage points — and the difference within the US between productivity growth in ICT and in the rest of the economy — 6.8 percentage points — I get 0.26 percentage points, or approximately 40% of the reported overall US-EU difference in productivity growth.

Takeaways by Macro Roundup® AI

  1. Productivity growth over 2000–2019 was higher in the US than in the EU11, essentially western Europe, but almost half of that gap disappears when the ICT sector is excluded.
  2. The tech sector accounted for 9.2% of US GDP versus 5.4% of EU GDP, a 3.8-percentage-point difference, while US productivity growth in ICT exceeded that in the rest of the US economy by 6.8 percentage points.
  3. Multiplying the 3.8-point ICT share gap by the 6.8-point within-US productivity growth gap yields an industrial mix effect of 0.26 percentage points, roughly 40% of the reported overall US-EU productivity growth difference.