Edward Conard

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Dorothy Theresa Sawchak Mankiw

Greg Mankiw Greg Mankiw's Blog
Date Posted:
June 22, 2020
Is Database:
Database

@GregMankiw, “Growing up, my father’s work in battery design at Western Electric seemed mundane, yet it underscores the critical role of innovation in economic growth, driving GDP & productivity.”

Growing up, the importance of my father's work in battery design at Western Electric seemed mundane, yet it underscores the critical role of innovation in economic growth. The evolution of technology and infrastructure, such as telecommunications, hinges on such foundational work, driving GDP and productivity. My mother's journey from a hairdresser to a vocational school teacher highlights the economic impact of education and skill development, crucial for labor market adaptability. Her pursuit of teacher certification while raising a family exemplifies the resilience and adaptability needed in a dynamic economy. These personal histories reflect broader economic themes: the significance of human capital investment and technological advancement in shaping economic landscapes.

“…In 1958, nine months after I was born, Mom, Dad, Peg, and I left Trenton for a newly built split-level house in Cranford, New Jersey. My father was working for Western Electric, an arm of AT&T, first as a draftsman and then as an electrical engineer. He worked there until his retirement. One of his specialties was battery design. When I was growing up, I thought it sounded incredibly boring. Now I realize how important it is….”

Greg Mankiw, "Dorothy Theresa Sawchak Mankiw,"Greg Mankiw's Blog, April 17, 2020, http://gregmankiw.blogspot.com/2020/04/dorothy-theresa-sawchak-mankiw.html

Dorothy Theresa Sawchak Mankiw

Dorothy Theresa Sawchak Mankiw: Extended Excerpt Image 1


Above is a picture of my mother as a young woman. I would like to tell you about her.

My mother was born on July 18, 1927, the second child of Nicholas and Catherine Sawchak.

Nicholas and Catherine were immigrants from Ukraine. They came to the United States as teenagers, arriving separately, neither with more than a fourth-grade education. Catherine was from a farming area in western Ukraine. She left because her family wanted her to marry an older man rather than her younger boyfriend, who had been conscripted into the army. Her first job here was as a maid. Nicholas was from Kiev, where he had been trained to be a furrier. In the United States, he worked as a potter, making sinks and toilettes. When Nicholas and Catherine came to the United States, they thought they might return home to Ukraine eventually. But World War I and the Russian Revolution intervened, causing a change of plans. Catherine’s boyfriend died in the war. Nicholas and Catherine met each other, married, and settled in a small row house in Trenton, New Jersey, where they lived the rest of their lives.

Catherine and Nicholas had two children, my uncle Walter and my mother Dorothy. When my mother was born, her parents chose to name her “Dorothy Theresa Sawchak.” But because Catherine spoke with a heavy accent, the clerk preparing the birth certificate did not understand her. So officially, my mother’s middle name was “Tessie” rather than “Theresa.” She never bothered to change it.

Nicholas and Catherine were hardworking and frugal. They saved enough to send Walter to college and medical school. He served as a physician in the army during the Korean war. Once I asked him if he worked at a MASH unit, like in the TV show. He said no, he worked closer to the front. He patched up the wounded soldiers the best he could and then sent them to a MASH unit to recover and receive more treatment. After the war, he became a pathologist in a Trenton-area hospital. He married and had two daughters, my cousins.

My mother attended Trenton High School (the same high school, I learned years later, attended by the economist Robert Solow at about the same time). She danced ballet. She water-skied on the Delaware River. She loved to read and go to the movies.

In part because of limited resources and in part because of the gender bias of the time, my mother was not given the chance to go to college. Years later, her parents would say that not giving her that opportunity was one of their great regrets. Instead, my mother learned to be a hairdresser. She was also pressured to marry the son of some family friends.

The marriage did not work. With my mother pregnant, her new husband started “running around,” my mother’s euphemism for infidelity. They divorced, and she kicked him out of her life. But the marriage did leave her with one blessing—my sister Peg.

My mother continued life as a single mother. Some years later, she met my father, also named Nicholas, through social functions run by local Ukrainian churches. They both loved to dance. He wanted to marry her, but having been burned once, she was reluctant at first. Only when she realized that he had become her best friend did she finally accept.

In 1958, nine months after I was born, Mom, Dad, Peg, and I left Trenton for a newly built split-level house in Cranford, New Jersey. My father was working for Western Electric, an arm of AT&T, first as a draftsman and then as an electrical engineer. He worked there until his retirement. One of his specialties was battery design. When I was growing up, I thought it sounded incredibly boring. Now I realize how important it is.

My mother then stopped working as a hairdresser to become a full-time mom. But she kept all the hairdresser equipment from her shop—chair, mirrors, scissors, razors, and so on—in our basement. She would cut the hair of her friends on a part-time basis. When I was a small boy, she cut my hair as well.

I attended the Brookside School, the public grade school which was a short walk from our house. When I was in the second or third grade, my mother was called in to see the teacher. The class had been given some standardized aptitude test. “Greg did well,” the teacher said. “We were very surprised.”

At that moment, my mother decided the school was not working out for me. I was talkative and inquisitive at home but shy and lackluster at school. I needed a change.

She started looking around for the best school she could find for me. She decided it was The Pingry School, an independent day school about a dozen miles from our house. She had me apply, and I was accepted.

The question then became, how to pay for it? Pingry was expensive, and we did not have a lot of extra money. My mother decided that she needed to return to work.

She started looking for a job, and an extraordinary opportunity presented itself. Union County, where we lived, was opening a public vocational school, and they were looking for teachers. She applied to be the cosmetology teacher and was hired.

There was, however, a glitch. The teachers, even though teaching trades like hairdressing, needed teacher certification. That required a certain number of college courses, and my mother had not taken any. So she got a temporary reprieve from the requirement. While teaching at the vocational school during the day, she started taking college courses at night to earn her certification, all while raising two children.

My mother taught at the vocational school until her retirement. During that time, she also co-authored a couple of books, called Beauty Culture I and II, which were teacher’s guides. From the summary of the first volume: “The syllabus is divided into six sections and includes the following areas of instruction: shop, school, and the cosmetologist; sterilization practices in the beauty salon; scalp and hair applications and shampooing; hair styling; manicuring; and hairpressing and iron curling.” I suppose one might view this project as a harbinger of my career as a textbook author.

When my parents both retired, they were still the best of friends. They traveled together, exploring the world in ways that were impossible when they were younger and poorer. During my third year as an economics professor, I was visiting the LSE for about a month. I encouraged my parents to come over to London for a week or so. They had a grand time. I believe it was the first time they had ever visited Europe. When I was growing up, vacations were usually at the Jersey shore.

My father died a few years later. My mother spent the next three decades living alone. She was then living full-time at the Jersey shore in Brant Beach on Long Beach Island. The house was close to the ocean and large enough to encourage her growing family to come for extended visits. Two children, five grandchildren, four great-grandchildren. The more, the merrier. Nothing made her happier than being surrounded by family.

My mother loved to cook, especially the Ukrainian dishes she learned in her childhood. Holubtsi (stuffed cabbage) was a specialty. Another was kapusta (cabbage) soup. One time, the local newspaper offered to publish her kapusta soup recipe. They did so, but with an error. Every seasoning that was supposed to be measured in teaspoons was printed as tablespoons. The paper later ran a correction but probably to no avail. I am not sure if anyone ever tried the misprinted recipe and, if so, to what end.

During her free time in her later years, my mother read extensively, played FreeCell on her computer, and watched TV. A few years ago, when she was about 90 years old, I was visiting her, and I happened to mention the show “Breaking Bad.” She had not heard of it. She suggested we watch the first episode. And then another. And another. After I left, she binge-watched all five seasons.

As she aged, living alone became harder. When she had trouble going up and down the stairs, an elevator was added to her house. But slowly her balance faltered, and she fell several times. She started having small strokes, and then a more significant one. She moved into a nursing home. Whenever I visited, I brought her new books to read. Her love of reading never diminished.

This is, I am afraid, where the story ends. Last week, Dorothy Theresa Sawchak Mankiw tested positive for Covid-19. Yesterday, she died. I will miss her.

  • Productivity
    • Institutional Capabilities
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Showing 484 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization

The College Wage Premium in the Generative AI Era

AI Summary. S. 575 between 2022 and 2026, the first sustained decline in relative demand for college-educated labor in four decades. AI exposure in white-collar occupations accounts for roughly 28% of that drop, as wage growth slowed disproportionately in high-AI-exposure jobs where college graduates are concentrated.

José Azar, Mireia Gine and Javier Sanz-Espín Social Science Research Network
Date Posted:
September 4, 2026
Is Database:
Database

The college wage premium flattened in the mid-2010s and has fallen ~8% since 2022. The authors argue that this compression reflects a broad decline in the returns to formal schooling, rather than a decline in the upper tail.

Is the college degree losing its economic value to artificial intelligence?

Core argument: The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.

After expanding for four decades, the U.S. college wage premium [dropped] sharply from 0.626 in 2022 to 0.575 in 2026. Current Population Survey data through 2026 implies an unprecedented drop in relative demand for college labor—the first sustained negative relative demand growth. Post-2022 wage growth slowed disproportionately in high-exposure occupations, which employ a disproportionate share of college graduates. By 2026, going from zero occupational AI exposure to full exposure had a negative effect on wages of−0.086. Combined with the college–non-college exposure gap, this mechanism accounts for roughly 28% of the total drop in the college wage premium from 2022 to 2026. While non-causal, these patterns indicate that task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in the recent compression of the aggregate skill premium.

Takeaways by Macro Roundup® AI

  1. The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.
  2. Moving from zero to full occupational AI exposure reduced wages by 0.086 log points by 2026.
  3. the college–non-college AI-exposure gap accounts for roughly 28% of the total premium compression over that period.

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  • How Students and Recent Grads are Responding to the Rise of AI — Far from shying away from AI, American undergraduates “are flocking towards the most-AI-exposed degrees,” with enrollment in these majors up 8% last year…
  • AI and Young-adult Jobs: The Real Mystery — Since the summer of 2023, the employment rate for Americans 22–25 has declined for both college grads and non-college workers, a phenomenon beyond both…
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Gross and Net US Investment

AI Summary. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment merely replaces depreciating assets. The shift toward faster-depreciating information technology assets requires larger gross investment increases to achieve any given gain in productive capital per worker.

Timothy Taylor Conversable Economist
Date Posted:
September 4, 2026
Is Database:
Database

U.S. real net private domestic investment—which adds to the American capital stock—is now only ~25% as large as gross investment, down from ~40% in the 1970s. Taylor suggests the widening gap between gross and net investment reflects the relatively rapid depreciation of IT-related capital.

Does faster asset depreciation explain slowing productivity growth?

Core argument: Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.

The figure divides net investment by gross investment. Back in the 1970s, net investment was often around 40% of gross investment, but the share has been slumping over time. For the last decade or so, net investment has been about 25% of the gross–that is, about three-quarters of gross investment is just making up for depreciation of the pre-existing capital stock. The likely reason for the growing gap between gross and net investment is that modern investment is more likely to be related to information technology [which] depreciates more rapidly and thus needs to be replaced and updated more often. If we want the average US worker to be using a greater amount of capital on the job–which was one of the key drivers of rising labor productivity in the past–it now takes a bigger rise in gross investment to lead to a given rise in net investment.

Takeaways by Macro Roundup® AI

  1. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.
  2. The shift toward information technology — which depreciates faster than physical machinery — is the primary driver of the widening gap between gross and net investment.
  3. Raising capital per worker, a historic engine of labor productivity growth, now requires a substantially larger increase in gross investment than it did several decades ago.

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The AI Re-Acceleration That Wasn’t

AI Summary. 615). Claims of re-acceleration result from cherry-picking frontier observations, selecting a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Paul Kedrosky Applied Complexity
Date Posted:
September 3, 2026
Is Database:
Database

Kedrosky argues AI capabilities continue to improve, but “the full composite data shows flattening relative gains, not acceleration…rolling relative model gains have fallen from their 2024 peak, while model dispersion has narrowed sharply.”

Are AI performance gains accelerating or just appearing to through selective measurement?

Core argument: Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.

Using all Epoch’s Capabilities Index observations, and controlling for developer and model family, there is no statistically significant breakpoint. A piecewise model—which splits the series into intervals and applies a sub-function to each segment—does not improve on a purely linear trend: p = 0.615, The estimated change in slope has a confidence interval of -8.4 to +23.4 points per year. In short, the maths shows there is no model acceleration, contrary to claims, and as expected. The result comes from selecting frontier observations only, choosing a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Takeaways by Macro Roundup® AI

  1. Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.
  2. Claims of AI re-acceleration rest on a methodological artifact: selecting only frontier model observations, pre-choosing a breakpoint, ignoring variance collapse, and fitting separate trend lines on each side of that breakpoint.

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  • Why .400 Hitters Disappeared — and What It Means for AI — As AI model performance converges toward a ceiling, relative gains per improvement cycle shrink, transforming frontier capability from a pricing moat into a commodity where price becomes the primary differentiator and margin pressure intensifies across leading providers.
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  • Anthropic’s Best AI Model Struggles To Attract Users As Cheaper Tools Thrive — Spending on the most expensive AI model from a leading provider has plateaued at 11% of total outlay, as cheaper, older models prove capable of handling most business tasks.
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Understanding AI and Productivity

AI Summary. U.S. productivity growth has accelerated to ~2.2% annually since mid-2022, above the 2010s baseline, though pandemic-era labor market and business formation dynamics likely contributed alongside AI. Historical general-purpose technology booms sustained labor productivity growth above 2.5% for a decade or more, making the current acceleration substantial but not unprecedented.

Chad Syverson Economic Innovation Group
Date Posted:
August 28, 2026
Is Database:
Database

Syverson is skeptical that AI initiated the rise in productivity growth that began in 2023. The acceleration began while AI investment was small, and pandemic-era labor market churn and business dynamism match the acceleration’s start.

Is AI-driven productivity growth sustainable at historical technology boom levels?

Core argument: U.S. labor productivity has grown at roughly 2.2% annually since mid-2022, a pace exceeding the 2010s trend and, if sustained, implying GDP per capita roughly 7% higher within a decade than the prior trajectory.

Productivity from mid-2022 on has maintained a faster-than-2010s trajectory involving annual growth of about 2.2%. Could this acceleration be due to AI? Perhaps. The timing leans against AI being the sole initial cause. Additionally, there were well-documented increases in economic dynamism (labor market churn and business formation) during the pandemic emergence whose timing matches the acceleration’s start. Regardless of AI’s current effect, the longer the aggregate productivity acceleration continues, the more plausible it is that AI is an important driver. As for the magnitude, a sustained increase from 1.5 to 2.2% annual productivity growth would be substantial (after a decade, GDP per capita would be 7% higher than otherwise), but hardly unprecedented. The 1995–2004 productivity boom saw annual productivity growth of nearly 3% per year, and other past general-purpose-technology-related productivity boosts saw labor productivity growth in excess of 2.5% for a decade or longer.

Takeaways by Macro Roundup® AI

  1. U.S. labor productivity has grown at roughly 2.2% annually since mid-2022, a pace exceeding the 2010s trend and, if sustained, implying GDP per capita roughly 7% higher within a decade than the prior trajectory.
  2. The 1995–2004 productivity boom averaged nearly 3.0% annual growth, establishing that a durable AI-driven acceleration to 2.2% would be meaningful but well within historical precedent for general-purpose-technology cycles.
  3. Pandemic-era surges in labor market churn and business formation align more precisely with the productivity acceleration’s start date than AI adoption does, complicating AI-as-sole-cause narratives.

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US Widens AI-Driven Investment Gap With Europe

AI Summary. US corporate investment in equipment and facilities is projected to grow 40% in real terms by the end of next year, versus 12% in the euro area, widening a productivity gap where output per hour worked rose $14 in the US compared with $2 in Europe since 2018.

Sam Fleming, Amy Borrett and Olaf Storbeck Financial Times
Date Posted:
August 24, 2026
Is Database:
Database

Oxford Economics projects US real business investment will rise 40% over 2021–2027, ~3x the euro area’s 12%. US investment growth since 2024 has been largely information processing and software, but high US growth in GDP/hour is not “merely digital.”

Is artificial intelligence investment widening the transatlantic productivity divide?

Core argument: U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.

Corporate spending on new equipment and facilities in the US is projected to increase 40% in real terms between 2021 and the end of next year, according to forecasts from Oxford Economics. The US surge compared with a real-terms increase of just 12% in the euro area, while German business investment is expected to have all but stagnated over the same period. Europe also faces a large and growing productivity gap with the US. “The United States has recently pulled further ahead of Europe,” Bart van Ark, a professor at the University of Manchester, told policymakers at the ECB Forum in Sintra. GDP per hour worked increased $14 in the US between 2018 and 2025, compared with just $2 in Europe. “The gap is not only a digital sector story,” added van Ark, stressing that the US outperformance extended to other sectors, including wholesale and retail as well as professional services.

Takeaways by Macro Roundup® AI

  1. U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.
  2. U.S. labor productivity rose $14 per hour worked between 2018 and 2025, versus $2 in Europe, with outperformance spanning wholesale, retail, and professional services—not solely the digital sector.

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Why Big Tech’s AI Spending Is $3 Trillion Higher Than It Seems

AI Summary. Nine major technology companies carry ~$3tn in off-balance-sheet AI commitments — 5x their ~$600bn in reported capital spending — obligations that are growing faster than traditional investment and triple their combined lease and debt liabilities.

Peter Rudegeair and Peter Santilli Wall Street Journal
Date Posted:
August 17, 2026
Is Database:
Database

A WSJ analysis finds 9 firms involved in the data center buildout have ~$3T in off-balance-sheet commitments largely tied to AI infrastructure. The growth in such obligations has outpaced the firms’ capex growth over the last year.

Are technology companies hiding the true cost of artificial intelligence?

Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings. Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.

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  • The Market Is Asking Questions — AI infrastructure debt spreads are widening as markets question whether returns on massive, front-loaded capital spending will outpace financing costs before assets depreciate. If compute demand plateaus from efficiency gains or slow adoption, the industry faces a glut of expensive, rapidly depreciating capacity.
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