“Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
“Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
“A full-throated defense of economic dynamism.” - The Wall Street Journal
“There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
Research on how firms reorganize work in response to technology, trade, changing skills, geography, and new business models. Coverage includes automation, occupational change, team design, outsourcing, remote work, and shifts between industries.
AI Summary.The authors find AI-native U.S. startups are 25% smaller than non-AI startups in the same industry and cohort, with 13% higher engineer shares, roughly 15% lower shares of entry-level workers and managers, flatter hierarchies, and comparable valuations. They argue embedding AI into products lets firms scale knowledge work without large teams.
Hyunjin Kim and Rembrand KoningSocial Science Research Network
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“AI native” firms – firms organized around AI founded btw 2020 and 2024 – had headcounts ~25% lower than their industry peers. These firms’ share of engineers is ~13% higher, while their share of entry-level workers and managers is ~15% lower.
How do AI-native startups differ from other startups in workforce structure?
Core argument: AI-native startups are 25% smaller than non-AI startups in the same industry-cohort, yet their valuations are comparable, which the authors say suggests higher value created per employee.
Drawing on Y Combinator batches W20–F24 and U.S. venture-backed startups whose first financing closed between 2020 and 2024, we classify each firm’s AI-native status and link it to workforce microdata on team size, function, seniority, and hierarchy. Relative to non-AI startups in the same industry-cohort, AI-native firms are 25% smaller. Their share of engineers is 13% greater, and the shares of entry-level workers and managers are each roughly 15% lower. Their hierarchies are half a seniority level flatter—yet valuations are comparable, suggesting higher value created per employee. We argue these patterns reflect two channels: a process channel, in which AI changes how people work inside the firm, and a product channel, in which AI capabilities are built into what the firm sells. Using text from product descriptions and job postings, we find that embedding AI into the product, beyond layering on AI tools into existing workflows, is a primary way startups are scaling “knowledge work” without large teams of knowledge workers.
Takeaways by Macro Roundup® AI
AI-native startups are 25% smaller than non-AI startups in the same industry-cohort, yet their valuations are comparable, which the authors say suggests higher value created per employee.
In AI-native firms, the share of engineers is 13% greater, the shares of entry-level workers and managers are each roughly 15% lower, and hierarchies are half a seniority level flatter.
Drawing on text from product descriptions and job postings for Y Combinator batches W20–F24 and U.S. venture-backed startups first financed between 2020 and 2024, the authors find embedding AI into the product is a primary way startups scale knowledge work without large teams.
Related Articles:
Attention (And Money) Is All You Need: Why Universities Are Struggling to Keep AI Talent— The annual compensation gap between top industry and academic artificial intelligence researchers widened from roughly $294,000 to $1.5m, coinciding with accelerating academic out-migration toward large incumbent technology firms. Researchers who move to industry cut paper production by about 63% while increasing patent filings nearly sixfold, consistent with a shift from open science toward proprietary innovation.
Which Entrepreneurs Boost Productivity?— The authors find that a minority of entrepreneurs are "transformative" — generating disproportionate productivity gains, scoring high on IQ tests, being well-educated, and hiring research and development workers. They argue that economies with more such entrepreneurs and R&D workers sustain higher long-run productivity growth.
Solopreneurs, Solow, and the SaaSpocalypse— For three decades after commercial electricity delivery began in 1882, inflation-adjusted output per worker grew just 0.5% annually, accelerating to more than double that pace only after factories were redesigned around electric motors rather than retrofitted from steam. The author argues a near-term absence of productivity acceleration from AI is not evidence none is coming.
AI Summary.A survey of firms finds 18% use AI in at least one business function (32% employment-weighted), with adoption concentrated in large firms and knowledge-intensive sectors such as information, professional services, and finance. Scope remains narrow: 57% of adopters use AI in three or fewer functions, 66% use it to augment tasks, and only 2% report employment reductions.
Kathryn Bonney, Cory Breaux, Emin Dinlersoz, Lucia Foster, et al.National Bureau of Economic Research
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Census data show that 18% of US firms used AI in at least one function (32% employment-weighted). 66% say it augments work; only 2% report job cuts, but the deeper firms embed AI into operations, the more employment falls.
How widely are firms adopting AI, and does it reduce employment?
Core argument: AI use concentrates in large firms and knowledge-intensive sectors, reaching 50%–60% (60%–70% employment-weighted) among very large firms in Information, Professional Services, and Finance.
During Nov 2025–Jan 2026, 18% of firms used AI in at least one function (32%, employment-weighted), with adoption expected to reach 22% within six months. Use is concentrated in large firms and knowledge-intensive sectors, reaching 50%–60% (60%–70%, employment-weighted) among very large firms in Information, Professional Services, and Finance. Among adopters, scope remains limited: 57% use AI in three or fewer functions, most often Sales and Marketing (52%), Strategy (45%), and IT (41%). Worker-level use appears in 23% (41%, employment-weighted) of firms, primarily for writing, document analysis, and information search; 65% restrict use to three or fewer tasks. Evidence suggests both top-down and bottom-up diffusion: worker use can occur without firm adoption, and vice versa. Most firms (66%) use AI for task augmentation, while employment reductions are rare (2%).
Takeaways by Macro Roundup® AI
AI use concentrates in large firms and knowledge-intensive sectors, reaching 50%–60% (60%–70% employment-weighted) among very large firms in Information, Professional Services, and Finance.
Among adopters, 57% use AI in three or fewer functions, most often Sales and Marketing (52%), Strategy (45%), and IT (41%); 66% use AI for task augmentation, while employment reductions are rare (2%).
Related Articles:
Firm Data On AI— Survey data show around 70% of businesses use some artificial intelligence technology, with larger, more productive, and higher-paying firms more likely to adopt it. Firms report little employment effect and a modest productivity boost so far, but predict adoption will raise productivity by about 1.4% and cut employment by about 0.7% over three years.
Attention (And Money) Is All You Need: Why Universities Are Struggling to Keep AI Talent— The annual compensation gap between top industry and academic artificial intelligence researchers widened from roughly $294,000 to $1.5m, coinciding with accelerating academic out-migration toward large incumbent technology firms. Researchers who move to industry cut paper production by about 63% while increasing patent filings nearly sixfold, consistent with a shift from open science toward proprietary innovation.
Waiting for the AI J-Curve— Artificial intelligence has yet to appear in employment, productivity, or inflation data, or in profit margins and earnings expectations outside the largest technology firms. With fierce competition pushing large language model prices toward zero for users, value creation depends on sector-level implementation, and the author argues AI will likely enhance labor in some sectors rather than replace it across all.
AI Summary.The US population is split evenly among urban, suburban, and rural areas, but 64% of those living at suburban densities reside in prosperous or comfortable ZIP codes, versus 36% in rural and 38% in urban ones. County distress scores correlate strongly with life expectancy, with prosperous counties averaging 5.3 years longer.
Kenan Fikri and Sarah EckhardtAgglomerations
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An analysis of census data finds residents of the mean “prosperous” (top-quintile) county live 5.3 years longer than those in the mean “distressed” (bottom-quintile) one, with the discrepancy between the top and bottom percentiles widening to 8 years.
Do suburban areas concentrate America's prosperity and longer life expectancy?
Core argument: 64% of the U.S. population living at semi-urban (suburban) densities resides in prosperous or comfortable ZIP codes, far above the 36% in rural areas and 38% in urban ones.
Nationally, the country’s population is evenly divided between urban, semi-urban (which we’ll call suburban), and rural areas. Fully 55% of the population in top-tier ZIP codes live at suburban densities, compared to only 20% in the bottom (distressed) tier. Put differently, fully 64% of the U.S. population living at semi-urban densities reside in prosperous or comfortable zip codes (Figure 2). That is far and above the 36% in rural areas or 38% in urban ones. County distress scores are strongly correlated with life expectancy. Residents of the average prosperous county live over five years longer (5.3 years to be exact) than those in the average distressed one, widening to 8 years in the top and bottom percentiles.
Takeaways by Macro Roundup® AI
64% of the U.S. population living at semi-urban (suburban) densities resides in prosperous or comfortable ZIP codes, far above the 36% in rural areas and 38% in urban ones.
55% of the population in top-tier ZIP codes lives at suburban densities, compared with 20% in the bottom, distressed tier, while the country overall splits evenly among urban, suburban, and rural areas.
County distress scores are strongly correlated with life expectancy, with residents of the average prosperous county living 5.3 years longer than those in the average distressed county, widening to 8 years between top and bottom percentiles.
Related Articles:
The Great “Transfer”-mation— Government transfers rose from 8.2% of Americans' total personal income in 1970 to 17.6% in 2022, making them the third largest income source after work and investments. The share of U.S. counties drawing a quarter or more of personal income from transfers rose from under 1% in 1970 to 53% in 2022.
“Compensate the Losers?” Economic Policy and Partisan Realignment in the US— The authors argue that partisan realignment by education reflects Democrats' declining supply of "predistribution" policies such as job guarantees, higher minimum wages, protectionism, and stronger unions, which less-educated Americans favor while more-educated Americans prefer taxes and transfers. They estimate changing views of the parties' economic policies explain roughly half the shift.
The Economic Geography Of The 2024 Elections— Counties where government transfer programs such as Social Security, Medicare, Medicaid, and unemployment insurance make up at least a quarter of personal income gave Trump 63% of the vote, while counties where transfers account for less than 15% gave Harris 56%. Transfers supplied 18% of total US personal income.
AI Summary.Government transfers rose from 8.2% of Americans' total personal income in 1970 to 17.6% in 2022, making them the third largest income source after work and investments. The share of U.S. counties drawing a quarter or more of personal income from transfers rose from under 1% in 1970 to 53% in 2022.
Kenan Fikri, Sarah Eckhardt and Benjamin GlasnerEconomic Innovation Group
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Transfer payments made up 18% of all US personal income in 2022, up from 8% in 1970. Social Security/Medicare made up 56% of the increase from 1970 to 2022, and income maintenance programs/Medicaid accounted for 35%. @kenanfikri @BenGlasner
How much have government transfers grown as a share of personal income?
Core argument: Government transfers rose from 8.2% of Americans' total personal income in 1970 to 17.6% in 2022, ranking third as an income source after work and investments.
Transfers’ share of Americans’ total personal income has more than doubled over the past 50 years, from 8.2% in 1970 to 17.6% in 2022. They are the third largest source of Americans’ personal income, after income from work and investments. The average American received $11,500 in income from government transfers in 2022, compared to $40,500 in income derived from work and $12,900 from investments. Today, most U.S. counties depend on a level of government transfer income that was once reserved only for the most distressed places. In 1970, not even 1% of counties derived a quarter or more of their total personal income from transfers. In 2000, just 10.4% did. But by 2022, 53% of counties were receiving a quarter or more of their income from transfers. Increasing participation (as populations age) and rising underlying costs (driving up the expense of health-related programs, in particular) are the most significant factors at play. Income maintenance programs only account for 11.4% of the real increase in transfers from 1970 to 2022. In stark contrast to the rising elderly share of the population, the poor share has been stable.
Takeaways by Macro Roundup® AI
Government transfers rose from 8.2% of Americans' total personal income in 1970 to 17.6% in 2022, ranking third as an income source after work and investments.
In 2022, 53% of U.S. counties derived a quarter or more of total personal income from transfers, up from 10.4% in 2000 and under 1% in 1970.
Rising participation as populations age and rising underlying costs, particularly for health-related programs, are the most significant factors behind the increase, while income maintenance programs account for 11.4% of the real rise in transfers from 1970 to 2022.
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The Distribution of Household Income in 2021— Income after transfers and taxes grew most for households in the highest quintile, a rate that would have been nearly the same without temporary pandemic policies such as recovery rebate credits, expanded unemployment compensation, and the expanded child tax credit. Those policies raised average income in the lowest quintile to $48,700 from $41,200 without them.
Welfare Is What’s Eating the Budget— The U.S. redistributes 29.4% of its gross domestic product through transfers and taxes, a larger share than any developed country except France at 30.1%. Counting transfers as income and taxes as income lost and adjusting for household size, average households in the bottom, second, and middle income fifths have roughly equal incomes despite large differences in work effort.
AI Summary.The authors find China's tariff cuts after joining the World Trade Organization exceeded the norm of reciprocity, which raised aggregate real incomes in the United States and the rest of the world through better terms of trade while amplifying the China Shock. They estimate this shifted employment from tradable to non-tradable sectors abroad.
Chad Bown, Lorenzo Caliendo, Fernando Parro, Robert Staiger, and Alan SykesNational Bureau of Economic Research
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.@ChadBown and team find that China’s tariff reductions in its WTO accession exceeded reciprocity norms, increasing real incomes but driving an incremental 4% decline in labor demand for tradable goods in the rest of the world.
Did one-sided tariff cuts raise incomes while amplifying trade shocks?
Core argument: China's tariff reductions after its WTO accession exceeded the norm of reciprocity, under which exchanged tariff cuts leave relative world prices between countries unchanged.
When countries exchange tariff cuts that abide by reciprocity, relative world prices (the terms of trade between them) do not change. We find the tariff reductions China implemented after its WTO accession exceeded the norm of reciprocity. This deviation from reciprocity increased aggregate real incomes in the United States and in the rest of the world through improvements in their terms of trade, but it also amplified the magnitude of the China Shock. Figure 2 presents the percentage change in employment in the tradable sector and in the non-tradable sector in the rest of the world due to the movement in the terms of trade resulting from the actual changes in tariff between China and the rest of the world from 1990-2007. We find that China exceeding reciprocity resulted in employment shifting from the tradable sector to the nontradable sector in the rest of the world.
Takeaways by Macro Roundup® AI
China's tariff reductions after its WTO accession exceeded the norm of reciprocity, under which exchanged tariff cuts leave relative world prices between countries unchanged.
China's deviation from reciprocity raised aggregate real incomes in the United States and the rest of the world through improvements in their terms of trade, while also amplifying the magnitude of the China Shock.
Terms-of-trade movements from the actual tariff changes between China and the rest of the world from 1990 to 2007 shifted employment in the rest of the world from the tradable sector to the non-tradable sector.
Related Articles:
Trading Places: Mobility Responses of Native and Foreign-Born Adults to the China Trade Shock— U.S. local labor markets more exposed to import competition from China saw substantially larger net reductions in their foreign-born populations, with no significant reduction among native-born residents. Within exposed areas, foreign-born and native-born workers had comparably sized declines in employment-to-population ratios.
The World’s Rust Belts: The Heterogeneous Effects of Deindustrialization on 1,993 Cities in Six Countries— A study of 1,993 cities in France, Germany, Italy, Japan, the UK and the US finds former manufacturing hubs lost employment on average after each country's manufacturing peak, though a large share fully recovered. After the peak, hubs with a higher initial share of college-educated workers grew significantly faster.
To Find Relative Earnings Gains After the China Shock, Look Outside Manufacturing and Upstream— Among workers initially employed in manufacturing, cumulative median nominal earnings growth from 2000 to 2007 varied widely by destination sector: 27% for those who stayed in manufacturing, higher for those moving to capital- or skill-intensive, higher-wage sectors, and negative for those moving to generally lower-wage sectors.
AI Summary.Administrative tax data show that entrants to the taxi and limousine services industry after online ridesharing platforms appeared were more likely to be young, female, White, and U.S.-born, and to combine driving income with wage and salary jobs. Average net earnings of entrants fell to $2,110, consistent with driving being used to supplement income.
Katharine Abraham, John Haltiwanger, Claire Hou, et al.National Bureau of Economic Research
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From 2013-2019, drivers in the taxi/rideshare industry grew six-fold to 1.3mm. The mean entrant in 2016 had net earnings of just $2,110, as the share of drivers supplementing other wage and salary earnings rose from <20% in 2011 to over 70% in 2015.
How did ridesharing platforms change who drives and what they earn?
Core argument: Average net earnings of entrants to the Taxi and Limousine Services industry (NAICS 4853) fell to $2,110 by 2016, while 2011 nonemployer incumbents averaged $12,290 in net earnings (in 2015 dollars), according to administrative tax data.
Using administrative tax data, we explore the effects of the introduction of online ridesharing platforms on entry, employment, and earnings in the Taxi and Limousine Services industry [NAICS 4853]. New entrants were more likely to be young, female, White, and U.S. born, and to combine earnings from ridesharing with wage and salary earnings. The first two panels of Figure 4 display the gross receipts and net earnings of the 2011 NAICS 4853 nonemployer incumbents and of entrants to the industry. Incumbents had net earnings that averaged just $12,290 (in 2015 dollars) in 2011. Beginning in 2013, the net earnings of NAICS 4853 entrants began to fall off sharply. By 2016, the average NAICS 4853 entrant had net earnings of just $2,110. The decline in average nonemployer earnings and increased propensity to combine nonemployer self-employment with a wage and salary job among NAICS 4853 entrants are consistent with earlier findings that many app-based drivers use driving to supplement or smooth their earnings rather than as a primary source of income
Takeaways by Macro Roundup® AI
Average net earnings of entrants to the Taxi and Limousine Services industry (NAICS 4853) fell to $2,110 by 2016, while 2011 nonemployer incumbents averaged $12,290 in net earnings (in 2015 dollars), according to administrative tax data.
Entrants to NAICS 4853 following the introduction of online ridesharing platforms were more likely to be young, female, White, and U.S. born, and to combine ridesharing earnings with wage and salary earnings.
The decline in average nonemployer earnings and the greater propensity of NAICS 4853 entrants to pair self-employment with a wage and salary job are consistent with earlier findings that many app-based drivers drive to supplement or smooth earnings rather than as a primary income source.
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Consumer Morsel: The Gig Is Up – In A Good Way— Bank of America internal data show the share of its customers receiving income from gig platforms reached 3.8% on a three-month moving average, above the prior peak, after stalling and then resuming an uptrend. Ridesharing has driven the increase and is now the largest gig category, while social commerce and delivery shares have moderated.
AI Summary.Survey data show 6.30% of US workers who switched to remote work after the pandemic moved states, compared with 3.58% of those who kept commuting, and plausibly exogenous changes in employer remote-work policies predict both higher remote work and higher interstate migration. Remote workers staying with the same employer are more likely to move states.
Alexander Bick, Adam Blandin, Karel Mertens, et al.Federal Reserve Bank of St. Louis Working Papers
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The rise in the work-from-home share accounts for more than half of the rise in overall interstate migration since 2019, and states with greater WFH opportunities experienced more out-migration.
Does remote work increase interstate migration?
Core argument: In the Real-Time Population Survey, 6.30% of workers who switched to work from home after Covid moved states since February 2020, compared with 3.58% of workers who commuted both before and after Covid.
In the American Community Survey (ACS), annual interstate migration [AIM] among adults ages 18-64 fell from 2.75% in 2005 to 2.23% in 2010. AIM rose from 2.40% in 2019 to 2.66% in 2022. Data from the Real-Time Population Survey (RPS) provide micro evidence that access to WFH increases interstate migration. [Fig. 4 shows] that 6.30% of workers who switched to WFH post-Covid moved states since February 2020, [compared to] 3.58% of workers who commuted both pre- and post-Covid. A potential concern [is]that the pandemic increased the desire to move across states and a large share of these moves involve WFH, but WFH itself was not necessary for those moves to happen [and thus not causal]. Using novel questions in the RPS - whether workers who commuted prior to the pandemic did so due to job characteristics, personal preferences, or employer requirements, and whether employer WFH policies had changed since 2020 - we find that plausibly exogenous changes in employer WFH policies predict higher rates of WFH and higher rates of interstate migration. Finally, consistent with the interpretation that WFH reduces the benefit of living near a workplace, we find that (i) among workers who remain with their pre-pandemic employer, WFH workers are more likely to move states, and (ii) among workers who move states WFH workers are less likely to change employers.
Takeaways by Macro Roundup® AI
In the Real-Time Population Survey, 6.30% of workers who switched to work from home after Covid moved states since February 2020, compared with 3.58% of workers who commuted both before and after Covid.
Annual interstate migration among U.S. adults ages 18–64 in the American Community Survey fell from 2.75% in 2005 to 2.23% in 2010, then rose from 2.40% in 2019 to 2.66% in 2022.
Plausibly exogenous changes in employer work-from-home policies predict higher rates of work from home and higher rates of interstate migration; among workers staying with pre-pandemic employers, those working from home are more likely to move states.
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Working From Density— Within the same metropolitan-area income quartile, denser neighborhoods have a higher share of jobs that can be done remotely. The authors argue this pattern suggests increased telework raises willingness to pay for housing in high-density places, where leisure amenities and nonwork social interaction are more available.
Remote Work Is Less Common Than We Thought— New Bureau of Labor Statistics estimates show hybrid remote work is substantially less common than other surveys indicated, while fully remote work matches other estimates at about one in ten workers, making fully remote slightly more common than hybrid. Nearly one in five college-educated workers are fully remote, and nearly 40% of advanced-degree holders work hybrid or fully remote.
Remote Work, Three Years Later— Remote work has reduced office utilization but not yet office occupancy, because most firms are locked into long-duration leases. Baseline estimates suggest remote work will exert 0.8 percentage points of upward pressure on the office vacancy rate near-term, 2.3 more over the following five years, and 1.8 thereafter.
AI Summary.Cultural individualism accounts for about one-third of cross-country differences in working from home, with the shift from the lowest-individualism country (Taiwan) to the highest (the US) associated with 0.7 more fully paid work-from-home days per week, and 1.1 more among college graduates. The authors find individualism explains more of this variation than industry mix, population density, or lockdown stringency.
Pablo Zarate, Mathias Dolls, Steven Davis, et al.National Bureau of Economic Research
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A measure of individualism accounts for about 1/3 of the cross-country variation in work-from-home rates, with WFH most prevalent in the Anglosphere and least in Asia. @PabloZarate98 @I_Am_NickBloom @Jose_MariaRD @cevatgirayaksoy
Does culture explain why working from home varies across countries?
Core argument: Cultural factors, specifically Individualism, account for about one-third of cross-country differences in work from home, with the move from Taiwan's lowest Individualism score to the highest in the US associated with 0.7 more full paid WFH days per week.
We examine how work from home varies across countries and across US workers. Our key finding is that cultural factors – specifically Individualism – account for about one-third of the differences across countries. Figure 3 shows the bivariate relationship between individualism and WFH. Moving from the country with the lowest Individualism score (Taiwan) to the one with the highest (the US) is associated with 0.7 more full paid WFH days per week (1.1 among college graduates). Individualism explains far more of the variation than industrial composition, population-weighted density, or lockdown stringency. Across US workers, industry and population density are the two most important determinants. But cultural factors are still statistically significant. Respondents who reside in counties that voted for Joe Biden in 2020 by a larger margin WFH at higher rates even after accounting for industry, state-level wages, population density, and a battery of demographics.
Takeaways by Macro Roundup® AI
Cultural factors, specifically Individualism, account for about one-third of cross-country differences in work from home, with the move from Taiwan's lowest Individualism score to the highest in the US associated with 0.7 more full paid WFH days per week.
Among college graduates, the gap between the lowest- and highest-Individualism countries is associated with 1.1 more full paid WFH days per week, and Individualism explains far more variation than industrial composition, population-weighted density, or lockdown stringency.
Across US workers, industry and population density are the two most important determinants of working from home, while respondents in counties that voted for Joe Biden in 2020 by larger margins work from home at higher rates even after accounting for industry, state-level wages, density, and demographics.
Related Articles:
Remote Work, Three Years Later— Remote work has reduced office utilization but not yet office occupancy, because most firms are locked into long-duration leases. Baseline estimates suggest remote work will exert 0.8 percentage points of upward pressure on the office vacancy rate near-term, 2.3 more over the following five years, and 1.8 thereafter.
Does Working from Home Boost Productivity Growth?— After controlling for each industry's pre-pandemic productivity trend, there is essentially no statistically significant relationship between how easily an industry's work can be done remotely and its excess productivity growth during the pandemic.
Work From Home and the Office Real Estate Apocalypse— The authors estimate New York City office values fell 45% in 2020 and 39% in the longer run, a $453bn value destruction, with lower-quality buildings hit hardest as tenants shifted to higher-quality space. They argue this has implications for local public finances and financial sector stability.
