Ch20: Economic Growth, the Financial System, and Business Cycles – Page 2

Is it 1987 for AI?

Image generated by ChatGPT 5 of a 1981 IBM personal computer.

The modern era of information technology began in the 1980s with the spread of personal computers. A key development was the introduction of the IBM personal computer in 1981. The Apple II, designed by Steve Jobs and Steve Wozniak and introduced in 1977, was the first widely used personal computer, but the IBM personal computer had several advantages over the Apple II. For decades, IBM had been the dominant firm in information technology worldwide. The IBM System/360, introduced in 1964, was by far the most successful mainframe computer in the world. Many large U.S. firms depended on IBM to meet their needs for processing payroll, general accounting services, managing inventories, and billing.

Because these firms were often reliant on IBM for installing, maintaining, and servicing their computers, they were reluctant to shift to performing key tasks with personal computers like the Apple II. This reluctance was reinforced by the fact that few managers were familiar with Apple or other early personal computer firms like Commodore or Tandy, which sold the TRS-80 through Radio Shack stores. In addition, many firms lacked the technical staffs to install, maintain, and repair personal computers. Initially, it was easier for firms to rely on IBM to perform these tasks, just as they had long been performing the same tasks for firms’ mainframe computers.

By 1983, the IBM PC had overtaken the Apple II as the best-selling personal computer in the United States. In addition, IBM had decided to rely on other firms to supply its computer chips (Intel) and operating system (Microsoft) rather than develop its own proprietary computer chips and operating system. This so-called open architecture made it possible for other firms, such as Dell and Gateway, to produce personal computers that were similar to IBM’s. The result was to give an incentive for firms to produce software that would run on both the IBM PC and the “clones” produced by other firms, rather than produce software for Apple personal computers. Key software such as the spreadsheet program Lotus 1-2-3 and word processing programs, such as WordPerfect, cemented the dominance of the IBM PC and the IBM clones over Apple, which was largely shut out of the market for business computers.

As personal computers began to be widely used in business, there was a general expectation among economists and policymakers that business productivity would increase. Productivity, measured as output per hour of work, had grown at a fairly rapid average annual rate of 2.8 percent between 1948 and 1972. As we discuss in Macroeconomics, Chapter 10 (Economics, Chapter 20 and Essentials of Economics, Chapter 14) rising productivity is the key to an economy achieving a rising standard of living. Unless output per hour worked increases over time, consumption per person will stagnate. An annual growth rate of 2.8 percent will lead to noticeable increases in the standard of living.

Economists and policymakers were concerned when productivity growth slowed beginning in 1973. From 1973 to 198o, productivity grew at an annual rate of only 1.3 percent—less than half the growth rate from 1948 to 1972. Despite the widespread adoption of personal computers by businesses, during the 1980s, the growth rate of productivity increased only to 1.5 percent. In 1987, Nobel laureate Robert Solow of MIT famously remarked: “You can see the computer age everywhere but in the productivity statistics.” Economists labeled Solow’s observation the “productivity paradox.” With hindsight, it’s now clear that it takes time for businesses to adapt to a new technology, such as personal computers. In addition, the development of the internet, increases in the computing power of personal computers, and the introduction of innovative software were necessary before a significant increase in productivity growth rates occurred in the mid-1990s.

Result when ChatGPT 5 is asked to create an image illustrating ChatGPT

The release of ChatGPT in November 2022 is likely to be seen in the future as at least as important an event in the evolution of information technology as the introduction of the IBM PC in August 1981. Just as with personal computers, many people have been predicting that generative AI programs will have a substantial effect on the labor market and on productivity.

In this recent blog post, we discussed the conflicting evidence as to whether generative AI has been eliminating jobs in some occupations, such as software coding. Has AI had an effect on productivity growth? The following figure shows the rate of productivity growth in each quarter since the fourth quarter of 2022. The figure shows an acceleration in productivity growth beginning in the fourth quarter of 2023. From the fourth quarter of 2023 through the fourth quarter of 2024, productivity grew at an annual rate of 3.1 percent—higher than during the period from 1948 to 1972. Some commentators attributed this surge in productivity to the effects of AI.

However, the increase in productivity growth wasn’t sustained, with the growth rate in the first half of 2025 being only 1.3 percent. That slowdown makes it more likely that the surge in productivity growth was attributable to the recovery from the 2020 Covid recession or was simply an example of the wide fluctuations that can occur in productivity growth. The following figure, showing the entire period since 1948, illustrates how volatile quarterly rates of productivity growth are.

How large an effect will AI ultimately have on the labor market? If many current jobs are replaced by AI is it likely that the unemployment rate will soar? That’s a prediction that has often been made in the media. For instance, Dario Amodei, the CEO of generative AI firm Anthropic, predicted during an interview on CNN that AI will wipe out half of all entry level jobs in the U.S. and cause the unemployment rate to rise to between 10% and 20%.

Although Amodei is likely correct that AI will wipe out many existing jobs, it’s unlikely that the result will be a large increase in the unemployment rate. As we discuss in Macroeconomics, Chapter 9 (Economics, Chapter 19 and Essentials of Economics, Chapter 13) the U.S. economy creates and destroys millions of jobs every year. Consider, for instance, the following table from the most recent “Job Openings and Labor Turnover” (JOLTS) report from the Bureau of Labor Statistics (BLS). In June 2025, 5.2 million people were hired and 5.1 million left (were “separated” from) their jobs as a result of quitting, being laid off, or being fired.

Most economists believe that one of the strengths of the U.S. economy is the flexibility of the U.S. labor market. With a few exceptions, “employment at will” holds in every state, which means that a business can lay off or fire a worker without having to provide a cause. Unionization rates are also lower in the United States than in many other countries. U.S. workers have less job security than in many other countries, but—crucially—U.S. firms are more willing to hire workers because they can more easily lay them off or fire them if they need to. (We discuss the greater flexibility of U.S. labor markets in Macroeconomics, Chapter 11 (Economics, Chapter 21).)

The flexibility of the U.S. labor market means that it has shrugged off many waves of technological change. AI will have a substantial effect on the economy and on the mix of jobs available. But will the effect be greater than that of electrification in the late nineteenth century or the effect of the automobile in the early twentieth century or the effect of the internet and personal computing in the 1980s and 1990s? The introduction of automobiles wiped out jobs in the horse-drawn vehicle industry, just as the internet has wiped out jobs in brick-and-mortar retailing. People unemployed by technology find other jobs; sometimes the jobs are better than the ones they had and sometimes the jobs are worse. But economic historians have shown that technological change has never caused a spike in the U.S. unemployment rate. It seems likely—but not certain!—that the same will be true of the effects of the AI revolution.

Which jobs will AI destroy and which new jobs will it create? Except in a rough sense, the truth is that it is very difficult to tell. Attempts to forecast technological change have a dismal history. To take one of many examples, in 1998, Paul Krugman, later to win the Nobel Prize, cast doubt on the importance of the internet: “By 2005 or so, it will become clear that the Internet’s impact on the economy has been no greater than the fax machine’s.” Krugman, Amodei and other prognosticators of the effects of technological change simply lack the knowledge to make an informed prediction because the required knowledge is spread across millions of people.

That knowledge only becomes available over time. The actions of consumers and firms interacting in markets mobilize information that is initially known only partially to any one person. In 1945, Friedrich Hayek made this argument in “The Use of Knowledge in Society,” which is one of the most influential economics articles ever written. One of Hayek’s examples is an unexpected decrease in the supply of tin. How will this development affect the economy? We find out only by observing how people adapt to a rising price of tin: “The marvel is that … without an order being issued, without more than perhaps a handful of people knowing the cause, tens of thousands of people whose identity could not be ascertained by months of investigation are made [by the increase in the price of tin] to use the material or its products more sparingly.” People adjust to changing conditions in ways that we lack sufficient information to reliably forecast. (We discuss Hayek’s view of how the market system mobilizes the knowledge of workers, consumers, and firms in Microeconomics, Chapter 2.)

It’s up to millions of engineers, workers, and managers across the economy, often through trial and error, to discover how AI can best reduce the cost of producing goods and services or improve their quality. Competition among firms drives them to make the best use of AI. In the end, AI may result in more people or fewer people being employed in any particular occupation. At this point, there is no way to know.

Why Were the Data Revisions to Payroll Employment in May and June So Large?

Image generated by ChatTP-4o

As we noted in yesterday’s blog post, the latest “Employment Situation” report from the Bureau of Labor Statistics (BLS) included very substantial downward revisions of the preliminary estimates of net employment increases for May and June. The previous estimates of net employment increases in these months were reduced by a combined 258,000 jobs. As a result, the BLS now estimates that employment increases for May and June totaled only 33,000, rather than the initially reported 291,000. According to Ernie Tedeschi, director of economics at the Budget Lab at Yale University, apart from April 2020, these were the largest downward revisions since at least 1979.

The size of the revisions combined with the estimate of an unexpectedly low net increase of only 73,000 jobs in June prompted President Donald Trump to take the unprecedented step of firing BLS Commissioner Erika McEntarfer. It’s worth noting that the BLS employment estimates are prepared by professional statisticians and economists and are presented to the commissioner only after they have been finalized. There is no evidence that political bias affects the employment estimates or other economic data prepared by federal statistical agencies.

Why were the revisions to the intial May and June estimates so large? The BLS states in each jobs report that: “Monthly revisions result from additional reports received from businesses and government agencies since the last published estimates and from the recalculation of seasonal factors.” An article in the Wall Street Journal notes that: “Much of the revision to May and June payroll numbers was due to public schools, which employed 109,100 fewer people in June than BLS believed at the time.” The article also quotes Claire Mersol, an economist at the BLS as stating that: “Typically, the monthly revisions have offsetting movements within industries—one goes up, one goes down. In June, most revisions were negative.” In other words, the size of the revisions may have been due to chance.

Is it possible, though, that there was a more systematic error? As a number of people have commented, the initial response rate to the Current Employment Statistics (CES) survey has been declining over time. Can the declining response rate be the cause of larger errors in the preliminary job estimates?

In an article published earlier this year, economists Sylvain Leduc, Luiz Oliveira, and Caroline Paulson of the Federal Reserve Bank of San Francisco assessed this possibility. Figure 1 from their article illustrates the declining response rate by firms to the CES monthly survey. The figure shows that the response rate, which had been about 64 percent during 2013–2015, fell significantly during Covid, and has yet to return to its earlier levels. In March 2025, the response rate was only 42.6 percent.

The authors find, however, that at least through the end of 2024, the falling response rate doesn’t seem to have resulted in larger than normal revisions of the preliminary employment estimates. The following figure shows their calculation of the average monthly revision for each year beginning with 1990. (It’s important to note that they are showing the absolute values of the changes; that is, negative change are shown as positive changes.) Depite lower response rates, the revisions for the years 2022, 2023, and 2024 were close to the average for the earlier period from 1990 to 2019 when response rates to the CES were higher.

The weak employment numbers correspond to the period after the Trump administration announced large tariff increases on April 2. Larger firms tend to respond to the CES in a timely manner, while responses from smaller firms lag. We might expect that smaller firms would have been more likely to hesitate to expand employment following the tariff announcement. In that sense, it may be unsurprising that we have seen downward revisions of the prelimanary employment estimates for May and June as the BLS received more survey responses. In addition, as noted earlier, an overestimate of employment in local public schools alone accounts for about 40 percent of the downward revisions for those months. Finally, to consider another possibility, downward revisions of employment estimates are more likely when the economy is heading into, or has already entered, a recession. The following figure shows the very large revisisons to the establishment survey employment estimates during the 2007–2010 period.

At this point, we don’t fully know the reasons for the downward employment revisions announced yesterday, although it’s fair to say that they may have been politically the most consequential revisions in the history of the establishment survey.

Glenn on Policies to Increase Economic Growth and Reduce the Federal Budget Deficit

Image generated by ChatGTP 40

Glenn, along with co-authors Douglas Elmendorf of Harvard’s Kennedy School and Zachary Liscow of the Yale Law School, has written a new National Bureau of Economic Research working paper: “Policies to Reduce Federal Budget Deficits by Increasing Economic Growth”

Here’s the abstract:

Could policy changes boost economic growth enough and at a low enough cost to meaningfully reduce federal budget deficits? We assess seven areas of economic policy: immigration of high-skilled workers, housing regulation, safety net programs, regulation of electricity transmission, government support for research and development, tax policy related to business investment, and permitting of infrastructure construction. We find that growth-enhancing policies almost certainly cannot stabilize federal debt on their own, but that such policies can reduce the explicit tax hikes, spending cuts, or both that are needed to stabilize debt. We also find a dearth of research on the likely impacts of potential growth-enhancing policies and on ways to design such policies to restrain federal debt, and we offer suggestions for ways to build a larger base of evidence.

The full working paper can be found at this link.

Glenn Proposes Tax Reforms to Aid Economic Growth

Photo of the Internal Revenue Service building in Washington, DC from the Associated Press via the Wall Street Journal

The following op-ed by Glenn first appeared in the Wall Street Journal.

The GOP Tax Bill Could Solve the Tariff Problem

The economy and financial markets nervously await the July 8 end of the 90-day pause of the Trump administration’s “reciprocal” tariffs. But four days earlier, Republicans can allay those fears with a pro-growth policy that advances President Trump’s Made in America agenda without tariffs. The fix: tax reform.

July 4 is the date that Treasury Secretary Bessent has predicted Congress and the White House will have ready a 2.0 version of the landmark Tax Cut and Jobs Act of 2017, or TCJA. Without congressional action, some of these reforms will expire at the end of 2025, killing changes that still benefit the economy. Corporate tax changes, in particular, boosted investment and growth. These should remain the focal point of this next tax bill—for many reasons. Done right, corporate tax reform could advance President Trump’s goal to bring investment to American production without using economy-roiling tariffs. Call it TCJA+.

Renewing some parts of the original TCJA will help investment in the U.S. The 2017 reform offered incentives for investment in new businesses by allowing them to expense the cost of assets, rather than writing them off over time. But these benefits were set to be phased out from 2023 to 2026, removing a key pro-growth element of the earlier law.

Then there are two new provisions Republicans should add to secure Mr. Trump’s goal to have more made in America. Both were in the original 2016 House Republican tax reform blueprint.

First, Congress should change business taxation from the current income tax to a cash-flow tax, which taxes a firm’s revenue, minus all its expenses, including investment. Long championed by economists and tax-law experts, a cash-flow tax would allow businesses to expense investment immediately. It would also disallow nonfinancial companies from making interest deductions, because unlike an income tax, a cash-flow tax treats debt and equity the same. This removes an important tax incentive for firms to allow themselves to be leveraged. A cash-flow tax is also much simpler than a corporate income tax, which requires complex depreciation schedules. Most important, the reform would stimulate business investment.

Second, legislators should add a border adjustment to corporate taxes. That would deny companies a tax deduction for expenses of inputs imported from abroad, while exempting U.S. exports from taxation. As the Trump administration has observed, other countries already use border adjustments in their value-added taxes on consumption. America uses a similar mechanism in state and local retail taxes, too. If you buy a kitchen appliance in New York, you pay New York sales tax, even if it was made in Ohio. Sales tax applies only where the good is sold, not where it originates.

Though distinct from tariffs, border adjustments can promote domestic production. Adding a border adjustment to the federal corporate tax would eliminate any extra tax businesses suffer now simply as a consequence of producing in the U.S. Though the 2017 law made the U.S. tax system more globally competitive by lowering the corporate income tax rate from 35% to 21%, it’s still higher than in many other countries, which attract production with low cost. A border adjustment would remove this incentive for American firms to locate profits or activities abroad, while increasing incentives for non-U.S. firms to locate activities within our borders.

As with the original pro-investment features in the 2017 law, this TCJA+ reform would increase investment and incomes—and thereby revenue. For the original TCJA, when the Congressional Budget Office included the macroeconomic effects of higher incomes buoyed by the reforms’ pro-growth elements, the CBO reduced the estimated revenue loss from the tax cuts by almost 30%. Changing the corporate income tax to a cash-flow levy would similarly increase revenue by removing taxes on what economists call the “normal return” on investment, or the cost of capital. Companies would instead pay only on profits above this amount. A border adjustment can likewise raise substantial revenue over the next decade because imports (which would receive no deduction) are larger than exports (which would no longer be taxed).

The higher revenue these two TCJA+ provisions promise could be used to lower the tax rate on business cash flow or to fund other tax-policy objectives. Importantly, that revenue can replace revenue raised from the tariffs the Trump administration had planned to implement on July 8. These two tax provisions would accomplish the president’s America-first and revenue objectives without destabilizing businesses with whipsawing tariffs.

Finally, TCJA+ would offer another big benefit: It would move the U.S. toward independence from political meddling in the economy via targeted protection or subsidies. This added predictability and breathing room for investment would be one more reason to produce in America. Pluses indeed.

Glenn on the Importance of Research

An image generated by GTP-4o illustrating research.

This opinion column by Glenn appeared in the Financial Times on March 10.

The Trump administration has wisely emphasised raising America’s rate of economic growth. But growth doesn’t just happen. It is the byproduct of innovation both radical (think of the emergence of generative artificial intelligence) and gradual (such as improvements in manufacturing processes or transport). Many economic factors influence innovation, but research and development is key. While this can be privately or publicly funded, the latter can support basic research with spillovers to many companies and applications.

Therein lies the rub: the new administration’s growth agenda is joined by a significant effort to reduce government spending, spearheaded by the so-called Department of Government Efficiency. Some spending restraint can enhance growth by reducing interest rates or reallocating funds towards more investment-oriented activities. But cuts to R&D, as the administration is advocating at the National Institutes of Health (NIH), National Science Foundation (NSF), Department of Energy (DoE) and NASA, are counter-productive. They will limit innovation and growth.

The link between R&D and productivity growth has a long pedigree in economics and has generally been acknowledged by US policymakers. In the mid-1950s, economist Robert Solow made the Nobel Prize-winning conclusion that sustained output growth is not possible without technological progress. Decades later, former World Bank chief economist Paul Romer added another Nobel Prize-winning insight: growth reflected the intentional adoption of new ideas, so could be affected by research incentives.

It is well known that research is undervalued by private companies. Private funders of R&D don’t capture all its benefits. The social returns of R&D are two to four times higher than private returns. These high returns are enabled in the US by federal funding. For example, publicly funded research at the NIH has been found to significantly impact private development of new drugs.

In a comprehensive study, Andrew Fieldhouse and Karel Mertens classify major changes in non-defence R&D funding by the DoE, Nasa, NIH and NSF over the postwar period. They estimate implied returns of as much as 200 per cent — raising US economic output by $2 per dollar of funding. This is substantially higher than recent estimates of returns to private R&D. According to the Congressional Budget Office, the high returns to public funding are more than 10 times that on public investment in infrastructure. With the higher tax revenue generated from additional GDP, an increase in R&D funding more than pays for itself.

In aggregate, productivity gains from federal R&D funding are substantial. Indeed, Fieldhouse and Mertens estimate that government-funded R&D amounts to about one-fifth of productivity growth (measured as output growth less all input growth) in the US since the second world war.

Combined with the high social returns of government-funded R&D, it is essential that policymakers in the current administration acknowledge the risks of underfunding R&D. Spending cuts are clearly harmful to productivity and even budget outcomes.

A shift towards government-financed R&D does not imply that policy in these areas should be beyond review. Some economists have questioned whether current R&D projects take sufficiently high scientific risks, particularly on the ideas of younger scholars. And policymakers can certainly investigate whether indirect cost subsidies to universities and laboratories—in addition to the direct costs of research—are set at the appropriate levels. But, if growth is the objective, the presumption must be that additional public spending on R&D is worthwhile.

Federal support for growth-oriented R&D can extend beyond research grants. Publicly supported applied research centres around the country offer a mechanism to collaborate with local universities and business networks to disseminate ideas to practice. This builds upon the agricultural and manufacturing extension services instituted by 19th-century land-grant colleges that enhanced productivity.

The Trump administration is right to promote growth as a public objective. Spending restraint and fiscal discipline can be growth-enhancing. But all spending is equal. Government-funded R&D is vitally important for innovation and productivity growth. The case is clear.

Are We in a Recession? Depends on Which Forecast You Believe

Image generated by GTP-4o of people engaging in economic forecasting

How do we know when we’re in a recession? Most economists and policymakers accept the decisions of the National Bureau of Economic Research (NBER), a private research group located in Cambridge, Massachusetts (see Macroeconomics, Chapter 10, Section 10.3). Typically, the NBER is slow in announcing that a recession has begun because it takes time to gather and analyze economic data. The NBER didn’t announce that a recession had begun in December 2007 until 11 months later in November 2008. When the NBER announced in June 2020 that a recession had begun in February 2020, it was considered to be an unusually fast decision.

On its website, the NBER notes that: “The NBER’s traditional definition of a recession is that it is a significant decline in economic activity that is spread across the economy and that lasts more than a few months.” The NBER lists the data it considers when determining whether a recession has begun (or ended), including: “real personal income less transfers (PILT), nonfarm payroll employment, real personal consumption expenditures, manufacturing and trade sales adjusted for price changes, employment as measured by the household survey, and industrial production.” In practice, it is normally the case that an NBER business cycle peak coincides with the peak in nonfarm payroll employment and an NBER business cycle trough coincides with a trough in the same employment series.

Of course, policymakers at the Fed don’t wait until the NBER announces that a recession has begun when formulating monetary policy. Members of the Fed’s policymaking Federal Open Market Committee (FOMC) monitor a wide range of data series as the series become available. The broadest measure of the state of the economy is real GDP, which is only available quarterly, and the data are released with a lag. For instance, the Bureau of Economic Analysis’s “advance” (first) estimate of real GDP in the first quarter of 2025 won’t be released until April 30.

Given the importance of GDP, there are several groups that attempt to nowcast GDP. A nowcast is a forecast that incorporates all the information available on a certain date about the components of spending that are included in GDP. The Federal Reserve Bank of New York and the Federal Reserve Bank of Atlanta both release nowcasts of GDP. They use different methodologies, so their forecasts are not identical. Today (March 3), the two estimates are surprisingly far apart. First, here is the nowcast from the NY Fed:

This nowcast indicates that real GDP will grow in the first quarter of 2025 at a 2.94 percent annual rate. That would be an increase from growth of 2.3 percent in the fourth quarter of 2024.

The nowcast from the Atlanta Fed—which they call GDPNow—is strikingly different:

The Atlanta Fed nowcast indicates that real GDP in the first quarter of 2025 will decline by 2.8 percent at an annual rate. If accurate, this forecast indicates that—far from the solid expansion in economic activity that the NY Fed is forecasting—the U.S. economy in the first quarter of 2025 will contract at the fastest rate since the first quarter of 2009, near the end of the severe 2007–2009 downturn (leaving aside the highly unusual declines in the first three quarters of 2020 during the Covid pandemic).

What explains such a large difference between these two forecasts? First, note that the Atlanta Fed includes in its graphic the range of forecasts from Blue Chip Indicators. These forecasts are collected from 50 or more economists who work in the private sector at banks, brokerages, manufacturers, and other firms. The graphic shows that the Blue Chip forecasters do not expect that the economy grew as much as the NY Fed’s nowcast indicates, but the forecasters do expect solid growth rate of 2 percent or more. So, the Atlanta Fed’s forecast appears to be an outlier.

Second, the NY Fed updates its nowcast only once per week, whereas the Atlanta Fed updates its forecast after the release of each data series that enters its model. So, the NY Fed nowcast was last updated on February 28, while the Atlanta Fed nowcast was updated today. Since February 28, the Atlanta Fed has incorporated into its nowcast data on the Institute for Supply Management (ISM) manufacturing index and data on construction spending from the Census Bureau. Incorporating these data resulted in the Atlanta Fed’s nowcast of first quarter real GDP growth declining from –1.5 percent on February 28 to –2.8 percent on March 3.

But incorporating more data explains only part of the discrepancy between the two forecasts because even as of February 28 the forecasts were far apart. The remaining discrepancy is due to the different methodologies employed by the economists at the two regional Feds in building their nowcasting models.

Which forecast is more accurate? We’ll get some indication on Friday (March 7) when the Bureau of Labor Statistics (BLS) releases its “Employment Situation” report for February. Economists surveyed are expecting that the payroll survey will estimate that there was a net increase of 160,000 jobs in February, up from a net increase of 143,000 jobs in January. If that expectation is accurate, it would seem unlikely that production declined in the first quarter to the extent that the Atlanta Fed nowcast is indicating. But, as we discuss in this blog post from 2022, macro data can be unreliable at the beginning of a recession. If we are currently in a recession, then even an initial estimate of a solid net increase in jobs in February could later be revised sharply downward.

Glenn on How the Trump Administration Can Hit Its Growth Target

Treasury Secretary nominee Scott Bessent. (Photo from Progect Syndicate.)

By setting an ambitious 3% growth target, U.S. Treasury Secretary nominee Scott Bessent has provided the Trump administration a North Star to follow in devising its economic policies. The task now is to focus on productivity growth and avoiding any unforced errors that would threaten output.

U.S. Treasury Secretary nominee Scott Bessent is right to emphasize faster economic growth as a touchstone of Donald Trump’s second presidency. More robust growth not only implies higher incomes and living standards—surely the basic objective of economic policy—but also can reduce America’s yawning federal budget deficit and debt-to-GDP ratio, and ease the sometimes difficult trade-offs across defense, social, and education and research spending.

But faster growth must be more than just a wish. Achieving it calls for a carefully constructed agenda, based on a recognition of the channels through which economic policies can raise or reduce output. While a pro-investment tax policy might boost capital accumulation, productivity, and GDP, higher interest rates from deficit-financed tax or spending changes might have the opposite effect. Similarly, since growth in hours worked is a component of growth in output or GDP, the new administration should avoid anti-work policies that hinder full labor-force participation, as well as sudden adverse changes to legal immigration.

While recognizing that some policy shifts that increase output might adversely affect other areas of social interest (such as the distribution of income) or even national security, policymakers should focus squarely on increasing productivity. The three pillars of any productivity policy are support for research, investment-friendly tax provisions, and more efficient regulation.

Ideas drive prospects in modern economies. Basic research in the sciences, engineering, and medicine power the innovation that advances technology, improvements in business organization, and gains in health and well-being. It makes perfect sense for the federal government to support such research. Since private firms cannot appropriate all the gains from their own outlays for basic research, they have less of an incentive to invest in it. Moreover, government support in this area produces valuable spillovers, as demonstrated by the earlier Defense Department research expenditures that became catalysts for today’s digital revolution.

This being the case, cuts in federal support for basic research are inconsistent with a growth agenda. Still, policymakers should review how research funds are distributed to ensure scientific merit, and they should encourage a healthy dose of risk-taking on newer ideas and researchers.

In addition to encouraging commercialization of spillovers from basic research and defense programs, federal support for applied research centers around the country would accelerate the dissemination of new productivity-enhancing technologies and ideas. Such centers also tend to distribute the economy’s prosperity more widely, by making new ideas broadly accessible—as agricultural- and manufacturing-extension services have done historically.

To address the second pillar of productivity growth, the administration should seek to extend the pro-investment provisions of the Tax Cuts and Jobs Act that Trump signed into law in 2017. While the TCJA’s lower tax rates on corporate profits remain in place, the expensing of business investment – a potent tool for boosting capital accumulation, productivity, and incomes – was set to be phased out over the 2023-26 period. This provision could be restored and made permanent by reducing spending on credits under the Inflation Reduction Act, or by rolling back the spending – such as $175 billion to forgive student loans – associated with outgoing President Joe Biden’s executive orders.

If the new administration wanted to go further with tax policy, it could build on the 2016 House Republican blueprint for tax reform that shifted the business tax regime from an income tax to a cashflow tax. By permitting immediate expensing of investment, but not interest deductions for nonfinancial firms, this reform would stimulate investment and growth, remove tax incentives that favor debt over equity, and simplify the tax system.

That brings us to the third pillar of a successful growth strategy: efficient regulation. The issue is not “more” versus “less.” What really matters for growth is how changes in regulation can improve the prospects for growth through innovation, investment, and capital allocation, while focusing on trade-offs in risks. Those shaping the agenda should start with basic questions like: Why can’t we build better infrastructure faster? Why can’t capital markets and bank lending be nimbler? Not only do such questions identify a specific goal; they also require one to identify trade-offs.

Fortunately, financial regulation under the new administration is likely to improve capital allocation and the prospects for growth, given the leadership appointments already announced at the Securities and Exchange Commission and the Federal Reserve. But policymakers also will need to improve the climate for building infrastructure and enhancing the country’s electricity grids to support the data centers needed for generative artificial intelligence. This will require a sharper focus on cost-benefit analysis at the federal level, as well as better coordination with state and local authorities on permitting. Using federal financial support programs as carrots or sticks can be part of such a strategy.

Bessent’s emphasis on economic growth is spot on. By setting an ambitious 3% target for annual growth, he has provided the new administration a North Star to follow in devising its economic policies.

This commentary first appeared on Project Syndicate.

1/17/25 Podcast – Authors Glenn Hubbard & Tony O’Brien discuss the pros/cons of tariffs and the impact of AI on the economy.

Welcome to the first podcast for the Spring 2025 semester from the Hubbard/O’Brien Economics author team. Check back for Blog updates & future podcasts which will happen every few weeks throughout the semester.

Join authors Glenn Hubbard & Tony O’Brien as they offer thoughts on tariffs in advance of the beginning of the new administration. They discuss the positive and negative impacts of tariffs -and some of the intended consequences. They also look at the AI landscape and how its reshaping the US economy. Is AI responsible for recent increased productivity – or maybe just the impact of other factors. It should be looked at closely as AI becomes more ingrained in our economy.

https://on.soundcloud.com/8ePL8SkHeSZGwEbm8

Is the United States Entering a Period of Higher Growth in Labor Productivity?

Image generated by GTP-4o illustrating labor productivity

Several articles in the business press have discussed the recent increases in labor productivity. For instance, this article appeared in this morning’s Wall Street Journal (a subscription may be required).

The most widely used measure of labor productivity is output per hour of work in the nonfarm business sector. The BLS calculates output in the nonfarm business sector by subtracting from GDP production in the agricultural, government, and nonprofit sectors. (The definitions used by the Bureau of Labor Statistics (BLS) in estimating labor productivity are discussed in the “Technical Notes” that appear at the end of the BLS’s quarterly “Productivity and Costs” releases.) The blue line in the following figure shows the annual growth rate in labor productivity in the nonfarm business sector as measured by the percentage change from the same quarter in the previous year. The green line shows labor productivity growth in manufacturing.

As the figure shows, both labor productivity growth in the nonfarm business sector and labor productivity growth in manufacturing are volatile. The business press has focused on the growth of productivity in the nonfarm business sector during the period from the third quarter of 2023 through the third quarter of 2024. During this time, labor productivity has grown at an average annual rate of 2.5 percent. That growth rate is notably higher than the growth rate that many economists are expecting over the next 10 years. For instance, the Congressional Budget Office (CBO) has forecast that labor productivity will grow at an average annual rate of only 1.6 percent over the period from 2025 to 2034.

The CBO forecasts that the total numbers of hours worked in the economy will grow at an average annual rate of 0.5 percent. Combining that estimate with a 2.5 percent annual rate of growth of labor productivity results in output per person—a measure of the standard of living—increasing by 34 percent by 2034. If labor productivity increases at a rate of only 1.6 percent, then output per person will have increased by only 23 percent by 2034.

The standard of living of the average person in United States increasing 11 percent more would make a noticeable difference in people’s lives by allowing them to consume and save more. Higher rates of labor productivity growth leading to a faster growth rate of income and output would also increase the federal government’s tax revenues, helping to decrease federal budget deficits that are currently forecast to be historically large. (We discuss the components of long-run economic growth in Macroeconomics, Chapter 16, Section 16.7; Economics, Chapter 26, Section 26.7, and the economics of long-run growth in Macroeconomics, Chapter 11; Economics, Chapter 21.)

Can the recent growth rates in labor productivity be maintained over the next 10 years? There is an historical precedent. Labor productivity in the nonfarm business sector grew at an average annual rate of 2.6 percent between 1950 and 1973. But growth rates that high have proven difficult to achieve in more recent years. For instance, from 2008 to 2023, labor productivity grew at an average annual rate of only 1.5 percent. (We discuss the debate over future growth rates in Macroeconomics, Chapter 11, Section 11.3; Economics, Chapter 21, Section 21.3.)

The Wall Street Journal article we cited earlier provides an overview of some of the factors that may account for the recent increase in labor productivity growth rates. The 2020 Covid pandemic may have led to some increases in labor productivity. Workers who temporarily or permanently lost their jobs as businesses closed during the height of the pandemic may have found new jobs that better matched their skills, making them more productive. Similarly, businesses that were forced to operate with fewer workers, may have found ways to restore their previous levels of output with lower levels of employment. These changes may have led to one-time increases in labor productivity at some firms, but are unlikely to result in increased rates of labor productivity growth in the future.

Some businesses have used newly available generative artificial intelligence (AI) software to increase labor productivity by, for instance, using software to replace workers who previously produced marketing materials or responded to customer questions or complaints. It will take at least several years before generative AI software spreads throughout the economy, so it seems too early for it to have had a broad enough effect on the economy to be visible in the productivity data.

Note also that, as the green line in the figure above shows, manufacturing productivity has been lagging recently. From the third quarter of 2023 to the third quarter of 2024, labor productivity in manufacturing has increased at an annual average rate of only 0.4 percent. This slowdown is surprising given that over the long run productivity in manufacturing has typically increased faster than has productivity in the overall economy. It seems unlikely that labor productivity in the overall economy can sustain its recent growth rates if labor productivity growth in manufacturing continues to lag.

Finally, the productivity data are subject to revision as better estimates of output and of hours worked become available. It’s possible that what appear to be rapid rates of productivity growth during the last five quarters may turn out to have been less rapid following data revisions.

So, while the recent increase in the growth rate of labor productivity is an encouraging sign of the strength of the U.S. economy, it’s too soon to tell whether we have entered a sustained period of higher productivity growth.

Glenn on the Economic Policies Necessary to Increase Growth

Image showing scientific research generated by GTP-4o

Note: The following op-ed first appeared in the Wall Street Journal.

The Trump Economic Awakening

Traditional policies like tax cuts, targeted aid and responsible spending can deliver stronger growth.

Political scientists will debate the forces that shaped Donald Trump’s victory, but one thing is clear: Americans yearn for a change in economic policy. Voters have rejected the interventionist policies that brought inflation and high deficits. They want an economic awakening, a new way forward that uses traditional economic policies to achieve Mr. Trump’s goal of more jobs for Americans whose fortunes have been harmed by technological change and globalization.

Any economic path to a successful awakening begins with growth: the engine that powers individual income and our collective ability to support the nation’s defense, economy, education and healthcare industry. To pursue this growth, the new administration should consider at least three measures:

First, by working with Congress, it should build on the successes of the Tax Cuts and Jobs Act of 2017 to make permanent the expensing of business investment. Second, it should increase support for science and defense research, which would have significant spillover to the commercial sector, particularly in space exploration. Third, it should build on this research by constructing applied research centers around the country, linked to regional university and city hubs. Like the land-grant colleges of the 19th century, these centers would generate and distribute knowledge, improving local capabilities in manufacturing and services.

Opportunity is also a pillar of the awakening. Community colleges are an underfunded source of skill-building and mobility. As Austan Goolsbee, Amy Ganz and I proposed in a 2019 report, a modest federal block grant to support community colleges on the supply side—rather than a demand-side emphasis on financial aid—can help these schools push more Americans toward better jobs by working with local employers on skill needs and curriculum development. Targeted aid to places with depressed economic activity can help distribute opportunity to communities better than one-size-fits-all Washington-directed programs.

Corporate tax reform can play a role, too, by improving incentives for companies to settle and invest in the U.S. This can magnify opportunities for Americans, all without having to rely on costly tariffs.

Working a job doesn’t merely generate income; it also promotes human dignity. Enlisting more people into the workforce is thus another element of the economic-policy awakening. While growth and opportunity policies can boost labor-force participation, strengthening the earned-income tax credit to boost the incomes of childless workers can help attract younger people to the workforce. Maintaining the child tax credit can also provide parents with easier pathways toward economic participation.

These ideas share several important themes with Mr. Trump’s campaign and the traditional conservative playbook. They emphasize that policy ideas should be practical and workable, not merely rhetorical. Each makes use of America’s federalist system and innovative ethos. Making a priority of strong local involvement in applied research centers and community colleges and as tailoring place-based aid are more effective approaches than Washington diktats. Programs need to be held accountable for results, not simply allocated money.

This economic-policy awakening requires a clear-eyed assessment of budget trade-offs. Profligate spending with little regard for debt and inflation—à la the American Rescue Plan—contributed to Mr. Trump’s victory. It is possible to accomplish the steps above in a fiscally responsible way by offsetting spending and tax changes.

Organizing for the policy awakening’s success will be essential. Lack of communication among cabinet agencies can stymie creative ideas for expanding the economic pie for American workers. Like the president’s Working Group on Financial Markets, created by Ronald Reagan in 1988 to convene disparate agencies, the new administration would benefit from a senior executive team that can coordinate economic ideas and learn from leaders in business, labor and social services. Such a body, unlike the National Economic Council, could more adeptly cut across silos related to tax, trade, regulatory and industrial policy.

Voters have signaled they’re ready for an economic awakening. The president-elect, equipped with a new playbook and vision, should seize the opportunity.