Key takeaways

The Trump administration’s unwise policy agenda has the potential to do great damage to U.S. families—and this is true even if it does not lead to recession or spiking inflation in the near term. While this agenda has heightened the risk of recession in coming years, the greatest future damage will come from slowing growth in the economy’s supply side and raising inequality. Trump’s economic policies will cause incomes and wages for typical families to grow more slowly, and this will lead to a less affordable life for many.  

How will Trump administration policies harm income growth for typical families? 

• The Trump administration inherited a fundamentally strong economy from the Biden administration. Yet the Trump administration’s policy agenda has raised the risk of a near-term recession by slowing growth in spending by households, businesses, and governments (aggregate demand).  

• • Federal workforce cuts, deportations and a slowdown in immigration, and chaos in trade policy and the administration’s approach to the Federal Reserve have all weighed on demand growth. 

• • The deportation agenda and cutbacks to the federal workforce will deeply damage the economy’s supply side as well. Further, deficit-financed tax cuts will also put headwinds in front of growth in the economy’s supply side in coming years. These growth reductions will be small in any given year but will accumulate quickly and lead to future incomes being significantly lower than they would have been under a different policy regime.  

• Finally, the 2025 Republican-led tax cuts favor the rich, while the spending cuts included in the same Republican megabill will sharply lower incomes for the bottom half of U.S. households (ranked by income) in coming years. This combination will lead to a very large spike in inequality.  

• • The Trump administration’s assaults on typical workers’ bargaining power and leverage, and its support for corporations with significant market power, will increase pre-tax inequality.  

Policy choices that fostered excess unemployment, slow growth of the economy’s supply side, and rising inequality have all contributed to making recent decades extremely difficult for typical families. The policies of the Trump administration double down on the worst policy decisions of this period and will make typical families reliably poorer in the future, even if an outright recession or spiking inflation does not happen.  

In the first months of the second Trump administration, the question that popped up frequently about its economic policy agenda was, “Will it cause a recession?” After a year and no clear signs of a recession (at least not yet), many looking to formulate an organized critique of the Trump agenda argue that it is making affordability for American families worse.

Both the concerns of heightened recession risks and deteriorating affordability are valid. Trump policies really are making a recession more likely and even if a recession does not occur, these policies will harm typical families’ ability to afford what they need. This affordability crunch will happen for two reasons: Trump policies will hamstring the economy’s ability to supply goods and services, and these policies aim to increase inequality by transferring income from the bottom and middle toward the top. Sometimes this affordability crunch will manifest as higher prices or faster inflation, but it is more likely to appear as slower wage growth and the rollback of public supports for households. But its root is always and everywhere poor economic choices, including prioritizing the interests of the rich and corporations over the concerns of typical American families.

This report provides an explanation and overview of how Trump policies will impact overall U.S. economic performance and the living standards and economic security of typical families.

• In the short run, Trump policies raise the risk of recession.
  • The U.S. economy might avoid a recession over the next year, but the Trump agenda has made a recession far more likely than it would have been without these policy choices.
    • The short-run danger from Trump policies stems from the chaotic implementation of tariff policies, the administration’s cuts to social spending in the 2025 Republican budget megabill, their rapid and random downsizing of the federal workforce, and the chilling effects their mass deportation aspirations have on spending.
• In the long run, the Trump policy agenda will significantly reduce the U.S. economy’s ability to supply goods and services without high and rising inflation.
  • The administration’s deportation agenda is slowing the size of the future U.S. labor force and has maybe even shrunk it.
  • Trump has backed mostly deficit-financed tax cuts for the rich, which will slow the size of the future U.S. capital stock.
  • His administration is attacking key federal agencies and has shown a lack of strategy in tariff policies, which are slowing the size of the future U.S. technology stock.
• In both the short and the long run, the Trump policy agenda is guaranteed to cause greater inequality.
  • In the short run, the huge tax cuts tilted mostly toward the rich and the spending cuts falling mostly on the bottom 40% will lead to an enormous rise in inequality.
  • A possible recession will damage the labor market and likely lead to rising inequality over any subsequent recovery as unemployment remains elevated.
  • Further, the Trump administration’s attacks on the leverage and bargaining power of typical workers and the administration’s toleration of monopolization and abusive financial practices will see income in the business sector reliably funneled away from typical workers and toward the already-rich owners and managers of large companies.
  • The Trump administration has hamstrung or downsized the key functions of the federal civilian workforce that work to level playing fields between the rich and corporations on one hand and typical workers and consumers on the other.
• Finally, many of the Trump administration’s policy choices will inflict significant damage on U.S. families that is not reflected in contemporaneous measures of GDP or income. Just because this damage is not reflected in real-time GDP or income data does not mean it is unimportant or cannot be measured well.
  • For example, regulations enforced by the federal government lead to greater air and water quality, and voluminous research indicates these save lives and many Americans highly value them. If the attack on the federal workforce and the Trump administration’s generally anti-regulatory stance lead to rollbacks in air and water quality, people will suffer, even as most of this suffering is not well captured in GDP.

In what follows, we provide the economic basis for these conclusions, focusing on Trump policy effects on aggregate demand, potential output (supply), and income distribution and how these drive real-world outcomes for typical families. Families will feel the bad outcomes from all three dimensions of macroeconomic performance as a deterioration in affordability.

Three key dimensions of macroeconomic performance: Demand, supply, and distribution

A quick overview of some important macroeconomic concepts can help organize thoughts about how the Trump policy agenda will tangibly affect U.S. families. The most important tasks policymakers must get right to offer typical families’ economic security are as follows: managing aggregate demand, fostering potential output (supply) growth, and ensuring equitable distribution of income.

Managing aggregate demand just means making sure unemployment and inflation stay low most of the time and are quickly returned to low levels when shocks push them higher for some stretch of time. The key to successful aggregate demand management is ensuring that spending by households, governments, and businesses is high enough to fully employ all resources in the economy—especially labor, but not so high as to generate ongoing inflation. This means ensuring that aggregate demand matches potential output.

Fostering growth in potential output (supply) involves making sure the economy’s productive capacity grows rapidly over the long run. Key elements include fostering growth in the labor force and productivity (a measure of how much output and income is generated in an average hour of work in the economy). Growth in productivity depends on the educational attainment and quality of the labor force, the size of the capital stock that workers can use to aid production, and the state of technology in the economy.

Ensuring an equitable distribution of growth means making sure the overall income growth generated in the economy is shared at least proportionally throughout the income distribution. Even better would be growth biased more toward households in the bottom half of the income distribution. This would help reverse some of the large increases in inequality that occurred over the past few generations of economic life in the U.S. Fostering an equitable distribution of growth matters for typical families for an obvious reason: If average living standards rise rapidly, but living standards for the large majority lag far behind as households at the very top see extreme above-average gains, it is hard to declare this an economic success for broad-based economic security. Without an equitable distribution of growth, too many people would be unable to afford daily life.

Trump policies will drag on aggregate demand and raise recession risks

Recessions happen and unemployment rises when spending by households, businesses, and governments (demand) lags behind potential output (supply). Because supply tends to change slowly and predictably, it is sharp cutbacks in demand that lead to recessions and rising unemployment.[1]

When demand falls short of supply, this means that there is more capacity in the economy to produce goods and services than demand to buy them. To illustrate, let’s take the example of a restaurant. It will not hire staff to cover every table and cook meals for a full house, unless there are paying customers at each table. If demand (or the number of customers) falls, then the restaurant will cut back staff and food purchases by roughly the same amount.

Figure A shows estimates of potential output and actual gross domestic product (GDP) over time. When actual GDP falls short of potential output, it can be inferred that GDP is demand-constrained (more could be produced if economic actors simply spent more). The shortfalls of actual GDP relative to potential may look small on the graph, but they correspond to significant economic distress. The growing gap between 2007 to 2009 was associated with the unemployment rate rising from 4.4% to just under 10%—meaning that roughly 9 million people lost their jobs during this time period. Others dropped out of the labor force, and wage growth even for those workers who kept their jobs was significantly damaged as well, as their main source of leverage to gain wage increases (the threat of—or ability to—leave their current job to find a higher-paying one) lost power in a labor market with huge pools of unemployed workers. Over the 2007–2017 period, excess unemployment translated into roughly 47 million years of avoidable unemployment for U.S. workers, and this period of soft labor markets kept wage growth firmly suppressed.[2]

Figure A

Throughout 2025, many have raised concerns that Trump administration policies could lead to a recession. It should be noted how surprising this development would be, considering the context. The economy handed over by the outgoing Biden administration in January 2025 was extremely strong, and there were no obvious macroeconomic threats moving forward that would have led one to forecast a recession in the next few years.[3]

For a recession to happen in the next year or two, there would need to be some short-run shock or drag on aggregate demand that forces it below the economy’s potential output. Despite the strength of the economy the Trump administration inherited, their subsequent policy agenda since his inauguration in 2025 contains plenty of reasons to worry about such drags.

For one, the Trump administration’s assault on the federal workforce directly destroys employment and incomes. Between January and December 2025, 290,000 federal workers have lost their jobs. While this is not enough by itself to drag an otherwise healthy national economy into recession (as it constitutes less than 0.2% of total employment), it certainly puts downward pressure on aggregate demand.

On top of this, the spending cuts in the 2025 Republican budget megabill (which the White House has referred to as the OBBB) will reduce aggregate demand in coming years. [4] For example, the Republican megabill will cut SNAP and Medicaid benefits by a combined $100 billion per year on average over the next decade. Households receiving Medicaid and SNAP benefits will cut back spending sharply when these benefits are reduced. Further, the megabill rolled back a set of Biden administration policies that sharply reduced student loan payments. In coming years, households will have to pay substantially higher student loan payments to the federal government.

Finally, another fiscal change that was not an explicit part of the megabill but was notable in its absence is the expiration of enhanced subsidies to buy health insurance in the marketplace exchanges established by the Affordable Care Act (ACA). The rollback of these enhanced subsidies—also passed during the Biden administration—will double out-of-pocket payments for the premiums of the 20 million Americans enrolled in these exchanges, increasing costs by more than $30 billion annually in coming years.[5]

The tax cuts in the Republican megabill are unlikely to do much to spur demand for two reasons. First, they are tilted toward high-income households whose spending is not constrained by their current incomes. Second, the tax cuts are small relative to a “current policy” baseline, meaning that they leave tax burdens unchanged, not appreciably lower, relative to 2025.[6]

The mass deportation agenda of the Trump administration will have its most predictably negative effects on the economy’s supply side, as millions of immigrant workers are forced out of the country.[7] But immigrants are not just workers; they are consumers as well. Further, immigrant workers are key complements to U.S.-born workers in many industries. Deporting these consumers and complementary workers and making it harder and more dangerous for those who remain to conduct the normal business of their lives will clearly have depressing effects on aggregate demand as well.

Most importantly, the radical uncertainty and chaotic implementation of Trump policies—particularly the trade policies—seem almost designed to freeze new business investment. Who would set up a new manufacturing facility if they had no idea what the competitive landscape of the sector was going to look like in coming years? Will tariffs protect domestic production? Will tariffs make imported inputs into the factory more expensive? Will protective tariffs vanish overnight when a foreign government meets the president’s demands of the day? Will future profits be reduced because the Trump administration arbitrarily demands ownership stakes in companies? Business investment is by far the most volatile component of aggregate demand, and it is the one that generally leads to recessions. It seems highly plausible that the Trump administration’s policies could cause business investment to seize up and slow growth.

Early in Trump’s second term, the administration’s “Liberation Day” tariffs led to most forecasters sharply raising the risk of a recession happening over the next year.[8] The sharp reversal of these historically high and broad tariffs to levels “only” half as high on average led to this risk receding a bit, yet still remaining sharply higher than it was in January 2025. So far, most of the “hard” economic data (that measure actual economic transactions like wages, employment, incomes, or gross domestic product) have yet to signal that a recession is coming.

Part of the relative robustness of macroeconomic measures likely owes to the fortuitous timing of a boom in AI-related spending, which largely began in mid-2023.[9] The valuation of stock markets has reached the second-highest levels in history—trailing only the stock market bubble of 2000–2001 (also driven by a boom in tech stocks). Much of these stock market gains have been driven by AI-related firms. A significant amount of consumption spending out of these wealth gains has likely contributed nontrivially to growth over the past year.

Further, capital expenditures related to the AI-boom have also been contributing to growth. Starting in 2023, year-over-year real growth (adjusted for inflation) in data centers, for example, has consistently exceeded 35%, peaking at just under 77% in late 2024 and remaining above 30% throughout most of 2025. While this AI-related spending has helped keep the U.S. economy well clear from recession through the third quarter of 2025, it is the kind of spending that would likely evaporate relatively quickly if business sentiment about the future use and profitability of AI investments dims.

If this happened, the depressing effect on wider business investment stemming from the uncertainty mentioned above might well dominate and lead to quick decelerations in growth. Evidence of this depressing effect seems already clear, as investment in components not related to the AI boom looks notably weak over the past year.

Figure B

The danger of a slowdown in aggregate demand highlights how much discussions about affordability need to go beyond prices.[10] Much of the discourse about affordability recently was driven by the outbreak of very high inflation in the early 2020s, following the COVID-19 pandemic. But absent very rare and sharp increases in inflation like that (and which tend to be driven by external events like pandemics and wars, not policy missteps), the main damage to affordability over time does not stem from fast inflation, but from slow growth in wages and incomes. A recession would return inflation in the U.S. to very low levels. The recession of 2008–2009, for example, led to inflation averaging below 2% for the following decade. And yet this low inflation provided next to no relief for affordability because the high unemployment of that period—which was the source of disinflation—sapped workers’ leverage and bargaining power in labor markets and led to slow wage growth.[11]

A recession in the next year would solve the price side of affordability in that it would lead to a sharp slowdown in inflation, but it would force wage growth down even faster, and hence, would exacerbate, not help, the ongoing problem of affordability properly defined.

All in all, it seems safe to say that the Trump administration’s policies have significantly elevated the risk of a recession over the next year. Their trade policy retreat has been sharp enough that a recession might well be avoided. But this hinges largely on the administration’s being able to resist whipsawing trade policy chaotically again—and this seems far from certain. But we may well navigate the next year without a recession—largely stemming from the momentum of the strong economy the current administration inherited and the lucky timing of much AI-related spending remaining strong through 2025.

Trump polices will quickly erode the economy’s ability to supply goods and services without inflation—this damages affordability for typical families

However, the avoidance of a recession would not mean the economic policy decisions of this administration were wise. If the only question on the table regarding the impact of Trump policies was “Will there be a recession?” the future of the U.S. economy would be much less bleak. Instead, the more predictable and larger amount of damage that the Trump administration’s policies will inflict will not come through downward pressure on aggregate demand but through the rapid erosion of the economy’s potential output and the upward redistribution of income instead. These influences will be experienced by typical families as wages, incomes, and public supports failing to outpace prices by sufficient margins over time, thereby damaging affordability.

In the previous section, we noted the sharp economic damage done by the aggregate demand shortfall of the early 2010s. The most obvious and acute damage stemming from this shortfall was the elevated unemployment rate of that time, along with the attendant damage to wage growth.

However, the worst lingering damage from that long period of deficient aggregate demand likely came from its spillover effect in destroying potential output. When employers see that customers are scarce and workers are cheap and plentiful, their imperative to invest in worker training or newer capital or innovative technological processes to economize on labor costs and boost productivity is blunted. And when jobless workers see elevated unemployment rates and the low probability of being hired, job seekers can get discouraged, and labor force participation can falter.[12]

Over time these dynamics lead to a lower-quality workforce and smaller capital stock, which reduce productivity growth and potential output. Figure A showed actual GDP and successive estimates of potential output over time. Between 2007 and 2019, these potential output estimates continually fall as the demand shortfall bends down potential output, as productive investment is blunted. By 2019, potential output was $2.2 trillion below where its 2007 trend would have left it in that year. This translates into $6,500 less income for every adult and child in the United States in 2019 (or $26,000 less income for a family of four). In short, over a 5–10-year period, even small bends in the growth of potential output have huge real-world consequences.

Supply destruction leads directly to unaffordability

This discussion of potential output growth likely sounds abstract to noneconomists. But it has profound effects on typical families’ economic security, and the way this slowing down of potential output translates into observable real-world effects is by making affordability worse for these families. For example, in the paragraph above, we said that the slowdown of potential output growth after 2007 translated by 2019 to $6,500 less in inflation-adjusted income for every person in the United States (or $26,000 less income for a family of four). The way this happens is by wages and incomes failing to outpace growth prices by satisfactory amounts—even during times (like the 2010s) when inflation was extremely low.

And, of course, the gap in the race between wages and prices differs depending on the specific goods and services examined. In the 2010s, the output that was produced less and less, relative to historic norms, was housing.[13] This reduced output of housing translated directly into higher relative prices for rents.

While there is a lot about this collapse in housing production and rise in rental prices that is housing-specific, the root of all of this pressure on affordability stems from macroeconomic choices. If potential output growth slows for the overall economy, then the production of something will lag, and its price is likely to rise. If we had somehow kept housing construction constant in the face of a fall in overall potential output, the biggest affordability problem would have shown up someplace else, but one surely would have emerged.

How Trump policies will slow potential output and exacerbate affordability concerns

In the current moment with unemployment that is still relatively low by historical standards and so-far adequate aggregate demand, the imminent threat to the economy’s supply side today is not an extended recession, but simply the direct effect of many Trump policies. When (not if, but when) potential output growth falters in coming years, it will again represent a sharp break from the economy the Trump administration inherited, an economy that saw rapid productivity growth in the years following the pandemic.

Figure C

The potential supply destruction stemming from Trump administration policies comes along many margins.

Loosening immigration restrictions and unleashing mass deportations

The most obvious blow to the economy’s potential output would be Trump’s mass deportation policy. If the U.S. relied solely on growth in U.S.-born workers, labor force growth would shrink rapidly over the next decade (see Bivens 2025c). Successful mass deportations—besides causing great and unnecessary human misery—would actually push labor force growth in the U.S. economy into negative territory in coming years.

Further, immigrant and U.S.-born labor supply are often complementary (Zipperer 2025). One obvious example is that child care centers are disproportionately staffed with foreign-born workers. If mass deportations cause significant closures of these centers, U.S.-born parents will often be forced into stopping work in order to care for children.

Cutting back federal spending and the workforce

We noted the cutbacks to federal workforce and spending previously as short-run threats to aggregate demand. But the federal government is not just a source of short-run demand; it also provides absolutely crucial inputs needed for robust private-sector growth.[14] Recent decades have seen sharp cuts in the size of the federal workforce and the investments in the functions it provides. By January 2025, the size of the federal workforce and the spending to support it were at historically low levels relative to the broader economy. In short, it seems clear that this workforce and the state capacity of the federal government were already significantly degraded even before the Trump administration took power. Since then, the administration has unleashed an unrelenting attack on this state capacity.

Perhaps the clearest way reduced federal spending will translate into slower potential output growth in coming years comes from cutbacks to science and research. Fieldhouse and Mertens (2025), for example, have estimated that nearly a third of total factor productivity (TFP) growth stems from federally financed research and development spending.

Further, the Trump administration has significantly cut back federal spending on universities. A key driver of productivity growth over time is a more educated and skilled workforce. Today’s higher education cuts are guaranteed to slow the growth of labor quality in the U.S. workforce in coming decades.

Other federal agencies collect, analyze, clean, and provide access to free, publicly available, high-quality data on the nation’s economy and demographics. These services provide enormous monetary value to private-sector actors (see Hughes-Cromwick and Coronado 2019).

Other agencies provide crucial monitoring services that help the nation avoid financial, epidemiological, or weather disasters. These investments provide a huge rate of return relative to the (likely too small) federal spending done on them. Even monitoring and surveillance that directly aim to constrain and manage private-sector decision-making can often actually lead to better private-sector outcomes. Hirtle, Kovner, and Plosser (2019), for example, examine the outcome of banks when they receive more or less regulatory scrutiny from federal banking supervisors. The authors find that “…banks that receive more supervisory attention hold less risky loan portfolios, are less volatile, and are less sensitive to industry downturns, but do not have slower growth or profitability.”

By far the biggest long-run threat to the U.S. and global economies’ ability to produce goods and services without inflation is the effect of climate change. Climate change can be thought of as an ongoing erosion of the economy’s productive capacity. For example, key swathes of land will become less valuable as flooding and disaster exposure rise, buildings and factories will be threatened by extreme weather, and the productivity of work that must be performed outside will suffer due to either extreme weather or needed spending to mitigate the effects of it on workers. Investments that mitigate greenhouse gas emissions (GHG) and reduce the effects of climate change are incredibly valuable in the long run for maintaining the economy’s supply side. By far the biggest and most effective investments in this type of mitigation ever made by the United States were the subsidies for clean energy and its adoption in the Inflation Reduction Act (IRA) of 2022. The Republican budget megabill, however, rolled back the majority of these IRA subsidies and will hence lead to far fewer reductions in GHG emissions in coming years. Essentially these rollbacks will accelerate the destruction to the economy’s supply side that is ongoing due to climate change.

Many federal agencies are responsible for providing and enforcing transparent rules for markets that channel economic competition into productivity improvements, instead of zero-sum opportunism. For example, the Securities and Exchange Commission and the Consumer Financial Protection Bureau provide protection to investors by enforcing rules against fraud or misappropriation of their funds from companies they invest in. This promotes trust and allows more liquid capital markets that are able to provide finance for more prospective and ongoing businesses. The Federal Trade Commission and the Antitrust Division at the Department of Justice aim to keep firms’ monopoly power from distorting markets. The Occupational Health and Safety Administration and the Wage and Hour Division at the Department of Labor protect employees from abusive workplaces, allowing them to choose among prospective employers without having to factor in whether there will be unsafe or exploitative working conditions with these employers.

Another key federal agency priority that has had profoundly beneficial effects on the U.S. economy’s supply side in recent decades is enforcement of anti-discrimination laws. The Equal Employment Opportunity Commission, for example, was established in 1965. Hsieh et al. (2019) have noted that since then, there has been an enormous increase in the share of high-wage, high-skill occupational employment that is accounted for by women and Black men. In turn, the authors estimate that this more efficient allocation of workers to occupations based on talent and merit accounted for up to 40% of all growth in the U.S. economy since 1960. Much of this better allocation of talent has stemmed directly from enforcement of anti-discrimination laws. Going forward from today, there is ample scope for ongoing and/or improved enforcement of anti-discrimination laws to support future growth. If instead, the enforcement of these laws withers, and there is a reduction in the efficient allocation of talent to occupation, this could be an outright headwind to growth going forward.

Haphazardly implementing poorly designed and chaotic tariff policy

The chaotic implementation of the administration’s tariff policy is surely a short-run drag on aggregate demand. But, if the end result of the policy is to leave the United States with historically high and broad tariff rates (which is where the tariff policy has landed as of December 2025, even with the sharp reversal of many of the highest tariffs), without any obvious corresponding benefit from well-designed industrial policy considerations, then this will also slow potential output growth.[15]

Tariffs are essentially a way to block the lowest-cost method of delivering goods to U.S. households and businesses, if this lowest-cost method involves imports. Sometimes this kind of blockage is fully justified by other policy concerns besides what is the cheapest production at the moment. For example, if foreign governments subsidize their producers in a specific sector, and if the U.S. deems it imperative to have productive capacity in that sector, then tariffs can help keep domestic producers from being forced out of business by the decisions of foreign governments.

Further, if the sectors that domestic producers are being forced out of looked poised to drive productivity gains in coming decades, there might be a strategic benefit to using tariffs to protect domestic production. The case of electric vehicles (EVs) is one potential example. There is clearly going to be a large global shift toward EVs in the coming decades. EV manufacturing will scale rapidly, and often this kind of scale produces huge leaps in productivity. If today’s constellation of EV production facilities and foreign countries’ subsidies of their own EV makers threaten to shove U.S. producers entirely out of the race for EV market share, it seems like industrial policy efforts to support domestic production of EVs would make a lot of sense—and this was indeed a priority of the Biden administration.

Similarly, if some or all of the cost advantage of imports in a sector stems from objectionable practices of producers in other countries—say, blatant disregard of fundamental labor rights—tariffs can protect U.S. producers from being forced out of business by these objectionable practices.

But the historically broad and high tariffs of the Trump administration are not being calibrated in any kind of strategic or careful way. Instead, they are blocking the lowest-cost means of delivering goods to U.S. households and businesses randomly. This essentially is the equivalent of a negative technology shock. Businesses (both foreign and domestic in the U.S.) that supply goods have been forced out of the most efficient way to produce goods, and without any countervailing benefit from smartly designed industrial policy considerations.

Finally, the chaotic implementation does not only affect aggregate demand. If ever-shifting tariff levels change the patterns of production that lead to the lowest-cost ways of producing goods in random ways, this makes it impossible to set up efficient supply chains, hence stunting potential output growth.

Financing tax cuts for the rich and corporations with higher debt

In 2000, the ratio of U.S. public debt to gross domestic product (GDP) stood at less than 35%. In 2024, the debt ratio nearly tripled, rising to almost 96%.[16] A large part of this increase was due to the two historically large economic crises experienced in those years: the financial crisis and Great Recession of 2008–2009, and the COVID-19 recession.

More worryingly, even in 2024—a year in which the unemployment rate averaged 4%, the Fed’s short-term interest rates stood at over 5%, and inflation was above the Federal Reserve’s target—the federal budget deficit was 6.2% of GDP. This is too large a deficit for an economy that is at roughly full employment and not in need of fiscal support.[17]

The 2024 deficit can essentially be entirely explained by the successive rounds of tax cuts engineered by Republican administrations since 2000. In 2009, the Congressional Budget Office (CBO) projected what federal revenue as a share of GDP would be if the tax cuts signed into law by George W. Bush in 2001 and 2003 were allowed to lapse (see CBO 2009). They projected that revenue would be 20.2% of GDP by 2019. However, in 2019—after the vast majority of the Bush-era tax cuts were maintained and President Trump signed the 2017 Tax Cuts and Jobs Act (TCJA)—federal revenue came in at just 16.1% of GDP.  If revenue had remained at 2000 levels going forward, even with the extra debt incurred by economic crises, budget deficits by 2024 would’ve been effectively zero.

In the decade after the onset of the Great Recession in 2008 and during the early stages of the 2020–2021 pandemic, large deficits were not harming the economy. In fact, they were usefully propping up aggregate demand even as private sources of demand were plummeting. This chronic shortfall of aggregate demand (sometimes labelled “secular stagnation”) kept spending weak and interest rates and inflation historically low (short-term interest rates stood at essentially zero in all these years).[18] And so long as interest rates were low, no damage was being done by higher deficits.

But in the post-pandemic recovery, aggregate demand (aided by a robust fiscal response to the crisis) has been stronger, and interest rates and inflation have moved decisively off their historic lows. In this environment—when the economy is no longer demand-constrained—further increases in federal debt now compete with private-sector borrowers to find available savings. This, in turn, pushes up interest rates and threatens to crowd out private sector investments in new factories, plants, and equipment. This slowdown in the growth of the nation’s capital stock, in turn, leaves U.S. workers with less capital to aid them in doing their jobs and hence slows the pace of productivity growth.

This potted history of fiscal policy debates in recent decades tells us that after a decade and a half of warnings about the crowding-out effect of higher deficits on investment not ever coming to pass, there is now strong evidence to suggest this might be an important influence on growth going forward. Figure D shows the “real debt service ratio,” a measure of how sharply the government’s borrowing costs are rising. After a long stretch of being under 1%, this measure has recently surpassed its historic high.

Figure D

This historic high was surpassed even before the passage of the 2025 Republican budget megabill—a bill that will add nearly $4 trillion to the federal debt over the next 10 years. Borrowing costs are guaranteed to spike further going forward from now in any time period when the economy sits near full employment.

If the worried-about recession comes to pass in the next year or so, the collapse in private spending will reduce competition for available savings and interest rates will fall and the supply destruction effect of higher interest rates will be muted. But so long as the underlying fiscal structure of the U.S. sees large budget deficits even when the economy is at full employment, this means that interest rates will be high during these full employment periods and investment will be suppressed, leading to slower future productivity growth.

A key aggravating factor of the supply-destroying effects of higher deficits in coming years is what they were used for: simply to give much higher disposable incomes to rich households in the United States.          

One could imagine a counterfactual in which instead of using debt to finance higher disposable incomes for the rich, the federal government used this debt to make significant investments to mitigate emissions of greenhouse gases. This would leave the country with a higher stock of “green” capital (capital used to mitigate greenhouse gas emissions) and a smaller stock of conventional capital. This would be an affirmatively good thing. It would effectively be leaving future generations with slightly lower productivity in producing conventional goods and services, but a more livable and viable climate. Consistent economic growth essentially guarantees that future generations will be significantly richer than the current one in their ability to buy conventional goods and services. Trading off a bit of this advantage for a livable planet would be welcomed by this future generation—and it’s a trade-off they won’t be able to make. Only their ancestors can make it for them.

Alternatively, one could imagine a world in which the federal government took on additional deficits of the size generated by the 2025 Republican megabill to radically increase investments in children: providing federal financing of universal, high-quality pre-kindergarten; boosting aid to K–12 public school systems; and providing a universal Child Allowance to end child poverty. This would not only raise human welfare much more than tax cuts to rich people would; it would also see some of the deficit costs defrayed in coming decades as today’s children grew up healthier and better educated and worked more and earned higher wages in the decades to come. Some of these offsets could be considerable.[19]

There are no such happy offsets that stem from running larger deficits simply to give tax cuts that are radically tilted toward households that don’t need them—the ones at the very top of the income distribution. These deficits are supply destruction for the sake of intentionally increasing inequality.

Threatening a political takeover of Federal Reserve policy decisions

The Trump administration has been far more forceful than previous ones in pressuring the Federal Reserve to fall in line with the administration’s economic goals. They have demanded that the Federal Reserve set interest rate policy to meet the administration’s short-term economic goals and have constantly demanded lower interest rates, even as conditions do not warrant cuts in interest rates (inflation remains above the Fed’s long-run target, and unemployment remains generally low).

If decision-makers throughout the economy—households, businesses, and state and local governments—begin to think that the Federal Reserve’s interest rate decisions will be managed entirely by the executive branch, they might well raise their expectations of inflation in the future. This, in turn, would likely require any future Federal Reserve that committed to reducing inflation (and inflation expectations) to raise interest rates higher than they would otherwise have to be. These higher long-run interest rates would, in turn, reduce investment and slow productivity growth (much like too-large deficits run during times of full employment).

How much will supply destruction slow growth in coming years?

It is very hard to provide any convincingly precise estimates as to how much supply destruction will result from this portfolio of Trump administration policies. What determines the ebb and flow of productivity growth in advanced economies is one of the most debated topics in economics, and one in which no consensus exists. Yet we can give some very rough bounds for how important each element of this potential supply destruction might be over the next decade. The sum of these negative effects would be highly significant for future living standards growth—or affordability.

We start with the Congressional Budget Office’s (2025b) forecasts of potential output growth for the next decade. Currently they forecast that annual growth will average 2.0% between 2025 and 2034.

About 30% of the 2.0% that CBO forecasts (or 0.6% of this growth) stems from their estimate of how much the labor force will grow in those years. However, if one accounts for the Trump administration’s meeting their mass deportation goal of removing 1 million immigrants each year from the United States, this would imply that the labor force will barely grow at all in those years, translating into a 0.4% slowdown of growth in potential output.[20]

More than half of the projected growth in potential output comes from CBO’s forecast of growth in total factor productivity—a measure of how much extra output can be obtained holding inputs constant. TFP growth is often interpreted as a measure of pure technological advance—using new processes and production techniques to get more output out of a given stock of inputs. However, as we noted before, Fieldhouse and Mertens (2025) have estimated that fully one-third of TFP growth in recent decades can be accounted for by direct federal spending on research and development. The Fieldhouse and Mertens (2025) results would imply that a 20% cut in federal research and development spending would reduce projected productivity growth in the U.S. over the next decade by 0.2% annually.[21] This, in turn, would reduce potential output enough by roughly $2,500 for every adult and child in the United States by 2035.[22]

Importantly, their estimates do not include the effect of federal support for institutions of higher education, and this support has been large and critical for these centers of scientific research—likely as important as the direct federal research and development spending. This could easily double the effects from direct federal research and development spending, especially if one accounts for the long-run loss in the labor supply of trained scientists and researchers capable of undertaking research and development that will occur as higher education funding erodes.

CBO (2025b) has estimated that the 2025 Republican megabill will add roughly 7.1 percentage points to the ratio of public debt to GDP by 2034. Using earlier estimates from CBO (2025e) to translate the effect of a higher debt ratio on economic growth, this level of debt increase (assuming no recession intervenes) would slow growth by 0.1%–0.2% by 2034 through its effect on interest rates and investment. Given that Figure D previously showed that higher interest rates really have emerged in recent years, this effect seems possible.[23]

Estimates of the growth effects of the Trump administration’s trade policy are more uncertain. The Yale Budget Lab indicates a long-run effect on the level of GDP of 0.4%. However, it is hard not to make a comparison between the strategy-free actions of the Trump administration and a similar lack of planning that went into the United Kingdom’s exit from the European free trade area (Brexit). Estimates of the effect of Brexit are substantially larger than 0.4%—on the order of 2%–3% of GDP over 10 years (Bloom et al. 2025). If we think that Brexit is a suitable potential model for the fallout from the Trump trade policy—similarly chaotic and unplanned—this would imply a reduction in productivity growth of around 0.25% over the next year.

The long-run growth effect of eroding the federal government’s state capacity through budget cuts and downsizing is harder to estimate. One suggestive paper on this is Klein Martins (2025), who looks at episodes of sharp permanent spending cutbacks in advanced countries over the past 30 years. He estimates highly persistent negative effects on GDP growth of these cutbacks, over timespans well longer (15 years) than could be explained simply by the effect of these spending reductions adding to demand shortfalls. Klein Martins finds that each 1% of GDP in public spending reductions leads to GDP that is 2% smaller 15 years later. Say that half of these effects were driven by the erosion to state capacity stemming from these cuts. The cuts to the federal workforce in 2025 will result in a reduction of federal government spending of roughly 0.1% of U.S. GDP, which would imply (using half of Klein Martins’ estimates) a reduction in GDP of about 0.1%.

Tedeschi (2024) estimates how much higher interest rates driven by political events (like the capture of Fed policymaking by the executive branch) could reduce growth in coming years.[24] He finds that if the political events just moved the “country risk premium” of the United States to look more like the United Kingdom, this could reduce growth by 0.1% annually. If instead, this country risk premium deteriorated enough to look more like other rich, stable economies like Spain, the damage could be closer to 0.3% annually.

Adding up supply destruction from Trump policies

The Trump deportation goals could reduce labor supply growth by 0.4% over the next decade. The cuts to direct public research and development spending and this spending supported by institutions of higher education could each slow productivity growth by 0.2% over this period. Financing the Trump administration’s tax cuts for the rich with debt could reduce capital investment and hence productivity by 0.2%. If Brexit is the best model for the administration’s strategy-free trade policy, this could also reduce productivity growth by 0.2%. If the Trump-led attacks on the Fed led to steep concerns in international financial markets that raise the U.S. country risk premium and other interest rates significantly, this could slow growth by up to 0.3% in coming years. The administration’s attacks on the state capacity of the federal government could reduce growth by 0.1%. Their capture of Federal Reserve policy—leading to rising interest rates—could slow growth by between 0.1%–-0.3%. Adding these up, this means growth could slow by just under 2% on average over the next decade, with productivity growth slowing by well over 1%.

Somewhat ironically, the optimistic projections of how much advances in AI could boost U.S. productivity growth over the next decade tend to cluster around 1% annually.[25] The damage being done by the Trump administration to the economy’s supply side over the next decade is hence potentially as large as the most optimistic projections for how much a new burst of technology could boost it. If this came to pass, it would constitute just the latest episode of poor policy decisions squandering the potential benefits of economic growth and technological advance. The typical U.S. household today is not poorer in absolute terms compared with decades ago. But they are shockingly poorer relative to the potential growth they could have enjoyed with smarter policy that prioritized their economic security over showering the rich with even more perks.

Trump policies will raise inequality—the worst blow to families’ affordability

As we noted before, affordability is determined simply by the race between families’ economic resources (wages, incomes, and publicly provided subsidies and benefits) and prices. When affordability is strained, it is overwhelmingly because something—a recession or slowing of potential output growth, for example—has dragged on growth in families’ economic resources. Moreover, even when the aggregate economy seems strong—free of recession or inflation and with adequate growth in potential output—affordability for the vast majority of families can be squeezed if growth in these families’ resources lags far behind average growth. This mismatch between growth in typical families’ resources and average growth is driven by strongly above-average growth at the top of the income scale—the precise problem that has afflicted the U.S. economy in recent decades and the true root of nearly all U.S. families’ concerns about affordability.

Figure E

The Trump policy agenda will push income away from low- and moderate-income families and toward the top along many different margins. Even if (as expected) inflation rates return to normal during the second Trump term, this will be unlikely to boost the inflation-adjusted resources available to most families because the policies of the administration will actively claw resources—or the market power to claim these resources—away from typical families.

In the short run, the Trump budget megabill will cause an enormous jump in inequality

The signature legislative achievement of the second Trump administration is the 2025 Republican megabill, a budget reconciliation package that continues the individual provisions (and some business provisions) of the 2017 Tax Cuts and Jobs Act. The megabill also enacts steep cuts to health care and nutrition programs (Medicaid and the Supplemental Nutrition Assistance Program, abbreviated as SNAP). On top of this, the megabill also failed to either roll back or otherwise modify the corporate income tax cuts of the 2017 TCJA, but also fails to extend the supplements to subsidies for purchasing health insurance in the marketplace exchanges established by the Affordable Care Act that were passed as part of the Biden-era American Rescue Plan.

To give a sense of scale of the bill’s impact, we compare the one-year change that will result directly from the 2025 megabill policy with the entire upward redistribution of income that happened between 1979–2019, a period widely recognized as one during which U.S. inequality exploded. The share of total income claimed by the top 10% of households over that period rose by roughly 10 percentage points over a period of 40 years (or about 0.25 percentage points per year). But the Republican megabill alone will in one year raise the share of income claimed by these top 10% of households by 1 full percentage point. The 40 years between 1979 and 2019 saw the top 10% gain an average of 0.25 percentage points in the share of income they claim. This means the Republican megabill will see the rate of inequality growth quadruple in its first year, and it will essentially accomplish 10% of the entire post-1979 rise in inequality in a single year.

In the longer run, Trump policies empower the rich and disempower everybody else

Besides these large fiscal changes, other policy priorities of the second Trump administration include stripping workers of the effective right to organize unions and bargain collectively, deregulating some of the most abusive parts of the financial sector, and shrinking the federal workforce. All of these will lead to rising inequality.[26]

Trump policies continue the conservative assault on labor and workers’ rights

The Trump administration has continued to move forward with parts of its first-term priorities like the assault on labor and the bargaining power of typical workers. Two obvious high-profile indications of this were the stripping of collective bargaining rights of more than a million federal workers (including terminating the collective bargaining agreement of the Transportation Security Administration and firing National Labor Relations Board (NLRB) Member Gwynne Wilcox for “unduly disfavoring the interests of employers.” Further, the Trump administration nominated a partner at the very law firm that is currently challenging the constitutionality of the NLRB to be the NLRB’s general counsel. [27]

The assaults on labor and the bargaining leverage of typical workers continue a long-term conservative effort that has been highly successful in suppressing wage growth for low- and middle-wage workers and which has been a primary contributor to the long-run rise of inequality in the U.S. economy. [28]

Normalizing the most abusive parts of the financial system

The rise of the financial sector’s power has played a large role in the upward redistribution of income in the U.S. economy in recent decades. Finance is possibly the economic sector that has most benefitted from the federal government’s intentional industrial policy support. Between deposit insurance, the day-to-day liquidity provisions of the Federal Reserve (like the discount window that provides overnight reserves at the Fed), and the regular occurrences of extraordinary support provided in financial crises, the financial sector is obviously far larger in capitalist economies than it would be without this public support.

Significant public support of the financial sector is warranted—finance provides needed services to the rest of the economy, and without public backing, market failures would prevent these necessary services from being continually available. But this public support also justifies a robust regulatory and supervisory framework surrounding the financial sector.

The history of finance in the United States is one of accepting public support (especially during bad times for finance) while constantly trying to escape regulation and supervision that constrain profits during good times. The period from the late 1970s to 2007 saw regulation and supervision atrophy. This resulted in exploding profits and incomes in the financial sector with very little obvious benefit to the rest of the economy and the spectacular crash of 2008 that demanded even more public support for the sector. In short, the industrial policy support that the financial sector has received is a case study for how complementary policies (regulation and supervision in this case) are needed to ensure public support for a specific sector is not siphoned off into the incomes of economic players with substantial market power.[29]

In the financial regulation space, the Trump administration has continued conservative efforts to keep public supports for finance strong while expanding the scope of what the sector can do to seek profits.[30] The administration has directed the Consumer Financial Protection Bureau to shrink its scope and cede regulatory oversight to state agencies and has supported congressional efforts to slash funding for the bureau. The administration has also stopped U.S. movements toward harmonizing regulations with the Basel III recommendations—essentially meaning that large banks are no longer required to hold as large a set of capital buffers to protect against financial market stress. These capital buffers are there to prevent the public sector from having to bail out large parts of the financial sector during these periods.

The administration has also endeavored to bring cryptocurrency into the realm of traditional financial institutions, but under a loose regulatory regime. This approach would essentially allow some parts of the crypto ecosystem to put the public sector on the hook for bailouts needed due to instability in the sector, but would also allow many of the worst abuses of the crypto ecosystem—its use in illegal transactions and its speculative excesses—to continue unregulated. The approach to crypto represents the worst of all possible worlds. It gives the public sector heavier responsibilities to ensure that crypto crashes are managed but robs them of the tools needed to supervise the sector.

Attacks on the federal workforce

Between January and December 2025, federal payroll employment fell by roughly 290,000 due to the cuts started by the so-called Department of Government Efficiency. We noted previously that these cuts would sharply hurt growth in potential output in coming years. They will also lead to a less equal economy.[31]

Besides providing key inputs to public-sector production that markets generally fail to provide, the activities of federal workers often involve providing a countervailing force against unchecked corporate power. The Federal Trade Commission and the Antitrust Division at the Department of Justice ensure that markets remain competitive and block firms from exercising monopoly power. The Centers for Medicaid and Medicare Services must set reimbursement rates for the health care delivered by private-sector providers but paid for by the federal government. Private-sector health providers have seen a wave of consolidation in recent years and often can exercise pricing power against patients and other payers—the price-setting decisions of the federal government are a key bulwark against this pricing power. The Occupational Safety and Health Administration and the Food and Drug Administration have workplace inspectors to ensure that firms do not try to maximize profits by underinvesting in basic protections for worker or consumer safety.

Further, in a country where the federal tax system remains at least moderately progressive (with richer households facing higher tax rates than low- and moderate-income households), effective administration of the nation’s tax laws is equality enhancing. The vast majority of unpaid taxes are owed by the very rich. As such, attacks on the capacity of the Internal Revenue Service to administer this tax law are intentionally designed to lighten the tax burden of the privileged without passing new legislation.

Measures of GDP and income understate harms of Trump policies

Most of the discussion above concerns economic forces that affect measured GDP and incomes. But the economic security and happiness of U.S. families cannot be captured entirely based on these measures. For example, many Americans report feeling overworked and wish they had more leisure time. Increases in leisure time do not show up as greater GDP or incomes, yet clearly are valuable to families.

A number of policy choices made by the Trump administration will have profoundly damaging effects on families’ welfare that are not captured by GDP or data on incomes. For example, much of the damage done by climate change will not be well captured in these statistics. At the starkest level, climate change is forecast to lead to worse health outcomes and more premature deaths. The famous Stern review of climate change (2021) noted that accounting for these non-GDP influences likely at least doubles the true economic cost of climate change.

Similarly, the cutbacks to health insurance coverage signed into law by the Trump administration will cause poorer health and excess deaths in the coming decade if they stand. These deaths will not directly affect GDP, but obviously they need to be accounted for when assessing the impact of these policy changes.

Some of the outcomes of public policy raise GDP but actually reduce welfare. As climate change makes people spend more money on air conditioning, for example, this shows up as an increase in GDP yet makes peoples’ lives worse. Similarly, an increase in health spending driven by maladies related to climate change will raise GDP yet reduce welfare.

Further, some government spending provides outputs that GDP does not measure well at all. The value of less air and water pollution, for example, is immense but not captured in contemporaneous GDP. Much of its value will implicitly show up in future GDP numbers, as less pollution will lead to a healthier and more productive workforce in the future, but in real time, the benefits are not precisely measured. A similar finding concerns investments in children generally. Some of the benefits might occur in the moment (say, child care subsidies that allow parents to work more and earn higher incomes), but most accrue over time as children grow up healthier and become more productive and higher-earning adults.

Just because the benefits of much public spending do not mechanically show up in contemporaneous GDP measures do not mean they cannot be measured. When they are measured, there is ample evidence that families value this spending and the output it produces immensely. Often the estimated value of such spending is on the order of $1.50 for each $1.00 spent, with most of the benefit coming from welfare gains not captured in GDP. Welfare gains this large from public spending are strong suggestive evidence that public spending is already extremely under-provided, and further cuts will make it far worse.

Conclusion

It is essentially a guarantee that the policy path charted by the second Trump administration will leave the U.S. economy poorer and less equal. But much of this damage will be subtle and hard to see in month-to-month or even year-to-year changes in economic statistics. The Trump administration’s inability to implement a policy agenda without rank chaos might lead to a short-run recession that will temporarily expose much of the damage being done. But even if the recession does not come and even when it passes, there will be a steady hollowing out of the U.S. economy’s simple ability to produce the goods and services families need, and the inadequate growth that does get generated will flow disproportionately to the richest households.

In short, the macroeconomic consequences of the second Trump administration are profound. They will leave the vast majority of American families poorer over the next decade, and if Trump’s successors continue in this vein, they will leave the current generation’s children far poorer.

Notes

[1] The obvious historical counterexample to the rule that supply tends to grow slowly and predictably occurred during and immediately after the COVID-19 pandemic and Russian invasion of Ukraine, when these shocks broke global supply chains and led to sharp supply disruptions that restored themselves only with lots of volatility. This was, however, an unprecedented behavior of supply in advanced economies over the past century and is highly unlikely to repeat in the future.

[2] For this calculation, assume a counterfactual in which the unemployment rate stood at 4.0% over the 2007–2017 period and multiply by the size of the labor force in each year. Then, subtract this level of unemployment from the actual rate and sum over the years. For evidence of the damage this excess unemployment did to wage growth, particularly for lower-wage workers, see Gould et al. 2025.

[3] For details on the strength of the economy the Trump administration inherited, see Bivens 2025a.

[4] Numbers in this paragraph about cuts in the 2025 Republican budget megabill are taken from CBO 2025b, c.

[5] See Lo et al. 2025.

[6] This current policy baseline is a wrong and dishonest one to use when grading a law’s fiscal impact in coming years, but it’s the right one to use when figuring out whether growth will accelerate or decelerate in coming years due to policy changes.

[7] See Zipperer 2025 for estimates of the employment impact of the Trump administration’s mass deportation goals.

[8] For a wide range of views on the “Liberation Day” tariffs, resulting pullback and recession risks, see Nathan, Grimberg, and Rhodes 2025.

[9] Numbers in this paragraph can largely be found in Bivens (forthcoming).

[10] See Shierholz 2025 for this broader argument.

[11] Stark evidence that it is the race between wages and prices (and not just prices) that determines affordability can be found in Gould et al. 2025. They show that inflation-adjusted wage growth for low- and middle-wage workers was extremely strong from 2019 to 2024 but was actually negative over the five years following the previous business peak (from 2007 to 2012), even as this 2007–2012 period saw much lower rates of inflation. The strength of the labor market dwarfed changes in inflation in these periods, for good and bad.

[12] See Bivens 2017 for evidence that healthy labor markets support faster productivity growth.

[13] For example, according to the National Income and Product Accounts (NIPA) Table 1.1.10, between 1979 and 2007 residential investment was about 4.7% of overall GDP, whereas between 2007 and 2019 it was just 3.3%.

[14] See Bivens 2025b for an overview of the short- and long-run effects of steep cutbacks in the federal workforce.

[15] See the Yale Budget Lab’s State of U.S. Tariffs feature for a real-time assessment of trade policy under the second Trump administration.

[16] Numbers in this section are taken from CBO 2025b.

[17] Bivens 2019 estimates that a budget deficit of 2.5% or lower is likely consistent with a roughly stable debt ratio when the economy is near full employment.

[18] See Banerjee and Bivens 2022 for an overview of secular stagnation and how it intersects with fiscal policy debates.

[19] See Lynch and Vaygul 2015 for an accounting of the costs and benefits of investments in early childhood education.

[20] Bivens 2025c looks at a scenario in which net immigration between 2025–2034 was halved relative to CBO projections made in January 2025. The goal of deporting 1 million immigrants would yield reductions in immigrant labor supply very close to that “halving net immigration scenario” in that report.

[21] Marr and Cureton 2025 note that the administration’s proposed budget calls for cuts larger than 20% in federal research and development spending.

[22] For this calculation, we compare a scenario in which the $204 billion spent on government research and development in 2024 is cut by 20% going forward and compare it with a scenario in which (as has been largely the norm) this spending was instead held constant as a share of GDP. By 2035 this implies a funding shortfall of nearly $80 billion. We multiply this funding shortfall by the high end of estimated returns to this kind of spending from Fieldhouse and Mertens to ascertain the total cumulative reduction in GDP by 2035, which is 2% of projected GDP in that year. We then divide this by 10 to get the average effect on productivity growth over that time.

[23] In CBO 2025e, they present the effect of GDP on two different scenarios regarding growth in the debt ratio over time. Using this, one could back out the implicit effect on GDP of a given increment of increase in the debt ratio. If this incremental effect holds for the increase in the debt ratio caused by the 2025 Republican budget megabill, one can hence get an estimate of its growth effects.

[24] While Tedeschi 2024 is not just writing about the takeover of the Fed, he absolutely mentions this as one thing that could threaten the very low current “country risk premium” enjoyed by the U.S. The country risk premium is essentially how much lower a return that international investors are willing to take on investments in the U.S. due to the perceived safety and stability of U.S. investments from political manipulation.

[25] See Bivens (forthcoming) for a quick discussion of these estimates.

[26] For a comprehensive assessment of policies undertaken by the Trump administration and their likely effect on typical working families, see Economic Policy Institute 2025–2026.

[27] For a comprehensive overview of actions taken by the Trump administration (including those mentioned in this paragraph) that harm workers’ leverage in labor markets, see McNicholas, Poydock, and Bivens 2026.

[28] See Farber et al. 2021 for the link between unionization and inequality throughout U.S. history.

[29] See Epstein 2018 for a good overview on how powerful economic actors in finance are able to claim a larger share of society’s incomes and resources than their economic contribution justifies.

[30] Much of this section relies on Gensler et al. 2025.

[31] Much of this discussion relies on Bivens 2025b.

References

Banerjee, Asha, and Josh Bivens. 2022. Will Secular Stagnation Return? The Stakes for Current Economic Debates and Fiscal Policy. Economic Policy Institute Report. August 4, 2022.

Bivens, Josh. 2017. A ‘High-Pressure’ Economy Can Help Boost Productivity and Provide Even More ‘Room to Run’ for the Recovery. Economic Policy Institute, March 2017.

Bivens, Josh. 2019. Thinking Seriously About What ‘Fiscal Responsibility’ Should Mean: Full Employment and Reduced Inequality Are the Most Important Targets of Fiscal Policy. Economic Policy Institute, September 2019.

Bivens, Josh. 2025a. “President-Elect Trump Is Inheriting a Historically Strong Economy.” Working Economics Blog (Economic Policy Institute), January 17, 2025.

Bivens, Josh. 2025b. “The Economic Effects of Rapid Federal Downsizing” in Gensler, Gary, Simon Johnson, Ugo Panizza, and Beatrice Weder di Mauro (eds), The Economic Consequences of the Second Trump Administration: A Preliminary Assessment. Centre for Economic Policy Research Press, December 2025.

Bivens, Josh. 2025c. The U.S.-Born Labor Force Will Shrink over the Next Decade: Achieving Historically ‘Normal’ GDP Growth Rates Will Be Impossible, Unless Immigration Flows Are Sustained. Economic Policy Institute, October 2025.

Bivens, Josh. Forthcoming. “How Are AI Investments Affecting the U.S. Economy?” Working Economics Blog (Economic Policy Institute).

Bloom, Nicholas, Philip Bunn, Paul Mizen, Pawel Smietanka, and Gregory Thwaites. 2025. “The Economic Impact of Brexit.” National Bureau of Economic Research (NBER) Working Paper no. 34459, November 2025.

Bureau of Economic Analysis (BEA). 2025. “National Income and Product Accounts (NIPA)” (web page). Accessed December 2025.

Bureau of Labor Statistics (BLS). 2025. “Major Sector Productivity and Costs Database” (web page). Accessed December 2025.

Congressional Budget Office. 2009. The Budget and Economic Outlook: 2009 to 2019. January 7, 2009.

Congressional Budget Office. 2024. The Distribution of Household Income in 2021. September 11, 2024.

Congressional Budget Office. 2025a. “Estimated Budgetary Effects of Public Law 119-21 to Provide for Reconciliation Pursuant to Title II of H. Con. Res. 14, Relative to CBO’s January 2025 Baseline” [Excel files]. Published July 21, 2025.

Congressional Budget Office. 2025b. Key Budget and Economic Data.

Congressional Budget Office. 2025c. The Budget and Economic Outlook: 2025 to 2035. January 17, 2025.

Congressional Budget Office. 2025d. The Estimated Effects of Enacting Selected Health Coverage Policies on the Federal Budget and on the Number of People with Health Insurance. September 18, 2025.

Congressional Budget Office. 2025e. The Long-Term Budget Outlook Under Alternative Scenarios for the Economy and the Budget. May 2025.

Economic Policy Institute (EPI). 2025–2026. Federal Policy Watch (Blog post series).

Epstein, Gerald. 2018. “On the Social Efficiency of Finance.” Development and Change 49, no. 2: 330–352. March 2018.

Farber, Henry S., Daniel Herbst, Ilyana Kuziemko, and Suresh Naidu. “Unions and Inequality over the Twentieth Century: New Evidence from Survey Data.” Quarterly Journal of Economics  136, no. 3: 1325–1385. August 2021.

Fieldhouse, Andrew J., and Karel Mertens. 2025. “The Returns to Government R&D: Evidence from U.S. Appropriations Shocks.” Society for Economic Dynamics Annual Meeting Working Paper, June 26, 2025.

Gensler, Gary, Simon Johnson, Ugo Panizza, and Beatrice Weder di Mauro, eds. 2025. The Economic Consequences of the Second Trump Administration: A Preliminary Assessment. Centre for Economic Policy Research, December 2025.

Gould, Elise, Katherine deCourcy, Joe Fast, and Ben Zipperer. 2025. Strong Wage Growth for Low-Wage Workers Bucks the Historic Trend. Economic Policy Institute, March 2025.

Hirtle, Beverly, Anna Kovner, and Matthew Plosser. 2019. “The Impact of Supervision on Bank Performance.” Federal Reserve Bank of New York Working Paper no. 768. May 2019.

Hsieh, Chang-Tai, Erik Hurst, Charles I. Jones, and Peter J. Klenow. 2019. “The Allocation of Talent and U.S. Economic Growth.” Econometrica 87, no. 5: 1439–1474. September 2019.

Hughes-Cromwick, Ellen, and Julia Coronado. 2019. “The Value of U.S. Government Data to U.S. Business Decisions.” Journal of Economic Perspectives 33, no. 1: 131–146.

Klein Martins, Guilherme. 2025. “Long-Run Effects of Austerity: An Analysis of Size Dependence and Persistence in Fiscal Multipliers.” Oxford Bulletin of Economics and Statistics 87, no. 2: 330–356.

Lo, Justin, Larry Levitt, Jared Ortaliza, and Cynthia Cox. 2025. ACA Marketplace Premium Payments Would More Than Double on Average Next Year If Enhanced Premium Tax Credits Expire. KFF, September 30, 2025.

Lynch, Robert, and Kavya Vaghul. 2015. The Benefits and Costs of Investing in Early Childhood Education. Washington Center for Equitable Growth, December 2015.

Marr, Chuck, and Josephine Cureton. 2025. Administration’s Proposed Cuts to Non-Defense R&D Pose Long-Term Risk to Rising Living Standards. Center on Budget and Policy Priorities, October 2025.

McNicholas, Celine, Margaret Poydock, and Josh Bivens. 2026. 47 Ways Trump Has Made Life Less Affordable in the Last Year. Economic Policy Institute, January 2026.

Nathan, Allison, Jenny Grimberg, and Ashley Rhodes. 2025. Top of Mind: Tariff-Induced Recession Risk. Issue 138. Goldman Sachs Research, April 2025.

Shierholz, Heidi. 2025. “Everyone Is Talking About Affordability—and Making the Same Mistake: Focusing on Just Prices Misses the Bigger Picture.” MS NOW, November 29, 2025.

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Tedeschi, Ernie. 2024. Political Risks to the U.S. Safe Harbor Premium. The Budget Lab at Yale, May 2024.

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Zipperer, Ben. 2025. Trump’s Deportation Agenda Will Destroy Millions of Jobs: Both Immigrants and U.S.-Born Workers Would Suffer Job Losses, Particularly in Construction and Child Care. Economic Policy Institute, July 2025.

Tennessee is committed to providing an attractive business climate with a focus on low debt and a pro-business regulatory environment. Tennessee is proud to be a right-to-work state with no personal income tax on wages. Our state and local tax burdens are among the lowest in the country (Memphis Moves n.d.).

These sentiments, especially around unions, are shared by many Southern governors. For example, the governor of Alabama recently wrote:

Alabama has become a national leader in automotive manufacturing, and all this was achieved without a unionized workforce. In other words, our success has been home grown – done the Alabama way (Ivey 2024).

The governor of South Carolina shared a similar sentiment:

One thing we do not need is more labor unions… We have gotten where we are without them, and we do not need them now.

He continued:

We will fight. All the way to the gates of hell. And we will win (Harris 2024).

These statements highlight some of the major components of the Southern economic development model that politicians and business interests across many Southern states advocate for.1 As noted in the Memphis Moves quote, a key component of this model is ensuring the absolute minimal levels of regulation on businesses, including a lack of enforcement of labor laws or safety standards for workers (Cooper and Kroeger 2017; Fleischman and Franklin 2017; FPI 2024).

Each of the quotes underline policymakers and business interests’ hostility toward unions. Unions empower workers to advocate collectively to ensure they are paid livable wages and provided with basic benefits, such as health insurance, paid time off, and retirement benefits. Instead, politicians defend low wages for workers and the lack of regulation on businesses in their states, as well as the threadbare safety net in place across the South (Childers 2023).

Cash payments to help support poor families, for example, are distributed through a program known as Temporary Assistance to Needy Families (TANF). In 13 of 17 Southern states, a single mother with two children would receive a maximum monthly cash benefit of $500 or less. Some of the least generous benefits are in states like Alabama ($215), Arkansas ($204), Georgia ($280), Kentucky ($262), Mississippi ($260), North Carolina ($272), and Oklahoma ($292). This is compared with the $492 maximum monthly benefit in Michigan—the median state for TANF benefits in July 2022—and $1,151 in New Hampshire, the state with the most generous benefit (Thompson, Azevedo-McCaffrey, and Carr 2023).

The unemployment insurance benefit systems across the South are similarly stingy, with maximum weekly benefit amounts across the South being as low as $235 in Mississippi and $275 in Alabama, Florida, Louisiana, and Tennessee (The Century Foundation 2023).

While politicians across the South keep benefit levels for workers and families remarkably low, they further enrich wealthier Southerners and corporations by funneling them money that could be used to build out the social safety net, adequately fund schools, and provide public transportation. States across the South provide corporations with massive subsidies, tax breaks, and other incentives, such as the $1.3 billion South Carolina agreed to spend to attract Scout Motors or Georgia’s $1.8 billion in incentives to Hyundai to build electric vehicles in the state (AP 2022; Bustos and Hughes 2023).

Public officials’ stated goals for these subsidies is that they will attract jobs—good jobs—and drive growth in the region. But as we show in Figure B, Southern states are among the states with the lowest gross domestic product (GDP), whose job growth consistently falls behind population growth, and that have the lowest labor force participation rates of any region.

Figure A

Southern states adopting the Southern economic development model have lower GDPs

The first indicator this report examines is the state-level gross domestic product (GDP). The GDP is the total value of goods and services produced in an economy. It is a comprehensive measure that represents overall spending by government, the output of businesses and their workers, investments made by actors in the economy, and the trade conducted with economic actors in other jurisdictions.

GDP grows when governments spend more money, when the demand for goods and services produced by businesses grows, when more resources are invested in the area, and when the productivity of workers and their employers increase. Productivity—the income generated from each hour of work—generally increases when workers, their employers, and their political leaders invest in education and job training; necessary capital investments such as machinery, digital technologies, and public infrastructure; and work supports such as living wages, fair scheduling policies, and paid leave. All these investments can help make workers more productive.

Overall GDP trends are heavily driven by national and often global macroeconomic forces, and regional or state GDP trends can differ significantly depending on how a particular state or region’s economy is implicated by those macroeconomic forces. For example, states with heavy tourism industries were particularly exposed to the effects of the COVID-19 pandemic, as the drop in travel and face-to-face services meant these states were more likely to experience a sharper drop in overall economic activity than states where tourism is less prominent.

Still, state and local policymakers have enormous power to shape the public services, educational opportunities, infrastructure, and other investments being made in regional economies. Similarly, policymakers and employers can powerfully influence both productivity and consumer demand through the choices they make governing job quality and investments in workers. As a result, GDP trends do vary by region and by state within regions.

Figure B shows the per capita GDP for the United States and for each region of the country in 2019, before the COVID-19 recession, and in 2022.2 If we look at the regions as the Census Bureau defines them, the South has the lowest per capita GDP in both 2019 ($67,220) and 2022 ($69,811).

The South, however, includes the District of Columbia (D.C.), which has the highest per capita GDP of any state. But D.C.’s unique position as a city-state and the seat of the federal government artificially raises the overall per capita GDP for the region. Notably, D.C. does not conform to the Southern economic development model. If we exclude D.C. from the analysis, Figure B shows that the GDP across the South is even lower. The GDP across the South falls even further if Maryland and Delaware—two more states that do not follow the Southern economic development model—are also excluded.

Figure B

While this report uses the Census Bureau’s definition of the South, one could argue that D.C., Maryland, and Delaware should not be included in the region, since they were not part of the Confederacy and have not followed the Southern economic development model with their relatively higher wages, higher minimum wages, and greater protections for workers. For example, most states across the South either have no state minimum wage or have the minimum wage set at the federal rate of $7.25 per hour. D.C., however, has a minimum wage of $17, Maryland’s is $15, and Delaware’s is $13.25 (EPI 2024; Hickey 2023). None of these are so-called right-to-work states and all three have passed paid family and medical leave laws for workers (Williamson 2023). Including these states in the analysis, however, allows the data to show how taking a different policy path can bring ample benefits to workers and families.

Next, we examine GDP trends for individual states. Figure C shows the 2022 per capita GDP for the 10 states with the highest per capita GDPs. Just one Southern state—Delaware—is included among the top 10. The District of Columbia would also be among the 10 jurisdictions with the highest GDP but was omitted from these rankings for the reasons mentioned above.

Figure C

Figure D shows the 10 states with the lowest per capita GDP. Seven of these 10 states are in the South—Alabama, Arkansas, Kentucky, Mississippi, Oklahoma, South Carolina, and West Virginia. Mississippi has the lowest GDP of all states. These Southern states follow the Southern economic development model, as opposed to states like Delaware and Maryland or the District of Columbia, which do not.

Figure D

GDP is an important measure of overall economic trends, but it is limited in its ability to provide an understanding of the experiences and living standards of average workers and their families. Only in the theoretical scenario where inequality is low and overall economic growth is equally shared do changes in GDP necessarily reflect the experience of the average individual or household. When economic gains go to fewer and fewer people, as has increasingly been the case in the South and across the country, changes in GDP disproportionately reflect the experiences of those at the top (Boushey and Clemens 2018; Clemens 2023).

To better understand the living conditions and experiences of average households in the South, we need to look at a variety of economic measures, particularly those describing households’ engagement with the labor market. Most households get the bulk, if not all, of their income through work. Thus, economic outcomes of workers in the South are key to understanding how the Southern model has impacted people in the region. Next, we examine various additional components of the Southern labor market to gain a more comprehensive understanding of work and workers across the South.

Before getting into labor market indicators, however, it’s important to emphasize how large the Southern population is, and how much it has grown. Population growth or decline is an important factor impacting a region’s GDP. And how the Southern population grows relative to other regions has consequential implications for the economic health of the country as a whole. The 17 states that make up the South were home to almost four in 10 Americans (38.1%) in 2020. Figure E shows how the share of the U.S. population that resides in each region has shifted between 1910 and 2020. The South not only has the largest population of any region in the nation, but also its population is the fastest growing.

Figure E

This growth in the share of the population living in the South reflects several factors, including natural increases—i.e., more births than deaths—as well as international immigration (especially across the Southern border, from South America and the Caribbean) and domestic migration to the region.

Politicians point to population growth across the South as evidence of the positive impacts of their policies, but strong population growth has not occurred uniformly across the region—despite the widespread adoption of the Southern model—and growth tends to be concentrated in specific states and cities. For example, Texas and Florida are two of the largest states in the nation and they had the highest population growth from 2021 to 2022, but Louisiana, West Virginia, Maryland, and Mississippi all saw their populations decline during the same period (U.S. Census Bureau 2022).

Many of those moving to the South from other regions are seeking cheaper housing (Henderson 2016). Land generally tends to be cheaper across the South, contributing to housing that is much more affordable than in places like California and New York. Texas and Florida’s populations also had some of the largest gains in international migration between 2021 and 2022 (U.S. Census Bureau 2022).

Another contributor that is largely ignored in discussions of population growth in the South is the fact that, beginning in the 1960s and 1970s, the use of air conditioning became more widespread across the region. Prior to that time, the oppressive heat during summer months along with high humidity left many Southerners in misery. This was fundamentally changed, however, by air conditioning in cars, homes, and businesses (Arsenault 1984). As Figure D shows, before 1970, the share of the population living in the South fluctuated between 30–32%. Since 1970, it has continued to increase in each subsequent decade.

Job growth across the South lags other regions

Many Southern politicians argue that the policies associated with the Southern economic development model that is common across Southern states—low wages, lax business regulations, zealous defense of so-called right-to-work laws—produce stronger job growth. When we examine the data, however, we find that job growth across the South has not outpaced population growth and, in most instances, it has failed to keep up with population growth.

The region’s underwhelming job growth is very likely a direct result of some of the Southern model’s intentional aims. Low wages and a weak safety net undermine workers and households’ spending power, reducing the overall demand for goods and services that might lead to stronger GDP and job growth. Similarly, with little union presence in the region, workers lack what has traditionally been a key vehicle for increasing workers’ share of overall income in the economy. Because low- and middle-income households tend to spend a larger share of their income than corporations and high-income households, if more income is captured by businesses and corporate shareholders, it can further depress overall demand for goods and services that could drive up GDP and spur faster job growth.

In Figure F, we compare job growth with the growth in the working-age population across each region. While the share of the U.S. population living in the Midwest and Northeast has declined since the late 1970s, the number of working-aged people in all regions increased. The data show that since the early 2000s, job growth has lagged population growth across the South. Figure F shows that between 1976 and the mid-2000s, Southern job growth had generally moved in tandem with population growth but never exceeded growth in the working-age population. Over the last decade and a half since the 2007–2009 Great Recession, job growth has failed to even keep up, indicating that job growth resulting from the Southern economic development model has not been particularly impressive. Indeed, compared with other regions, the Southern model fared no better than the rest of the country over the same period.

Figure F

Figure G shows the same data for the remaining regions. While all regions experienced a decline in the number of jobs in the region during the 2007–2009 recession, Figure G shows that in the Northeast and Midwest, job growth consistently exceeded population growth although neither region grew as quickly as the South. Population and job growth in the West region was similar to that in the South.

Figure G

Figure H shows that this regional pattern is not being driven by just a few large states such as Florida and Texas, which experienced large increases in their populations. In most states in the region, whether the population was growing or declining, job growth lagged population growth. Mississippi is one example of a state that has had its overall population as well as its working-age population decline over the past few years but still has been unable to generate enough jobs to match the size of the working-age population (St. Louis Federal Reserve 2023).3

Figure H

Labor force participation

The fact that job growth lags growth in the working-age population across the South indicates that labor force participation across the South may also lag other regions. The labor force participation rate is a key indicator used to convey the health of the labor market, as it shows the share of the population ages 16 and older that is either employed or unemployed but actively looking for work.

Figure I shows trends in the labor force participation rate for workers 16 and older from 1979 through 2022 for each region of the country. The South had the second lowest labor force participation rate of any region from 1979 until the 2007–2009 Great Recession. Since the Great Recession, the South has had the lowest labor force participation rate of all regions. The Midwest has had the highest labor force participation rates since the 1990s, followed by Western states.4

Figure I

Notably, Figure J shows that this lower labor force participation rate across the South is driven by white Southerners who make up the largest share of the population (U.S. Census Bureau 2023). The labor force participation rate for white Southerners is below 60%, lower than for white workers in any other region. It is also in contrast to the labor force participation rates of other racial and ethnic groups in the region. Black Southerners have a labor force participation rate of 62%, higher than Black Americans in any other region except those living in Western states, where it stands at 63.3%. Hispanic Southerners have a labor force participation rate of 66% and their labor force participation rate is only higher in the Midwest, where it is 70.8%. Asian Southerners have a labor force participation rate of 65.7%, the second highest of all groups.

Figure J

The labor force participation rate provides a good overview of the health of the region’s economy but is also limited in the amount of information it provides when considered in isolation. It cannot tell us, for example, what shares of people are unemployed or actually have a job. In the next two sections, we examine unemployment, and employment as a share of the prime-age population. While these measures provide limited information in isolation, together, they offer a better understanding of how well the economic development model in place across the South is utilizing labor.

Unemployment

We begin with the unemployment rate, which is one of the most frequently referenced statistics to describe labor market conditions. Generally, an unemployment rate lower than 5% is considered a sign of a relatively healthy labor market (Wolla n.d.). However, changes in the unemployment rate can occur for a variety of reasons—some good and some bad—so it is important to understand what this indicator actually measures.

Individuals are counted as unemployed if they do not have a job but are actively looking for and are available for work (BLS 2015). When workers become discouraged and stop looking for work, they are no longer considered unemployed and are no longer counted as part of the labor force. This can cause the unemployment rate to fall even though large segments of the population who would like to work still do not have a job. It is also the case that when discouraged workers believe the economy is tightening or improving, they often reenter the labor market to actively look for work. This can cause the unemployment rate to increase as the labor force grows, even though the economy is recovering.

Figure K shows the unemployment rate by region for 2019, 2020, and 2021. Except for the Midwest in 2021, the South consistently has the lowest rates of unemployment, both before and after the pandemic. In 2019, unemployment rates were well below 5% for all regions. Data for 2020 and 2021 are provided to show how regions were impacted by the pandemic. While unemployment rates increased in all regions, they remained lower in the South than in other regions of the country with the exception of the Midwest in 2021.

The lower unemployment rate for the South might be seen as an indicator of the success of the Southern economic development model, but this is unlikely given the lower labor force participation rates above. Their lower unemployment rate, rather, masks a lot of discouraged job seekers and economic hardship as the data in Figure K will show. While unemployment rates fell further by 2021, they remained much higher than their pre-pandemic levels across regions.

Figure K

While pre-pandemic unemployment rates were low nationally and across the South, it is important to note that they were not equally low for all groups of workers. In fact, whether unemployment rates are high or low, Black workers have historically had unemployment rates twice that of their white counterparts nationally and across every region of the country, including the South (Williams and Wilson 2019).

Figure L shows the unemployment rate for workers across the South by race and ethnicity for the largest racial and ethnic groups in the region. Consistent with national data, Black workers across the South have unemployment rates at least twice that of their white counterparts across the last four decades. While Hispanic workers have lower unemployment rates than Black workers, their rates are also consistently higher than that of their white counterparts.

Figure L

Black workers in particular have higher rates of unemployment overall and higher rates when compared with white workers of the same age and with the same levels of education (Ajilore 2020; Williams and Wilson 2019; Wilson and Darity Jr. 2022). This reflects the discrimination that Black workers face when looking for jobs. Black men without a criminal record, for example, are less likely to receive a call back from employers than White workers with a criminal record (Pager 2003).

Black workers in the U.S. and across the South also face much higher rates of criminalization and incarceration which further disadvantages them in the labor market (Childers 2024; Mast forthcoming). In 2021, the U.S. imprisoned 664 per 100,000 people. This is much higher than our peer nations including the United Kingdom (129) and Canada (104).

Despite the U.S. having an exceedingly high incarceration rate internationally, 13 of the 17 Southern states have incarceration rates that are even higher, with the highest rates in Louisiana (1094), Mississippi (1031), and Oklahoma (993). Black Americans are disproportionately represented among these prison populations, with Black Americans being just over 32% of the population in Louisiana but accounting for 66% of the state’s incarcerated population in prisons (PPI 2021a). In Mississippi, Black Americans are 37% of the population, but 61% of the prison population (PPI 2021b). And in Oklahoma, Black Americans are just over 7% of the population, but they are 27% of the incarcerated population (PPI 2021c).

Prime-age (25–54) employment-to-population ratio (EPOP) 

Finally, the prime-age employment-to-population ratio (EPOP) is arguably the best individual measure of the health of the labor market. The prime-age EPOP refers to the share of the population ages 25–54 that is currently employed. One strength of this measure is that the EPOP does not fluctuate based on the movement of workers into or out of the labor market—be it due to dissatisfaction with job prospects or more innocuous movements typical at different life stages. For instance, by focusing on just workers ages 25 through 54, this measure is unaffected by young people finishing their education and older workers moving into retirement. This is particularly important for states like Florida, with large retiree populations. Figure M shows the EPOP for each region of the country. Consistent with the data on labor force participation, the South lags much of the country in the share of the prime-age population that is employed.

Differences across regions were negligible in the late 1970s and early 1980s, but in the late 1980s, the regions began to show a significant divergence. The South and West fell behind the Northeast and Midwest in the early 2000s. The South continues to have one of the lowest EPOPs of all regions since the early 2000s.

Figure M

As with other indicators, there are substantial differences across Southern states in the share of the prime-age population that has a job. Nationally, 78.2% of residents aged 25 through 54 are employed. This percentage is pulled down by the much lower rates across many Southern states. Of the 10 states with the highest prime-age employment-to-population ratios, none are in the South. Among states with an EPOP of 80% or higher, only 4 of 21 are in the South: D.C., Delaware, Maryland, and Virginia (data not shown in Figure M).

Figure N shows the 10 states with the smallest shares of their prime-age population employed. Seven of these states are in the South, and all have lower employment rates than the national rate. In West Virginia, Mississippi, Alabama, and Louisiana, along with the Western state of New Mexico, more than one in every four prime-age residents are without a job. This means that the economy created with the Southern economic development model has left many Southerners out of the labor market either because they can’t find a job or because they face barriers to pursuing employment.

Figure N

Beyond the lower overall EPOP across the South, the large and intersecting racial and gender disparities in employment are indicative of both inequities across the region and policy failures in the Southern economy. Figure O shows that across racial and ethnic groups, prime-age men are much more likely to be employed than are women. The smallest gender gap is between Black men and women as prime-age Black men are employed at a rate 3.4 percentage points higher than Black women. White men are 12.9 percentage points and Asian men are 19.7 percentage points more likely to be employed than their same-race female counterparts. The largest gap, however, is among Hispanic workers, with Hispanic men 24.3 percentage points more likely to be employed than Hispanic women.

Figure O

Figure O also shows that among men, only Black men have an EPOP below 80%. Asian and Hispanic men have the highest EPOP among men, followed by white men. Among women, however, Black women have the highest EPOP followed by white women. Hispanic and Asian women have the lowest EPOPs.

The smaller gap between Black men and women reflects, at least in part, the fact that historically Black women have been more likely than women of other racial and ethnic groups to be in the labor force because they often had few alternatives (see Frye 2016). Since slavery, Black women have been viewed as workers rather than mothers or wives (Frye 2016). Fewer employment opportunities for Black men also meant that Black women’s labor was even more important to the economic well-being of Black households. This continues today as Black women are more likely than women from other racial and ethnic groups to have their earnings be essential to household income, since they are more likely to be either breadwinners or co-breadwinners providing a substantial source of total household income (Banks 2019; Frye 2016; Glynn 2019).

Conclusion

In this report we have shown that Southern states are overrepresented among states with the lowest per capita GDP; that job growth across the South has failed to keep up with growth in the working-age population; and that the South lags in labor force participation and prime-age EPOP. The apparent lower levels of unemployment across the region are misleading, because the low labor force participation rate and the prime-age employment-to-population ratio show that smaller shares of the available workforce are employed in the South relative to other regions. Together, they indicate that many Southerners have become discouraged either because they have been unable to find a job, or because they face serious obstacles to paid employment—such as the need to care for a child or family member that prevents them from seeking employment, or an illness or a disability that prevents them from working.

The relationship between race, ethnicity, gender, employment, and unemployment are no doubt complex. But the fact that, on indicator after indicator, specific states and regions consistently underperform points to systemic factors shaping these outcomes across states, race, ethnicity, and gender. The Southern economic development model is a key factor shaping the results we see.

For example, Figure O showed that Black women have higher employment-to-population ratios across the South than women from other racial and ethnic groups. There are several reasons for this, including the greater need for their incomes as breadwinners and co-breadwinners relative to women of other racial and ethnic backgrounds. The fact that Black women’s earnings are so central to their households means it is critical that they have access to resources like affordable childcare and eldercare to enable them to participate in the labor market. This is not limited to Black women, however, as families across the South—and the nation—need access to these resources to fully participate in the labor market.

Access to affordable, reliable transportation is also critical to the ability of Southerners to participate in the labor market. Research shows that cities and metro areas across the South tend to provide less access to public transportation than cities in the Northeast and West. For example, McCann (2019) ranked 100 cities’ public transit systems on criteria including accessibility, convenience, safety, and reliability. The resulting ranking showed that the District of Columbia was the only Southern jurisdiction among the 10 highest ranking cities, but eight of the 10 lowest ranking cities were Southern cities.5

The lack of access to public transportation is made worse by other policies that further reduce workers’ geographic mobility. For instance, when states suspend workers’ driver’s licenses when they have unpaid fees and fines (Khalfani 2021). Khalfani (2021) reports that, of the 430,000 Georgians who were on the probation rolls in 2018, almost four in 10 were on probation for misdemeanors related to the inability to pay traffic fines, including minor traffic or parking violations. In many jurisdictions, driver’s licenses are revoked or suspended for the inability to pay for these tickets or court costs, or for a failure to attend a court hearing, which sometimes occurs simply because the relevant individuals are incarcerated. These court costs are a key way that some county and local jurisdictions fund their criminal legal systems, increasing the incentive to abuse them and further depriving Southerners of access to the very transportation they need to participate in the labor market.

In addition to lacking access to workforce supports, Black and brown men and women are more likely to face discrimination in hiring and job assignment. Research shows that simply having a name that “sounds Black” results in a lower likelihood of getting a job interview (Bertrand and Mullainathan 2003). And, as noted above, Pager (2003) showed that Black men who did not have a criminal record were less likely than white men who did have a criminal record to be called for a job interview. Discrimination is particularly salient when trying to understand why Black men have the lowest employment-to-population ratio among men in the principal racial and ethnic groups across the South.

The Southern economic development model has not produced the good jobs or prosperity it promised. Instead, workers across the South face lower rates of labor force participation and employment, with substantial racial and gender disparities in employment rates. States across the South have lower per capita GDPs, and job growth across the region has failed to keep up with population growth. Further, the inability to provide jobs for the working-age population may exacerbate the exodus of people from states like Louisiana and Mississippi that are already losing population.

Notes

1. For a more detailed description of the Southern economic development model, see Childers 2023 and Childers 2024, which provide a more in-depth description of the model, along with a description of its historical development.

2. This is the per capita GDP in 2019 and 2022, both in 2022 dollars.

3. While population growth exceeded job growth in most Southern states, two states with highly variable population growth over the past 40 years—Louisiana and West Virginia—had greater job growth than population growth over the most recent time periods. The District of Columbia also had greater job growth than population growth. Job growth and population growth occurred at similar rates in Maryland, Kentucky, and Oklahoma.

4. Midwestern states include Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. Western states include Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

5. Southern cities with the lowest ranking on public transit are Baton Rouge, LA, Arlington, TX, Oklahoma City, OK, Tulsa, OK, New Orleans, LA, Charlotte, NC, Tampa, FL, and St. Petersburg, FL.

References

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Wolla, Scott A. n.d. Making Sense of Unemployment Data. Federal Reserve Bank of St. Louis.

This quote is not from an op-ed written by a political pundit; it’s from a 2016 peer-reviewed article in the American Economic Review. Adding in data since this article was written does not change its conclusion. There is still a pronounced Democratic advantage in nearly every measure of macroeconomic performance. Positive indicators like growth in gross domestic product (GDP), income, and wages are faster, while negative indicators like unemployment, inflation, and interest rates are lower. For those who want to skip right to this bottom line, see Table 1.

Besides the pronounced superiority of macroeconomic performance under Democratic presidents, the fruits of economic growth are also distributed substantially more equally under Democratic presidents. This is true even for data that are dominated by market-based incomes and exclude most of the federal government’s safety net and income support payments. It is, in short, not just driven by Democrats being more supportive of using taxes and spending to reduce inequality.

Given how clear the data are, it is striking that public opinion polling has consistently shown that voters rate Republicans more highly as the party that is better at managing the economy.1

The data presented in Blinder and Watson (2016) and in this report obviously cannot claim to measure the causal effect of partisan White House control on economic performance. The president does not have total control over the economy, and there is a lot of luck and chance that determine economic outcomes. Those who prefer Republican stewardship may consider it just bad luck again and again that keeps their preferred policy ideas from translating into faster growth in real time. And bad luck is a real issue in these examinations. For example, both the Obama administration and the Biden administration inherited a depressed macroeconomy that had been buffeted by severe shocks whose aftereffects had to be managed.

But it is our sense that the simple facts on real-time economic performance during Democratic and Republican administrations—and how starkly better this performance is during Democratic administrations—aren’t particularly well known. And these facts constitute important information people should have during this time of rampant misinformation.

Aggregate results

It is difficult to tell what respondents to opinion polls have in mind when they are asked about “the economy.” For example, respondents often rate the Republican party higher as economic managers yet rate the Democratic party more highly on issues related to health care. But health care is, by far, the single largest sector of the U.S. economy, affecting economic outcomes of households, businesses, and governments in significant ways. It seems hard to imagine that one could manage health care poorly and yet be a decent economic manager overall since health care is nearly a fifth of the U.S. economy.

But it seems fair to guess that what constitutes good management of “the economy” in the minds of polling respondents is fast economic growth, fast income growth, low unemployment, and low inflation. In the jargon of economists, it means successful macroeconomic stabilization.2 Further, these variables can indeed respond relatively quickly (within a year) to decisions of policymakers. Besides these variables, we also include a few others (measures of business investment and market incomes) that more closely measure the performance of the private sector.

Table 1 shows the average performance of a range of key macroeconomic variables under Democratic and Republican administrations since 1949, the beginning of Harry Truman’s first elected term.3 There is a Democratic advantage in every measure.

Table 1

Below we provide a brief description of each variable and why it is included in our analysis.

Real (inflation-adjusted) gross domestic product (annual % growth) is by far the most cited variable for assessing overall macroeconomic health. Gross domestic product (GDP) is the value of all final goods and services produced and sold in the United States—a measure of total economic activity and incomes. The Democratic advantage in this measure (1.2 percentage points) is very large. Given the $27.4 trillion GDP in the United States in 2023, just a single year of growing 1.2% faster would translate into an additional $330 billion in income that could accrue to U.S. families.

Real net domestic product per capita (annual % growth) is a lesser-known measure of economic health, but it measures with even greater precision the potential that economic growth provides for living standards to rise. It provides this greater precision by accounting for population growth and depreciation of the nation’s capital stock. Both are important influences for potential growth in living standards.

For example, real GDP growth has decelerated significantly in recent decades. Some of this reflects genuinely worse economic performance, but part of it simply reflects slower population growth (and hence slower labor force growth) in more recent decades. To account for this, a measure of real domestic product per capita is valuable. Second, recent decades have also seen a large rise in the rate of depreciation in the economy, as more and more of the nation’s capital stock is composed of short-lived computers that need to be replaced regularly. Because depreciation is a drain on gross domestic product, a measure that accounts for this drain—net domestic product—more faithfully tracks the potential of the economy to deliver rising living standards (rather than to simply maintain the nation’s capital stock).

Making these adjustments for population growth and depreciation yields a measure of net domestic product per capita—and here the Democratic advantage is even larger than it is for real GDP growth. At the 2.6% annual growth rate that has characterized Democratic administrations, per capita living standards could double every 28 years—within a single worker’s career. At the 1.3% rate that has characterized Republican administrations, this doubling would take 56 years or twice as long.

Total job growth is the percentage increase in total employment (private-sector plus government employment) over the past year. During Democratic administrations, total job growth has averaged 2.5% annually, while it is barely over 1% annually during Republican administrations. Applied to today’s total workforce, this would imply nearly 2.4 million more jobs created every year under Democratic administrations.  

Private job growth is the percentage increase in private-sector employment over the past year. The Democratic advantage is even larger in private job growth than it is for total job growth. During Democratic administrations, private job growth has averaged 2.6% annually, while it is less than 1% annually during Republican administrations. This also means that private-sector job growth is faster than overall job growth during Democratic administrations but that private-sector job-growth is slower than overall job growth during Republican administrations.  

The unemployment rate is a straightforward measure of how easy it is for jobseekers to find work. Applied to today’s labor force, the Democratic advantage in this measure (0.6 percentage points) would translate into roughly 1 million more people being able to find jobs. Since 1972 the Bureau of Labor Statistics (BLS) has also gathered data on the unemployment rate of Black workers, and there is a consistent Democratic advantage in this measure as well. We show this comparison in Table 2 when we compare performance for the post-1980 periods.

Real (inflation-adjusted) wages for production and nonsupervisory workers measure hourly wages for the roughly 80% of U.S. workers who are not managers. Here again the Democratic advantage is substantial.

Real business investment (annual % growth) is a measure of investment in structures, equipment, and intellectual property made by private-sector businesses (excluding both residential investments and changes to inventories). Some have argued that Republican policy mainstays (lower taxes on corporations and rollbacks of federal regulations) boost economic growth through their alleged effect on business investment. Yet this category shows the largest Democratic advantage in performance by far, with investment growth running at more than double the pace during Democratic administrations than it does during Republican ones.

Real personal income excluding transfers per capita (annual % growth) is a measure of market incomes, excluding the effect on personal incomes of tax changes or public benefits (like Social Security). Again, to the degree that Republican rhetoric reflected actual results, there should be a Republican advantage in generating greater growth in market-driven incomes. Yet again the Democratic advantage is large in this category, with market-driven personal incomes rising at almost double the pace of growth compared with times when Republicans hold the presidency.

Inflation. Average rates of inflation—both overall and “core” measures that exclude volatile food and energy prices—are lower during Democratic administrations. The gap is very small, but it tilts toward Democratic administrations. It’s worth noting that because all of the income and activity measures above are adjusted for inflation already, it should be mostly irrelevant which party generally presides over lower inflation. But recent years have shown that even after accounting for the impact of inflation on wages and incomes, the public does seem to care about inflation in and of itself.

The federal funds rate is a measure of the interest rate set by the Federal Reserve. This rate roughly sets the level of most of the economy’s other interest rates (like mortgage rates, or rates on car loans or credit cards), so it serves as a good barometer for the pressure that interest rates generally might be putting on household budgets. The federal funds rate (only tracked since 1954) is also lower during Democratic administrations than Republican, which means that borrowing money is generally cheaper during Democratic administrations.

Table 2 shows an almost identical set of indicators as Table 1 but measured only since 1981, the first term of the Reagan administration. There are two reasons to look at this set of more recent administrations. First, if the Democratic advantage mostly stems from the performance of very long-past administrations (say that the Kennedy/Johnson administration had superior performance relative to the Eisenhower administration), perhaps many will simply find these comparisons irrelevant.

The second reason has a bit more of a quantitative basis: Some of the variables examined above have possible time trends (both real GDP and inflation, for example), with clear differences between the period before the Reagan administration and the period after the Reagan administration.4 Before 1980, Democratic and Republican control of the White House was split exactly equally, with both parties having 16 years in power between 1948 and 1980. But because Republicans held the White House in 60% of the years after this, perhaps Republican performance is penalized simply by having held power disproportionately in later decades when variables like GDP growth were trending downward, driven by structural forces that neither party had any control over. “Leveling the playing field” by focusing solely on the later period addresses this issue. If slow growth rates after 1980 were driven by structural forces that neither party could affect, then only focusing on this period would penalize both parties equally.

Table 2

Even looking only at the post-1980 period, there is a pronounced (and mostly similar in size) Democratic advantage across all variables, including inflation. Focusing on this more recent period also lets us add a measure—the unemployment rate for Black jobseekers (which the Bureau of Labor Statistics only began collecting in 1972).

Distributional results

Tables 3 and 4 show economic performance by income class under Democratic and Republican administrations. In earlier work looking at trends through the early 2000s, Bartels (2016) documented that household income growth was faster on average and far more equal during Democratic administrations than during Republican ones. Tables 3 and 4, updated with data through 2022, show that this pattern continues.

These two tables show changes in pre-tax money income by income fifth (and the top 5%) using data from the Census Bureau, and the tables also show changes in post-tax, post-transfer income (including in-kind transfers like food stamps or Medicaid) for the bottom half of the income distribution, the 50th through the 90th percentile, the 90th–99th percentile, and the top 1%, using data from the World Inequality Database (WID), which is in turn based on estimations from Piketty, Saez, and Zucman (2018).

Table 3

In Table 3, showing income growth over the full post–1948 period, the Census Bureau data show a Democratic advantage in income growth for every percentile measured, and the advantage uniformly becomes larger the lower one goes down the income distribution.

For example, income growth for families at the 95th percentile of the income distribution (those making more income than 95% of other families) is about 10% faster during Democratic administrations (1.95% average annual growth compared with 1.74% growth in Republican administrations). But families in the middle fifth of the income distribution see growth that is 48% faster during Democratic administrations (1.9% average annual growth compared with 1.3% growth during Republican administrations). And for families in the bottom fifth of the income distribution, income growth is 188% faster during Democratic administrations (2.1% average annual growth compared with 0.7% growth during Republican administrations).

In data from the World Inequality Database, the Democratic advantage also holds for every income grouping, and it is largest for the 50th to 90th percentile and smallest for the top 1%.

Table 4

In Table 4, showing income growth only since 1980, there is again a Democratic“ advantage in pre-tax money income in every income group, and it is by far the largest for the lowest income fifth. In the WID data, there is a Democratic advantage in every income group, and it is by far the smallest for the top 1%.

In short, the clear sweep of income trends is that during Democratic control of the White House, overall income rises faster, income for every measured income class rises faster, and income growth is far more equalizing.

Conclusion

The matching of economic policy decisions and real-time economic performance is far from perfect. Some presidential administrations enact smart policies and run into bad luck, and others enact short-sighted policies and are blessed with good luck. Some might even get the results their policy decisions deserve. One would expect that the large role of chance would (almost by definition) cut uniformly across the partisan composition of presidential administrations. And yet the Democratic advantage in economic performance by partisan control of the presidency is striking.

As Blinder and Watson (2016) note in their abstract, the performance gap between Democratic and Republican administrations is “so large, in fact, that it strains credulity, given how little influence over the economy most economists (or the Constitution, for that matter) assign to the president of the United States.”

All of this seems worth knowing as people make their decisions about which candidates are likely to be better economic managers.

Appendix: Methods and sources

For the measures of aggregate economic performance in Appendix Tables 1 and 2, all data are collected at a quarterly frequency. We date the start of a presidential administration as the third quarter of the year following their election. So, for example, the Biden administration is dated as starting in June 2021. We think this very roughly allows some scope of administration decisions to affect variables, and it lines up relatively closely with the annual data series we use to undertake the analysis of distributional outcomes. Importantly, none of the results or partisan rankings is sensitive at all to reasonable changes in the “window” over which administrations are defined.

For growth measures (including inflation), we average the growth rates measured each quarter relative to the same quarter a year ago. For level variables (like the unemployment and federal funds rate), we use quarterly averages of the unemployment rate. For our comparisons by partisan control of the White House, we simply collapse the averages of all our variables by either Democratic or Republican control of the White House.

For the distributional variables, we measure the start of a presidential administration as the year that they are inaugurated—for example, the Biden administration starts in 2021. So, the first growth rate measured under the Biden administration is average income in 2021 relative to average income in 2020. For making partisan comparisons, we again simply collapse the average growth rate of all variables by either Democratic or Republican control of the White House.

Our variables have the following sources:

Real gross domestic product: The National Income and Products Account (NIPA) Table 1.1.6 compiled by the Bureau of Economic Analysis (BEA).

Net domestic product per capita: NIPA Tables 1.7.6 (net domestic product) and 2.1 (population) compiled by the BEA.

Total job growth: The Current Employment Statistics (CES) online database from the Bureau of Labor Statistics.  

Private job growth: The Current Employment Statistics (CES) online database from the Bureau of Labor Statistics.  

Unemployment rate (including for Black jobseekers): The Current Population Survey (CPS) online database from the Bureau of Labor Statistics (BLS).

Real wages of production and nonsupervisory workers: This is an EPI-derived series using data from the BLS Current Employment Statistics (CES) online database. For 1964 on, we use the overall series for wages for production and nonsupervisory workers for the entire private sector. This series was not available before 1964, so we use the broadest economic sector where wage data is available—goods-producing industries. We use the growth rate for the goods-producing sector and apply it to the overall private-sector levels from 1964 to backcast a consistent series.

Real business investment: NIPA Tables 1.1.6, (Nonresidential fixed investment) from the Bureau of Economic Analysis.

Inflation (including “core” inflation removing food and energy prices): The price deflator for personal consumption expenditures, from BEA NIPA Table 2.3.4. Importantly, the Democratic advantage in inflation performance holds for alternative inflation series as well, like the CPI-U (overall and core) or the CPI-U-RS (overall and core).

Federal funds rate: These data are obtained from the Federal Reserve Economic Database (FRED) of the Federal Reserve Bank of St. Louis.

Money income by income fifth and top 5%: These data are obtained from the Historical Income Statistics: Family Program of the Census Bureau. To measure incomes consistently over time, we have to account for two breaks in the series that represent methodological changes made by the Census Bureau in 2013 and 2017. In both cases, the Census Bureau has provided an income measure using both the old and the new method of calculating incomes. This lets us use the growth rate between the “break year” estimate using the new method and the subsequent year (which also is calculated with the new methodology). Because we are only interested in growth rates and not income levels, this lets us use a growth rate each year that is not infected by methodological changes.

Post-tax, post-transfer incomes of the bottom 50%, the 50th to 90th percentiles, the 90th to 99th percentiles, and the top 1%: These data are obtained from the World Inequality Database (WID) using methods first laid out by Piketty, Saez, and Zucman (2018). We use post-tax, post-transfer income of all adults, assuming an equal split of household income between married adults.

Notes

1. See Newall and Feldman (2023) for one example of this polling advantage. Over the longer run (since the 1950s, when Gallup first began asking a regular question) neither party has held a public opinion monopoly on being more trusted to “keep the country prosperous,” but since 2000, Republicans have been slightly more likely to have an advantage. See Saad (2023) and the linked data in that report for an examination of longer-run trends on this issue.

2. Macroeconomic stabilization is, of course, only one target of policymakers (though a very important one). There are also policies potentially meant to influence the rate of long-run growth. By definition, the effect of such policies will be far less related to contemporaneous control of the White House, so we do not look at these in the present study. It is worth noting, however, that there are few instances where policies that spur successful macroeconomic stabilization would be somehow bad for long-run growth. Further, political realism argues that presidents would generally not choose to sacrifice successful near-term macroeconomic stabilization (tolerating high unemployment or high inflation) to bequeath higher long-run growth (even if that were a viable trade-off). Given all of this, it would be hard to credit arguments that Republicans care so much about long-run growth that they are willing to sacrifice near-term performance.

3. For details on each variable, including its construction, see the Appendix.

4. See McConnell and Quiros (1997) for some of this evidence.

References

Bartels, Lawrence M. 2016. Unequal Democracy: The Political Economy of the New Gilded Age. Princeton, N.J.: Princeton Univ. Press.

Blinder, Alan S., and Mark W. Watson. 2016. “Presidents and the US Economy: An Econometric Exploration.” American Economic Review 106, no. 4: 1015–1045.

McConnell, Margaret M., and Gabriel Perez Quiros. 1997. “Out Put Fluctuations in the United States : What Has Changed Since the Early 1980s?” Federal Reserve Bank of New York Research Paper No. 9735, November 1997.

Newall, Mallory, and Sarah Feldman. 2023. “One Year from Election Day, Republicans Perceived as Better at Handling the Economy.” Ipsos website, November 5, 2023.

Piketty, Thomas, Emmanuel Saez, and Gabriel Zucman. 2018. “Distributional National Accounts: Methods and Estimates for the United States.” Quarterly Journal of Economics 133, no. 2: 553–609.

Saad, Lydia. 2023. “Neither Party Well-Liked, but GOP Holds Advantage on Issues.” Gallup. October 3, 2023.

Myth: “The economy is simply bad under the Biden administration”

In January 2020, the share of Americans saying that the U.S. economy was in “poor” shape was below 10%, but in recent months that share was above 40%. However, the unemployment rate in early 2020 and the middle of 2023 was essentially identical. The share of adults between the ages of 25 and 54 with a job was actually higher in the more recent period. Economic growth in the last quarter of 2019 was 2.6%, while it was 4.9% in the third quarter of 2023. The economy is growing (at least) as fast as it was pre-pandemic, and jobs are more plentiful.

This higher-pressure labor market has substantially eroded inequality in wages. Consider one metric of inequality—the ratio of the 90^th-percentile wage (the wage earned by the worker who has higher pay than 90% of the workforce) to the 10^th-percentile wage. Between 1980 and 2019, this ratio rose enormously by about 34%. But a full third of this 39-year increase has been erased in less than three years after 2019 because of rapid growth in pay for low-wage workers, which has not been a historical norm. If this inequality reduction sticks, it could well be the single most important development in the economy in decades.

Myth: “Inflation has crushed living standards and that was the Biden administration’s fault”

Inflation was too high for most of the past two years. But, average wages for most Americans are higher today than pre-pandemic even after accounting for inflation. So, jobs are both more plentiful and pay more than they did pre-pandemic.

As for blame, the case for the Biden administration causing inflation is extraordinarily weak. Inflation was global, with every single advanced economy in the world seeing a pronounced increase in inflation, even as these countries took widely divergent responses to the pandemic recession. As of today, the U.S. has substantially lower inflation and lower unemployment than nearly all of our advanced country peers.

Myth: “But prices won’t ever go back down”

That’s mostly right if we’re talking about an average index of all prices in the economy. But these broad indices never really do go down in absolute terms (at least not in modern times). And that’s fine—what matters is the relative growth of wages and prices, and so long as wage growth outpaces price growth, living standards rise. The economy has seen a significant reset of both wages and prices relative to pre-pandemic times. It would be nice to enjoy today’s nominal wages that are 20% higher than in December 2019 while still being able to pay December 2019 prices for everything, but it’s always true that it would be nice to have today’s wages and last year’s (or last decade’s) prices. But that’s not how the economy works.

For specific goods like energy and food, however, prices do often go down. And energy prices are way down relative to recent peaks—peaks driven by global events like the Russian invasion of Ukraine.

Further, some genuine progress has been made in ameliorating long-running cost pressures on U.S. families stemming from health care and education. The American Rescue Plan (ARP) lowered drug prices and provided more generous aid to families buying health insurance in the individual market. And the administration has tried to cancel significant amounts of student debt and expand programs that allow less burdensome repayment plans.

Myth: “The Biden administration kept gas prices high by stopping domestic oil and gas drilling”

For good or bad, this is flat untrue—the U.S. hit an all-time high in gas and oil production in 2023.

Myth: “Debt is out of control in recent years”

The federal government’s debt measured as a share of the nation’s gross domestic product (GDP) has fallen since the first quarter of 2021 (the Biden administration’s first quarter in office). While it is true that the American Rescue Plan boosted deficits substantially in the first quarter of 2021, that was by design and the ARP was the reason why unemployment recovered so quickly in the wake of the pandemic recession as compared with previous crises. But since this planned boost to the deficit jump-started recovery, we have seen some of the largest one-year reductions in federal government borrowing in history. Key Biden administration deficit-reducing actions include a tax on stock buybacks, a minimum corporate income tax, boosted Internal Revenue Service (IRS) enforcement to stop rampant tax evasion and avoidance among the rich and corporations, and reforms to stop pharmaceutical price gouging of public health insurance programs like Medicare.

“So, you’re saying everything’s great now?”

The economy still has plenty of challenges and problems. We allowed a significant and compassionate expansion of the U.S. welfare state undertaken in response to the pandemic to roll back in 2022, causing a huge one-year rise in poverty (and particularly child poverty). Despite a pronounced upsurge in workers’ interest and activism about joining unions, we have not fixed the legal and policy roadblocks to protect this vital right. The federal minimum wage remains at $7.25, and in inflation-adjusted terms has hit its lowest level since the 1950s. Our care economy institutions are in near-crisis and need public investment. Tax rates faced by the richest households and corporations are at the lowest levels in decades.

On each of these issues, however, progress would be made if even a sliver of Republicans in Congress would get on the right side of these issues.

That said, it has been a highly unusual few years and no economic analyst has called every zig and zag of the inflation debate exactly. Given that this unusual period seems to be ending, it’s a useful time for a retrospective look at predictions I made. This retrospective can be divided into four categories:

• Unambiguously wrong: I predicted a relatively short and narrow burst of inflation, with very little spillover into faster nominal wage growth. Much of this was wrong due to new shocks occurring after this initial prediction, but not all of it.
• Unambiguously right: Higher unemployment was not needed to pull down inflation or even the pace of nominal wage growth.
• Probably wrong: I thought interest rate increases as fast and high as what was done over the past year would have appreciably slowed the economy far more than they have so far.
• Probably right: The role of generic macroeconomic overheating in driving inflation has been far overemphasized. Instead, the evidence is more consistent with a story of extreme shocks causing unexpectedly large ripple effects in the wider economy.
• Totally mixed: What role, if any, did higher interest rates contribute to normalizing inflation?

Below, I’ll say a bit more about each of these.

Unambiguously wrong: My prediction of a shorter and narrower burst of inflation—with little spillover into fast wage growth—was made before the Omicron wave and the Russian invasion of Ukraine, both of which represented very large new shocks, which substantially exculpates my wrongness on this. But, this prediction was also wrong simply because U.S. workers were able to protect their inflation-adjusted wages from price shocks to a greater degree than I thought possible.

For decades, price increases had not been met by any reliable boost to wages. In essence, when price shocks occurred, workers just immediately had to absorb lower real incomes. It was not always this way in every historical period. In the period of the “Great Compression” (roughly the mid-1930s to the late 1970s) when institutions granting workers leverage and bargaining power in the labor market were strong, price shocks did spur countervailing wage growth. For a brief period in 2021 and early 2022, workers had some real leverage and were able to demand higher wages in response to price shocks. This leverage was the result of short-lived and sui generis features of the 2021–2022 economy, such as pandemic aid boosting wealth buffers for workers and the “severed monopsony power” stemming from mammoth layoffs in low-wage sectors followed quickly by extraordinarily rapid hiring spurred by fiscal relief and recovery measures. In short, atypically fast nominal wage growth was indeed mostly just ripple effects stemming from large pandemic- and war-driven price shocks—but these ripples were larger and longer-lived than I imagined they would be. Once the shocks stopped, the ripples did eventually begin dying out, and the specter of wage-price spirals needing much higher unemployment to tame never emerged.

Unambiguously right: Higher unemployment was not needed to pull down inflation or even the pace of nominal wage growth. From the start of the elevated inflation in mid-2021 through the next year, nominal wages clearly dampened—not amplified—the upward spike of prices. The source of the successive waves of inflationary pressure was huge pandemic and war shocks, not excessively tight labor markets. Nominal wage growth moved higher than its pre-COVID pace (again, a development I didn’t predict), but it always lagged other sources of price growth and hence muffled, not amplified, the inflationary shocks. As such, targeting inflation reduction through wage restraint imposed by higher rates of unemployment was always going to be a poorly calibrated response. Further, from early 2022 onwards, nominal wage growth stopped rising and began moderating even as many quantity-side measures of labor slack (like unemployment or the prime-age employment-to-population ratio) tightened.

Nothing about the 2021–2022 inflationary episode signaled extraordinary excess heat in labor markets except a spike in vacancies. There is not zero information embedded in the vacancy data, but it was the outlier indicator, and there are many questions about how well it is measured and predicts inflation—particularly early in recoveries. As such, this vacancy spike should have been taken with a large grain of salt, not elevated above other labor market indicators. The possibility that a soft landing was in reach and should be targeted was identified early in this debate and it seems to be quite likely now.

Finally, the fact that nominal wage growth has been normalizing even with very low unemployment puts to rest the often-claimed trade-off for real wage growth between tight labor markets and inflation. Inflation hawks frequently post graphs of real (inflation-adjusted) wages over recent years and invite readers to draw the conclusion that inflation was a policy mistake that led to real wages being lower than they would have been had we not pursued as-aggressive a fiscal response to the pandemic recession and early recovery. This is almost surely not true—a counterfactual macroeconomic response that was contractionary enough to noticeably reduce inflation would have pulled down nominal wage growth even faster than price inflation. The choice, in short, is not between the low unemployment and relatively slow real wage growth we have seen in recent years, and a scenario with higher unemployment but faster real wage growth. It’s between our current recovery and one where unemployment was higher but real wage growth was even lower.

Probably wrong: I thought interest rate increases as fast and large as we’ve seen over the past year would have appreciably slowed the economy and labor market by now. I was not strongly opposed to any increase, but I would have gone more slowly and stopped at a lower interest rate, which now looks like it was perhaps too risk-averse in terms of harming the labor market. This caution could still end up being vindicated—the lag between interest rate hikes and slower economic activity can be considerable and quite non-linear. For example, there is still a scenario where acute financial distress emerges in banks due to higher interest rates, particularly in those banks whose main assets are commercial real estate, which is almost surely going to see a large negative price reset in coming years. It is, of course, maddening that banks have such trouble adjusting to higher interest rates when a high level of rates is clearly good for their profitability, but the experience of the past year should tell us they do have this trouble adjusting, and further shoes could drop in the financial sector.

Finally, one key thing that may have absorbed much of the force of recent rate increases is the trio of industrial policy bills passed in 2021 and 2022—the Infrastructure Investment and Jobs Act (IIJA), the CHIPS and Science Act, and the Inflation Reduction Act (IRA). These bills boosted exactly that component of economic activity—fixed investment—that interest rate increases are generally expected to dampen. If the economy does indeed make it into 2024 without a recession even in the face of the steepest interest rate increase in decades, it will likely have these industrial policy bills to thank for it.

Probably right: The role of generic macroeconomic overheating has been far overemphasized relative to a “shocks and ripples” view of what drove recent years’ inflation. The role of relative price shocks in driving the inflationary spike of 2021–2022 was dismissed far too early in this debate. It is true that some of the discussion linking relative price changes to overall inflation was unsound reasoning in the form of “if you remove this and that component then the rest of overall inflation looks tame.” But there is a long and respectable intellectual vein in inflation analysis that highlights the importance of relative price shocks and the economy’s reactions to them as possible sources of relatively durable overall inflation.

One key piece of evidence arguing against a generalized macroeconomic overheating view of recent inflation came from factor shares. I pointed out that large increases in corporate profit margins could disproportionately account for the first year of inflation. This finding largely got slotted into a less useful discussion about the role of corporate greed or concentration in driving inflation. But what too many missed is that in every single U.S. business cycle since World War II, tight labor markets and higher levels of aggregate demand relative to supply had been associated with thinner profit margins and a lower profit share of income. The fact that inflation and factor shares in 2022 instead displayed the opposite pattern of the 11 other business cycles since World War II really should have been seen as a signal that there was something different going on this time. High profit shares being evidence against macroeconomic overheating still remains underrecognized.

What remains very unclear: How big a role have interest rate hikes played in the inflation deceleration? The most common chain of reasoning linking higher interest rates and lower inflation runs through the labor market. Higher rates are assumed to reduce consumption and business spending, and the resulting drop in demand filters through to higher unemployment and reduced upward pressure on wages. Given that many quantity-side indicators of labor market tightness have instead increased during the year of interest rate hikes, the role of these higher rates in pulling down inflation seems far from obvious. There is one oversimplified narrative being told that should be refuted—that rate hikes did indeed slow aggregate demand growth, but because the economy was on a vertical section of the aggregate supply curve (or sometimes the “non-linear” section of this curve), this did not lead to output and employment losses but instead just pulled down inflation.

The intuition is shown below in Figure A. It shows the normal downward sloping aggregate demand (AD) curve and an extremely non-linear aggregate supply curve. In this view, outward shifts in the AD curve (say from AD1 to AD2) lead to no boost in output when the economy is initially on the “elbow” of the L-shaped AS curve, but instead only translate one-for-one into inflation increases. Inward shifts in the AD curve (say when interest rate increases pull it from AD2 to AD1) just walk the economy down the vertical part of the AS curve and lead to lower prices, but with no loss at all to output.

This explanation makes logical sense, and because there are no observable economic data labeled “aggregate demand” or “aggregate supply”, it is impossible to debunk (or confirm) simply by plotting these data. However, we can make inferences about aggregate demand and supply from empirical proxies, and these proxies make clear that this story does not fit the historical data at all. This sort of extreme “plunging” behavior—with increases in aggregate demand translating near-entirely into inflation acceleration with no increase in output and vice-versa—has never happened before in post-war U.S. economic history. Instead, even when the economy is starting from an unemployment rate well below estimates of the long-run natural rate, boosts to aggregate demand have reliably increased output and quantity-side measures of employment.

Some have implicitly argued that it is self-evident that aggregate demand exceeds aggregate supply by far more today than it has at any point in recent economic history and hence this kind of plunging story makes sense. But the only evidence ever presented for this is graphs of nominal spending, which has indeed accelerated sharply in the past two years. But these graphs only prove that inflation happened. When prices rise quickly, then nominal spending also rises by definition. These graphs are not explanations for why inflation has risen, they are simply proof that it did, and that’s not in any dispute.

Further, we do have some empirical measures that can give us some sense of whether aggregate demand is indeed far in excess of aggregate supply in recent years. An oft-used “output gap” measure examines how much actual gross domestic product (GDP) exceeds potential GDP (which is an estimate of how much output the economy can produce without putting upward pressure on inflation). Crucially, potential GDP is not a measure of maximum feasible output—it is only a ceiling on how much output can be produced without upward pressure on inflation.

The gap between actual and potential GDP should be a good proxy for how much aggregate demand is exceeding aggregate supply—this gap is essentially a measure of how hard aggregate demand is pushing the economy to produce in excess of its ability to do so without any upward pressure on inflation. Currently, the economy’s real GDP and estimates of real potential GDP are essentially exactly where pre-pandemic forecasts predicted they would be this year—and those predictions did not include any upward spike in inflation. In Banerjee and Bivens (2023), we make some adjustments to estimates of potential GDP to reflect the reductions in labor force participation, capital investment, and productivity that have occurred post-COVID (and which were most likely caused by the pandemic). But, even with this “scarring” effect accounted for, we have seen significantly larger and more persistent positive output gaps (which signal aggregate demand pushing the economy to produce in excess of aggregate supply) in the past with no outbreak of inflation.

Figure B below shows the recent positive output gap (excess of actual GDP over potential GDP) and how it compares with past episodes. In short, there is nothing in U.S. economic history that suggests the aggregate supply curve becomes vertical at relatively normal levels of production—and today’s levels of production are quite normal.

Figure B

Conclusion

A soft landing has not been secured yet—inflation remains above the Fed’s 2% target. Yet a huge lesson has already been learned: sharp deterioration in the labor market was not necessary to put inflation on a downward path. The lessons of the past couple of years should continue to provoke much study and reflection. This is not even necessarily because they will hold generalizable lessons—it really has been an extraordinarily weird few years in macroeconomic terms (and along a bunch of other margins as well). But instead as a reminder that inflation is a multi-faceted phenomenon, and the existence of inflation is not by itself evidence that its cause is an imbalance of aggregate demand and supply.

• We’re very likely not in recession currently, even though we’ve had two straight quarters of negative GDP growth. The “two straight quarters” criterion for a recession is a rough rule of thumb. The more generally accepted arbiter of business cycles in the U.S. is the National Bureau of Economic Research Business Cycle Dating Committee, which weighs changes in many economic variables to determine the start and end dates of recessions. The most notable statistics arguing against the view that we’re in recession currently are unemployment and employment growth. Both remain quite strong.
  • The negative growth in the first quarter of 2022 was mostly driven by statistical quirks that hid some real strength in the economy. Specifically, exports were quite weak and imports quite strong, but both of these measures can be pretty volatile. Net exports in the second quarter, for example, were positive and added to growth. But, if I had to choose one measure of the strength of the domestic economy that stripped out volatile measures that could be introducing noise in our assessment, I’d choose domestic demand growth (known officially as final sales to domestic purchasers)—this is a measure of spending by U.S.-based households, businesses, and governments that strips out volatile changes in firms’ inventories. In the first quarter, this domestic demand growth was acceptably strong, rising at a 2.0% rate.
  • Conversely, fundamental growth in the second quarter was weak. Domestic demand growth actually shrank in the quarter. On top of that fundamental weakness, a statistical quirk—a huge decline in the contribution to GDP made by changes in firms’ inventories—also weighed unusually on growth.
  • In short, the negative growth in the first quarter of 2022 looked much worse than it was. This is far less true for the negative growth in the second quarter.

• The weakness in yesterday’s report has the fingerprints of Federal Reserve interest rate increases on it. Before this week, the Fed had sharply raised interest rates (by 0.75%—its largest single rate increase since 1994) in its last meeting. Interest rate hikes tend to weigh heavily on business investment and residential building. These were key indicators of weakness in the second quarter report. Business spending on structures and equipment contracted, and residential investment fell at its fastest rate since the pandemic recession in the second quarter of 2020. If you ignore that quarter, residential investment fell at its fastest pace since 2010—on the heels of the Great Recession.
  • On Wednesday, the Fed again pushed rates up sharply by another 0.75%, a very large rate hike that will layer on top of yesterday’s GDP weakness. The Fed may already have overshot and secured a recession in the coming months. But either way, they should slow the pace of rate increases substantially in coming months and be ready to go into neutral or even cut rates if weakness persists.
• Yesterday’s report showed a significant slowdown in the most relevant price index the Fed should be watching. Core prices (stripping out food and energy) rose by just 4.4% at an annualized rate, the slowest pace since the first quarter of 2021.

A recession in the coming months would be exceptionally troubling. It would largely result from a policy mistake of too-rapid interest rate tightening by the Fed—one that could have been avoided. If a recession hits when inflation remains high—mostly driven by global developments in energy and food markets—the Fed might feel pressure to not cut rates in order to bleed remaining inflation out of the economy. This would be extremely damaging and threaten to prolong the recession.

Finally, if the wrong narrative—that today’s inflation was driven by too-generous fiscal relief—takes hold, it could make it even harder for Congress to undertake necessary recovery measures. In short, the inflationary episode we’re in could induce political hesitancy to address a future recession, and that could end up being inflation’s greatest cost to U.S. households.

What this report finds: Rising inequality has had serious economic and fiscal effects. Key among them: It has hurt economic growth. By 2018, the rise in income inequality since 1979 was reducing growth in aggregate demand by about 1.5% of GDP. Rising inequality constrains overall economic growth by reducing economywide spending: Spending falls as inequality redistributes income from lower-income households (that need to spend more of their income to meet living expenses) to higher-income families (that have the luxury to save money). EPI’s estimate of the “all else equal” drag on household spending growth from rising inequality is based in part on a careful calculation of savings rates by income group that allows even savings decisions of very high-income households to be examined. For example, the data show that by 2018, the top 1% were securing 16.4% of income (income before taxes and benefits), up from 8.9% in 1979. And they were saving 30.6% of their income, over 60 times as much as the bottom fifth of households.

Why it matters: Our key fiscal and monetary policymaking institutions have far too often failed to stabilize demand growth and counter the effects of rising inequality. Congress has not made the tax and benefits (aka “transfers”) system more progressive—taxing more at the top and spending more at the bottom—to counteract the rise in inequality. It has also tolerated long stretches of weak aggregate demand without raising government spending enough to boost that demand. And with interest rates until just recently sitting at or near zero since the Great Recession, the Fed has had little room to boost spending with interest rate cuts. Without policy changes, inequality will likely drag on household spending, further slowing overall economic growth in the future as well.

What we can do about it: Because reduced worker bargaining power in the labor market is a key driver of the rise in income inequality before taxes and spending, policies that build worker power can help offset these trends. Policymakers can also enact tax policies that reduce the upward redistribution of income and spend more on tax credits and benefits that raise the overall share of income going to lower-income households.

Introduction and key findings

Starting in the late 1970s, inequality in the U.S.—measured both by income before taxes and government benefits and income after taxes and benefits—began rising rapidly. This growth lasted until at least the early 2000s and never significantly reversed. The rise in income inequality reflects a failure of policy on two fronts. First, policy choices were made to intentionally weaken the bargaining power of workers, and this erosion of workers’ power fueled inequality in market-based incomes. Second, the U.S. system of federal taxes and benefits (known as transfers), while income-equalizing generally, did not become more progressive in the face of rising inequality and thus largely failed to slow it. These policy failures were not just detrimental to workers—they had a negative effect on macroeconomic growth.

Our analysis of rising inequality, its main drivers, and its potential effects on U.S. economic performance and on fiscal outcomes finds that:

• Our tax and transfer system is designed to be progressive (taxing more at the top and spending more at the bottom) but it barely slowed the expanding gap between incomes of high- and low-earning families over the last four decades.
  • In technical terms, rising inequality in disposable incomes (i.e., income after taxes and benefits, or “post-fiscal income”) is more than explained by developments in pre-tax and benefits (or pre-fiscal) incomes: pre-fiscal income gaps grew significantly, and taxes and benefits only very modestly reduced the rise in pre-fiscal income inequality. For example, the top 1% of households saw their share of total income before taxes and government benefits rise by 7.5 percentage points between 1979 and 2018 (from 8.9% to 16.4%), while their share of income after taxes and benefits rose by a still sizable 6.0 percentage points (from 7.4% to 13.4%).
• Due to their expanding share of income over the last four decades, the top 10% (as of 2018) secured 34.5% of all post-tax-and-benefits income in 2018, more than the bottom 60% (31.9%).
• By redistributing income from lower-income households that spend money to higher-income households that have the luxury to save money, the rise in inequality reduces growth in aggregate demand by about 1.5% of GDP annually. This “all else equal” drag on household spending growth imposed by rising inequality in post-tax-and-benefits income was even higher in 2007, when it peaked at 2.0% of GDP.
• Policy failures in the labor market have helped fuel the rise in inequality. Pre-tax-and-benefits income inequality grew largely because typical workers lost bargaining power, thanks in part to intentional policy decisions that shifted the balance of power in labor markets away from typical workers. The reduced bargaining power of workers is evident in the split between productivity growth and pay: Between 1979 and 2019 economywide productivity rose by nearly 60%, while hourly pay for nonsupervisory workers rose less than 14%. In the first three decades following World War II, when policy was oriented much more strongly to give rank-and-file workers more power, these measures rose much more tightly together.
• The pronounced shift in labor market power had profound effects on a number of economic outcomes (at least before the COVID-19 pandemic). In addition to the rapid rise in pre-fiscal income inequality discussed, the shift in labor market power reduced the pace of wage growth at any given unemployment rate. And, by fueling the rapid rise in pre-fiscal income inequality, the shift in labor market power contributed immensely to the significant drag on growth in household consumption spending (documented above) that led to reduced growth in aggregate demand more generally.
• The policy institutions meant to stabilize demand growth have proved not up to the task in the face of this large rise in inequality. Both fiscal and monetary policy failures failed to significantly slow inequality’s expansion or stem its effects.
  • As noted, policymakers (i.e., Congress) have not made the tax and transfer system more progressive—taxing more at the top and spending more at the bottom—to counteract the rise in inequality. And they have tolerated long stretches of weak aggregate demand without raising government spending enough to boost that demand.
  • Monetary policymakers (i.e., at the Federal Reserve) for a time try to keep an increase in household savings from dragging on demand by lowering interest rates to spur investment. But when rates settled near zero around 2008 and mostly remained there, the Fed had little room left to boost sluggish spending with interest rate cuts. 
• The large rise in pre-fiscal income inequality has also had large potential effects on the nation’s fiscal balance and public debt. However, many of these effects are likely counterbalancing.
  • All else equal, the rise in pre-fiscal inequality likely reduced budget deficits as tax payments collected from higher-income households increased more than benefits and tax expenditures going to lower-income households (the first-round mechanical effect). These effects reduced the federal budget deficit by almost 3.5% of GDP in 2018. Federal income taxes collected from the top 10% of the income distribution were a significant contributor: as their share of income essentially doubled, the progressive income tax system meant that more revenue was collected even as their federal tax rates fell.
  • But at the same time there was pressure increasing budget deficits arising from another large potential effect of rising pre-fiscal income inequality, specifically its effect on overall economic growth. If overall growth was demand-constrained for a significant number of years in recent decades, and if this demand constraint was exacerbated by the rise in pre-tax fiscal income inequality, then budget deficits would be pushed up in those years by the rise in inequality. We find that the effect of demand-constraints generally on growth over the post-1979 period likely increased budget deficits enough to add roughly 30 to 70 percentage points to public debt over that period. This is about half as large in absolute terms as the mechanical effects stemming from changing income shares highlighted in the previous bullet point.

The sections that follow begin with some important data clarifications, namely that the measure of income before taxes and government benefits actually includes labor-related benefits payments like Social Security and unemployment benefits because that is what is included in the data set from the Congressional Budget Office. We then document trends in inequality, present evidence that changing relative bargaining power in labor markets is the root cause of many of these trends, and discuss the large potential macroeconomic effects of rising inequality and how policy institutions meant to stabilize macroeconomic outcomes have been overwhelmed by the rise in inequality. Finally, we examine the channels through which rising income inequality may affect budget deficits and debt.

Trends in inequality

Since 1979, U.S. incomes have become increasingly dispersed (spread over a wider range, and thus more unequal), and the share of total income held by the top 10%, 5%, and 1% has risen rapidly (Piketty, Saez, and Zucman 2018; CBO 2021). We assess trends in income growth using data from the latest report in the Congressional Budget Office’s Distribution of Household Income series, which includes data through 2018 (CBO 2021). The CBO data set compiles comprehensive income information for all five income quintiles or “fifths,” as well as the top 10%, top 5%, and top 1% of the income distribution.

Definitions of household income

CBO’s income data set includes many different definitions of income. Market income consists solely of income derived from wages and other forms of labor income (including cash wages, employer contributions to health insurance premiums, payroll taxes, business income), and capital income (such as capital gains, dividends, rent, interest payments, and business income). Market income does not include any effects from taxation by the government, or from the payments and benefits that individuals and families with specific needs get from government-run safety net and social insurance programs. These benefits, such as food stamps and unemployment benefits, are known as transfers because they transfer resources from the government’s tax coffers to individuals and families in need.

Income before taxes and transfers per the CBO is a little bit of a misnomer. It actually consists of market income plus some of what is generally considered transfers income, specifically social insurance benefits, including Social Security, Medicare, unemployment insurance, and workers’ compensation. The reasoning for including this income is that receiving income from these programs is conditional upon labor earnings earlier in one’s career. In our summary and figures, this is the income we are discussing when we refer to income before government taxes and benefits. In our detailed analyses, we use the shorthand term pre-fiscal income, as it represents income largely before the effects of fiscal policy (government taxing and spending) kick in.

Income after taxes and transfers as calculated by CBO includes market income and social insurance benefits and adds means-tested transfers and tax credits then subtracts federal tax payments. Means-tested transfers are cash payments and in-kind benefits from safety net and anti-poverty programs operated by local, state, and federal governments, programs such as Supplemental Nutrition Assistance Program (SNAP, commonly known as food stamps), Medicaid, and Temporary Assistance to Needy Families (TANF). In our summary and figures, this is the income we are discussing when we refer to income after government taxes and benefits (it is also commonly referred to as disposable income). In our detailed analyses, we use the shorthand term post-fiscal income, as it represents income largely after the effects of fiscal policy (government taxing and spending) kick in.

The inclusion of social insurance payments in pre-fiscal income is slightly odd. Social Security, for example, is clearly a government transfer, and it is far from obvious why a measure of households’ resources before fiscal policy (government taxing and spending) is accounted for should include it. Often for ease of comparability and exposition we stick with the CBO definitions and our shorthand terms for the CBO categories. But occasionally we include social insurance incomes in a measure of transfer payments, and when we do, we note that explicitly in the text.

Obtaining a measure of income inequality requires ranking households by the level of income. Because there are a number of definitions of income, there are also a number of options for how households are ranked to define inequality. For this paper, we rank households by post-fiscal, or disposable, income. This essentially ranks households on the basis of the resources available to them in the real world (i.e., after the influence of the tax and transfer system is exerted).

Large income share changes between 1979 and 2018

By either measure of income, both pre-fiscal and post-fiscal, inequality has risen sharply since 1979. This trend has led to a sharp concentration of income in the top of the distribution. Figure A shows the percentage-point change in the share of total income held by each income group between 1979 and 2018, for both types of income. Both sets of income follow the same general trend: the top 1% increased its share of income the most, followed by the 96th-99th percentile, while households in each of the fifths below the top fifth saw steep declines in their income shares. Figure A also documents that the income share increases for households in the higher-income groups and corresponding income share decreases for households in the lower-income groups are consistently larger for pre-fiscal income than for post-fiscal income.

Figure A

Figure B shows what these trends mean in terms of actual income shares held by each group. As the figure shows, the bottom fifth held just 4.9% of all pre-fiscal and 7.0% of post-fiscal income in 2018, down from 5.6% and 7.5% respectively in 1979. The middle fifth saw its share of income shrink from 16.1% to 12.8% (pre-fiscal) and from 16.6% to 14.4% (post-fiscal) from 1979 to 2018. Meanwhile, over this same period, the share of income held by the top 10% increased from 29.9% to 39.8% (pre-fiscal) and from 26.9% to 34.5% (post-fiscal).

Figure B

The steeper gains and losses in income shares before taxes and government benefits suggest that it is trends and developments in pre-fiscal income that have been driving inequality since 1979. Perhaps most strikingly, the loss of pre-fiscal income shares for the bottom fifth from 1979 to 2018 barely changes when the effect of government taxes and benefits factor in (a 0.7 percentage-point decline becomes a 0.5 percentage-point decline). In short, the tax and transfer system seems to be doing quite little to shield the poorest fifth of households from the effects of rising inequality.

Labor market power as the root cause of rising inequality 

As the previous section suggests, it is trends in pre-fiscal income that have driven the post-1979 rise in inequality. Bivens and Mishel (2021) document more specifically that is trends in market income—and particularly in the U.S. labor market—that have been the root cause of this rise in inequality. Figure C highlights this labor market weakness, tracking growth in economywide productivity and real (inflation-adjusted) hourly pay for typical U.S. workers over the long run. Productivity is a measure of the national income (or output) generated in the average hour of work in the economy. It includes not just wages and benefits paid to workers, but corporate profits, business income, proprietor’s income, property rent, and all other income flows. Because productivity growth means more income is being generated per each hour of work, it represents the ceiling on how much living standards can grow on average in the economy.

Figure C

From 1948 to the mid-1970s, the typical workers’ hourly pay grew in lockstep with productivity growth. During this period, the United States was in the second half of a period of declining inequality known as the “Great Compression” of incomes that began following the Great Depression and the run-up to World War II. Since the business cycle peak of 1979 these measures have diverged sharply, with hourly pay of production/nonsupervisory workers in the private sector lagging further and further behind productivity. This growing wedge between pay and productivity meant that large amounts of income were being generated in the U.S. economy but were not ending up in typical workers’ paychecks. Instead, this income ended up, mostly, in large increases in pay for corporate managers and executives and, to a lesser degree, in corporate profits and other measures of business income. This transfer of income that once went to typical workers’ pay going toward the salaries of managers and executive and toward profits led to large increases in overall inequality. By 2019 (before the COVID-19 pandemic and its unusual impact on the economy), productivity was up nearly 60% since 1979, while worker pay was up just under 14%.

The labor market changes that led to this wedge between pay and productivity have been intensely debated for decades. Through the 1980s and 1990s most explanations were generally centered in competitive models of the labor market where workers’ wages are assumed to track their marginal productivity. This competitive models–based research focused on influences—like “skill-biased technological change” or the effect of international trade on the structure of U.S. production—that could shift the relative demand and supply of workers in competitive markets. But, as much research has documented, these explanations fail to account for a number of salient facts about the U.S. labor market (Card and DiNardo 2002; Schmitt, Shierholz, and Mishel 2013; Bivens and Mishel 2021). For example, workers with highly similar observable characteristics (age and years of education, say) often earn very different hourly wages and these differences are correlated tightly with race and gender. In competitive labor markets, one would expect similar workers to earn similar pay. As another example, in the late 1990s, we stopped seeing a correlation between increasing wage inequality and a rising return for having a four-year college degree. In competitive labor markets, one would expect that workers who obtain college degrees would be able to leverage the demand for their education and skills to secure wage increases. But since 2000, wages for college graduates as a whole have grown anemically, while wages for the overall top 5%—and especially the top 1% of workers—have grown at an ever-accelerating rate.

The failure of competitive labor market models to explain key wage and employment trends has led to increasing calls to adopt models of the labor market in which some sort of market power is present. In fact, the 2021 Nobel Memorial Prize in Economics winner David Card focused on this in his speech for the 2022 Annual Meeting of the American Economic Association (Card 2022), noting that “the time has come to recognize that many—or even most—firms have some wage-setting power. Such a shift was made with respect to firm’s price-setting power many decades ago.” Stansbury and Summers (2020) note the strong evidence that declining worker power is the key factor in driving inequality in recent decades. Bivens and Mishel (2021) analyze a range of discrete policy changes that shifted bargaining power in labor markets away from typical workers, and survey the research literature to assess how much each change explains the wedge between productivity and pay that has emerged since the late 1970s.

They find that discrete policy changes likely explain three-quarters of the entire wedge between productivity and pay by 2019.1 And of these policy changes, there are three that account for half of the gap between productivity and pay:

• The turn to more-austere macroeconomic policy driven largely by a desire to keep inflation (rather than unemployment) very low at all times, but sometimes simply by partisan politics.2
• The decline in unionization driven by the failure of labor law and its enforcement to protect workers seeking to form unions from employer tactics that thwart collective bargaining rights.
• The integration of the U.S. economy and the much-poorer global economy on terms deeply disadvantageous to U.S. workers.

For those concerned that the growth in inequality has been a bad thing for American society, the strong link between inequality and policy changes is in some ways promising news. If policy efforts that changed the rules of the labor market so effectively redistributed income upward in the past, rewriting these rules to orient them toward boosting wage growth for typical workers could progressively redistribute income toward working families. Too often, federal policy debates assume that the only way we can reliably reduce inequality is to use taxes and transfers to claw back some income for the bottom parts of the income distribution. In other words, they assume that income inequality before taxes and government benefits is a given, and that all we can do is try to use taxes and benefits to shrink inequality. This clearly isn’t true—there are a range of other policies with the power to deliver a more-equitable distribution of income, if that’s what policymakers choose.

How inequality affects economic growth

As the previous two sections have shown, pre-fiscal incomes have driven overall inequality since the late 1970s, and the dramatic decrease in workers’ bargaining power in the labor market is the largest contributor to the rise in pre-fiscal income inequality over the same period. This section documents that it is not just low- and middle-income households who suffer, but the economy as a whole. In addition to shunting more and more income growth away from low- and middle-income households, rising inequality also hurts the macroeconomy. Most obviously, the rise in inequality slows aggregate household spending by redistributing income from households with higher propensities to spend their current income (i.e., lower-income households) and toward households with higher propensities to save (i.e., higher-income households). If this drag on household spending growth is not somehow counterbalanced by increased spending by businesses and governments, then it will pull down aggregate demand and potentially constitute a large drag on economic growth.

Inequality’s drag on demand was documented in Bivens 2017, which found that relative to a 1979 baseline, by 2007, rising inequality lowered aggregate demand growth by over 4 percentage points of GDP annually. Decreased aggregate demand growth is not the only way in which inequality can affect growth. Cingano (2014), for example, has found that among nations of the Organisation for Economic Co-operation and Development (OECD), a period of an expanding gap between low-income households and the rest of the population has a negative impact on subsequent growth, specifically through the channel of human capital. Specifically, Cingano (2014) finds that the gap between low-income households and the rest of the population depresses skill developments for those with lower parental education background. Cingano (2014) concludes that redistribution policies in the tax and transfer system are critical to sustaining growth by making sure the benefits of growth are fairly distributed.

Higher-income households have much higher savings rates

The way that income inequality drags on aggregate demand is relatively straightforward: It redistributes income away from low- and middle-income households (which save a lower share of their income, because basic living expenses consume so much of their income) toward higher-income households (which save a higher share of income, because they have the luxury to do so). Thus, rising inequality means that each dollar of income in the economy now supports less household spending, and more savings. The resulting lower household spending due to income redistribution then, all else equal, weakens aggregate demand. In theory, there are countervailing economic forces that can keep an increase in household savings from dragging on aggregate demand. For example, if interest rates fall then businesses might desire to invest more in new plant, equipment and processes, and hence the increase in savings could be seamlessly channeled into new spending, keeping aggregate demand stable. In practice, most of these countervailing forces depend on active policy decisions, and they have largely not been able to keep aggregate demand stable in the face of rising inequality.

While intuitively it makes sense that higher-income households save a higher share of their income, efforts to quantify savings rates by income group—especially small groups at the top of the income distribution—are quite difficult. For example, the Consumer Expenditure Survey conducted by the U.S. Census Bureau on behalf of the Bureau of Labor Statistics is widely thought to miss lots of consumption spending by rich households (see Aguiar and Bils 2015), and its income measures are “top-coded” so that the true incomes at the top of the income distribution cannot be calculated (Yang and Toth 2014). See Gould 2019 for an explanation of top-coding, which essentially involves protecting the confidentiality of top wage earners by recording wages only up to a certain threshold, which hasn’t increased in decades.

Our methodology for constructing savings rates for even small income groups at the top of the distribution builds from Maki and Palumbo 2001, Cynamon and Fazzari 2015, and Bivens 2017, and involves tracking net new assets acquired by households (which is essentially the definition of savings).

We build on Bivens 2017 in one key way. Bivens 2017 used data from the Survey of Consumer Finances (SCF) from the Federal Reserve to obtain the share of various assets held by income groups.3 This distributional data was then combined with aggregate macroeconomic data from the Financial Accounts of the United States (FAUS) showing the net acquisition of each asset in a given year.

This 2022 analysis, like Bivens 2017, uses the macroeconomic data from the FAUS on net acquisition of various assets. But it exploits a new distributional data set that does not require using the microdata from the SCF: the Distributional Financial Accounts of the United States (DFA), also compiled by the Federal Reserve. The DFA provides data on the share of fairly detailed assets and liabilities held by each income grouping. We can then map this onto the macroeconomic data showing the aggregate net acquisition of these assets and liabilities in a given year. Appendix Table 1 provides the precise mapping between the DFA and the FAUS.

For each income group, this allows us to construct a measure of the value of total assets (net of liabilities) newly acquired each year. As noted, this acquisition of net new assets is essentially the definition of savings. For each income group we then obtain an estimate of aggregate income by multiplying the number of households in each group by the average household income—both of which are provided in the CBO data on household income distribution. Finally, by dividing the net acquisition of financial assets by total income for each group, we derive a group-specific (average) savings rate.

Most macroeconomic measures of personal savings do not include realized capital gains in their income measure. To make our measure more comparable with standard measures, we pull out realized capital gains from the measure of post-fiscal income that we use in the denominator of our savings rate. Figure D clearly shows a staggering difference in saving rates for each income group. The top 1% of the income distribution saves 30.6%, compared with 0.5% for the bottom 20%, a 61-fold difference.

Figure D

The redistribution of income toward higher-saving households translates into slowed spending economywide

With these estimates of savings rates by income group, we can quantify more specifically the impact of inequality’s redistribution of income on economic growth and aggregate demand. We know that redistribution of income from the bottom and middle to the top of the income distribution should be expected to curtail household consumption spending, all else equal. In Figure E, we calculate this spending drag as a percentage of GDP, using the savings rates from Figure D. To construct this, we multiply the change in income share for each income group by one minus their savings rate. This provides a rough estimate for how much consumption spending changed for each income grouping. Then, we sum across income groups to obtain a measure of how much aggregate consumption spending changed due to the inequality-induced changes in income shares. This implied fall in aggregate household spending we initially calculate is implicitly expressed as a share of total household income (because that is what the CBO data is measuring). Because many macroeconomic variables traditionally are scaled to overall gross domestic product (GDP), we convert this into a fall in household spending as a share of overall GDP by multiplying it by the ratio of aggregate personal income to GDP.

Figure E

By doing this, we provide a measure of the “all else equal” effect of how much the rise in inequality dragged on household spending relative to the baseline of no increase in inequality. By 2007 this drag reached 2% of overall GDP. The temporary decline in inequality that occurred when financial markets and stock prices collapsed during the Great Recession of 2008–2009 reduced this drag, but by 2018 rising inequality was still curbing spending by almost 1.5% of GDP.

To put a raw dollar figure on this drag, imagine policymakers wanted to undo this drag through a fiscal stimulus package (presumably because the Fed has little room to boost sluggish spending with interest rate cuts). Imagine further that the stimulus package was reasonably well-structured, with a “fiscal multiplier” of 1.25—meaning that every dollar spent generated $1.25 in additional economic activity. This package would have to be nearly $300 billion, and it would have to recur each year.

Failure of countervailing levers lets inequality slow growth

Slowed household spending doesn’t always reduce aggregate demand or impinge on economic growth. If other countervailing forces in the economy allow the expanding pool of household savings to be channeled into more investing by businesses or other activities that boost aggregate demand, or if governments spend more (via expansionary fiscal policy), then aggregate demand can be held constant in the face of the drag on household spending. But in recent decades, the policy institutions meant to stabilize the macroeconomy have not been able to either curb rising inequality or lessen the resulting drag on aggregate demand.

Monetary policy as deployed by the Federal Reserve has been more consistently applied to spur growth in recent decades than in the more distant past, but it has proved too weak as an expansionary policy tool. Most notably, the Fed has regularly ratcheted down the federal funds rate (the interest rate that banks pay on overnight loans) in the hope of prompting declines in credit card, mortgage, and other rates thereby stimulating investment and consumption. However, this policy lever has all but been maximized since the early 2000s, as shown in Figure F. It shows the federal funds rate since 1960 along with decade averages (the horizontal bars). As the decade averages show, the Fed’s policy interest rate has continued to decline, resting at essentially 0 since after the 2007–2009 Great Recession.

Figure F

The failure of low interest rates to reliably spur growth has sometimes been analogized to “pushing on a string.” Despite low and falling interest rates, the economic recoveries from each of the three most recent recessions before 2020 were agonizingly slow. Essentially, interest rates have been effectively pushed to near zero for decades, making it impossible to further cut rates in an attempt to neutralize economic phenomena—like rising inequality—that may be dragging on aggregate demand. As a result, the slowdowns in aggregate demand have become the binding constraint on overall growth.

Fiscal policy has been even less effective than monetary policy in reining in inequality and alleviating demand constraints. The federal tax and transfer system can impact inequality by spending more on safety net and social insurance programs (transfer programs) that benefit households at the bottom, and taxing more at the top. One way we can estimate the efficacy of the tax and transfer system is by calculating the transfer rate, which is tax credits and deductions and transfers a household receives as a share of its pre-tax-and-transfer income. The transfer rate differs among households at different points in the income distribution just as tax rates do. Figure G shows the net transfer rate (the transfer rate minus the federal tax rate), to depict how much pre-fiscal incomes for each income group have been buoyed or reduced by the effect of taxes and transfers since 1979. In this period, the net effect of taxes and transfers has always boosted incomes for the bottom 40%, and since the early 1990s has even boosted incomes for the middle fifth of the income distribution. The fact that the transfer rate for the bottom fifth of households exceeds 40% confirms that our progressive tax system is working at least somewhat as designed: the lowest-income households don’t have to pay large amount of taxes and they receive government benefits to help them meet their needs (though the U.S. social safety net is weaker than in other countries).4

Figure G

Conversely, the net effect of taxes and transfers reduces incomes for all groups with incomes at or above the 60th percentile, and, by growing amounts (an outcome of the progressive federal tax system). But, over the entire 1979 to 2018 period, these net transfer rates have not changed nearly enough to neutralize most of the rise in pre-fiscal inequality. Even when the net transfer rate has increased dramatically, it often turns out to have been driven as much by the denominator growing much slower (due to weak pre-fiscal income growth) as by increased absolute generosity of the tax and transfer system (the numerator). For example, in 1979 the net tax and transfer rate for the bottom fifth was 34.7%. It remained beneath this level until 2011. Between 2011 and 2018, it rose from 34.7% to 42.7%. But over that same period, the bottom fifth’s market income grew at well under 1% per year (only about a third as fast as overall growth in market incomes).

The effect of all these intragroup changes in net transfer rates on the trajectory of income inequality can, of course, simply be seen in the change in post-fiscal income shares presented in figures A and B. These changes are a bit smaller than the pre-fiscal income share changes so fiscal policy has blunted some of the rise in inequality, but the changes in post-fiscal income shares are still quite dramatic.

Fiscal impact of the rise in inequality

Given its large potential macroeconomic effects, it follows that the rise in inequality also likely had large potential fiscal effects. There are two particularly important channels through which rising inequality can affect the federal fiscal balance: by changes in income growth mechanically raising or lowering tax collections and transfers for a given income group, and by the reduced tax collections and larger transfers that accompany slowing economic growth widening budget deficits.

Differential tax and transfer rates by income group

In Figure H, we estimate the implications for the federal budget balance if the net tax and transfer rate for each income group evolved as it did between 1979 and 2019, but each group’s pre-fiscal income share remained at its 1979 level in subsequent years. The difference between the actual and the counterfactual can be thought of as the “all else equal” effect of changing just income shares (i.e., rising inequality) on the federal budget deficit. Of course, this entire paper is about how inequality and growth and other macroeconomic variables are all interrelated, so this “all else equal” assumption is important—but it does help sharpen some intuition about the channels through which inequality can affect the federal budget deficit. The figure breaks out the effect of taxes and transfers separately.

Figure H

Before reporting the results as depicted in the figure, it is instructive to take an example to understand how the various changes of the components impact the aggregate changes. For example, though not shown in the figure, the effective tax rate for the top 1% fell from 35.1% % in 1979 to 30.2% in 2018. (The effective tax rate is how much of their income these households paid in income, payroll, excise, and other taxes, not the tax rate for their tax bracket.) With just that information, one might think that there is no way that taxes collected overall from the top 1% as a share of the economy could rise. But over this same period, total pre-fiscal income claimed by the top 1% rose by 7.5 percentage points (from Figure A). This means that in 2018, taxes (net of transfers) collected from the top 1% actually rose by 2.3% of total household income relative to a scenario where income shares had remained constant at their 1979 level. The amount of this increase is simply the 7.5 percentage increase in the top 1%’s income share multiplied by the 30.2% tax rate in 2018. We repeat this exercise for transfers, and for all other income groupings in our data.5

Overall, these effects indicate that the federal budget deficit in 2018 was lower by roughly 3.4% of GDP due to the effect of changing income shares. This is a substantial amount. The overall effect is split through lower government outlays in the form of transfer payments and higher government revenues in the form of taxes paid that resulted from changing income shares. Higher tax collections from the top 1% account for roughly 60% of the overall effect (again, even as tax rates for the top 1% fell). But lower transfer payments flowing to the bottom 60% of households can account for nearly half of the overall effect as well. (Two different groups can contribute more than 100% to the total net effect if the influence of other groups is negative.) Households in the bottom 60% were net recipients of taxes and transfers in 2018 overall, but their lower income shares due to rising inequality reduced the effect of these transfers on the budget deficit.6

One way to understand this last point is to look at net tax and transfers rates, and then compare them to the aggregate amount of net taxes and transfers for the bottom fifth of households measured as a share of aggregate income. Between 1979 and 2015, the net tax and transfer rate for the bottom fifth rose from 34.5% to just under 39%. But, as a share of total household income economywide, net taxes and transfers going to the bottom fifth actually fell slightly, from 1.93% to 1.91%. This is because the higher tax and transfer rate was being multiplied in 2015 by an income share that significantly fell. In a sense, the federal budget moved closer to balance because falling income shares for the bottom 60% led, all else equal, to less money flowing to these households, as we devoted fewer of the economy’s overall resources to pushing up incomes at the bottom.

Chronic weak demand contributes to budget deficits

As noted above, the “all else equal” scenario that led our analysis to find a significant estimated reduction in the deficit from rising inequality is unlikely the case in the real world. When the economy operates below its potential for a sustained period, tax collections fall and more households rely on federal transfer payments for a higher share of income. In previous sections, we highlighted the potentially large drag on aggregate demand—and hence economic growth—exerted by rising inequality. If demand were weakened by rising inequality and this slowed overall growth, the slowdown would have large implications for the federal budget deficit. In this section, we provide a rough estimate of how much a slack in demand may have pushed up deficits during the period of rising inequality. We then examine whether this upward pressure on the deficit from slack demand cancels out part of the downward pressure on the deficit from the tax and transfer system.

For this estimate, we draw on two data resources provided by the Congressional Budget Office. The first, an estimate of the “unemployment gap,.” draws on the most-used measure of the economy’s long-run potential level of resource utilization: the “natural” rate of unemployment (sometimes called the “non-accelerating inflation rate of unemployment” or NAIRU). This is the unemployment rate below which it is assumed that inflation will begin to accelerate and hence constitutes the lowest unemployment rate that is sustainable over more than a short time period. The unemployment gap is simply the difference between the estimated natural rate of unemployment and the actual unemployment rate. The gap is negative when the economy is operating beneath potential.

The second CBO estimate we use is the “cyclically adjusted” budget deficit. This is an estimate of what the budget deficit would have been in a given year if the economy had experienced an actual unemployment rate that matched the estimate of the natural rate. The difference between the actual budget deficit and the cyclically adjusted deficit hence provides an estimate of how much slack resource utilization (which is in turn caused by slack aggregate demand) increased budget deficits.

It could be argued that the CBO estimates of the natural rate of unemployment are too high, and that lower unemployment was possible over sustained periods (see Bivens 2021). But, even using the CBO estimates, large (negative) unemployment gaps put substantial upward pressure on budget deficits. Figure I shows the effect of tight or slack resource utilization (a positive or negative unemployment gap) on taxes and transfers for two long stretches of time: 1965–1979 and 1980–2020. We choose the first period because 1965 is the first year for which CBO calculates the cyclically adjusted budget deficit.

Figure I

Between 1965 and 1979, the actual unemployment rate was 0.7 percentage points lower on average than estimates of the natural rate, providing a positive employment gap of 0.7%. Estimates of the relationship between the unemployment gap and the cyclical effect of budget deficits indicate that this positive unemployment gap reduced budget deficits by 0.3% of GDP on average in those years.

In comparison, between 1980 and 2020, the unemployment rate was higher on average than estimates of the natural rate and the gap averaged -0.9 percentage points. This negative employment gap led to budget deficits that were roughly 0.45% of GDP higher on average.

The bottom set of bars in the figure shows the effect of the negative unemployment gap on the budget deficit under the likely scenario that CBO’s estimates of the natural rate of unemployment are too high (see Tasci 2019 for evidence and a discussion of the difficulty in estimating the natural rate of unemployment in real time). If the economy could actually hum along quite well (at optimum resource utilization but low inflation) at a lower rate of unemployment, the actual effect of slack demand on budget deficits might be substantially higher. If, for example, the real natural rate of unemployment is 1 percentage point lower than what CBO estimates, then the negative unemployment gap would be even larger, reflecting an even greater degree of economic underperformance. Under this scenario, too-slack demand arising from increasing inequality after 1980 actually boosted budget deficits by roughly 1% of GDP on average in each year.

If demand slack really did increase budget deficits by 1% annually over the 1979–2020 period, and if this demand slack was largely due to rising inequality, this would imply that about half of the decline in public debt due to changing income shares (discussed in the previous section’s Figure H) was offset by higher budget deficits resulting from the drag on demand growth imposed by inequality.7 Further, if slack demand raised budget deficits by between 0.45 and 1.0% of GDP on average each year since 1980 (as in Figure I), over the entire 39-year period, this translates into an overall public debt to GDP ratio that was 30 to 70 percentage points higher by 2018.

More fundamentally, if the rise in inequality really did put the economy in a position where economic growth was being regularly held back by lower than necessary economic demand in recent decades, this implies that there is essentially no beneficial economic effect of the lower budget deficits caused by inequality-induced changes in income shares. That is because deficits, and the resulting additions to debt from them, affect the economy very differently depending on whether growth is constrained by demand (workers and other resources are idling because firms don’t expect enough paying customers) or by supply (all resources are fully utilized thus any increase in aggregate demand growth spills over into inflationary pressures rather than output growth). As noted in Bivens 2020, deficits only harm economic growth when economy growth is supply-constrained, not when it is demand-constrained. And as we have shown, rising inequality has produced demand constraints.

Conclusion

Since the late 1970s, income inequality has risen sharply enough and been sustained long enough to have significant macroeconomic and fiscal effects. This inequality has led to chronic shortfalls of demand stemming from weakened household spending. These chronic demand shortfalls have constrained economic growth—by as much as 3.4% of GDP per year—and contributed strongly to the very slow recoveries following the most recent three recessions predating the coronavirus recession. The early 1990s recovery was the first one dubbed “jobless,” but employment recovered even more slowly in the early 2000s recovery and the recovery from the Great Recession of 2007–2009 (Bivens 2016).

Even as rising inequality dragged on demand growth and harmed recovery from these three recessions, policy levers meant to help the economy bounce back faster were either becoming less effective (interest rates were near or at zero and couldn’t be lowered further) or were left unused (Congress failed to provide sufficient fiscal stimulus by boosting spending). So far, the recovery from the recession caused by the COVID-19 shock has been happily much more rapid, almost entirely due to the much greater fiscal effort—spending increases—put into recovery. But the fiscal push that aided recovery so far is gone, while almost certainly little progress has been made in lessening inequality. As time marches on, the demand-depressing effect of this higher inequality could start to reassert itself.

In fiscal terms, the key effect of rising inequality has been to redistribute income from the low- and moderate-income households that tend to be net recipients of disposable income from the tax and transfer system toward the higher-income households that tend to be net payers to this system. As income gets transferred from low-savings to high-savings households, where is the increased savings going? A good chunk of it goes to reducing measured budget deficits. As we note in the text box explaining why some measures of aggregate personal savings haven’t risen over recent decades, the reduced budget deficits that accompanied the rise in inequality is in some sense “where” the extra savings one would expect from a rise in inequality have shown up.

There are economic circumstances in which moving closer to federal budget balance might aid economic growth. But the U.S. economy has not enjoyed those circumstances for much of the last four decades. Economic growth has been constrained by weakened demand for sustained periods since 1979, which means that there was no particular economic benefit from lower budget deficits. Essentially, the macroeconomic downside of higher inequality—the drag on economic growth—likely neutralized any fiscal upside.

Acknowledgments

The authors thank Katie DeCourcy for research assistance and Lora Engdahl for editing. This project was made possible by financial support from the Peter G. Peterson Foundation.  

Appendix: Constructing the savings rates in Figure D

To calculate savings rates by income group, we combined asset and liability information from two different data sets compiled by the Federal Reserve. The Distributional Financial Accounts provide data on the share of fairly detailed assets and liabilities held by each income grouping (FRB 2021a). We can then map this onto the macroeconomic data from the Financial Accounts of the United States (FRB 2021b) showing the aggregate net acquisition of these assets and liabilities in a given year. Appendix Table 1 provides the precise mapping between the two data sets.

Appendix Table 1

Endnotes

1. Bivens and Mishel use the median worker as proxy for the typical worker, rather than the average production/nonsupervisory worker. The median worker is, by definition, the one in the exact middle of the wage distribution, while production and nonsupervisory workers are the 80% of the private-sector workforce who are not managers. From our perspective, either work well as a proxy for typical workers. The nonsupervisory data series goes back further in history, so is more useful for long-run comparisons. The median wage is more precisely defined and inarguably representative of a middle-wage worker, so might be better to use when it is available. Finally, because the rise in wage inequality is overwhelmingly generated by rapid growth in the pay of workers well above the median and not included in nonsupervisory workforces, either measure works well in the sense that it tracks wages for the group left behind in recent economic growth.

2. See Bivens 2016 and Bivens 2019 for very short histories of fiscal and monetary austerity in recent economic history.

3. Relative to those estimated in Bivens 2017, savings rates for high-income households are lower in this paper. This is because we account for increased liabilities of households (i.e., increases in debt, for example) in the current paper and register them as offsets to savings. Accordingly, the savings rates in Bivens 2017 can be seen as gross savings, whereas the current savings rates are net savings.

4. EPI’s U.S. Tax & Spending Explorer compares the inequality reducing effects of the U.S. tax and transfer system with that in other countries. See EPI n.d., specifically https://www.epi.org/explorer/international.

5. Again, these estimates take the evolution of tax and transfers rates as given and only isolate the effect of changing income share (i.e., inequality). If one looks at the total taxes actually collected by the top 1% in 1979 (their 8.9% income share multiplied by a 35.1% net tax rate) and in 2018 (their 16.6% income share multiplied by their 30.2% net tax rate), then it just declines by 1.8%, not the 2.3% we highlight. But our estimates neutralized the effect of the changing tax rate—taking that as given—and only estimate the effect of rising inequality.

6. In some sense, this example of transfers going to the bottom fifth falling as a share of aggregate personal income highlights the limit of this “all else equal” approach to holding tax and transfer rates constant even as income shares change radically. Many of the transfers going to the bottom fifth of households are means-tested, with the test often involving fixed income thresholds. If slow growth in incomes of the bottom fifth led to more and more families falling beneath these fixed income thresholds, this would mechanically boost the measured tax and transfer rate. But given that tax and transfer rates in the CBO data have steadily increased for the bottom three-fifths since 1979, this just means that any effect of changing income shares in raising these rates that is missed in our analysis would strengthen the implied relationship between rising inequality and a smaller budget deficit. That is to say that if the transfer rate for the bottom fifth only rose because their pre-fiscal income share declined, this means that our estimate of the inequality effect in directing resources toward (or away) from this group over time is overstated as we should also be holding the transfer rate more-constant and not allowing it to be mechanically pulled up by income declines. More importantly, it is clear that large swings in net tax/transfer rates for any income group are driven much more by exogenous policy changes than by the mechanical operation of pre-existing means-tests.

7. While the average per year contribution to higher budget deficits stemming from income drag (1% per year in the high estimate) is less than a third of the end-year contribution of changing income shares (3.4%), the demand-drag effect happens each year, while the contribution of changing income shares starts very small in the early 1980s and then grows gradually as inequality grows. The overall effect on accumulated public debt over the entire time period stemming from rising income shares is hence only about twice as large as the effect stemming from demand-drag.

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Key takeaways:

• While the economy remains 10 million jobs below pre-pandemic levels and job growth is slowing significantly as the pandemic surges, the remaining suite of pandemic unemployment insurance (UI) programs are set to expire on December 26, even as one of the most important—the extra $600 per week—has already expired and millions of workers have already exhausted benefits or had them significantly slashed.
• The economic shock from COVID-19 has been ongoing long enough that roughly one-third of unemployed workers have been unemployed for 27 weeks or longer. Unemployment insurance benefits should not just be made much more generous, they should also have their durations extended substantially. Once again, this highlights that UI benefit generosity and duration should never be tied to arbitrary dates but should rather be dictated by economic conditions (preferably tied to employment rates).
• If these programs—including the extra $600—are reinstated and extended through 2021, and if the virus is brought under control so that economic growth for 2021 returns to being simply a function of aggregate demand growth, the economy would be boosted by 3.5% and 5.1 million more jobs would be added in 2021.

By the end of this month, several key pandemic relief programs will expire, ending the valuable lifeline provided to workers and their families experiencing job loss, facing eviction/foreclosure, or needing emergency paid sick days and family and medical leave. While spending is needed across multiple avenues to provide relief and recovery, this post examines the importance of reinstating and extending the suite of pandemic unemployment insurance (UI) programs enacted in the Coronavirus Aid, Relief, and Economic Security (CARES) Act: Pandemic Unemployment Assistance (PUA), Pandemic Emergency Unemployment Compensation (PEUC), and Pandemic Unemployment Compensation (PUC) payments.

If the effective safety net functions provided by these programs were maintained through 2021, millions of workers would be better able to avoid economic catastrophe while out of work due to the pandemic. Maintaining these programs’ effectiveness in providing relief and aid for recovery in the face of rising long-term unemployment rates over the next year requires adding additional weeks of UI eligibility duration.

All told, we find that if these programs’ effectiveness is maintained through 2021, and if the virus is brought under control so that economic growth for 2021 returns to being simply a function of aggregate demand growth, the economy would be boosted by 3.5% and 5.1 million more jobs would be added in 2021.

While this past summer’s rebound from the 22.2 million jobs lost in March and April started strong, job growth has since slowed considerably. The first dose of austerity was the expiration of the enhanced UI benefit in July—specifically, the PUC program that provided an extra $600 per week in benefits. Although the economy grew strongly in the third quarter based on momentum from businesses reopening and strong income support from the CARES programs, these PUC cuts will continue to take a serious toll on job creation going forward. As of October, the U.S. economy is still down 10 million jobs from where it was in February. If job growth continues to slow or even reverse course in the winter months as COVID-19 caseloads rise, states reshutter large swaths of businesses, and federal policymakers provide no additional aid to unemployed workers or state and local governments, it will be years before we return to anything resembling the pre-pandemic economy. It would be a tragedy to force U.S. workers to yet again wait a full decade between brief periods of tight labor markets that drive acceptable wage growth.

When the PUA program—which expanded eligibility to millions of workers usually excluded from state UI programs—and PEUC—which extended regular state programs an additional 13 weeks—expire on December 26 of this year, millions will be left out in the cold. In a Century Foundation report, Andrew Stettner and Elizabeth Pancotti found that 12 million workers will be on either PUA or PEUC when the programs expire in less than four weeks. Further, they found that 4.4 million additional workers will have already faced expiration of the benefits on one of those programs before December 26.

Long-term unemployment (unemployment for 27 weeks and over) has been rising quickly over the last several months, hitting 32% of total unemployment in October. It is likely that the rates of long-term unemployment will continue to rise in coming months—as happened in the aftermath of the Great Recession when long-term unemployment exceeded 40% of total unemployment for three years. This means that maintaining the protectiveness of the pandemic UI programs over the next year requires providing additional weeks of eligibility for workers who fall into long-term unemployment. Maintaining the effectiveness of these pandemic-related UI programs over the next year would help workers and their families keep their heads above water while breathing necessary life into the economic recovery.

We estimate the income gains, gross domestic product (GDP) growth, and employment growth that would result from maintaining the PUA, PEUC, and PUA programs through 2021. Like prior estimates of the loss in the $600 boost to UI benefits last July, we use the 2020 relationships between personal income from each UI program and the level of unemployment (or long-term unemployment) to project the income boost provided by continued UI support based on projections of unemployment through 2021. Based on that projected income boost, we then project GDP growth and job growth. Our methodology is described in further detail below.

Table 1 illustrates that these UI extensions would create huge economic gains: an income boost of $441 billion, a gain of 3.5% GDP, and 5.1 million jobs created or saved over the next year. Figure A breaks down the job gains by state.

Having more generous and longer-lasting UI benefits turn off in the midst of a recovery that is still 10 million jobs short of pre-recession levels illustrates why these benefits should be dictated by economic conditions, not by the whims of Congress. Implementing effective automatic stabilizers—both in UI and for programs like federal fiscal aid to state and local governments—should be a pressing priority for the incoming administration.

Table 1

Figure A

Methodology

Our estimates of the income, GDP, and employment boost from maintaining the pandemic UI programs use the relationship between unemployment (or long-term unemployment) and personal income from the PUA and PEUC programs, and then apply this relationship to predicted unemployment in each quarter of 2021. In general terms, we use the relationship between income from the variety of pandemic UI programs and unemployment (overall or long-term) as a proxy for how much money will be put into the economy if the pandemic UI programs are expanded or reinstated in 2021. Then, we use this estimated income boost to estimate growth in GDP and jobs.

Our estimates employ an adjusted unemployment rate, which includes workers who were misclassified as employed and workers who left the labor force for pandemic-related reasons since February 2020. This actually makes our estimates more conservative, as the income boost per percentage point of unemployment in 2020 is lower with the use of the adjusted (higher) unemployment rate in the denominator. The long-term unemployment rate is defined here as the percent of the labor force who is employed 27+ weeks as a share of the labor force. We assume the relationship between the adjusted unemployment rate and long-term unemployment rate and the Bureau of Economic Analysis Personal Income Data for July, August, and September holds for 2021 and apply these ratios to the predicted unemployment and long-term unemployment rate for 2021 to determine the PUA and PEUC personal income boost. Similarly, the personal income boost from PUC is determined by the relationship between personal income from the PUC and the adjusted unemployment rate in April through August (a conservative estimate given that PUC was only just ramping up in April and had officially ended at the end of July, though back payments were still being made in August).

Predicted unemployment rates for 2021 are estimated by applying quarterly changes in the Congressional Budget Office’s 10-year economic projections of the unemployment rate to our adjusted unemployment rate starting in the third quarter of 2020. To construct predictions of long-term unemployment for 2021, we assume that 40% of the unemployed will be facing long-term unemployment in 2021. This calculation is based on the fact that long-term unemployment as a share of the unemployed has been rising steadily over the last several months and recently hit 32% in October, and that long-term unemployment as a share of total unemployment peaked at 45.1% in the aftermath of the Great Recession and exceeded 40% for three years straight (between December 2009 to November 2012). It is certainly possible this is an understatement, and that long-term unemployment is even a bigger problem in 2021 than it was during the worst years of the Great Recession.

We apply a multiplier of 1.5 to the personal income boost for each UI program separately and divide by GDP to get the resulting percentage boost to GDP. We apply this percent change in economic activity to CBO’s prediction for payroll employment in each quarter of 2021. Normally GDP growth runs faster than employment growth early in recoveries. However, because so much of the job loss associated with the COVID-19 shock has been in sectors with low pay and high labor intensity, we think employment growth will respond more rapidly and robustly to a given increment of GDP growth than during normal recessions. The numbers in the chart are the average boost to personal income, GDP, and employment across all quarters of 2021 for the extension or reinstatement of each pandemic unemployment insurance program separately.

The state-level estimates allocate national employment effects to each state with a weight that is the simple average of a state’s current share of initial and continuing UI claims in that state (as of the Department of Labor Unemployment Insurance Weekly Claims dated November 19, 2020) and total nonfarm employment from the Current Employment Statistics averaged from November 2019 to October 2020. In the table, each state’s change in employment is also expressed as a percentage of its October 2020 employment level.

But it’s obviously not a normal year, and the GDP data released on Thursday will be for a quarter following the single fastest contraction of GDP in history, when the economy shrank at an annualized rate of 31.4% in the second quarter of 2020 due to the COVID-19 shock. The third-quarter data will show historically fast GDP growth—it could conceivably even see growth at a 31.4% annualized rate, for example. Some might be tempted to take too much solace in this rapid growth, and if growth in the third quarter looks to match the pace of contraction in the second quarter, some might even be tempted to declare the economic crisis nearly over.

This post highlights some reasons to temper enthusiasm (some that overlap with points made in this excellent Vox post), even in the face of a very large third-quarter growth number. There are five main reasons that I detail further below:

• The enormous contraction of GDP in the second quarter means any growth in the third quarter is coming off of a significantly smaller base of GDP.
• The COVID-19 shock caused rapid contraction of the economy even in the first quarter of 2020—so it’s not just the record-setting contraction of the second quarter that needs to be clawed back.
• It’s not just the level of pre-shock GDP that needs restored to make labor markets healthy; it’s the level this GDP would be at if it had continued to grow at its pre-shock rate.
• Because the COVID-19 shock has been so centered in low-wage sectors, any given dollar value of GDP lost translates into far more people who have lost jobs.
• Third-quarter growth was driven by the momentum of economic reopening and occurred with the tailwind of the generous recovery measures included in the CARES Act. Neither of these boosts will help in the future, absent radical policy change.

The second-quarter GDP disaster means growth going forward is off of a much smaller base

U.S. GDP shrank at a 31.4% annualized rate in the second quarter of 2020 (this means the economy shrank by 9% in that quarter—if that same pace was sustained for an entire year, the economy would be 31.4% smaller than it started). Say that the third-quarter GDP data shows growth at a 31.4% annualized rate, would that mean the economy is back to the pre-shock status quo? No, because the third quarter’s growth is being measured against a smaller base GDP (the greatly eroded GDP of the second quarter). Concretely, 31.4% growth in the third quarter would still leave GDP that was about 2.5% smaller than it was before the second quarter COVID-19 shock hit.

The COVID-19 economic shock hit in the first quarter, too

While the economy shrank at—by far—the fastest pace in recorded history in the second quarter of 2020, COVID-19 inflicted a large shock in the first quarter as well. In fact, the 5% annualized pace of contraction in the first quarter of 2020 was one of the worst quarters in recorded history for GDP. Since 1947—the first year of quarterly data available—293 quarters of GDP data have been recorded. The 5% contraction in the first quarter of 2020 is tied for the eighth worst in history. The Great Recession and financial crisis of 2008–2009 lasted six quarters and was widely considered the worst economic shock since the Great Depression of the 1930s. Only the single worst quarter during that crisis (the third quarter of 2008) saw a faster contraction than what the U.S. economy felt during the first quarter of 2020.

So, again, say that Thursday’s GDP data indicates that growth proceeded at a 31.4% rate in the third quarter. This would mean that the economy in the third quarter of 2020 was still 3.8% smaller than it was in the fourth quarter of 2019.

The GDP baseline is growth, not stasis

This 3.8% gap between GDP in the fourth quarter of 2019 and what would occur even if growth proceeded at a 31.4% rate in the third quarter of 2020 is still an underestimate of just how damaged the economy remains. GDP grows consistently outside of recessions, it doesn’t stand still. A conservative measure of growth that would have happened without the COVID-19 shock is 2%. In this case, even if growth proceeded at a 31.4% pace in the third quarter, the “output gap”—the gap between actual GDP and GDP in an economy with the same unemployment rate as what prevailed in the last quarter of 2019—would be closer to 5.2% of this potential GDP.

In the COVID-19 shock, more jobs are lost for each dollar of GDP lost

If the jobs lost due to the COVID-19 shock were of average labor intensity, then a 5.2% output gap would translate into a jobs gap of 5.2%, or roughly 7.5 million jobs (this is actually better expressed as total hours of work, not jobs, but for now we’ll stick with the slightly more intuitive concept). But we know that COVID-19 was felt most acutely by economic sectors that provide face-to-face services (restaurants, travel accommodations, personal services, and retail). These jobs are low-paid and far more labor-intensive than the economywide average, with each dollar of income generated in these sectors being associated with far more jobs. This means that the jobs gap associated with any given “output gap” based on GDP is going to be much larger.

In the real world, GDP is a pretty abstract concept. Jobs and paychecks are not. If a given GDP shortfall is associated with a larger jobs shortfall (as it is during the COVID-19 shock), this means the human welfare implications of this output gap is larger than normal.

Sources of third-quarter growth are spent

It is possible GDP in the third quarter of this year grew at a 30% annualized rate or even more. The cautions above are mostly about the arithmetic of this growth. But the economic sources of rapid third-quarter growth are obvious: momentum from reopening following coronavirus-driven shutdowns and income growth buoyed by the generous relief measures included in the CARES Act. Neither of these sources of growth will recur going forward, unless there are radical policy changes. The virus is resurgent across much of the country, and smart public health measures are needed to allow many aspects of life to continue safely in the face of this resurgence. The income support of the CARES Act has completely evaporated, and very soon growth will become throttled by income-constrained households forced to cut back spending. This household pullback in spending will be quickly followed by pullbacks in public spending from state and local governments. Policy can relieve this demand gap stemming from both household and state and local public-sector constraints, but if this demand gap is not addressed, then growth will falter badly in coming quarters.

Given all of this, what is the best summary measure of how far the economy still has to go before it reaches anything like pre-COVID health? I’d say that a good rough barometer is the employment levels that prevailed in February 2020, plus at least 100,000 jobs for each month thereafter to account for trend growth that should be generated in normal times. By this measure—which is much more relevant for the vast majority of Americans who need to find work to economically survive—the U.S. economy was short by a staggering 11 million jobs or more at the end of the third quarter of this year, and nothing about GDP data released this week will change that grim story. The need for aggressive policy action to help families through this horrible period and to spur a faster recovery once it begins remains critical.

Policymakers should realize two things about this completely expected data. First, it shows the utterly enormous scale of recovery the economy needs to mount before it is anywhere close to healthy. To put it simply, it could take years of historically fast GDP growth just to return the economy to the pre-COVID-19 status quo. Second, the today’s quarterly data mask important intra-quarter trends, and they miss troubling developments in the month of July, which we won’t see until the next quarterly data release. Concretely, the economic collapse of the second quarter largely happened in April (though it began in March in the first quarter of the year), with May and June seeing some rapid (but still woefully insufficient) bounceback. But this bounceback is likely to have already ended in July. Next Friday (August 7th) we’ll see data on employment growth in July. Many early data indicators strongly forecast flat or even negative employment changes in July.

The policy response to this should be clear. Even when the economy saw rapid bounceback in May and June, the COVID-19 economic shock inflicted so much damage in earlier months that the net result was an economic catastrophe for the second quarter. Even if this early bounceback had persisted in July, it would’ve needed substantial fiscal aid from Congress to continue. The fact that this bounceback has almost certainly stalled means this aid is even more necessary. Congress and the president need to restore the extra $600 in unemployment insurance so long as the job market remains damaged, and needs to provide large-scale, flexible aid to state and local governments to keep the coming revenue shortfalls facing these governments from translating into spending cuts and austerity that will starve U.S. households of needed help and drag on recovery in coming months.