Category: Disaggregating Growth

Five or 6 years ago, economists and pundits frequently debated whether the incomes of low- and middle-class households in the United States were stagnating. I wrote about it, and I think what I said then more or less holds up. Wage growth for these households wasn’t zero, so “stagnant” might be inaccurate in the strict sense, yet wage growth was slow at the bottom of the income ladder—and particularly so in the wake of the Great Recession, which left lower-income workers mired in a poor job market until about 2014.

At the time I wrote that column in 2018, income data from a Congressional Budget Office report was only available through 2015, so the evidence of the effects of a tightening U.S. labor market after that year were only just emerging. Moreover, income growth at the top of the income distribution was much stronger than what other households were experiencing, making “stagnant” a more apt description, relative to those households at the top. And prior to 2014, the vast majority of households underperformed the average as high-income households captured large shares of economic growth.

Now, some economists and analysts are revisiting that debate. Michael Strain of the American Enterprise Institute says that income stagnation was a myth, while the online pundit Matthew Yglesias thinks we should take more note of recent declines in income inequality. I don’t fundamentally disagree with either of these views. Income inequality does appear to have declined slightly between 2007 and 2019, but the pace of that decrease was excruciatingly slow. And while I don’t think income stagnation was a complete myth, strong wage growth from 2014 onward modestly improved the situation for low- and middle-income households up to 2019. Yglesias and Strain stop their analysis in 2019, but economic relief packages during the COVID-19 pandemic and recession significantly decreased inequality to levels we haven’t seen this century. As temporary relief from the government fades, however, those gains will probably prove fleeting.

In this column, I provide additional context on the recent trajectory of wages and income and wealth inequality in the U.S. economy. I use recent data releases from the Congressional Budget Office, which are the data Strain and Yglesias use in their separate analyses, as well as data from the U.S. Bureau of Economic Analysis and the Federal Reserve, to look more closely at these trends. The BEA data series, which I have written about frequently, is an exciting new tool for evaluating how income inequality and economic growth are related in the U.S. economy. It is similar to the Federal Reserve’s Distributional Financial Accounts, which provide a timely view of trajectories in U.S. wealth inequality.

Both Yglesias and Strain are correct to point out the strong wage recovery of the late 2010s and the resulting dip in income inequality metrics. I add three bits of context in this column. First, the decline in inequality since 2007 has been extremely slow. Second, the better part of that decline is attributable to so-called government transfers—programs such as the Child Tax Credit and the Supplemental Nutrition Assistance Program—and the way both the Congressional Budget Office and Bureau of Economic Analysis account for government healthcare spending. Third, in an encouraging sign, wealth inequality was rising through 2015 but has been mostly flat since.

Income inequality is declining really slowly

Both Strain and Yglesias focus on the period from 2007 to 2019, a span of years that begins with the Great Recession. Initially, the Great Recession decreased inequality. Recessions usually take a larger toll, in percentage terms, on higher-income households due to the fall in business revenues and the value of assets, such as stocks and bonds. But shortly after the Great Recession, the stock market and top incomes came surging back, while incomes for most U.S. households languished. It’s the second half of this period, from 2014 to 2019, when the incomes for lower-income households really started to pick up. And this did reduce income inequality.

But economists and pundits alike should have some humility about the pace of that reduction because it was very, very slow. According to CBO estimates, in the 28 years from 1979 to 2007, the share of income held by the highest-income quintile of U.S. households rose from 41.7 percent to 51 percent—a 9.3 percentage point rise (I use the after-tax-and-transfer income measure for all CBO calculations). From 2007 to 2019, that share dropped 2.4 percentage points to 48.6 percent of income earned. That trend would have to continue for another 35 years to get us back to 1979 levels of income inequality.

There’s nothing magical about 1979. It’s simply the year the Congressional Budget Office started its analysis. According to the two economists perhaps most famous for identifying rising income inequality—Thomas Piketty at the Paris School of Economics and Emmanuel Saez at the University of California, Berkeley—income inequality started rising in the early 1970s, based on their own, much longer time series of data, and the more equitable distribution of income in the United States in the mid-20^th century won’t return anytime soon.

It’s good to keep in mind that this recent dip in inequality is pretty small, compared to the huge run-up in income inequality experienced over the past 50 years after the prior two decades of equitable income growth and economic growth.

The CBO income measure includes realized capital gains from the sale of assets, such as stocks and bonds and property, which makes its measure of income inequality more volatile than income measures that exclude realized capital gains. It’s therefore important not to infer too much based on arbitrary beginning and end points: 2007 was the highest top quintile share of income ever recorded by the Congressional Budget Office, at 51 percent, and the top quintile share fluctuated quite a bit between 2007 and 2019. If one instead uses a 3-year average around 2007 and 2018 to smooth the volatility in realized capital gains, then the drop in the top quintile share of income is half as large, at just 1.2 percentage points.

The BEA distributing personal income dataset, which excludes realized capital gains, tells a more ambiguous story. Between 2007 and 2019, the agency reports that the share of income held by the top quintile increased, from 47.3 percent to 47.6 percent. The Congressional Budget Office’s top quintile share is probably higher in large part due to their accounting for unrealized capital gains, and this may also account for a significant portion of the drop in top incomes.

That’s not to say that the Bureau of Economic Analysis finds no reduction in income inequality. According to the agency, the Gini coefficient—a common one-number summary of inequality in an economy, where higher numbers mean more inequality—for after-tax-and-transfer income decreased 2 percent in this period. But that’s not because of losses at the top. Rather, it’s because of some compression in the middle. On average, incomes grew by 30 percent over this 12-year period. The lowest decile of income saw the largest gains, and the top decile saw the second-largest increases. The losers are upper-income households; the eighth decile saw income growth of 27 percent, the lowest among all deciles. (See Figure 1.)

Figure 1

Given these caveats, I’m hesitant to lean into the “inequality is declining” narrative. It might be more accurate for now to say that income inequality has been essentially flat since 2007.

While the CBO dataset currently only runs through 2019, the BEA data runs through 2021, and the story of the COVID-19 pandemic is pretty interesting. The massive increase in government transfers intended to help people weather the pandemic was a game-changer for income inequality. According to BEA data, the Gini coefficient fell almost 8 percent from 2019 to 2021. That’s a huge change for 2 years, and the lowest Gini coefficient in the BEA dataset, which starts in 2000.

That’s impressive, but the transfers that made this drop possible—including the expanded Child Tax Credit, enhanced Unemployment Insurance, and direct stimulus checks—have all expired now, so the Gini coefficient will likely go up again. And it will be a few years from now before the data are available to calculate the long-run impact of the pandemic on income inequality.

Wages increased, but most of the rise for low- and middle-income U.S. households was from government transfers

Policy changes during the Obama administration, particularly the Affordable Care Act, led to gradually increasing benefits for low-income U.S. households from 2014 onward, when Obamacare first began to be implemented. In the 2007–2019 window, according to the BEA data, 50 percent of the cumulative increase in income for the bottom 50 of households came from transfers, which includes government-provided healthcare in BEA’s accounting, while 36 percent came from increasing wages, and 14 percent was from other sources, such as business income, rental income, and stock dividends. (See Figure 2.)

Figure 2

In 2020, the percentage of income coming from transfers went up dramatically, thanks to pandemic cash transfer programs. (BEA publishes initial data for 2021 but does not include breakdowns by type of income, so 2020 is the most recent year where income can be decomposed and we can see the impact of transfers.)

The Affordable Care Act boosted in-kind transfer income because both the Congressional Budget Office and the Bureau of Economic Analysis treat government-provided health insurance as income. That is, if a household receives Medicare or Medicaid, several thousand dollars are added to their income to represent the value of that insurance (average per-capita spending on Medicaid enrollees in 2019 was $6,556). 

Some economists object to this kind of accounting. Notably, Nobel-Prize winner Angus Deaton at Princeton University objects to valuing the inputs to healthcare instead of the outputs. That is, the amount of money the government spends on providing healthcare to a person doesn’t necessarily correlate to the value that individuals get out of that insurance because the cost and quality of healthcare varies from place to place across the country. Medicare and Medicaid are undoubtedly beneficial to those who receive it. The issue is whether and how to value the money spent on those programs when determining income inequality.

But the main reason to be concerned about a drop in inequality that is driven by in-kind transfers, and the Affordable Care Act in particular, is that it may not be sustainable. The Affordable Care Act started providing most of its benefits in 2014, and its benefits expanded over the years as more states opted into the expansion of Medicaid as part of Obamacare. There are still states that haven’t opted in, so ACA spending might grow and provide further in-kind transfer income to those at the bottom. But in states where Medicaid expansion has already occurred, ACA benefits are already included in income, so that income will stop contributing to CBO-measured income growth (and declining inequality) in those states, and low- or middle-income households will be more dependent on wage increases to raise their incomes.

Paradoxically, the only way for the Affordable Care Act to drive further income increases in states that have already expanded Medicaid is to have more people become eligible for coverage under the program because their incomes drop. That could push more people into eligibility and compensate for lower earnings from employment. But this kind of income “increase” wouldn’t exactly be good news for low-income households.

A similar situation played out in the years between the Great Recession and the implementation of the Affordable Care Act. In the aftermath of the Great Recession, federal economic stimulus and falling wages led to rising transfer income for low-income U.S. households. But by 2011, these additional transfers dried up, and these households started to receive lower transfer payments each year. In 2014, the Affordable Care Act started to provide significant boosts that continued into 2019 as states opted into Medicaid expansion. These dynamics added to the incomes for bottom 50 percent of households from 2007 to 2019. (See Figure 3.)

Figure 3

Wealth inequality has leveled off

Finally, I took a quick look at wealth inequality in this period using the Federal Reserve’s Distributional Financial Accounts. Last year, the Federal Reserve fielded its triennial Survey of Consumer Finances. The results from that survey won’t be available until September of this year, but the Distributional Financial Accounts provide a reasonable estimate in the meantime. According to the DFA data, the share of wealth held by the wealthiest 10 percent of U.S. households increased from just more than 66 percent in 2007 to about 69 percent in 2015, and has dropped moderately since then, following a similar trajectory as income inequality. (See Figure 4.)

Figure 4

It’s probably too early to read much into the 2021 and 2022 data as wealth tends to be volatile for high-wealth households around economic recessions. Still, it’s encouraging that wealth concentration at least appears to have stalled out starting in 2015. That suggests that increased parity in income growth is also leading to more parity in wealth growth.

Wealth and income inequality don’t have to move together like this. An ongoing debate in economics centers on the extent to which rising wealth inequality is driven by either income inequality or by high-wealth households having access to investments with higher rates of return.

Don’t plan the ticker-tape parade yet

The trajectory of income and wealth inequality in the United States has changed since 2007. But it’s far too soon to celebrate. Income inequality is down very marginally and remains far from the levels seen in the mid-20th century United States. Moreover, much of the decrease in this inequality was caused by implementation of the Affordable Care Act, which will probably not provide significant income boosts going forward. That makes it possible that decreasing income inequality will not be lasting.

Absent an increase in the federal minimum wage or increased worker power, a tight U.S. labor market will be necessary to prolong the success of the post-2014 U.S. economy in terms of more equitable income growth and thus more sustainable overall economic growth. Likewise, income trends will eventually be reflected in wealth inequality, and absent strong wage growth for low- and middle-income households, the current pause in increasing U.S. wealth inequality may prove fleeting.

Statistics Canada, the umbrella agency for national statistics in Canada, is the first statistical agency in the world to release subannual data on how economic growth is distributed among rich and poor households. In January of this year, it released a quarterly dataset of these distributional statistics for the first quarter of 2020 through the third quarter of 2021 called the Distributions of Household Economic Accounts, or DHEA. Then, in April, it released new statistics for the fourth quarter of 2021. Statistics Canada plans to continue releasing these quarterly snapshots on a one-quarter lag.

The production of these statistics at a quarterly frequency with relatively little lag is a watershed moment for the worldwide effort to produce more comprehensive and useful statistics on income inequality. For decades, reporting on income inequality has been dominated by the production of opaque “Gini coefficients,” which are difficult for nonspecialists to understand and are often constructed using incomplete measures of income.

This new vintage of inequality statistics will be easier for nonspecialists to interpret, while also offering more comprehensive measures of income. These data build on the pioneering work of Joseph Stiglitz at Columbia University, Amartya Sen at Harvard University, and Jean-Paul Fitoussi at the Institut d’Etudes Politiques de Paris—an expert group at the 38-member-nation Organisation for Economic Co-operation and Development on distributional national accounts—in their “Report by the Commission on the Measurement of Economic Performance and Social Progress,” as well as the many economists who have participated in the WID.world project to create comparable metrics of inequality for a large number of countries.

The U.S. version of these statistics, which does not currently offer subannual estimates and is released on a 2-year lag, provides simple answers to questions such as “What share of income is earned by middle-income Americans?” or “What percentage of annual growth accrued to the richest 10 percent of households?” The chart below shows how aggregate economic growth was divided in each year between the lowest-income half of U.S. households, the upper-income 40 percent above that, and the richest 10 percent of households. (See Figure 1.)

Figure 1

A number of other countries are also beginning to produce these statistics, mostly in an experimental capacity. Reducing the amount of lag in the production of these statistics and increasing the frequency of their release is the next frontier in inequality statistics. Statistics Canada’s example could provide a template to produce inequality statistics that provide up-to-date guidance for the public, businesses, and governments to act on.

In this column, I describe how Statistics Canada produced these data and detail some remaining caveats. Notably, Statistics Canada also produces distributional statistics for consumption and wealth, but I focus only on income here.

Overview of datasets and methods used to create Canada’s DHEA

A key advantage Statistics Canada has over the U.S. statistical agencies is that they start with their Social Policy Simulation Database. The SPSD is a high-quality synthetic dataset based on a blend of survey and administrative data that Statistics Canada has maintained in some form since 1990. It is primarily based on four data sources:

• The annual Canadian Income Survey
• The biennial Survey of Household Spending
• Annual income tax return data
• Employment insurance claimant history records

These data sources are blended, and disclosure avoidance techniques are applied to ensure privacy.

The SPSD is publicly available and comes packaged with a simulation program that can be used to look at the impacts of various policy options on Canadian households. Analysts can simulate different kinds of tax changes, for example, or changes in levels of social spending using the simulation program.

This database is fairly different from what the U.S. Bureau of Economic Analysis has access to in the United States. There is no current synthetic dataset that blends administrative and survey data that is widely available to agencies and other researchers. This may change soon, as the U.S. Census Bureau is currently working on some large data blending projects, as well as disclosure avoidance strategies that may make blended synthetic data files available.

Nor can BEA start with confidential administrative data, such as IRS tax returns, because the tax code specifically prohibits this sharing. It is a serious flaw in the U.S. tax code that our economic statistical agencies are prohibited access to these data, which would allow them to construct more innovative and ambitious statistical products that could be informative for the public. Consequently, BEA starts with one part of the Census Bureau’s Current Population Survey, the Annual Social and Economic Supplement, which is somewhat comparable to the Canadian Income Survey.

Statistics Canada follows the lead of the OECD expert group on distributional national accounts, as does BEA in the United States. The aggregate income concepts targeted by each agency are a bit different, but both come relatively close to reproducing the System of National Accounts’ definition of disposable income (BEA’s statistics are available for both personal income and disposable personal income).

Because both methods are grounded in the OECD approach, they have some core similarities. Both make some use of a technique called scaling, for example, to ensure that aggregate income in the microdata match aggregate income in the national accounts. This approach has been criticized, but more research is necessary to determine exactly why these aggregates disagree and how to correct for discrepancies. For now, scaling is the best answer we have. Statistics Canada also makes adjustments to the SPSD data to ensure compatibility with System of National Accounts’ income definitions. These include deriving imputed rent estimates and adjustments for tax-sheltered income.

Methods used in the subannual DHEA

Statistics Canada goes beyond any other country in offering subannual distributions. The agency began producing these in response to demand from the public and government officials to better understand the effects of the COVID-19 pandemic. For the most part, the datasets used to create the SPSD are available only at annual frequency, so statisticians had to get a little creative to provide subannual measures.

An easy way to create subannual distributions is to take existing distributions of income in wages, business income, and other income categories and simply apply them to new national accounts aggregates. This can be an inaccurate approach. In this approach, if the first quintile of households by income earned 10 percent of wage income in the previous year, statisticians would simply assume this distribution continues to apply in current-year quarters, but economists know that these distributions change, so this is not generally going to produce very accurate results.

If agencies want to construct accurate subannual estimates of household income, then it is important for them to try to redistribute at least some income sources on a subannual basis. In other words, they must find a data source that can be used to make a new estimate of how some sources of income are spread across the income distribution. As I have previously explained, the vast majority of income for the lower 90 percent of households comes from wages and government transfers. So, these are the most important categories of income to redistribute.

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More frequent disaggregated data on U.S. income inequality would improve economic research and inform policymaking

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The U.S. economy is in its fourth decade of rising inequality amid the need for more accurate data on its consequences

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Statistics Canada’s solution is very close to the one adopted by the website Realtime Inequality. Realtime Inequality is a project from University of California, Berkeley economists Thomas Blanchet, Gabriel Zucman, and Emmanuel Saez that reports distributional measures of growth in the United States on a quarterly cadence. Both Statistics Canada and Realtime Inequality redistribute government transfers using rules-based simulation. That is, they look at known data on households, such as income, household size and composition, and other relevant information, and use those criteria to determine whether or not the household is eligible for government transfers, such as the stimulus checks issued by the U.S. government during the pandemic.

For wages, another data source is necessary. Statistics Canada uses the monthly Canadian Labour Force Survey. This survey asks respondents a number of questions about employment status and earnings from employment. Statistics Canada uses the responses to these questions to simulate the number of weeks worked and wages for individuals each quarter.

Additionally, the Labour Force Survey asks questions about respondents’ business income. This allows Statistics Canada to redistribute that category of income as well. Unfortunately, in the United States, there are no similar sources of high-frequency business income data to draw on.

Realtime Inequality distributes wages using the Quarterly Census of Employment and Wages. The QCEW does not provide disaggregated data on the wages of particular employees, but it does report out the total money spent on wages in highly disaggregated cells. Pioneering work by Byoungchan Lee of the Hong Kong University of Science and Technology demonstrates that the QCEW can be used to proxy for the distribution of income.

For other sources of income, among them interest and dividends, both Statistics Canada and Realtime Inequality use old, known distributions mapped onto current quarter aggregates. These sources of income account for relatively small percentages of household income outside the top 10 percent of households, making them less important as contributors to inequality.

Implications for the methods of disaggregating U.S. economic data

One significant caveat is necessary. To date, Statistics Canada has not released a public analysis of the accuracy of their method. Subannual estimation of inequality necessarily relies on a modeling approach that may make these estimates less accurate. So-called nowcasting techniques, such as the one described above, require revisions when more accurate annual data are released.

Until that analysis is released, some caution is warranted. The Realtime Inequality website, which uses a similar approach to nowcasting, has shown that the approach can be relatively accurate. In their methods paper, the creators of Realtime Inequality show that they only rarely make errors in the direction of growth. That is, they very rarely find that income for a particular income group—say, the bottom 50 percent—is shrinking when it is, in fact, growing. This is an encouraging sign for the methodology, but more error analysis is necessary.

Statistics Canada may have some advantages that are unavailable to the U.S. Bureau of Economic Analysis. Canada’s Social Policy Simulation Database provides blended administrative and survey data to use as a base for the statistics. The U.S. Bureau of Economic Analysis has no similar starting point and begins instead with the Annual Social and Economic Supplement to the Current Population Survey. The Realtime Inequality team starts with IRS tax data. It is not entirely clear how these differing data sources affect accuracy. More work is necessary to better understand why all these data sources fall short of national income aggregates.

In short, considerable research is still necessary. In 2021, BEA issued a report on the feasibility of creating quarterly distributions. At the time, no one had demonstrated a working prototype of quarterly reporting using current distributions of government transfers and earnings. BEA did not investigate the possibility of using the QCEW data or another dataset to make those redistributions.

In light of continued interest from policymakers and proof of concept from both Realtime Inequality and Statistics Canada, it would be appropriate for BEA to revisit this decision. A prototype dataset blending existing BEA data with QCEW data would provide an opportunity to test the accuracy of more timely predictions and decide whether they meet the necessary standards of national statistical reporting.

Overview

Economic inequality in the United States continued to rise over the first two decades of the 21st century, according to new data from the U.S. Bureau of Economic Analysis—a trend that builds upon the sharp divergence between the fortunes of the truly rich and the rest of society that began around 1980. As we enter the fourth straight decade of rising income and wealth inequality and their attendant social inequities—such as the widening gulfs in college attendance and life expectancy—U.S. policymakers, now more than ever, need more accurate data to deal with the baleful consequences of inequitable growth.

Most of this rise in inequality occurred before 2000, but the latest data show that U.S. households are still slowly drifting apart from one another. December’s BEA data release expanded the bureau’s distributional data series to cover 2000 through 2019, where it previously only covered 2007 through 2018. These more complete data encompass the entirety of two business cycles: the dot-com bubble-induced recession of 2001 and subsequent expansion, and the Great Recession of 2007–2009 and subsequent expansion, before the arrival of the coronavirus pandemic and ensuing recession in 2020.

Until the Bureau of Economic Analysis extends the series backward further, it’s difficult to say how increases in inequality now compare to those that happened in the 1980s and 1990s—but two things are clear. First, inequality continues to rise. Second, these data are critical to understanding the consequences of economic inequality in the United States.

This issue brief provides analysis of the most recent release. The evident utility of the data series, as shown here, demonstrates why the Bureau of Economic Analysis should request more resources from Congress to expand and improve this dataset, especially by adding more current data that would allow us to analyze trends now, rather than 2 years in the past.

What the latest economic data show about rising economic inequality

The U.S. economy is in its fourth decade of expanding inequality. One way to see this is to compare average growth in incomes to the growth realized within particular income brackets. Average growth in disposable personal income over the period from 2000 to 2019 was about 2.3 percent but ranged as high as 2.7 percent for those in the top 10 percent of household income and as low as 1.6 percent for the lowest-income households. Even within the top 10 percent, there is significant dispersion: The top 1 percent had an average growth rate of about 3.3 percent. (See Figure 1.)

Figure 1

As Figure 1 shows, “headline” personal income growth figures—those that are most commonly reported by analysts, the media, and other sources—overstate the actual growth experienced by virtually all U.S. households. Only the top decile exceeds headline growth. Headline growth is simply an average, and in an age of inequality, the average is largely determined by very rapid income growth for the highest incomes.

These differences add up over a 20-year window: A household experiencing the same income growth rate as top 1 percent of households would end the 20-year period with income 20 percent higher than if they had instead experienced just average growth. The difference between experiencing the income growth of a bottom 10 percent household in this period or experiencing average growth is a 15 percent income premium over the period. And this represents just a small part of the run-up in inequality that started sometime around 1980. The Bureau of Economic Analysis should continue to extend the series back in time, so comparisons to this period can be made.

The federal statistical system needs to be resourced to expand and continue reporting on inequality

Four decades of rising inequality calls for a more robust policy response to ensure broad-based growth in the U.S. economy. An important first step is to develop the data infrastructure to track growth in inequality over time, so that policymakers can monitor and respond to the problem, and voters can hold them accountable to producing strong growth for all U.S. households.

Existing data series are insufficient. One of the few reports by the federal government on economic inequality is the U.S. Census Bureau’s September Income and Poverty Report. The Bureau of Economic Analysis has already significantly improved on this report by using a more comprehensive income concept, releasing more granular income groups (the Census Bureau releases quintiles), and fully accounting for income in one national account (the Bureau of Economic Analysis fully accounts for all Personal Income). But there is much more to do.

Congress must give the Bureau of Economic Analysis the resources to continue developing this work. There are three important avenues for further development of this product. First, the current BEA product underestimates top incomes because it does not account for capital gains. This is not an intentional omission but rather a reflection of the fact that capital gains are not included in any national accounts.

Economists Thomas Piketty at the Paris School of Economics and Emmanuel Saez and Gabriel Zucman at the University of California, Berkeley, in their pathbreaking 2018 article on distributional national accounts, attempted to mitigate this omission by using National Income as their income concept. National Income includes retained corporate earnings, which Piketty, Saez, and Zucman argue can act as a proxy for unrealized capital gains.

Distributing retained corporate earnings, however, requires making several simplifying assumptions that may be prone to error. Statistical agencies should research a separate series of the distribution of capital gains that is compatible with the distributional personal income data. Economist Jacob Robbins at the University of Illinois at Chicago defines a measure of Gross National Capital Gains that could be a model for such a data series.

Second, these data must be released on a shorter delay. Currently, the Bureau of Economic Analysis plans to release these data once a year in December, adding data for the period 2 years prior. This is what happened last month—2019 data were released in 2021. This is a useful tool for understanding the near past, but it will not help policymakers make real-time decisions, and it will not help households understand and respond to the economic conditions they currently face.

There are challenges with publishing more current data, but they are surmountable. With proper resources and a willingness to impute and model current data, BEA staff can release this data series at a higher frequency and with less latency.

Finally, expanding this new data series back in time to include the 1980s and 1990s would enable policymakers to have an even better grasp of rising economic inequality trends over generations. This year’s data release demonstrates how much policymakers can learn about inequality in the U.S. economy—and how much more they could learn if the data were released more frequently and collated over longer periods of time.

Examining U.S. income inequality over the past 20 years

U.S. households at different levels of earnings had significantly different experiences of the past 20 years, with the top 1 percent experiencing more income growth. Those with high incomes saw more immediate and deeper drops in income during recessions, while those with lower incomes were partially supported by government “transfers,” economic parlance for social infrastructure programs such as Unemployment Insurance that underpin the economy during downturns. But the flip side is that low-income households experienced years of stagnation after recessions had subsided, with very little of overall economic growth accruing to those in the bottom half of the income distribution despite moderate headline growth. (See Figure 2.)

Figure 2

Figure 2 shows income growth in each year subdivided into four groups that some researchers and academics commonly use. They are the bottom half of all income earners in the distribution, the next 40 percent of earners (50th to 90th percentile), the next 9 percent at the top (90th to 99th percentile), and the top 1 percent. The blue portion of each bar shows the amount of growth that benefitted earners in the bottom half of the distribution. Warm colors—yellow, red, and orange—show the amount of growth in each year that accrued to the top.

U.S. households move between deciles frequently. The Bureau of Economic Analysis does not follow people over time, so those in the bottom 50 percent of income in one year may not be there the next year. This means these data do not chart economic mobility, only the shape of the overall income distribution. But the overall shape of the distribution is important. When wages for the bottom 50th percentile aren’t increasing, it means that there is little wage growth in the kinds of jobs occupied by those with low educational attainment, young workers, and those in vulnerable populations. The U.S. economy used to deliver real growth to this end of the distribution, but for the past four decades, it has been increasingly stingy.

Households in the bottom 50 percent of the income distribution experienced relatively little growth in incomes over the past 20 years: Over that entire period, the bottom 50 percent of the distribution captured just 20 percent of all growth, even though they represent half the population. Meanwhile, the top 10 percent—a group just one-fifth the size of the bottom 50th percentile—captured 37 percent of overall growth. (See Figure 3.)

Figure 3

This pattern was stable across the two economic expansions in this time period. The expansion after the Great Recession was slightly more equal, with the top 1 percent of income earners benefitting less, compared to other groups, but differences were slight.

The BEA data series also provides detail on how particular components of income, such as wages, transfers, or business income, changed for households in each decile of income. Looking at fluctuations in these components is a useful way to understand how the economy works for U.S. households at different points on the economic ladder.

Households in the bottom half of the income distribution, for example, are largely dependent on wages and government transfers. In 2019, wages represented 49 percent of positive income for this group, and government transfers made up another 40 percent (you can see all the components that make up personal income here), with most other components contributing very little to incomes of the bottom 50 percent of income earners. So, fluctuations in overall income for this group are largely due to wages and transfers. (See Figure 4.)

Figure 4

The top panel of Figure 4 shows annual growth in each component, while the bottom panel shows cumulative growth. The majority of all growth for this group came from growth in government transfers. Wage growth contributed 30 percent of all growth for the group, while the contribution of all other income sources was negligible.

The “upper 40” group of households derive most their income from wages, but government transfers still provided about 13 percent of income for this group in 2019. Unsurprisingly, the relative importance of these two categories for income growth is flipped for this group of households, compared to “bottom 50” households. Over the 2000­–2019 period, 64 percent of income growth for this group came from growth in wages, but the Great Recession hit the wages of this group particularly hard. Subsequent years saw a rapid recovery. Transfers accounted for about 26 percent of all growth for this group over this time period. Yet this group did earn some interest and dividend income, accounting for about 8 percent of income. (See Figure 5.)

Figure 5

The top 10 percent of households by income have the most diverse sources of income. Although wages are still important for this group, making up about 51 percent of positive sources of household income, these households also have significant amounts of business income and interest and dividends income. About 27 percent of all growth over the period came from interest and dividends, while about 20 percent of growth came from business income. (See Figure 6.)

Figure 6

Conclusion

The BEA distributing personal income data series has progressed rapidly in just a couple of years. Every year has seen significant improvements in the statistical methodology and the amount and types of data available.

The current product allows for useful analysis of the recent past, with important applications to current policy debates. Knowing how growth in the economy is distributed is just as important as knowing how much the economy is growing. Congress must resource this effort, so the Bureau of Economic Analysis can expand the data series. This should include:

• Improve frequency and latency: The bureau should investigate ways to decrease the lag in the release of estimates. Right now, data are released in December for 2 years prior. In December 2022, we will get data for 2020, giving us our first glimpse of the coronavirus pandemic, yet this lag is too long. Ideally, estimates would be released quarterly, putting this product on the same footing as GDP growth.
• Extend the time series back: There also is significant value in having a complete history of inequality in the modern U.S. economy. The bureau should make efforts to extend the data series back in time to allow comparisons with earlier eras.
• Account for capital gains: Although this has traditionally not been the purview of the bureau, capital gains are increasing and contribute significantly to economic inequality. The Biden administration or Congress should consider tasking the bureau or another agency with tracking this trend.

Together, these three steps would vastly improve our nation’s economic statistics and enable policymakers to act on the harmful consequences of four decades of rising economic inequality.