Tag Archives: income distribution

The Growing Inequality in Wealth in the US, Part 1 – Basic Facts

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Chart 1

A.  Introduction

The extreme, and growing, inequality in wealth ownership in the US has been receiving increasing attention.  In its most recent annual listing of the 400 most wealthy Americans (from September 15, 2026), Forbes estimates that 15 individuals (and their immediate families) each have a wealth of $100 billion or more, seven have a wealth of $200 billion or more, and the top one – Elon Musk – had a wealth (as of the date of publication) of over $900 billion.  Concern with such concentration of wealth has also grown as a number of these ultra-wealthy individuals have become increasingly politically active, using their wealth in ways that can determine election outcomes and overwhelm basic democratic institutions.  This became especially evident in the 2024 presidential election, with consequences that are now clear.

This post, and several to follow, will review the basic data that estimate the inequality in wealth holdings and how that inequality has grown in recent decades.  This first post will look at wealth per household (more precisely net worth, but the terms will be used interchangeably) with the households ranked by their level of wealth from least to most:  the bottom 50%, the 50 to 90%, the 90 to 99%, the 99.0 to 99.9%, and the top 0.1%.

The chart at the top of this post shows how that net worth has grown (in real terms) over the last several decades.  Because of the immense wealth of the top 0.1% – whose average net worth grew to over $200 million per household – it is hard to see on the chart the average wealth of the bottom 99% of the population.  Their net worth is just too small.  Charts below will split out the bottom 99% to show their histories, but the point is that the disparity between the top 0.1% and the rest is so large that – other than for the top 1% – their average holdings disappear on the scale of the chart.

The post will first discuss in Section B below the data for these estimates.  They were calculated from data estimated by staff at the Federal Reserve Board and released as its Distributional Financial Accounts.  This source is widely considered the best set of estimates on household wealth available for the US.  These are, of course, estimates, however, and while the best available, it is important still to recognize their limitations.  Wealth is difficult to estimate, for both conceptual and practical reasons.

Section C will then present the basic estimates of per-household wealth (net worth) when grouped by households ranked by the wealth they hold.  The figures going back to 1989Q3 for all five groups the Fed distinguishes are as in the chart at the top of this post, and charts will then be provided also for the bottom 99% (and its sub-groups) and for the bottom 50%.  Each has a story to tell.

Section D examines how the wealth estimates have changed over time in real terms and per household relative to what they were in the earliest period in this data series (1989Q3).  Inequality was already high in 1989, but then grew much more.  Section E will decompose the figures for average overall wealth, linking their increase to the growth in GDP over the period, to the increasing share of profits in GDP (at the expense of wages), and to the increase in how that wealth is valued as a multiple of the profits the owners of the assets are entitled to.

Section F will conclude.  Future posts will examine other aspects of wealth holdings, using the same Fed data, including on wealth holdings when households are ranked by their income levels rather than their wealth levels, and on wealth holdings at the same age for different generations – i.e. for Baby Boomers compared to generations before and after.  A final post will then examine what might – or might not – be done through policy to address these issues.

B.  The Fed’s Distributional Financial Accounts

The measurement of wealth is not at all straightforward, and poses both conceptual and practical issues.  This is in contrast to the measurement of incomes.  For incomes, there are actual market transactions where firms earn revenues from what they sell.  Out of those revenues the firms pay a portion for the inputs used and for the wages of their employees, with the remainder then being the gross profits that will cover taxes, the interest they pay on their debt, and their investment costs (counted as depreciation).  What remains is their net profits.  All of these transactions (other than depreciation) are measurable in cash being transferred and can be observed.

The measurement of wealth is different.  Wealth is the value that others would be willing to pay for the assets owned.  That is not observed and can only be estimated.  Guessed may be a better term.  It is also in a fundamental sense socially determined, as the measured value of the wealth is not inherent in the asset itself, but rather an estimate of what others – given the state of the economy, their expectations of what the future might be, the availability of financing, and other such factors – might be willing to pay for the asset.  The transfer of the asset does not take place, however, and hence cannot be observed in the manner that incomes are observed.  The value can only be estimated as a hypothetical.

Thus if someone is willing to pay twice as much for an asset (perhaps real estate, or a company, or whatever) than what it was valued at the previous day, the owner of the asset would be richer.  His wealth would be greater.  But nothing fundamental has changed in the economy; the assets are all the same as before.  Only the valuation has changed.

In the face of such issues, statisticians do the best they can.  They need to make assumptions about what others might be willing to pay for the assets.  And they can assemble data on transfers of assets that have recently taken place and hence can be observed, where those assets may be similar – to a greater or lesser degree – to those they are trying to value.  This can inform – to some extent – the valuations they assign to assets that did not change hands.

The calculations discussed in this post (and those that follow in this series) are based on data assembled by staff at the Board of Governors of the Federal Reserve System in Washington, DC, and specifically its Distributional Financial Accounts (DFA).  The DFA estimates are published quarterly, usually about 11 weeks after the end of each calendar quarter.  The accounts for the second quarter of 2026 were released on September 18, and provide estimates for wealth held by households as of the end of the quarter, i.e. on June 30.

The DFA data sets include quarterly data going back to the third quarter of 1989, but were only first produced starting in 2019.  They are based on a combination of two other data sets produced by staff at the Fed Board of Governors, the Survey of Consumer Finances (SCF) – a survey undertaken once every three years –  and the quarterly Financial Accounts of the United States (FA).

The SCF is a survey of households undertaken on behalf of the Fed where household assets and liabilities are recorded in detail, along with household income and characteristics of the household such as the age, education, race, and similar of its members.  Because of this, the Fed can then produce estimates of wealth not simply of households in general, but also of households categorized by their income, their age, education, and/or race, and similar.

A representative sample of households across the country is chosen for the SCF, along with an oversampling of higher-income households in order to get meaningful sample sizes of the more wealthy households that hold a substantial share of household wealth.

The response rate of the general sample is reasonably good – at about 70% – but that for the oversampled wealthy households is only 10%.  This may lead to biases in the results.  While it would not matter if the wealthy households not responding were random (one could then simply start with a sample size ten times larger), it is likely that the relatively more wealthy households within that group are less likely to respond.  The share of the nation’s wealth held by the most wealthy would then be underestimated.  Despite this possible bias in the estimates, we will see below that the share held by the most wealthy groups is nonetheless extremely high.

A point to note is that the SCF specifically excludes any sampling of the extremely wealthy households on the Forbes 400 list of richest Americans.  This might be due to privacy concerns, or simply the recognition that they would be unlikely to respond to the SCF survey even if asked.  However, the DFA accounts specifically add them back in, with assets and liabilities either obtained from Forbes or drawn from the same sources used by Forbes.  The net worth of the top 400 is significant.  In the most recent September 2026 ranking, their total net worth came to $8 trillion.  The DFA estimate of the net worth of the top 0.1% of households as of June 30, 2026, was $28 trillion.  Thus the top 400 alone account for 29% of the total wealth of the group, even though they account for less than 0.3% of the approximately 135,000 households in the top 0.1%.

The SCF is, as noted, conducted only once every three years.  To arrive at the quarterly estimates, the Fed staff use the quarterly Financial Accounts of the United States (FA) data to estimate how the wealth holdings of the households in the groupings analyzed may have changed quarter to quarter.  The FA accounts date back to 1955, and were formerly called the Flow of Funds Accounts.  They track – for the nation as a whole – both the stocks and flows of capital assets (both real and financial) in all their forms across economic sectors such as households, businesses, governments, and financial intermediaries.  They are normally released ten weeks after the end of the quarter, and present financial flows during that quarter along with end-of-period stocks.

The DFA estimates combine the triennial SCF estimates on wealth holdings of categories of households with the quarterly FA estimates of total assets and liabilities held by households in the country as a whole.  But this is not always a simple scaling, for several reasons.  For a full discussion, see this chapter from a NBER conference held in March 2020 (and published in 2022) where Fed staff describe in some detail how the then new DFA accounts were assembled.

Arriving at household holdings of wealth – both assets and liabilities – consistent with national-level estimates of the totals of such assets and liabilities in the economy is not always straightforward.  A couple of examples illustrate this.

One is the accounting of the assets backing up commitments to retirees in defined benefit pension plans.  The SCF does not attempt to include at the household level the assets held in such schemes.  Workers expect they will receive a pension of some amount, which will be tied to how many years they ended up working for the firm and what their final income was (often averaged over some period, such as their final three years).  They will have little idea what assets are being invested to back up those commitments.  But at the national level, the assets of the company pension plans backing up those commitments have been invested and are reflected in the totals in the FA accounts.

To resolve this, the DFA makes an estimate of what a household’s share of the assets held by the pension fund managers would be given the characteristics of the households.  For those who are already retired and are receiving such a pension, plus for those who expect to receive a pension at a certain level based on their past employment with some firm, they calculate the present discounted value (which will depend on their age) of their expected future pension benefits.  They then subtract those present value totals from the assets of the defined benefit pension plans at the national level to arrive at a figure for the pensions of workers still employed, who will start to receive pension benefits once they retire at some future date. They use a similar approach for the determination of the value at the household level of wealth held indirectly in annuity schemes and in life and other insurance plans.  This approach can be easily criticized, but is probably the best that can be done.

As another example of the type of assumptions they had to make, there is an issue with how equity in privately held businesses as well as closely-held corporate entities should be valued.  The SCF reported figures based on the household responses for both the cost basis of such equity and for what the households believed the market value would be.  Aggregated to the national level, the market value estimates were well above the totals in the FA accounts while the cost basis estimates were well below.  However, the average of the two turned out to be close, so they used that.

It should be noted that while estimates are made of the value to the household of the assets held (directly or indirectly) in their defined benefit pension plans as well as their defined contribution plans (401k’s, IRAs, and so on) and annuities from insurers, the DFA accounts do not include the implicit value of their Social Security retirement (and other) benefits.  But the value of those Social Security commitments are significant, and especially so (as a share of their total wealth) for lower-income households.

To illustrate, a standard rule-of-thumb in retirement calculations is to spend each year no more than 4% of the balance of assets held in the retirement accounts at the time of retirement.  The remaining assets will continue to grow in value, but at that 4%, retirees are unlikely to outlive the savings they had to cover retirement.  Based on this, one can calculate the assets that would be required in a private pension savings account to cover benefits equal to those paid by Social Security.

As of December, 2025, the average monthly benefits paid to retirees, along with the average paid to the bottom 50% of retirees and top 50%, were:

Average Monthly Benefit

Annual Equivalent

Capital-Value Using 4% Rule
All Retired Workers

$2,071

$24,854

$621,350
Bottom 50%

$1,338

$16,060

$401,505
Top 50%

$2,813

$33,761

$844,017

Source:  Calculated from data at Social Security – Beneficiaries by benefit level.  For December 2025.

To cover the average benefit to retirees of $2,071 per month, assets of over $620,000 would have been needed if one followed the 4% rule.  For the bottom 50% of retirees (bottom 50% of the recipients of Social Security benefits), it would have been over $400,000, and over $840,000 for the top 50%.  These could be counted as part of household wealth (assuming Social Security keeps to its commitments – which certainly can be done but will require Congress to act).

These are significant amounts.  As will be discussed in the following section below, the average net worth as of June 30, 2026, of the bottom 50% of households (as ranked by their net worth) is only $63,000 in the DFA figures.  For those in the 50 to 90% percentile, the average net worth was $988,000.

At the same time, keep in mind that the assets required by the 4% rule to match what Social Security is paying retirees would be the assets as of the year they retire and start collecting Social Security retirement benefits.  Any individual builds up this entitlement to benefits over their working life, and thus the equivalent value when they are younger would be proportionally less.  Similarly, retirement assets will be drawn down during the years one is retired, so the equivalent assets as one ages after retirement will then also be less.

However, the important point is that Social Security could be seen as a significant addition to household wealth, and especially so for the least wealthy.  The DFA could have included that implicit wealth (with then a suitable adjustment in the treatment of the Social Security Trust Account in the federal government accounts) just as they did for private defined contribution pension plans, but chose not to.

I have given these examples to make clear that the wealth figures really are estimates, where those estimates need to be based on assumptions that can be questioned.  The approach taken is probably the best that has been done to estimate the distribution of wealth in the US, but the limitations should not be forgotten.

C.  Real Net Worth per Household, Stratified by Rank in Terms of Net Worth

There is extreme disparity in the distribution of wealth in the US.  Specific figures by category of household may provide a better sense of this, along with the ownership of the types of assets of each group.  As of the end of the second quarter of 2026:

2026Q2 per Household – in $thousands

Bottom 50%

50 to 90%

90 to 99%

99.0 to 99.9%

Top 0.1%
A.  Total Assets

$152

$1,153

$5,906

$27,454

$205,938
1) Real Estate

$71

$429

$1,240

$3,876

$14,377
2) Consumer durables

$32

$71

$158

$347

$5,413
3) Bank deposits & Money mkt accts

$12

$113

$583

$2,345

$17,790
4) Bonds/Loans

$1

$24

$214

$1,066

$9,418
5) Equities

$8

$180

$2,412

$17,164

$153,056
6) Retirement accts

$19

$304

$1,210

$2,234

$3,873
7) Other

$8

$30

$90

$421

$2,012
B. Total Liabilities

$89

$164

$346

$732

$2,200
1) Home mortgages

$47

$126

$280

$373

$857
2) Consumer credit

$39

$33

$43

$92

$383
3) Other

$3

$5

$23

$267

$960
C.  Net Worth

$63

$988

$5,559

$26,722

$203,738
o/w Net Real Estate

$25

$303

$959

$3,503

$13,520

Source:  Calculated from the data at Fed Board of Governors, Distributional Financial Accounts.  Downloaded September 18, 2026.

Households are ranked here by their net worth – from those with the least (and indeed negative) net worth to those with the most.  The DFA shows the totals held in categories for the bottom 50% of households, for those ranked between 50 and 90%, between 90 and 99%, between 99.0 and 99.9%, and for the top 0.1%, along with the number of households in each group (so one can calculate the per household averages).  As of the end of 2026Q2, there were approximately 135 million households in the US, so the bottom 50% covered the 67.5 million with the least net worth, and then so on to the top 0.1% with approximately 135,000 households.

The DFA data (in what is made publicly available) identify 15 categories of assets and 5 categories of liabilities.  The table above aggregates some of these into broader categories of interest.  The figures shown here are rounded to thousands, and for the reasons discussed in the preceding section of this post, need to be taken as approximate estimates.

Keep in mind also that these are averages over the households in each of the given categories.  The minimum net worth to be included in the categories (in 2022Q2 – the date of the most recent SCF survey used for this categorization) was $240,000 for the 50 to 99% group, $2.1 million for the 90 to 99% group, $11.0 million for the 99.0 to 99.9% group, and $46 million for the top 0.1%.

Also keep in mind that the categories cover individuals of all ages.  The trends over time do not follow the experience of any given individual, who might start out their working life with a very low net worth – and thus is in the bottom 50% of households – but who then accumulates wealth as they grow older and ends up in one of the wealthier groups.  Rather, the figures on the wealth holdings over time are for those in each of the groups (the bottom 50%, the 50 to 90%, and so on) at each point in time.

Furthermore, because the distribution of net worth is highly skewed to the right – both overall and within each category of net worth – the average holdings of a group will be greater than the median holdings.  That is, the median holding – the net worth where half of the households in the group hold less and half hold more – will be less than the averages shown in this table.  Since the DFA files released by the Fed only show total holdings (in nominal terms) and the number of households in each group, one can calculate averages but not medians.

The one exception where a median can be found is the dividing line in net worth between the bottom 50% of households and the next group up (the 50 to 90%).  As noted above, that dividing line in 2022Q2 was $240,000.  Since 50% of households have a net worth less than this, that will be the median for the nation as a whole.  But the average net worth of households in the nation as a whole was $1,043,000 as of the end of 2022Q2.  The average is more than four times the median.  As of the end of 2026Q2, the average net worth was $1,373,000.  (The figures in this paragraph are all in current price terms.)

But even with the qualifiers on the impact of age and the difference between the average and the median due to the skewness of the distributions, I still find it mind-boggling that the DFA estimates indicate that the top 0.1% – 135,000 households in the US – have on average a net worth as of the end of June 2026 of $203.7 million.

The chart at the top of this post shows how the average net worth of households ranked by their wealth percentile has grown since 1989Q3.  To allow for meaningful comparisons over time, these have been adjusted for inflation using the CPI-U index – the most commonly used consumer price index measure in the US.  They are expressed in terms of the prices of 2026Q2.

The average net worth of the top 0.1% of wealth-holders rose from $45.2 million in 1989Q3 (in prices of 2026Q2) to $203.7 million as of the end of 2026Q2.  The substantial increase in wealth of those in the 99.0 to 99.9% group can also be seen in the chart (from $9.4 million on average at the start to $26.7 million now).  But the scale of the chart is such that what happened for those in the bottom 99% is not clear.

Limiting the chart to just the groups in the bottom 99%, one has:

Chart 2

The average net worth of the 90 to 99% group rose over the period from $2.5 million to $5.6 million as of the end of 2026Q2.  The average net worth for the 50 to 90% group rose from $524,000 to $988,000.

The line for the bottom 50% is still not clear, so in a chart on its own:

Chart 3

The path is far from steady:  While average net worth for the bottom 50% rose over the full period from $41,200 to $63,300, it fell to a low of just $6,400 in 2010Q4 as a result of the 2008/09 economic and financial crisis.  This primarily reflected the crash in real estate values – assets which account for a disproportionate share of the wealth of the bottom 50%.  The average net value of their real estate wealth (i.e. the value of their real estate assets less that owed on home mortgages) was a negative $15,850 at the end of 2010.

Furthermore, while their net worth then recovered, it did not recover fully until 2020.  The average net worth of the bottom 50% was $44,500 in 2005Q4, and an even higher $50,500 in 2000Q3 near the end of the Clinton presidency.  But as of the end of the third quarter of 2016 (September 30, just before the November election that Trump won), the average net worth of the bottom 50% was only $25,600.  This was only half of what it had been 16 years before.  The disquiet and concern on economic prospects among many in the bottom half of the population should not be surprising.

There is tremendous inequality in net worth across households.  As of mid-2026, the bottom half had an average net worth of only $63,300.  The average net worth of the top 0.1% was over $200 million.  That is over 3,200 times as much.

D.  Inequality is Not Just High, But Also Has Grown

The disparities are gigantic.  But the disparities aren’t only huge:  they’ve grown.  Taking the net worth of each group as a ratio to their level in 1989Q3 (with all in real terms per household), one has:

Chart 4

The real net worth per household of the top 0.1% at the end of 2026Q2 grew to be 4.5 times what it was at the end of 1989Q3.  That of those in the 99.0 to 99.9% stratum was 2.8 times as high.  And so on to the bottom 50%, where it was 1.53 times as high.  The wealthier the group to start, the greater the growth in their wealth over the period.

There was no necessity for this.  Indeed, until around 2005, there were times when the relative growth in the net worth of less wealthy groups grew by more than that of some of the more wealthy groups.  But over extended periods, they did not.

But it is also not terribly surprising.  The more affluent can invest in riskier assets, and over extended periods such assets will generally achieve a higher rate of return.  The returns on riskier assets are subject to greater volatility, but over time their higher growth will win out.  This is aside from advantages the wealthy can engineer for themselves through their advantageous capacity to intervene in political processes and decision-making.

E.  Decomposing the Growth in Overall Wealth

The average real value of wealth per household in the US as a whole has gone up substantially over this time period.  It is shown as the thick line in black in Chart 4, and is – as one would expect – in the middle of the increases recorded for the various groups.  Overall net worth (in real terms and per household) in 2026Q2 was 2.34 times higher than what it was in 1989Q3.

What accounts for this?  To start, GDP has grown, but as we will see, the value of wealth has grown by far more.  Breaking this down, the rise has been due to a combination of real GDP growth, an increase in the share of GDP going to earnings on assets (profits), and a higher valuation multiple being placed on those earnings.

To start, profits grew as a share of GDP while the share of wages fell:

Chart 5

Gross profits (i.e. including depreciation allowances) rose from 36.4% of Gross Domestic Product as of the end of 1989Q2 to 43.6% as of the end of 2026Q2 – an increase (relative to the 36.4% share at the start) of almost 20%.  The share paid to employees (wages) fell from 55.4% of GDP to 50.3%.  As lower income groups are more dependent on wages for their income rather than profits, that fall in the wage share is significant.

[Side note:  The shares of wages plus profits in the chart do not add up to 100% of GDP.  The difference primarily reflects indirect taxes (such as excise taxes on fuels and tobacco, tariffs on imports, state and local property taxes, and more).  Indirect taxes are not counted as being paid out of wages or profits (direct income taxes are), and hence constitute a third category of income in the National Income and Product Accounts.  Those taxes have totaled a steady 6 to 7% of GDP throughout this period (except for a short period in 2020-2021 when they fell with the fall in spending due to Covid), and hence do not affect the trends seen for the wage and profit shares.

There will also be variation in the “statistical discrepancy” – the name the BEA uses for the difference between the estimated values of Gross Domestic Product (GDP) and Gross Domestic Income (GDI).  GDP and GDI should in principle be identical, as the value of all that is produced (GDP) will be someone’s income (GDI).  The statistical discrepancy is small – less than +/- 2% of GDP – but varies over time.  Wages, profits, and indirect taxes are estimated in the GDI accounts, and will sum to 100% of GDI.  But they are expressed here as shares of GDP, which, while close, will not sum to 100% of GDP due to the statistical discrepancy.]

A “valuation multiple” can next be defined as the ratio of wealth (i.e. net worth) for the country as a whole to the profits being earned.  It is akin to a price/earnings ratio often used in valuation measures of equity.  The valuation multiple over this period also rose:

Chart 6

The valuation multiple rose from 9.9 in 1989Q3 to 13.1 in 2026Q2 – an increase of 33%.  But it fluctuated a lot.  It rose to 11.9 in 2007, fell to as low as 9.6 in 2012 following the economic and financial collapse of 2008/2009, and then rose again.  It reached 13.6 in the last quarter of 2021, fell to 12.0 in 2022Q3, and ended at 13.1.

An astute observer might note that the gross profits being assessed here (as a share of GDP in Chart 5, and then as the denominator in the ratio in Chart 6) are the overall profits in the economy.  The corresponding wealth being valued based on those profits is then the wealth held by households.  Will this coincide with the total wealth in the economy as a whole?  The answer is that it will be close, although not exact.  The Fed’s Financial Accounts of the United States (FA), discussed in Section B above, estimates that the aggregate wealth of the US economy as of the end of 2026Q2 was $181.0 trillion (Table S1.b). The Fed’s Distributional Financial Accounts (DFA) estimate the aggregate wealth of households as of the end of 2026Q2 was $185.7 trillion.

These are close to each other but not the same for several reasons.  One is that some of the estimation methods and definitions are different in the DFA accounts than those in the FA accounts, as discussed in Section B above.  But they are close because the ownership of most assets and liabilities is ultimately attributed to households in the DFA.  Households may hold assets directly (such as homes), or via their ownership of equity in both corporate and non-corporate businesses, or their ownership of financial assets (e.g. deposits in banks, or direct ownership of bonds).  These account for most assets in the economy.

The main exception will be government assets – including federal, state, and local government assets.  These totaled $23.1 trillion as of the end of 2026Q2 in the FA accounts.  But largely offsetting this is the net claims on US assets by foreign entities.  These totalled $20.7 trillion, leaving a small net difference.  Coupled with definitional and estimation differences, the total net worth of all US households is reasonably close to the net assets of the economy as a whole and adequate for the purposes here.

Putting this together:  GDP per household rose in real terms, profits rose as a share of GDP, and the valuation multiple assigned to those profits also rose:

Chart 7

Putting numbers on this, real GDP per household rose by 47.2% over the period, profits as a share of GDP rose by 19.7%, and the valuation multiple rose by 32.7%.  Multiplying this out, the result is:  1.472 x 1.197 x 1.327 = 2.338.  That is, as of 2026Q2, overall real net worth per household rose to 2.338 times what it was in 1989Q3.  That corresponds to the line in blue in Chart 7, as well as the identical line for households as a whole in Chart 4 above (the thick black line).

Decomposed in this way, one sees that the increase in real GDP per household was the largest single factor behind the increase in overall wealth (with its increase of 47.2%).  The economy is producing more now than before.  The increase in the valuation multiple (by 32.7%) was the second most important factor.  Higher valuations are being assigned to assets for any given level of current profits.  And these were then amplified by profits growing as a share of GDP (by 19.7%).  All three were significant, and together led to wealth that is now valued at 2.338 times (i.e. 133.8% higher than) what it was in 1989Q3.

In stable conditions, one would expect wealth to grow in line with the growth in GDP.  But it increased by substantially more over the past 37 years because an increasing share of the income being generated in the economy (GDP) is going to profits rather than wages, and because the value being placed on the assets for a given level of profits (the valuation multiple) has also increased.  Together, they led to an increase in the value placed on that wealth for any given level of GDP by almost 59% (since 1.197 x 1.327 = 1.588).  A good question is why, but that is a topic for a separate discussion.

F.  Conclusion

Overall real wealth per household has grown significantly over the almost 37 years covered by this data series.  Adjusting for inflation using the CPI, it is now 2.338 times what it was at the start in 1989Q3.

But its distribution is highly skewed.  Ranking households by their net worth, the average net worth of the bottom 50% of households was an estimated $63,300 as of 2026Q2.  The average in the 50 to 90% group was an estimated $988,200.  In contrast, the average net worth of the top 0.1% was an estimated $203.7 million, and that in the 99.0 to 99.9% range was $26.7 million.

The disparities were already huge in 1989Q3 – the earliest period in this data series.  But those disparities then grew, with the estimated real net worth per household of the top 0.1% growing to 4.5 times what it was at the start, and that of the 99.0 to 99.9% growing to 2.8 times what it was at the start.  In contrast, while the real net worth per household of the bottom 50% grew, it grew to just 1.53 times what it was at the start.  It also fluctuated widely, and for 13 years (from 2007 to 2020) was less than what it was in 1989Q3.  This was primarily due to depressed values for their net real estate assets.  The value of those assets collapsed in 2007/08, remained depressed for several years, and recovered only slowly.  And real estate accounts for a high share of the net worth of the bottom 50%.

The purpose of this post has been to provide some of the basic figures on the distribution of wealth in the US and to describe how the underlying basic data were estimated by the Fed.  Future posts will examine other aspects of the distribution of wealth, including its distribution when groups are classified by income rather than their wealth holdings, and how wealth accumulation has changed across generations.  A final post will discuss what might be done to address these disparities.

A Carbon Tax with Redistribution Would Be a Significant Help to the Poor

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A.  Introduction

Economists have long recommended taxing pollution as an effective as well as efficient way to achieve societal aims to counter that pollution.  What is commonly called a “carbon tax”, but which in fact would apply to all emissions of greenhouse gases (where carbon dioxide, CO2, is the largest contributor), would do this.  “Cap and trade” schemes, where polluters are required to acquire and pay for a limited number of permits, act similarly.  The prime example in the US of such a cap and trade scheme was the program to sharply reduce the sulfur dioxide (SO2) pollution from the burning of coal in power plants.  That program was launched in 1995 and was a major success.  Not only did the benefits exceed the costs by a factor of 14 to 1 (with some estimates even higher – as much as 100 to 1), but the cost of achieving that SO2 reduction was only one-half to one-quarter of what officials expected it would have cost had they followed the traditional regulatory approach.

Cost savings of half or three-quarters are not something to sneer at.  Reducing greenhouse gas emissions, which is quite possibly the greatest challenge of our times, will be expensive.  The benefits will be far greater, so it is certainly worthwhile to incur those expenses (and it is just silly to argue that “we cannot afford it” – the benefits far exceed the costs).  One should, however, still want to minimize those costs.

But while such cost savings are hugely important, one should also not ignore the distributional consequences of any such plan.  These are a concern of many, and rightly so.  The poor should not be harmed, both because they are poor and because their modest consumption is not the primary cause of the pollution problem we are facing.  But this is where there has been a good deal of confusion and misunderstanding.  A tax on all greenhouse gas emissions, with the revenue thus generated then distributed back to all on an equal per capita basis, would be significantly beneficial to the poor in purely financial terms.  Indeed it would be beneficial to most of the population since it is a minority of the population (mostly those who are far better off financially than most) who account for a disproportionate share of emissions.

A specific carbon tax plan that would work in this way was discussed in an earlier post on this blog.  I would refer the reader to that earlier post for the details on that plan.  But briefly, under this proposal all emissions of greenhouse gases (not simply from power plants, but from all sources) would pay a tax of $49 per metric ton of CO2 (or per ton of CO2 equivalent for other greenhouse gases, such as methane).  A fee of $49 per metric ton would be equivalent to about $44.50 per common ton (2,000 pounds, as commonly used in the US but nowhere else in the world).  The revenues thus generated would then be distributed back, in full, to the entire population in equal per capita terms, on a monthly or quarterly basis.  There would also be a border-tax adjustment on goods imported, which would create the incentive for other countries to join in such a scheme (as the US would charge the same carbon tax on such goods when the source country hadn’t, but with those revenues then distributed to Americans).

The US Treasury published a study of this scheme in January 2017, and estimated that such a tax would generate $194 billion of revenues in its initial year (which was assumed to be 2019).  This would allow for a distribution of $583 to every American (man, woman, and child – not just adults).  Furthermore, the authors estimated what the impact would be by family income decile, and concluded that the bottom 7 deciles of families (the bottom 70%, as ranked by income) would enjoy a net benefit, while only the richest 30% would pay a net cost.

That distributional impact will be the focus of this blog post.  It has not received sufficient attention in the discussion on how to address climate change.  While the Treasury study did provide estimates on what the impacts by income decile would be (although not always in an easy to understand form), views on a carbon tax often appear to assume, incorrectly, that the poor will pay the most as a share of their income, while the rich will be able to get away with avoiding the tax.  The impact would in fact be the opposite.  Indeed, while the primary aim of the program is, and should be, the reduction of greenhouse gas emissions, its redistributive benefits are such that on that basis alone the program would have much to commend it.  It would also be just.  As noted above, the poor do not account for a disproportionate share of greenhouse gas emissions – the rich do – yet the poor suffer similarly, if not greater, from the consequences.

This blog post will first review those estimated net cash benefits by family income decile, both in dollar amounts and as a share of income.  To give a sense of how important this is in magnitude, it will then examine how these net benefits compare to the most important current cash transfer program in the US – food stamp benefits.  Finally, it will briefly review the politics of such a program.  Perceptions have, unfortunately, been adverse, and many pundits believe a carbon tax program would never be approved.  Perhaps this might change if news sources paid greater attention to the distribution and economic justice benefits.

B.  Net Benefits or Costs by Family Income Decile from a Carbon Tax with Redistribution

The chart at the top of this post shows what the average net impact would be in dollars per person, by family cash income decile, if a carbon tax of $49 per metric ton were charged with the revenues then distributed on an equal per capita basis.  While prices of energy and other goods whose production or use leads to greenhouse gas emissions would rise, the revenues from the tax thus generated would go back in full to the population.  Those groups who account for a less than proportionate share of greenhouse gas emissions (the poor and much of the middle class) would come out ahead, while those with the income and lifestyle that lead to a greater than average share of greenhouse gas emissions (the rich) will end up paying in more.

The figures are derived from estimates made by the staff of the US Treasury – staff that regularly undertake assessments of the incidence across income groups of various tax proposals.  The study was published in January 2017, and the estimates are of what the impacts would have been had the tax been in place for 2019.  The results were presented in tables following a standard format for such tax incidence studies, with the dollars per person impact of the chart above derived from those tables.

To arrive at these estimates, the Treasury staff first calculated what the impact of such a $49 per metric ton carbon tax would be on the prices of goods.  Such a tax would, for example, raise the price of gasoline by $0.44 per gallon based on the CO2 emitted in its production and when it is burned.  Using standard input-output tables they could then estimate what the price changes would be on a comprehensive set of goods, and based on historic consumption patterns work out what the impacts would be on households by income decile.  The net impact would then follow from distributing back on an equal per capita basis the revenues collected by the tax.  For 2019, the Treasury staff estimated $194 billion would be collected (a bit less than 1% of GDP), which would allow for a transfer back of $583 per person.

Those in the poorest 10% of households would receive an estimated $535 net benefit per person from such a scheme.  The cost of the goods they consume would go up by $48 per person over the course of a year, but they would receive back $583.  They do not account for a major share of greenhouse gas emissions because they cannot afford to consume much.  They are poor, and a family earning, say, $20,000 a year consumes far less of everything than a family earning $200,000 a year.  In terms of greenhouse gas emissions implicit in the Treasury numbers, the poorest 10% of Americans account only for a bit less than 1.0 metric tons of CO2 emissions per person per year (including the CO2 equivalent in other greenhouse gases).  The richest 10% account for close to 36 tons CO2 equivalent per person per year.

As one goes from the lower income deciles to the higher, consumption rises and CO2 emissions from the goods consumed rises.  But it is not a linear trend by decile.  Rather, higher-income households account for a more than proportionate share of greenhouse gas emissions.  As a consequence, the break-even point is not at the 50th percentile of households (as it would be if the trend were linear), but rather something higher.  In the Treasury estimates, households up through the 70th percentile (the 7th decile) would on average still come out ahead.  Only the top three deciles (the richest 30%) would end up paying more for the carbon tax than what they would receive back.  But this is simply because they account for a disproportionately high share of greenhouse gas emissions.  It is fully warranted and just that they should pay more for the pollution they cause.

But it is also worth noting that while the richer household would pay more in dollar terms than they receive back, those higher dollar amounts are modest when taken as a share of their high incomes:

In dollar terms the richest 10% would pay in a net $1,166 per person in this scheme, as per the chart at the top of this post.  But this would be just 1.0% of their per-person incomes.  The 9th decile (families in the 80 to 90th percentile) would pay in a net of 0.7% of their incomes, and the 8th decile would pay in a net of 0.3%. At the other end of the distribution, the poorest 10% (the 1st decile) would receive a net benefit equal to 8.9% of their incomes.  This is not minor.  The relatively modest (as a share of incomes) net transfers from the higher-income households permit a quite substantial rise (in percentage terms) in the incomes of poorer households.

C.  A Comparison to Transfers in the Food Stamps Program

The food stamps program (formally now called SNAP, for Supplemental Nutrition Assistance Program) is the largest cash income transfer program in the US designed specifically to assist the poor.  (While the cost of Medicaid is higher, those payments are made directly to health care providers for their medical services to the poor.)  How would the net transfers under a carbon tax with redistribution compare to SNAP?  Are they in the same ballpark?

I had expected they would not be close.  However, it turns out that they are not that far apart.  While food stamps would still provide a greater transfer for the very poorest households, the supplement to income that those households would receive by such a carbon tax scheme would be significant.  Furthermore, the carbon tax scheme would be of greater benefit than food stamps are, on average, for lower middle-class households (those in the 3rd decile and above).

The Congressional Budget Office (CBO) has estimated how food stamp (SNAP) benefits are distributed by household income decile.  While the forecast year is different (2016 for SNAP vs. 2019 for the carbon tax), for the purposes here the comparison is close enough.  From the CBO figures one can work out the annual net benefits per person under SNAP for households in the 1st to 4th deciles (with the 5th through the 10th deciles then aggregated by the CBO, as they were all small):

The average annual benefits from SNAP were estimated to be about $1,500 per person for households in the poorest decile and $690 per person in the 2nd decile.  These are larger than the estimated net benefits of these two groups under a carbon tax program (of $535 and $464 per person, respectively), but it was surprising, at least to me, that they are as close as they are.  The food stamp program is specifically targeted to assist the poor to purchase the food that they need.  A carbon tax with redistribution program is aimed at cutting back greenhouse gas emissions, with the funds generated then distributed back to households on an equal per capita basis.  They have very different aims, but the redistribution under each is significant.

D.  But the Current Politics of Such a Program Are Not Favorable

A carbon tax with redistribution program would therefore not only reduce greenhouse gas emissions at a lower cost than traditional approaches, but would also provide for an equitable redistribution from those who account for a disproportionate share of greenhouse gas emissions (the rich) to those who do not (the poor).  But news reporters and political pundits, including those who are personally in favor of such a program, consider it politically impossible.  And in what was supposed to be a personal email, but which was part of those obtained by Russian government hackers and then released via WikiLeaks in order to assist the Trump presidential campaign, John Podesta, the senior campaign manager for Hillary Clinton, wrote:  “We have done extensive polling on a carbon tax.  It all sucks.”

Published polls indicate that the degree of support or not for a carbon tax program depends critically on how the question is worded.  If the question is stated as something such as “Would you be in favor of taxing corporations based on their carbon emissions”, polls have found two-thirds or more of Americans in support.  But if the question is worded as something such as “Would you be in favor of paying a carbon tax on the goods you purchase”, the support is less (often still more than a majority, depending on the specific poll, but less than two-thirds).  But they really amount to the same thing.

There are various reasons for this, starting with that the issue is a complex one, is not well understood, and hence opinions can be easily influenced based on how the issue is framed.  This opens the field to well-funded vested interests (such as the fossil fuel companies) being able to influence votes by sophisticated advertising.  Opponents were able to outspend proponents by 2 to 1 in Washington State in 2018, when a referendum on a proposed carbon tax was defeated (as it had been also in 2016).  Political scientists who have studied the two Washington State referenda believe they would be similarly defeated elsewhere.

There appear to be two main concerns:  The first is that “a carbon tax will hurt the poor”.  But as examined above, the opposite would be the case.  The poor would very much benefit, as their low consumption only accounts for a small share of carbon emissions (they are poor, and do not consume much of anything), but they would receive an equal per capita share of the revenues raised.

In distinct contrast, but often not recognized, a program to reduce greenhouse gas emissions based on traditional regulation would still see an increase in costs (and indeed likely by much more, as noted above), but with no compensation for the poor.  The poor would then definitely lose.  There may then be calls to add on a layer of special subsidies to compensate the poor, but these rarely work well.

The second concern often heard is that “a carbon tax is just a nudge” and in the end will not get greenhouse gas emissions down.  There may also be the view (internally inconsistent, but still held) that the rich are so rich that they will not cut back on their consumption of high carbon-emission goods despite the tax, while at the same time the rich can switch their consumption (by buying an electric car, for example, to replace their gasoline one) while the poor cannot.

But the prices do matter.  As noted at the start of this post, the experience with the cap and trade program for SO2 from the burning of coal (where a price is put on the SO2 emissions) found it to be highly effective in bringing SO2 emissions down quickly.  Or as was discussed in an earlier post on this blog, charging polluters for their emissions would be key to getting utilities to switch use to clean energy sources.  The cost of both solar and wind new generation power capacity has come down sharply over the past decade, to the point where, for new capacity, they are the cheapest sources available.  But this is for new generation.  When there is no charge for the greenhouse gases emitted, it is still cheaper to keep burning gas and often coal in existing plants, as the up-front capital costs have already been incurred and do not affect the decision of what to use for current generation.  But as estimated in that earlier post, if those plants were charged $40 per ton for their CO2 emissions, it would be cheaper for the power utilities to build new solar or wind plants and use these to replace existing fossil fuel plants.

There are many other substitution possibilities as well, but many may not be well known when the focus is on a particular sector.  For example, livestock account for about 30% of methane emissions resulting from human activity.  This is roughly the same share as methane emissions from the production and distribution of fossil fuels.  And methane is a particularly potent greenhouse gas, with 86 times the global warming potential over a 20-year horizon of an equal weight of CO2.  Yet a simple modification of the diets of cows will reduce their methane emissions (due to their digestive system – methane comes out as burps and farts) by 33%.  One simply needs to add to their feed just 100 grams of lemongrass per day and the digestive chemistry changes to produce far less methane.  Burger King will now start to purchase its beef from such sources.

This is a simple and inexpensive change, yet one that is being done only by Burger King and a few others in order to gain favorable publicity.  But a tax on such greenhouse gas emissions would induce such an adjustment to the diets of livestock more broadly (as well as research on other dietary changes, that might lead to an even greater reduction in methane emissions).  A regulatory focus on emissions from power plants alone would not see this.  One might argue that a broader regulatory system would cover emissions from such agricultural practices, and in principle it should.  But there has been little discussion of extending the regulation of greenhouse gas emissions to the agricultural sector.

More fundamentally, regulations are set and then kept fixed over time in order to permit those who are regulated to work out and then implement plans to comply.  Such systems are not good, by their nature, at handling innovations, as by definition innovations are not foreseen.  Yet innovations are precisely what one should want to encourage, and indeed the ex-post assessment of the SO2 emissions trading program found that it was innovations that led to costs being far lower than had been anticipated.  A carbon tax program would similarly encourage innovations, while regulatory schemes can not handle them well.

There may well be other concerns, including ones left unstated.  Individuals may feel, for example, that while climate change is indeed a major issue and needs to be addressed, and that redistribution under a carbon tax program might well be equitable overall, that they will nonetheless lose.  And some will.  Those who account for a disproportionately high share of greenhouse gas emissions through the goods they purchase will end up paying more.  But costs will also rise under the alternative of a regulatory approach (and indeed rise by a good deal more), which will affect them as well.  If they do indeed account for a disproportionately high share of greenhouse gas emissions, they should be especially in favor of an approach that would bring these emissions down at the lowest possible cost.  A scheme that puts a price on carbon emissions, such as in a carbon tax scheme, would do this at a lower cost than traditional approaches.

So while many have concerns with a carbon tax with redistribution scheme, much of this is due to a misunderstanding of what the impacts would be, as well as of what the impacts would be of alternatives.  One sees this in the range of responses to polling questions on such schemes, where the degree of support depends very much on how the questions are worded or framed.  There is a need to explain better how a carbon tax with redistribution program would work, and we have collectively (analysts, media, and politicians) failed to do this.

There are also some simple steps one can take which would likely increase the attractiveness of such a program.  For example, perceptions would likely be far better if the initial rebate checks were sent up-front, before the carbon taxes were first to go into effect, rather than later, at the end of whatever period is chosen.  Instead of households being asked to finance the higher costs over the period until they received their first rebate checks, one would have the government do this.  This would not only make sense financially (government can fund itself more cheaply than households can), but more important, politically.  Households would see up-front that they are, indeed, receiving a rebate check before the prices go up to reflect the carbon tax.

And one should not be too pessimistic.  While polling responses depend on the precise wording used, as noted above, the polling results still usually show a majority in support.  But the issue needs to be explained better.  There are problems, clearly, when issues such as the impact on the poor from such a scheme are so fundamentally misunderstood.

E.  Conclusion 

Charging for greenhouse gases emitted (a carbon tax), with the revenues collected then distributed back to the population on an equal per capita basis, would be both efficient (lower cost) and equitable.  Indeed, the transfers from those who account for an especially high share of greenhouse gas emissions (the rich) to those who account for very little of them (the poor), would provide a significant supplement to the incomes of the poor.  While the redistributive effect is not the primary aim of the program (reducing greenhouse gases is), that redistributive effect would be both beneficial and significant.  It should not be ignored.

The conventional wisdom, however, is that such a scheme could not command a majority in a referendum.  The issue is complex, and well-funded vested interests (the fossil fuel companies) have been able to use that complexity to propagate a sufficient level of concern to defeat such referenda.  The impact on the poor has in particular been misportrayed.

But climate change really does need to be addressed.  One should want to do this at the lowest possible cost while also in an equitable manner.  Hopefully, as more learn what carbon tax schemes can achieve, politicians will obtain the support they need to move forward with such a program.

The Performance of the Stock Market During Trump’s Term in Office: Not So Special

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A.  Introduction

Stock market performance is often taken to be a good measure of how the economy as a whole is performing.  But it is not.  For most Americans it is simply irrelevant, as the overwhelming share of investments in the stock markets are held by only a small segment of the population (the wealthy).  And its track record as a broader indicator of how the economy is performing is imperfect at best.

Still, many do focus on stock market returns, and Trump brags that the performance of the market during his term in office has been spectacular.

That is not the case.  This post will look at how the stock market has performed during Trump’s term in office thus far, and compare it to what that performance was under presidents going back to Reagan up to the same point in their terms.

First, however, we will briefly discuss to what extent one should expect stock market prices to reflect actions a president might be taking.  And the answer is some, but there is much more going on.

B.  Presidential Policies and the Stock Market

Owning shares of a firm entitles the owner to a share of the profits generated by that firm, both now and into the future.  And while there are many complications, a simple metric commonly used to assess the price of a share in a firm, is the price/earnings ratio.  If earnings (profits) go up, now and into the future, then for a given price/earnings ratio the price of the stock would go up in proportion.

Economic policies affect profits.  And in a thriving economy, profits will also be rising.  The policies of a presidential administration will affect this, and although the link is far from a tight one (with important lags as well), policies that are good for the economy as a whole will generally also lead to a rising stock market.

But there is also a more specific link to policy.  What accrues to the shareholders are not overall profits, but profits after taxes.  And this changed significantly as a result of the new tax law pushed through Congress by Trump and the Republicans in December 2017.  It resulted in the effective corporate profits (income) tax being cut by more than half:

This chart is an update of one prepared for an earlier post on this blog (where one can see a further discussion of what lies behind it).  It shows corporate profit taxes at the federal level as a share of corporate profits (calculated from figures in the national income accounts issued by the BEA).  While Trump and the Republicans in Congress asserted the 2017 tax bill would not lead to lower corporate profit taxes being paid (as loopholes would be closed, they asserted), in fact they did.  And dramatically so, with the effective corporate tax rate being slashed by more than half –  from around 15 to 16% prior to 2017, to just 7% or so since the beginning of 2018 (and to just 6.3% most recently).

This cut therefore led to a significant increase in after-tax profits for any given level of before-tax profits, which has accrued to the shareholders.  Note that this would not be due to the corporations becoming more productive or efficient, but rather simply from taxing profits less and shifting the tax burden then on to others (i.e. a redistributive effect).  And based on a reduction in the taxes from 16% of corporate profits to 7%, after-tax profits would have gone from 84% of profits to 93%, an increase of about 11%.  For any given price/earnings ratio, one would then expect stock prices, for this reason alone, to have gone up by about 11%.

[Side note:  Technically one should include in this calculation also the impact of taxes on profits by other government entities – primarily those of state and local governments.  These have been flat at around 3 1/2% of profits, on average.  With these taxes included, after-tax profits rose from 80 1/2% of before-tax profits to 89 1/2%, an increase that is still 11% within round-off.]

One should therefore expect that stock prices following this tax cut (or in anticipation of it) would have been bumped up by an additional 11% above what they otherwise would have been.  Other things equal, the performance of the stock market under Trump should have looked especially good as a result of the shift in taxes away from corporations onto others.  But what has in fact happened?

C.  Trump vs. Obama

The chart at the top of this post compares the performance of the stock market during Trump’s term in office thus far (through December 31, 2019) to that under Obama to the same point in his first term in office.  The difference is clear.  Other than during Obama’s first few months in office, when he inherited from George W. Bush an economy in freefall, stock market performance under Obama was always better than it has been under Trump.  Even after slashing corporate profit taxes by more than half, the stock market under Trump did not do exceptionally well.

The S&P500 Index is being used as the measure of the US stock market.  Most professionals use this index as the best indicator of overall stock market performance, as it is comprehensive and broad (covering the 500 largest US companies as measured by stock market value, with the companies weighted in the index based on their market valuations).  The data were downloaded from Yahoo Finance, where it is conveniently available (with daily values for the index going back to 1927), but can be obtained from a number of sources.  The chart shows end-of-month figures, starting from December 31 of the month before inauguration, and going through to December 31 of their third year in office.  The index is scaled to 100.0 on exactly January 20 (with this presented as “month” 0.65).

So if one wants to claim “bragging rights” for which president saw a better stock market performance, Obama wins over Trump, at least so far in their respective terms.

D.  Trump vs. All Presidents Since Reagan

A comparison to just one president is limited.  How does the performance under Trump compare to that under other US presidents up to the same points in their terms in office?  Trump is roughly in the middle:

This chart tracks the performance under each president since Reagan up through the third year of their first terms in office.  I have adjusted here for inflation (using the CPI), as inflation was substantially higher during the Reagan and Bush Sr. terms in office than it has been since.  (I left the chart at the top of this post of just Obama vs. Trump in nominal terms as inflation in recent years has been steady and low.  But for those interested in the impact of this, one can see the Obama and Trump numbers in real terms in the current chart.)  I have included in this chart only the first terms of each president (with one exception) as the chart is already cluttered and was even more so when I had all the presidential terms.

The exception is that I included for perspective the stock market performance during Clinton’s second term in office.  The stock market rose over that period by close to 80% in real terms, which was substantially higher than under any other president since at least before Reagan in either their first or second terms.  The performance in Obama’s first term (of 146% in real terms) was the second-highest.  There was then a set of cases which, at the three-year mark, showed surprising uniformity in performance, with increases of between 32% and 34% in the second Reagan term, the first Clinton term, the second Obama term, and Trump’s term so far.  Bush Sr. was not far behind this set with an increase of 28%.

The worst performances were under Bush Jr. ( a fall of 22% to the third-year point in his first term), and Reagan (an increase of just 8% to that point in his first term).

So the performance of the market under Trump is in the middle – not the worst, but well below the best.

E.  Single Year Increases in the S&P500 from 1946 to 2019

Finally, was the increase under Trump in his best single year so far (2019) a record?  No, it was not.  Looking at the single year performances (in real terms) since 1946, the top 15 were:

The increase in 2019, of 25.9%, was good, but only the sixth-highest of the 74 years between 1946 and 2019 (inclusive).  The stock market rose by more in 2013 during Obama’s term in office (by 27.7%), and in 1997 (28.8%) and 1995 (30.8%) which were both Clinton years.  And the highest increases were in 1958 (35.7%) and 1954 (45.6%) when Eisenhower was president.

The market also rose substantially in 2017, in Trump’s first year in office, by 16.9%.  But it then fell by 8.0% in 2018, in Trump’s second year in office.  Overall, the average rank (out of the 74 years from 1946 to 2019) of the individual year performances over the three years Trump has been in office so far, would place Trump in the middle third.  Not the worst, but also far from the best.  And comparing the three-year average while Trump has been president to rolling three-year averages since 1946, Trump’s average (of 11.6%) is well below the best.  The highest was an average return of 25.3% in 1995-97 during Clinton’s term in office.  And the three-year average return was also higher at 16.7% in 2012-14 during Obama’s term.

F.  Summary and Conclusion

Trump likes to brag that the performance of the stock market during his term in office has been exceptional.  But despite a slashing of corporate profit taxes (which, other things being equal would be expected to increase stock prices by 11%), the performance of the market during Trump’s term in office would put him in the middle.  Specifically:

a)  The market rose by more during the first three years of Obama’s term in office than it has under Trump;

b)  Compared to the first three years in office of all presidents since Reagan (whether first terms only, or first and second terms) would place Trump in the middle.  Indeed, the increase under Trump so far was almost exactly the same as the increases seen (at the three-year point) in Obama’s second term, in Reagan’s second term, and in Clinton’s first term.  And the return under Trump was well below that seen in Obama’s first term, and especially far below that in Clinton’s second term.

c)  The individual year performances during Trump’s three years have also not been exceptional.  While the performance in 2019 was good, it was below that of a number of other years since World War II, and below that of individual years during Obama’s and Clinton’s terms in office.

But as noted at the start of this post, stock market returns should not be over-emphasized.  An increase in the stock market does little for those who do not have the wealth to have substantial holdings in the stock market, and as a broader indicator of how the overall economy is performing, stock market returns are imperfect at best.

Still, one should be accurate in one’s claims.  And as on many things, Trump has not been.