Taxes on Corporate Profits Have Collapsed

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A.  Introduction:  The Plunge in Corporate Profit Tax Revenues

Corporate profit tax revenues have collapsed following the passage by Congress last December of the Trump-endorsed Republican tax plan.  And this is not because corporate profits have decreased:  They have kept going up.  The initial figures, for the first half of 2018, show federal corporate profit taxes (also referred to as corporate income taxes) collected have fallen to an annual rate of roughly just $150 billion.  This is only half, or less, of the $300 to $350 billion collected (at annual rates) over the past several years.  See the chart above.

The estimates on corporate profit taxes actually being paid through the first half of 2018 come from the National Income and Product Accounts (NIPA, and commonly also referred to as the GDP accounts) produced by the Bureau of Economic Analysis.  The figures are collected as part of the process of producing the GDP accounts, but for various reasons the figures on corporate profit taxes are not released with the initial GDP estimates (which come out at the end of the month that follows the end of each quarter), but rather one month later (i.e. on August 29 this time, for the estimates for the April to June quarter).  The quarterly estimates are seasonally adjusted (which is important, as tax payments have a strong seasonality to them), and are then shown at annual rates.  While we already saw such a collapse in corporate tax revenues in the figures for the first quarter of 2018 (first published in May), it is always best with the estimates of GDP and its components to wait until a second quarter’s figures are available to see whether any change is confirmed.  And it was.

This initial data on what is actually now being collected in taxes following the passage of the Republican tax plan last December suggests that the revenue losses will be substantially higher than the $1.5 trillion over ten years that the staff at the Joint Committee on Taxation (the official arbiters for Congress on such matters) forecast.  Indeed, the plunge in corporate profit tax collections alone looks likely to well exceed this.  On top of this, there were also sharp cuts in non-corporate business taxes and in income taxes for those in higher income groups.

This blog post will look at what the initial figures are revealing on the tax revenues being collected, as estimated in the GDP accounts.  The focus will be on corporate income taxes, although in looking at the total tax revenue losses we will also look briefly at what the initial data is indicating on reductions in individual income taxes being paid.

The chart above shows what the reduction has been in corporate profit taxes in dollar terms.  In the next section below we will look at this in terms of the taxes as a share of corporate profits.  That implicit average actual tax rate is more meaningful for comparisons over time, and it has also plunged.  And the implicit actual rate now being paid, of only about 7% for the taxes at the federal government level, shows how misleading it is to focus on the headline rate of tax on corporate profits of 21% (down from 35% before the new law).  The actual rate being paid is only one-third of this, as a consequence of the numerous loopholes built into the law.  The Republican proponents of the bill had argued that while they were cutting the headline rate from 35% to 21%, they were also (they asserted) ending many of the loopholes which allowed corporations to pay less.  But in fact numerous loopholes were added or expanded.

The next section of the post will then look at this in the longer term context, with figures on the implicit corporate profit tax rate going back to 1950.  The implicit rate has fallen steadily over time, from a rate that reached over 50% in the early 1950s, to just 7% now.  While Trump and his Republican colleagues argued the cut in corporate taxes was necessary in order for the economy to grow, the economy in fact grew at a faster pace in the 1950s and 1960s, when the rate paid varied between 30 and 50%, than it has in recent decades despite the now far lower rates corporations face.

But this is for the federal tax on corporate profits alone.  There are also taxes on corporate profits imposed at the state and local level, as well as by foreign governments (although such foreign taxes are then generally deductible from the taxes due domestically).  This overall tax burden is more meaningful for understanding whether the overall burden is too high.  But, as we shall see below, that rate has also fallen steadily over time.  There is again no evidence that lower rates lead to higher growth.

The final substantive section of the post will then look more closely at the magnitude of the revenue losses from the December bill.  They are massive, and based on the initial evidence could very well total over $2 trillion over ten years for the losses on the corporate profit tax alone.  The losses from the other tax cuts in the new law, primarily for the wealthy and for non-corporate business, will add to this.  A very rough estimate is that the losses in individual income tax revenues may total an additional $1 trillion, bringing the total to over $3 trillion.  This is double the $1.5 trillion loss in revenues originally forecast.

But first, an analysis of what we see from the initial evidence on what is being paid.

B.  Profit Taxes as a Share of Corporate Profits

The chart at the top of this post shows what has been collected, by quarter (but shown at an annual rate), by the federal tax on corporate profits over the last several years.  Those figures are in dollars, and show a fall in the first half of 2018 of a half or more compared to what was collected in recent years.  But for comparisons over time, it is more meaningful to look at the implicit corporate tax rate, as corporate profits change over time (and generally grow over time).  And this can be done as the National Income and Product Accounts include an estimate of what corporate profits have been, as part of its assessment of how national income is distributed among the major functional groups.

That share since 2013 has been:

Between 2013 and 2016, the implicit rate (quarter by quarter) varied between about 15 and 17%.  It came down to about 14% for most of 2017 for some reason (possibly tied to the change in administration in Washington, with its new interpretation of regulatory and tax rules), but one cannot know from the aggregate figures alone.  But the rate then fell sharply, by half, to just 7% after the new tax law entered into effect.

A point to note is that the corporate profit figures provided here are corporate profits as estimated in the National Income and Product Accounts.  They are a measure of what corporate profits actually are, in an economic sense, and will in general differ from what corporate profits are as defined for tax purposes.  Thus, for example, accelerated depreciation allowed for tax purposes will reduce taxable corporate profits.  But the BEA estimates for the NIPA accounts will reflect not the accelerated depreciation allowed for tax purposes, but rather an estimate of what depreciation actually was.  Thus the figures as shown in the chart above will be a measure of what the true average corporate tax rate actually was, before the adjustments made (as permitted under tax law) to arrive at taxable corporate profits.

That average rate is now just 7%.  That is only one-third of the headline rate under the new law of 21%.  Provisions in the tax code allow corporations to pay far less in tax than what the headline rate would suggest.  This is not new (the headline rate previously was 35%, but the actual average rate paid was just 15 to 17% between 2013 and 2016, and 14% in most of 2017).  But Trump administration officials had asserted that many of the loopholes allowing for lower taxes would be ended under the new tax law, so that the actual rate paid would be closer to the headline rate.  But this clearly did not happen.  As many independent analysts pointed out before the bill was passed, the new tax law had numerous provisions which would allow the system to be gamed.  And we now see the result of that in the figures.

C.  Corporate Taxes in a Longer Term Context

The cuts in corporate profit taxes are not new.  Taxes on corporate profits in the US used to be far higher:

In the early 1950s, the federal tax on corporate profits (actually paid, not the headline rate) reached over 50%.  While it then fell, it kept to a rate of between about 30% and 50% through the 1950s and 60s.  And this was a period of good economic growth in the US – substantially faster than it has been since.  A high tax rate on corporate profits did not block growth.  Indeed, if one looked just at the simple correlation, one might conclude that a higher tax on corporate profits acts as a spur to growth.  But this would be too simplistic, and I would not argue that.  But what one can safely conclude is that a high rate of tax on corporate profits does not act as a block to more rapid growth.

There have also been important distributional consequences, however.  Corporate wealth is primarily owned by the wealthy (duh), and the sharp decline in taxes paid on corporate profits means that a larger share of the overall tax burden has been shifted to taxes on individual incomes, which are primarily borne by the middle classes.  Based on figures in the NIPA accounts, in 1950 taxes on individual incomes (including Social Security taxes) accounted for 47% of total federal taxes, while taxes on corporate profits accounted for 35% (with the rest primarily various excise taxes such as on fuels, liquor, tobacco, etc., plus import duties).  By 2017, however, the share of taxes on individual incomes had grown to 87.4%, while the share on corporate profits had declined to just 8.6%.  There was a gigantic shift away from taxes on wealth to taxes on individual incomes – taxes that are borne primarily by the middle class.  And that share will now fall further in 2018, by about half.

The chart above is for federal corporate profit taxes alone.  It could be argued that what matters to growth is not just the corporate profit taxes paid at the federal level, but all such taxes, including those paid at the state and local level, as well as to foreign governments (although the taxes paid abroad are generally deductible on their domestic taxes, so that will be a wash).

That chart looks like:

This follows the same path as the chart for federal corporate profit taxes alone, with a similar decline.  With the federal share of such taxes averaging 84% over the period (up to 2017), this is not surprising.  The federal share will now fall sharply in 2018, due to the new tax law.  But over the 1950 to 2017 period, the chart covering all taxes on corporate profits is basically a close to proportionate increase over what the tax has been at the federal level alone.

So the same pattern holds, and the total of the taxes on corporate profits varied between 33% to over 50% in the 1950s and 60s, to between 15 and 20% in recent years before the plunge in the first half of 2018 to just 10%.  But the relatively high taxes in the 1950s and 60s did not lead to slow growth in those years, nor did the low taxes in recent decades lead to more rapid growth.  Rather, one had the reverse.

D.  An Estimate of the Revenue Losses Due to the Tax Bill

These initial figures on the taxes actually being paid following the passage of the Republican tax bill allow us to make an estimate of what the revenue losses will turn out to be.  These will be very rough estimates, as we only have data for half a year, and one should be cautious in extrapolating this to what the losses will be over a decade.  But they can give us a sense of the magnitude.  And it is large.  As we will see below, based on the evidence so far the revenue losses (from the cuts in both corporate taxes and in personal income taxes) might be over $3 trillion over ten years, or about double the $1.5 trillion loss estimate originally forecast.

First, for the federal taxes on corporate profits, as the largest changes are there:  As was discussed before (and seen in the charts above), corporate profit taxes paid as a share of corporate profits were relatively flat between 2013 and 2016, varying between 15 and 17% each quarter, before falling to 14% for most of 2017.  For the full 2013 to 2017 period, the simple average was 15.3%.  The implicit rate then fell to just 7.0% in the first half of 2018.  Had the rate instead remained at 15.3%, corporate profit taxes collected in 2018 would have been $184 billion higher (on an annual basis).

This is not small, and is twice as high as the estimate of the staff of the Joint Committee on Taxation of revenue losses of $91 billion in FY2019 (the first full year under the new tax regime) from all the tax measures affecting businesses (including non-corporate businesses, and covering both domestic business and overseas business).  It is three times as high as the estimated loss of $60 billion in FY18, but the new tax law did not affect the first quarter of FY2018 (October to December).

One should be cautious with any extrapolation of this loss estimate going forward, as not only is the time period of actual experience under the new tax regime short (only a half year), but the law is also a complicated one, with certain provisions changing over time.  But a simple extrapolation over ten years, based on the assumption that corporate profits grow at just a modest 3% a year in nominal terms (meaning 1% a year in real terms if inflation is 2% a year), and that the tax rate on corporate profits will be 7.0% a year (as seen so far in 2018) rather than the 15.3% of recent years, implies that the reduction in corporate profit tax revenues will sum to about $2.1 trillion.

Note that the losses would be greater (everything else equal) if the assumed growth rate of corporate profits is higher.  But the results are not very sensitive to this.  The total losses over ten years would be $2.2 trillion, for example, at an assumed nominal growth rate of 4% (i.e. with inflation still at 2%, then with corporate profits growing at 2% a year in real terms, or double the 1% rate of the base scenario).  Note this also counters the argument of some that such tax cuts will lead to such a large spurt in growth that total tax collections will rise despite the cut in the rates.  As will be discussed below, there is no evidence that this has ever been the case in the US.  But even assuming there were, the argument is undermined by the basic arithmetic.  In the example here, a doubling of the assumed growth rate of profits (from 1% in real terms to 2%) would imply taxes on corporate profits would still fall by $2.0 trillion over the next ten years.  This is not far from the $2.1 trillion loss if there is no rise at all in the growth of corporate profits.  And a doubling of the real growth rate is far above what anyone would reasonably assume could follow from such a cut in the tax rate.

Second, there were also substantial cuts in individual income taxes, although primarily for the wealthy.  While far less in proportional terms, the substantially higher taxes that are now paid by individuals than by corporations means that this is also significant for the totals.

Specifically, individual (federal) income taxes as a share of GDP in the NIPA accounts were quite steady in the quarterly GDP accounts for the period from 2015Q1 to 2017Q4, varying only between 8.22% and 8.44%.  The average was 8.31%.  But then this fell to an average of 7.89% in the first half of 2018 (7.90% in the first quarter, and 7.87% in the second quarter).  Had the rate remained at 8.31%, then $86 billion more in revenues (at an annual rate) would have been collected.

Extrapolating this out for ten years, assuming again just a modest rate of growth for GDP of 3% a year in nominal terms (i.e. just 1% a year in real terms if inflation is 2% a year), the total loss would be $1.0 trillion.  With a higher rate of growth, and everything else the same, the losses would again be larger.  This extrapolation is, however, particularly fraught, as the Republicans wrote into their bill that the cuts in individual taxes would be reversed in 2026.  They did this to keep the forecast cost of the tax bill to the $1.5 trillion envelope they had set, and an effort is already underway to make this permanent (Speaker Paul Ryan has said he will schedule a vote on this in September).  But even if we left out the tax revenue losses in the final two years of the period, the losses in individual taxes would still reach about $0.8 trillion.

Adding the lower revenues from the taxes on corporate profits and the taxes on individual incomes, the total revenue losses would come, over the ten years, to about $3 trillion.  This is double the $1.5 trillion loss that had been forecast.  It is not a small difference.

To give a sense of the magnitude, the loss in 2018 alone (a total of $270 billion) would allow a doubling of the entire budgets (based on FY2017 actual outlays) of the Departments of Education, Housing and Urban Development, and Labor; the Environmental Protection Agency; all international assistance programs (foreign aid); NASA; the National Science Foundation; the Army Corps of Engineers (civil works); and the Small Business Administration.  Note I am not arguing that all of their budgets should necessarily be doubled (although many should, indeed, be significantly increased).  Rather, the point is simply to give readers a sense of the size of the revenues lost as a consequence of the tax cut bill.

As another comparison to give a sense of the magnitude, just half of the lost revenue (now and into the future) would suffice to fund fully the Social Security Trust Fund for the foreseeable future.  If nothing is done, the Social Security Trust Fund will run out at some point around 2034.  Republicans have asserted that nothing can be done for Social Security except to scale back (already low) Social Security pensions.  This is not true.  Just half of the revenues that will be lost under the tax cut bill would suffice to ensure the pensions can be paid in full for at least 75 years (the forecast period used by the Social Security trustees).

But as noted above, proponents of the tax cuts argue that the lower taxes will spur growth.  This has been discussed in earlier posts on this blog, where we have seen that there is no evidence that this will follow.  There are not only basic conceptual problems with this argument (a misreading of basic economics), but also no indication in what we have in fact observed for the economy that this has ever been the case (whether in the years immediately following the major tax cuts of Reagan or Bush, nor if one focuses on the longer term).

Administration officials have not surprisingly argued that the relatively rapid pace of growth in the second quarter of 2018 (of 4.2% at an annual rate in the end-August BEA estimates) is evidence of the tax cut working as intended.  But it is not.  Not only should one not place much weight on one quarter’s figures (the quarterly figures bounce around), but this followed first-quarter figures which were modest at best (with GDP growth of an estimated 2.2% at an annual rate).

But more fundamentally, one should dig into the GDP figures to see what is going on.  The argument that tax cuts (especially cuts in corporate profit taxes) will spur growth is based on the presumption that such cuts will spur business investment.  More such investment, especially in equipment, could lead to higher productivity and hence higher growth.  But growth in business investment in equipment has slowed in the first half of 2018.  Such investment grew at the rates of 9.1%, 9.7%, 9.8%, and 9.9% through the four quarters of 2017 (all at annual rates).  It then decelerated to a pace of 8.5% in the first quarter of 2018 and to a pace of 4.4% in the second quarter.  While still early (these figures too bounce around a good deal), the evidence so far is the exact opposite of what proponents have argued the tax cut bill would do.

So what might be going on?  As noted before, there is first of all a good deal of volatility in the quarterly figures for GDP growth.  But to the extent growth has accelerated this year, a more likely explanation is simple Keynesian stimulus.  Taxes were cut, and while most of the cuts went to the rich, some did go to the lower and middle classes.  In addition, government spending is now rising, while it been kept flat or falling for most of the Obama years (since 2010).  It is not surprising that such stimulus would spur growth in the short run.

The problem is that with the economy now running at or close to full capacity, such stimulus will not last for long.  And when it was needed, in the years from 2010 until 2016, as the economy recovered from the 2008/09 downturn (but slowly), such stimulus measures were repeatedly blocked by a Republican-controlled Congress.  This sequence for fiscal policy is the exact opposite of the path that should have been followed.  Contractionary policies were followed after 2010 when unemployment was still high, while expansionary fiscal policies are being followed now, when unemployment is low.  The result is that the fiscal deficit is rising soon to exceed $1 trillion in a year (5% of GDP), which is unprecedented for a period with the economy at full employment.

E.  Conclusion

We now have initial figures on what is being collected in taxes following the tax cut bill of last December.  While still early, the figures for the first two quarters of 2018 are nonetheless clear for corporate profit taxes:  They have fallen by half.  Corporate profit taxes paid would be an estimated $184 billion higher in 2018 had the tax rate remained at the level it had been over the last several years.

While this post has not focused on personal income taxes, they too were cut.  The reduction here was more modest – only by about 5% overall (although certain groups got far more, while others less).  But with their greater importance in overall federal tax collections, this 5% reduction is leading to an estimated $86 billion reduction in revenues (in 2018) from this source.

Based on what has been observed in the first two quarters of 2018, the two taxes together (corporate and individual) will see a combined reduction in taxes paid of about $270 billion in 2018.  Extrapolating over ten years, the combined losses may be on the order of $3 trillion.

These losses are huge.  And they are double what had been earlier forecast for the tax bill.  Just half of what is being lost would suffice to ensure Social Security would be fully funded for the foreseeable future.  And the rest could fund programs to rebuild and strengthen the physical infrastructure and human capital on which growth ultimately depends.  Or some could be used to reduce the deficit and pay down the public debt.  But instead, massive tax cuts are going to the rich.

Why Do the Quarterly GDP Figures Bounce Around So Much?: Econ 101

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A.  Introduction

The Bureau of Economic Analysis (BEA) released on July 27 its initial estimate of GDP growth in the second quarter of 2018 (what it technically calls its “advance estimate”).  It was a good report:  Its initial estimate is that GDP grew at an annualized rate of 4.1% in real terms in the quarter.  Such growth, if sustained, would be excellent.

But as many analysts noted, there are good reasons to believe that such a growth rate will not be sustained.  There were special, one-time, factors, such as that the second quarter growth (at a 4.1% annual rate) had followed a relatively modest rate of growth in the first quarter of 2.2%.  Taking the two together, the growth was a good, but not outstanding, rate of 3.1%.

More fundamentally, with the economy now at full employment, few (other than Trump) expect growth at a sustained rate of 4% or more.  Federal Reserve Board members, for example, on average expect GDP growth of 2.8% in 2018 as a whole, with this coming down to a rate of 1.8% in the longer run.  And the Congression Budget Office (in forecasts published in April) is forecasting GDP growth of 3.0% in 2018, coming down to an average rate of 1.8% over 2018 to 2028.  The fundamental issue is that the population is aging, so the growth rate of the labor force is slowing.  As discussed in an earlier post on this blog, unless the productivity of those workers started to grow at an unprecedented rate (faster than has ever been achieved in the post-World War II period), we cannot expect GDP to grow for a sustained period going forward at a rate of 3%, much less 4%.

But there will be quarter to quarter fluctuations.  As seen in the chart at the top of this post covering the period just since 2006, there have been a number of quarters in recent years where GDP grew at an annualized rate of 4% or more.  Indeed, growth reached 5.1% in the second quarter of 2012, with this followed by an also high 4.9% rate in the next quarter.  But it then came back down.  And there were also two quarters (setting aside the period of the 2008/09 recession) which had growth of a negative 1.0%.  On average, GDP growth was around 2% (at an annual rate) during Obama’s two terms in office (2.2% annually from the end of the recession in mid-2009).

Seen in this context, the 4.1% rate in the initial estimate for the second quarter of 2018 was not special.  There have been a number of such cases (and with even substantially higher growth rates for a quarter or even two) in the recent past, even though average growth was just half that.  The quarterly rates bounce around.  But it is of interest to examine why they bounce around so much, and that is the purpose of this blog post.

B.  Reasons for this Volatility

There are a number of reasons why one should not be surprised that these quarter to quarter growth rates in GDP vary as they do.  I will present several here.  And note that these reasons are not mutually exclusive.  Several of them could be acting together and be significant factors in any given quarter.

a)  There may have been actual changes in growth:

To start, and to be complete, one should not exclude the possibility that the growth in the quarter (or the lack of it) was genuine.  Perhaps output did speed up (or slow down) as estimated.  Car plants might go on extra shifts (or close for a period) due to consumers wanting to buy more cars (or fewer cars) in the period for some reason.  There might also be some policy change that might temporarily spur production (or the opposite).  For example, Trump’s recent trade measures, and the response to them from our trading partners, may have brought forward production and trade that would have been undertaken later in the year, in order to avoid tariffs threatened to be imposed later.  This could change quarterly GDP even though GDP for the year as a whole will not be affected positively (indeed the overall impact would likely be negative).

[Side note:  But one special factor in this past quarter, cited in numerous news reports (see, for example, here, here, here, here, and here), was that a jump in exports of soybeans was a key reason for the higher-than-recently-achieved rate of GDP growth.  This was not correct.  Soybean exports did indeed rise sharply, with this attributed to the response threatened by China and others to the new tariffs Trump has imposed.  China and others said they would respond with higher tariffs of their own, targeted on products such as soybeans coming from the US.  There was thus a rush to export soybeans in the period between when China first announced they would impose such retaliatory tariffs (in late March) and when they were then imposed (ultimately on July 6).

But while soybean exports did indeed increase sharply in the April to June quarter, soybeans are a crop that takes many months to grow.  Whatever increase in shipments there was had to come out of inventories.  An increase in exports would have to be matched by a similar decrease in inventories, with this true also for corn and other such crops.  There would be a similar issue for any increase in exports of Kentucky bourbon, also a target of retaliatory tariffs.  Any decent bourbon is aged for at least three years.

One must keep in mind that GDP (Gross Domestic Product) is a measure of production, and the only production that might have followed from the increased exports of soybeans or similar products would have been of packing and shipping services.  But packing and shipping costs are only a relatively small share of the total value of the products being exported.

Having said that, one should not then go to the opposite extreme and assume that the threatened trade war had no impact on production and hence GDP in the quarter.  It probably did.  With tariffs and then retaliatory tariffs being threatened (but to be imposed two or three months in the future), there were probably increased factory orders to make and ship various goods before such new tariffs would enter into effect.  Thus there likely was some impact on GDP, but to an extent that cannot be quantified in what we see in the national level accounts.  And with such factory orders simply bringing forward orders that likely would have been made later in the year, one may well see a fallback in the pace of GDP growth in the remainder of the year.  But there are many other factors as well affecting GDP growth, and we will need to wait and see what the net impact will be.]

So one should not exclude the possibility that the fluctuation in the quarterly growth rate is real.  But it could be due to many other factors as well, as we will discuss below.

b)  Change at an Annualized Rate is Not the Change in a Quarter:

While easily confused, keep in mind also that in the accounts as normally published and presented in the US, the rates of growth of GDP (and of the other economic variables) are shown as annual equivalent rates.  The actual change in the quarter is only about one-fourth of this (a bit less due to compounding).  That is, in the second quarter of 2018, the BEA estimated that GDP (on a seasonally adjusted basis, which I will discuss below as a separate factor) grew by 1.00% (and yes, exactly 1.00% within two significant digits).  But at an annualized rate (some say “annual rate”, and either term can be used), this would imply a rate of growth of 4.06% (which rounded becomes 4.1%).  It is equal to slightly more than 4.0% due to compounding.  [Technically, 1% growth in the quarter means 1.00 will grow to 1.01, and taking 1.01 to the fourth power yields 1.0406, or an increase of 4.06%.]

Thus it is not correct to say that “GDP grew by 4.1% in the second quarter”.  It did not – it grew by 1.0%.  What is correct is to say that “GDP grew at an annualized rate of 4.1% in the second quarter”.

Not all national statistical agencies present such figures in annualized terms.  European agencies, for example, generally present the quarterly growth figures as simply the growth in the quarter.  Thus, for example, Eurostat on June 7 announced that GDP in the eurozone rose by an estimated 0.4% in the first quarter of 2018.  This 0.4% was the growth in the quarter.  But that 0.4% growth figure would be equivalent to growth of 1.6% on an annualized basis (actually 1.61%, if the growth had been precisely 0.400%).  Furthermore, the European agencies will generally also focus on the growth in GDP over what it had been a year earlier in that same quarter.  In the first quarter of 2018, this growth over the year-earlier period was an estimated 2.5% according to the Eurostat release.  But the growth since the year-earlier period is not the same as the growth in the current quarter at an annualized rate.  They can easily be confused if one is not aware of the conventions used by the different agencies.

c)  Don’t confuse the level of GDP with the change in GDP:

Also along the lines of how we might misleadingly interpret figures, one needs to keep in mind that while the quarterly growth rates can, and do, bounce around a lot, the underlying levels of GDP are really not changing much.  While a 4% annual growth rate is four times as high as a 1% growth rate, for example, the underlying level of GDP in one calendar quarter is only increasing to a level of about 101 (starting from a base of 100 in this example) with growth at a 4% annual rate, versus to a level of 100.25 when  growth is at an annual rate of 1%.  While such a difference in growth rates matters a great deal (indeed a huge deal) if sustained over time, the difference in any one quarter is not that much.

Indeed, I personally find the estimated quarter to quarter levels of GDP in the US (after seasonal adjustment, which will be discussed below) to be surprisingly stable.  Keep in mind that GDP is a flow, not a stock.  It is like the flow of water in a river, not a stock such as the body of water in a reservoir.  Flows can go sharply up and down, while stocks do not, and some may mistakenly treat the GDP figures in their mind as a stock rather than a flow.  GDP measures the flow of goods and services produced over some period of time (a calendar quarter in the quarterly figures).  A flow of GDP to 101 in some quarter (from a base of 100 in the preceding quarter) is not really that different to an increase to 100.25.  While this would matter (and matter a good deal) if the different quarterly increases are sustained over time, this is not that significant when just for one quarter.

d)  Statistical noise matters:

Moving now to more substantive reasons why one should expect a significant amount of quarter to quarter volatility, one needs to recognize that GDP is estimated based on surveys and other such sources of statistical information.  The Bureau of Economic Analysis (BEA) of the US Department of Commerce, which is responsible for the estimates of the GDP accounts in the US (which are formally called the National Income and Product Accounts, or NIPA), bases its estimates on a wide variety of surveys, samples of tax returns, and other such partial figures.  The estimates are not based on a full and complete census of all production each quarter.  Indeed, such an economic census is only undertaken once every five years, and is carried out by the US Census Bureau.

One should also recognize that an estimate of real GDP depends on two measures, each of which is subject to sampling and other error.  One does not, and cannot, measure “real GDP” directly.  Rather, one estimates what nominal GDP has been (based on estimates in current dollars of the value of all economic transactions that enter into GDP), and then how much prices have changed.  Price indices are estimated based on the prices of surveyed samples, and the components of real GDP are then estimated from the nominal GDP of the component divided by the relevant price index.  Real GDP is only obtained indirectly.

There will then be two sets of errors in the measurements:  One for the nominal GDP flows and one for the price indices.  And surveys, whether of income flows or of prices, are necessarily partial.  Even if totally accurate for the firms and other entities sampled, one cannot say with certainty whether those sampled in that quarter are fully representative of everyone in the economy.  This is in particular a problem (which the BEA recognizes) in capturing what is happening to newly established firms.  Such firms will not be included in the samples used (as they did not exist when the samples were set up) and the experiences of such newly established firms can be quite different from those of established firms.

And what I am calling here statistical “noise” encompasses more than simply sampling error.  Indeed, sampling error (the fact that two samples will come up with different results simply due to the randomness of who is chosen) is probably the least concern.  Rather, systemic issues arise whenever one is trying to infer measures at the national level from the results found in some survey.  The results will depend, for example, on whether all the components were captured well, and even on how the questions are phrased.  We will discuss below (in Section C, where we look at a comparison of estimates of GDP to estimates of Gross Domestic Income, or GDI, which in principle should be the same) that the statistical discrepancy between the estimates of GDP and GDI does not vary randomly from one quarter to the next but rather fairly smoothly (what economists and statisticians call “autocorrelation” – see Section C).  This is an indication that there are systemic issues, and not simply something arising from sample randomness.

Finally, even if that statistical error was small enough to allow one to be confident that we measured real GDP within an accuracy of just, say, +/- 1%, one would not then be able to say whether GDP in that quarter had increased at an annualized rate of about 4%, or decreased by about 4%.  A small quarterly difference looms large when looked at in terms of annualized rates.

I do not know what the actual statistical error might be in the GDP estimates, and it appears they are well less than +/- 1% (based on the volatility actually observed in the quarter to quarter figures).  But a relatively small error in the estimates of real GDP in any quarter could still lead to quite substantial volatility in the estimates of the quarter to quarter growth.

e)  Seasonal adjustment is necessary, but not easy to do:

Economic activity varies over the course of the year, with predictable patterns.  There is a seasonality to holidays, to when crops are grown, to when students graduate from school and enter the job market, and much much more.  Thus the GDP data we normally focus on has been adjusted by various statistical methods to remove the seasonality factor, making use of past data to estimate what the patterns are.

The importance of this can be seen if one compares what the seasonally adjusted levels of GDP look like compared to the levels before seasonal adjustment.  Note the level of GDP here is for one calendar quarter – it will be four times this at an annual rate:

There is a regular pattern to GDP:  It is relatively high in the last quarter of each year, relatively low in the first quarter, and somewhere in between in the second and third quarters.  The seasonally adjusted series takes account of this, and is far smoother.  Calculating quarterly growth rates from a series which has not been adjusted for seasonality would be misleading in the extreme, and not of much use.

But adjusting for seasonality is not easy to do.  While the best statisticians around have tried to come up with good statistical routines to do this, it is inherently difficult.  A fundamental problem is that one can only look for patterns based on what they have been in the past, but the number of observations one has will necessarily be limited.  If one went back to use 20 years of data, say, one would only have 20 observations to ascertain the statistical pattern.  This is not much.  One could go back further, but then one has the problem that the economy as it existed 30 or 40 years ago (and indeed even 20 years ago) was quite different from what it is now, and the seasonal patterns could also now be significantly different.  While there are sophisticated statistical routines that have been developed to try to make best use of the available data (and the changes observed in the economy over time), this can only be imperfect.

Indeed, the GDP estimates released by the BEA on July 27 incorporated a number of methodological changes (which we will discuss below), one of which was a major update to the statistical routines used for the seasonal adjustment calculations.  Many observers (including at the BEA) had noted in recent years that (seasonally adjusted) GDP growth in the first quarter of each year was unusually and consistently low.  It then recovered in the second quarter.  This did not look right.

One aim of the update to the seasonal adjustment statistical routines was to address this issue.  Whether it was fully successful is not fully clear.  As seen in the chart at the top of this post (which reflects estimates that have been seasonally adjusted based on the new statistical routines), there still appear to be significant dips in the seasonally adjusted first quarter figures in many of the years (comparing the first quarter GDP figures to those just before and just after – i.e. in 2007, 2008, 2010, 2011, 2014, and perhaps 2017 and 2018.  This would be more frequent than one would expect if the residual changes were now random over the period).  However, this is an observation based just on a simple look at a limited sample.  The BEA has looked at this far more carefully, and rigorously, and believes that the new seasonal adjustment routines it has developed have removed any residual seasonality in the series as estimated.

f)  The timing of weekends and holidays may also enter, and could be important:

The production of the goods and services that make up the flow of GDP will also differ on Saturdays, Sundays, and holidays.  But the number of Saturdays, Sundays, and certain holidays may differ from one year to the next.  While there are normally 13 Saturdays and 13 Sundays in each calendar quarter, and most holidays will be in the same quarter each year, this will not always be the case.

For example, there were just 12 Sundays in the first quarter of 2018, rather than the normal 13.  And there will be 14 Sundays in the third quarter of 2018, rather than the normal 13.  In 2019, we will see a reversion to the “normal” 13 Sundays in each of the quarters.  This could have an impact.

Assume, just for the sake of illustration, that production of what goes into GDP is only one-half as much on a Saturday, Sunday, or holiday, than it is on a regular Monday through Friday workday.  It will not be zero, as many stores, as well as certain industrial plants, are still open, and I am just using the one-half for illustration.  Using this, and based on a simple check of the calendars for 2018 and 2019, one will find there were 62 regular, Monday through Friday, non-holiday workdays in the first quarter of 2018, while there will be 61 such regular workdays in the first quarter of 2019.  The number of Saturdays, Sundays, and holidays were 28 in the first quarter of 2018 (equivalent to 14 regular workdays in terms of GDP produced, assuming the one-half figure), while the number of Saturdays, Sundays, and holidays will be 29 in the first quarter of 2019 (equivalent to 14.5 regular workdays).  Thus the total regular work-day equivalents will be 76 in 2018 (equal to 62 plus 14), falling to 75.5 in 2019 (equal to 61 plus 14.5).  This will be a reduction of 0.7% between the periods in 2018 and 2019 (75.5/76), or a fall of 2.6% at an annualized rate.  This is not small.

The changes due to the timing of holidays could matter even more, especially for certain countries around the world.  Easter, for example, was celebrated in March (the first quarter) in 2013 and 2016, but came in April (the second quarter) in 2014, 2015, 2017, and 2018.  In Europe and Latin America, it is customary to take up to a week of vacation around the Easter holidays.  The change in economic activity from year to year, with Easter celebrated in one quarter in one year but a different one in the next, will make a significant difference to economic activity as measured in the quarter.

And in Muslim countries, Ramadan (a month of fasting from sunrise to sunset), followed by the three-day celebration of Eid al-Fitr, will rotate through the full year (in terms of the Western calendar) as it is linked to the lunar cycle.

Hence it would make sense to adjust the quarterly figures not only for the normal seasonal adjustment, but also for any changes in the number of weekends and holidays in some particular calendar quarter.  Eurostat and most (but not all) European countries make such an adjustment for the number of working days in a quarter before they apply the seasonal adjustment factors.  But I have not been able to find how the US handles this.  The adjustment might be buried somehow in the seasonal adjustment routines, but I have not seen a document saying this.  If no adjustment is made, then this might explain part of the quarterly fluctuations seen in the figures.

g)  There have been, and always will be, updates to the methodology used:

As noted above, the GDP figures released on July 27 reflected a major update in the methodology followed by the BEA to arrive at its GDP estimates.  Not only was there extensive work on the seasonal adjustment routines, but there were definitional and other changes.  The accounts were also updated to reflect the findings from the 2012 Economic Census, and prices were changed from a previous base of 2009 to now 2012.  The July 27 release summarized the changes, and more detail on what was done is available from a BEA report issued in April.  And with the revisions in definitions and certain other methodological changes, the BEA revised its NIPA figures going all the way back to 1929, the first year with official GDP estimates.

The BEA makes such changes on a regularly scheduled basis.  There is normally an annual change released each year with the July report on GDP in the second quarter of the year.  This annual change incorporates new weights (from recent annual surveys) and normally some limited methodological changes, and the published estimates are normally then revised going back three and a half years.  See, for example, this description of what was done in July 2017.

On top of this, there is then a much larger change once every five years.  The findings from the most recent Economic Census (which is carried out every five years) are incorporated, seasonal adjustment factors are re-estimated, and major definitional or methodological changes may be incorporated.  The July 2018 release reflected one of those five-year changes.  It was the 15th such comprehensive revision to the NIPA accounts undertaken by the BEA.

I stress this to make the point that the GDP figures are estimates, and as estimates are always subject to change.  The professionals at the BEA are widely admired around the world for the quality of their work, and do an excellent job in my opinion.  But no estimates will ever be perfect.  One has to recognize that there will be a degree of uncertainty surrounding any such estimates, and that the quarter to quarter volatility observed will derive at least in part from the inherent uncertainty in any such estimates.

C.  Estimates of GDP versus Estimates of GDI

One way to develop a feel for how much the changes in quarterly GDP may be due to the inherent uncertainty in the estimates is to compare it to the estimated quarterly changes in Gross Domestic Income (GDI).  GDP (Gross Domestic Product) measures the value of everything produced.  GDI measures the value of all incomes (wages, profits, rents, etc.) generated.  In principle, the totals should be the same, as the value of whatever is produced accrues to someone as income.  They should add up to the same thing.

But the BEA arrives at its estimates of GDP and of GDI by different routes.  As a consequence, the estimates of the totals will then differ.  The differences are not huge in absolute amount, nor have they grown over time (as a share of GDP or of GDI).  That is, on average the estimates match each other over time, with the same central tendency.  But they differ by some amount in any individual quarter, and hence the quarter to quarter growth rates will differ.  And for the reasons reviewed above, those slight changes in the levels in any individual quarter can translate into often major differences in the growth rates from one quarter to the next.  And these differences may appear to be particularly large when the growth rates are then presented in annualized terms.

For the period since 2006, the two sets of growth rates were (where the initial estimate for the second quarter of 2018 will not be available until the end-August figures come out):

As is seen, the alternative estimates of growth in any individual quarter can be quite different.  There was an especially large difference in the first quarter of 2012, when the estimated growth in GDP was 3.2% at an annual rate, while the estimated growth in GDI was a giant 8.7%.

Which is correct?  Was the growth rate in the first quarter of 2012 3.2% (as found with the GDP estimate) or 8.7% (as found with the GDI estimate)?  The answer is we do not know, and indeed that probably neither is correct.  What is most likely is that the true figure is probably somewhere in between.

Furthermore, and also moderating what the impact on the differences in the respective estimated growth rates will be, it is not the case that the estimates of GDP and GDI are statistically independent of each other, with the two bouncing around randomly with respect to each other.  Rather, if one looks at what the BEA calls the “statistical discrepancy” (the difference between GDP and GDI), one finds that if, say, the estimate of GDP were above the estimate of GDI in one quarter, then it likely would also be above in the next quarter.  Not by the same amount, and the differences would evolve over time, but moving more like waves than as balls ricocheting around.  Economists and statisticians refer to this as “autocorrelation”, and it indicates that there is some systemic error in the estimates of GDP and of GDI, which carries over from one quarter to the next.  What the source of that is, we do not know.  If we did know, then it would be eliminated.  But the fact such autocorrelation exists tells us that there is some source of systemic error in the measures of GDP and GDI, and we have not been able to discover the source.

Estimates are estimates.  We need to recognize that there will be statistical uncertainty in any such figures.  Even if they even out over time, the estimated growth from one quarter to the next will reflect such statistical volatility.  The differences seen in the estimated rates of growth in any one quarter for total output (estimated by way of GDP versus by way of GDI) provides a useful benchmark for how to judge the reported changes seen in growth for GDP in any individual quarter.  The true volatility (for purely statistical reasons) is likely to be at least as much, if not more.

D.  Conclusion

There are many reasons, then, to expect the quarterly growth figures to bounce around.  One should not place too much weight on the estimates from any individual quarter.  It is the longer term trends that matter.  The estimated figure for growth in GDP of 4.1% in the second quarter was not out of line with what has been seen in a number of quarters in recent years.  But growth since mid-2009 has only been about one half as much on average, despite several quarters when estimated growth was well in excess of 4.1%.

To conclude, some may find of interest three country cases I am personally familiar with which illustrate why one needs to exercise care, and with an understanding of the country context, when considering what is meaningful or not for reported figures on GDP growth.  The countries are Japan, China, and an unidentified, but newly independent, former colony in the 1960s.

a)  Japan:  In the late 1990s / early 2000s, while holding a position within the World Bank Group, I was responsible for assessments of the prospects and risks of the countries of East Asia where the World Bank was active.  This was not long after the East Asia crisis of 1997, and the countries were just beginning to recover.  Japan was important, both as a trading partner to the others and because Japan itself had gone through a somewhat similar crisis following 1990, when the Japanese financial bubble burst.

As part of this, I followed closely the quarterly GDP growth figures for Japan.  But as many analysts at the time noted, the quarter to quarter figures behaved in ways that were difficult to understand.  Components went up when one would have thought they would go down (and vice versa), the quarterly changes were far more extreme than seen elsewhere, and in general the quarter to quarter fluctuations were difficult to make sense of.  The volatility in the figures was far greater than one would have expected for an economy such as Japan’s.

This view among analysts was such a common one that the government agency responsible for the estimates felt it necessary to issue a news release in June 2000 defending its work and addressing a number of the concerns that had been raised.

I have no doubt that the Japanese government officials responsible for the estimates were well-qualified and serious professionals.  But it is not easy to estimate GDP and its components, the underlying data on which the statisticians relied might have had problems (including sample sizes that were possibly too small), and there may have been segments of the economy (in the less formal sectors) which might not have been captured well.

I have not followed closely in recent years, and do not know if the issues continue.  But Japan’s case illustrates that even a sophisticated agency, with good professionals, can have difficulty in arriving at GDP estimates that behave as one would expect.

b)  China:  The case of China illustrates the mirror image problem of what was found in Japan.  While the Japanese GDP estimates bounced around far too sharply from one quarter to the next, the GDP estimates for China showed remarkable, and not believable, stability.

Chinese growth rates have normally been presented as growth of GDP in the current period over what it was in the same period one year ago.  Seasonal adjustment is then not needed, and indeed China only started to present seasonally adjusted figures in 2011.  However, these estimates are still not fully accepted by many analysts.  Comparing GDP in the current quarter to what it was in the same quarter a year before overcomes this, but at the cost that it does not present information on growth just in the quarter, as opposed to total growth over the preceding year.

And the growth rates reported over the same quarter in the preceding year have been shockingly smooth.  Indeed, in recent years (from the first quarter of 2015 through to the recently released figures for the second quarter of 2018), China’s reported growth of its GDP over the year-earlier period has not been more than 7.0% nor less than 6.7% in each and every quarter.  Specifically, the year on year GDP growth rates from the first quarter of 2015 through to the second quarter of 2018 were (in sequence):  7.0%, 7.0%, 6.9%, 6.8%, 6.7%, 6.7%, 6.7%, 6.8%, 6.9%, 6.9%, 6.8%, 6.8%, 6.8%, and 6.7% (one can find the figures in, for example, the OECD database).  Many find this less than credible.

There are other problems as well in the Chinese numbers.  For example, it has often been the case that the reported growth in provincial GDP of the 31 provincial level entities in China was higher in almost all of the 31 provinces, and sometimes even in all of the provinces, than GDP growth was in China as a whole.  This is of course mathematically impossible, but not surprising when political rewards accrue to those with fast reported growth.

With such weak credibility, analysts have resorted to coming up with proxies to serve as indicators of what quarter to quarter might have been.  These might include electricity consumption, or railway tonnage carried, or similar indicators of economic production.  Indeed, there is what has been labeled the “Li index”, named after Li Keqiang (who was vice premier when he formulated it, and later China’s premier).  Li said he did not pay much attention to the official GDP statistics, but rather focused on a combination of electricity production, rail cargo shipments, and loan disbursements.  Researchers at the Federal Reserve Bank of San Francisco who reproduced this and fitted it through some regression analysis found that it worked quite well.

And the index I found most amusing is calculated using nighttime satellite images of China, with an estimation of how much more night-time illumination one finds over time.  This “luminosity” index tracks well what might be going on with China’s GDP.

c)  An unidentified, newly independent, former colony:  Finally, this is a story which I must admit I received third hand, but which sounds fully believable.  In the mid-1970s I was working for a period in Kuala Lumpur, for the Government of Malaysia.  As part of an economic modeling project I worked closely with the group in the national statistical office responsible for estimating GDP.  The group was led by a very capable, and talkative, official (of Tamil origin), who related a story he had heard from a UN consultant who had worked closely with his group in the early 1970s to develop their system of national accounts.

The story is of a newly independent country in the mid-1960s (whose name I was either not told or cannot remember), and its estimation of GDP.  An IMF mission had visited it soon after independence, and as is standard, the IMF made forecasts of what GDP growth might be over the next several years.  Such forecasts are necessary in order to come up with estimates for what the government accounts might be (as tax revenues will depend on GDP), for the trade accounts, for the respective deficits, and hence for what the financing needs might be.

Such forecasts are rarely very good, especially for a newly independent country where much is changing.  But something is needed.

As time passed, the IMF received regular reports from the country on what estimated GDP growth actually was.  What they found was that reported GDP growth was exactly what had been forecast.  And when asked, the national statisticians responded that who were they to question what the IMF officials had said would happen!

What Has Been Happening to Real Wages? Sadly, Not Much

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A.  Introduction

There is little that is more important to a worker than his or her wages.  And as has been discussed in an earlier post on this blog, real wages in the US have stagnated since around 1980.  An important question is whether this has changed recently.  Trump has claimed that his policies (of lifting regulations, slashing corporate taxes, and imposing high tariffs on our trading partners) are already leading to higher wages for American workers.  Has that been the case?

The answer is no.  As the chart at the top of this post shows, real wages have been close to flat.  Nominal wages have grown with inflation, but once inflation is taken into account, real wages have barely moved.  And one does not see any sharp change in that trend after Trump took office in January 2017.

It is of course still early in Trump’s term, and the experience so far does not mean real wages will not soon rise.  We will have to see.  One should indeed expect that they would, as the unemployment rate is now low (continuing the path it has followed since 2010, first under Obama and now, at a similar pace, under Trump).  But the primary purpose of this blog post is to look at the numbers on what the experience has been in recent years, including since Trump took office.  We will see that the trend has not much changed.  And to the extent that it has changed, it has been for the worse.

We will first take an overall perspective, using the chart at the top of this post and covering the period since 2006.  This will tell us what the overall changes have been over the full twelve years.  For real wages, the answer (as noted above) is that not much has changed.

But the overall perspective can mask what the year to year changes have been.  So we will then examine what these have been, using 12 month moving averages for the changes in nominal wages, the consumer price index, and then the real wage.  And we will see that changes in the real wage have actually been trending down of late, and indeed that the average real wage in June 2018 was below where it had been in June 2017.

We will then conclude with a short discussion of whether labor market trends have changed since Trump took office.  They haven’t.  But those trends, in place since 2010 as the economy emerged from the 2008/09 downturn, have been positive.  At some point we should expect that, if sustained, they will lead to rising real wages.  But we just have not seen that yet.

B.  Nominal and Real Wages Since 2006

It is useful first to start with an overall perspective, before moving to an examination of the year to year changes.  The chart at the top of this post shows average nominal wages in the private sector, in dollars per hour, since March 2006, and the equivalent in real terms, as deflated by the consumer price index (CPI).  The current CPI takes the prices of 1982-84 (averaged over that period) as the base, and hence the real wages shown are in terms of the prices of 1982-84.  For June 2018, for example, average private sector wages were $26.98 per hour, equivalent to $10.76 per hour in terms of the prices of 1982-84.

The data series comes from the Current Employment Survey of the Bureau of Labor Statistics, which comes out each month and is the source of the closely watched figures on the net number of jobs created each month.  The report also provides figures on average private sector wages on a monthly basis, but this particular series only started being reported in March 2006.  That is part of the reason why I started the chart with that date, but it is in any case a reasonable starting point for this analysis as it provides figures starting a couple of years before the economic collapse of 2008, in the last year of Bush’s presidential term, through to June 2018.

The BLS report also only provides figures on average wages in the private sector.  While it would be of interest also to see the similar figures on government wages, they are not provided for some reason.  If they had been included, the overall average wage would likely have increased at an even slower pace than that shown for the private sector only, as government wages have been increasing at a slower pace than private wages over this period.  But government employment is only 15% of total employment in the US.  Private wages are still of interest, and will provide an indication of what the market pressures have (or have not) been.

The chart shows that nominal wages have increased at a remarkably steady pace over this period.  Many may find that lack of fluctuation surprising.  The economy in 2008 and early 2009 went through the sharpest economic downturn since the Great Depression, and unemployment eventually hit 10.0% (in October 2009).  Yet nominal private sector wages continued to rise.  As we will discuss in more detail below, nominal wages were increasing at about a 3% annual pace through 2008, and then continued to increase (but at about a 2% pace) even after unemployment jumped.

But while nominal wages rose at this steady pace, it was almost all just inflation.  After adjusting for inflation, average real wages were close to flat for the period as a whole.  They were not completely flat:  Average real wages over the period (March 2006 to June 2018) rose at an annual rate of 0.57% per year.  This is not much.  It is in fact remarkably similar to the 0.61% growth in the average real wage between 1979 and 2013 in the data that were discussed in my blog post from early 2015 that looked at the factors underlying the stagnation in real wages in the decades since 1980.

But as was discussed in that blog post, the average real wage is not the same as the median real wage.  The average wage is the average across all wage levels, including the wages of the relatively well off.  The median, in contrast, is the wage at the point where 50% of the workers earn less and 50% earn more.  Due to the sharp deterioration in the distribution of income since around 1980 (as discussed in that post), the median real wage rose by less than the average real wage, as the average was pulled up by the more rapid increase in wages of those who are relatively well off.  And indeed, the median real wage rose by almost nothing over that period (just 0.009% per year between 1979 and 2013) when the average real wage rose at the 0.61% per year pace.  If that same relationship has continued, there would have been no increase at all in the median real wage in the period since 2006.  But the median wage estimates only come out with a lag (they are estimated through a different set of surveys at the Census Bureau), are only worked out on an annual basis, and we do not yet have such estimates for 2018.

C.  12 Month Changes in Nominal Wages, the Consumer Price Index, and Real Wages Since 2006

While the chart at the top of this post tracks the cumulative changes in wages over this period, one can get a better understanding of the underlying dynamics by looking at how the changes track over time.  For this we will focus on percentage changes over 12 month periods, worked out month by month on a moving average basis.  Or another way of putting it, these will be the percentage changes in the wages or the CPI over what it had been one year earlier, worked out month by month in overlapping periods.

For average nominal wages (in the private sector) this is:

Note that the date labels are for the end of each period.  Thus the point labeled at the start of 2008 will cover the percentage change in the nominal wage between January 2007 and January 2008.  And the starting date label for the chart will be March 2007, which covers the period from March 2006 (when the data series begins) to March 2007.

Prior to the 2008/09 downturn, nominal wages were growing at roughly 3% a year.  Once the downturn struck they continued to increase, but at a slower pace of roughly 2% a year or a bit below.  And this rate then started slowly to rise over time, reaching 2.7% in the most recent twelve-month period ending in June 2017.  The changes are remarkably minor, as was also noted above, and cover a period where unemployment was as high as 10% and is now just 4%.  There has been very little year to year volatility.

[A side note:  There is a “bump” in late 2008/early 2009, with wage growth over the year earlier period rising from around 3% to around 3 1/2%.  This might be considered surprising, as the bump up is precisely in the period when jobs were plummeting and unemployment increasing, in the worst period of the economic collapse.  But while I do not have the detailed microdata from the BLS surveys to say with certainty, I suspect this is a compositional effect.  When businesses start to lay off workers, they will typically start with the least experienced, and lowest paid, workers.  That will leave them with a reduced labor force, but one whose wages are on average higher.]

There have been larger fluctuations in the consumer price index:

But note that “larger” should be interpreted in a relative sense.  The absolute changes were generally not all that large (with some exceptions), and can mostly be attributed to changes in the prices of a limited number of volatile commodities, namely for food items and energy (oil).  The prices of such commodities go up and down, but over time they even out.  Thus for understanding inflationary trends, analysts will often focus instead on the so-called “core CPI”, which excludes food and energy prices.  For the full period being examined here, the regular CPI rose at a 1.88% annual pace while the core CPI rose at a 1.90% pace.  Within round-off, these are essentially the same.

But what matters to wage earners is what their wages earn, including for food and energy.  Thus to examine the impact on real living standards, what matters is the real wage defined in terms of the regular CPI index.  And this was:

With the relatively steady changes in average nominal wages, year to year, the fluctuations will basically be the mirror image of what has been happening to inflation.  When prices fell, real wages rose, and when prices rose more than normal, real wages fell.

Prices are now again rising, although still within the norm of the last twelve years.  For the 12 months ending in June 2018, the CPI (using the seasonally adjusted series) rose at a 2.8% rate.  The average nominal wage rate rose at a rate of 2.74% and thus the real wage fell slightly by 0.05% (calculated before rounding).  Average real wages are basically the same as (and formally slightly below) where they were a year ago.

D.  Employment and Unemployment

There is thus no evidence that the measures Trump has trumpeted (of deregulation, slashing taxes for corporations, and launching a trade war) have led to a step up in real wages.  This should not be surprising.  Deregulation which spurs industry consolidation increases the power of firms to raise prices while holding down wages.  And there is no reason to believe that tax cuts will lead quickly to higher wages.  Corporations do not pay their workers out of generosity or out of some sense of charity.  In a market economy they pay their employees what they need to in order to get the workers in the number and quality they need.  And although there can be winners in a trade war, there will also certainly be losers, and overall there will be a loss.  Workers, on average, will lose.

But what is surprising is that wages are not now rising by more in an economy that has reached full employment.  Federal Reserve Chair Jerome Powell, for example, has called this “a puzzle”.  And indeed it is.

The labor market turned around in the first two years of the Obama administration, and since then employment has grown consistently:

This has continued (although at a slightly slower pace) since Trump took office in January 2017.  The same trend as before has continued.  And this trend growth in net jobs each month has meant a steady fall in the unemployment rate:

Again, the pace since Trump took office is similar to (but a bit slower than) the pace when Obama was still in office.  But the somewhat slower pace should not be surprising.  With the economy at close to full employment, one should expect the pace to slow.

Indeed, the unemployment rate cannot go much lower.  There is always a certain amount of “churn” in the job market, which means an unemployment rate of zero is impossible.  And many economists in fact have taken a somewhat higher rate of unemployment (or at least 5.0%) as the appropriate target for “full employment”, arguing that anything lower will lead to a wage and price spiral.

But we have not seen any sign of that so far.  Nominal wages are rising at only a modest pace, and indeed over the last year at a pace less than inflation.

E.  Conclusion

There has been no step up in real wages since Trump took office.  Indeed, over the past twelve months, they fell slightly.  But while there is no reason to believe there should have been a jump in real wages following from Trump’s economic policies (of deregulation, tax cuts for corporations, and trade war), it is surprising that the economy is not now well past the point where low unemployment should have been spurring more substantial wage gains.

This very well could change, and indeed I would expect it to.  There is good reason to believe that the news for the real wage will be a good deal more positive over the next year than it has been over the past year.  But we will have to wait and see.  So far it has not happened.