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Social Security Could be Saved With the Revenues Lost Under the Trump Tax Plan

As is well known, the Social Security Trust Fund will run out in about 2034 (plus or minus a year) if nothing is done.  “Running out” means that the past accumulated stock of funds paid in through Social Security taxes on wages, plus what is paid in each year, will not suffice to cover what is due to be paid out that year to beneficiaries.  If nothing is done, Social Security payments would then be scaled back by 23% (in 2034, rising to 27% by 2091), to match the amount then being paid in each year.

This would be a disaster.  Social Security does not pay out all that much:  An average of just $15,637 annually per beneficiary for those in retirement and their survivors, and an average of just $12,452 per beneficiary for those on disability (all as of August 2017).  But despite such limited amounts, Social Security accounts for almost two-thirds (63%) of the incomes of beneficiaries age 65 or older, and 90% or more of the incomes of fully one-third of them.  Scaling back such already low payments, when so many Americans depend so much on the program, should be unthinkable.

Yet Congress has been unwilling to act, even though the upcoming crisis (if nothing is done) has been forecast for some time.  Furthermore, the longer we wait, the more severe the measures that will then be necessary to fix the problem.  It should be noted that the crisis is not on account of an aging population (one has pretty much known for 64 years how many Americans would be reaching age 65 now), nor because of a surprising jump in life expectancies (indeed, life expectancies have turned out to be lower than what had been forecast).  Rather, as discussed in an earlier post on this blog, the crisis has arisen primarily because wage income inequality has grown sharply (and unexpectedly) since around 1980, and this has pulled an increasing share of wages into the untaxed range above the ceiling for annual earnings subject to Social Security tax ($127,200 currently).

But Congress could act, and there are many different approaches that could be taken to ensure the Social Security Trust Fund remains adequately funded.  This post will discuss just one.  And that would be not to approve the Trump proposal for what he accurately calls would be a huge cut in taxes, and use the revenues that would be lost under his tax plan instead to shore up the Social Security Trust Fund.  As the chart at the top of this post shows (and as will be discussed below), this would more than suffice to ensure the Trust Fund would remain in surplus for the foreseeable future.  There would then be no need to consider slashing Social Security benefits in 2034.

The Trump tax plan was submitted to Congress on September 27.  It is actually inaccurate to call it simply the Trump tax plan as it was worked out over many months of discussions between Trump and his chief economic aides on one side, and the senior Republican leadership in both the Senate and the Congress on the other side, including the chairs of the tax-writing committees.  This was the so-called “Gang of Six”, who jointly released the plan on September 27, with the full endorsement of all.  But for simplicity, I will continue to call it the Trump tax plan.

The tax plan would sharply reduce government revenues.  The Tax Policy Center (TPC), a respected bipartisan nonprofit, has provided the most careful forecast of the revenue losses yet released.  They estimated that the plan would reduce government revenues by $2.4 trillion between 2018 and 2027, with this rising to a $3.2 trillion loss between 2028 and 2037.  The lost revenue would come to 0.9% of GDP for the 2018 to 2027 period, and 0.8% of GDP for the 2028 to 2037 period (some of the tax losses under the Trump plan are front-loaded), based on the GDP forecasts of the Social Security Trustees 2017 Annual Report (discussed below).  While less than 1% of GDP might not sound like much, such a revenue loss would be significant.  As we will see, it would suffice to ensure the Social Security Trust Fund would remain fully funded.

The chart at the top of this post shows what could be done.  The curve in green is the base case where nothing is done to shore up the Trust Fund.  It shows what the total stock of funds in the Social Security Trust Fund have been (since 1980) and would amount to, as a share of GDP, if full beneficiary payments would continue as per current law.  Note that I have included here the trust funds for both Old-Age and Survivors Insurance (OASI) and for Disability Insurance (DI).  While technically separate, they are often combined (and then referred to as OASDI).

The figures are calculated from the forecasts released in the most recent (July 2017) mandated regular annual report of the Board of Trustees of the Social Security system.  Their current forecast is that the Trust Fund would run out by around 2034, as seen in the chart.

But suppose that instead of enacting the Trump tax plan proposals, Congress decided to dedicate to the Social Security Trust Funds (OASDI) the revenues that would be lost as a consequence of those tax cuts?  The curve in the chart shown in red is a forecast of what those tax revenue losses would be each year, as a share of GDP.  These are the Tax Policy Center estimates, although extrapolated.  The TPC forecasts as published showed the estimated year-by-year losses over the first ten years (2018 to 2027), but then only for the sum of the losses over the next ten years (2028 to 2037).  I assumed a constant rate of growth from the estimate for 2027 sufficient to generate the TPC sum for 2028 to 2037, which worked out to a bit over 6.1%.  I then assumed the revenue losses would continue to grow at this rate for the remainder of the forecast period.

Note this 6.1% growth is a nominal rate of growth, reflecting both inflation and real growth.  The long-run forecasts in the Social Security Trustees report were for real GDP to grow at a rate of 2.1 or 2.2%, and inflation (in terms of the GDP price index) to grow at also 2.2%, leading to growth in nominal GDP of 4.3 or 4.4%.  Thus the forecast tax revenue losses under the Trump plan would slowly climb over time as a share of GDP, reaching 2% of GDP by about 2090.  This is as one would expect for this tax plan, as the proposals would reduce progressivity in the tax system.  As I noted before on this blog and will discuss further below, most of the benefits under the Trump tax plan would accrue to those with higher incomes.  However, one should also note that the very long-term forecasts for the outer years should not be taken too seriously.  While the trends are of interest, the specifics will almost certainly be different.

If the tax revenues that would be lost under the Trump tax plan were instead used to shore up the Social Security Trust Fund, one would get the curve shown in blue (which includes the interest earned on the balance in the Fund, at the interest rates forecast in the Trustees report).  The balance in the fund would remain positive, never dipping below 12% of GDP, and then start to rise as a share of GDP.  Even if the TPC forecasts of the revenues that would be lost under the Trump plan are somewhat off (or if Congress makes changes which will reduce somewhat the tax losses), there is some margin here.  The forecast is robust.

The alternative is to follow the Trump tax plan, and cut taxes sharply.  As I noted in my earlier post on this blog on the Trump tax plan, the proposals are heavily weighted to provisions which would especially benefit the rich.  The TPC analysis (which I did not yet have when preparing my earlier blog post) has specific estimates of this.  The chart below shows who would get the tax cuts for the forecast year of 2027:

The estimate is that 87% of the tax revenues lost under the Trump plan would go to the richest 20% of the population (those households with an income of $154,900 or more in 2027, in prices of 2017).  And indeed, almost all of this (80% of the overall total) would accrue just to the top 1%.  The top 1% are already pretty well off, and it is not clear why tax cuts focused on them would spur greater effort on their part or greater growth.  The top 1% are those households who would have an annual income of at least $912,100 in 2027, in prices of 2017.  Most of them would be making more than a million annually.

The Trump people, not surprisingly, do not accept this.  They assert that the tax cuts will spur such a rapid acceleration in growth that tax revenues will not in fact be lost.  Most economists do not agree.  As discussed in earlier posts on this blog, the historical evidence does not support the Trumpian view (the tax cuts under Reagan and Bush II did not lead to any such acceleration in growth; what they did do is reduce tax revenues); the argument that tax cuts will lead to more rapid growth is also conceptually confused and reveals a misunderstanding of basic economics; and with the economy having already reached full employment during the Obama years, there is little basis for the assertion that the economy will now be able to grow at even 3% a year on average (over a mulit-year period) much less something significantly faster.  Tax cuts have in the past led to cuts in tax revenues collected, not to increases, and there is no reason to believe this time will be different.

Thus Congress faces a choice.  It can approve the Trump tax plan (already endorsed by the Republican leadership in both chambers), with 80% of the cuts going to the richest 1%.  Or it could use those revenues to shore up the Social Security Trust Fund.  If the latter is done, the Trust Fund would not run out in 2034, and Social Security would be able to continue to pay amounts owed to retired senior citizens and their survivors, as well as to the disabled, in accordance with the commitments it has made.

I would favor the latter.  If you agree, please call or write your Senator and Member of Congress, and encourage others to do so as well.

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Update, October 22, 2017

The US Senate passed on October 19 a budget framework for the FY2018-27 period which would allow for $1.5 trillion in lost tax revenues over this period, and a corresponding increase in the deficit, as a consequence of new tax legislation.  It was almost fully a party line vote (all Democrats voted against it, while all Republicans other than Senator Rand Paul voted in favor).  Importantly, this vote cleared the way (under Senate rules) for it to pass a new tax law with losses of up to $1.5 trillion over the decade, and pass this with only Republican votes.  Only 50 votes in favor will be required (with Vice President Pence providing a tie-breaking vote if needed).  Democrats can be ignored.

The loss in tax revenues in this budget framework is somewhat less than the $2.4 trillion that the Tax Policy Center estimates would follow in the first decade under the Trump tax plan.  But it is still sizeable, and it is of interest to see what this lesser amount would achieve if redirected to the Social Security Trust Fund instead of being used for tax cuts.

The chart above shows what would follow.  It still turns out that the Social Security Trust Fund would be saved from insolvency, although just barely this time.

One has to make an assumption as to what would happen to tax revenues after 2027, as well as for what the time pattern would be for the $1.5 trillion in losses over the ten years from FY2018 to 27.  With nothing else available, I assumed that the losses would grow over time at the same rate as what is implied in the Tax Policy Center estimates for the losses in the second decade of the Trump tax plan as compared to the losses in the final year of the first decade.  As discussed above, these estimates implied a nominal rate of growth of 6.1% a year.  I assumed the same rate of growth here, including for the year to year growth in the first decade (summing over that decade to $1.5 trillion).

The result again is that the Social Security Trust Fund would remain solvent for the foreseeable future, although now just marginally.  The Trust Fund (as a share of GDP) would just touch zero in the years around 2080, but would then start to rise.

We therefore have a choice.  The Republican-passed budget framework has that an increase in the fiscal deficit of $1.5 trillion over the next decade is acceptable.  It could be used for tax cuts that would accrue primarily to the rich.  Or it could be used to ensure the Social Security system will be able, for the foreseeable future, to keep to its commitments to senior citizens, to their survivors, and to the disabled.

 

Lower Corporate Taxes Have Not Led to Higher Real Wages

A recently released report from the president’s Council of Economic Advisers (CEA) claims that cutting the headline corporate income tax rate from the current 35% to 20% would lead to a jump in the real incomes of American households by a minimum of $4,000 a year and possibly by as much as $9,000.  Others have criticized those forecasts for a variety of reasons, and Larry Summers has called the estimates “absurd”.

Indeed, they are absurd.  One way to see this is by looking at the historical evidence.  This is not the first time the US would cut its corporate tax rates.  Did such cuts in the past then lead to a jump in real wages?  As the chart above suggests, the answer is no.  This blog post will discuss that evidence, as well as other issues with the CEA analysis prepared under (and it appears largely by) its new chair Kevin Hassett.  But first some background on the CEA and its new chair, and what this recent incident portends for the Council and its previous reputation for professionalism.

The Council of Economic Advisers has, until now, been a highly respected office in the White House, set up to provide the president with objective and professional economic advice on the key economic issues of the day.  The Council was established in 1946 during the Truman administration, and has had as its chair and its members many illustrious and well-respected economists.  A number later received the Nobel Prize in Economics and similar awards.  While the CEA can be and has been political at times (it is located in the Office of the President, after all), it has had an able staff who were expected to provide professional assessments of the issues as a service to the president.  Many came on leave from academic posts.  As an example of the type of staff they could draw, both Larry Summers and Paul Krugman, then young and rising economists, were on the Council staff in the early 1980s during the Reagan administration.

The current chair is Kevin Hassett.  Trump did not nominate someone to the position until April, and Hassett took up his post (following Senate approval) only on September 13.  Prior to this post, Hassett was perhaps best known for co-authoring (with James K. Glassman) the 1999 book titled “Dow 36,000”, in which he forecast the Dow Jones Industrial Average would reach 36,000 by 2002 and certainly no later than by 2004.  In the event, the Dow never exceeded 11,750 (in January 2000) and dropped to 7,200 in October 2002, as the Bush administration’s first recession took hold.

Hassett has now, as one of his first official acts, released a formal CEA study that claims that if the Trump Tax Plan were enacted, with the headline corporate income tax cut from 35% to 20%, household incomes in the US would rise by a minimum of $4,000 per year, and possibly by as much as $9,000.  Larry Summers has termed it “dishonest, incompetent, and absurd”, and other economists have been similarly scathing.

The study really is pretty bad, and must be an embarrassment to the CEA staff. The report starts (Figure 1) with a chart that shows average real wage growth over the last several years (2013 to 2016) among the 10 OECD member countries with the highest statutory corporate income tax rates, as compared to that for the 10 OECD members with the lowest rates.  Between 2013 and 2016 (but essentially just in 2015) the wage growth was higher by a few percentage points in the set of OECD countries with the lower tax rates.  But the 10 OECD member countries with the lowest corporate tax rates were mostly countries from Central and Eastern Europe (Estonia, Latvia, and so on to Slovenia).  They were starting from lower wage rates than in the richer countries, and benefited as they opened up to globalization and in particular to the EU markets.  It is difficult to see how this simplistic correlation tells us much about what would happen if the US cut its corporate income tax rate.

Hassett then quantifies his estimate of the dollar gains per household by citing a number of obscure articles (several of which were never published in a peer-reviewed journal) to come up with estimates of possible elasticities (explained below) that relate how much household incomes would rise if corporate taxes were cut.  He concludes this review by asserting that an elasticity in the range of -0.16 to -0.33 would be reasonable, in his view.  The -0.16 figure came from a study from 2009 published in the “Federal Reserve Bank of Kansas City Economic Review”.  That is not exactly a prestigious journal.  And the -0.33 figure came from a 2007 paper that was presented at a conference, and does not appear to have ever been published.

An elasticity of -0.16 means that if the corporate tax rate were cut by 1% (not 1 percentage point, but rather by actually 1%, e.g. from 35% to 34.65%), then real wages would rise by 0.16%.  A 10% cut in the corporate tax rates (e.g. 35% to 31.5%) would lead, according to this assumption, to real wages rising by 1.6%.  And a cut in the corporate income tax rate from 35% to 20% (a 43% fall), as proposed in the Trump tax plan, would raise real wages by 6.9% under this assumption.  Hassett then applies this to the wage portion of household incomes to arrive at his calculated gain of $4,000 per household.  And the $9,000 gain is based on assuming an elasticity of -0.33.

There are numerous problems with this analysis, starting with the assumption that correlations are the same as causation.  There is also a question of what correlations are relevant.  The study that came up with the -0.33 elasticity, for example, looked at correlations across a panel of 50 countries.  It is not clear that such correlations would be of much relevance to judging the impact on real wages of a change in the US on corporate tax rates, as real wages across such a range of countries are driven by many factors (including, not least, the level of development).  And the -0.16 elasticity came from a study that examined correlations between real wages and corporate tax rates across the different states of the US.  But labor is mobile across US states, as is capital, plus the range of variation (state to state) in corporate tax rates is relatively modest as state taxes are relatively modest in size.  And indeed, it is not even clear how many companies actually pay the headline corporate income tax rates on the books, as states routinely grant them special tax holidays and other favors in order to try to get them to move to their states.

One would have thought that the most interesting investigation as to whether changes in corporate income taxes would matter in the US to real wages, would have been to see what actually happened in the US when such rates were cut in the past.  The fact that Hassett ignored this obvious question in the new CEA report is telling.  And there have indeed been earlier changes in the corporate tax rate, most notably (in recent decades) in 1987/88, following from the Tax Reform Act of 1986 during the Reagan administration.

The impact (or rather the lack of it) can be seen in the chart at the top of this post.  As had been discussed in earlier posts on this blog, real wages have been stagnant in the US (for the median wage earner) since around 1980.  The chart at the top of this post is an update of one prepared for a post from February 2015 that looked at the proximate causes of stagnant wages over this period, despite growth of real GDP per capita of more than 80% over the period.  While real GDP per capita is now 82% above what it was at the start of 1979, real wages (as measured by real median weekly earnings of full-time workers) are just 5.7% above where they were at the start of 1979.  Furthermore, the current “peak” of 5.7% growth can all be attributed to growth in the period since mid-2014, as the economy finally approached full employment levels in the later years of the Obama administration (having been held back by government spending cuts from 2010), with this carrying over into 2017.

The top corporate tax rate on profits was cut from 46% in the years up through 1986, to 40% in 1987 and then to just 34% in 1988 and thereafter to 1993 (when it was raised to the current level of 35%).  Did the cuts in 1987/88 lead to a sharp jump in real wages?  There is no indication of that at all in the chart.  Indeed, real wages fell by close to 6% between late 1986 and 1990, and then stayed at close to that low level until they started to rise some in the mid to late 1990s.  And there is no indication that the small increase in the corporate tax rate in 1993 to 35% led to wages then declining – indeed, they started to rise a few years later.

Based on this, one might come to the conclusion that a cut in corporate tax rates will lead to a fall (not an increase) in real wages, as seen following the 1987/88 cuts.  And also that a modest rise in the tax rate (such as in 1993) would lead to a gain in real wages a few years later.  But I would not claim this.  Rather, I would say that real wages and corporate tax rates are simply not closely linked to one another.  But for Hassett and others to claim that cuts in corporate taxes will lead to a significant jump in real wages, the exact opposite outcome following the 1987/88 cuts needs to be explained.

The CEA report was badly done, and must be an embarrassment to the professional staff there who certainly know better.  And as Larry Summers remarked in his blog post:  “Considering all this, if a Ph.D student submitted the CEA analysis as a term paper in public finance, I would be hard pressed to give it a passing grade.”

An Analysis of the Trump Tax Plan: Not a Tax Reform, But Rather a Massive Tax Cut for the Rich

A.  Introduction

The Trump administration released on September 27 its proposed tax plan.  It was exceedingly skimpy (only nine pages long, including the title page, and with all the white space could have been presented on half that number of pages).  Importantly, it was explicitly vague on many of the measures, such as what tax loopholes would be closed to partially pay for the tax cuts (simply saying they would do this somehow).  One can, however, examine measures that were explicitly presented, and from these it is clear that this is primarily a plan for massive tax cuts for the rich.

It is also clear that this is not a tax reform.  A tax reform would be revenue neutral.  The measures proposed would not be.  And a reform would focus on changes in the structure of the tax system.  There is little of that here, but rather proposals to cut various tax rates (including in several cases to zero), primarily for the benefit of those who are well off.

One can see this in the way the tax plan was approached.  In a true tax reform, one would start by examining the system, and whether certain deductions and tax exemptions are not warranted by good policy (but rather serve only certain vested interests).  Closing such loopholes would lead to higher revenues being collected.  One would then determine what the new tax rates could be (i.e. by how much they could be cut) to leave the overall level of tax collection the same.

But that was not done here.  Rather, they start with specific proposals on what the new tax rates “should” be (12%, 25%, and 35% for individuals, and 20% for corporations), and then make only vague references to certain, unspecified, deductions and tax exemptions being eliminated or reduced, in order not to lose too much in revenues (they assert).  They have the process backward.

And it is clear that these tax cuts, should they be enacted by Congress, would massively increase the fiscal deficit.  While it is impossible to come up with a precise estimate of how much the tax plan would cost in lost revenues, due to the vagueness on the parameters and on a number of the proposals, Republicans have already factored into the long-term budget a reduction in tax revenues of $1.5 trillion over ten years.  And estimates of the net cost of the Trump plan range from a low of $2.2 trillion over ten years ($2.7 trillion when additional interest is counted, as it should be), to as high as $5 trillion over ten years.  No one can really say as yet, given the deliberate lack of detail.

But any of these figures on the cost are not small.  The total federal debt held by the public as of the end of September, 2017, was $14.7 trillion.  The cost in lost revenue could equal more than a third of this.  Yet Republicans in Congress blocked the fiscal expenditures we desperately needed in the years from 2010 onwards during the Obama years, when unemployment was still high, there was excess capacity in our underutilized factories, and the country needed to rebuild its infrastructure (as we still do).  The argument then was that we could not add to our national debt.  But now the same politicians see no problem with adding massively to that debt to cover tax cuts that will primarily benefit the rich.  The sheer hypocrisy is breath-taking.

Not surprisingly, Trump officials are saying that there will be no such cost due to a resulting spur to our economic growth.  Trump himself asserted that his tax plan would lead the economy to grow at a 6% pace.  No economist sees this as remotely plausible.  Even Trump’s economic aides, such as Gary Cohn who was principally responsible for the plan, are far more cautious and say only that the plan will lead to growth of “substantially over 3 percent”.  But even this has no basis in what has been observed historically after the Reagan and Bush tax cuts, nor what one would expect from elementary economic analysis.

The lack of specificity in many of the proposals in the tax plan issued on September 27 makes it impossible to assess it in full, as major elements are simply only alluded to.  For example, it says that a number of tax deductions (both personal and corporate) will be eliminated or reduced, but does not say which (other than that they propose to keep the deductions for home mortgage interest and for charity).  As another example, the plan says the number of personal income tax brackets would be reduced from seven currently to just three broad ones (at 12%, 25%, and 35%), but does not say at what income levels each would apply.  Specifics were simply left out.

For a tax plan where work has been intensively underway for already the eight months of this administration (and indeed from before, as campaign proposals were developed), such vagueness must be deliberate.  The possible reasons include:  1) That the specifics would be embarrassing, as they would make clear the political interests that would gain or lose under the plan; 2) That revealing the specifics would spark immediate opposition from those who would lose (or not gain as others would); 3) That revealing the specifics would make clear that they would not in fact suffice to achieve what the Trump administration is asserting (e.g. that ending certain tax deductions will make the plan progressive, or generate revenues sufficient to offset the tax rate cuts); and/or 4) That they really do not know what to do or what could be done to fix the issue.

One can, however, look at what is there, even if the overall plan is incomplete.  This blog post will do that.

B.  Personal Income Taxes

The proposals are (starting with those which are most clear):

a)  Elimination of the Estate Tax:  Only the rich pay this.  It only applies to estates given to heirs of $10.98 million or more (for a married couple).  This only affects the top 0.2%, most wealthy, households in the US.

b)  Elimination of the Alternative Minimum Tax:  This also only applies to those who are rich enough for it to apply and who benefit from a range of tax deductions and other benefits, who would otherwise pay little in tax.  It would be better to end such tax deductions and other special tax benefits that primarily help this group, thus making the Alternative Minimum Tax irrelevant, than to end it even though it had remained relevant.

c)  A reduction in the top income tax rate from 39.6% to 35%:  This is a clear gain to those whose income is so high that they would, under the current tax brackets, owe tax at a marginal rate of 39.6%.  But this bracket only kicks in for households with an adjusted gross income of $470,700 or more (in 2017).  This is very close to the minimum income of those in the top 1% of the income distribution ($465,626 in 2014), and the average household income of those in that very well-off group was $1,260,508 in 2014.  Thus this would be a benefit only to the top 1%, who on average earn over $1 million a year.

The Trump plan document does include a rather odd statement that the congressional tax-writing committees could consider adding an additional, higher, tax bracket, for the very rich, but it is not at all clear what this might be.  They do not say.  And since the tax legislation will be written by the congressional committees, who are free to include whatever they choose, this gratuitous comment is meaningless, and was presumably added purely for political reasons.

d)  A consolidation in the number of tax brackets from seven currently to just three, of 12%, 25%, and 35%:  Aside from the clear benefit to those now in the 39.6% bracket, noted above, one cannot say precisely what the impact the new tax brackets would have for the other groups since the income levels at which each would kick in was left unspecified.  It might have been embarrassing, or contentious, to do so.  But one can say that any such consolidation would lead to less progressivity in the tax system, as each of the new brackets would apply to a broader range of incomes.  Instead of the rates rising as incomes move up from one bracket to the next, there would now be a broader range at which they would be kept flat.  For example, suppose the Trump plan would be for the new 25% rate to span what is now taxed at 25% or 28%.  That range would then apply to household incomes (for married couples filing jointly, and in 2017) from $75,900 on the low end to $233,350 at the high end.  The low-end figure is just above the household income figure of $74,869 (in 2016) for those reaching the 60th percentile of the income distribution (see Table A-2 of this Census Bureau report), while the top-end is just above the $225,251 income figure for those reaching the 95th percentile.  A system is not terribly progressive when those in the middle class (at the 60th percentile) pay at the same rate as those who are quite well off (in the 95th percentile).

e)  A ceiling on the tax rate paid on personal income received through “pass-through” business entities of just 25%:  This would be one of the more regressive of the measures proposed in the Trump tax plan (as well as one especially beneficial to Trump himself).  Under current tax law, most US businesses (95% of them) are incorporated as business entities that do not pay taxes at the corporate level, but rather pass through their incomes to their owners or partners, who then pay tax on that income at their normal, personal, rates.  These so-called “pass-through” business entities include sole proprietorships, partnerships, Limited Liability Companies (LLCs), and sub-chapter S corporations (from the section in the tax code).  And they are important, not only in number but also in incomes generated:  In the aggregate, such pass-through business entities generate more in income than the traditional large corporations (formally C corporations) that most people refer to when saying corporation.  C corporations must pay a corporate income tax (to be discussed below), while pass-through entities avoid such taxes at the company level.

The Trump tax plan would cap the tax rate on such pass-through income at 25%.  This would not only create a new level of complexity (a new category of income on which a different tax is due), but would also only be of benefit to those who would otherwise owe taxes at a higher rate (the 35% bracket in the Trump plan).  If one were already in the 25% bracket, or a lower one, that ceiling would make no difference at all and would be of no benefit.  But for those rich enough to be in the higher bracket, the benefit would be huge.

Who would gain from this?  Anyone who could organize themselves as a pass-through entity (or could do so in agreement with their employer).  This would include independent consultants; other professionals such as lawyers, lobbyists, accountants, and financial advisors; financial entities and the partners investing in private-equity, venture-capital, and hedge funds; and real estate developers.  Trump would personally benefit as he owns or controls over 500 LLCs, according to Federal Election Commission filings.  And others could reorganize into such an entity when they have a tax incentive to do so.  For example, the basketball coach at the University of Kansas did this when Kansas created such a loophole for what would otherwise be due under its state income taxes.

f)  The tax cuts for middle-income groups would be small or non-existent:  While the Trump tax proposal, as published, repeatedly asserts that they would reduce taxes due by the middle class, there is little to suggest in the plan that that would be the case.  The primary benefit, they tout (and lead off with) is a proposal to almost double the standard deduction to $24,000 (for a married couple filing jointly).  That standard deduction is currently $12,700.  But the Trump plan would also eliminate the personal exemption, which is $4,050 per person in 2017.  Combining the standard deduction and personal exemptions, a family of four would have $28,900 of exempt income in 2017 under current law ($12,700 for the standard deduction, and personal exemptions of four times $4,050), but only $24,000 under the Trump plan.  They would not be better off, and indeed could be worse off.  The Trump plan is also proposing that the child tax credit (currently a maximum of $1,000 per child, and phased out at higher incomes) should be raised (both in amount, and at the incomes at which it is phased out), but no specifics are given so one cannot say whether this would be significant.

g)  Deduction for state and local taxes paid:  While not stated explicitly, the plan does imply that the deduction for state and local taxes paid would be eliminated.  It also has been much discussed publicly, so leaving out explicit mention was not an oversight.  What the Trump plan does say is the “most itemized deductions” would be eliminated, other than the deductions for home mortgage interest and for charity.

Eliminating the deduction for state and local taxes appears to be purely political.  It would adversely affect mostly those who live in states that vote for Democrats.  And it is odd to consider this tax deduction as a loophole.  One has to pay your taxes (including state and local taxes), or you go to jail.  It is not something you do voluntarily, in part to benefit from a tax deduction.  In contrast, a deduction such as for home mortgage interest is voluntary, one benefits directly from buying and owning a nice house, and such a deduction benefits more those who are able to buy a big and expensive home and who qualify for taking out a large mortgage.

h)  Importantly, there was much that was not mentioned:  One must also keep in mind what was not mentioned and hence would not be changed under the Trump proposals.  For example, no mention was made of the highly favorable tax rates on long-term capital gains (for assets held one year or more) of just 20%.  Those with a high level of wealth, i.e. the wealthy, gain greatly from this.  Nor was there any mention of such widely discussed loopholes as the “carried interest” exception (where certain investment fund managers are able to count their gains from the investment deals they work on as if it were capital gains, rather than a return on their work, as it would be for the lawyers and accountants on such deals), or the ability to be paid in stock options at the favorable capital gains rates.

C.  Corporate Income Taxes

More than the tax cuts enacted under Presidents Reagan and Bush, the Trump tax plan focuses on cuts to corporate income (profit) taxes.  Proposals include:

a)  A cut in the corporate income tax rate from the current 35% to just 20%:  This is a massive cut.  But it should also be recognized that the actual corporate income tax paid is far lower than the headline rate.  As noted in an earlier post on this blog, the actual average rate paid has been coming down for decades, and is now around 20%.  There are many, perfectly legal, ways to circumvent this tax.  But setting the rate now at 20% will not mean that taxes equal to 20% of corporate profits will be collected.  Rather, unless the mechanisms used to reduce corporate tax liability from the headline rate of 35% are addressed, those mechanisms will be used to reduce the new collections from the new 20% headline rate to something far less again.

b)  Allow 100% of investment expenses to be deducted from profits in the first year, while limiting “partially” interest expense on borrowing:  This provision, commonly referred to as full “expensing” of investment expenditures, would reduce taxable profits by whatever is spent on investment.  Investments are expected to last for a number of years, and under normal accounting the expense counted is not the full investment expenditure but rather only the estimated depreciation of that investment in the current year.  However, in recent decades an acceleration in what is allowed for depreciation has been allowed in the tax code in order to provide an additional incentive to invest.  The new proposal would bring that acceleration all the way to 100%, which as far as it can go.

This would provide an incentive to invest more, which is not a bad thing, although it still would also have the effect of reducing what would be collected in corporate income taxes.  It would have to be paid for somehow.  The Trump proposal would partially offset the cost of full expensing of investments by limiting “partially” the interest costs on borrowing that can be deducted as a cost when calculating taxable profits.  The interest cost of borrowing (on loans, or bonds, or whatever) is currently counted in full as an expense, just like any other expense of running the business.  How partial that limitation on interest expenses would be is not said.

But even if interest expenses were excluded in full from allowable business expenses, it is unlikely that this would come close to offsetting the reduction in tax revenues from allowing investment expenditures to be fully expensed.  As a simple example, suppose a firm would make an investment of $100, in an asset that would last 10 years (and with depreciation of 10% of the original cost each year).  For this investment, the firm would borrow $100, on which it pays interest at 5%.  Under the current tax system, the firm in the first year would deduct from its profits the depreciation expense of $10 (10% of $100) plus the interest cost of $5, for a total of $15.  Under the Trump plan, the firm would be able to count as an expense in the first year the full $100, but not the $5 of interest.  That is far better for the firm.  Of course, the situation would then be different in the second and subsequent years, as depreciation would no longer be counted (the investment was fully expensed in the first year), but it is always better to bring expenses forward.  And there likely will be further investments in subsequent years as well, keeping what counts as taxable profits low.

c)  Tax amnesty for profits held abroad:  US corporations hold an estimated $2.6 trillion in assets overseas, in part because overseas earnings are not subject to the corporate income tax until they are repatriated to the US.  Such a provision might have made sense decades ago, when information systems were more primitive, but does not anymore.  This provision in the US tax code creates the incentive to avoid current taxes by keeping such earnings overseas.  These earnings could come from regular operations such as to sell and service equipment for foreign customers, or from overseas production operations.  Or such earnings could be generated through aggressive tax schemes, such as from transferring patent and trademark rights to overseas jurisdictions in low-tax or no-tax jurisdictions such as the Cayman Islands.  But whichever way such profits are generated, the US tax system creates the incentive to hold them abroad by not taxing them until they are repatriated to the US.

This is an issue, and could be addressed directly by changing the law to make overseas earnings subject to tax in the year the earnings are generated.  The tax on what has been accumulated in the past could perhaps be spread out equally over some time period, to reduce the shock, such as say over five years.  The Trump plan would in fact start to do this, but only partially as the tax on such accumulated earnings would be set at some special (and unannounced) low rates.  All it says is that while both rates would be low, there would be a lower rate applied if the foreign earnings are held in “illiquid” assets than in liquid ones.  Precisely how this distinction would be defined and enforced is not stated.

This would in essence be a partial amnesty for capital earnings held abroad.  Companies that have held their profits abroad (to avoid US taxes) would be rewarded with a huge windfall from that special low tax rate (or rates), totalling in the hundreds of billions of dollars, with the precise gain on that $2.6 trillion held overseas dependant on how low the Trump plan would set the tax rates on those earnings.

It is not surprising that US corporations have acted this way.  There was an earlier partial amnesty, and it was reasonable for them to assume there would be future ones (as the Trump tax plan is indeed now proposing).  In one of the worst pieces of tax policy implemented in the George W. Bush administration, an amnesty approved in 2004 allowed US corporations with accumulated earnings abroad to repatriate that capital at a special, low, tax rate of just 5.25%.  It was not surprising that the corporations would assume this would happen again, and hence they had every incentive to keep earnings abroad whenever possible, leading directly to the $2.6 trillion now held abroad.

Furthermore, the argument was made that the 2004 amnesty would lead the firms to undertake additional investment in the US, with additional employment, using the repatriated funds.  But analyses undertaken later found no evidence that that happened.  Indeed, subsequent employment fell at the firms that repatriated accumulated overseas earnings.  Rather, the funds repatriated largely went to share repurchases and increased dividends.  This should not, however, have been surprising.  Firms will invest if they have what they see to be a profitable opportunity.  If they need funds, they can borrow, and such multinational corporations generally have no problem in doing so.  Indeed, they can use their accumulated overseas earnings as collateral on such loans (as Apple has done) to get especially low rates on such loans.  Yet the Trump administration asserts, with no evidence and indeed in contradiction to the earlier experience, that their proposed amnesty on earnings held abroad will this time lead to more investment and jobs by these firms in the US.

d)  Cut to zero corporate taxes on future overseas earnings:  The amnesty discussed above would apply to the current stock of accumulated earnings held by US corporations abroad.  Going forward, the Trump administration proposes that earnings of overseas subsidiaries (with ownership of as little as 10% in those firms) would be fully exempt from US taxes.  While it is true that there then would be no incentive to accumulate earnings abroad, the same would be the case if those earnings would simply be made subject to the same current year corporate income taxes as the US parent is liable for, and not taxable only when those earnings are repatriated.

It is also not at all clear to me how exempting these overseas earnings from any US taxes would lead to more investment and more jobs in the US.  Indeed, the incentive would appear to me to be the opposite.  If a plant is sited in the US and used to sell product in the US market or to export it to Europe or Asia, say, earnings from those operations would be subject to the regular US corporate income taxes (at a 20% rate in the Trump proposals).  However, if the plant is sited in Mexico, with the production then sold in the US market or exported from there to Europe or Asia, earnings from those operations would not be subject to any US tax.  Mexico might charge some tax, but if the firm can negotiate a good deal (much as firms from overseas have negotiated such deals with various states in the US to site their plants in those states), the Trump proposal would create an incentive to move investment and jobs to foreign locations.

D.  Conclusion

The Trump administration’s tax plan is extremely skimpy on the specifics.  As one commentator (Allan Sloan) noted, it looks like it was “written in a bar one evening over a batch of beers for a Tax 101 class rather than by serious people who spent weeks working with tax issues”.

It is, of course, still just a proposal.  The congressional committees will be the ones who will draft the specific law, and who will then of necessity fill in the details.  The final product could look quite different from what has been presented here.  But the Trump administration proposal has been worked out during many months of discussions with the key Republican leaders in the House and the Senate who will be involved.  Indeed, the plan has been presented in the media not always as the Trump administration plan, but rather the plan of the “Big Six”, where the Big Six is made up of House Speaker Paul Ryan, Senate Majority Leader Mitch McConnell, House Ways and Means Committee Chairman Kevin Brady, Senate Finance Committee Chairman Orrin Hatch, plus National Economic Council Director Gary Cohn and Treasury Secretary Steven Mnuchin of the Trump administration.  If this group is indeed fully behind it, then one can expect the final version to be voted on will be very similar to what was outlined here.

But skimpy as it is, one can say with some certainty that the tax plan:

a)  Will be expensive, with a ten-year cost in the trillions of dollars;

b)  Is not in fact a tax reform, but rather a set of very large tax cuts;

and c)  Overwhelmingly benefits the rich.

How Fast Can GDP Grow?: Not as Fast as Trump Says

A.  Introduction

A debate now underway between the Trump Administration and others is on the question of how fast the economy can and will grow.  Trump claimed during the presidential campaign that if elected, he would get the economy to grow at a sustained rate of 5% or even 6%.  Since then the claim has been scaled back, to a 4% rate over the next decade according to the White House website (at least claimed on that website as I am writing this).  And an even more modest rate of growth of 3% for GDP (to be reached in 2020, and sustained thereafter) was forecast in the budget OMB submitted to Congress in May of this year.

But many economists question whether even a 3% growth rate for a sustained period is realistic, as would I.  One needs to look at this systematically, and this post will describe one way economists would address this critically important question.  It is not simply a matter of pulling some number out of the air (where the various figures presented by Trump and his administration, varying between 6% growth and 3%, suggests that that may not be far removed from what they did).

One way to approach this is to recognize the simple identity:  GDP will equal GDP per worker employed times the number of workers employed.  Over time, growth in the number of workers who can be employed will be equal to the growth in the labor force, and we have a pretty good forecast for that will be from demographic projections.  The other element will then depend on growth in how much GDP is produced per worker employed.  This is the growth in productivity, and while more difficult to forecast, we have historical numbers which can provide a sense for what its growth might be, at best, going forward.  The chart at the top of this post shows what it has been since 1947, and will be discussed in detail below.  Forecasts that productivity will now start to grow at rates that are historically unprecedented need to be viewed with suspicion.  Miracles rarely happen.

I should also be clear that the question being examined is the maximum rate at which one can expect GDP to grow.  That is, we are looking at growth in what economists call capacity GDP.  Capacity GDP is what could be produced in the economy with all resources, in particular labor, being fully utilized.  This is the full employment level of GDP, and the economy has been at or close to full employment since around 2015.  Actual GDP can be less than capacity GDP when the economy is operating at less than full employment.  But it cannot be more.  Thus the question being examined is how fast the economy could grow, at most, for a sustained period going forward, not how fast it actually will grow.  With mismanagement, such as what was seen in the government oversight of the financial markets (or, more accurately, the lack of such oversight) prior to the financial and economic collapse that began in 2008 in the final year of the Bush administration, the economy could go into a recession and actual GDP will fall below capacity GDP.  But we will give Trump the benefit of the doubt and look at how fast capacity GDP could grow at, assuming the economy can and will remain at full employment.

We will start with a look at what is expected for growth in the labor force and hence in the number of workers who can be employed.  That is relatively straightforward, and the answer is not to expect much possible growth in GDP from this source.  We will then look at productivity growth:  what it has been in the past and whether it could grow at anything close to what is implicit in the Trump administration forecasts.  Predicting what that actual rate of productivity growth might be is beyond the scope of this blog post.  Rather, we will be looking at it whether it can grow as fast as is implied by the Trump forecasts.  The answer is no.

B.  Growth in the Labor Force 

Every two years, the Bureau of Labor Statistics provides a detailed ten-year forecast of what it estimates the US labor force will be.  The most recent such forecast was published in December 2015 and provided its forecast for 2024 (along with historical figures up to 2014).  The basic story is that while the labor force is continuing to grow in the US, it is growing at an ever decreasing rate as the population is aging, the baby boom generation is entering into retirement, and decades ago birth rates fell.  The total labor force grew at a 1.2% annual rate between 1994 and 2004, at a 0.6% rate between 2004 and 2014, and is forecast by the BLS to grow at a 0.5% rate between 2014 and 2024.

But it is now 2017.  With a decelerating rate of growth, a growth rate in the latter part of a period will be less than in the early part of a period.  Taking account of where the labor force is now, growth going forward to 2024 will only be 0.3% (with these figures calculated based on the full numbers before round-off).  This is not much.

A plot of the US civilian labor force going back to 1948 puts this in perspective:

The labor force will be higher in 2024 than it is now, but not by much.  The labor force grew at a relatively high rate from the 1950s to the 1970s (of a bit over 2% a year), but then started to level off.  As it did, it continued to grow but at an ever slower rate.  There was also a dip after the economic collapse of 2008/09, but then recovered to its previous path.  When unemployment is high, some workers drop out of the labor force for a period. But we are now back to what the path before would have predicted.  If the BLS forecasts are correct, growth in the labor force will continue, but at a rate of just 0.3% from where it is now to 2024, to the point shown in red on the chart.  And this is basically a continuation of the path followed over the last few decades.

One should in particular not expect the labor force to get back to the rapid growth rate (of over 2% a year) the US had from the 1950s to the 1970s.  This would require measures such as that immigration be allowed to increase dramatically (which does not appear to enjoy much support in the Trump administration), or that grandma and grandpa be forced back into the labor force in their 70s and 80s rather than enjoy their retirement years (where it is not at all clear how this would “make America great again”).

I have spoken so far on the figures for the labor force, since that is what the BLS and others can forecast based largely on demographics.  Civilian employment will then be some share of this, with the difference equal to the number of unemployed.  That curve is also shown, in blue, in the chart.  There will always be some unemployment, and in an economic downturn the rate will shoot up.  But even in conditions considered to be “full employment” there will be some number of workers unemployed for various reasons. While economists cannot say exactly what the “full employment rate of unemployment” will be (it will vary over time, and will also depend on various factors depending on the make-up of the labor force), it is now generally taken to be in the range of a 4 to 5% unemployment rate.

The current rate of unemployment is 4.4%.  It is doubtful it will be much lower than this in the future (at least not for any sustained period).  Hence if the economy is at full employment in 2024, with unemployment at a similar rate to what it is now, the rate of growth of total employment from now to 2024 will be the same as the rate of growth of the labor from now to then.  That is, if unemployment is a similar share of the total labor force in 2024 as it is now, the rates of growth of the labor force and of total employment will match.  And that rate of growth is 0.3% a year.

This rate of growth in what employment can be going forward (at 0.3%) is well below what it was before.  Total employment grew at an annual rate of 2.1% over the 20 years between 1947 and 1967, and a slightly higher 2.2% between 1967 and 1987.  With total employment able to grow only at 1.8 or 1.9% points per annum less than what was seen between 1947 and 1987, total GDP growth (for any given rate of productivity growth) will be 1.8 or 1.9% points less.  This is not a small difference.

C.  Growth in Productivity 

Growth in productivity (how much GDP is produced per worker employed) is then the other half of the equation.  What it will be going forward is hard to predict; economists have never been very good at this.  But one can get a sense of what is plausible based on the historical record.

The chart below is the same as the one at the top of this post, but with the growth rates over 20 year periods from 1947 (10 years from 2007) also shown:

These 20 year periods broadly coincide with the pattern often noted for the post-World War II period for the US:  Relatively high growth (2.0% per year) from the late 1940s to the late 1960s; a slowdown from then to the mid 1980s (to 0.9%); a return to more rapid growth in productivity in the 1990s / early 2000s, although not to as high as in the 1950s and 60s (1.5% for 1987 to 2007); and then, after the economic collapse of 2008/2009, only a very modest growth (0.8% for 2007 to 2017, but much less from 2010 onwards).

Note also that these break points all coincide, with one exception (1987), with years where the economy was operating at full employment.  In the one exception (1987, near the end of the Reagan administration) unemployment was still relatively high at 6.6%.  While one might expect productivity levels to reach a local peak when the economy is at or close to full employment, that is not always true (the relationship is complex), and is in any case controlled for here by the fact the break points coincide (with the one exception) with full employment years.

Another way to look at this is productivity growth as a rolling average, for example over continuous 10 year periods:

 

Productivity, averaged over 10 year periods, grew at around 2% a year from the late 1940s up to the late 1960s.  It then started to fall, bottoming out at roughly 0.5% in the 1970s, before reverting to a higher pace.  It reached 2% again in the 10 year period of 1995 to 2005, but only for a short period before starting to fall again.  And as noted before, it fell to 0.8% for the 2007 to 2017 period.

What productivity growth going forward could at most be will be discussed below, but first it is useful to summarize what we have seen so far, putting employment growth and productivity growth together:

Growth Rates

Employment

GDP per worker

GDP

1947-1967

2.1%

2.0%

4.1%

1967-1987

2.2%

0.9%

3.1%

1987-2007

1.6%

1.5%

3.1%

2007-2017

0.6%

0.8%

1.4%

Employment grew at over 2% a year between the late 1940s and 1987.  This was the period of the post-war recovery and baby boom generation coming of working age.  With GDP per worker growing at 2.0% a year between 1947 and 1967, total GDP grew at a 4.1% rate.  It still grew at a 3.1% rate between 1967 and 1987 despite productivity growth slowing to just 0.9%, as the labor force continued to grow rapidly over this period.  And total GDP continued to grow at a 3.1% rate between 1987 and 2007 despite slower employment (and labor force) growth, as a recovery in productivity growth (to a 1.5% pace) offset the slower availability of labor.

It might, at first glance, appear from this that a return to 3% GDP growth (or even 4%) is quite doable.  But it is not.  Employment growth fell to a pace of just 0.6% between 2007 and 2017 (and the unemployment rates were almost exactly the same in early 2007, at 4.5%, and now, at 4.4%, so this matched labor force growth).  Going forward, as discussed above, the labor force is forecast to grow at a 0.3% pace between now and 2024.  To get to a 3% GDP growth rate now at such a pace of labor growth, one would need productivity to grow at a 2.7% pace.  To get a 4% GDP growth, productivity would have to grow at a 3.7% pace.  But productivity growth in the US since 1947 has never been able to get much above a 2% pace for any sustained period.  To go well beyond this would be unprecedented.

D.  Why Does This Matter?  And What Can Be Achieved?

Some readers might wonder why all this matters.  On the surface, the difference between growth at a 2% rate or 3% rate may not seem like much.  But it is, as some simple arithmetic illustrates:

  Alternative Growth Scenarios

 Growth Rates:

GDP 

Population

GDP per capita

Cumulative

Over 30 years

1.0%

0.8%

0.2%

6%

2.0%

0.8%

1.2%

43%

3.0%

0.8%

2.2%

91%

4.0%

0.8%

3.2%

155%

This table works out the implications of varying rates of hypothetical GDP growth, between 1.0% and 4.0%.  Population growth in the US is forecast by the Census Bureau at 0.8% a year (for the period to the 2020s).  It is higher than the forecast pace of labor force growth (of 0.3% in the BLS figures) primarily because of the aging of the population, so a higher and higher share of the adult population is entering their retirement years.

The result is that GDP growth at 1.0% a year will be just 0.2% a year with a 0.8% population growth rate.  After 30 years (roughly one generation) this will cumulate to a total growth in per capita income of just 6%.  But GDP growth at 2% a year will, by the same calculation, cumulate to total per capita income growth of 43%, to 91% with GDP growth of 3%, and to 155% with GDP growth of 4%.  These differences are huge.  What might appear to be small differences in GDP growth rates add up over time to a lot.  It does matter.

[I should note, as an aside, that this does not address the distribution issue.  GDP in total may grow, as it has over the last several decades, but all or almost all may go only to a few.  As a post on this blog from 2015 showed, only the top 10% of the income distribution saw any real income growth at all between 1980 and 2014 – real incomes per household fell for the bottom 90%.  And the top 1%, or richer, did very well.

But total GDP growth is still critically important, as it provides the resources which can be distributed to people to provide higher standards of living.  The problem in the US is that policies followed since 1980, when Ronald Reagan was first elected, have led to the overwhelming share of the growth the US has achieved to go to the already well off. Measures to address this critically important, but separate, issue have been discussed in several earlier posts on this blog, including here and here.]

Looking forward, what pace of productivity growth might be expected?  As discussed above, while the US has been able to achieve productivity growth at a rate of about 2.0% in the 1950s and 1960s, and very briefly between 1995 and 2005, it has not been able to reach a rate higher than this for any sustained period (of 10 years or more).  And over time, there is some evidence that reaching the rates of productivity growth enjoyed in the past is becoming increasingly difficult.

A reason for this is the changing structure of the economy.  Productivity growth has been, and continues to be, relatively high in manufacturing and especially in agriculture. Mechanization and new technologies (including biological technologies) can raise productivity in manufacturing and in agriculture.  It is more difficult to do this in services, which are often labor intensive and personal.  And with agriculture and manufacturing a higher share of the economy in the past than they are now (precisely because their higher rates of productivity growth allowed more to be produced with fewer workers), the overall pace of productivity growth in the economy will move, over time, towards the slower rate found in services.

The following table illustrates this.  The figures are taken from an earlier blog post, which looked at the changing shares of the economy resulting from differential rates of productivity growth.

Productivity Growth

Agriculture

Manufacturing

Services

Overall (calculated)

1947 to 2015:

3.3%

2.8%

0.9%

1.4%

At GDP Shares of:

   – 1947 shares

8.0%

27.7%

64.3%

1.7%

   – 1980 shares

2.2%

23.6%

74.2%

1.4%

   – 2015 shares

1.0%

13.9%

85.2%

1.2%

The top line (with the figures in bold) shows the overall rates of productivity growth between 1947 and 2015 in agriculture (3.3%), manufacturing (2.8%), services (0.9%), and overall (1.4%).  The overall is for GDP, and matches the average for growth in GDP per employed worker between 1947 and 2017 in the chart shown at the top of this post.

The remaining lines on the table show what the pace of overall productivity growth would then have been, hypothetically, at these same rates of productivity growth by sector but with the sector shares in GDP what they were in 1947, or in 1980, or in 2015.  In 1947, with the sector shares of agriculture and manufacturing higher than what they were later, and services correspondingly lower, the pace of productivity growth overall (i.e. for GDP) would have been 1.7%.  But at the sector shares of 2015, with services now accounting for 85% of the economy, the overall rate of productivity growth would have been just 1.2%, or 0.5% lower.

This is just an illustrative calculation, and shows the effects of solely the shifts in sector shares with the rates of productivity growth in the individual sectors left unchanged.  But those individual sector rates could also change over time, and did.  Briefly (see the earlier blog post for a discussion), the rate of productivity growth in services decelerated sharply after the mid-1960s; the pace in agriculture was remarkably steady; while the pace in manufacturing accelerated after the early 1980s (explaining, to a large extent, the sharp fall in the manufacturing share of the economy from 24% in 1980 to just 14% in 2015).  But with services dominating the economy (74% in 1980, rising to 85% in 2015), it was the pace of productivity growth in services, and its pattern over time, which dominated.

What can be expected going forward?  The issue is a huge one, and goes far beyond what is intended for this post.  But especially given the headwinds created by the structural transformation in the economy of the past 70 years towards a dominance by the services sector, it is unlikely that the economy will soon again reach a pace of 2% productivity growth a year for a sustained period of a decade or more.  Indeed, a 1.5% rate would be exceptionally good.

And with labor force growth of 0.3%, a 1.5% pace for productivity would imply a 1.8% rate for overall GDP.  This is well below the 3% rate that the Trump administration claims it will achieve, and of course even further below the 4% (and 5% and 6%) rates that Trump has claimed he would get.

E.  Conclusion

As a simple identity, GDP will equal GDP per worker employed (productivity) times the number of workers employed.  Growth in GDP will thus equal the sum of the growth rates of these two components.  With a higher share of our adult population aging into the normal retirement years, the labor force going forward (to 2024) is forecast to grow at just 0.3% a year.  That is not much.  Overall GDP growth will then be this 0.3% plus the growth in productivity.  That growth in the post World War II period has never much exceeded 2% a year for any 10-year period.  If we are able to get to such a 2% rate of productivity growth again, total GDP would then be able to grow at a 2.3% rate.  But this is below the 3% figure the Trump administration has assumed for its budget, and far below the 4% (or 5% or 6%) rates Trump has asserted he would achieve.  Trump’s forecasts (whether 3% or 4% or 5% or 6%) are unrealistic.

But a 2% rate for productivity growth is itself unlikely.  It was achieved in the 1950s and 1960s when agriculture and manufacturing were greater shares of the economy, and it has been in those sectors where productivity growth has been most rapid.  It is harder to raise productivity quickly in services, and services now dominate the economy.

Finally, it is important to note that we are speaking of growth rates in labor, productivity, and GDP over multi-year, sustained, periods.  That is what matters to what living standards can be achieved over time, and to issues like the long-term government budget projections.  There will be quarter to quarter volatility in the numbers for many reasons, including that all such figures are estimates, derived from surveys and other such sources of information.  It is also the case that an exceptionally high figure in one quarter will normally soon be followed by an exceptionally low figure in some following quarter, as the economy, as well as the statistical measure of it, balances out over time.

Thus, for example, the initial estimate (formally labeled the “advance estimate”) for GDP growth in the second quarter of 2017, released on July 28, was 2.6% (at an annual rate). Trump claimed this figure to be “an unbelievable number” showing that the economy is doing “incredibly well”, and claimed credit for what he considered to be a great performance.  But it is a figure for just one quarter, and will be revised in coming months as more data become available.  It also follows an estimate of GDP growth in the first quarter of 2017 of just 1.2%.  Thus growth over the first half of the year averaged 1.9%. Furthermore, productivity (GDP per worker) grew at just a 0.5% rate over the first half of 2017.  While a half year is too short a period for any such figure on productivity to be taken seriously, such a performance is clearly nothing special.

The 1.9% rate of growth of GDP in the first half of 2017 is also nothing special.  It is similar to the rate achieved over the last several years, and is in fact slightly below the 2.1% annual rate seen since 2010.  More aptly, in the 28 calendar quarters between the second quarter of 2010 and the first quarter of 2017, GDP grew at a faster pace than that 2.6% estimated rate a total of 13 times, or almost half. The quarter to quarter figures simply bounce around, and any figure for a single quarter is not terribly meaningful by itself.

It therefore might well be the case that a figure for GDP growth of 3%, or even 4% or higher, is seen for some quarter or even for several quarters.  But there is no reason to expect that the economy will see such rates on a sustained basis, as the Trump administration has predicted.

 

The Purple Line Ridership Forecasts Are Wrong: An Example of Why We Get Our Infrastructure Wrong

Executive Summary

There are several major problems with the forecast ridership figures for the Purple Line, a proposed 16-mile light rail line that would pass in a partial arc around Washington, DC, in suburban Maryland.  The forecasts, as presented and described in the “Travel Forecasts Results Technical Report” of the Final Environmental Impact Statement for the project, are in a number of cases simply impossible.

Problems include:

a)  Forecast ridership in 2040 between many of the Transit Analysis Zone pairs along the Purple Line corridor would be higher on the Purple Line itself than it would be for total transit ridership (which includes bus, Metrorail, and commuter rail ridership, in addition to ridership on the Purple Line) between these zones.  This is impossible. Such cases are not only numerous (found in more than half of the possible cases for zones within the corridor) but often very large (12 times as high in one case).  If the forecasts for total transit ridership are correct, then correcting for this, with Purple Line ridership some reasonable share of the totals, would lead to far lower figures for Purple Line ridership.

b)  Figures on forecast hours of user benefits (primarily forecast time savings from a rail line) in a scenario where the Purple Line is built as compared to one where it is not, are often implausibly high.  In two extreme cases, the figures indicate average user benefits per trip between two specific zones, should the Purple Line be built, of 9.7 hours and 11.5 hours.  These cannot be right; one could walk faster.  But other figures on overall user benefits are also high, leading to an overall average predicted benefit of 30 minutes per trip.  Even with adjustments to the pure time savings that assign a premium to rail service, this is far too high and overestimates benefits by at least a factor of two or even three.  The user benefit figures are important for two reasons:  1) An overestimate leads to a cost-effectiveness estimate (an estimate of the cost of the project per hour of user benefits) that will be far off;  and 2) The figures used for user benefits from taking the proposed rail line enter directly into the estimation of ridership on the rail line (as part of the choice on whether to take the rail line rather than some other transit option, or to drive).  If the user benefit figures are overstated, ridership will be less.  With the user benefit figures overstated by a large margin, ridership will be far less.

c)  Figures on ridership from station to station are clearly incorrect.  They indicate, for example, that far more riders would exit at the Bethesda station (an end point on the line) each day (19,800) than would board there (10,210).  This is impossible.  More significantly, the figures indicate system capacity must be sufficient to handle 21,400 riders each day on the busiest segment (on the segment leaving Silver Spring heading towards Bethesda).  Even if the overall ridership numbers were correct, the figure for ridership on this segment is clearly too high (and it is this number which leads to the far higher number of those exiting the system in Bethesda than would enter there each day).  The figure is important as the rail line has been designed to a capacity sufficient to carry such a load.  With the true number far lower, there is even less of a case for investing in an expensive rail option.  Upgraded bus services could provide the capacity needed, and at far lower cost.

There appear to be other problems as well.  But even just these three indicate there are major issues with these forecasts.  This may also explain why a number of independent observers have noted for some time that the Purple Line ridership forecasts look implausibly high.  The figure for Purple Line ridership in 2040 of 69,300 per day is three times the average daily ridership actually observed in 2012 on 31 light rail lines built in the US over the last three decades.  It would also be 58% higher on the Purple Line than on the highest amongst those 31.  Yet the Purple Line would pass solely through suburban neighborhoods, of generally medium to low density.  Most of these other light rail lines in the US serve travel to and from downtown areas.

The causes of these errors in the ridership forecasts for the Purple Line are not always clear.  But the issues suggest at a minimum that quality checks were insufficient.  And while the Purple Line is just one example, inadequate attention to such issues might explain in part why ridership forecasts for light rail lines have often proven to be substantially wrong.

 

A.  Introduction

The Purple Line is a proposed light rail line that would be built in Suburban Maryland, stretching in a partial arc from east of Washington, DC, to north of the city.  I have written several posts previously in this blog on the proposed project (see the posts here, here, here, and here) and have been highly critical of it.  It is an extremely expensive project (the total cost to be paid to the private concessionaire to build and then operate the line for 30 years will sum to $5.6 billion, and other costs borne directly by the state and/or local counties will add at least a further $600 million to this).  And the state’s own analyses of the project found that upgraded bus services (including any one of several bus rapid transit, or BRT, options) to provide the transit services that are indeed needed in the corridor, would be both cheaper and more cost-effective.  Such alternatives would also avoid the environmental damage that is inevitable with the construction of dual rail lines along the proposed route, including the destruction of 48 acres of forest cover, the filling in of important wetland areas, and the destruction of a linear urban park that has the most visited trail in the state.

The state’s rationale for building a rail line rather than providing upgraded bus services is that ridership will be so high that at some point in the future (beyond 2040) only rail service would be able to handle the load.  But many independent analysts have long questioned those ridership forecasts.  A study from 2015 found that the forecast ridership on the Purple Line would be three times as high as the ridership actually observed in 2012 on 31 light rail lines built in the US over the last three decades.  Furthermore, the forecast Purple Line ridership would be 58% higher than ridership actually observed on the highest line among those 31.  And with the Purple Line route passing through suburban areas of generally medium to low density, in contrast to routes to and from major downtown areas for most of those 31, many have concluded the Purple Line forecasts are simply not credible.

Why did the Purple Line figures come out so high?  The most complete description provided by the State of Maryland of the ridership forecasts are provided in the chapter titled “Travel Forecasts Results Technical Report”, which is part of Volume III of the Final Environmental Impact Statement (FEIS) for the Purple Line, dated August 2013 (which I will hereafter often refer to simply as the “FEIS Travel Forecasts chapter”).  A close examination of that material indicates several clear problems with the figures.  This post will discuss three, although there might well be more.

These three are:

a)  The FEIS forecast ridership for 2040 on the Purple Line alone would be higher (in a number of cases far higher) in most of the 49 possible combinations of travel between the 7 Transit Analysis Zones (TAZs) defined along the Purple Line route, than the total number of transit riders among those zones (by bus, Metrorail, commuter rail, and the Purple Line itself).  This is impossible.

b)  Figures on user benefits per Purple Line trip (primarily the time forecast to be saved by use of a rail line) are implausibly high.  In two cases they come to 9.7 hours and 11.5 hours, respectively, per trip.  This cannot be.  One could walk faster.  But these figures for minutes of user benefits per trip were then passed through in the computations to the total forecast hours of user benefits that would accrue as a consequence of building the Purple Line, thus grossly over-estimating the benefits. Such user benefit figures would also have been used in the estimation of how many will choose to ride the Purple Line.  If these user benefit figures are overestimated (sometimes hugely overestimated), then the Purple Line ridership forecasts will be overestimated.

c)  The figure presenting rail ridership by line segment from station to station (which then was used to determine what ridership capacity would be needed to service the proposed route) shows almost twice as many riders exiting at the Bethesda station (an end of the line) as would board there each day (19,800 arriving versus 10,210 leaving each day).  While there could be some small difference (i.e. some people might take transit to work in the morning, and then get a car ride home with a colleague in the evening), it could not be so large.  The figures would imply that Bethesda would be accumulating close to 9,600 new residents each day.  The forecast ridership by line segment (which is what determines these figures) is critical as it determines what the capacity will need to be of the transit system to service such a number of riders.  With these figures over-stated, the design capacity is too high, and there is even less of a rationale for building a rail line as opposed to simply upgrading bus services in the corridor.

These three issues are clear just from an examination of the numbers presented.  But as noted, there might well be more.  We cannot say for sure what all the errors might be as the FEIS Travel Forecasts chapter does not give a complete set of the numbers and assumed relationships needed as inputs to the analysis and then resulting from it, nor more than just a cursory explanation of how the results were arrived at.  But with anomalies such as these, and with no explanations for them, one cannot treat any of the results with confidence.

And while necessarily more speculative, I will also discuss some possible reasons for why the mistakes may have been made.  This matters less than the errors themselves, but might provide a sense for why they arose.  Broadly, while the FEIS Travel Forecasts chapter (and indeed the entire FEIS report) only shows the Maryland Transit Administration (MTA) as the source for the documents, the MTA has acknowledged (and as would be the norm) that major portions of the work – in particular the ridership forecasts – were undertaken or led by hired consulting firms.  The consulting firms use standard but large models to prepare such ridership forecasts, but such models must be used carefully to ensure reliable results.  It is likely that results were generated by what might have been close to a “black box” to the user, that there were then less than sufficient quality checks to ensure the results were reasonable, and that the person assigned to write up the results (who may well have differed from the person generating the numbers) did not detect these anomalous results.

I will readily admit that this is speculation as to the possible underlying causes, and that I could be wrong on this.  But it might explain why figures were presented in the final report which were on their face impossible, with no explanation given.  In any case, what is most important is the problems themselves, regardless of the possible explanations on why they arose.

Each of the three issues will be taken up in turn.

B.  Forecast Ridership on the Purple Line Alone Would Be Higher in Many Cases than Total Transit Ridership

The first issue is that, according to the forecasts presented, there would be more riders on the Purple Line alone between many of the Transit Analysis Zones (TAZs) than the number of riders on all forms of transit.  This is impossible.

Forecast Ridership on All Transit Options in 2040:

Forecast Ridership on Purple Line Alone in 2040:

These two tables are screenshots of the upper left-hand corners of Table 16 and 22 from the FEIS Travel Forecasts chapter.  While they show the key numbers, I would recommend that the reader examine the full tables in the original FEIS Travel Forecasts chapter. Indeed, if your computer can handle it, it would be best to open the document twice in two separate browsers and then scroll down to the two tables to allow them to be compared side by side on your screen.

The tables show forecast ridership in 2040 on all forms of transit in the “Preferred Alternative” scenario where the Purple Line is built (Table 16), or for the sub-group of riders just on the Purple Line (Table 22).  And based on the total ridership figures presented at the bottoms of the full tables, the titles appear to be correct. That is, Table 16 forecasts that total transit ridership in the Washington metro region would be about 1.5 million trips per day in 2040, which is plausible (Table 13 says it was 1.1 million trips per day in 2005, which is consistent with WMATA bus and rail ridership, where WMATA accounts for 80 – 85% of total ridership in the region).  And Table 22 says the total number of trips per day on the Purple Line in 2040 would be 68,650, which is consistent (although still somewhat different from, with no explanation) with figures given elsewhere in the chapter on forecast total Purple Line trips per day in 2040 (of 69,330 in Table 24, for example, or 69,300 in Tables 25 and 26, with that small difference probably just rounding). So it does not appear that the tables were mislabeled, which was my first thought.

The full tables show the ridership between any two pairs of 22 defined Transit Analysis Zones (TAZs), in production/attraction format (which I will discuss below).  The 22 TAZs cover the entire Washington metro region, and are defined as relatively compact geographic zones along the Purple Line corridor and then progressively larger geographic areas as one goes further and further away.  They have seven TAZs defined along the Purple Line corridor itself (starting at the Bethesda zone and ending at the New Carrollton zone), but Northern Virginia has just two zones (where one, labeled “South”, also covers most of Southern Prince George’s County in Maryland).  See the map shown as Figure 4 on page 13 of the FEIS Travel Forecasts chapter for the full picture.  This aggregation to a manageable set of TAZs, with a focus on the Purple Line corridor itself, is reasonable.

The tables then show the forecast ridership between any two TAZ pairs.  For example, Table 16 says there will on average be 1,589 riders on all forms of transit each day in 2040 between Bethesda (TAZ 1, as a “producer” zone) and Silver Spring (TAZ 3, as an “attractor” zone).  But Table 22 says there will be 2,233 riders each day on average between these same two TAZs on the Purple Line alone.  This is impossible.  And there are many such impossibilities.  For the 49 possible pairs (7 x 7) for the 7 TAZs directly on the Purple Line corridor, more than half (29) have more riders on the Purple Line than on all forms of transit.  And for one pair, between Bethesda (TAZ 1) and New Carrollton (TAZ 7), the forecast is that there would be close to 12 times as many riders taking the Purple Line each day as would take all forms of public transit (which includes the Purple Line and more).

Furthermore, if one adds up all the transit ridership between these 49 possible pairs (where the totals are presented at the bottom of the tables; see the FEIS Travel Forecasts chapter), the total number of trips per day on all forms of transit sums to 29,890 among these 7 TAZs (Table 16), while the total for the Purple Line alone sums to 30,560 (Table 22).

How could such a mistake have been made?  One can only speculate, as the FEIS chapter had next to no description of the methods they followed.  One instead has to infer a good deal based on what was presented, in what sequence, and from what is commonly done in the profession to produce such forecasts.  This goes into fairly technical issues, and readers not interested in these details can skip directly to the next section below.  But it will likely be of interest at least to some, provides a short review of the modeling process commonly used to generate such ridership forecasts, and will be helpful to an understanding of the other two obvious errors in the forecasts discussed below.

To start, note that the tables say they are being presented in “production/attraction” format.  This is not the more intuitive “origin/destination” format that would have been more useful to show.  And I suspect that over 99% of readers have interpreted the figures as if they are showing travel between origin and destination pairs.  But that is not what is being shown.

The production/attraction format is an intermediate stage in the modeling process that is commonly used for such forecasts.  That modeling process is called the “four-step model”.  See this post from the Metropolitan Washington Council of Governments (MWCOG) for a non-technical short description, or this post for a more academic description.  The first step in the four-step model is to try to estimate (via a statistical regression process normally) how many trips will be “produced” in each TAZ by households and by businesses, based on their characteristics.  Trips to work, for example, will be “produced” by households at the TAZ where they live, and “attracted” by businesses at the TAZ where those businesses are located.  The number of trips so produced will be forecast based on some set of statistical regression equations (with parameters possibly taken from what might have been estimated for some other metro area, if the data does not exist here).  The number of trips per day by household will be some function of average household size in the TAZ, average household income, how many cars the households own, and other such factors.  Trips “attracted” by businesses in some TAZ will similarly be some function of how many people are employed by businesses in that TAZ, perhaps the nature of the businesses, and so on.  Businesses will also “produce” their own trips, for example for delivery of goods to other businesses, and statistical estimates will be made also for such trips.

Such estimates are unfortunately quite rough (statistical error is high), and the totals calculated for the region as a whole of the number of trips “produced” and the number of trips “attracted” will always be somewhat different, and often far different.  But by definition the totals have to be the same, as all trips involve going from somewhere to somewhere. Hence some scaling process will commonly be used to equate the totals.

This will then yield the total number of trips produced in each TAZ, and the total number attracted to each TAZ.  But this does not tell us yet the distribution of the trips.  That is, one will have the total number of trips produced in TAZ 1, say, but not how many go from TAZ 1 to TAZ 2 or to TAZ 3 or to TAZ 4, and so on.  For this, forecasters generally assume the travel patterns will fit what is called a “gravity model”, where it is assumed the trips from each TAZ will be distributed to the “attractor” TAZs in some statistical relationship which is higher depending on the “mass” (i.e. the number of jobs in some TAZ) and lower depending on the distance between them (typically measured in terms of travel times). This is also rough, and some iterative rescaling process will be needed to ensure the trips produced in each TAZ and attracted to each TAZ sum to the already determined totals for each.

This all seems crude, and it is.  Many might ask why not determine such trip distributions from a straightforward survey of households asking where they travel to.  Surveys are indeed important, and help inform what the parameters of these functions might be, but one must recognize that any practicable survey could not suffice.  The 22 TAZs defined for the Purple Line analysis were constructed (it appears; see below) from a more detailed set of TAZs defined by the Metropolitan Washington Council of Governments.  But MWCOG now identifies 3,722 separate TAZs for the Washington metro region, and travel between them would potentially involve 13.9 million possible pairs (3,722 squared)!  No survey could cover that.  Hence MWCOG had to use some form of a gravity model to allocate the trips from each zone to each zone, and that is indeed precisely what they say they did.

At this point in the process, one will have the total number of trips produced by each TAZ going to each TAZ as an attractor, which for 2040 appears as Table 8 in the FEIS chapter. This covers trips by all options, including driving.  The next step is to separate the total number of trips between those taken by car from those taken by transit, and then, at the level below, the separation of those taken by transit into each of the various transit options (e.g. Metrorail, bus, commuter rail, and the Purple Line in the scenario where it is built). This is the mode choice issue, and note that these are discrete choices where one chooses one or the other.  (A combined option such as taking a bus to a Metrorail station and then taking the train would be modeled as a separate mode choice.)  This separation into various travel modes is normally then done by what is called a nested logit (or logistic) regression model, where the choice is assumed to be a function of variables such as travel time required, out of pocket costs (such as for fares or tolls or parking), personal income, and so on.

Up to this stage, the modeling work as described above would have been carried out by MWCOG as part of its regular work program (although in the scenario of no Purple Line). Appendix A of the FEIS Travel Forecasts chapter, says specifically that the modelers producing the Purple Line ridership forecasts started from the MWCOG model results (Round 8.0 of that model for the FEIS forecasts).  By aggregating from the TAZs used by MWCOG (3,722 currently, but possibly some different number in the Round 8.0 version), to the 22 defined for the Purple Line work, the team doing the FEIS forecasts would have been able to arrive at the table showing total daily trips by all forms of transportation (including driving) between the 22 TAZs (Table 8 of the FEIS chapter), as well as the total trips by some form of transit between the 22 in the base case of no Purple Line being built (the “No Build” alternative; Table 14 of the FEIS chapter).

The next step was then to model how many total transit trips would be taken in the case where the Purple Line has been built and is operating in 2040, as well as how many of such transit trips will be taken on the Purple Line specifically.  The team producing the FEIS forecasts would likely have taken the nested logit model produced by MWCOG, and then adjusted it to incorporate the addition of the Purple Line travel option, with consequent changes in the TAZ to TAZ travel times and costs.  At the top level they then would have modeled the split in travel between by car or by any form of transit, and at the next level then modeled the split of any form of transit between the various transit options (bus, Metrorail, commuter rail, and the Purple Line itself).

This then would have led to the figures shown in Table 16 of the FEIS chapter for total transit trips each day by any transit mode (with the Purple Line built), and Table 22 for trips on the Purple Line only.  Portions of those tables are shown above.  They are still in “production/attraction” format, as noted in their headings.

While understandable as a step in the process by which such ridership forecasts are generated (as just described), trips among TAZs in production/attraction format are not terribly interesting in themselves.  They really should have gone one further step, which would have been to convert from a production/attraction format to an origin/destination format.  The fact that they did not is telling.

As discussed above, a production/attraction format will show the number of trips between each production TAZ and each attraction TAZ.  Thus a regular commute for a worker from home (production TAZ) to work (attraction TAZ) each day will appear as two trips each day between the production TAZ and the attraction TAZ.  Thus, for example, the 1,589 trips shown as total transit trips (Table 16) between TAZ 1 (Bethesda) and TAZ 3 (Silver Spring) includes not only the trips by a commuter from Bethesda to Silver Spring in the morning, but also the return trip from Silver Spring to Bethesda in the evening.  The return trip does not appear in this production/attraction format in the 4,379 trips from Silver Spring (TAZ 3) to Bethesda (TAZ 1) element of the matrix (see the portion of Table 16 shown above).  The latter is the forecast of the number of trips each day between Silver Spring as a production zone and Bethesda as an attractor.

This is easy to confuse, and I suspect that most readers seeing these tables are so confused.  What interests the reader is not this production/attraction format of the trips, which is just an intermediate stage in the modeling process, but rather the final stage showing trips from each origin TAZ to each destination TAZ.  And it only requires simple arithmetic to generate that, if one has the underlying information from the models on how many trips were produced from home to go to work or to shop or for some other purpose (where people will always then return home each day), and separately how many were produced by what they call in the profession “non-home based” activities (such as trips during the workday from business to business).

I strongly suspect that the standard software used for such models would have generated such trip distributions in origin/destination format, but they are never presented in the FEIS Travel Forecasts chapter.  Had they been, one would have seen what the forecast travel would have been between each of the TAZ pairs in each of the two possible directions. One would probably have observed an approximate (but not necessarily exact) symmetry in the matrix, as travel from one TAZ to another in one direction will mostly (but not necessarily fully) be matched by a similar flow in the reverse direction, when added up over the course of a day.  For that reason also, the row totals will match or almost match each of the column totals.  But that will not be the case in the production/attraction format.

That the person writing up the results for this FEIS chapter did not understand that an origin/destination presentation of the travel would have been of far greater interest to most readers than the production/attraction format is telling, I suspect.  They did not see the significance.  Rather, what was written up was mostly simply a restatement of some of the key numbers from the tables, with little to no attempt to explain why they were what they were.  It is perhaps then not surprising that the author did not notice the impossibility of the forecast ridership between many of the TAZ pairs being higher on the Purple Line alone (Table 22) than the total ridership on all transit options together (Table 16).

C.  User Benefits and Time Savings

The modeling exercise also produced a forecast of “user benefits” in the target year. These benefits are measured in units of time (minutes or hours) and arise primarily from the forecast savings in the time required for a trip, where estimates are made as to how much less time will be required for a trip if one has built the light rail line.  I would note that there are questions as to whether there would in fact be any time savings at all (light rail lines are slow, particularly in designs where they travel on streets with other traffic, which will be the case here for much of the proposed route), but for the moment let’s look at what the modelers evidently assumed.

“User benefits” then include a time-value equivalent of any out-of-pocket cost savings (to the extent any exists; it will be minor here for most), plus a subjective premium for what is judged to be the superior quality of a ride on a rail car rather than a regular bus. The figures in the AA/DEIS (see Table 6-2 in Chapter 6) indicate a premium of 19% was added in the case of the medium light rail alternative – the alternative that evolved into what is now the Purple Line.  The FEIS Travel Forecasts chapter does not indicate what premium they now included, but presumably it was similar.  User benefits are thus largely time savings, with some markup to reflect a subjective premium.

Forecast user benefits are important for two reasons.  One is that it is such benefits which are, to the extent they in fact exist, the primary driver of predicted ridership on the Purple Line, i.e. travelers switching to the Purple Line from other transit options (as well as from driving, although the forecast shifts out of driving were relatively small).  Second, the forecast user benefits are also important as they provide the primary metric used to estimate the benefit of building the Purple Line. Thus if the inputs used to indicate what the time savings would be by riding the Purple Line as opposed to some other option were over-estimated, one will be both over-estimating ridership on the line and over-estimating the benefits.

And it does appear that those time savings and user benefits were over-estimated.  Table 23 of the FEIS chapter presents what it labels the “Minutes of User Benefits per Project Trip”.  A screenshot of the upper left corner, focussed on the travel within the 7 TAZs through which the Purple Line would pass, is:

Note that while the author of the chapter never says what was actually done, it appears that Table 23 was calculated implicitly by dividing the figures in Table 21 of the FEIS Travel Forecasts chapter (showing calculated total hours of time savings daily for each TAZ pair) by those in Table 22 (showing the number of daily trips on the Purple Line, the same table as was discussed in the section above).  This would have been a reasonable approach, given that the time savings figures include that saved by all the forecast shifts among transit alternatives (as well as from driving) should the new rail line be built.  The Table 23 numbers thus show the overall time saved across all travel modes, per Purple Line trip.

But the figures are implausible.  Taking the most extreme cases first, the table says that there would be an average of 582 minutes of user benefits per trip for travel on the Purple line between Bethesda (TAZ 1) and Riverdale Park (TAZ 6), and 691 minutes per trip between Bethesda (TAZ 1) and New Carrollton (TAZ 7).  This works out to user benefits per trip of 9.7 hours and 11.5 hours respectively!  One could walk faster!  And this does not even take into account that travel between Bethesda and New Carrollton would be faster on Metrorail (assuming the system is still functioning in 2040).  The FEIS Travel Forecasts chapter itself, in its Table 6, shows that Metrorail between these two stations currently requires 55 minutes.  That time should remain unchanged in the future, assuming Metrorail continues to operate.  But traveling via the Purple Line would require 63 minutes (Table 11) for the same trip.  There would in fact be no time savings at all, but rather a time cost, if there were any riders between those two points.

Perhaps some of these individual cases were coding errors of some sort.  I cannot think of anything else which would have led to such results.  But even if one sets such individual cases aside, I find it impossible to understand how any of these user benefit figures could have followed from building a rail line.  They are all too large.  For example, the FEIS chapter provides in its Table 18 a detailed calculation of how much time would be saved by taking a bus (under the No Build alternative specifically) versus taking the proposed Purple Line.  Including average wait times, walking times, and transfers (when necessary), it found a savings of 11.4 minutes for a trip from Silver Spring (TAZ 3) to Bethesda (TAZ 1); 2.6 minutes for a trip from Bethesda (TAZ 1) to Glenmont (TAZ 9); and 8.0 minutes for a trip from North DC (TAZ 15) to Bethesda (TAZ 1).  Yet the minutes of user benefits per trip for these three examples from Table 23 (see the full table in the FEIS chapter) were 25 minutes, 19 minutes, and 25 minutes, respectively.  Even with a substantial premium for the rail options, I do not see how one could have arrived at such estimates.

And the figures matter.  The overall average minutes of user benefits per project trip (shown at the bottom of Table 23 in the FEIS chapter) came to 30 minutes.  If this were a more plausible average of 10 minutes, say, then with all else equal, the cost-effectiveness ratio would be three times worse.  This is not a small difference.

Importantly, the assumed figures on time savings will also matter to the estimates made of the total ridership on the Purple Line.  The forecast number of daily riders in 2040 of 68,650 (Table 22) or 69,300 (in other places in the FEIS chapter) was estimated based on inputs of travel times required by each of the various modes, and from this how much time would be saved by taking the Purple Line rather than some other option.  With implausibly large figures for travel time savings being fed in, the ridership forecasts will be too high.  If the time savings figures being fed in are far too large, the ridership forecasts will be far too high.  This is not a minor matter.

D.  Ridership by Line Segment

An important estimate is of how many riders there will be between any two station to station line segments, as that will determine what the system capacity will need to be.  Rail lines are inflexible, and completely so when, as would be the case here, the trains would be operated in full from one end of the line to the other.  The rider capacity (size) of the train cars and the spacing between each train (the headway) will then be set to accommodate what is needed to service ridership on what would be the most crowded segment.

Figure 10 of the FEIS Travel Forecasts chapter provides what would be a highly important and useful chart of ridership on each line segment, showing, it says, how many riders would (in terms of the daily average) arrive at each station, how many of those riders would get off at that station, and then how many riders would board at that station.  That would then produce the figure for how many riders will be on board traveling to the next station.  And one needs to work this out for going in each direction on the line.

Here is a portion of that figure, showing the upper left-hand corner:

Focussing on Bethesda (one end of the proposed line), the chart indicates 10,210 riders would board at Bethesda each day, while 19,800 riders would exit each day from arriving trains.  But how could that be?  While there might be a few riders who might take the Purple Line in one direction to go to work or for shopping or for whatever purpose, and then take an alternative transportation option to return home, that number is small, and would to some extent balance out by riders going in the opposite direction.  Setting this small possible number aside, the figures in the chart imply that close to twice as many riders will be exiting in Bethesda as will be entering.  They imply that Bethesda would be seeing its population grow by almost 9,600 people per day.  This is not possible.

But what happened is clear.  The tables immediately preceding this figure in the FEIS Travel Forecasts chapter (Tables 24 and 25) purport to show for each of the 21 stations on the proposed rail line, what the daily station boardings will be, with a column labeled “Total On” at each station and a column labeled “Total Off”.  Thus for Bethesda, the table indicates 10,210 riders will be getting on, while 19,800 will be getting off.  While for most of the stations, the riders getting on at that station could be taking the rail line in either direction (and those getting off could be arriving from either direction), for the two stations at the ends of the line (Bethesda, and at the other end New Carrollton) they can only go in one direction.

But as an asterisk for the “Total On” and “Total Off” column headings explicitly indicates, the figures in these two columns of Table 24 are in production/attraction format.  That is, they indicate that Bethesda will be “producing” (mostly from its households) a forecast total of 10,210 riders each day, and will be “attracting” (mostly from its businesses) 19,800 riders each day.  But as discussed above, one must not confuse the production/attraction presentation of the figures, with ridership according to origin/destination.  A household where a worker will be commuting each day to his or her office will be shown, in the production/attraction format, as two trips each day from the production TAZ going to the attraction TAZ.  They will not be shown as one trip in each direction, as they would have been had the figures been converted to an origin/destination presentation.  The person that generated the Figure 10 numbers confused this.

This was a simple and obvious error, but an important one.  Because of this mistake, the figures shown in Figure 10 for ridership between each of the station stops are completely wrong.  This is also important because ridership forecasts by line segment, such as what Figure 10 was supposed to show, are needed in order to determine system capacity.  The calculations depicted in the chart conclude that peak ridership in the line would be 21,400 each day on the segment heading west from the Woodside / 16th Street station (still part of Silver Spring) towards Lyttonsville.  Hence the train car sizes and the train frequency would need to be, according to these figures (but incorrectly), adequate to carry 21,400 riders each day. That is their forecast of ridership on the busiest segment.  The text of the chapter notes this specifically as well (see page 56).

That figure is critically important because the primary argument given by the State of Maryland for choosing a rail line rather than one of the less expensive as well as more cost-effective bus options, is that ridership will be so high at some point (not yet in 2040, but at some uncertain date not too long thereafter) that buses would be physically incapable of handling the load.  This all depends on whether the 21,400 figure for the maximum segment load in 2040 has any validity.  But it is clearly far too high; it leads to almost twice as many riders going into Bethesda as leave.  It was based on confusing ridership in a production/attraction format with ridership by origin/destination.

Correcting for this would lead to a far lower maximum load, even assuming the rest of the ridership forecasts were correct.  And at a far lower maximum load, there is even less of a case against investing in a far less expensive, as well as more cost-effective, system of upgraded bus services for the corridor.

E.  Other Issues

There are numerous other issues in the FEIS Travel Forecasts chapter which leads one to question how carefully the work was done.  One oddity, as an example and perhaps not important in itself, is that Tables 17 and 19, while titled differently, are large matrices where all the numbers contained therein are identical.  Table 17 is titled “Difference in Daily Transit Trips (2040 Preferred Alternative minus No Build Alternative) (Production/Attraction Format)”, while Table 19 is titled “New Transit Trips with the Preferred Alternative (Production/Attraction Format)”.  That the figures are all identical is not surprising – the titles suggest they should be the same.  But why show them twice?  And why, in the text discussing the tables (pp. 41-42), does the author treat them as if they were two different tables, showing different things?

But more importantly, there are a large number of inconsistencies in key figures between different parts of the chapter.  Examples include:

a)  New transit trips in 2040:  Table 17 (as well as 19) has that there would be 19,700 new transit trips daily in the Washington region in 2040, if the Purple Line is built (relative to the No Build alternative).  But on page 62, the text says the number would be 16,330 new transit trips in 2040 if it is built.  And Table B-1 on page 67 says there would be 28,626 new transit trips in 2040 (again relative to No Build).  Which is correct?  One is 75% higher than another, which is not a small difference.

b)  Total transit trips in 2040:  Table 16 says that there would be a total of 1,470,620 total transit trips in the Washington region in 2040 if the Purple Line is built, but Table B-1 on page 67 puts the figure at 1,683,700, a difference of over 213,000.

c)  Average travel time savings:  Table 23 indicates that average minutes of “user benefits” per project trip would be 30 minutes in 2040 if the Purple Line is built, but the text on page 62 says that average travel time savings would “range between 14 and 18 minutes per project trip”.  This might be explained if they assigned a 100% premium to the time savings for riding a rail line, but if so, such an assumed premium would be huge.  As noted above, the premium assigned in the AA/DEIS for the Medium Light Rail alternative (which was the alternative later chosen for the Purple Line) was just 19%.  And the 14 to 18 minutes figure for average time savings per trip itself looks too large. The simple average of the three representative examples worked out in Table 18 of the chapter was just 7.3 minutes.

d)  Total user benefit hours per day in 2040:  The text on page 62 says that the total user benefit hours per day in 2040 would sum to 17,175.  But Table B-5 says the total would come to 24,073 hours (shown as 1,444,403 minutes, and then divided by 60), while Table 21 gives a figure of 33,960 hours.  The highest figure is almost double the lowest.  Note the 33,960 hours figure is also shown in Table 20, but then shows this as 203,760 minutes (but should be 2,037,600 minutes – they multiplied by 6, not 60, for the conversion of hours to minutes).

There are other inconsistencies as well.  Perhaps some can be explained.  But they suggest that inadequate attention was paid to ensure accuracy.

F.  Conclusion

There are major problems with the forecasts of ridership on the proposed Purple Line.  The discussion above examined several of the more obvious ones.  There may well be more. Little explanation was provided in the documentation on how the forecasts were made and on the intermediate steps, so one cannot work through precisely what was done to see if all is reasonable and internally consistent.  Rather, the FEIS Travel Forecasts chapter largely presented just the final outcomes, with little description of why the numbers turned out to be what they were presented to be.

But the problems that are clear even with the limited information provided indicate that the correct Purple Line ridership forecasts would likely be well less than what their exercise produced.  Specifically:

a)  Since the Purple Line share of total transit use can never be greater than 100% (and will in general be far less), a proper division of transit ridership between the Purple Line and other transit modes will result in a figure that is well less than the 30,560 forecast for Purple Line ridership for trips wholly within the Purple Line corridor alone (shown in Table 22).  The corridor covers seven geographic zones which, as defined, stretch often from the Beltway to the DC line (or even into DC), and from Bethesda to New Carrollton.  There is a good deal of transit ridership within and between those zones, which include four Metrorail lines with a number of stations on each, plus numerous bus routes.  Based on the historical estimates for transit ridership (for 2005), the forecasts for total transit ridership in 2040 within and between those zones look reasonable.  The problem, rather, is with the specific Purple Line figures, with figures that are often higher (often far higher) than the figures for total transit use.  This is impossible.  Rather, one would expect Purple Line ridership to be some relatively small share (no more than a quarter or so, and probably well less than that) of all transit users in those zones.  Thus the Purple Line ridership forecasts, if properly done, would have been far lower than what was presented.  And while one cannot say what the precise figure would have been, it is a mathematical certainty that it cannot account for more than 100% of total transit use within and between those zones.

b)  The figures on user benefits per trip (Table 23) appear to be generally high (an overall average of 30 minutes) and sometimes ridiculously high (9.7 hours and 11.5 hours per trip in two cases).  At more plausible figures for time savings, Purple Line ridership would be far less.

c)  Even with total Purple Line ridership at the official forecast level (69,300), there will not be a concentration in ridership on the busiest segment of 21,400 (Figure 10).  The 21,400 figure was derived based on an obvious error – from a confusion in the meaning of the production/attraction format.  Furthermore, as just noted above, correcting for other obvious errors imply that total Purple Line ridership will also be far less than the 69,300 figure forecast, and hence the station to station loads will be far less.  The design capacity required to carry transit users in this corridor can therefore be far less than what these FEIS forecasts said it would need to be.  There is no need for a rail line.

These impossibilities, as well as inconsistencies in the figures cited at different points in the chapter for several of the key results, all suggest insufficient checks in the process to ensure the forecasts were, at a minimum, plausible and internally consistent.  For this, or whatever, reason, forecasts that are on their face impossible were nonetheless accepted and used to justify building an expensive rail line in this corridor.

And while the examination here has only been of the Purple Line, I suspect that such issues often arise in other such transit projects, and indeed in many proposed public infrastructure projects in the US.  When agencies responsible for assessing whether the projects are justified instead see their mission as project advocates, a hard look may not be taken at analyses whose results support going ahead.

The consequence is that a substantial share of the scarce funds available for transit and other public infrastructure projects is wasted.  Expensive new projects get funded (although only a few, as money is limited), while boring simple projects, as well as the maintenance of existing transit systems, get short-changed, and we end up with a public infrastructure that is far from what we need.