The Increasingly Attractive Economics of Solar Power: Solar Prices Have Plunged

A.  Introduction

The cost of solar photovoltaic power has fallen dramatically over the past decade, and it is now, together with wind, a lower cost source of new power generation than either fossil-fuel (coal or gas) or nuclear power plants.  The power generated by a new natural gas-fueled power plant in 2018 would have cost a third more than from a solar or wind plant (in terms of the price they would need to sell the power for in order to break even); coal would have cost 2.4 times as much as solar or wind; and a nuclear plant would have cost 3.5 times as much.

These estimates (shown in the chart above, and discussed in more detail below) were derived from figures estimated by Lazard, the investment bank, and are based on bottom-up estimates of what such facilities would have cost to build and operate, including the fuel costs.  But one also finds a similar sharp fall in solar energy prices in the actual market prices that have been charged for the sale of power from such plants under long-term “power purchase agreements” (PPAs).  These will also be discussed below.

With the costs where they are now, it would not make economic sense to build new coal or nuclear generation capacity, nor even gas in most cases.  In practice, however, the situation is more complex due to regulatory issues and conflicting taxes and subsidies, and also because of variation across regions.  Time of day issues may also enter, depending on when (day or night) the increment in new capacity might be needed.  The figures above are also averages, particular cases vary, and what is most economic in any specific locale will depend on local conditions.  Nevertheless, and as we will examine below, there has been a major shift in new generation capacity towards solar and wind, and away from coal (with old coal plants being retired) and from nuclear (with no new plants being built, but old ones largely remaining).

But natural gas generation remains large.  Indeed, while solar and wind generation have grown quickly (from a low base), and together account for the largest increment in new power capacity in recent years, gas accounts for the largest increment in power production (in megawatt-hours) measured from the beginning of this decade.  Why?  In part this is due to the inherent constraints of solar and wind technologies:  Solar panels can only generate power when the sun shines, and wind turbines when the wind is blowing.  But more interestingly, one also needs to look at the economics behind the choice as to whether or not to build new generation capacity to replace existing capacity, and then what sources of capacity to use.  Critical is what economists call the marginal cost of such production.  A power plant lasts for many years once it is built, and the decision on whether to keep an existing plant in operation for another year depends only on the cost of operating and maintaining the plant.  The capital cost has already been spent and is no longer relevant to that decision.

Details in the Lazard report can be used to derive such marginal cost estimates by power source, and we will examine these below.  While the Lazard figures apply to newly built plants (older plants will generally have higher operational and maintenance costs, both because they are getting old and because technology was less efficient when they were built), the estimates based on new plants can still give us a sense of these costs.  But one should recognize they will be biased towards indicating the costs of the older plants are lower than they in fact are.  However, even these numbers (biased in underestimating the costs of older plants) imply that it is now more economical to build new wind and possibly solar plants, in suitable locales, than it costs to continue to keep open and operate coal-burning power plants.  This will be especially true for the older, less-efficient, coal-burning plants.  Thus we should be seeing old coal-burning plants being shut down.  And indeed we do.  Moreover, while the costs of building new wind and solar plants are not yet below the marginal costs of keeping open existing gas-fueled and nuclear power plants, they are on the cusp of being so.

These costs also do not reflect any special subsidies that solar and wind plants might benefit from.  These vary by state.  Fossil-fueled and nuclear power plants also enjoy subsidies (often through special tax advantages), but these are long-standing and are implicitly being included in the Lazard estimates of the costs of such traditional plants.

But one special subsidy enjoyed by fossil fuel burning power plants, not reflected in the Lazard cost estimates, is the implicit subsidy granted to such plants from not having to cover the cost of the damage from the pollution they generate.  Those costs are instead borne by the general public.  And while such plants pollute in many different ways (especially the coal-burning ones), I will focus here on just one of those ways – their emissions of greenhouse gases that are leading to a warming planet and consequent more frequent and more damaging extreme weather events.  Solar and wind generation of power do not cause such pollution – the burning of coal and gas do.

To account for such costs and to ensure a level playing field between power sources, a fee would need to be charged to reflect the costs being imposed on the general population from this (and indeed other) such pollution.  The revenues generated could be distributed back to the public in equal per capita terms, as discussed in an earlier post on this blog.  We will see that a fee of even just $20 per ton of CO2 emitted would suffice to make it economic to build new solar and wind power plants to substitute not just for new gas and coal burning plants, but for existing ones as well.  Gas and especially coal burning plants would not be competitive with installing new solar or wind generation if they had to pay for the damage done as a result of their greenhouse gas pollution, even on just marginal operating costs.

Two notes before starting:  First, many will note that while solar might be fine for the daytime, it will not be available at night.  Similarly, wind generation will be fine when the wind blows, but it may not always blow even in the windiest locales.  This is of course true, and should solar and wind capacity grow to dominate power generation, there will have to be ways to store that power to bridge the times from when the generation occurs to when the power is used.

But while storage might one day be an issue, it is mostly not an issue now.  In 2018, utility-scale solar only accounted for 1.6% of power generation in the US (and 2.3% if one includes small scale roof-top systems), while wind only accounted for 6.6%.  At such low shares, solar and wind power can simply substitute for other, higher cost, sources of power (such as from coal) during the periods the clean sources are available.  Note also that the cost figures for solar and wind reflected in the chart at the top of this post (and discussed in detail below) take into account that solar and wind cannot be used 100% of the time.  Rather, utilization is assumed to be similar to what their recent actual utilization has been, not only for solar and wind but also for gas, coal and nuclear.  Solar and wind are cheaper than other sources of power (over the lifetime of these investments) despite their inherent constraints on possible utilization.

But where the storage question can enter is in cases where new generation capacity is required specifically to serve evening or night-time needs.  New gas burning plants might then be needed to serve such time-of-day needs if storage of day-time solar is not an economic option.  And once such gas-burning plants are built, the decision on whether they should be run also to serve day-time needs will depend on a comparison of the marginal cost of running these gas plants also during the day, to the full cost of building new solar generation capacity, as was discussed briefly above and will be considered in more detail below.

This may explain, in part, why we see new gas-burning plants still being built nationally.  While less than new solar and wind plants combined (in terms of generation capacity), such new gas-burning plants are still being built despite their higher cost.

More broadly, California and Hawaii (both with solar now accounting for over 12% of power used in those states) are two states (and the only two states) which may be approaching the natural limits of solar generation in the absence of major storage.  During some sunny days the cost of power is being driven down to close to zero (and indeed to negative levels on a few days).  Major storage will be needed in those states (and only those states) to make it possible to extend solar generation much further than where it is now.  But this should not be seen so much as a “problem” but rather as an opportunity:  What can we do to take advantage of cheap day-time power to make it available at all hours of the day?  I hope to address that issue in a future blog post.  But in this blog post I will focus on the economics of solar generation (and to a lesser extent from wind), in the absence of significant storage.

Second, on nomenclature:  A megawatt-hour is a million watts of electric power being produced or used for one hour.  One will see it abbreviated in many different ways, including MWHr, MWhr, MWHR, MWH, MWh, and probably more.  I will try to use consistently MWHr.  A kilowatt-hour (often kWh) is a thousand watts of power for one hour, and is the typical unit used for homes.  A megawatt-hour will thus be one thousand times a kilowatt-hour, so a price of, for example, $20 per MWHr for solar-generated power (which we will see below has in fact been offered in several recent PPA contracts) will be equivalent to 2.0 cents per kWh.  This will be the wholesale price of such power.  The retail price in the US for households is typically around 10 to 12 cents per kWh.

B.  The Levelized Cost of Energy 

As seen in the chart at the top of this post, the cost of generating power by way of new utility-scale solar photovoltaic panels has fallen dramatically over the past decade, with a cost now similar to that from new on-shore wind turbines, and well below the cost from building new gas, coal, or nuclear power plants.  These costs can be compared in terms of the “levelized cost of energy” (LCOE), which is an estimate of the price that would need to be charged for power from such a plant over its lifetime, sufficient to cover the initial capital cost (at the anticipated utilization rate), plus the cost of operating and maintaining the plant,

Lazard, the investment bank, has published estimates of such LCOEs annually for some time now.  The most recent report, issued in November 2018, is version 12.0.  Lazard approaches the issue as an investment bank would, examining the cost of producing power by each of the alternative sources, with consistent assumptions on financing (with a debt/equity ratio of 60/40, an assumed cost of debt of 8%, and a cost of equity of 12%) and a time horizon of 20 years.  They also include the impact of taxes, and show separately the impact of special federal tax subsidies for clean energy sources.  But the figures I will refer to throughout this post (including in the chart above) are always the estimates excluding any impact from special subsidies for clean energy.  The aim is to see what the underlying actual costs are, and how they have changed over time.

The Lazard LCOE estimates are calculated and presented in nominal terms.  They show the price, in $/MWHr, that would need to be charged over a 20-year time horizon for such a project to break even.  For comparability over time, as well as to produce estimates that can be compared directly to the PPA contract prices that I will discuss below, I have converted those prices from nominal to real terms in constant 2017 dollars.  Two steps are involved.  First, the fixed nominal LCOE prices over 20 years will be falling over time in real terms due to general inflation.  They were adjusted to the prices of their respective initial year (i.e. the relevant year from 2009 to 2018) using an inflation rate of 2.25% (which is the rate used for the PPA figures discussed below, the rate the EIA assumed in its 2018 Annual Energy Outlook report, and the rate which appears also to be what Lazard assumed for general cost escalation factors).  Second, those prices for the years between 2009 and 2018 were all then converted to constant 2017 prices based on actual inflation between those years and 2017.

The result is the chart shown at the top of this post.  The LCOEs in 2018 (in 2017$) were $33 per MWHr for a newly built utility-scale solar photovoltaic system and also for an on-shore wind installation, $44 per MWHr for a new natural gas combined cycle plant, $78 for a new coal-burning plant, and $115 for a new nuclear power plant.  The natural gas plant would cost one-third more than a solar or wind plant, coal would cost 2.4 times as much, and a nuclear plant 3.5 times as much.  Note also that since the adjustments for inflation are the same for each of the power generation methods, their costs relative to each other (in ratio terms) are the same for the LCOEs expressed in nominal cost terms.  And it is their costs relative to each other which most matters.

The solar prices have fallen especially dramatically.  The 2018 LCOE was only one-tenth of what it was in 2009.  The cost of wind generation has also fallen sharply over the period, to about one-quarter in 2018 of what it was in 2009.  The cost from gas combined cycle plants (the most efficient gas technology, and is now widely used) also fell, but only by about 40%, while the cost of coal or nuclear were roughly flat or rising, depending on precisely what time period is used.

There is good reason to believe the cost of solar technology will continue to decline.  It is still a relatively new technology, and work in labs around the world are developing solar technologies that are both more efficient and less costly to manufacture and install.

Current solar installations (based on crystalline silicon technology) will typically have conversion efficiencies of 15 to 17%.  And panels with efficiencies of up to 22% are now available in the market – a gain already on the order of 30 to 45% over the 15 to 17% efficiency of current systems.  But a chart of how solar efficiencies have improved over time (in laboratory settings) shows there is good reason to believe that the efficiencies of commercially available systems will continue to improve in the years to come.  While there are theoretical upper limits, labs have developed solar cell technologies with efficiencies as high as 46% (as of January 2019).

Particularly exciting in recent years has been the development of what are called “perovskite” solar technologies.  While their current efficiencies (of up to 28%, for a tandem cell) are just modestly better than purely crystalline silicon solar cells, they have achieved this in work spanning only half a decade.  Crystalline silicon cells only saw such an improvement in efficiencies in research that spanned more than four decades.  And perhaps more importantly, perovskite cells are much simpler to manufacture, and hence much cheaper.

Based on such technologies, one could see solar efficiencies doubling within a few years, from the current 15 to 17% to say 30 to 35%.  And with a doubling in efficiency, one will need only half as many solar panels to produce the same megawatts of power, and thus also only half as many frames to hold the panels, half as much wiring to link them together, and half as much land.  Coupled with simplified and hence cheaper manufacturing processes (such as is possible for perovskite cells), there is every reason to believe prices will continue to fall.

While there can be no certainty in precisely how this will develop, a simple extrapolation of recent cost trends can give an indication of what might come.  Assuming costs continue to change at the same annual rate that they had over the most recent five years (2013 to 2018), one would find for the years up to 2023:

If these trends hold, then the LCOE (in 2017$) of solar power will have fallen to $13 per MWHr by 2023, wind will have fallen to $18, and gas will be at $32 (or 2.5 times the LCOE of solar in that year, and 80% above the LCOE of wind).  And coal (at $70) and nuclear (at $153) will be totally uncompetitive.

This is an important transition.  With the dramatic declines in the past decade in the costs for solar power plants, and to a lesser extent wind, these clean sources of power are now more cost competitive than traditional, polluting, sources.  And this is all without any special subsidies for the clean energy.  But before looking at the implications of this for power generation, as a reality check it is good first to examine whether the declining costs of solar power have been reflected in actual market prices for such power.  We will see that they have.

C.  The Market Prices for Solar Generated Power

Power Purchase Agreements (PPAs) are long-term contracts where a power generator (typically an independent power producer) agrees to supply electric power at some contracted capacity and at some price to a purchaser (typically a power utility or electric grid operator).  These are competitively determined (different parties interested in building new power plants will bid for such contracts, with the lowest price winning) and are a direct market measure of the cost of energy from such a source.

The Lawrence Berkeley National Lab, under a contract with the US Department of Energy, produces an annual report that reviews and summarizes PPA contracts for recent utility-scale solar power projects, including the agreed prices for the power.  The most recent was published in September 2018, and covers 2018 (partially) and before.  While the report covers both solar photovoltaic and concentrating solar thermal projects, the figures of interest to us here (and comparable to the Lazard LCOEs discussed above) are the PPAs for the solar photovoltaic projects.

The PPA prices provided in the report were all calculated by the authors on a levelized basis and in terms of 2017 prices.  This was done to put them all on a comparable basis to each other, as the contractual terms of the specific contracts could differ (e.g. some had price escalation clauses and some did not).  Averages by year were worked out with the different projects weighted by generation capacity.

The PPA prices are presented by the year the contracts were signed.  If one then plots these PPA prices with a one year lag and compare them to the Lazard estimated LCOE prices of that year, one finds a remarkable degree of overlap:

This high degree of overlap is extraordinary.  Only the average PPA price for 2010 (reflecting the 2009 average price lagged one year) is off, but would have been close with a one and a half year lag rather than a one year lag.  Note also that while the Lawrence Berkeley report has PPA prices going back to 2006, the figures for the first several years are based on extremely small samples (just one project in 2006, one in 2007, and three in 2008, before rising to 16 in 2009 and 30 in 2010).  For that reason I have not plotted the 2006 to 2008 PPA prices (which would have been 2007 to 2009 if lagged one year), but they also would have been below the Lazard LCOE curve.

What might be behind this extraordinary overlap when the PPA prices are lagged one year?  Two possible explanations present themselves.  One is that the power producers when making their PPA bids realize that there will be a lag from when the bids are prepared to when the winning bidder is announced and construction of the project begins.  With the costs of solar generation falling so quickly, it is possible that the PPA bids reflect what they know will be a lag between when the bid is prepared and when the project has to be built (with solar panels purchased and other costs incurred).  If that lag is one year, one will see overlap such as that found for the two curves.

Another possible explanation for the one-year shift observed between the PPA prices (by date of contract signing) and the Lazard LCOE figures is that the Lazard estimates labeled for some year (2018 for example) might in fact represent data on the cost of the technologies as of the prior year (2017 in this example).  One cannot be sure from what they report.  Or the remarkable degree of overlap might be a result of some combination of these two possible explanations, or something else.

But for whatever reason, the two estimates move almost exactly in parallel over time, and hence show an almost identical rate of decline for both the cost of generating power from solar photovoltaic sources and in the market PPA prices for such power.  And it is that rapid rate of decline which is important.

It is also worth noting that the “bump up” in the average PPA price curve in 2017 (shown in the chart as 2018 with the one year lag) reflects in part that a significant number of the projects in the 2017 sample of PPAs included, as part of the contract, a power storage component to store a portion of the solar-generated power for use in the evening or night.  But these additional costs for storage were remarkably modest, and were even less in several projects in the partial-year 2018 sample.  Specifically, Nevada Energy (as the offtaker) announced in June 2018 that it had contracted for three major solar projects that would include storage of power of up to one-quarter of generation capacity for four hours, with overall PPA prices (levelized, in 2017 prices) for both the generation and the storage of just $22.8, $23.5, and $26.4 per MWHr (i.e. 2.28 cents, 2.35 cents, and 2.64 cents per kWh, respectively).

The PPA prices reported can also be used to examine how the prices vary by region.  One should expect solar power to be cheaper in southern latitudes than in northern ones, and in dry, sunny, desert areas than in regions with more extensive cloud cover.  And this has led to the criticism by skeptics that solar power can only be competitive in places such as the US Southwest.

But this is less of an issue than one might assume.  Dividing up the PPA contracts by region (with no one-year lag in this chart), one finds:

Prices found in the PPAs are indeed lower in the Southwest, California, and Texas.  But the PPA prices for projects in the Southeast, the Midwest, and the Northwest fell at a similar pace as those in the more advantageous regions (and indeed, at a more rapid pace up to 2014).  And note that the prices in those less advantageous regions are similar to what they were in the more advantageous regions just a year or two before.  Finally, the absolute differences in prices have become relatively modest in the last few years.

The observed market prices for power generated by solar photovoltaic systems therefore appear to be consistent with the bottom-up LCOE estimates of Lazard – indeed remarkably so.  Both show a sharp fall in solar energy prices/costs over the last decade, and sharp falls both for the US as a whole and by region.  The next question is whether we see this reflected in investment in additions to new power generation capacity, and in the power generated by that capacity.

D.  Additions to Power Generation Capacity, and in Power Generation

The cost of power from a new solar or wind plant is now below the cost from gas (while the cost of new coal or nuclear generation capacity is totally uncompetitive).  But the LCOEs indicate that the cost advantage relative to gas is relatively recent in the case of solar (starting from 2016), and while a bit longer for wind, the significant gap in favor of wind only opened up in 2014.  One needs also to recognize that these are average or mid-point estimates of costs, and that in specific cases the relative costs will vary depending on local conditions.  Thus while solar or wind power is now cheaper on average across the US, in some particular locale a gas plant might be less expensive (especially if the costs resulting from its pollution are not charged).  Finally, and as discussed above, there may be time-of-day issues that the new capacity may be needed for, with this affecting the choices made.

Thus while one should expect a shift towards solar and wind over the last several years, and away from traditional fuels, the shift will not be absolute and immediate.  What do we see?

First, in terms of the gross additions to power sector generating capacity:

The chart shows the gross additions to power capacity, in megawatts, with both historical figures (up through 2018) and as reflected in plans filed with the US Department of Energy (for 2019 and 2020, with the plans as filed as of end-2018).  The data for this (and the other charts in this section) come from the most recent release of the Electric Power Annual of the Energy Information Agency (EIA) (which was for 2017, and was released on October 22, 2018), plus from the Electric Power Monthly of February, 2019, also from the Energy Information Agency (where the February issue each year provides complete data for the prior calendar year, i.e. for 2018 in this case).

The planned additions to capacity (2019 and 2020 in the chart) provide an indication of what might happen over the next few years, but must be interpreted cautiously.  While probably pretty good for the next few years, biases will start to enter as one goes further into the future.  Power producers are required to file their plans for new capacity (as well as for retirements of existing capacity) with the Department of Energy, for transparency and to help ensure capacity (locally as well as nationally) remains adequate.  But these reported plans should be approached cautiously.  There is a bias as projects that require a relatively long lead time (such as gas plants, as well as coal and especially nuclear) will be filed years ahead, while the more flexible, shorter construction periods, required for solar and wind plants means that these plans will only be filed with the Department of Energy close to when that capacity will be built.  But for the next few years, the plans should provide an indication of how the market is developing.

As seen in the chart, solar and wind taken together accounted for the largest single share of gross additions to capacity, at least through 2017.  While there was then a bump up in new gas generation capacity in 2018, this is expected to fall back to earlier levels in 2019 and 2020.  And these three sources (solar, wind, and gas) accounted for almost all (93%) of the gross additions to new capacity over 2012 to 2018, with this expected to continue.

New coal-burning plants, in contrast, were already low and falling in 2012 and 2013, and there have been no new ones since then.  Nor are any planned.  This is as one would expect based on the LCOE estimates discussed above – new coal plants are simply not cost competitive.  And the additions to nuclear and other capacity have also been low.  “Other” capacity is a miscellaneous category that includes hydro, petroleum-fueled plants such as diesel, as well as other renewables such as from the burning of waste or biomass. The one bump up, in 2016, is due to a nuclear power plant coming on-line that year.  It was unit #2 of the Watts Bar nuclear power plant built by the Tennessee Valley Authority (TVA), and had been under construction for decades.  Indeed the most recent nuclear plant completed in the US before this one was unit #1 at the same TVA plant, which came on-line 20 years before in 1996.  Even aside from any nuclear safety concerns, nuclear plants are simply not economically competitive with other sources of power.

The above are gross additions to power generating capacity, reflecting what new plants are being built.  But old, economically or technologically obsolete, plants are also being retired, so what matters to the overall shift in power generation capacity is what has happened to net generation capacity:

What stands out here is the retirement of coal-burning plants.  And while the retirements might appear to diminish in the plans going forward, this may largely be due to retirement plans only being announced shortly before they happen.  It is also possible that political pressure from the Trump administration to keep coal-burning plants open, despite their higher costs (and their much higher pollution), might be a factor.  We will see what happens.

The cumulative impact of these net additions to capacity (relative to 2010 as the base year) yields:

Solar plus wind accounts for the largest addition to capacity, followed by gas.  Indeed, each of these accounts for more than 100% of the growth in overall capacity, as there has been a net reduction in the nuclear plus other category, and especially in coal.

But what does this mean in terms of the change in the mix of electric power generation capacity in the US?  Actually, less than one might have thought, as one can see in a chart of the shares:

The share of coal has come down, but remains high, and similarly for nuclear (plus miscellaneous other) capacity.  Gas remains the highest and has risen as a share, while solar and wind, while rising at a rapid pace relative to where it was to start, remains the smallest shares (of the categories used here).

The reason for these relatively modest changes in shares is that while solar and wind plus gas account for more than 100% of the net additions to capacity, that net addition has been pretty small.  Between 2010 and 2018, the net addition to US electric power generation capacity was just 58.8 thousand megawatts, or an increase over eight years of just 5.7% over what capacity was in 2010 (1,039.1 thousand megawatts).  A big share of something small will still be small.

So even though solar and wind are now the lowest cost sources of new power generation, the very modest increase in the total power capacity needed has meant that not that much has been built.  And much of what has been built has been in replacement of nuclear and especially coal capacity.  As we will discuss below, the economic issue then is not whether solar and wind are the cheapest source of new capacity (which they are), but whether new solar and wind are more economic than what it costs to continue to operate existing coal and nuclear plants.  That is a different question, and we will see that while new solar and wind are now starting to be a lower cost option than continuing to operate older coal (but not nuclear) plants, this development (a critically important development) has only been recent.

Why did the US require such a small increase in power generation capacity in recent years?  As seen in the chart below, it is not because GDP has not grown, but rather because energy efficiency (real GDP per MWHr of power) improved tremendously, at least until 2017:

From 2010 to 2017, real GDP rose by 15.7% (2.1% a year on average), but GDP per MWHr of power generated rose by 18.3%.  That meant that power generation (note that generation is the relevant issue here, not capacity) could fall by 2.2% despite the higher level of GDP.  Improving energy efficiency was a key priority during the Obama years, and it appears to have worked well.  It is better for efficiency to rise than to have to produce more power, even if that power comes from a clean source such as solar or wind.

This reversed direction in 2018.  It is not clear why, but might be an early indication that the policies of the Trump administration are harming efficiency in our economy.  However, this is still just one year of data, and one will need to wait to see whether this was an aberration or a start of a new, and worrisome, trend.

Which brings us to generation.  While the investment decision is whether or not to add capacity, and if so then of what form (e.g. solar or gas or whatever), what is ultimately needed is the power generated.  This depends on the capacity available and then on the decision of how much of that capacity to use to generate the power needed at any given moment.  One needs to keep in mind that power in general is not stored (other than still very limited storage of solar and wind power), but rather has to be generated at the moment needed.  And since power demand goes up and down over the course of the day (higher during the daylight hours and lower at night), as well as over the course of the year (generally higher during the summer, due to air conditioning, and lower in other seasons), one needs total generation capacity sufficient to meet whatever the peak load might be.  This means that during all other times there will be excess, unutilized, capacity.  Indeed, since one will want to have a safety margin, one will want to have total power generation capacity of even more than whatever the anticipated peak load might be in any locale.

There will always, then, be excess capacity, just sometimes more and sometimes less.  And hence decisions will be necessary as to what of the available capacity to use at any given moment.  While complex, the ultimate driver of this will be (or at least should be, in a rational system) the short-run costs of producing power from the possible alternative sources available in the region where the power is needed.  These costs will be examined in the next section below.  But for here, we will look at how generation has changed over the last several years.

In terms of the change in power generation by source relative to the levels in 2010, one finds:

Gas now accounts for the largest increment in generation over this period, with solar and wind also growing (steadily) but by significantly less.  Coal powered generation, in contrast, fell substantially, while nuclear and other sources were basically flat.  And as noted above, due to increased efficiency in the use of power (until 2017), total power use was flat to falling a bit, even as GDP grew substantially.  This reversed in 2018  when efficiency fell, and gas generated power rose to provide for the resulting increased power demands.  Solar and wind continued on the same path as before, and coal generation still fell at a similar pace as before.  But it remains to be seen whether 2018 marked a change in the previous trend in efficiency gains, or was an aberration.

Why did power generation from gas rise by more than from solar and wind over the period, despite the larger increase in solar plus wind capacity than in gas generation capacity?  In part this reflects the cost factors which we will discuss in the next section below.  But in part one needs also to recognize factors inherent in the technologies.  Solar generation can only happen during the day (and also when there is no cloud cover), while wind generation depends on when the wind blows.  Without major power storage, this will limit how much solar and wind can be used.

The extent to which some source of power is in fact used over some period (say a year), as a share of what would be generated if the power plant operated at 100% of capacity for 24 hours a day, 365 days a year, is defined as the “capacity factor”.  In 2018, the capacity factor realized for solar photovoltaic systems was 26.1% while for wind it was 37.4%.  But for no power source is it 100%.  For natural gas combined cycle plants (the primary source of gas generation), the capacity factor was 57.6% in 2018 (up from 51.3% in 2017, due to the jump in power demand in 2018).  This is well below the theoretical maximum of 100% as in general one will be operating at less than peak capacity (plus plants need to be shut down periodically for maintenance and other servicing).

Thus increments in “capacity”, as measured, will therefore not tell the whole story.  How much such capacity is used also matters.  And the capacity factors for solar and wind will in general be less than what they will be for the other primary sources of power generation, such as gas, coal, and nuclear (and excluding the special case of plants designed solely to operate for short periods of peak load times, or plants used as back-ups or for cases of emergencies).  But how much less depends only partly on the natural constraints on the clean technologies.  It also depends on marginal operating costs, as we will discuss below.

Finally, while gas plus solar and wind have grown in terms of power generation since 2010, and coal has declined (and nuclear and other sources largely unchanged), coal-fired generation remains important.  In terms of the percentage shares of overall power generation:

While coal has fallen as a share, from about 45% of US power generation in 2010 to 27% in 2018, it remains high.  Only gas is significantly higher (at 35% in 2010).  Nuclear and other sources (such as hydro) accounts for 29%, with nuclear alone accounting for two-thirds of this and other sources the remaining one-third.  Solar and wind have grown steadily, and at a rapid rate relative to where they were in 2010, but in 2018 still accounted only for about 8% of US power generation.

Thus while coal has come down, there is still very substantial room for further substitution out of coal, by either solar and wind or by natural gas.  The cost factors that will enter into this decision on substituting out of coal will be discussed next.

E.  The Cost Factors That Enter in the Decisions on What Plants to Build, What Plants to Keep in Operation, and What Plants to Use

The Lazard analysis of costs presents estimates not only for the LCOE of newly built power generation plants, but also figures that can be used to arrive at the costs of operating a plant to produce power on any given day, and of operating a plant plus keeping it maintained for a year.  One needs to know these different costs in order to address different questions.  The LCOE is used to decide whether to build a new plant and keep it in operation for a period (20 years is used); the operating cost is used to decide which particular power plant to run at any given time to generate the power then needed (from among all the plants up and available to run that day); while the operating cost plus the cost of regular annual maintenance is used in the decision of whether to keep a particular plant open for another year.

The Lazard figures are not ideal for this, as they give cost figures for a newly built plant, using the technology and efficiencies available today.  The cost to maintain and operate an older plant will be higher than this, both because older technologies were less efficient but also simply because they are older and hence more liable to break down (and hence cost more to keep running) than a new plant.  But the estimates for a new plant do give us a sense of what the floor for such costs might be – the true costs for currently existing plants of various ages will be somewhat higher.

Lazard also recognized that there will be a range of such costs for a particular type of plant, depending on the specifics of the particular location and other such factors.  Their report therefore provides both what it labels low end and high end estimates, and with a mid-point estimate then based usually on the average between the two.  The figures shown in the chart at the top of this post are the mid-point estimates, but in the tables below we will show the low and high end cost estimates as well.  These figures are helpful in providing a sense of the range in the costs one should expect, although how Lazard defined the range they used is not fully clear.  They are not of the absolutely lowest possible cost plant nor absolutely highest possible cost plant.  Rather, the low end figures appear to be averages of the costs of some share of the lowest cost plants (possibly the lowest one third), and similarly for the high end figures.

The cost figures below are from the 2018 Lazard cost estimates (the most recent year available).  The operating and maintenance costs are by their nature current expenditures, and hence their costs will be in current, i.e. 2018, prices.  The LCOE estimates of Lazard are different.  As was noted above, these are the levelized prices that would need to be charged for the power generated to cover the costs of building and then operating and maintaining the plant over its assumed (20 year) lifetime.  They therefore need to be adjusted to reflect current prices.  For the chart at the top of this post, they were put in terms of 2017 prices (to make them consistent with the PPA prices presented in the Berkeley report discussed above).  But for the purposes here, we will put them in 2018 prices to ensure consistency with the prices for the operating and maintenance costs.  The difference is small (just 2.2%).

The cost estimates derived from the Lazard figures are then:

(all costs in 2018 prices)

A.  Levelized Cost of Energy from a New Power Plant:  $/MWHr

Solar

Wind

Gas

Coal

Nuclear

low end

$31.23

$22.65

$32.02

$46.85

$87.46

mid-point

$33.58

$33.19

$44.90

$79.26

$117.52

high end

$35.92

$43.73

$57.78

$111.66

$147.58

B.  Cost to Maintain and Operate a Plant Each year, including for Fuel:  $/MWHr

Solar

Wind

Gas

Coal

Nuclear

low end

$4.00

$9.24

$24.38

$23.19

$23.87

mid-point

$4.66

$10.64

$26.51

$31.30

$25.11

high end

$5.33

$12.04

$28.64

$39.41

$26.35

C.  Short-term Variable Cost to Operate a Plant, including for Fuel:  $/MWHr

Solar

Wind

Gas

Coal

Nuclear

low end

$0.00

$0.00

$23.16

$14.69

$9.63

mid-point

$0.00

$0.00

$25.23

$18.54

$9.63

high end

$0.00

$0.00

$27.31

$22.40

$9.63

A number of points follow from these cost estimates:

a)  First, and as was discussed above, the LCOE estimates indicate that for the question of what new type of power plant to build, it will in general be cheapest to obtain new power from a solar or wind plant.  The mid-point LCOE estimates for solar and wind are well below the costs of power from gas plants, and especially below the costs from coal or nuclear plants.

But also as noted before, local conditions vary and there will in fact be a range of costs for different types of plants.  The Lazard estimates indicate that a gas plant with costs at the low end of a reasonable range (estimated to be about $32 per MWHr) would be competitive with solar or wind plants at the mid-point of their cost range (about $33 to $34 per MWHr), and below the costs of a solar plant at the high end of its cost range ($36) and especially a wind plant at its high end of its costs ($44).  However, there are not likely to be many such cases:  Gas plants with a cost at their mid-point estimate would not be competitive, and even less so for gas plants with a cost near their high end estimate.

Furthermore, even the lowest cost coal and nuclear plants would be far from competitive with solar or wind plants when considering the building of new generation capacity.  This is consistent with what we saw in Section D above, of no new coal or nuclear plants being built in recent years (with the exception of one nuclear plant whose construction started decades ago and was only finished in 2016).

b)  More interesting is the question of whether it is economic to build new solar or wind plants to substitute for existing gas, coal, or nuclear plants.  The figures in panel B of the table on the cost to operate and maintain a plant for another year (all in terms of $/MWHr) can give us a sense of whether this is worthwhile.  Keeping in mind that these are going to be low estimates (as they are the costs for newly built plants, using the technologies available today, not for existing ones which were built possibly many years ago), the figures suggest that it would make economic sense to build new solar and wind plants (at their LCOE costs) and decommission all but the most efficient coal burning plants.

However, the figures also suggest that this will not be the case for most of the existing gas or nuclear plants.  For such plants, with their capital costs already incurred, the cost to maintain and operate them for a further year is in the range of $24 to $29 (per MWHr) for gas plants and $24 to $26 for nuclear plants.  Even recognizing that these costs estimates will be low (as they are based on what the costs would be for a new plant, not existing ones), only the more efficient solar and wind plants would have an LCOE which is less.  But they are close, and are on the cusp of the point where it would be economic to build new solar and wind plants and decommission existing gas and nuclear plants, just as this is already the case for most coal plants.

c)  Panel C then provides figures to address the question of which power plants to operate, for those which are available for use on any given day.  With no short-term variable cost to generate power from solar or wind sources (they burn no fuel), it will always make sense to use those sources first when they are available.  The short-term cost to operate a nuclear power plant is also fairly low ($9.63 per MWHr in the Lazard estimates, with no significant variation in their estimates).  Unlike other plants, it is difficult to turn nuclear plants on and off, so such plants will generally be operated as baseload plants kept always on (other than for maintenance periods).

But it is interesting that, provided a coal burning plant was kept active and not decommissioned, the Lazard figures suggest that the next cheapest source of power (if one ignores the pollution costs) will be from burning coal.  The figures indicate coal plants are expensive to maintain (the difference between the figures in panel B and in panel C) but then cheap to run if they have been kept operational.  This would explain why we have seen many coal burning plants decommissioned in recent years (new solar and wind capacity is cheaper than the cost of keeping a coal burning plant maintained and operating), but that if the coal burning plant has been kept operational, that it will then typically be cheaper to run rather than a gas plant.

d)  Finally, existing gas plants will cost between $23 and $27 per MWHr to run, mostly for the cost of the gas itself.  Maintenance costs are low.  These figures are somewhat less than the cost of building new solar or wind capacity, although not by much.

But there is another consideration as well.  Suppose one needs to add to night-time capacity, so solar power will not be of use (assuming storage is not an economic option).  Assume also that wind is not an option for some reason (perhaps the particular locale).  The LCOE figures indicate that a new gas plant would then be the next best alternative.  But once this gas plant is built, it will be available also for use during the day.  The question then is whether it would be cheaper to run that gas plant during the day also, or to build solar capacity to provide the day-time power.

And the answer is that at these costs, which exclude the costs from the pollution generated, it would be cheaper to run the gas plant.  The LCOE costs for new solar power ranges from $31 to $36 per MWHr (panel A above), while the variable cost of operating a gas plant built to supply nighttime capacity ranges between $23 and $27 (panel C).  While the difference is not huge, it is still significant.

This may explain in part why new gas generation capacity is not only being built in the US, but also is then being used more than other sources for additional generation, even though new solar and wind capacity would be cheaper.  And part of the reason for this is that the costs imposed on others from the pollution generated by burning fossil fuels are not being borne by the power plant operators.  This will be examined in the next section below.

F.  The Impact of Including the Cost of Greenhouse Gas Emissions

Burning fossil fuels generates pollution.  Coal is especially polluting, in many different ways. But I will focus here on just one area of damage caused by the burning of fossil fuels, which is that from their generation of greenhouse gases.  These gases are warming the earth’s atmosphere, with this then leading to an increased frequency of extreme weather events, from floods and droughts to severe storms, and hurricanes of greater intensity.  While one cannot attribute any particular storm to the impact of a warmer planet, the increased frequency of such storms in recent decades is clearly a consequence of a warmer planet.  It is the same as the relationship of smoking to lung cancer.  While one cannot with certainty attribute a particular case of lung cancer to smoking (there are cases of lung cancer among people who do not smoke), it is well established that there is an increased likelihood and frequency of lung cancer among smokers.

When the costs from the damage created from greenhouse gases are not borne by the party responsible for the emissions, that party will ignore those costs.  In the case of power production, they do not take into account such costs in deciding whether to use clean sources (solar or wind) to generate the power needed, or to burn coal or gas.  But the costs are still there and are being imposed on others.  Hence economists have recommended that those responsible for such decisions face a price which reflects such costs.  A specific proposal, discussed in an earlier post on this blog, is to charge a tax of $40 per ton of CO2 emitted.  All the revenue collected by that tax would then be returned in equal per capita terms to the American population.  Applied to all sources of greenhouse gas emissions (not just power), the tax would lead to an annual rebate of almost $500 per person, or $2,000 for a family of four.  And since it is the rich who account most (in per person terms) for greenhouse gas emissions, it is estimated that such a tax and redistribution would lead to those in the lowest seven deciles of the population (the lowest 70%) receiving more on average than what they would pay (directly or indirectly), while only the richest 30% would end up paying more on a net basis.

Such a tax on greenhouse gas emissions would have an important effect on the decision of what sources of power to use when power is needed.  As noted in the section above, at current costs it is cheaper to use gas-fired generation, and even more so coal-fired generation, if those plants have been built and are available for operation, than it would cost to build new solar or wind plants to provide such power.  The costs are getting close to each other, but are not there yet.  If gas and coal burning plants do not need to worry about the costs imposed on others from the burning of their fuels, such plants may be kept in operation for some time.

A tax on the greenhouse gases emitted would change this calculus, even with all other costs as they are today.  One can calculate from figures presented in the Lazard report what the impact would be.  For the analysis here, I have looked at the impact of charging $20 per ton of CO2 emitted, $40 per ton of CO2, or $60 per ton of CO2.  Analyses of the social cost of CO2 emissions come up with a price of around $40 per ton, and my aim here was to examine a generous span around this cost.

Also entering is how much CO2 is emitted per MWHr of power produced.  Figures in the Lazard report (and elsewhere) put this at 0.51 tons of CO2 per MWHr for gas burning plants, and 0.92 tons of CO2 per MWHr for coal burning plants.  As has been commonly stated, the direct emissions of CO2 from gas burning plants is on the order of half of that from coal burning plants.

[Side note:  This does not take into account that a certain portion of natural gas leaks out directly into the air at some point in the process from when it is pulled from the ground, then transported via pipelines, and then fed into the final use (e.g. at a power plant).  While perhaps small as a percentage of all the gas consumed (the EPA estimates a leak rate of 1.4%, although others estimate it to be more), natural gas (which is primarily methane) is itself a highly potent greenhouse gas with an impact on atmospheric warming that is 34 times as great as the same weight of CO2 over a 100 year time horizon, and 86 times as great over a 20 year horizon.  If one takes such leakage into account (of even just 1.4%), and adds this warming impact to that of the CO2 that is produced by the gas that has not leaked out but is burned, natural gas turns out to have a similar if not greater atmospheric warming impact as that resulting from the burning of coal.  However, for the calculations below, I will leave out the impact from leakage.  Including this would lead to even stronger results.]

One then has:

D.  Cost of Greenhouse Gas Emissions:  $/MWhr

Solar

Wind

Gas

Coal

Nuclear

Tons of CO2 Emitted per MWHr

0.000

0.000

0.510

0.920

0.000

Cost at $20/ton CO2

$0.00

$0.00

$10.20

$18.40

$0.00

Cost at $40/ton CO2

$0.00

$0.00

$20.40

$36.80

$0.00

Cost at $60/ton CO2

$0.00

$0.00

$30.60

$55.20

$0.00

E.  Levelized Cost of Energy for a New Power Plant, including Cost of Greenhouse Gas Emissions (mid-point figures):  $/MWHr

Solar

Wind

Gas

Coal

Nuclear

Cost at $20/ton CO2

$33.58

$33.19

$55.10

$97.66

$117.52

Cost at $40/ton CO2

$33.58

$33.19

$65.30

$116.06

$117.52

Cost at $60/ton CO2

$33.58

$33.19

$75.50

$134.46

$117.52

F.  Short-term Variable Cost to Operate a Plant, including Fuel and Cost of Greenhouse Gas Emissions (mid-point figures):  $/MWHr

Solar

Wind

Gas

Coal

Nuclear

Cost at $20/ton CO2

$0.00

$0.00

$35.43

$36.94

$9.63

Cost at $40/ton CO2

$0.00

$0.00

$45.63

$55.34

$9.63

Cost at $60/ton CO2

$0.00

$0.00

$55.83

$73.74

$9.63

Panel D shows what would be paid, per MWHr, if greenhouse gas emissions were charged for at a rate of $20 per ton of CO2, of $40 per ton, or of $60 per ton.  The impact would be significant, ranging from $10 to $31 per MWHr for gas and $18 to $55 for coal.

If these costs are then included in the Levelized Cost of Energy figures (using the mid-point estimates for the LCOE), one gets the costs shown in Panel E.  The costs of new power generation capacity from solar or wind sources (as well as nuclear) are unchanged as they have no CO2 emissions.  But the full costs of new gas or coal fired generation capacity will now mean that such sources are even less competitive than before, as their costs now also reflect, in part, the damage done as a result of their greenhouse gas emissions.

But perhaps most interesting is the impact on the choice of whether to keep burning gas or coal in plants that have already been built and remain available for operation.  This is provided in Panel F, which shows the short-term variable cost (per MWHr) of power generated by the different sources.  These short-term costs were primarily the cost of the fuel used, but now also include the cost to compensate for the damage from the resulting greenhouse gas emissions.

If gas as well as coal had to pay for the damages caused by their greenhouse gas emissions, then even at a cost of just $20 per ton of CO2 emitted they would not be competitive with building new solar or wind plants (whose LCOEs, in Panel E, are less).  At a cost of $40 or $60 per ton of CO2 emitted, they would be far from competitive, with costs that are 40% to 120% higher.  There would be a strong incentive then to build new solar and wind plants to serve what they can (including just the day time markets), while existing gas plants (primarily) would in the near term be kept in reserve for service at night or at other times when solar and wind generation is not possible.

G.  Summary and Conclusion

The cost of new clean sources of power generation capacity, wind and especially solar, has plummeted over the last decade, and it is now cheaper to build new solar or wind capacity than to build new gas, coal, and especially nuclear capacity.  One sees this not only in estimates based on assessments of the underlying costs, but also in the actual market prices for new generation capacity (the PPA prices in such contracts).  Both have plummeted, and indeed at an identical pace.

While it was only relatively recently that the solar and wind generation costs have fallen below the cost of generation from gas, one does see these relative costs reflected in the new power generation capacity built in recent years.  Solar plus wind (together) account for the largest single source of new capacity, with gas also high.  And there have been no new coal plants since 2013 (nor nuclear, with the exception of one plant coming online which had been under construction for decades).

But while solar plus wind plants accounted for the largest share of new generation capacity in recent years, the impact on the overall mix was low.  And that is because not that much new generation capacity has been needed.  Up until to at least 2017, efficiency in energy use was improving to such an extent that no net new capacity was needed despite robust GDP growth.  A large share of something small will still be something small.

However, the costs of building new solar or wind generation capacity have now fallen to the point where it is cheaper to build new solar or wind capacity than it costs to maintain and keep in operation many of the existing coal burning power plants.  This is particularly the case for the older coal plants, with their older technologies and higher maintenance costs.  Thus one should see many of these older plants being decommissioned, and one does.

But it is still cheaper, when one ignores the cost of the damage done by the resulting pollution, to maintain and operate existing gas burning plants, than it would cost to build new solar or wind plants to generate the power they are able to provide.  And since some of the new gas burning plants being built may be needed to add to night-time generation capacity, this means that such plants will also be used to generate power by burning gas during the day, instead of installing solar capacity.

This cost advantage only holds, however, because gas-burning plants do not have to pay for the costs resulting from the damage their pollution causes.  While they pollute in many different ways, one is from the greenhouse gases they emit.  But if one charged them just $20 for every ton of CO2 released into the atmosphere when the gas is burned, the result would be different.  It would then be more cost competitive to build new solar or wind capacity to provide power whenever they can, and to save the gas burning plants for those times when such clean power is not possible.

There is therefore a strong case for charging such a fee.  However, many of those who had previously supported such an approach to address global warming have backed away in recent months, arguing that it would be politically impossible.  That assessment of the politics might be correct, but it really makes no sense.  First, it would be politically important that whatever revenues are generated are returned in full to the population, and on an equal per person basis.  While individual situations will of course vary (and those who lose out on a net basis, or perceive that they will, will complain the loudest), assessments based on current consumption patterns indicate that those in the lowest seven deciles of income (the lowest 70%) will on average come out ahead, while only those in the richest 30% will pay more.  It is the rich who, per person, account for the largest share of greenhouse gas emissions, creating costs that others are bearing.  And a redistribution from the richest 30% to the poorest 70% would be a positive redistribution.

But second, the alternative to reducing greenhouse gas emissions would need to be some approach based on top-down directives (central planning in essence), or a centrally directed system of subsidies that aims to offset the subsidies implicit in not requiring those burning fossil fuels to pay for the damages they cause, by subsidizing other sources of power even more.  Such approaches are not only complex and costly, but rarely work well in practice.  And they end up costing more than a fee-based system would.  The political argument being made in their favor ultimately rests on the assumption that by hiding the higher costs they can be made politically more acceptable.  But relying on deception is unlikely to be sustainable for long.

The sharp fall in costs for clean energy of the last decade has created an opportunity to switch our power supply to clean sources at little to no cost.  This would have been impossible just a few years ago.  It would be unfortunate in the extreme if we were to let this opportunity pass.

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.

The Purple Line: New Thinking is Needed to Address Our Transportation Problems

The Washington Post in recent weeks has published a number of pieces, both articles and editorials, on purportedly massive economic benefits from building the Purple Line (a 16 mile light rail line in the Maryland suburbs of Washington, DC).  See, among others, the items from the Post herehere, here, and here.  Their conclusion is based on uncritical acceptance of the results of a consultant’s report which concluded that the funds invested would generate a return (in terms of higher incomes in the region) of more than 100% a year.  But transit projects such as this do not generate such huge returns.  This should have been a clear red flag to any reporter that something was amiss.

As discussed in an earlier post on this blog, the consultant’s report was terribly flawed. There were obvious blunders (such as triple counting the construction expenditures) as well as more complex ones.  While one should not expect a reporter necessarily to have the expertise to uncover such problems, one would expect a good reporter and news organization to have sought out the assessment of a neutral observer with the necessary expertise.  This was not done.

The consultant’s report was so badly done that one cannot conclude anything from it as to what the economic impact would be of building such a rail line.  But more importantly, the report did not even ask the right question.  It looked at building the Purple Line versus doing nothing.  But no one is advocating that we should do nothing.  We have real transportation issues, and they need to be addressed.

The proper question, then, is what is the best use of the scarce funds we have available for public transit.  If an alternative approach can provide better service for more riders at similar or lower cost, then the impact of building the Purple Line versus proceeding with that alternative is negative.  As discussed below and as an illustration of what could be done (there are other alternatives as well), instead of the Purple Line one could provide free bus service on the entire county wide local bus systems.  These bus systems cover poor communities as well as the rich (the Purple Line will pass through some of the richest zip codes in the nation), and for the cost of the Purple Line the bus systems could be expanded to provide free bus service to as many as four times the ridership the Purple Line is projected to carry (with such projections, based on past experience, likely to prove optimistic).  The local bus systems already carry twice the projected ridership of the Purple Line.

With the aim of providing an alternative view to the discussion being carried, I submitted the attached to the Post for consideration as a local opinions column.  The Post decided, however, not to publish it, so I am making it available here.

 

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Solving Our Transportation Problems Will Require New Thinking, Not Old White Elephants

As Governor Hogan comes to a decision on the Purple Line, there is increasing pressure from proponents arguing the 16 mile light rail line will yield huge developmental benefits.  A recently released and highly publicized study by the consulting firm TEMS concluded the line would raise incomes by $2.2 billion a year, for a capital cost that TEMS took to be just $1.9 billion (the cost estimate is now higher, at close to $2.5 billion).  Thus the return, TEMS says, will be well in excess of 100% a year.

As the saying goes, when something sounds too good to be true, it usually is.  The TEMS study was badly flawed.  More importantly, it did not address the right question.  What we should be asking is how best to address our very real transportation needs, given the limited public resources available.

There are numerous problems with the TEMS study.  To start with more obvious blunders:  It estimated impacts during the construction period as if the entire Purple Line would be built in Montgomery County, would be built again in Prince George’s, and built again in Washington, DC (even though it will not even touch Washington).  That is, it triple counted the construction expenditures and therefore its income and jobs impacts.  It also assumed that all the inputs (other than the rail cars) would be sourced in Washington or these counties.  But steel rail cannot come from here:  Neither Washington nor its suburbs have any steel mills.

There were more fundamental problems as well.  Among them was the fallacy of cause and effect.  In their statistical analysis, TEMS found that higher income neighborhoods are associated with lower transportation costs.  From this they jumped to the conclusion that lower transportation costs led to those higher incomes.  That is not the case.  Rich people live in Georgetown and, being close to downtown, transportation costs there are relatively low.  But moving to Georgetown does not suddenly make you rich due to low transportation costs.  Rather, one can afford to buy a home in Georgetown if you are already rich.

Perhaps the most basic problem is that TEMS assumed it was either the Purple Line or nothing.  But no one is advocating doing nothing.  We face real transportation issues, and they need to be addressed.  Unfortunately, alternatives have not been seriously examined.  Part of the problem has been narrow-minded thinking that has failed to consider broader alternatives than solely a line on this fixed corridor.

As an example of what might be done, consider the locally run RideOn and TheBus systems in these counties.  These two systems already carry double the projected ridership of the Purple Line.

The annual operating cost of the Purple Line is expected to be $55 million a year.  This is in addition to the $2.5 billion capital cost.  Taking just half of that annual operating cost, net of fares expected to be collected due to the Purple Line, one could cover the full amount currently collected in fares on the entire county-wide RideOn and TheBus systems.  That is, one could provide free bus service on the entire systems for just half of the cost of operating the Purple Line.

Some might say that with zero fares, there would be the “problem” that many more riders will want to take these buses.  But that would be fantastic.  One would need to cover the additional costs, but this could be done.  Note first that filling empty seats on buses costs nothing, and there are a lot of empty seats now.  There is then the second half of the annual operating cost of the Purple Line, and finally the $2.5 billion capital cost.  Taken together, these funds could cover the full costs (including costs covered in county and state budgets) of doubling the scale of the RideOn and TheBus systems.

We can therefore have the Purple Line, serving riders on a narrow 16 mile corridor that runs through some of the most affluent areas of these counties, or for the same cost provide free bus service on systems that could carry four times as many riders.  Furthermore, the bus systems serve not just affluent areas but also the poorest communities of the counties.  For the poor, earning at or close to the minimum wage with perhaps two jobs to get by, daily bus fares of $6 or $8 or more are not insignificant.

We need to be open to broader options for how to address our transportation crisis.  The debate on the Purple Line has not done that.  And by treating the issue as the Purple Line or nothing, proponents are increasing the likelihood that the outcome will be nothing.

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Note on Sources:

a)  The TEMS consultant report, March 2015, commissioned by Montgomery County, Prince George’s County, and the Greater Washington Board of Trade.

b)  Current cost estimates for building and operating the Purple Line, along with ridership projections, are from the most recently issued Federal Transit Administration Purple Line Profile Sheet, November 2014.

c)  Cost and ridership data for the local transit systems (RideOn and TheBus) are from the National Transit Database of the Federal Transit Administration.

The TEMS Study of the Economic Impact of the Purple Line: A Good Example of a Badly Flawed Report

A.  Introduction

A review of a recently released report, purportedly on the economic impact of the Purple Line, should be of interest not only to those with a direct interest in the Purple Line project itself, but also to those interested in how such work is now used as part of a political process to influence decisions on major public projects.  It is a badly flawed report. Nonetheless, its results were announced with great fanfare, and treated without question by news organizations such as the Washington Post.

The Purple Line is a proposed light rail line which would run in a 16 mile arc through suburban Washington, DC, from east of the city to its north.  It is a controversial project, due to its high financial as well as environmental costs while serving relatively few riders.

An earlier analysis on this blog calculated that the full cost per trip on the proposed system would be an estimated $10.42 (and double this per day for a round-trip).  But the system would take in only 38 cents in average additional fares per trip (since a large share of the riders will be free or reduced rate transfers from the bus or existing rail systems), leading to a subsidy of over $10 per ride.  And this analysis assumes that the current cost and ridership projections will hold true.  Such projections have generally proven to be highly optimistic on other light rail projects.

Despite the high cost, there are significant vested interests pushing strongly for the project.  In particular, land developers along the proposed corridor would see the value of their properties rise, possibly by the hundreds of millions of dollars.  And local government authorities (in particular those of Montgomery and Prince George’s Counties) have come out in favor:  Almost all of the cost would be borne by the State of Maryland or the Federal Government, and the subsidy payments from the State of Maryland would be locked in (under the proposed PPP contract) for 30 years beyond the estimated 5 year construction period.

In this environment of controversy, a consultant’s report was commissioned and recently released with great fanfare.  The report (dated March 2015) is titled “Purple Line Preliminary Impact Study:  Update”, and was prepared by the firm Transportation Economics & Management Systems, Inc. (TEMS).  The conclusions from the report were provided publicly in a presentation to business leaders on April 20, reported on by the Washington Post that day, and used also that same day as the basis for an editorial by the Post advocating that the Purple Line should be built (a position the Post has long taken).

There are numerous and major flaws with the report.  This blog post will go through some of the more important ones.  But first it will summarize several of the red flags that should have signaled to any serious analyst and news organization that there could be problems with the report, and that a more careful reading would have been warranted before its conclusions were widely publicized.

A number of the problems with the report are quite technical.  I would not suggest that a general news reporter would have the technical knowledge necessary to have discovered these himself or herself.  But the red flags are obvious, and should have signaled to the journalist that there could very well be issues here, and that if he did not have the skills to assess the report, then he should have consulted with some neutral third party to do such an assessment for him.  The Purple Line project is controversial, and an experienced reporter and news organization should have recognized that a report such as this, commissioned by and released by advocates for the project, may not be one to take on faith.  But this was not done.

B.  Red Flags

Some obvious issues should have raised attention:

1)  Gigantic Returns:  The TEMS report concludes that building the Purple Line will lead to an “Increase in income to local households of $2.2 billion per year” (Chapter 8, Conclusion).  This is astounding.  The cost estimate to build the line used in the study (from the August 2013 Final Environmental Impact Study) was only $1.9 billion (expressed in 2014 prices).  (Note:  The most recently published estimate, from November 2014, puts the expected cost a good deal higher, at $2.45 billion in current dollars, or about $2.3 billion in terms of 2014 prices.  But the TEMS study used the earlier cost estimate.)

A $2.2 billion increase in annual incomes on a one-time $1.9 billion cost implies an annual rate of return of 116%!  One is generally content with annual rates of return of perhaps 16%, or even 10%, on public projects.  Yet this one claims a return of 116%.  This should have been an immediate flag that something is questionable in what was done.  As the adage goes, if something is too good to be true, it probably is.

2)  Implausibly Precise Statistical Results:  While a more technical issue, any observer conversant with basic statistics and regression analysis would have been surprised to see that the t-statistic was as high as 250 in the cross-section regressions in the simple model of travel demand (Exhibit 4.2 of Chapter 4).  The t-statistic is a measure of how tight the data fits around the estimated coefficients of a regression equation.  Any t-statistic greater than 2.0 is generally taken to imply the coefficient is statistically different from zero (with a 95% confidence).  In cross-section regressions, one is normally happy to find t-statistics of 2 or 3.  But in the statistical regression reported on here, for the estimated number of trips by commuters between two geographic zones as a function of just two simple variables, the t-statistics varied between 200 and 250.

Such precision in results in statistical work such as this is highly surprising.  In the real world there are many other determinants of travel demand between two zones than just the two variables used in the TEMS study (one for the cost of such travel, and one a constructed variable based on population, incomes, and employment).  While I do not have access to the data they used to determine what is going on, any statistician would be highly suspicious of such precise results.

3)  Who Sponsored and Paid for the Report:  An assessment of any report such as this starts with finding out who commissioned and paid for it.  The Washington Post article and editorial both state that the report was commissioned by Montgomery and Prince George’s Counties, the two Maryland counties through which the Purple Line will run.  No one else was mentioned.  Yet a report on the same presentation that day by the Gazette (a local newspaper of Suburban Maryland) noted that the Greater Washington Board of Trade was also a commissioner of the work, along with the two counties.  The Board of Trade is an industry group, whose members include construction companies and property developers, a number of whom will benefit directly if Maryland proceeds with this project.

Good journalism would have called for full disclosure on who sponsored and paid for the report.  If this was misunderstood at the time, a correction should have been reported later.  And an obvious question at the presentation of the report would have addressed not only who commissioned the report, but also what was the total cost and how was that cost shared among the sponsors.  Given the tight budgetary situation of all governments these days, it would not be surprising if a disproportionate share of the costs came from the Board of Trade (and some sub-set of its members who might have an interest in the outcome).

C.  Problems With the Report

Once one delves into the details of the work done, a number of flaws become clear.  This section will summarize a few of them.  The sequence followed is that of the report, starting with the theoretical construct, through the statistical work, and then the results.  However, this sequence unfortunately means that the more important issues are the ones further down on the list, rather than at the top.  I hope the reader will be patient.

1)  Confusion in the Theoretical Framework:  There are two major parts to the report. The first seeks to estimate what it terms to be the long-term supply side impacts of the project, while the goal of the second is to estimate the immediate impacts on the region from the construction spending itself.  We will focus first on the report’s supply side analysis, starting with the theoretical framework presented.  A separate section below will review how the immediate impacts were estimated.

The report provides an elaborate theoretical framework (in Chapter 2) for the approach they say they are taking, but there are issues.  It starts by saying they will work through a supply side analysis to determine how a transportation investment such as the Purple Line will increase productivity and output, and assert that this will be equivalent to (the “mirror image” of) the more traditional approach of valuing transportation investments by how much cost and time they save for drivers and riders.  But in fact this will not be the case. Measured levels of household incomes simply do not include as one element the time saved (or as a negative, the time consumed) in travel.  Yet the TEMS report uses the standard measures of household incomes and other such economic variables in their statistical work.  Thus the TEMS approach and the traditional approach of valuing the benefits from transportation investments will not be mirror images of each other.  They will produce totally different results.

They also define what they call “economic rent”, to be a function of the variables: population structures, industrial structures, education levels, cultural characteristics, and “transportation efficiency”.  They do not further define the five variables other than transportation efficiency, but argue that they will be largely unchanged over a period of 10 to 20 years or so.  Thus any changes over such a period will only be due to changes in transportation efficiency.  Actually, this will not be the case, as any geographic area will see its population and incomes changing over time.  But while incorrect, it should not matter to their analysis.  One could interpret their approach as looking for the partial effect of transportation efficiency on what they call economic rents.

But there are problems with how this is implemented.  First, they take as their measure of “transport efficiency” a weighted average cost of automobile travel (for both time and financial costs) from a specific geographic zone to all other geographic zones in the region. Why they should include only automobile travel in a study looking at the impacts of a light rail line is not clear.  But more of a concern is that “economic rent” is measured by a series of what they call “proxies” (specifically:  employment, household income density, and residential property value density), and that they assume that each of these variables is separately a function of transportation costs (and transportation costs alone).

This is a simplistic framework.  It is not at all clear why the specific variables they define as “proxies” for economic rent do indeed capture what economic rent really is. They merely assert they do.  Economic rent corresponds to the value of land in a particular location. Land rent, with all else equal, will generally be higher in more central locations with lower transportation costs.  But land rent is not synonymous with employment or with household incomes, for example.  Thus while there may well be a relationship between land rents and transportation costs, it is not at all clear why there should be the same such relationship between household incomes and transportation costs.

There are therefore issues with the theoretical framework used.

2)  Flawed Statistical Analysis:  I noted above that at least certain of their statistical results appear to be too good to be true.  But there are other issues as well.

One mistake is to assume that a relationship that might apply at a broad geographic scale will apply in the same way in a more limited jurisdiction.  Their basic statistical work is based on an analysis of the relationship between their socio-economic proxies and average transportation costs over a set of 299 geographic zones in the Washington and Baltimore metropolitan areas.  This is a large area, stretching from the Pennsylvania border to south of Fredericksburg, Virginia (a distance of over 150 miles), and from the Shenandoah Valley in Virginia to the Chesapeake Bay.

Distances such as this matter a good deal in deciding where to live and commute.  There will not be many people commuting from Fredericksburg to Baltimore and beyond, or from Warrenton to Annapolis.  Even if one found a nice house and neighborhood in such areas, the cost of commuting will dominate in the decision not to live there.  And a statistical regression, when properly done, should pick up such relationships and show that the commuting costs of course matter.

But there is then a problem is assuming that the same statistical relationship will apply similarly, and with the same parameters, when examining housing and commuting choices on a much smaller scale.  If your commute would be five miles, say, from one possible home location, and seven miles from another, the difference in commuting times might not be all that important.  Rather, one might choose the location that is further away based on how much one likes the specific house or neighborhood, where your friends live, and other such factors.  It would be a mistake to assume the statistical relationship with transportation costs will be the same.

Yet the authors of this report do assume this.  They assume that the relationship they estimate based on the region wide data stretching over 150 miles and many hours of potential commuting time will apply similarly at the scale relevant to riders deciding whether or not to take the Purple Line.  The Purple Line will only be relevant to largely local riders, living and/or working within a few miles of the 16 mile long rail line.  Statistically, the authors made the mistake of presuming that relationships in a data set that is largely “out-of-sample” will apply similarly in the more limited scale relevant to the Purple Line.

There are other issues as well.  As already noted, the t-statistics for their travel demand model estimations are implausibly high.  It is also odd that the estimated slope coefficients in their regressions relating employment, household income density, and property value density (in Exhibit 5.4), and later housing density and housing units density (in Exhibit 5.7), as a function of average transportation costs, are all in the relatively narrow range of -3.30 to -3.97.  By the way the equations were structured, these coefficients are all what economists call “elasticities”, meaning that a 1% decrease in average transportation costs in the zone will lead to increases of between 3.30% and 3.97% in the various socio-economic variables.  It is surprising that these response rates are all so close to each other, for such very different variables as employment, household income densities, property values, and so on.  While I cannot say what might be causing this without knowing more on precisely what was done, the similarity in response rates over such disparate variables is probably a flag that something was not done properly in the statistics.

There is also a, possibly related, technical statistical issue in that they assume in one set of relationships that their socio-economic measures (income, etc.) are a function just of their average transportation cost figures (equation 12), while in another equation (equation 6) they postulate that travel demand will be a function of certain constructed socio-economic variables (which are themselves built up from the basic set of socio-economic variables) and average transportation costs. This implies in their system that the variables they are using (the socio-economic variables and average transportation costs) to explain travel demand are not in fact independent of each other.  When this is the case, ordinary least squares regressions will not work, and one needs to utilize a more sophisticated statistical approach.

3)  The Elasticity Estimates Are Just Not Plausible:  While the similarity across the elasticity estimates is curious, it is more important to recognize the implications of the values themselves.

Using the case of the response of household income density to transportation costs, the equation the TEMS study estimated found an elasticity of -3.79.  That is, for a 1% fall in transportation costs in the area, household income density will rise by 3.79%.  Some of this might come from higher average household incomes in the area and some by more homes being built in the area, both of which will increase the income of the area.

This would be a huge response, if true.  Transportation costs (private plus intracity public transit) on average accounts for about 15% of the consumer price index (BLS data on the CPI weights).  Median household income along the Purple Line is roughly $80,000 (based on a simple average of the median household incomes at the four major stations where there are now regular MetroRail lines).  15% of $80,000 is $12,000 spent directly on transportation costs.  To this one should add the value of time spent commuting (as an additional cost).  Based ultimately on Census Bureau data, a study found that residents of Washington, DC, spend an additional 11% of their working hours each week on commuting.  Applying this 11% to the $80,000 median household income, the total cost of transportation for an average household is 26% of $80,000, or $20,800.

The TEMS regression results, if they are to be believed, imply that a 1% reduction in transportation costs ($208 = 1% x $20,800) will lead to a 3.79% rise in household incomes ($3,032 = 3.79% x $80,000) through either a rise in per household incomes or in the number of households in the zone or by some combination.  This implies that a subsidy of just $208 per household for what they spend on transportation will lead to a rise in household incomes in the area by $3,032!

This would be amazing, if true.  A small $208 cost would be converted into more than a $3,000 gain in annual incomes!  And with government income tax rates averaging roughly 25% (the figure the TEMS study uses), the government tax take would rise by over $750. Only 28% of this increase in the tax take could then be used to pay for a further $208 subsidy, and one would have the equivalent of a perpetual motion machine (or in this case a perpetual wealth machine).

Unfortunately, it is not likely that there will be such a response to transportation investments.  Perpetual wealth machines do not exist.  The parameter estimates are simply implausible.  The reason why the result may have been found (assuming the statistics was done properly, which is itself not clear) will be discussed immediately below. The implausible parameter values also explains why the TEMS study found such purported high returns (of 116% a year) for an investment as costly and as inefficient as the Purple Line.  But as the next section will discuss, the interpretation was wrong.

4)  Lower Transportation Cost Is Not the Main Cause of Higher Incomes – Correlation Is Not Causation:  The regression equations summarized in Exhibits 5.4 and 5.7, regress variables such as employment, household income density, and so on, on average transportation costs in the zone.  But it is a well known principle in regression analysis that such regressions do not demonstrate causation.  Rather, they can only show correlation.

Nevertheless, the TEMS report asserts that the correlations found in their regressions do show that employment, household income density, and so on, will rise as a direct result of average transportation costs falling.  The percentage rise will be in accordance with the elasticities estimated, they assert, and will be a consequence of the higher productivity of the economy that lower transportation costs leads to.

But it is not at all clear that the causation goes in the direction the TEMS report asserts. The correlations may rather be showing that people with high incomes prefer to live in areas where transportation costs (and commuting times, which are part of transportation costs) are relatively low.  In the Washington, DC, area, to take an example, the Georgetown neighborhood is a high income area in the city, close to the central downtown office zone, and hence an area with relatively low transportation costs.  Many rich people who can afford it like to live in the area, and home prices are high reflecting this preference.  But the residents of Georgetown did not become rich because transportation costs are on average relatively low there.  Rather, rich people have sought to live in Georgetown for, among other reasons, the relatively low cost of getting to work from there.

Thus one finds in the regression results a correlation between high incomes (and the other variables estimated) and relatively low average transportation costs.  But the residents did not become rich as a result of some reduction in transportation costs. They were already rich, which allowed them to move into an area such as Georgetown.

Thus it is incorrect to conclude, as the TEMS study does (see the beginning of Chapter 6, page 36), that building the Purple Line will “create more than 27 thousand jobs; will increase property value (sic) by 12.8 $ billion (sic) and the household income (sic again) is estimated to increase by $2.2 billion”.  Building a rail line (or any other transportation improvement) will not itself raise household incomes in such a way or create thousands of jobs.  Rather, the correlation observed (and assuming the statistical analysis was done correctly) can arise due to the choices people make between living in one neighborhood and another.

Note also that a decision of a relatively high income households to move to a location such as Georgetown in preference to a location further away from their job, will lead not only to higher income households concentrating in Georgetown, but to a symmetrical reduction in such households in the other locations they chose not to move to.  Similarly for property values:  Home prices will be bid up in Georgetown, and will see a reduction relative to what they would otherwise be in other locations.  But this is arising not because lower transportation costs is making people richer in Georgetown (that is, not due to a supply side effect increasing productivity, as the TEMS study asserts), but due to shifts in location preferences.

This is important.  A reduction in transportation costs is not making the region richer through some supply side effect, and certainly not in accordance with regression coefficients such as those found (with an elasticity of -3.79 for income, for example). Rather, the regression equations (and assuming again that the statistics were done properly, even though there are questions on that) are picking up at best a locational preference that shifts households from one location to another, and has limited or no effect on household incomes or property values in the region as a whole.

5)  The Multiplier Analysis Fails on Several Counts:  In addition to the “supply side” analysis reviewed above, the TEMS study undertook to estimate the immediate impact on employment and incomes in the areas immediately surrounding the Purple Line corridor during the construction period.  It was this analysis that led to the stated figure in the news reports that the project would create 4,000 jobs per year during the construction period (see here and here for example).

The multiplier analysis is decidedly not supply side analysis, but rather a purely demand side assessment of how much incomes and jobs would rise to produce what goes into the project.  And in a multiplier analysis, one takes into account not only what is used directly in the project, but also the production of the inputs that go into what is used directly and then the inputs into the inputs, and so on.

When unemployment is high and factories are underutilized, a multiplier analysis can be of interest.  An earlier post on this blog discussed what the fiscal multiplier means at the national level, and how the value of the multiplier will differ across countries and under different conditions, in particular whether one is assessing the multiplier at a time of high unemployment or low.  It can certainly be a useful tool if properly applied.  But one needs to be careful in how it is applied, and here the TEMS study fails.

There are multiple issues:

a)  The TEMS study failed to recognize that the major share of the inputs to the project will come from outside the region:  The expenditures that are the basis for the multiplier analysis come from the FEIS, which was finalized in August 2013.  The FEIS study has the capital cost figures in 2012$, and the TEMS authors puts them into 2014$. The capital cost estimate in the FEIS would then be $1.9 billion in 2014$ (it is now projected to be higher).  From this, the TEMS authors subtracted the cost of the train vehicles of $0.2 billion, as these vehicles would be built somewhere outside the Washington, DC, metropolitan region (the initial set of streetcar / light rail line vehicles purchased for a new line in Washington, DC, indeed came from the Czech Republic). Thus building such cars would have no multiplier effects here.  This was correct.

But then the TEMS study assumed that the entire remaining $1.7 billion would be used to purchase items for the Purple Line from production in Montgomery County, Prince George’s Country, or Washington, DC.  This is of course not true.  There are no steel mills in Washington, DC, or its Maryland suburbs that produce steel rails.  There are no plants that produce the sophisticated electronics that goes into the communications and other systems of the control centers (Siemens of Germany is one of the main global suppliers of such systems).  The overhead power lines are not made from copper and other materials mined locally.  And so on.  The primary and perhaps sole local component would be the share of the $1.7 billion paid to local labor for the installation.  This will be a significant cost item, of course, but far less than the full $1.7 billion.

It is thus a gross error to have assumed that the purchase of the steel rails, the communications equipment, the overhead power lines, and much of the rest, will lead to local multiplier impacts in the Washington region from their production.  Their production is elsewhere.  Thus the true multiplier impacts in the Washington region, even if one accepts their methodology, will be nowhere close to those they estimate.

But it gets worse.

b)  The Construction Cost Estimates Were Triple-Counted, Once Each for Montgomery County, Prince George’s County, and for Washington, DC:  The TEMS study concluded that there would be an additional $7.0 billion in gross regional product as a consequence of the $1.7 billion in construction expenditure for the Purple Line.  This implies a multiplier of 4.1 (= $7.0 billion / $1.7 billion).  Such a multiplier would be huge.  At the national level, one might expect a multiplier of 2 to 3 when unemployment is high, and many economists have argued that it might be more like 1.5.  It really depends on the degree of unemployment and other conditions.  But no one says it will be more than 4.

Furthermore, the multiplier at the national level will be much higher than the multiplier at a regional level.  If my income goes up due to employment on some project, I will spend that income not only on goods and services produced in the immediate Washington, DC, region, but also on pork from Iowa, wines from California, vegetables from Florida, cars from Michigan (or Germany), and so on.  Hence the local multiplier will be far below what it will be at the national level, and will be smaller the smaller one defines the local region (less for the city of Washington, DC, than for the Washington, DC, metropolitan region, for example).

So how did the TEMS authors arrive at such a high multiplier of 4.1?  They made a big blunder.  Examination of the tables showing their calculated Gross Regional Product figures for Montgomery County, Prince George’s County, and Washington, DC (Exhibits 7.10, 7.11, and 7.12) shows increased construction sector product of $1.66 billion in each case.  But this is (to three significant digits) the estimated total construction expenditure assumed for the Purple Line (the $1.7 billion figure is rounded from a more precise figure of $1.656 billion that one can obtain by reproducing the process they followed to arrive at their $1.7 billion).  The individual figures for Montgomery County, Prince George’s County, and Washington, DC, differ very slightly (in the fourth digit) since the feedback effects in the input-output matrices used for the multiplier analysis will differ a bit across these jurisdictions.

The TEMS authors triple counted the expenditures on the Purple Line.  Not only did they assume the entire $1.7 billion non-vehicle cost of the line would be spent locally, but they presented figures based on $1.7 billion being spent in Montgomery County, $1.7 billion being spent again in Prince George’s County, and $1.7 billion being spent again in Washington, DC (and the Purple Line will not even touch Washington).

The results for the multiplier analysis are therefore completely wrong, even if one takes their methodology for granted.  They made a big blunder.  But what is perhaps even more worrying is that the multiplier they reported of 4.1 was clearly far too high for what one would expect in any such analysis at a regional level.  Despite what should have been a big flag that something was amiss, the results were reported without the authors reviewing how they had arrived at such a large and implausible number.

c)  The Multiplier Methodology is Mechanical, and Implies That Cost Overruns are Good:  Finally, one should note that a multiplier methodology such as that used here, even if applied without the mistakes that were made, is a mechanical one.  One takes construction expenditures, at whatever level they are, and multiplies out the implied levels of employment, regional product, and personal incomes that follow based on this multiplier approach.

An implication of this is that every time the cost goes up, the calculated “benefits” rise also.  Indeed, under a multiplier analysis such as that done here, the benefits will rise in proportion.  If the project ends up costing twice as much, then the “benefits” in terms of higher jobs and incomes will be twice as much.  But this is of course silly.  Cost overruns are not good.

The problem is that the wrong question is being asked.  A project is not a good one because it requires more (rather than less) labor to build it.  Higher costs are not a good thing.  Rather, the objective of a transportation investment is to provide transportation services, and the question that should be asked is what is the lowest cost and most efficient way to provide those services.  If one can achieve the transportation aims with a project that only costs half as much, then one should follow that approach rather than the more expensive one.  And if one then has additional budget resources available through following the lower cost approach, one can then consider undertaking other projects, for transportation or whatever.  In the end, the number of jobs involved will be similar if similar amounts are spent.

6)  The most basic flaw in the TEMS study was that it was asking the wrong question:  The question the TEMS study sought to address was what the economic impacts would be of building this project compared to doing nothing.  But this was the wrong question.

No one is advocating that nothing should be done to address the very real transit issues in the area of the Purple Line corridor.  The issue, rather, is how best to address the transit needs.  Any assessment of the Purple Line should not be relative to doing nothing, but rather relative to what the best other alternative would be.  If the best other alternative is superior to the Purple Line, then the actual impact of building the Purple Line (instead of the alternative) is negative.

The Alternatives Analysis / Draft Environmental Impact Statement (AA/DEIS), did look at a number of bus alternatives.  All turned out to be far cheaper than light rail both in total amount and per rider (see Summary Table 6-2 of Chapter 6 of the AA/DEIS).  The most cost effective (in terms of cost per new rider) was a simple upgrade of the regular bus system, with a cost per new rider that was 60% less than the light rail alternative chosen. Furthermore, a bus system can be easily scaled up or down, with frequency and routes adjusted depending on ridership and changing development patterns.  A light rail system is fixed, and fixed forever.  It is also basically either all the way on or all the way off.  There is little flexibility.

It should also be noted that the true alternative should have recognized that not just buses provide transit to riders in this corridor.  One also has the existing MetroRail system. The four larger stations of the Purple Line would be at intersections with four MetroRail stations, and existing MetroRail service would often require less time for the journey than the Purple Line would.  Light rail lines are slow.  For example, the FEIS highlights (see Table 9-1 of Chapter 9 of the FEIS) that in the year 2040, a bus journey from Bethesda to New Carrollton (the two end points on the Purple Line) would require 108 minutes, while the Purple Line light rail would require 63 minutes, a saving of 42% they state.  But the FEIS failed to recognize that no rational person would take the Purple Line for such a journey, since one could make the same trip by MetroRail (today and in 2040) in just 51 minutes.  The Purple Line would take substantially longer for this journey than simply taking the existing MetroRail service.  Nevertheless, having failed to take into account the MetroRail alternative, the FEIS (and then the TEMS study as well) calculated benefits as if a transit rider would save 45 minutes ( =108 – 63) from Bethesda to New Carrollton by taking the Purple Line rather than the “no build” alternative of a bus following the same route.

The alternative considered in the FEIS to the light rail line was therefore a straw man.  They did not take into account the MetroRail alternative, which would be as fast or faster for many of the riders, nor did they consider seriously what an upgraded bus system could do.  And much could be done to upgrade bus service from the second class system it has been treated as, through use of a combination of redesigned routes, express routes on some corridors, perhaps bus rapid transit on some routes, and more.  But even the straw man they did consider was far more cost effective than the light rail alternative chosen.

D.  Conclusion

There are major flaws in the TEMS study, both in its structure and in its implementation. Some are outright blunders, such as the triple counting in the multiplier analysis by treating the Purple Line as if it were to be built completely in Montgomery County, completely again in Prince George’s County, and completely again in Washington, DC.  But even without such mistakes, the approach taken has major issues, such as from confusing correlation with causation, failure to recognize that the bulk of the inputs would come from elsewhere, the statistical issues, and more.

While a number of the issues are technical, there were also easy to spot clear red flags that something was wrong.  A public project such as this does not generate an annual rate of return of 116%.  One does not get fantastically precise statistical results with real world data.  These and other results should have served as flags first to the authors of the study that something was wrong, second as a warning to those commissioning the study that the results looked odd, and third as a signal to the journalists covering the release that they should consult with some neutral third party who would have the necessary expertise to advise on whether there might be issues.  When something looks too good to be true, it usually is.

But such a review was not done, and the results were announced as if they were valid.

The High Cost of the Purple Line Light Rail Transit Project: Free Bus Service Would Be Cheaper For Everyone, and Provide a Better Service

Purple Line Costs vs BRT

A.  Introduction

The Purple Line is a proposed light rail transit project that would thread itself through suburban neighborhoods over 16 miles in an arc from the east of Washington, DC, to its north.  It is a controversial project, but with strong political pressure to sign soon a contract with a private concessionaire who would construct and then operate the rail line over a 30 year life.  The aim is to begin construction in 2015, complete construction by late 2020, and open the line to ridership by early 2021.

The project is controversial for several reasons.  There are environmental and noise concerns, as a portion of the line will be routed over what is now a park (on an old, abandoned, rail line) with a walking and biking trail that is the most popular in Maryland in terms of usage.  Two parallel rail lines would be built on this trail, with a new trail then built alongside the tracks, necessitating the clear cutting of the mature trees along the trail to allow for the much wider right of way.  There will also be major noise issues, as frequent trains (every 10 minutes in each direction during the off-peak hours, and every 6 minutes during the peak) will go by, until 3:00 am on weekends and starting at 5:00 am on week-day mornings.  Homes now backing on to a quiet park will instead have to contend with the noise of the frequent passing trains.  No compensation will be provided to those adversely impacted, and it should not be surprising that they, as well as others, are opposed.

The line is also expensive.  The most recent estimate, from July 2014, puts the capital cost alone at $2.4 billion, with annual operating costs then of $58 million.  But the Purple Line will only serve suburban neighborhoods of medium to low density, so ridership will not be high.  The cost estimates are of course only estimates, and the final costs will not be known until the work is completed (when it is too late to do anything).  Based on past experience with such projects, one should expect that the final costs will be substantially higher than these estimates.  And as will be discussed below, the published cost estimates do not even cover all of the costs that will be incurred for the Purple Line.  Finally, even these estimates have increased substantially from what they were initially.  As late at June 2007, with initial design work well under way and alternatives being considered, the estimated capital cost was only $1 billion.  Subsequent estimates were $1.5 billion (in August 2009), $1.9 billion (in September 2011), and $2.2 billion (in September 2012).  The most recent estimate is $2.4 billion.  Few will be surprised if this goes higher, and perhaps much higher.

These cost totals by themselves do not tell us much, however, unless they are put in the context of how many riders will use the system.  While thousands of pages of documents have been posted on the web on the proposed project, with the Final Environmental Impact Statement (FEIS, August 2013) the most comprehensive review, I have not been able to find any serious economic analysis of the project, nor of the alternatives to provide such transit services.  The FEIS does describe in great detail a set of alternatives it states they considered, and I am sure such work was done.  There are full chapters in the FEIS on the alternatives (see in particular Chapter 2 and Chapter 9).  But figures are not presented which would allow one to compare one alternative to another.

Evaluating major projects such as this is something I did during my career at the World Bank.  This blog post will summarize estimates I have made of what the full costs of the Purple Line will be, and will compare these to some alternatives.

B.  The Cost of the Purple Line

A transit project such as the Purple Line will incur both upfront capital costs to build the system, and then annual operations and maintenance (O&M) costs to operate it.  Ridership will start only once the system is built, and then should grow over time.  Determining the full cost of the system per boarding (one rider getting on board for one trip) is therefore complex.  While it would be easy to determine the annual O&M costs per boarding once the system is up and running, one should not ignore the up-front capital costs that are incurred.  And since the capital costs are incurred up-front, there will be interest costs, either explicit (for what the private operator borrows) or implicit (if government grants are used –  but such funds will still need either to be borrowed or to come from some other use, so there will be an opportunity cost in such usage of the limited funds available).  One cannot simply ignore the costs of these funds, and yet the published analysis appears to do just that.

One therefore needs to use a spreadsheet which separates out by year when the costs are incurred (both capital and O&M costs), and when the ridership occurs.  One can then calculate what the cost would be per boarding which, over some given lifetime, would cover the full costs incurred by building and then operating the Purple Line.  If riders are charged this cost per boarding (and assuming the projected ridership would still be the same, even though such a fare was charged), the system would cover its costs from the ridership.  While transit systems rarely cover their full costs from the fare box, one will still need to know what this cost will be to judge whether the system is worthwhile, as well as to judge whether some alternative would be a better use of the funds.

The Technical Note at the bottom of this post describes in some detail the methodology followed, the sources for the data used, and the assumptions then made.  The end result is that the estimated full cost for the Purple Line comes out to be $10.42 per boarding, in terms of constant dollars of 2012.  This is a lot.  The riders on the Purple Line will mostly be making only short trips of just a few miles, connecting to Metrorail lines and/or traditional bus routes to get to and from work.  At $10.42, private taxi service would likely normally to be cheaper.

The busiest portion of the route is expected to be between Silver Spring and Bethesda, connecting two business centers each on two effectively separate Metrorail lines (although in fact they are the same line, after looping through downtown Washington, DC).  This is the portion of the route that would destroy the existing park.  It is only 4.3 miles long, and the time savings would be small.  Existing local bus service between these two points only requires 17 minutes, and that is during rush hour.  The Purple Line light rail service would require 9 minutes, producing a savings of only 8 minutes.

It is expected that few if any travelers would ride the full 16 miles of the line.  Traveling that route on the Purple Line would take an estimated 63 minutes based on the current design.  But one could travel between the same two points on the existing Metrorail service in 51 minutes now, during rush hour.  The Purple Line is designed for local service.

Riders would of course not pay that $10.42.  If they were charged such fares for the short trips being taken, very few would take the Purple Line (as noted, taxis would likely be cheaper).  The FEIS (Chapter 3, page 3-8) estimates that the additional fare box revenue in 2040 (but in 2012 dollars) would be $9,615,564 (which is more precise than one would think they intend).  Based on the FEIS ridership projections, this comes to just 38 cents per boarding.  It is so low because most of the riders would be transfers to and from Metrorail and traditional bus services, or would displace ridership on existing services.  Transfers pay zero or small additional fares.

The cost per boarding of $10.42 and the fare per boarding of $0.38 implies that the subsidy that would be provided to those riding the Purple Line would be $10.04 per boarding.  These figures are shown in the chart at the top of this post.  A subsidy of over $10 per ride is huge.

C.  Comparison to a Bus Rapid Transit System for Montgomery County

To put the $10.42 per boarding cost of the Purple Line in perspective, one needs to look at alternative forms of transit.  Montgomery County, Maryland (through which roughly half of the Purple Line will run) is also looking closely at use of Bus Rapid Transit (BRT) systems for certain of its public transit routes.  A consultant’s report completed in 2011 commissioned by the county provides figures that can be used to provide perspective on the Purple Line costs.

A Bus Rapid Transit system provides high-capacity and streamlined bus services along selected routes.  By use of larger buses, dedicated stations where one will pay the fares before boarding (thus streamlining boarding), various road improvements and perhaps dedicated bus lanes, one can provide transit services that are significantly faster than, and more comfortable than, traditional bus services.

The Montgomery County BRT study looked at a system whose capital cost came to an estimated $2.4 to $2.6 billion (in 2012 dollars).  This was roughly the same, coincidently, as the current estimated cost of the Purple Line Light Rail project.  But what one would obtain for that similar investment would be far more:

Comparison of Purple Line to BRT BRT Purple Line Difference
Capital Cost $2.4 to $2.6b $2.43b similar
Number of routes 16 1 16 times
Number of miles covered 150 16 9.4 times
Daily boardings, 2040 (mid-point) 186,300 59,130 3.2 times
O&M cost per boarding (mid-point) $2.424 $2.688 10% less
Total cost per boarding $4.16 $10.42 60% less

The Montgomery County BRT system would cover 16 routes, versus only one for the Purple Line.  It would cover 150 miles, versus only 16 for the Purple Line.  The projected daily boardings in 2040 of 186,300 (based on the mid-point of the range projected) would be over three times the 59,130 projected for the Purple Line.  And the operational and maintenance (O&M) costs per boarding (again based on the mid-point of the range in the BRT study) would be 10% less.  Normally one justifies the higher capital expenditures per mile of a rail system by its then lower O&M costs.  But the O&M costs of the Purple Line would be higher.

The full cost (including capital costs) per boarding of the BRT system is then far below the cost of the Purple Line.  As discussed above, the estimated full cost of the Purple Line would be $10.42 (in 2012 dollars).  Using a similar methodology, but with the BRT cost and ridership estimates, the full cost of the BRT system would be $4.16 per boarding, or 60% less.

The BRT system would be a far better investment, then, of the scarce transit dollars available.  Many more people would be served, at a far lower cost.  For the Purple Line corridor itself, various BRT systems (as well as alternative light rail systems and other options) were examined by the Purple Line consultants, but rejected in favor of the light rail system selected.  However, I cannot find in any of the thousands of pages of documentation now posted any presentation of figures on the total cost per boarding of a light rail system versus a BRT for the selected route.  It is not clear if this was ever examined.  And some have argued that the BRT alternative was never seriously considered as an option, but rather that the light rail approach was chosen early, with the analysis then done by the hired consultants directed at justifying this choice.

It is possible that the BRT alternative was rejected for the Purple Line corridor due to the nature of the streets it would pass through, in particular on the Prince George’s County portion of it.  However, a BRT would likely work quite well for the section between Silver Spring and Bethesda, where there is a four-lane major road connecting the two centers.  A BRT could simply run along that.  A BRT would also provide an option to loop up to another major employment center just north of Bethesda, where the Naval Medical Center and headquarters (and main labs) of the National Institutes of Health are located.  The proposed light rail system would not do that.

Use of a BRT line between Silver Spring and Bethesda would also mean that the linear park between the two would not be destroyed.  A hybrid system of light rail up to Silver Spring, and then BRT between Silver Spring and Bethesda, would be a possible compromise.  The BRT could then join up with north-south BRT lines being planned separately for Bethesda, as well as BRT lines being planned for Silver Spring.

D.  A Cheaper and Better Alternative:  Free Bus Service

As noted above, the subsidy of over $10.00 per boarding for the Purple Line is huge.  The cost will be borne in one form or another (either capital subsidies or operational payments) by the government, and hence ultimately by the taxpayer.  Recognizing that government would be providing a subsidy of $10.00 per boarding to transit users in this corridor, provides a new and better perspective on how best to provide transit services.  Instead of asking the question of how much will it cost to build and then operate a light rail transit line, the question shifts to how best to use the funds that would be made available for transit in this corridor.

When one looks at the issue this way, one alternative stands out:  Why not simply charge a zero fare for bus service along the Purple Line corridor (and perhaps more broadly)?  While I was not able to find figures to allow a calculation of the full cost of operating a traditional bus system in an area of similar density as the Purple Line corridor, the cost should be expected to be less than the cost of a BRT system in Montgomery County.  That is, the cost will likely be less than $4.16 per boarding.

And note that with no fare being collected, there will be at least two additional advantages gained over current bus service.  First, the new bus system will have a similar advantage in terms of speed as a BRT system.  BRT buses are able to move more quickly on regular roads primarily because they can load passengers quickly, since fares have already been paid at the special bus stations built at each stop along a BRT line.  But if no fares are being collected, one can simply get on a traditional bus quickly, with no delays due to people lining up to pay their fare.  Over time, one could also replace current buses with ones with multiple entrances and exits, since everyone would not need anymore to pass through the front door by the driver, to ensure fares were being paid.  This would allow even speedier boarding.

Second, collecting individual fares is costly in itself.  Cash fares need to be kept secure and later counted and deposited, and one needs special equipment and technology to keep track of fares paid by those using electronic smart cards or similar devices.  In addition, speedier bus trips mean that the number of driver-hours one needs to pay for (the most significant expense in operating a bus system) will be reduced in per rider terms.  Both of these factors reduce costs, and significantly so.

But even assuming the traditional bus system will have full costs of $4.16 per boarding (the same as the BRT), one could still carry 2.4 times as many passengers as the Purple Line would carry, for the same net cost (of $10.04).  With a likely cost of well less than $4.16 per boarding, one could carry even more.  And with a larger number of riders, a higher frequency of bus service on each route (say every five minutes instead of every 15 minutes) could then be supported.  Free fares for riders coupled with more frequent service would then be expected to attract even more riders, and possibly many more.  The main concern public officials should probably have is that such bus service would become so popular that many more than 2.4 times as many riders would want to ride the system.  While economies of scale (more riders on each bus, on average) will reduce costs per rider to even less, a large number of new riders eager to take buses is a “problem” that public officials should welcome.

One would then also expect that such ridership shifts to public transit would start to have a significant impact on car usage and hence road congestion, even with additional bus service.  An individual bus with reasonable ridership levels displaces many cars from the roads along the corridor.

Even if it were argued that such a shift to free and frequent bus service were not possible for much of the Purple Line, it is clear that it would work well for at least the Bethesda to Silver Spring section.  As noted above, there is an existing four lane road, and even during congested rush hour traffic, the current traditional bus line (with its frequent stops, and passengers lining up at each stop to step aboard and pay their fare) only requires 17 minutes.  This could be sped up significantly with a shuttle service where no fare is paid (so need to line up to pay it) and perhaps a limited number of stops.  Such a service would likely match or almost match the 9 minutes the Purple Line light rail system would require for this 4.3 mile segment.  Furthermore, one could start to offer this free shuttle service immediately.  There is no need to wait until 2021 for the Purple Line to be built.  This alternative would also save the park that the Purple Line would destroy, and the residents whose land now backs on to this park would not need to contend with the noise of rail cars passing their windows every 5 minutes until midnight (as a train will pass every 10 minutes in each direction), and until 3:00 am on weekends.

E.  Reality Check:  Why the Better Solution is Unlikely to be Followed

So far, the analysis above has kept to what would make most sense to provide transit services along the corridor the Purple Line would serve.  But just because a simpler, cheaper, and better service might be available, does not mean that it is likely to be done.  There are at least three reasons in this case:

a)  Bureaucratic rules:  Government support for transit projects is biased to providing capital support to build things, rather than operational support to run things.  State and especially federal government support is biased in this way.  This creates distortions when decisions are made, as an option requiring much up-front capital will be favored over a solution which instead has primarily on-going operational expenses.  Funds for the capital investment may be available as a grant, while operational expenses are not covered (or are not covered to the same degree).

There would likely be such an issue here, as the state and federal funding is focussed on providing grants for construction.  Those advocating the expensive light rail system will argue that while they can get these funds for construction, they could not obtain such funds to operate improved bus services along this corridor.

But these are bureaucratic rules.  Such rules can be changed.  If a cheaper option than a light rail system (such as free and frequent bus service) provides a better solution, then elected politicians should be able to find a way to make this possible.

b)  Some parties will gain by an expensive light rail system:  Even though transit users as well as taxpayers might lose by building the expensive option, there are some groups that may gain.  Two in particular should be noted.  One is developers who own land parcels close to the proposed stations of the Purple Line.  These parcels will gain significantly in value as transit users are channeled to those locations (and not to others), with land values that may well rise by hundreds of millions of dollars.  Someone else will be paying the $2.4 billion construction cost.

The second is the group of private construction and engineering companies that will participate in the construction, as well as the ultimate concessionaire.  Profits on a $2.4 billion project are substantial.

c)  The embarrassment factor from admitting your choice was wrong:  Finally, one should not neglect that politicians and others will be extremely reluctant to admit that they made a mistake on a project they had previously supported and indeed championed.  But they should not be criticized if they recognize that the information they had before was perhaps insufficient, or that conditions have changed as more information has been gathered.

The Governor of Maryland announced in August 2009 that a light rail line would be the “locally preferred alternative” for the corridor the Purple Line would serve.  At that time, the capital cost was estimated to total just $1.5 billion, with construction that could start in 2013 and be competed by 2016, and with projected daily boardings of 64,800 by 2030.  But the current estimates are that the capital cost will come to $2.4 billion (60% more), construction will not begin until 2015 and only be completed in 2020 (four years later), and that daily boardings now projected for 2030 are only 53,000 (18% less).

Estimates are of course only estimates, and one cannot know for certain beforehand what the costs and ridership will be, nor how long it will take to build such a system.  But how high do the costs need to go before one agrees that earlier decisions need to be reconsidered?  A 60% increase is not small.

One way to resolve this:  Why not hold a vote?  Arrange for a ballot referendum in the areas impacted, where the population would be allowed to vote on whether they prefer the Purple Line light rail system (to be built as currently proposed, and with regular fares then to be paid to ride it), or the alternative of using the funds to provide free bus service along this corridor, starting immediately.  Since the issue is one of service preferences, as the costs would be similar, the general population should be given a say in how the funds are utilized.

 

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Technical Note on Methodology, Data, and Assumptions Used

This technical note presents in some detail the methodology, sources of data, and assumptions made, to come up with an estimate of the full cost per boarding of the proposed Purple Line Light Rail transit project.  The basic approach is to develop a spreadsheet which estimates the full costs (for each year over the lifetime of the project) of building and then operating the rail line.  One then subtracts from these costs what would need to be “charged” per boarding, so that the “revenues” thus generated (given the ridership estimates) will suffice so that the project will have paid for itself in full by the end of the time horizon chosen.  The “shadow fare” thus computed is not the fare that would actually be charged, but rather the cost per boarding that would need to be covered for the full cost of the project to be covered by the end of the time horizon.  Riders are not in fact charged this fare, but rather something far less.  The purpose of the exercise is to calculate what the full cost per boarding will be.

The spreadsheet needs to break out the costs by year since, like any project, capital costs are incurred up-front, ridership starts only when the project is completed, and ridership generally will grow over time as the region grows and develops.  Annual operations and maintenance budgets will also grow over time to cover the costs incurred from carrying more riders (with more frequent train service, for example).

Importantly, because major capital expenses are incurred up front, there will be a cost from providing the necessary funds up front, to be repaid only later.  These will be interest costs.  These interest costs will be incurred whether the project itself borrows directly the funds necessary for the construction, or if some level of government (federal, state, or local) provides the funds as a grant.  The grant funds need to come from somewhere, and governments need to borrow.  Even if the governments were currently running a budget surplus, they could have used the funds being provided to the transit project instead to pay down some of the government’s existing outstanding debt, or for some other use.  Economists call this the opportunity cost of capital, and it exists even when the transit project itself is receiving the funds as a grant.  This cost cannot be ignored, even though it often is.

Thus the basic structure of the spreadsheet starts by accounting for the capital costs during the construction period, by year, and including the interest costs incurred (implicit or explicit) to cover those capital costs (and after the first period, also the costs of covering the accumulated interest itself).  The construction period is primarily 2015 to 2020 according to the current planned schedule.  Operation then begins in early 2021, with annual operations and maintenance costs starting then and ridership beginning.  Since the current plan is to provide a concession to a private firm to build and operate the system, with the operations concession lasting for 30 years from the end of the construction period, the spreadsheet was used to determine what “shadow fare” would be necessary so that at the end of this 30 year concession, the “revenues” thus generated (given the ridership projections) less the annual operations and maintenance expenditures, would have covered the up-front capital costs incurred (along with accrued interest on the outstanding annual balances).  An iterative process was used to arrive at that shadow fare.  That shadow fare will be the full cost incurred, per boarding, of this light rail line.

The calculations were done all in current dollar terms.  That is, certain inflation rates were assumed and the implicit interest rate on the capital costs was defined in nominal terms.  However, all the figures reported here on cost per boarding are expressed in terms of prices of 2012.  One could have set up the spreadsheet to do all the calculations in real, inflation-adjusted, terms, but the results (if everything was done correctly) would be the same.  For the purposes here, working in current price (or nominal) terms, was simpler.

Data were taken from the documents posted on the internet for this project.  Most important were the most recently updated summary sheet from the US Federal Transportation Agency (FTA) of July 2014; the Final Environmental Impact Statement (FEIS) of August 2013, in particular its Chapters Two, Three, and Nine, plus its Volume III Technical Report on Capital Costs; and the “Request for Proposals (RFP) to Design, Build, Finance, Operate, and Maintain the Purple Line Project”, issued by the State of Maryland in July 2014.

One would have expected that with all these reports, totaling thousands of pages, the project designers would have made available a spreadsheet of their own with the expected costs by year as well as ridership.  But the information from such a spreadsheet does not appear to have been posted.  I am sure they would have themselves made such calculations, but they evidently chose not to make them available to the public.  I therefore had to make various estimates of my own, drawing on the figures they did make available and anchoring the projections in the figures they provided for only certain of the outlying years (most commonly 2035 or 2040).

Due to the inherent uncertainties in all this, I erred on the side of conservatism whenever assumptions needed to be made.  That is, I aimed to err on the side of keeping estimated costs low.  The estimated cost per boarding (in 2012 dollars) of $10.42 in the base case is therefore probably low.  The true figure will probably be higher.  But I have some confidence it will be at least this high.

Specific figures used included:

1)  Estimated capital costs (construction costs) was taken from the FTA summary sheet.  The figure reported there of $2,427.97 million includes, however, $126.0 million in “finance charges”.  These finance charges appear to include the financing costs that will be incurred only on the private borrowing portion of the total costs (estimated to cover $800 million of the overall $2.4 billion cost) and only during the construction period.  Since the total financing cost (including on government borrowed funds) will be accounted for separately, the capital cost figure used for construction expenses only was $2,302 million ($2,428 million less $126 million).  Like all the cost figures presented in the FEIS and RFP, it is assumed these are expressed in prices of 2012.  They were then spread evenly (in real terms) over the construction period of 2015 to 2020.

2)  While this capital cost figure of $2,302 million was used, it should be noted that all of the capital costs of the project have not been accounted for in this widely reported figure.  In particular, it does not include the cost of perhaps the most complex and difficult light rail station to construct, at the western end of the line (Bethesda).  This will be fitted into an existing underground tunnel under a building (where the old train line had run), with underground connections made there to link it to an existing subway line station.  Consideration was given to tearing down the existing building above the lines to allow the construction, but a recent decision was made not to, as the costs would be even higher.  The capital cost figure also does not include the cost of re-building the existing walking/biking path that the new rail line will take over, as this cost will be covered by Montgomery County.  However, it is still a cost, and should have been included.  Finally and perhaps most importantly, the capital cost figure of $2,302 million does not include anything for the significant costs incurred (mostly by the State of Maryland) for the design work, environmental impact and other assessments, and all else that has been done to bring the project to this point.  As has been noted, thousands of pages of analysis have been posted on the internet, consultants were hired to produce these reports, and public officials have devoted a good deal of time to organizing and overseeing this work.  These costs should not be ignored.  While it can be argued that these costs are already incurred and hence should not be a factor in what to do now, one should then not present the capital cost estimate (of $2,302 million currently) as the total capital cost of the project.  Rather, it is an estimate of the additional capital cost now needed to complete the project.  But in any case, since I do not have figures on the costs already incurred, I have had to leave them out.  The true total capital costs are higher.

3)  Also left out is any valuation for the cost of the public lands taken (including public park lands) for the rail line.  The public park and other public lands taken have been treated as if they were free, with zero value.  In particular, the western section of the line, from Silver Spring to Bethesda, will be built over an existing walking/biking path, and will need to clear-cut the existing trees on both sides to allow for the two new parallel rail lines plus a re-built path adjacent to it.  The park will be effectively destroyed.  Instead of a walk through the woods, one will have a utilitarian paved path next to a busy rail line.  If this project were being financed by the World Bank in a developing country, the World Bank would have required (by its environmental standards) that a new similarly sized park be created near-by, as an environmental offset to the land taken for the transit project.  The cost of acquiring this new park land would then be reflected in the project cost.  The cost would not be small, which is probably why it was never seriously considered here, but that high cost (reflecting the high value of such land) is precisely the point.  And while poor countries are expected to follow such measures to protect the environment, there is no such plan here, even though Montgomery County (where this section of the line will run) is one of the richest counties in one of the richest countries in the world.

4)  The implicit interest rate used (the opportunity cost of capital) to cover the cost of the up-front capital expenditures will also be important.  The project documents appear to have all left this out (except for the relatively minor $126 million finance charge included in the most recent FTA summary sheet, discussed above).  The current financing plan is for two-thirds of the cost to be covered by government grants (federal and state) and one-third by private borrowing by the project concessionaire.  The private borrower will of course need to cover its interest costs.  While interest rates are currently low, and have been since the Lehman Brothers collapse in September 2008 (as the Fed has kept rates low to spur the recovery), it is expected that interest costs will return to normal once full employment is recovered.  Over the ten year period leading up to September 2008, the average corporate bond borrowing rate for a AAA borrower averaged 6.2%, while it averaged 7.1% for a BBB borrower over this same period.  To be conservative, I assumed the borrowing rate would be 6.0% for this project, even though this is likely to be low.  Note that this is a nominal, not real, interest rate.

5)  More importantly, one also needs to include a cost for the government funds being provided.  It is certainly not zero, even if the project itself receives the funds as a grant.  The government has to obtain the funds from somewhere.  And while the government can borrow, in this case it is choosing to have the private concessionaire borrow funds for a substantial share of the project, rather than provide additional government borrowed funds.  This implies that the government would rather have the private entity borrow funds for the project, and that it views this cost (assumed to be 6.0%) as preferable to whatever it would pay for directly borrowed funds.  Therefore, the spreadsheet calculations were done based on a 6.0% interest cost, implicit or explicit, for the full project cost.

6)  Finally, all the calculations were undertaken in nominal terms, and hence one needed to make certain inflation assumptions.  Based on figures from the RFP and the FEIS, I assumed inflation rates of 3.1% for the construction costs, 2.5% for operations and maintenance costs, and 2.0% for general consumer prices (reflected in the shadow fare rates).

7)  Ridership forecasts were taken from the most recent FTA summary sheet, which shows figures for 2014 (which I interpret reflect what ridership would be today, if the system were operational today) and for 2035.  It was assumed ridership between these dates would grow at a steady growth rate.  This worked out to 1.113% a year, which is reasonable for the already developed region the rail line would go through.

Based on these cost and ridership assumptions, the cost per boarding for the proposed Purple Line comes to $10.42.  This is a lot, for what is designed to be basically a local service (providing connections to and from Metrorail lines and traditional bus services).

There is of course uncertainty in this single point estimate.  It depends on the accuracy of the underlying cost and other estimates used.  One needs to know the sensitivity of this point estimate to the data assumptions made, in order to judge how meaningful the point estimate is.  Several different scenarios were therefore examined to test the sensitivity.  Most of the scenarios tested looked at changes that would lead to higher costs, but the impacts would be similar going in the opposite direction:

Purple Line Scenarios Cost per Boarding
         (prices of 2012)
Base Case $10.42
Interest rate 6.0% → 7.0% $11.62
Time horizon 30 → 40 years $9.28
Ridership 20% less $13.02
Capital Cost + 20% $11.92
Construction Period + 2 years $11.02
Capital Cost + 20%, and also
    Construction Period + 2 years $12.64

The base case assumptions, as noted, lead to an estimated break-even cost per boarding of $10.42.  If the borrowing costs (implicit or explicit) were 7.0% rather than 6.0%, then the cost per boarding would rise to $11.62.  Some would argue that a 7% borrowing rate over the long term would likely be a better estimate of what it will be for such a project entity in coming decades than 6% (the BBB borrowing rate averaged 7.1% over the decade before Lehman Brothers collapsed), but the Base Case was deliberately conservative.

Extending the time horizon would also affect the break-even cost.  The private concession is planned to extend for thirty years of operation following completion of construction, so determining the break-even cost per boarding at that point is of interest.  But some of the assets would likely last longer.  Offsetting this, however, is that there will also be major rehabilitation costs periodically, and I was not able to find any estimates for what those would be.  They were therefore implicitly set at zero.  But even assuming rehabilitation costs were zero, and that assets were all able to last for 40 years rather than 30, the break-even cost per boarding would still be high at $9.28.

Ridership is also difficult to predict with great confidence.  Ridership that turns out to be 20% less than projected would raise the break-even cost per boarding to $13.02.

Construction costs (capital costs) also often turn out to be higher than projected, and/or completion takes longer than planned, and these often come together (delays in completion lead to higher costs).  If the capital cost turns out to be 20% higher, then the break-even cost per boarding rises to $11.92.  If completion is delayed by two years (but with no additional capital cost), the cost per boarding would be $11.02.  And if both the capital cost turns out to be 20% higher and completion is delayed by two years, the break-even cost per boarding rises to $12.64.

Finally, one could have (and indeed generally will have) a combination of differences.  Some might be offsetting, but one could also have some combination of lower ridership, higher construction costs, delays in completion, and higher borrowing costs.  But the degree of difference in each case might well be less than those tested here.

Based on the sensitivities in these scenarios, the estimated cost per boarding of $10.42 in the base case is probably accurate within a dollar or perhaps two.  Given past experience with such projects, there is a greater likelihood that costs will turn out to be higher than expected rather than lower.  I would therefore doubt that the final cost per boarding turns out to be less than the base scenario estimate of $10.42, while there is a significant risk that it could be $12 or even more.

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October 1, 2014:  Update

The Washington Post reported (in its print edition today, and in an on-line note yesterday) that the official estimate of the capital cost of the Purple Line has increased again, by $21 million this time from the estimate published in July.  The total is now $2.45 billion.  While the $21 million increase should perhaps not be considered large in itself, it comes as the most recent such increase that has steadily raised the estimated cost of the Purple Line from just $1 billion in 2007, to the estimated $2.45 billion now.

I have not changed any of the text above.  With this new capital cost estimate and assuming nothing else has also been changed, the cost per boarding would now work out to $10.48, a bit more than the $10.42 estimated before.