Why Voters Are Upset 3: Not Enough Homes Are Being Built

Chart 1

A.  Introduction

One of the more important reasons many voters are upset is that buying a home has become increasingly difficult.  Not enough homes are being built, and with the need for housing (one has to live somewhere), home prices have shot up to record levels.  While they had also gone up in the housing bubble that peaked in 2006/7 and then crashed (leading to the economic and financial collapse of 2008), that was a demand-driven bubble.  Mortgages were provided with very little down and with scant attention to affordability to borrowers who could not then repay them.  This soon came crashing down, along with home prices.

The recent spike in home prices is different.  Not only are prices substantially higher now than at their pre-2008 peak, but they are also far higher (in real terms, not just nominal) than they have ever been in the US in data going back to 1890.  See the chart above.  For over a century (i.e. from 1890 to 2000), real home prices fluctuated between index values of around 70 on the low side and at most 130 on the high side (where the price in 1890 = 100).  They are now at 220.  While homeowners can have good reason to be pleased by this rise in the value of their homes, those who are not homeowners see the rising prices as an ever-rising bar that will prevent them from ever being able to afford to buy a home.  For good reason, they are upset.

This post is the third in a series that have examined the economic factors behind why voters are upset.  Earlier posts looked at the overall figures on the slowdown in growth (and hence in incomes) following the 2008 economic and financial collapse, and at the structural factors behind that slowdown (with roughly equal shares due to: 1) a slowdown in labor force growth as a consequence of an aging population; 2) a slowdown in private investment despite record high profits and slashing taxes on profits from 35% to 21% in Trump’s 2017 tax measure; and 3) a slowdown in the growth in productivity of the resulting labor and capital).

This post will examine the reasons behind the recent sharp rise in home prices.  There are numerous home builders in the country, and with competition one might normally expect at least some to step in and build more homes to take advantage of those high prices.  That has not happened, and the interesting – and important – question is why.

What we will find:

a)  First, home prices in the US are at historic highs and are now far higher than where they have ever been (in real terms) going back to 1890 – 135 years ago.

b)  Second, the number of homes being built has not been enough to keep up with the growing number of households in the country.   But people have to live somewhere, so people do what they can to pay for the housing (whether owned or rented) they need.  This pushes up the price of housing.

c)  With those high prices, why are more homes not being built?  What one reads in the news media is that home builders claim they cannot build enough and have to charge such high prices because they are facing higher costs themselves – of labor, lumber, and other inputs – and because of burdensome regulation.

d)  If this were true, then the profitability of home building would be going down.  With higher costs, profitability would fall.  However, this has not been the case.  The profitability of the major home builders is remarkably high.

e)  There is also the odd result that productivity in the construction sector (of which home building is a major part) has gone down in recent decades in absolute terms.  Productivity almost always goes up, as productivity comes from knowledge of how to do things better.  Over time, one learns more.  The rate of increase in productivity can and does vary by sector, but what is puzzling is why it would go down in absolute terms.  Yet the construction sector produced 25% less per unit of labor in 2024 than it did in 1998.  Labor productivity in the overall private economy was 50% higher in 2024 than it was in 1998.

f)  The question, then, is why has the construction of new homes fallen far short of what is needed despite the high profits the homebuilders are enjoying?  With competition, one would expect that if some home builders do not build more, then others will step in and do it.  The technology on how to build a home is not secret or proprietary, and at a national level there are hundreds if not thousands of firms building homes.

g)  This has not happened.  It can be explained by what we see at the local level, where one needs to recognize that the relevant market for home building is not national but local.  Home building is not like making cars, for example, where one factory can serve the entire nation.  What we will find is that at the level of local markets – metro areas – home building has become much more concentrated over the last couple of decades, with a limited number of firms in each metro area taking an increasing share of the metro area market.  Homebuilders have been merging with each other or acquiring smaller firms, with the result that a small number of firms have grown to dominate the individual local markets.  The market shares of the top firms in each local market have grown, even though there can be (and normally are) different sets of firms in the different local markets across the nation.

h)  At a national level, therefore, the relatively modest market shares of individual home builders can make it look like the market for home building is diverse, with numerous builders each of whom is small compared to the overall national market.  But the national market is not the relevant market for home building:  local markets are.  And at the local market level, a few firms dominate in each and their dominance has grown in recent decades.

i)  By dominating their local markets, those few firms in each market can then have the market power to limit the building of new homes despite the high demand.  They face little pressure to invest to develop greater capacity to build more homes, and little pressure to improve their productivity.  Their productivity can fall in absolute terms – as has happened – yet their profitability can be high and indeed even grow despite that fall in productivity.  Without the pressure of competition, they can charge high prices for the homes they do build and thus be highly profitable.

This post will cover each of these points in the sections below, documenting them and illustrating the developments through a series of charts.

An annex to this post will then present, through basic supply and demand diagrams, an analysis of what to expect under such market conditions.  Economists love supply and demand diagrams, but few others do.  You will not miss much by skipping the annex, but some may enjoy the exercise of working through the charts.

Cases such as this are called instances of “monopolistic competition” by economists.  The annex will first review the base case of firm-level supply and demand under conditions of perfect competition and, alternatively, then of monopolistic competition where there are limits to the entry of new competitors in those markets.  Under each, we will see how much the firm will choose to build (the answer is they will choose to build less – possibly far less – under conditions of monopolistic competition than they would under conditions of perfect competition), the price the firm will charge for what it produces (higher – and possibly far higher – than they would if they faced more competition), and the resulting profits (higher as well – and again possibly far higher).  All this can be found in any basic introductory microeconomics textbook.

But the conditions in the local housing markets in the US then deviate from those covered in the standard textbooks.  In the standard textbook case, the high profitability in a market with monopolistic competition will induce at least some new firms to enter the market and provide a similar product.  After price and quantity adjustments, no exceptional profits will then be earned.  But in the local housing markets of the US, concentration among home-building firms has increased over the last couple of decades, not decreased.  There is now less competition, rather than more.

The annex will show that under such conditions the exceptional profits will then grow even higher with that increase in market concentration.  And in a third case, the annex will show that with both growing market concentration and growing demand for the product (housing), the exceptional profits will grow yet higher again.

B.  The High Price of Homes

Home prices in the US are exceptionally high.  The chart at the top of this post provides an estimate of real home prices (adjusted based on the general CPI) for the period from 1890 (with an index value set equal to 100) through to April 2025.  The data were assembled by Professor Robert Shiller of Yale, and was originally constructed for his book Irrational Exuberance.  The data in it is now updated monthly, and is available at Shiller’s personal website.

The series was assembled by Shiller by splicing together the estimates of several researchers, with the data through 1952 on an annual basis and since then on a monthly basis.  The data from April 1975 onward is from the Case-Shiller house price index that Shiller originally developed along with Karl Case and other colleagues, and is now a product of S&P/Corelogic.  While there will be more uncertainty in such data as one goes back in time, the Case-Shiller home price indices are carefully done, and it is the data for the last half-century (i.e. 1975 to now) that are of most interest to us.  Note that the prices incorporate adjustments to reflect changes in the quality of the homes being sold.  The Case-Shiller index does this by tracking the repeat sale prices of individual homes, adjusted for the cost of major renovations.  But it would have been increasingly difficult to do this accurately the further one goes back in time.

But it is the overall trends that are of most interest, plus what has happened to such home prices in recent years.  And the story is clear:  Real home prices fluctuated in a relatively narrow range (narrow given the length of time being considered) of between index values of 70 and 130 in the 110 years between 1890 and 2000 (with 1890 = 100.0).

This then changed in the period leading up to 2006.  Home prices in real terms reached a peak of 195 in 2006 and then fell – at first slowly and then quickly – as the demand-led housing bubble burst.  Financial markets discovered that home prices – driven as they had been by easy mortgage lending boosting demand – would not keep going up forever, as mortgage delinquency rates rose:

Chart 2

As home prices fell, the housing assets that backed the mortgages would not suffice to allow for a full recovery of what had been lent to the borrowers now going into default.  Mortgage lenders became more careful, the effective demand for housing fell, and home prices crashed.

The current run-up in home prices is different.  Mortgage delinquency rates, as seen in Chart 2, are now roughly where they were before the run-up to the 2007/08 mortgage-led crisis.  Easy mortgage lending is not now driving up home prices.  Rather, and as we will see in the next section, the cause has been supply-led rather than demand-led.  Home building has not kept pace with the growing number of households.

C.  Not Enough Homes Are Being Built

One can look at the adequacy of the number of new homes being built each year – adding to the existing stock of housing – in a number of different ways.  We will examine several in this section, and they all point to the problem of not enough homes being built.

First, there are figures on the number of housing units being completed each month:

Chart 3

The number being completed has fluctuated widely over the years but fell especially sharply following the bursting of the housing bubble in 2007.

But the figures on the absolute number of new homes built each period tell only part of the story, as the population of the US has grown substantially as have the number of households.  There were 60 million households in the US in 1968 but more than double that now with 132 million households as of 2024.  The number of new housing units being built each year per thousand US households has come down sharply:

Chart 4

The 10-year average number of new housing units being built each year per thousand households was 24.2 in the 1970s.  The most recent 10-year average (ending in 2024) was just 9.9 (60% less than in the 1970s), and hit a low of just 7.1 in 2018 (70% less).  Home building has not kept up.

The fall in residential investment is also clear in the National Income and Product Accounts (NIPA, and more commonly referred to as the GDP accounts, produced by the Bureau of Economic Analysis.  Net residential fixed investment (i.e. in housing, and “net” refers to net of depreciation) as a share of GDP has fluctuated widely in recent decades, but around a declining trend:

Chart 5

I have included in the chart the share of private non-residential net fixed investment as a share of GDP for context.  It has also been declining, although not by as much as net investment in residential fixed assets.  Net residential investment fell to essentially zero as a share of GDP in 2009-11, following the bursting of the housing bubble, and then recovered to only between 1 and 2% of GDP.  As of 2023 it was around 1% of GDP –  well below where it was in the 1960s and 70s.

[Side note:  This and the following chart were prepared in December 2024, as part of my preparation for my earlier post on the slowdown in overall GDP growth.  I then decided that the slowdown in housing investment should be addressed in a separate post – this one.  But the underlying data – through 2023 here – are still the most recent available.  They are updated only annually, and the data for 2024 will be released only in late September 2025.]

The growth in the resulting stock of residential fixed assets in real terms (i.e. the housing stock) was then:

Chart 6

The chart is on a logarithmic scale on the vertical axis.  A straight line on a logarithmic scale will reflect a constant rate of growth (with that rate of growth equal to the slope of the line).  The straight line in black is thus the trend growth in the stock of residential fixed assets between around 1980 and 2007.  It closely tracks that growth over the 1980 to 2007 period, with little fluctuation around it.  But then the growth in housing assets diverges sharply below the previous trend. The stock of housing would have been 32% higher in 2023 had it kept growing at its pre-2007 trend.  That is huge.  It should be no wonder that home prices were consequently bid up by so much.

While new home building has been slowing for some time in the US, it is noteworthy that the divergence from the previous trend in the real stock of residential fixed assets came only in 2008.  That divergence was then sustained and the relative gap continues to widen.  The increase in home prices under such conditions is not then surprising.  But why have home builders not responded by building more new homes?  If, as they often argue, they could not produce more because their costs had risen (costs of labor, materials, regulatory burdens, and other such costs), then their profitability would have gone down.  But as we will see in the next section, profits have instead been high, and have indeed been exceptionally high for some time.

D.  But Home Building is Highly Profitable

Possibly the best measure of whether the profitability of a firm has been increasing – and is expected to continue to do so – comes from observing the price of its publicly traded shares.  Investors buy equity in firms based on their expected profitability, and they will pay prices that will rise faster over time than the prices of other possible investments when that profitability is (and is expected to be) increasing faster than others.

And the observed prices of what investors are willing to pay for equity in the major home builders have increased spectacularly:

Chart 7

The chart shows the percentage increases in the stock prices (including reinvested dividends and capital gain distributions, and adjusted for any stock splits) of the five largest homebuilders in the nation (in terms of gross revenues earned in 2024) over the more than 25 years from January 2000 to August 12, 2025.  For comparison, the percentage increase in an investment in the S&P500 stock index (and again including reinvested dividends and any other distributions) over the same period is also shown.  The equity price figures were obtained from Yahoo Finance historical stock data.  For example, see here for the figures on D.R. Horton.

The figures for the resulting investment returns are summarized in this table:

             Value of a $10,000 Investment Made in January 2000

Value as of August 12, 2025

Rate of Return

S&P500                      $74,279               8.2%
D.R. Horton                    $685,108             18.0%
Lennar Corp                    $228,805             13.0%
PulteGroup                    $347,408               14.9%
NVR, Inc                 $1,759,624             22.4%
Toll Brothers, Inc                    $332,358             14.7%

An investment of $10,000 in January 2000 in the S&P500 stock index would have grown to $74,279 as of August 12, 2025, for an annual rate of return of 8.2%.  This is a nominal rate of return, but one can adjust for inflation by subtracting 2.6% – the average rate of inflation per annum over the period (as measured by the CPI).

An investment in the S&P500 index over the period – with a $10,000 investment rising to $74,279 – would have provided an excellent return.  But a $10,000 investment over the same period in any of the large homebuilders would have been far better.  A $10,000 investment in Lennar Corporation would have grown to almost $230,000.  And that would have been the worst among the five.  A $10,000 investment in NVR would have grown to over $1.7 million!

Furthermore, it appears that at least one prominent investor expects these excellent returns to continue.  Berkshire Hathaway – with Warren Buffett as CEO – revealed this month through a regular filing with the SEC that it had recently made major investments in Lennar Corporation and D.R. Horton.

There is no evidence here that home builder profits have been squeezed in recent years by high costs, forcing them to cut back on their home building.  Rather, the stock price data would be consistent with the opposite line of causation:  That the reduction in the pace of housing being built (as seen since 2008) has led to much higher profits.

Another indication of profitability can be found in the income statements of the different home builders, with measures such as the return on equity (ROE) generated in any given year.  I looked at the case of D.R. Horton – currently the largest home builder in the US in terms of the number of homes built each year (as well as in gross revenues).  ROE figures can be found in the various annual reports of D.R. Horton.  These were then compared to the overall average ROE figure of all US publicly traded firms (compiled annually by Professor Aswath Damodaran of NYU, for over 6,000 publicly traded firms on US stock exchanges):

Chart 8

With the major exception of negative returns in 2007-09 following the collapse of the housing price bubble, and a relatively low return in 2011, the return on equity of D.R. Horton has generally been higher than the average ROE of firms traded on US stock exchanges – and often far higher.  The gap has been especially high in recent years (as it was earlier when the demand-led home price bubble was building up in the years before 2007).  Home building has been a highly profitable activity.

The profitability of home building has remained exceptionally high in recent years.  There is no evidence that rising home prices should be blamed on rising costs of materials, labor, regulatory burdens, or other such factors – as is often asserted.  If rising costs were the cause, then the profitability of home builders would be low.  They are not.

E.  Profitability Has Been High Despite a Large Fall in Productivity

Another clue to what has been happening in the home building sector – with too few homes being built despite the exceptionally high profitability of home-building firms – can be found in how productivity in the sector has changed over time.  One always expects productivity to grow over time, as productivity reflects knowledge (the knowledge of how best to build what one is building), and knowledge only goes in one direction.  Knowledge is gained as one learns how to do things better, and whatever one knew before will presumably not be forgotten.

Yet remarkably, productivity in the construction sector has gone down over the past several decades, not up.  Government statistics on this are unfortunately only available for the construction sector as a whole – not for residential construction (home building) alone.  But residential construction is a major part of what is covered by the construction sector, accounting for 35% of it in 2023 (in value-added terms).

While productivity figures for residential construction alone are not available, the productivity growth figures for residential construction are almost certainly worse than what they were for construction as a whole.  The remainder of construction includes activities such as the building of bridges, roads, and highways, as well as of office buildings and commercial structures.  Those non-residential construction activities can make more extensive use of heavy equipment (such as bulldozers and excavators), tall cranes (for the building of multi-story office structures), and other such equipment that have gotten better over time.  Building individual homes is smaller scale and more decentralized, and heavy equipment is not as helpful to productivity.

But productivity has declined over time even for the overall construction sector.  In terms of simple labor productivity (what is produced in terms of the sector’s real value-added per employee, with those employed measured in full-time equivalent terms – i.e. with part-time workers included and weighted by their hours relative to full-time workers):

Chart 9

Labor productivity by sector can be calculated on a fully consistent basis for the construction sector only going back to 1998 in the current BEA statistics.  There was a change in how sectors were defined in 1997/98, so the prior series are not always fully consistent with the more recent ones.  But over the 26 years since 1998, labor productivity in the construction sector actually fell by 2023 to just 73% of what it was in 1998 and to 75% of what it was in 2024 (based on a 2024 estimate where I assumed employment of full-time equivalent workers grew at the same rate as the number of full-time workers – data on part-time workers are not yet available).  The fall in productivity mostly came in two periods:  the years leading up to 2008 (after which there was a partial recovery to 2010) and then again very recently in 2022 and 2023.  Between 2010 and 2021 productivity in construction was flat, without the growth over time that one sees in other sectors.

In contrast, labor productivity for the overall private economy grew by 50% between 1998 and 2024 – an annual rate of growth of 1.7% a year.  While the 1.7% per year might not appear to be high, it compounds over time.  If labor productivity in construction had grown at the same pace as it had in the overall private economy, the construction sector in 2024 would have been producing twice as much per worker (=1.50/0.75) as it was.

Labor productivity is simple to calculate as one only needs data on how much is produced in the sector and how many people are employed.  For certain purposes it is also the more meaningful concept, e.g. when one is interested in living standards that are possible.  But a more comprehensive measure of productivity will take into account other inputs used in production and in particular how much capital is employed (i.e. machinery and equipment, vehicles such as trucks, and so on).  The Bureau of Labor Statistics (BLS) provides an estimate of such a concept, which is called total factor productivity (TFP) – how much is produced (in real value-added terms) per unit of labor and capital inputs together.

We again see a sharp divergence in recent decades between growth in productivity in the overall economy and a large fall in the construction sector:

Chart 10

The earliest year in this data set is 1987, and the respective TFP estimates have each been indexed to 100 in 1987.  Since then, total factor productivity for the overall private business sector grew to an index value of 136.3 as of 2023 and 138.1 as of 2024 – an average growth rate of 0.9% per year since 1987.  Total factor productivity in construction fell, however, to an index value of 79.7 in 2023 – a fall of an average 0.6% per year since 1987.  The figure for 2024 is not yet available.  Had TFP grown in construction at the average for the overall private business sector, the construction sector in 2023 would be producing 71% more ( = 136.3/79.7) per unit of labor and capital input.  That is huge.

Why did productivity fall (and fall by so much) in construction over this period?  That is not normal.  As noted above, one does not expect productivity to fall over time, as productivity comes from knowledge of how things can best be organized and produced.  Knowledge over time only increases.  It would certainly be possible (and indeed normal) that productivity growth will be faster in certain sectors than in others.  But the mystery is not that productivity growth was slow in construction, but rather that it fell in absolute terms – and fell by a lot.  And productivity fell despite the high profits among home builders, as discussed above.  It cannot be attributed to a failure of not being able to fund investments to add to (or make more efficient) the capacity in the sector.

One possibility to consider might be that the cost of labor in the sector had gone down, perhaps due (in this theory) to immigrant labor driving down wages.  According to the National Association of Home Builders, immigrants make up about one-quarter of all those employed in the construction sector (which would include office employees), and almost one-third of those in the construction trades themselves.  Those shares are high.  The argument might then be that with cheaper labor becoming available, home building firms chose not to invest in new machinery and equipment as they could instead use cheap – and perhaps increasingly cheap – labor to build the homes.

But total compensation per worker in the construction sector since 1998 has not gone down.  It has gone up.  And it has gone up at a remarkably similar pace as compensation per worker in the overall private economy:

Chart 11

Furthermore, while this is a chart of how compensation per worker has changed (in real terms) since 1998 in construction versus the overall private economy, it is also the case that the average compensation levels themselves were remarkably similar.  In terms of current prices, average per worker total compensation (which will include the cost of benefits such as for health and pensions) in 1998 was $42,049 in construction and $41,694 in the overall private economy.  In 2023, the rates (again in current prices) were $94,191 in construction and $94,373 in the overall private economy.  And over the full 1998 to 2023 period, they never deviated by more than 3% from each other.

Thus wages in construction are not unusually low, nor did they increase at a slower pace than overall wage rates.  And this was not a consequence of some economic principle linking sector wages to overall wages.  In other sectors they could and did vary substantially from the overall average:

Chart 12

This chart is similar to Chart 11 above, but for all the major sectors of the economy (such as agriculture, mining, manufacturing, and so on) as defined by the BEA.  The paths are all over the place.  It just turned out that the figures for construction are very close to those for the overall private economy.  There was no necessity in this.

Another argument some might make for the fall in productivity in construction is that regulations on health and safety conditions at the work sites have become increasingly strict in recent decades.  It is probably correct that such regulations are stricter now than before – although I know of no figures or statistics that might measure this.  But if the burden of such measures were indeed significant and increasing over time, and were the cause of the lower productivity seen in the charts above for the sector, then profitability in the sector would have gone down.  Costs would be higher.  But profitability has not gone down; it has been high.

So once again:  Why did productivity fall in construction over this period, and fall despite profitability among home builders being especially high (so they could afford the capital investments had they chosen to make them)?  The high profitability itself might provide a clue.  One can conceive of productivity falling when home builders are not facing competitive pressures to stay efficient.  Lacking competitive pressures, they can defer investments, build few homes in inefficient ways, but still see high profits as no one else is stepping in to compete against them.  Put loosely, it is then easy to be lazy and not worry about producing for the lowest cost possible, as no one is pressuring you to do so.  Fewer homes are being built than would be the case if the home builders were facing strong competitive pressures, but with fewer homes being built the prices of those they did build then rose to unprecedented levels.  And profits could then be staggeringly high.

There will be less competitive pressure when a limited number of home builders in the relevant markets account for an increasingly higher share of the homes built in each of the markets.  The next section will show that such consolidation has indeed been the norm in housing markets across the US.

F.  The Increase in Home Building Firm Concentration in Local Markets 

The relevant markets for home building are local – i.e. metro areas – and not national.  This is key.  It may look like there are numerous competing home building firms when viewed at the national level, but what is relevant to anyone seeking to purchase a home is not some “national” market but rather what is available in the area where one will live.  Thus one needs to look at concentration in the new home markets not at the national level but rather by metro area.

Data on concentration among firms in local markets are rarely easy to access, if available at all.  Fortunately, there is such data on home builders.  Builder Online – basically a trade journal for home builders – provides figures each year (going back to 2005) on the share of the new housing market (in terms of the number of home sales closed) of the top 10 builders in each of 50 metro areas in the US.  From this, we can track whether – and the extent to which – the home building market has grown more concentrated by metro area over the last two decades.

One can examine various sets of markets with these figures.  For the 10 largest new home markets in 2024 (largest in terms of number of closings of newly built homes), we have:

Chart 13

The pattern is clear:  Concentration rose in each of these markets over the last two decades.  The increases were especially sharp between 2008 and 2011 following the economic and financial collapse of 2008/2009 (except for Phoenix, where there had been an especially large jump in concentration between 2005 and 2008).  This increase in concentration also coincides with the point at which growth in the net stock of fixed assets fell below its previous trend path (Chart 6 above).  The start of the sharp rise in home prices of recent years (shown in the chart at the top of this post) came soon after.  The trough in the Shiller real home price index was in February 2012.

There was then a second jump in market concentration between 2020 and 2022, which may have been related to the disruptions surrounding the Covid pandemic crisis plus the very low interest rates of that period (making it easy to borrow to buy out competitors).  The increase in concentration then continued in most of these markets between 2022 and 2024.  In all of the markets the concentration was higher in 2024 than in 2020, and usually substantially higher.

One can also look at other sets of markets.  For example, 11 of the top 50 markets in 2024 saw market shares of the top 10 home builders in each accounting for more than 90% of the number of new homes built and sold.  A few were among the smaller markets, but there was also:

Chart 14

One again sees the sharp increase in concentration between 2008 and 2011 and then a further increase after 2020.

And in some other major markets:

Chart 15

The pattern is again similar.

Finally, the pattern comes out clearly in the simple average of the top 10 home builder concentration across all of the top 50 housing markets in the US each year:

Chart 16

There was a large increase in concentration following the 2008/2009 economic and financial collapse, concentration then leveled off at those higher levels for a period, and then it rose again following the 2020/2021 Covid disruptions.

Home building markets by metro area have become substantially more concentrated over the past two decades.  Fewer home builders are competing with each other in each metro area.  This will reduce competitive pressures.  While it is impossible to say what this might mean in absolute terms, what is relevant when looking at the impact on the pace of home building is what it means in relative terms over time.  As we will discuss in the next section, with greater concentration production will be less than it would have been had the home-building markets not grown more concentrated.

G.  Monopolistic Competition and Home Building

Markets for new homes are what economists call “monopolistically competitive” markets, and in this case one where entry of new firms is limited for some reason.  Such markets differ from what economists call “perfectly competitive” markets – markets that represent more of an ideal than what one will normally see (with a few exceptions).  In a perfectly competitive market, any supplier can sell all that he produces at some market price, and whatever amount he sells will have no observable effect on that market price.  There are a few markets like this, such as a farmer growing a standard commodity such as wheat or soybeans.  They can sell all the wheat or soybeans that they produce at the market price of that day and have no observable effect on it.  If they try to ask for a higher price than that, they will not be able to sell any, and there is no reason why they should be interested in selling at a price lower than that market price.

Homes, and most products in the modern economy, are different.  Take breakfast cereals as a simple example.  People have different preferences for different cereals from different brands, such as, for example, for Kellogg’s Corn Flakes.  Because of this, if Kellogg should choose to raise its price by some small amount, most of those now purchasing the cereal will continue to do so, although some might switch to a different brand or a different cereal (or even no cereal).  The fact that most consumers will still buy their Corn Flakes gives Kellogg some power to set prices where it chooses, a power that the wheat or soybean farmer does not have.  Kellogg will then choose to price its Corn Flakes at a level that it finds most advantageous – meaning most profitable.

In a simple, static, system, Kellogg will choose to adjust its price to the point where the revenues it loses from lower sales (at the margin) from a somewhat higher price exceed what it saves in lower costs (again at the margin) from having to produce less due to those lower sales.  That is, Kellogg will choose to price its product so that – at the consequent level of sales – its marginal revenues will equal its marginal costs.  And at that point, it will be earning a substantial profit.

This is all standard economics, as taught in an introductory Econ 101 course on microeconomics.  The Annex to this post works through this using standard supply and demand diagrams.

Homes are similar in that each one is different.  Not only do different home builders build different types of homes, with at least perceived differences in quality and style, but they also build those homes in different places in any metro area.  As any real estate agent will tell you, the three most important attributes in buying a home are location, location, and location.  And by definition, every home built will be in a different location – with advantages and disadvantages to any interested buyer – even if the lots are adjacent to each other.

Home builders will thus have some degree of power to set prices for the homes they build.  It is not absolute: If they price too high, they will not be able to sell any.  But in general if they raise their price by some amount they will still be able to sell, but not as much as before (or, more properly for an asset such as a home, it will take them a longer time to make the sale, while they are incurring carrying costs such as interest on the loans they took out to build it).  In such a monopolistically competitive market, they will be able to earn a substantial profit.

But the recent home building markets in the US then deviate from the standard model taught in Econ 101 classes for what will happen next.  In the standard Econ 101 classes, students are taught that the high profits being earned by existing firms in those markets will attract new firms to compete with them.  With that additional supply and competition, the excess profits that were first earned by the prior firms in the markets will be bid down, eventually to the point where no excess profits are being earned by any firms in those markets.  The final outcome will still differ in some important respects from that in the model of perfect competition, but the main assumption is that excess profits will draw in new firms to the point where there are no more excess profits.

The home building markets in recent years have not behaved in this way.  Instead of new firms entering the markets and thus making them less concentrated, the home building firms in those markets have been able to take an increased (not decreased) share of the relevant markets: the markets in each metro area.  Mergers and acquisitions in the sector have been described as “red hot” in recent years and this has been underway for some time.  In principle, enforcement of laws on competition should limit such consolidation, but the rules and regulations set by the federal government do not fit well with the conditions in the local markets of home builders.  To start, concentration in the home builder market is not great at the national level.  While the rules and regulations should in principle also apply in the smaller local markets, those are not always closely examined by national regulators.

Also important is that regulators do not focus on concentration at, for example, the top ten share.  They focus, rather, on the share of an individual firm in the relevant market, with a normal “rule” that no individual firm accounts for more than 30% of the market.  The assumption is that purchasers can easily switch to an alternative supplier from the 70%.  Markets with ten competitors would normally be considered highly competitive.  But there is not, in fact, such flexibility in purchasing a home.  Due to the importance of location and other factors unique to each home builder, purchasers do not have an effective degree of choice such as they would have in purchasing, for example, groceries at ten different supermarket chains.

But for whatever reason, concentration among home builders has risen in the relevant markets over the past two decades.  Relative to where it was in 2005, concentration in these markets are now all higher.  And when there is an increase in concentration in the market (from whatever level), the home builders operating in those markets will be able to earn an even higher level of profits than they were earning before.  They will be able to charge a higher price than before, and can adjust their prices (and the pace at which they build new homes) to take advantage of this.  This is shown with supply and demand diagrams in the Annex to this post.

Finally, when markets have become both more concentrated and the demand for housing has increased (as it will with a growing population), their profitability will grow by even more.  This makes intuitive sense as the limited number of home builders will see an increase in demand for what they produce, and is also shown diagrammatically in the Annex.

H.  Putting It All Together

The story is straightforward.  Local housing markets have become progressively more concentrated over the last two decades, with a small number of home builders accounting for higher shares of the relevant markets.  They have been able to limit competition from new firms entering these markets, and hence the builders have been able to earn exceptionally high profits without those profits being competed away by new entrants.  The lack of competition has also allowed them to function profitably even while they allowed their productivity to fall over time.

The result is that too few homes are being built.  Or to be more precise, the result is that home building has not kept up with the growing demand from an expanding population.  This became especially important following the economic and financial collapse of 2008/09, which was itself caused by the collapse of a housing bubble that had reached its peak in 2006/07.  The result has been the unprecedented increase in home prices.

This does not mean that new home prices might, in the short run, fall from their current heights.  As seen in Chart 1 at the top of this post, new home prices (in real terms) went dramatically up until the spring of 2022 and have since fluctuated around that high level.  The spring of 2022 was when the Fed began to raise interest rates from the lows they had brought them to during the Covid pandemic in 2020 and 2021.

As a result, 30-year US home mortgage rates – which had been below 3% from mid-2020 through most of 2021, rose to over 7% by late 2022 and into 2023..  As I write this, they are still at around 6 1/2%.  The higher mortgage rates mean that a purchaser who needs a mortgage will pay much more each month on that mortgage, even if the home price is the same as before.

This would normally lead to a reduction in home prices.  The fact that they have remained largely unchanged over the last three years is unusual, and can be explained by special factors.  One is that those with a low interest rate mortgage – taken out or refinanced when interest rates were low – will be reluctant to sell that home and move to a new one as they would then need to take out a new mortgage at the current much higher rates.  This has reduced turnover and increased rigidities in the housing markets.

But home prices might fall from their current heights at some point in the next year or two.  While the long-term trend for new home building has been down (Charts 4 and 5 above), there has been an increase since around 2012 as construction emerged from the depths of the 2008-2011 collapse.  This might eventually have an impact on home prices.

Such short-term fluctuations should not be surprising, and are in fact the norm for home prices.  But one should not confuse such short-term fluctuations with the long-term trend in home prices of the last few decades.  And that trend is up.

Before ending, I should mention an alternative argument for why home prices have risen by so much in recent years.  This argument puts the blame on local housing regulation, asserting that these regulations have become more stringent over time and are primarily responsible for the lack of adequate new housing being built despite the record high home prices.

These arguments have been made under the label of the “Abundance” agenda – a term that came from the title of the recent book of Ezra Klein and Derek Thompson (although they address more than just housing).  It is also behind what has been called the “Missing Middle” and similar terms.  The Missing Middle agenda is that home builders should be given the option to build higher density structures (e.g. small apartment buildings) on the existing land footprint of areas now occupied by single-family homes.

It is not my purpose here to address these arguments in full.  Local land use policies can certainly matter, and increased concentration of home builders in their local markets and changes in land use policies may both have had an impact on home prices.  But I do not see the basis for arguing that only local land use policies (and other increasingly costly or restrictive regulations) have been the cause of high home prices:

a)  If the constraint on the building of more new homes comes from restrictions on the use of available land, then the ones who will profit from this are not the home builders (who must purchase land for any new home construction, including for what is being built now) but rather the land owners.  That is, this would not explain why home building itself has become so highly profitable.  What economists call the “economic rents” here will be accruing to the land owners, not the home builders.

b)  One can see why owners of available land may welcome the chance to sell their lots for high density development.  They will be moving elsewhere, and it will be those who continue to live in the neighborhood who will bear the costs of greater congestion and pressure on public infrastructure, and have to live with fewer trees and other green space in their neighborhoods.  The benefits of a pleasant neighborhood are basically an externality produced by all the lots in the neighborhood.  Converting the first lot to a high density structure will reduce that marginally.  But as more and more are converted, the value of that externality will be steadily reduced and property values will go down.

c)  This may well lead to lower home prices in the neighborhood, both due to the greater supply and due to the neighborhood not being as pleasant as before.  Homeowners who have not moved will bear that cost.  But this is basically a zero-sum (indeed possibly negative-sum) game:  The benefits to those now able to move in at a lower cost (and those who sold their lots and moved away) will be offset by the losses of those who had lived and remain in the neighborhood.

d)  An alternative approach would be to follow a transportation (or transit corridor) oriented development policy.  Rather than placing high density structures into the middle of low density neighborhoods (where the newcomers will need to rely on cars to get around), development should be directed to neighborhoods built up along transit corridors.  The transit corridors could be rail lines in certain cases, but more commonly various levels of bus service from standard up to express or bus rapid transit services.  There is substantial low density commercial development (surrounded by large expanses of surface parking lots) around all American cities.  Diverse neighborhoods could be developed on such land, with the highest density close to the main transit stops and lower density as one goes further away.

As noted, land use constraints – either by changes in land use regulations or simply a matter of space being used up as cities have grown – may be a contributing factor to higher home prices.  But they do not explain why home builders have been so highly profitable.  More fundamentally, if land use constraints were the primary cause of the higher home prices now observed, one would expect this to have led to a gradual but steady increase in home prices over several decades, rather than the sharp jump observed more recently.  Residential assets had risen on a steady trend up to around 2007 (Chart 6 above).  The question is what caused the deviation from this trend that began in 2008 and was then sustained.  The observed increase in market concentration of home builders in individual metro areas after 2005 can explain this.

A natural question is what to do now in terms of policy.  That has not been the focus of this post, where the aim was to examine what has led to our current very high home prices.  Nor are there any easy answers.  But a few points can be made.

First, as the proverb says:  “When you’re in a hole, the first thing to do is stop digging”.  Home building has become a substantially more concentrated industry in individual local markets in recent decades, and more serious enforcement of competition policy could stop this from getting worse.  That should be done.  It will be more difficult to unwind this to return to the less concentrated markets of the past, but measures might be possible to encourage greater competition between home builders.  Signs of collusion should be monitored.

Beyond this, government has a direct role to play in developing and expanding transportation corridors where new, diverse, neighborhoods can be developed (with a mix of high, medium, and low density).  New housing would be built and would add to available supply.  Development of such corridors depends on public investment, primarily in the development of suitable public transit options (which can vary, as noted, from bus service at an appropriate standard to rail options).  Government plays a direct role in making such development possible.

The bottom line is that there is a need to ensure more housing is built.  Transit-oriented development can be a key part of this.  Government can play an important role here and needs to.

 

Annex:  Supply and Demand Curves Under Monopolistic Competition

Firms (such as home builders) can make substantial profits under conditions of monopolistic competition.  And those profits can be sustained if the entry of new potential competitors is limited for some reason.  Furthermore, under such conditions the profitability of the home builders will increase if the markets become even more concentrated (with a small number of home builders accounting for an increasing share of the relevant markets), and even more so if demand is also growing.

This annex will back up each of these propositions via standard supply and demand diagrams, the same diagrams that anyone would be taught in an introductory Econ 101 microeconomics course.  They will be built up in steps, starting with the most simple situation (the assumption of perfect competition) and moving from there by steps to the more complex.  In the end, the shifts in the supply and demand curves may look complicated, but they in fact simply reflect a step-by-step buildup.

Note also that this supply-demand diagram (and the subsequent ones below) are for what an individual firm faces.  While such diagrams are sometimes used to depict conditions in a sector as a whole, that is not the use here.

Economists start with the assumption that the firm operates in a market of perfect competition.  This is not because such markets are common or even realistic, but rather because they provide a starting point as a basis of comparison.  As discussed in the text, under perfect competition a producer can sell all that he wishes to produce at a certain market price, and whatever he sells will not affect that price.  One can find such markets in cases such as farmers selling a standard commodity (e.g. wheat or soybeans).  In such markets, producers will choose to produce and sell up to an amount where their marginal cost of producing the good will equal that market price.

In cases where products are differentiated for any reason (e.g. brand identity, differences – actual or perceived – in what the product actually provides or in quality, and for any other reason), the producer has some power to set the price at which they will sell their product.  If they raise their price by some amount, the total amount they can then sell may go down (and likely will go down) by some amount, but not immediately to zero.  Thus they have some degree of flexibility to decide what price to charge for their particular product (such as a new home of a certain design and quality in a particular location).

The situation is then depicted in the following supply and demand diagram:

Chart 17

First, if this were in fact a perfectly competitive market, the producer would choose to produce a quantity Q0 which it could sell at a price P0:  that is, at point A in the diagram.  Their marginal and average costs of production are assumed to follow the curves shown (rising with increasing production after some point).  The demand curve they face (not explicitly shown) would be a horizontal line at price P0 – the market price they face which they cannot affect through how much they choose to sell.  Since they can receive price P0 for whatever amount they offer, they will choose to produce and sell as long as their marginal cost of production is less than the price at which they can sell it, and thus will produce Q0.

The firm being depicted here will also be making a profit when they produce quantity Q0 that they sell at price P0 (i.e. at point A in the diagram).  Their average cost of production is less than their marginal cost at that point, and the profits they would then be earning would be the quantity produced Q0 times the difference between the price they receive P0 and their average cost at that level of production AC0.  In general, both the average cost and marginal cost curves will be rising at that point, with the marginal cost curve above the average cost curve.  Indeed, the marginal cost curve will pass through the lowest point of the average cost curve, since average cost will be falling as long as the marginal cost is below it and rising as long as the marginal cost is above it.

When the firm operates in a market with product differentiation, in contrast, the demand curve they will face is not horizontal (at price P0), but rather some downward sloping curve such as the one depicted here as D1.  For simplicity, it is drawn as a straight line, but in general it can be any curve that slopes downward throughout.  The demand curve shows how much they will be able to sell in a period for any given price.  Or put the other way, it shows what price they will be able to obtain for any given quantity that they choose to provide.

Their decision on how much to produce and at what price now differs from the case of perfect competition.  What matters now is what revenue they will earn – at the margin – at any given level of production (with the associated price they can charge at that level of production).  If they scale back production by some amount, they will be able to charge and receive a higher price.  Or put the other way, if they choose to charge a higher price, the amount they will be able to sell will be reduced by some amount.

The average revenue they will earn for sales of any given quantity will simply be the price they can get at that level of sales (i.e. what is shown on the demand curve).  Hence the demand curve can be referred to as the average revenue curve.  But the marginal revenue they will earn when they charge a higher price will be less than that price since the quantity they can sell will be less.  Hence for any given quantity along the horizontal axis in the chart, the marginal revenue curve will be below the average revenue curve.

And that is all that we need to know.  In the special case where the demand curve is a straight line, one can easily show (as is always done in the introductory Econ 101 microeconomics class) that the marginal revenue curve will also be a straight line with a slope that is twice the negative slope of the demand curve (average revenue curve).  This is a result of some elementary calculus that will not be repeated here.  For the purposes here, all one needs to understand is that the marginal revenue curve will be uniformly below the associated demand (average revenue) curve.

A firm facing such supply and demand conditions will then choose to scale back production to the point where their marginal cost of production will equal the marginal revenue they will earn from that production. That is, they will not remain at a point such as A, as at that point their marginal cost is higher than the marginal revenue that they earn at that level of production.  (In the perfect competition case, where the demand curve they face is not the D1 curve shown in the diagram but rather a horizontal line at price P0 – as noted before – their marginal revenue curve will also be a horizontal line at that same price P0.  The slope of the demand curve is zero, and the slope of the marginal revenue curve – which is double that of the demand curve – will also be zero as double zero is still zero.)

Producing a quantity Q0 for sale at price P0 will therefore not be as profitable to them as scaling back production to Q1, where their marginal cost is no longer higher than the marginal revenue they can earn but rather equal to it.  This is point B in the diagram.  Or going from the opposite direction, they will expand production as long as the marginal revenue they earn at that level of production exceeds their marginal cost of producing it.  And they will stop expanding at the point where their marginal cost becomes equal to their marginal revenue.

When they are producing at point B with quantity Q1, their average cost of production will be at point C with cost AC1.  And they will be able to sell their output at point D on the demand curve, i.e. at price P1.  Their profits will then be equal to quantity produced Q1 times the price they will receive P1 minus their average cost AC1, i.e. the area shown in the box in light blue in the diagram.  They are producing less than they would in a situation of perfect competition, but they are receiving a higher price and their average cost will be less.  Since their marginal revenues are below their marginal costs for production above that point, scaling back production to Q1 from what it would be under perfect competition will always be more profitable for such firms.

[And as a point of clarification:  The particular way I drew the diagram here has the marginal revenue curve MR1 intersecting the quantity-axis in the chart at the same point as quantity Q0.  This is a coincidence, and will not in general be the case.  It happened here as I drew the initial point A at a center-point in the diagram – six units on each axis – and the demand curve as a 45-degree line.  The quantity Q0 will then be at the same point where the MR1 curve hits the axis.  This will not in general be the case, but I did not want to redraw all the charts.]

Starting from this, one can then look at what will happen to the firm’s choice on how much to produce (and the impact on its profitability) if the market should become even more concentrated.  This now deviates from the standard textbook treatment of monopolistic competition, in that in the standard treatment, it is assumed that the high profit the firm is able to earn (shown as the box in light blue in the chart above) will attract new competitors.  The new competitors will add to production in the market, which will lead the prices to be bid down and possibly increase costs for all (as they compete to buy some of the inputs needed in production).  This will reduce profits for the firms, and it is assumed (in the standard treatment) that new entrants will continue to come in as long as exceptional profits are being made.

But the home building industry has become more concentrated rather than less in the relevant local markets for new homes, as discussed in the text.  And by being able to increase concentration in those markets, home builders will become even more profitable than before.

This is shown in this second supply and demand diagram:

Chart 18

In a more concentrated market, the home builder depicted here faces less competition than before.  Should he raise his price, the amount he will be able to sell will still be less, but not as much less as before.  With fewer competitors for the purchaser to turn to, the firm will be able to keep a higher share of its customers (should they raise their prices) than would have been the case had market concentration not increased.

The result is that the demand curve for the firm will “twist” clockwise relative to where it was before – i.e. become steeper.  Their demand curve will now be the one in green (D2) rather than the one in blue (D1).  The associated marginal revenue curve will similarly twist to MR2 from MR1.  Their profit maximizing point will be where their new marginal revenue equals their marginal cost, and this point will have shifted to the left, with production now at Q2 rather than Q1.  (I left out letters to label the intersection points as the chart would have been too crowded with them.)  With lower production, the associated average cost AC2 will be below the prior AC1.  And the price they will be able to charge will now be P2 – above the prior P1.  Prices of new homes will be higher.  Profits will be higher as well, and are shown as the box in green in the chart.

Finally, if there is an increase in demand over time while the home building market is becoming more concentrated, new home prices (and profits) will grow by even more:

Chart 19

In this comparison, both concentration among home builders in the local market and the demand for homes in that market have increased.  Due to the growth in demand, the demand curve has shifted to the right from D2 to D3.  Production would rise from Q2 to Q3, i.e. to where the marginal revenue curve MR3 intersects the marginal cost curve. The average cost AC3 will be higher due to the rising average cost curve.  But the price will be substantially higher, rising to P3 from P2.  The firm’s profits will now grow to the area shaded in pink.  They can be much larger.

It is worth noting that while production will have gone up (from Q2 to Q3), that increase in production is less than the growth in demand.  The increase in demand can be measured by how much higher demand would have grown to at a constant price (the starting price of P2 – although this does not matter in the simple example here of a straight line demand curve shifted out by the same distance at all prices).  With a rising marginal cost curve as well as a falling marginal revenue curve, the increase from Q2 to Q3 will always be less than the distance that the demand curve has shifted at the original price of P2.  Or put another way, demand is constrained to grow from Q2 to Q3 rather than what the increase would have been at a constant price, by the producer raising the price from P2 to P3 in order to raise production and sales only to the point where his marginal revenue is equal to his marginal cost (i.e. only to Q3).

Note that with the growth in demand and an unchanged average cost curve, the average cost will go up (from AC2 to AC3).  This could be due to lower productivity at the higher demand (due, for example, to inadequate investment), but this could in principle be due to other factors as well.

The Impact of Covid-19 on Mortality

Chart 1

Chart 2

A.  Introduction

As a diversion from the more strictly economic posts usually on this blog, this post will examine data that can be used to better understand the impact the Covid pandemic had on US mortality rates.  The Social Security Administration provides figures each year on historical mortality rates as part of the legislatively mandated Annual Report of the Board of Trustees of the Social Security Trust Funds.  The 2025 Trustees Report was released on June 18, and as part of the background material, it provides mortality rates by year of age (for males and for females) for the year 2022.  Prior Trustee Reports provide the figures for earlier years (always with a three-year lag).  Comparing the mortality rates of one year to another allows us to see the impact of an event such as the Covid pandemic.

The charts above show the probabilities of dying within a year in 2020 (the first year of Covid) for someone of a given age compared to what it was on average over 2016 to 2019.  The gap between the respective curves is a measure of the impact of the special circumstances of 2020 compared to what would have been expected based on past experience.  One can look at such figures in different ways, which provide alternative perspectives.  As will be discussed below, in terms of the absolute difference in mortality rates (i.e. in percentage points), the impact was greatest for the elderly.  In terms of the relative difference in mortality rates (i.e. as a percentage of what they were in prior years), the impact was greatest on those in middle age – in their 30s and 40s.

The data can also be used (together with data from the Census Bureau on population by age) to calculate the number of “excess deaths” due to the special circumstances of 2020 (and similarly for 2021 and 2022).  This impact will depend on the combination of the greater likelihood of dying combined with the population in each age group.  We will see that from this perspective, the impact (the number of excess deaths) was greatest for those in their 60s to their early 90s.

Also of interest (and indeed what first led me to look at these patterns) are the basic figures on mortality rates themselves by year of age.  Before seeing such figures, I would have guessed that mortality rates did not rise by all that much between the ages of 20 and 60 or so.  After that they would be higher, and I would have guessed progressively higher at an accelerating rate for those who were older.

But they do not follow such a pattern.  Rather, while the mortality rates rise with age, they rise at a remarkably steady rate from around age 20 to age 70 – basically doubling with each decade of life.  They then accelerate for those in their 70s to 90s (roughly tripling with each decade, up from doubling), before decelerating – although still increasing – for those older than around 95.

I found this pattern remarkable.  While I am an economist and not a biologist, I suspect that this pattern (with mortality rates doubling each decade up to around age 70), reflects something profound in how our biological systems function.

This pattern of mortality by age will be discussed in the first section below.  The section following will then look at charts similar to that for 2020 above but for the 2021 and 2022 figures.  The same basic pattern holds.  While deaths due to Covid diminished in 2022, they remained significant in that year (based on CDC estimates on deaths due to Covid), before dropping sharply in 2023 and by more in 2024.  The section will examine the percentage and percentage point differences in the mortality rates by age, focusing on the 2020 data.  It will then look at excess deaths by year of age in 2020 – as well as the totals for 2021 and 2022 – compared to what they would have been at the 2016-2019 average mortality rates.  The estimates calculated here of excess deaths in 2020 – and especially in 2021 and 2022 – are remarkably close to the CDC estimates on deaths due to Covid in those respective years.

An annex to this post will then briefly examine material from a presentation by Sir David Spiegelhalter on the impact of being infected with Covid on death rates.  He compared them to pre-Covid death rates by age (based on UK data).  It was this work by Spiegelhalter that led me to look at US data on mortality rates.

Spiegelhalter found that the death rates by age were similar to the death rates of those infected with the virus that causes Covid.  That is, for those infected by the virus, the likelihood of dying due to Covid was similar to the likelihood of dying (pre-Covid) due to any cause within a year.

This was then grossly misinterpreted in the press.  As Spiegelhalter noted to his great dismay, instead of recognizing that this evidence pointed to a Covid infection as doubling the likelihood of dying within a year, the chart was interpreted by some in the press as saying the likelihood of dying within a year was the same whether or not one had come down with Covid.

The episode illustrates well how basic (and in this case highly important) statistics can be easily misinterpreted.

B.  Mortality Rates by Age

To start:  consider the basic pattern of mortality by age.  For males and for females, and using the 2016-2019 average (although any year could have been used for illustrating the basic pattern), the figures are:

Chart 3

The chart shows the probability of dying within a year for someone of a given age, with the vertical scale in logarithms.  It reaches a trough of around just 0.0001 ( = 0.01%) at around age 10 (following substantially higher rates as an infant, and especially for those in the first year after birth).  It then rises until the probability approaches 1 at the upper end.  The Social Security figures go all the way out to 120, but this is a modeled extrapolation as few are alive beyond age 110.

The death rates for males are uniformly above those for females.  They are especially higher at around age 20, and then remain higher (although at a diminishing proportion) until the age of 100 or more.  The higher rates for males are in part due to higher deaths for males from accidents, violence, and suicides.  For this reason, it is better to focus on the female rates to get a sense of the basic biological processes leading to the observed mortality rates.

As one may remember from their high school math, a straight line in a chart with a vertical scale in logarithms will follow a constant rate of growth, with the slope of that line giving the rate of growth.  In the chart above, the mortality rate for females rises at a remarkably steady rate (i.e. along a straight line) from age 20 to around age 70.  From the underlying mortality figures by age, one can calculate that the rate basically doubles (i.e. increases by about 100%, plus or minus around 20%) every decade over that age span.  The rate accelerates (the slope becomes steeper) from age 70 to around 95 – roughly tripling with each decade rather than doubling – after which it slows (as eventually it must:  the probability can never exceed 100%).

The mortality rates start, of course, at very low levels.  As noted above, the rate of dying within a year at age 10 (males or females) is only 0.0001 ( = 0.01%).  For females at age 20, it is around 0.0004 ( = 0.04%).  It then doubles to 0.0008 ( = 0.08%) at age 30, almost doubles again to 0.0014 ( = 0.14%) at age 40, basically doubles again to 0.0031 ( = 0.31%) at age 50, again to 0.0069 ( = 0.69%) at age 60, and again to 0.0150 ( = 1.5%) at age 70.  The pace then rises (roughly tripling with each decade) between ages 70 and 95 before slowing down.

The mortality rates before age 70 are all low, of course.  But it is interesting that they basically double each decade from age 20.  I suspect that this represents something fundamental about how biological systems function.

In any case, we can now look at how those mortality rates were affected by the special circumstances of 2020 (as well as in 2021 and 2022) during the height of the Covid pandemic.  Mortality rates rose, but far from uniformly by age.

C.  Mortality Rates in 2020, 2021, and 2022

Charts 1 and 2 at the top of this post show what mortality rates were according to age for males and females, respectively, in 2020 compared to the average over 2016 to 2019.  The charts are similar for 2021 and 2022:

Chart 4

Chart 5

The gap between the lines shows the impact of the special circumstances of the respective years relative to pre-Covid mortality rates.  One can look at this gap in different ways.  Most commonly, the differences in the mortality rates have been shown in absolute terms (i.e. in percentage points).  That impact is greatest for the elderly.  For 2020 compared to the 2016-2019 average rates (the basic pattern is similar for 2021 and 2022, although at a different level):

Chart 6

The increase in mortality rates in 2020 relative to what they were before was far higher in absolute terms (i.e. in percentage points) for the elderly than for the young.  They were also consistently higher for males than for females.

And the differences by age are huge.  Keep in mind that the impact on mortality depends on a combination of the likelihood of being infected by the virus that causes Covid, and the likelihood that one will die if infected.  Based on these figures for 2020 compared to the average mortality rates between 2016 and 2019, the increase in mortality of males at age 90, say, was 1.95% points.  The increase for males at age 20 was, in contrast, 0.028% points.  That is, the increase was 70 times higher for males at age 90 compared to those at age 20.  For females, the increase was even greater:  almost 250 times higher for those at age 90 compared to those at age 20.  The impact of the 2020 events on the elderly was huge.

A different way to look at the figures is in terms of the percentage increase in mortality rates for someone of a given age.  For 2020 compared to the 2016-2019 averages (where again, the basic pattern is similar for 2021 and 2022):

Chart 7

In terms of the relative increase in mortality rates, the impact of the 2020 events was highest for those between the ages of 20 and 50 (along with a peak at ages 10 and 11 for males).  The absolute increase in mortality rates for those in this age range was not high (as seen in Chart 6).  But relative to the normally small probabilities of dying for those who are young or middle-aged, the relative increase was greater than for the elderly.

From this, coupled with figures from the Census Bureau on the US population for each year of age, one can calculate the number of “excess deaths” arising due to the special circumstances of 2020 (Covid and its indirect as well as direct effects), compared to what the mortality would have been at the mortality rates of prior years (where the 2016 to 2019 average was used as this base).

For 2020, the excess deaths by year of age were:

Chart 8

The impact was largest on the elderly, and especially so for females more than for males.  Up to age 15 or so, there was almost no impact.  Indeed, the data indicate that for those in their first year of life, excess deaths were substantially reduced.  There were then very small effects – some positive and some negative – up to age 15.

Adding up the number of excess deaths across all ages – and with similar calculations for 2021 and 2022 – leads to:

Calculated Excess Deaths CDC Covid Deaths % difference
2020 418,076 385,676 8.4%
2021 467,992 463,267 1.0%
2022 245,081 247,196 -0.9%
2020 to 2022 1,131,150 1,096,139 3.2%

The CDC estimates of deaths due to Covid by year are shown in the second column of the table.  The two estimates turn out to be remarkably similar, especially for 2021 and 2022 where they are within +/- 1% of each other.  And the methodologies are completely different.  The CDC figures are based on death certificate data reported to it for its National Vital Statistics System.  The excess death figures here are calculated from a comparison of mortality (by year of age) in each year compared to what the mortality rates were on average between 2016 and 2019, applied to Census Bureau figures on the US population by year of age in each of these years.

This surprising congruence might be a coincidence – although the figures are extremely close in two of the three years so it would have to be an extreme coincidence.  And while this is speculation, the calculated excess deaths figure in 2020 – which is 8.4% higher than the recorded number of Covid deaths in that year – might reflect the special circumstances of the health care system (and especially of hospitals) in that year.  Due to the rapidly spreading virus that year and with no vaccine yet available, hospitals were crowded with patients being treated for Covid.  One avoided going to a hospital except under dire circumstances.  That led to patients with conditions that would have benefited from being treated at a hospital avoiding such care when they would have benefited from it, and consequent higher death rates.  While not a direct consequence of being infected with the virus that causes Covid, their higher mortality would have been an indirect consequence.

This indirect impact of Covid then largely went away in 2021 and 2022, as the Covid vaccine led to lower case loads, less hospital overcrowding, and less reason to avoid going to a hospital when needed for non-Covid reasons.

D.  Conclusion

The Covid-19 pandemic was a tragedy.  Over 1.2 million Americans have died, which is more than double the number of American soldiers who have died in combat in all of the country’s wars since 1775.  The Trump administration terribly mismanaged the response to the then spreading pandemic in the first half of 2020, with Trump asserting that his bans on travel – first from China, later from Europe and elsewhere – would stop the spread of the virus and that it would soon “go away”.  It did not.  And by his statement that he would not wear a mask in public despite the CDC recommendation to do so, Trump made the refusal to wear a mask into a sign of political fealty.  This later carried over into a refusal to be vaccinated.

This had real consequences.  People died.  And they died at higher rates in proportion to the share of the vote in a state for Trump.

Management of the Covid pandemic would have been difficult by even the most capable of administrations.  But it was not capably managed in the US.  As a reasonable comparator of what should have been possible, one can consider the case of Canada.  Deaths from Covid in Canada were 1,538 per million of population (as of April 2024, when cross-country comparable data collection stopped).  For the same period, it was 3,642 per million of population in the US:  2.4 times as high as in Canada.  If the US had had the same mortality rate as Canada, deaths would not have been 1.2 million (as of the end of 2024), but rather about 500,000.  An additional 700,000 Americans would be alive today.

The virus that leads to Covid will now be with us for the foreseeable future.  The peak number of deaths came in 2021 as it was the first full calendar year when the virus had spread to all parts of the nation.  In 2020, there were very few cases nationally until mid-March, and it did not spread to all corners of the nation until several months later.  Vaccines became available in 2021, but were in short supply for most of the first half of the year.  And even when fully available without restriction, a substantial share of the population refused to be vaccinated.

But with the vaccinations in 2021, as well as the immunity obtained by those who were infected by Covid at some point and survived, the number of deaths from Covid fell by almost half in 2022 to 247,000 in the CDC data.  See the table above.  Deaths then fell further to 76,000 in 2023 and to 47,500 in 2024.  Deaths in the coming years will likely be in the tens of thousands each year, similar to the pattern seen for deaths due to the influenza (flu) virus.  On average, about 30,000 have died each year since 2011 from influenza, but this has varied widely from a low of 6,300 in the 2021/22 season (when measures taken by many to limit exposure to Covid also served to limit exposure to the flu virus) to a high of 52,000 in the 2017/2018 season.  Deaths from Covid-19 may be similar in the coming years, with a good deal of variability and levels that depend on measures such as how many will be vaccinated each year against the evolving variants of the virus.

Mortality rates will also vary by age.  While the variation by age may be moderating, it is likely that the elderly will remain the most severely affected in absolute terms (as in Chart 6 above).  However, one should still recognize that in relative terms (relative to mortality rates at the given age), those in middle age may well remain the most affected (as in Chart 7 above).

Covid-19 was a tragedy.  There is, unfortunately, little indication that the mistakes that were made in the management of it will not be repeated when the next pandemic comes.

 

——————————————————————————————————-

Annex:  Mortality Rates by Age from Infections by Covid Compared to Pre-Covid Mortality Rates from All Causes 

The impetus for this post came from a chart I saw in a video of a November 2021 lecture by Sir David Spiegelhalter (a professor at the University of Cambridge, and on the board of the UK Statistics Authority).  See the section starting at around minute 18.  Based on very early (March 2020) UK data on the Covid fatality rate (later confirmed with much more data), he showed that the fatality rate if infected by Covid was similar for any given age as that of dying for any reason (before Covid began to spread) at that age.  The chart, with the vertical scale in logarithms, was:

Chart 9

Two points to note:

a)  Leaving aside the impact of a Covid infection, the mortality rate (from all causes, pre-Covid) in this UK data rises with age (from around age 10) at a remarkably steady rate through to age 90.  As noted in the post above, I was surprised that the pace at which mortality increases with age is close to the same for those in their 20s and 30s as it is for those much older.

b)  The mortality rate of those infected with Covid was basically the same as the mortality rate pre-Covid.  That is, being infected with Covid basically meant that the mortality rate doubled for any given age.  Note that this is not the same as what is in Chart 7 above, which shows the increase in the mortality rate for any given age in 2020 during the Covid pandemic.  Those increases were in the range of 20 to 30% for those in their 30s and early 40s, declining to 10 to 15% for the elderly.  It is not the same in Chart 7 because not everyone caught the virus that causes Covid in 2020 (and was also US rather than UK data, although this was probably not a factor).  Chart 9 from Spiegelhalter shows mortality rates from Covid for those who were infected with the virus that causes it.

Spiegelalter’s finding was then profoundly misunderstood.  Some in the British news media were soon citing this as “evidence” from a highly esteemed scholar that said (as in a headline in The Sun newspaper):  “Your risk of dying is NO different this year – despite coronavirus epidemic, says expert”.

Spiegelhalter was not saying that at all.  He had thought the correct interpretation would be obvious, but clearly it was not.  The chart is saying that, at each age group, the likelihood of dying if infected with the virus that causes Covid is close to the same as dying at that age for any reason (pre-Covid).  Hence, if you have been infected by Covid, your probability of dying that year has doubled.  As Spiegelhalter later noted, this is a good example of how statistics can be easily misinterpreted.

(After Spiegelhalter complained, the Sun changed the title to:  “Your risk of dying from coronavirus is roughly the same as your annual risk, says expert”.  A bit better, but still easy for readers to misinterpret.)

Larry Hogan’s Purple Line Fiasco: A Case Study in Poor Judgment, Poor Management, and Poor Decision-Making

Paris Metro Line 14 Tunnel:

 

Maryland Purple Line Light Rail – Route adjacent to roadway:

A.  Introduction

Few dispute that the management of the Purple Line project has been a fiasco.  Construction costs that are now well more than double what they were in the original contract and a forecast ten years (at least) for construction rather than the originally planned five, are just two of the more obvious problems with what has been a poorly planned and managed project.  As the governor of Maryland at the time, Larry Hogan signed the contracts that launched the project (and then the contracts that re-launched the project when the initial contractor walked away), and must bear responsibility for the consequences.

The Purple Line is a 16.2 mile light rail line that will arc north and east outside of Washington, DC, through the Maryland suburbs from Bethesda on the west to New Carrollton on the east.  It was supposed to be built at a fixed price in what was called an “innovative” PPP (Public-Private-Partnership) contract, where the risk of any cost overruns was to be the responsibility of the private concessionaire.  That has not been the case.

The cost – even the original cost – is also high for what the State of Maryland is getting.  The Purple Line is a relatively simple and straightforward light rail line of two parallel tracks built mostly at ground level, along existing roadways or over what had previously been public parks.  Yet, as we will see below, its cost per mile is comparable to that of a heavy rail line recently completed in Paris.  That rail line – a major extension of Line 14 of the Paris Metro – is entirely in tunnels and has a capacity to handle more than 14 times the number of passengers the Purple Line is being built to carry.  The photos at the top of this post show a portion of Paris’s new Line 14 in comparison to the simple tracks along the side of the road of the Purple Line.

The high cost of the Purple Line is, however, only one aspect of a terribly mismanaged program.  Despite years, indeed decades, to prepare, there have been repeated delays by the Maryland Transit Authority (MTA, part of the Maryland Department of Transportation – MDOT) in fulfilling its design and other responsibilities.  This increased what were already high costs.  The MTA has been responsible for the supervision of the contract, the basic design work, as well as the acquisition of the required land parcels along the right-of-way and arranging for the movement of utility lines along that right-of-way.  It has repeatedly failed to complete this on a timely basis, leading to delays in the work the construction contractor could do and consequent higher costs.

This led the original contractor responsible for the work to walk away, where a poorly designed PPP contract made that easy for them to do as they had little equity invested in the project.  The total now being paid by the State of Maryland for the construction phase of the project is $4.5 billion – an increase of $2.5 billion over the originally contracted cost of $2.0 billion.  And that is assuming there will be no further increases in the cost, which has happened multiple times already.  The time required to build what should have been a relatively straightforward project is also now expected to be ten years – if it is completed under the current schedule in late 2027 – rather than the originally scheduled five.

Then Governor Hogan sought to blame a citizen lawsuit opposed to the project for these delays and higher costs.  But that is nonsense.  The lawsuit argued that the project had not been adequately prepared, and the judge agreed, putting a hold on the start of construction for the project while calling for the State of Maryland to undertake further work on the justification for the project.  The judicial order was issued in August 2016, construction had been scheduled to start in October 2016, and then started instead in August 2017 after the appeals court decided that deference should be given to the State of Maryland on this matter.  The construction contractor, in their formal letter stating they would withdraw from the project, said this delayed the start of construction by 266 calendar days:  less than nine months.  That cannot account for an extra five years (i.e. ten years rather than five) to build the project from when construction began in 2017.

Indeed, if anything the extra nine months provided to the MTA to prepare the project should have reduced – rather than lengthened – the time required to build the project.  The MTA continued to work on the design of the project, as well as on the land acquisition and utility work needed, during the nine months when construction was on hold.  Yet despite that extra nine months to prepare, the MTA was still not able to keep up with the schedule.  The letter of May 2020 formally notifying the MTA that the original construction firm intended to withdraw stated:

“MTA was late in providing nearly every ROW [right-of-way] parcel out of the original 600+ parcels it was required to acquire … by more than two years in some cases”.

Construction obviously cannot begin on some portion of the line until they have the land to do it on, yet the MTA was consistently late in providing this despite the extra 9 months they had due to the judicial order.

And those MTA delays have continued.  Under the revised contract for the new construction firm, compensation is paid for any further delays resulting from the MTA not fulfilling its responsibilities.  Payments from the State of Maryland have been made twice under that provision – in July 2023 and in March 2024 – totaling $563 million thus far.  That is equal to 29% of what was supposed to be the original cost of building the entire rail line.

The problems have not been just with the construction.  There were major problems also with the assessments that were done to determine whether the project was the best use of scarce funds to serve the transit needs of these communities.  Issues in two key areas illustrate this.   One was with the forecasts made of what the ridership on the Purple Line might be (where expected ridership is fundamental to any decision on how best to serve transit needs, what the capacity of that service should be, and whether the expected ridership can justify the costs).  The second was the assessment of the economic impacts to be expected from the project.

But a close examination of the work done on those two key issues often shows absurd results that are simply impossible – mathematically impossible in some cases.  I have looked at this in prior posts on this blog (see here and here), and will only summarize below some of the key findings from those analyses.  The official assessments of whether the Purple Line was warranted were simply not serious.  A moderately competent but neutral professional could have pointed out the errors.  But none was evidently consulted.

As the governor at the time, Larry Hogan was responsible for the decision to proceed with the project.  While there is no expectation that he should have undertaken any kind of technical review himself of whether the project could be justified, he should have insisted that such assessments be made and that they be honest.  And he should have listened to neutral professionals on the adequacy of the assessments.

Rather, it appears Maryland state staff were expected to approach the issue not to determine whether the Purple Line was justified, but instead to find a way to justify a decision that had already been made and then to get it built.

Staff were especially praised by then Governor Hogan in 2022 when the contract was re-negotiated with a new construction firm (at a far higher cost than the original contract – see below).  As recorded in the transcript of the meeting of the Maryland Board of Public Works in January 2022 at which the new contract with the concessionaire was approved, Governor Hogan said:

“So I’m very proud of the team that in spite of incredible — it’s a huge project and it has incredible obstacles. But they have kept pushing, you know, moving the ball forward no matter how many times there was a setback from outside. It’s not the fault of anyone in any of these positions. They kept moving.”

Indeed, the more costly the project became, and thus the less justifiable it was as a use of scarce public funds, the more the staff were praised for their skill in nonetheless being able to keep pushing it forward.

To be fair, Governor Hogan was not the first Maryland governor to favor building the Purple Line.  Governor O’Malley, his predecessor and a Democrat, favored it as well.  But Governor Hogan made the final decision to proceed when the high cost was clear.  He signed the PPP contract and he is ultimately responsible.

Hogan is now running to represent Maryland in the US Senate as a Republican.  Should he win, he could very well represent the key vote to give Republicans control over what is expected to be a closely divided Senate.  While Hogan has stated he will not personally vote for Donald Trump to be president, that is not the vote of Hogan that will matter.  Rather, a Republican-controlled US Senate – possibly as a consequence of Hogan’s vote for a Republican as the Leader – could allow Trump, should he become president, to carry through his radical program with his openly stated aim of revenge.  Senate approval of Trump’s judicial and other appointees could also then be easily granted.  And if Harris should win the presidency, a Republican-controlled Senate could block much of what she would seek to achieve.

The experience with the Purple Line provides a good basis for assessing Hogan’s judgment.  It merits a review.  We will examine below first what the Purple Line is costing to build, starting with the original 2016 contract and through to now.  The line is not yet finished so the costs may rise even further, but we will examine the contracted costs as they stand now.

The section following will then compare those costs to the cost per mile of building a major extension to the Paris Metro Line 14 heavy rail subway line.  That extension was recently opened (in June 2024, just prior to the Paris Olympics this past summer), so we have good figures on what the cost was.  It was built entirely as a tunnel – which would be expected to be far more expensive than laying rails at ground level – and has a capacity to carry more than 14 times the number of passengers the Purple Line has been designed for.  But its cost per mile is only about a third more.  The cost per unit of capacity is more than ten times higher for the Purple Line than for the new Paris Metro line.

Sections will then briefly review the problems in the analyses of forecast Purple Line ridership and of its economic impacts.  These have been covered in previous posts on this blog, so only the highlights will be summarized here.  Finally, issues with the design of the PPP contract and the incentives it created will be briefly reviewed, along with Maryland governance issues.

B.  The Purple Line’s Construction Cost

1)  Cost

The cost of the Purple Line PPP contract first to build and then to operate this light rail line is summarized in the following table, with figures both for the original contract (signed in 2016) and for the contract as it stands now (most recently amended in March 2024):

Purple Line Cost
   in $ millions

Original Contract

New Contract

$ Increase

% Increase

A. Construction Costs $1,971.9 $4,467.8 $2,495.9 126.6%
Design-Build Contract $1,971.9 $3,435.8 $1,463.9
Settlement    $250.0    $250.0
MTA supervised work    $218.7    $218.7
Compensation for MTA Delays: July 2023 *    $148.3    $148.3
Compensation for MTA Delays: March 2024 *    $415.0    $415.0
B.  Operational Period $2,306.0 $2,613.7    $307.7 13.3%
Operations & Maintenance $1,744.3 $1,977.2     $232.9
Insurance    $272.6     $340.6      $68.0
Capital Renewal    $289.1    $295.9         $6.8
C.  Financing Costs (as of Feb 2022) $1,312.0 $2,765.8 $1,453.8 110.8%
Overall Total Cost (as of March 2024) $5,589.9 $9,847.3 $4,257.4 76.2%

*  Includes an unspecified amount for financing costs.

Sources:  MDOT Briefing prepared for Maryland Legislature, February 2022, and Board of Public Works Agenda, March 13, 2024

These figures do not include all the costs of the Purple Line, but rather just the costs covered under the PPP contract with the private concessionaire.  Thus the figures here exclude associated projects, such as the cost to connect the Purple Line station in Bethesda to the nearby Metro station (at a cost currently estimated to be $130.4 million), and the cost to restore what had been a tunnel for walkers and bikers at what will now be the Purple Line Bethesda station (at a cost currently estimated to be $82.5 million, although there is a good chance this promised restoration will be canceled).

More significantly although difficult to estimate, there have also been the direct costs incurred by MTA for both its staff and the consulting firms it contracted as the Purple Line project was designed, assessed, and now supervised.  The then Maryland Secretary of Transportation Pete Rahn had indicated in a court filing in 2017 that those costs over the years were already at that time at least $200 million.  They would be significantly higher now.  There have also been the costs covered directly by both the state and county governments and by other entities for some share of the road and utility work necessitated by the placement of the Purple Line.

And while less easy to estimate in dollar value, they should also have recognized that there has been the cost of taking over what had previously been parkland for use now as a rail line.  No compensation was paid for that parkland.  Had this been a World Bank funded project, the entities responsible for the project would have been required to acquire a similar acreage of land of a similar nature and relatively close by as an offset to the parkland lost to the project.  It does not appear there was ever any consideration given to acquiring such an offset, possibly because it would have been expensive.  But this loss of parkland for the rail line should still be recognized as a cost.

The overall costs of the Purple Line are therefore substantially higher than just the costs reflected in the PPP contract with the concessionaire.  But we will focus only on the costs of that contract.

The original PPP contract was approved and signed in the Spring of 2016.  The table above shows the costs of what would be covered under that contract broken down for both the construction period and then the subsequent 30 years of operation.  But it was just one unified contract for all of those costs.  While there would be certain payments made during the period of construction as various milestones were reached, the bulk of the payments would be made by the State of Maryland on a monthly basis once the line was operational.  These are called “availability payments” as the State of Maryland is obliged to make those payments as long as the Purple Line is available to be operated.  And those payments will be the same regardless of ridership; they must be made in the set amounts even if no riders show up.

The original construction firms responsible for the actual building of the Purple Line (primarily Fluor Enterprises – a large Texas construction firm – with two smaller partners) decided in 2020 that, due primarily to the frequent delays and other issues that arose after construction began in 2017, they would abandon the project unless compensated for the resulting costs.  As noted above, they explained that MTA was late (and as much as two years late – with this at a point where construction had been underway for only two and a half years) in delivering almost all of the more than 600 land parcels they needed to build the line.

They filed a claim for $800 million in additional payments from Maryland for the extra costs they had incurred, but Maryland refused and decided instead to seek a new construction firm to complete the project.  A negotiated settlement was eventually reached where the State of Maryland agreed to pay $250 million to the original construction firms for their additional costs, with the firms then leaving the project.

In early 2022 Maryland reached an agreement with the concessionaire on a new construction firm (with Dragagdos, of Spain, as the lead contractor), at a new cost of $3.4 billion for the rail line.  See the table above.  To this one needs to add $250 million for the settlement payment to Fluor and its partners, plus $219 million for construction work by the numerous sub-contractors that had been working on the project in 2020 when Fluor left, and which the MTA then supervised directly in order for the work to continue.

The State of Maryland has since agreed to provide the Purple Line concessionaire two additional payments – of $148 million approved in July 2023 and of $415 million approved in March 2024 – as compensation for the extra costs the concessionaire incurred as a consequence of continued delays in the MTA fulfilling its responsibilities under the project.  Such MTA delays was the issue that frustrated Fluor.  It appears that Dragados was careful in the new, 2022, contract to ensure there was a clear formula for what it would be compensated for the costs arising from such delays.

Taken together, the overall cost of construction is now at $4.5 billion instead of the $2.0 billion in the original contract – an increase of $2.5 billion or 127%.  MTA staff were praised by Governor Hogan for their negotiation of this new contract, and there is no doubt they worked hard at it.  But one should question the wisdom of the decision to start over with a new contract when the original contractor (Fluor) had indicated it would continue if it were fairly compensated for the costs it incurred as a consequence of the MTA delays – primarily delays in receiving the cleared land parcels it needed before it could begin its work.  It asked for an additional $800 million (which could probably have been negotiated down by some amount), but even at the full $800 million, the total cost would have been $2.8 billion to complete the line.  Under the new contract, the cost will be $3.9 billion for this same work (i.e. the $4.5 billion cost less the later payments of $148 million and $415 million).

It is hard to see how the new contract at $3.9 billion represented a better deal for Maryland taxpayers when the original contractor would have continued if paid $2.8 billion.  It is $1.1 billion more.  Yet Governor Hogan praised MTA staff for their skill at arriving at what he praised as a well-negotiated new contract.

Section B of the table above shows the costs associated with the operating stage of the concession (the 30 years following the completion of construction).  They increased by a total of $308 million in the new contract.  This part of the original contract would not have changed had Maryland agreed to compensate the original construction contractors the extra $800 million requested (or some lower negotiated amount).  The actual cost of the new contract is therefore not just the $1.1 billion for the construction, but rather $1.4 billion when one takes into account the higher cost of what will be paid for the operation of the line.

Section C then provides a figure for what the total financing costs will sum to over the life of the concession.  These financing costs are the sum of the interest and other fees associated with the funds borrowed by the concessionaire for the project, as well as a return on its equity investment.  It is of interest to the State of Maryland as it will be paying these financing costs via the availability payments, in addition to the repayment of the principal on the loans (where that principal is already reflected in what is shown for the construction costs and should not be double-counted).

[Side note:  The additional payments of $148 million agreed in July 2023 and $415 million in March 2024 are primarily for additional construction costs, and have been included in that section of the cost table above.  But some unspecified share of each appears to include additional financing costs to cover the interest on what will be borrowed and then repaid only over the 30-year concession period via consequent higher availability payments.  However, a breakdown of those payments to show the financing portion was not provided by MTA staff at the Board of Public Works meetings (see here and here) that approved the additional spending commitments, and thus could not be shown here.]

The financing costs (as of the February 2022 contract) more than doubled between the old and the new contracts:  from $1.3 billion to $2.8 billion, or an increase of $1.5 billion.  This was not due to higher interest rates when the new contract was signed in early 2022 compared to what they were when the original contract was signed in 2016 (or finalized in 2017).  Interest rates were in fact similar in those two periods.  They had declined in 2019 and especially 2020 during the Covid crisis, and in early 2022 were roughly where they were in 2016 / 2017.

Rather, one needs to examine the funding structure.

2)  Funding 

The original funding structure and that as of the February 2022 new contract were:

Purple Line Funding
in $ millions

Original Contract

Shares

New Contract

Shares

A.  Federal Government $1,775.0 79.7% $2,706.0 67.7%
  Grant – New Starts Program    $900.0 40.4% $1,006.0 25.2%
  Loan – TIFIA    $875.0 39.3% $1,700.0 42.5%
B.  Tax-exempt Private Activity Bond    $313.0 14.1% $1,013.0 25.3%
C.  Private Equity    $138.0   6.2%    $280.0   7.0%
Total (incl. for working capital) $2,226.0 100.0% $3,999.0 100.0%
Sub-Total:  Borrowed funds only $1,326.0 59.6% $2,993.0 74.8%

Sources:  MDOT Presentation of April 2019, and MDOT Briefing of February 2022

Even though the Purple Line PPP was proclaimed to be a “privately-funded” project, the federal government in fact covered 80% of the original costs.  Half of this was as a straight grant, and half as a low interest loan under the federal TIFIA (Transportation Infrastructure Finance and Innovation Act) program, which lends funds for such projects at an interest rate set equal to the funding costs of the federal government.  That is, funds under the TIFIA program basically pay the low US Treasury rate.

Under the revised contract signed in early 2022, there will only be a relatively minor increase in federal grant support: an additional $106 million that Maryland would have been able to use for a range of transportation projects but chose to give to the Purple Line.  Rather, the major increase in funding will be borrowed funds under the TIFIA program, with an increase to $1.7 billion from the prior $875 million.

More is also being borrowed through tax-exempt Private Activity Bonds, which enable the borrower for such projects to issue bonds that are exempt from Maryland state income taxes.  While Private Activity Bonds are granted exemption from Maryland income taxes because the investments are deemed to be in the public interest, the Maryland tax exemption is certainly appropriate here.  These are in essence State of Maryland borrowing obligations as Maryland will be repaying these bonds through the monthly availability payments to the concessionaire.  As of February 2022, MDOT stated that “new” Private Activity Bonds of $700 million would be issued.  While it is not fully clear from the MDOT presentation, it appears these new bonds are being counted in terms of the net increase in such bonds relative to those originally issued (as the originally issued bonds would be retired and replaced under the new contract). The equity stake was also increased to $280 million from the prior $138 million.

With the far higher construction costs ($3.9 billion as of February 2022) and the federal grant funding only modestly increased, there had to be far greater borrowing to cover the cost of the project.  That was the cause of the 111% increase in the financing costs, not (as some have asserted) higher interest rates in early 2022.  Indeed, the total funds borrowed for the project (including via equity) rose from $1,326 million in the original funding structure to $2,993 million in the new one – an increase of 126%.  This is even a bit more than the 111% increase in financing costs.

3)  Lack of Justification to Continue When the Higher Costs Became Clear

A critical question that should have been addressed when the far higher costs of the new concession contract became clear was whether there was a justification to continue with the project.  A project is an investment, and whether any investment is worthwhile depends on the relationship of its cost to its benefits.  At a low enough cost or high enough benefits, it may be a good use of funds.  But if the costs turn out to be substantially higher, or the benefits substantially lower, that may no longer be the case.  The issue therefore needs to be re-examined when there has been a substantial increase in costs.  But it does not appear it was ever seriously considered for the Purple Line.

Two questions should have been addressed by MTA staff and then considered by Governor Hogan in deciding whether to proceed.  One was whether at the new cost (which turned out to be $3.9 billion once the new bids became known, although with the possibility – and later the reality – that there could be further cost increases later), the rail line could be justified.  The second was whether given what had already been spent by that time on the project ($1.1 billion), was the further spending justified?  Treating the $1.1 billion as a sunk cost already lost, was it worth spending a further $2.8 billion on the project to cover the $3.9 billion cost of construction (minimum – assuming no further increases)?  One should also have taken into account that operating the line for the 30 years of the concession period would now also cost the state $300 million more, but I will set that aside for the purposes here.

It is clear that the answer to both of these questions is no.  The project was at best highly marginal when it was approved.  As was discussed in an earlier post on this blog, at the time the light rail line alternative was chosen as the “preferred alternative” by the State of Maryland (in 2008), a measure of its cost relative to its benefits placed it just barely in a “medium” cost category where it might be considered for federal support.  But with a cost only a tiny 0.7% higher (or benefits from expected ridership of just 0.7% less), it would have been in the “medium-low” category and unlikely to receive federal financial support.  And at costs 27% higher (or benefits 27% lower) it would have been in the “low” category and federal financial support would have been impossible.

Adjusting for inflation, the construction cost expected in 2008 ($1.2 billion) would have come to $1.6 billion as of late 2021 when decisions were being made on whether to proceed with the new contract.  The $3.9 billion construction cost being considered at that time was 144% higher than this, and the $2.8 cost excluding the $1.1 expense already incurred was 75% higher.  Even leaving the expected benefits the same (where we will see below that they will likely be far less than forecast), any of these costs are far above the 27% higher cost that would have placed the project in a category where it never would have been considered for approval.

The criteria for federal approval used in 2008 were later revised during the Obama administration to ones where qualitative judgment factors entered in addition to the cost factors.  A multi-level, weighted, point scoring system was then used to arrive at an overall rating.  But even at the original costs – costs that were known in early 2022 to be far too low – the project only obtained a minimally adequate rating in 2016 because of a system where the weighted averages of the point scores were rounded up at each level.  Without such rounding – i.e. with each of the specific weighted scores kept rather than rounded up – the Purple Line would not have obtained a minimally adequate rating for federal approval.

But there is no evidence that there was any consideration in early 2022 of whether the project could be justified at the then known costs (and minimum costs, as they could – and did – go even higher).  Rather, it appears MTA staff were directed to find a way to have it built regardless of the costs, as long as those costs would not appear in the current Maryland state budget.  Those costs would instead be paid for by future Maryland budgets when Governor Hogan would already have left office.  And that is what they did, with the increased costs loaded into higher availability payments that would begin when the rail line was completed.

C.  A Comparison to the Cost of the Extension of Line 14 of the Paris Metro 

A major extension of Paris Metro Line 14 has recently been completed, and good figures are available on what that cost.  It extends Line 14 south by 14 kilometers (8.7 miles) to Orly Airport, and was opened in June 2024, in time for the Paris Olympics.  There was also a more minor extension of the line north, but by only 1.1 kilometers (0.7 miles) to a new station.  The cost and other figures here will, however, only be for the extension south to Orly.

The cost figures can be found in a report published in April 2024 by the “Cour des Comptes” of France – the supreme audit institution in France, under the control of the courts and hence independent of the executive and legislative branches.  It audits major government programs.  The extension of Line 14 is the first part of a major (indeed huge) program expanding the Paris Metro system, which will add 200 kilometers of lines (almost all underground) and where in addition to the extension of Line 14 there will be four new lines (15, 16, 17, and 18) circling Paris.  The new lines are scheduled to open in phases between late 2025 and 2030, although it would not be a surprise if there are delays.  Figures on the costs so far (and what is expected to be the costs for the new lines when they are completed) can be found in Annex 11 of the Cour des Comptes report, while Annex 10 provides figures on the costs of the new trains that will run in these lines (including Line 14).

Comparing the new Paris Line 14 South extension with the Purple Line:

Paris Line 14 South Purple Line Ratio
Length 14 km = 8.7 miles 16.2 miles
Type of line Heavy rail Light rail
Tunnel 100% one of 1,020 feet
Depth 100 feet below ground Mostly at grade
Stations 7, o/w 6 new, all underground 21, mostly at surface
Stations per mile 0.8 1.3
Length of train 394 ft 142 ft
Driver required? Fully automated Driver required
Average speed incl. stops 37.3 mph on new section 15.5 mph
Cost per mile $365.9 million $275.8 million 1.34 times
Daily passenger capacity 1 million 70,000 14.3 times
Cost per unit of capacity   $365.9

 

$3,940.0 10.8 times (inverse)

The length of both lines is substantial:  8.7 miles for the Line 14 extension and 16.2 miles for the Purple Line.  Line 14 passes through the center of Paris, and now has a full length of 16.8 miles.  This extension south is thus more than doubling the length of the line.  It is a heavy rail line while the Purple Line is only light rail, and is designed to carry far more passengers.  In contrast to the Purple Line, which is mostly being built at ground level (with just a few bridges and elevated sections, and one tunnel of just 1,020 feet), 100% of Line 14 is built as a tunnel deep underground – on average about 30 meters (about 100 feet) underground.  Tunneling is far more expensive than a rail line that is simply built mostly at ground level alongside existing roads or over what had previously been public parks.

Six new stations have been built on the Line 14 extension, and it was connected at a seventh to the existing Line 14.  This comes to 0.8 stations per mile on average.  Such stations are major projects in themselves, as they have to be built to bring the passengers down to 100 feet underground (the height of a 10-story building) to where the rail line is.  The Purple Line will have 21 stations – or 1.3 per mile on average – but these should be far less expensive.  Most will be not much more than simple platforms with something to cover them.

To carry the passengers, each Line 14 train will consist of eight rail cars and will be 120 meters (394 feet) long.  The Purple Line trains are just 142 feet long.  The Line 14 trains are also fully automated, with no need to employ a driver on each train.  The Purple Line trains, in contrast, will require drivers.  This is necessary for safety as the Purple Line trains will be at ground level and will cross many roads at intersections.  While this will reduce the upfront capital costs (as there is then no need to spend the money needed to make the trains fully automated), the need for drivers on the Purple Line will increase operating expenses.

Paris’s Line 14 trains will also be far faster.  For the newly completed southern extension, they will average 37.3 miles per hour while the Purple Line will average just 15.5 miles per hour.

The Line 14 South Extension has cost Euro 2.71 billion according to the Cour des Comptes report.  Using an exchange rate of 1.085 Dollars per Euro (the current exchange rate as I write this), this comes to $2.94 billion. This includes the cost of the six new stations (and the connection to a seventh).  To this, one should add the cost of the train cars for comparability as that cost is included in the Purple Line construction cost contract.  They have purchased new trains for the entire Line 14 route (the old train cars on the line will be used elsewhere in the Paris Metro system), at a total cost of Euro 431 million.  Prorating this based on distance for the South Extension vs. the entire Line 14 route (51.9%), and with the 1.085 Dollars per Euro exchange rate, the cost for the train cars for the South Extension is $243 million.  Adding this to the $2.94 billion construction cost and dividing the total cost by the 8.7 mile length leads to an overall cost per mile of $365.9 million.

The Purple Line total construction cost, as noted above, will now be $4,467.8 million (assuming no further cost increases).  Over 16.2 miles, this comes to $275.8 million per mile.

The cost per mile of Line 14 is thus 34% higher than for the Purple Line, but Line 14 is a heavy rail line, built entirely as a tunnel rather than mostly at grade, with stations that will bring passengers to 100 feet below ground level, operating at a far higher speed.  One would expect that such a heavy rail line would cost many times the cost of a light rail line built mostly at grade.  But it is just 34% more.

And Line 14 will have a far greater passenger capacity.  The new line has been built to be able to carry 1 million passengers per day.  While the 1 million passengers would be for the entire Line 14 – including the existing section – the extensions will have the same capacity as the same trains at the same frequency will pass through them.  One might also note that the length of the full Line 14 – at 27 kilometers or 16.8 miles – is also similar to the length of the Purple Line.

The Purple Line, in contrast, is being built to be able to handle daily ridership that has been forecast to total 69,300 in 2040.  It is being built to handle a peak load that is forecast to be on the segment between Silver Spring and Bethesda.  I have rounded this up to 70,000 for the purposes here.  While “capacity” on such a system is not always easy to define (what degree of crowding should one assume, for example), the reader is welcome to substitute a higher figure for the calculations should they wish.  The story is fundamentally the same for any reasonable capacity figure that might be assumed.

The capacity to carry 1 million passengers per day on the Line 14 extension is more than 14 times the capacity to carry 70,000 passengers per day on the Purple Line.  But the cost is not 14 times higher; it is only 34% higher.  Or in terms of the cost per unit of capacity, the Purple Line is 10.8 times as expensive as the Paris Line 14 South Extension.  That is huge.

Prior to his decision to proceed with the Purple Line, Governor Hogan asked his Secretary of Transportation to find ways to reduce its cost.  There were some cuts, but primarily by removing some “nice-to-haves” by cutting what would be spent on artwork, on the visible surfaces at the stations, and on the promise that the area between the rails would be planted with grasses and other natural vegetation instead of covered with something harsher such as gravel.  These were all removed, or reduced in scale.  But this had only a modest effect on the costs, and the costs remained high, as discussed above.

What Governor Hogan should have asked was why were the basic costs simply so high.  Even at the original cost of the PPP contract (and thus excluding all the other costs associated with the project), $1,971.9 million for 16.2 miles works out to over $23,000 for each linear foot.  It is hard to understand why it should cost so much to lay down two parallel rail lines, mostly at ground level, with some concrete and other materials (and some pro-rated share of the costs of the trains and other equipment).  At the current cost of $4,467.8 million, the cost is $52,200 per linear foot.  That is over a half million dollars for every ten feet of the line!

Governor Hogan could also have asked why such lines cost so much less in a country such as France.  Wages of construction workers are not less in France than in the US; unions are certainly more powerful in France than in the US; and safety, environmental, and other regulations are certainly stricter in France than in the US.  Yet in terms of the cost per unit of capacity, the Purple Line is costing more than ten times what the Paris Metro Line 14 South Extension has cost.

But such questions were evidently never asked.

D.  Mismanagement Prior to Construction

The mismanagement of the Purple Line has not been limited only to the implementation of the construction phase of the project.  Prior to that, there were also major issues in the process that was followed to assess the project, determine what alternative would best serve the very real transit needs of the neighborhoods in the most cost-effective way, the approach taken in structuring the concession contract (including whether it should indeed be structured as a concession), and the role of the Maryland state legislature in approving or not the financial commitments the State of Maryland has made.

These issues have been reviewed in some detail in prior posts on this blog.  See the posts here, here, here, and here.  I will only summarize in this post some of the key issues and the more blatant mistakes.  These mistakes illustrate that the process followed simply was not serious; it was not professional.  Rather, it appears the aim of the process was not to assess how best to provide public transit services to meet the needs of these neighborhoods, but rather to provide a justification for a decision that had already been made and then to work out a way to get that particular approach done, regardless of the cost.

1)  The Ridership Forecasts

To start, a central issue is what ridership should be expected if the rail line is built.  A figure for expected ridership (for a specific project design) is needed to determine whether the project is being sized appropriately in terms of capacity, what the economic benefits might be should it be built, and whether those benefits justify the costs.

Ridership forecasts are inherently difficult to do, even with the best of intentions.  At this point – with the project not yet completed – we cannot say for sure what the ridership will be.  But with a rail line, it is too late once the line is built to change the design or choose a different option to provide transit services.  The money has already been spent, and a rail line does not have the flexibility to adapt should ridership turn out to be different than was forecast.  This is in contrast to expanded bus service, for example, where one can easily add to or reduce capacity depending on what the ridership turns out to be.  Also, in the specific case of the Purple Line, the concession contract has been structured so not only will the money for the construction have been spent, but the State of Maryland will also be committed to pay the concessionaire to operate the system for 30 years and in the same amount regardless of how many people choose to ride.

The ridership forecasts for the Purple Line are therefore especially important to taxpayers in the State of Maryland.  They do not matter to the concessionaire, as they will be paid the same regardless of how many riders show up.  Indeed, they would prefer fewer riders rather than more, as their costs will then be lower (less wear and tear on equipment due to use, for example, and less to clean up each day).

This was an important flaw in how the concession contract was designed, and will be discussed below.  But first on the ridership forecasts themselves.

The earlier post on this blog on ridership pointed out that there are numerous – and often obvious – errors in the ridership forecasts.  Some involved forecast figures that were simply mathematically impossible.  Among the more important (as well as obvious) mistakes:

a)  Mathematically Impossible Ridership Forecasts:  More than half of the figures for the forecast ridership between groups of Purple Line stations are mathematically impossible.  Briefly (see the earlier blog post for more detail) ridership forecasts are produced in a multi-level process by first dividing up the metro area (in this case the Washington, DC, metro area) into basically a large “checkerboard” (although with uneven borders and areas, not squares), with each defined area a “traffic analysis zone”.  A forecast for some future year (2040 in the case of the Purple Line forecasts) is produced via a model for the number of daily trips that will be taken from each zone to each other zone by all modes of transportation.  The number of trips is modeled based on the population of each zone, the number of jobs in each zone that workers will travel to, distances between the zones, and other factors such as income levels.

They then model what share of those trips will be taken by private vehicles (cars mainly) and what share by public transit, based on factors such as relative costs and travel times by each mode.  At the level below, they then model the share of the trips by public transit that will be taken between each zone by bus, by the Washington Metrorail system, by commuter rail, or by – in the scenario where the Purple Line is built – by the Purple Line.  Again, the shares are modeled based on factors such as the relative costs and travel times of each.

For the Purple Line, the objective is to forecast how many people will choose to travel each day between the Purple Line stations.  While there will be 21 Purple Line stations, they combined the stations into 7 adjacent groups based on the traffic analysis zones, with anywhere from one station in the zone (in the case of Bethesda) to several.  There would therefore be 7 x 7 = 49 different combinations of trips that are being modeled – from one traffic zone along the Purple Line route to another, in both directions.

In such a multi-level process, the number of trips that would be taken on the Purple Line alone cannot be more than the number of trips that will be taken by all forms of public transit, i.e. by bus, or the Metrorail, or commuter rail, or the Purple Line.  Yet, if one compares the forecast number of daily trips provided in the Travel Forecasts chapter of the Final Environmental Impact Statement (FEIS, Volume III, available here and with the relevant table reproduced in my blog post), one finds this not always to be the case.  For the figures on the number of trips from each zone to each other zone (in what is called the “Production/Attraction format” – see my post for more on that if interested), the forecast ridership on the Purple Line is higher than the forecast ridership on all forms of public transit in 29 of the 49 possible cases.

This is mathematically impossible and indeed simply nonsense.  The consultants responsible for this analysis (Parsons Brinckerhoff) made some kind of mistake.  Indeed, with 29 of the 49 possible cases, it is conceivable that the figures are simply random.  One would expect half to be higher and half lower if these are random figures, and 29 of 49 is not far from that.

b)  The Forecast Capacity Requirement:  There is another impossibility as well, although here the cause of the error is clear.  The number of daily riders that the Travel Forecasts chapter estimates will be exiting the Bethesda station each day is 19,800.  But they state that only 10,210 will be going from Bethesda to elsewhere on the Purple Line each day.  (See Figure 10 in the Travel Forecasts chapter, reproduced in my blog post.)  This is obviously silly; Bethesda will not be gaining a population of close to 10,000 each day.

The cause of this error is clear, however.  The analysts responsible for this work used ridership figures from the “Production/Attraction” format tables.  But that format does not present the total ridership that will be entering or leaving a station each day.  Rather, it is one step in the modeling process, and is an estimate of how many of the daily trips will start in a given zone (based mainly on how many people live there – this is the “Production”) and how many daily trips will have this as the destination (where the jobs are – this is the “Attraction”).  Bethesda is a relatively significant jobs center on the line, and hence “Attracts” a higher number of riders than it “Produces”.

The final step is to convert the figures to an “Origin/ Destination” format to show the number of daily trips that will originate at a given station and will end at a given destination.  The figures in origin/destination format will be close to the average for the figures in the production/attraction framework.  Thus the daily number of entries and exits at Bethesda would be roughly 15,000:  the average of the 19,800 and 10,210 figures.  And the Travel Forecasts chapter presents the origin/destination figures in its Tables 24 and 25, although in one they provide a figure of 15.010 and 14,990 in the other, with similar small differences for all of the stations as well in the overall daily ridership forecasts (69,330 in Table 24 and 69,300 in Table 25).  The cause of these small differences was never explained and suggests sloppiness.

But due to this error, the report indicates in its Figure 10 and in the associated text that the number of Purple Line riders going into Bethesda each day will be 19,800, and that the peak ridership (and hence the capacity that will be necessary) will be in the segment from the Woodside/16th Street station to Lyttonsville (a portion of the line between Silver Spring and Bethesda), where there would be 21,400 travelers each day in the westbound direction.  This is wrong, and a misinterpretation of the forecasts due to confusion between figures in a production/attraction format and ones in origin/destination.

c)  Time Savings:  The forecast time that would be saved by travelers choosing to take the Purple Line is central both to the forecast of how many would take the Purple Line (instead of some other mode of public transit) as well as for the estimate of the social benefits from the construction of the Purple Line (which is based on how much time would be saved by travelers).

But there were also major problems here.  Table 23 of the Travel Forecasts chapter of the FEIS provides the estimates they used for the average number of minutes that would be saved by travelers per trip (from each traffic zone to each other) if they have the Purple Line as a choice.  But some of the figures are obviously absurd.  They indicate an average time savings for travelers from the Bethesda traffic zone (Bethesda station) to the New Carrollton zone at the other end of the line of 691 minutes.  That is 11.5 hours!  That is 1.4 miles per hour for the 16.2 mile route of the Purple Line; one could walk faster!  Similarly, the time savings from Bethesda to the traffic zone just before New Carrollton (Riverdale Park) is 582 minutes, or 9.7 hours.  This is also absurd.  My guess is that they misentered the data, with a decimal point in the wrong place.

But there are more general and much more significant problems.  One is that in the data they entered on alternative travel options in the absence of the Purple Line, they failed to include use of the existing Washington Metrorail system as such an option.  And some of the trips would require less time on the Metro than they would if one rode on the Purple Line.  For example, the Travel Forecasts report itself indicated that traveling from Bethesda to New Carrollton via Metro currently takes only 55 minutes (Table 6 in the report).  On the Purple Line, the same trip would require 62.6 minutes (Table 11).  There would not be any time savings, but rather a time cost.  And obviously not 691 minutes of time savings.

More generally, the time savings per trip across the 7×7 zones being forecast (Table 21) appear to be far too high on average.  The summary at the bottom of the table indicates an average savings of 30 minutes per trip for trips in the metropolitan region as a whole.  Yet in the three detailed examples they provided for how such calculations are made, they calculated an average time savings of a much more plausible 7.3 minutes per trip.  The sample is small, but a savings of 10 minutes per trip is probably a better estimate than 30 minutes. This is especially the case if one allows (as one should) the option of taking the existing Metrorail system between several of the stations – often for a faster trip than the Purple Line will provide.

An average time savings of 10 minutes per trip rather than 30 reduces the benefits of the Purple Line by a far from insignificant two-thirds even assuming the number of trips would remain the same as forecast.  But it would also significantly reduce the number of Purple Line trips in the forecast, as that forecast number depends primarily on the assumed time savings.  If linear, it would imply that the number of trips should also be reduced by two-thirds, and the overall benefits (the number of trips times the time savings per trip) would fall by eight-ninths.  That is far from a minor adjustment in the forecast benefits.

A reduction in the forecast ridership on the Purple Line by two-thirds would also be more in line with the ridership seen on other light rail lines in the US.  As I noted in my earlier blog post:

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.

d)  Uncertainty and the Lack of Resilience in a Rail System:  Finally, one should always keep in mind that there will be uncertainties with any such forecasts.  The Purple Line ridership forecasts are not plausible, but a more fundamental issue is that future ridership can never be known with certainty.  The experience with ridership on Washington’s Metrorail system over the past decade and a half illustrates well the uncertainties, and also has a direct bearing on the Purple Line forecasts.

The Purple Line will intersect with four Metrorail lines, and a major share of the forecast Purple Line ridership is of riders who would use the Purple Line for part of their journeys to connect to a regular Metro line or to return home from a trip on the Metro.  The ridership forecasts for the Purple Line were first developed around 2008 and then refined later, and assumed that ridership on Metrorail as a whole would grow steadily over time.  But it turned out that Metrorail ridership peaked in 2008 and then fell steadily.  This was indeed a major issue raised by Judge Richard Leon in his August 2016 ruling, where he indicated the State of Maryland should review whether, in light of the steady decline in Metrorail ridership in the years leading up to 2016, the Purple Line would still be justified.  By 2016, Metrorail ridership was already 14% below where it had been in 2008, and by even more, of course, relative to where it would have been had it kept to the pre-2008 trend (a rising trend the ridership forecasts assumed would continue).  The response by Maryland was that they expected Metrorail ridership would soon recover fully to its pre-2008 trend path, and then continue at that trend growth rate in the future.

Metrorail ridership did not.  It continued to fall and then collapsed in 2020 due to the Covid crisis.  Average daily ridership on the Metrorail system in 2020 was 72% below what it was in 2019, and in 2021 was 78% below 2019.  There has since been some recovery, but in 2024 (up to October 11 – the most recent data as I write this) it is still 38% below what it was in 2019.  Given the shift to working from home for many office workers, there is the question of whether Metrorail ridership will ever recover to where it was in 2008, much less to the trend growth path that was assumed for the Purple Line ridership forecasts.

Defenders of the Purple Line might say that it was impossible to predict the Covid crisis and the shift to working from home.  That is true, and is precisely the point!  The future is uncertain, and major surprises are always possible.  It is for this reason that one should favor resilient and flexible systems that can respond as the future unfolds.  A light rail line such as the Purple Line is not flexible.  Once the rail lines have been laid, they cannot be moved.  And with the train sets purchased and other equipment installed, the capacity is basically fixed.  This is in direct contrast to bus systems, where routes can be changed based on how development proceeds and capacity on any given route can easily be adjusted by running more or fewer buses.

Bus options existed for serving the neighborhoods through which the Purple Line is being run.  Bus routes could have been adjusted, capacity on those routes could have been adjusted, and express routes could have been added (such as between Silver Spring and Bethesda – if there is indeed a demand).  And while less flexible, they could also have built – at a far lower cost than the Purple Line – bus-only lanes in key locations (such as into and through Bethesda), or they could have extended planned Bus Rapid Transit routes to include segments the Purple Line will be covering (such as between Silver Spring and Bethesda).

2)  The Economic Impact

A key question for a project such as the Purple Line is what economic impact can be expected, and will that impact justify the costs.  Studies are commissioned for this, and the key one for the Purple Line was prepared by the consulting firm TEMS (for Transportation Economics and Management Systems, Inc.).  Its March 2015 report was an update of one it had prepared in 2010, and will be the focus here.

The release of the TEMS report on April 20, 2015, was highly orchestrated, with a press conference, an article in the Washington Post, an editorial that same day in the Washington Post extolling the reported benefits, as well as letters to Governor Hogan from local political figures citing the report.  Presumably, the TEMS report was circulated before this date for review by selected entities (including the Washington Post) but embargoed until the press conference on that day.

It does not appear, however, that any of these entities undertook a serious review of the report prior to citing it as proof that the Purple Line would have highly beneficial economic impacts on the communities and the state and region more generally.  It is a report that could be highly technical in areas, and it would not have been reasonable to assume that members of the Editorial Board at the Washington Post would have fully understood what was being done.  However, before jumping to the conclusion that the report’s conclusions were soundly based, the Editorial Board (as well as others) could have requested a review by a neutral professional or scholar to advise on whether the report was sound.  But they did not.  There are major problems with the report, and indeed obvious problems that any professional would have quickly seen.

a)  The “Statistical” Analysis:  To start with a simple but obvious issue, and one that any professional in the field would have seen immediately just by glancing at the report, the TEMS study provided what it said were the results of a statistical regression analysis of travel demand between different zones of the Washington metro area.  The report said it used a database constructed of travel patterns in the region between each of the defined traffic zones, the cost of travel between the zones, and socioeconomic variables such as population, employment, and average household incomes.  The results are presented in Exhibit 4.2 on page 25.

They state that the statistical results they found were very good.  If properly done, such results would not only have been good, but far too good.  Indeed impossibly good.  For example, and most blatantly, they claimed to have obtained a statistical measure of goodness of fit (called a t-statistic) in one of the regressions of 250 for one of the parameters estimated and over 200 for each of two others.  The higher the t-statistic, the better (the tighter) the statistical fit.  For most such regressions, analysts are happy with a t-statistic of 2.0.  At 2.0, there is a 95% likelihood that a statistically meaningful correlation is being found, and only one chance in 20 that it is not.  At a t-statistic of 3, it is one chance in 370, at a t-statistic of 4 it is one in almost 16,000, at a t-statistic of 5 it is one in 1.7 million, at a t-statistic of 6 it is one in half a billion and at a t-statistic of 7 it is one in almost 400 billion.  As you can see, it diminishes rapidly.  It is impossible to imagine what it would be at a t-statistic of 250.  Something is clearly wrong here.  No statistical analysis of real-world data produces such results.

This would have been obvious to any professional with just a glance at the report.  But there were other issues as well.

b)  Correlation is Not Causation:  The TEMS report states that it estimates that annual household incomes in the region will increase by $2.2 billion each year as a return on the $1.9 billion investment in the building of the Purple Line (where $1.9 billion was the estimated cost at the time the study was done).  This would be a rate of return of 116% ($2.2 billion per year for a one-time investment of $1.9 billion).  Any professional would immediately see that this is certainly wrong.  Transportation investments – especially passenger rail investments – do not generate anything close to such returns.

How did they arrive at this figure?  Going through the report’s presentation, one sees that the estimate of the impact on household incomes is based on another statistical regression analysis.  That one looked at the relationship between existing household incomes in a particular geographic zone and a figure for average transportation costs for those living in that zone (along with other variables for that zone).

The problem was that they confused correlation with causation.  As any introductory statistical course in college will teach, a regression equation provides estimates of correlations, and one should not assume these necessarily imply causation.  There may be neighborhoods (Georgetown in Washington, DC, would be an example) where travel costs to work might be relatively low on average (Georgetown is close to jobs in downtown DC) and incomes are relatively high.  But it is a mistake then to jump to the conclusion that the reason rich people in Georgetown became rich was that their commuting costs were relatively low.

The TEMS study made this simple mistake.  Correlation is not causation.

c)  Errors in the Analysis of Construction Impacts:  In addition to their (mistaken) estimates of the economic benefits to households and others in the region once the Purple Line is running, the TEMS report also provided what it claimed to be estimates of benefits accruing from what will be spent for the construction of the line.  There were also fundamental problems here.

First, and most basic, the TEMS analysis is based on the presumption that the more that is spent on the construction, the greater the benefit.  That is, if the construction costs doubled to $4 billion, say, from the initial estimate of $2 billion, then the “benefits” would be twice as much.  And if the costs blew up to $20 billion, say, the benefits would be ten times as much.

This is, of course, not just perverse but also silly.  Cost overruns are not benefits.  While they are claiming that jobs would be “created” for those working on the construction (and hence twice as many jobs if twice as much is spent), the mistake they make is assuming the alternative is that such funds for public transit would not be spent at all.  But that is not the alternative.  There are huge needs for public transit – and for transportation projects more generally – and the alternative that should have been examined is not to do nothing, but rather to make use of the funds for one or more of the high-priority needs.  That is, instead of covering cost overruns on a poorly managed project, those funds could have been used to meet the very real transit needs of these communities.  The number of jobs “created” (if that is the objective) would have been similar.

It gets worse.  The TEMS study used what is called an “input-output” analysis to estimate what it said would be the impacts on the various sectors of the economy in the region from the expenditures for items that would be needed for the construction.  They excluded (properly) the $0.2 billion in expenditures on the rail cars for the Purple Line (as they have been built elsewhere), leaving $1.7 billion for the “construction” cost from their $1.9 billion overall cost estimate.  But they then assumed – completely improperly – that all of the $1.7 billion would be spent locally.  This is obviously not true.  While a portion would be spent for  local construction labor, a far greater portion would be for the materials and equipment needed for the rail line.  But the steel rails being laid down, for example, would not be an expenditure on a locally produced item.  There are no steel mills in the DC metro area.  And that would be the case for most of the items purchased for the project.  Yet the TEMS input-output analysis assumed all of the construction expenditure would be for locally produced products.

And then it gets even worse.  The TEMS study calculated such direct and feedback effects separately for Montgomery County and for Prince George’s County (the two Maryland counties of the 16.2 mile Purple Line route), and then also for Washington, DC (where the Purple Line will not run at all).  But one look at the detailed estimates of the purported economic impacts (Tables 7.10, 7.11, and 7.12, in the TEMS report for Montgomery, PG County, and DC, respectively).  They show that they assigned the full $1.7 billion of construction costs as if it were spent entirely in Montgomery County, spent entirely again in Prince George’s County, and spent entirely again in Washington, DC.

That is, they triple-counted those construction expenditures!  Even if one accepts the problematic methodology of TEMS, the implementation is replete with errors.

This was not a competent analysis.  Before extolling the purported “findings” of a report of the tremendous impact building the Purple Line would have on the region, the Washington Post editorial board, as well as others, should have first asked a neutral professional or academic to review it and indicate whether it was sound.

3)  The Structuring of the PPP Contract, Maryland Debt, and the Role of the State Legislature

There are also major issues with the process followed.  Only three will be addressed here:  the structuring of the PPP contract, the impact on State of Maryland debt, and the role of the state legislature in voting approval for (or, in this case, not voting) on what will be a major public expenditure commitment lasting over 35 years.

a)  How the PPP Contract Was Structured:  The Purple Line project is the first project of the State of Maryland that has gone forward under legislation passed in 2013 for PPP contracts.  It has important lessons on what not to do.

A PPP (Public-Private Partnership) contract divides responsibilities between state and private parties on investments where there is a direct public interest.  It can be a broad concept, covering a variety of contract structures, but the aim normally is to encourage the party best able to manage a particular type of risk to take responsibility for that risk and have an incentive to manage it well.

The Purple Line PPP does not do that.  Indeed, it is not much different from Maryland using a standard fixed-price procurement contract for the work to be done.  The private consortium that won the contract committed to building the Purple Line in accordance with the basic design provided by the state, and then to operate the line for 30 years following its opening in compliance with agreed service standards (such as for the hours and frequency of the train operations).  They were supposed to do this for a fixed cost that, as noted in Section B above, was to total $5.6 billion in payments over the planned 35 years (5 years for construction and then 30 years for operation).  The cost of the PPP contract will now be at least $9,8 billion over the full period.

This could have been structured as a standard fixed-price procurement contract.  Indeed, a standard procurement contract could have been written with greater flexibility by not combining the building of the rail line with its later operation.  Different types of firms have expertise in each.  There could have been a fixed-price contract to build it, and then separate fixed-price contracts for operating it for a given period as a concession.  And the operating concessions need not be for a very long, 30-year, period, but perhaps 5 years at a time after which it could be adjusted to reflect what ridership actually turned out to be and then re-bid.

It is certainly true that public procurement contracts that are supposed to be for a “fixed price” often end up with major cost increases.  But this would not differ from how the Purple Line contract turned out.  Its cost (over the full concession period) is now $4.3 billion higher than the original “fixed price” of $5.6 billion.

Private firms do not appear to be very good at managing the risk that costs may turn out to be substantially higher than initially agreed in such major public sector infrastructure projects.  It was a mistake to assume – as the structure of the Purple Line PPP assumed – that costs would not rise.  In the case of the Purple Line, the public sector (MTA) has been responsible for delivering on time the parcels of land required to build the rail line, as well as for ensuring utility lines and pipes are moved as required and for the basic design of the rail system.  But MTA has been repeatedly late in fulfilling those obligations (even with the lawsuit, that provided the MTA an extra 9 months to get a headstart on this work).  The most recent payment of compensation for the higher construction costs arising from the continued MTA delays was approved just last March – six and a half years after construction began.

In contrast, private firms can be good at assessing “market risk” – in this case assessing what ridership demand might be.  The Purple Line PPP contract was not, however, structured so that the private concessionaire would care whether ridership turns out to be anywhere close to the forecasts that were made.  The concessionaire will be paid the same regardless.  Whatever is collected in fares will be passed directly to the State of Maryland.

A better structure of the PPP contract would have been for the concessionaire to receive the fares.  This would have been much like how many private toll road PPP contracts are structured.  A significant difference is that while tolls on toll roads can usually suffice to cover the costs of building and then operating the road, fares on rail lines rarely cover their costs – and they certainly will not in the case of the Purple Line.  However, this could be addressed by basing the bidding criterion on the level of state subsidy the concessionaire would receive each month, say, during the concession period.  There would be a competitive bidding process, and the pre-qualified private consortium submitting the lowest bid for the subsidy required would win the contract.

Note that in the existing structure that was used for the PPP for the Purple Line, the State of Maryland is paying the concessionaire a similar “subsidy” for building and then operating the concession, with the subsidy then paid out monthly through the availability payments.  But the critical difference would be that the fares would instead go to the concessionaire rather than the state.  Instead of bidding an amount for which they would build and then operate the project (as in the current PPP contract), those competing for this contract would instead submit a bid that was reduced from that amount based on what they expected to receive in fares.

The structure would still be one where the private concessionaire would build the project and then operate it for a period such as 30 years.  The receipts – fares plus the fixed subsidies – over those 30 years would cover what it cost to build the rail line plus the operating and maintenance costs.  While this might appear to be close to the PPP structure used for the Purple Line, there is an important difference.  In this new structure, the concessionaire would have an incentive to build and operate a rail line that is attractive to riders, with a service that will maximize the number of riders and hence the fares.  The concessionaire would be receiving the fares.  Under the current structure, the concessionaire will be paid the same regardless of riders, would prefer fewer to more riders, and will need to be supervised closely to ensure they are not cutting corners to save money.  In the current PPP structure, they do not care whether fewer riders would choose the system (and indeed would prefer fewer).

In this new PPP structure, the potential bidders will pay close attention to the ridership forecasts, as they will lose directly should those forecasts turn out to be overly optimistic.  One can therefore be sure that they would be more carefully done than those – discussed above (with their numerous errors) – used by the State of Maryland to establish what they considered to be a justification for building the line.

However, there would be a major drawback to such a PPP structure, as neither the private concessionaires nor the politicians and others pushing for the project would have been happy.  Such a PPP structure would have made clear upfront that building the Purple Line was simply not warranted.  There would have been more carefully worked out estimates of the ridership (providing a more realistic estimate of what ridership to expect) as well as greater clarity on the state subsidies that would then have been required for the Purple Line to be built.  The political figures seeking a justification for building the rail line would not have wanted this.  Nor would the vested interests that are benefiting privately from it while others are paying.

The private consortia bidding on the line would also not favor such an approach.  They of course favor a structure where, as in the present one, they do not bear the risk of ridership not materializing.  They certainly prefer the State of Maryland to take on this risk.

There was an additional flaw in the structuring of the PPP contract.  Section B above noted that in the original financial structure agreed to in 2016, the equity investment of the Purple Line concessionaire was only $138 million – equal to just 6.2% of the overall cost to build the line.  This was small, and gave the concessionaire the credible threat to walk away should problems develop leading to higher costs.  Furthermore, the primary construction contractor – Fluor – held only a 15% share in the consortium responsible for the project, and hence would have put up only 15% of $138 million in equity, or $20.7 million.

Fluor walked away from the contract in 2020 when the persistent delays and other issues that raised their costs led them to demand $800 million in compensation in order to continue.  They eventually settled for $250 million, but both of these figures are far in excess of the $20.7 million they had invested in the project in equity.  The State of Maryland had claimed that the PPP contract would ensure that the private consortium responsible for the project would be responsible for any cost overruns, with Maryland protected.  This proved not to be the case, and the very low share of equity in the project gave the private consortium strong negotiating leverage when costs turned out to be higher.

b)  The Impact on Maryland’s Public Debt Commitments

The availability payments that Maryland will be obliged to pay on the Purple Line are essentially the same as debt commitments.  Maryland will be obliged to pay them in the amounts set in the contract, with the sole condition that the rail line is available for operations.  The availability payments due will not be reduced should ridership turn out to be less than forecast – even far less than forecast.  Nor will they be reduced should there be, for example, an economic downturn so that public funds are especially tight.  They will have to be maintained at the level agreed in the PPP contract.

The payments are thus basically like payments on a state bond obligation.  But unlike bonds, the payment obligations under the Purple Line PPP are to be for 35 years (5 years for the anticipated construction period, and then 30 years for operations).  In contrast, the maximum term of a State of Maryland public bond is set by law to be no more than 15 years – a maximum term that was presumably set for prudential borrowing reasons.

But Maryland is not counting these availability payment obligations as part of the state’s debt obligations.  If they had, then the Purple Line payment obligations would be included in debt limits set by the state’s Capital Debt Affordability Committee – a committee of senior state officials chaired by the State Treasurer.  The limits are for tax-supported state debt not to exceed 4% of personal (i.e. household) income in the state, nor for debt service obligations to exceed 8% of state revenues.

The state has been close to these limits, where according to the most recent report of the Capital Debt Affordability Committee (issued in December 2023 for FY2025 borrowing), Maryland state debt was 3.1% of state personal income in FY2023 and debt service on such debt was 6.5% of state revenues.  Scaling these up as if the Purple Line debt (construction cost less federal grant) and estimated availability payments were owed and due, Maryland debt would have been 3.9% of personal income in the state (just below the 4% limit), and debt service would have been 7.4% (versus the 8% limit).  Maryland was closer to the limits in FY2019 (as well as FY2020).  Had the Purple Line obligations been due and recognized then, the borrowing limits would have been breached, with state debt at 4.3% of personal income in FY2019 (and 4.4% in FY2020) and debt service payments at 8.7% of state revenues in FY2019 (and 8.5% in FY2020).

These past fiscal year figures are just taken for illustration.  The additional amounts that will be due on the Purple Line would be booked in a future year, and the base amounts for Maryland debt and debt service obligations in those future years are not now known.  But the recent figures indicate that including the Purple Line debts could lead to a breach of the prudential borrowing limits the state has set.  And even if not breached, the Purple Line obligations would reduce the headroom the state has for meeting its other needs.

The State of Maryland under then Governor Hogan decided, however, to exclude the Purple Line debt obligations and the 30-year commitment on the availability payments from Maryland state debt accounts.  As was discussed in more detail in an earlier post on this blog, Maryland officials structured the payment obligations for the Purple Line as if they would be made through a newly created trust account.  When the Purple Line is operational with the availability payments due, that trust account will receive whatever fare revenues are collected on the Purple Line plus then transfers of sufficient revenues from MARC (the state’s public commuter rail system) to suffice to cover the payments due.

Excluding the Purple Line debt and availability payments due would not be inappropriate if fare revenues from the Purple Line could be expected to cover what will be due.  It would not then need to be covered by general state tax revenue.  But the Purple Line fares will not come anywhere close to what is needed to cover the costs.  The average annual availability payments required will be approximately $280 million.  This is based on the $250 million figure provided in February 2022 by the MTA in a briefing to a legislative committee, accounting for the portion that will cover operations and maintenance ($87 million per year on average), and scaling up the debt repayment portion to reflect the additional compensation agreed in July 2023 ($148.3 million) and in March 2024 ($415.0 million).

The fares collected will be far less.  The Travel Forecasts chapter of the FEIS estimates that the net increase in fares on all public transit services when the Purple Line is in operation will only be $9.6 million in 2040.  This figure is especially low as it takes into account that much of the forecast ridership on the Purple Line will be riders who have shifted from other forms of public transit – primarily buses.  Based just on the fares to be collected on the Purple Line itself, with a forecast ridership in 2040 of 69,300 per weekday (certainly highly optimistic, as discussed above) and a fare per trip of $2 (a figure MTA has provided), and using the standard rule of thumb that ridership on weekends is about half the rate of that per weekday, then the gross fares collected on the Purple Line would be $21.6 million per year.

Fare collection of even $21.6 million per year is far below the $280 million needed for the availability payments, and the net fare collection of $9.6 million is even less.  And both figures are certainly overestimates due to the optimistic ridership forecasts.  The difference in what is needed to cover the availability payments would then be covered by a notional transfer of MARC fare revenues.  The argument made is that what is needed to cover the Purple Line payments will thus not come from the Maryland state budget and its regular tax revenues.

But this is not true.  MARC, like most commuter rail systems, does not run a surplus, but rather needs regular budgetary transfers as a subsidy to its operations.  Hence, whatever is transferred from the MARC accounts to cover the Purple Line availability payments will need to be matched dollar-for-dollar by an increase in budgetary transfers to MARC to cover its costs.

This is then just a shell game.  The payment obligations for the Purple Line availability payments are the same – and are being covered by the general state budget – whether the budget transfers are made directly to a Purple Line account or are made indirectly first to a MARC account and then from MARC to a Purple Line account.

The main rating agencies – S&P, Moody’s, and Fitch – recognize that availability payment commitments as have been made for the Purple Line are a state financial commitment, and cannot be ignored when they arrive at their decisions on state bond ratings.  They differ in the details of precisely how they account for what they call “debt-like obligations”, and when those obligations should be taken into account (e.g. as milestone payments are made during construction, or only when the project is operational) but they are unanimous in saying the obligations cannot be excluded in the debt and debt service ratios they examine.

Maryland has a AAA rating, which allows it to borrow on exceptionally good terms.  The Purple Line obligations could have an impact on this.

c)  The Role (or Non-Role) of the State Legislature in Setting the State Budget

The state legislature in Maryland, as in most states, approves major project commitments as well as the regular annual budget of the state.  As was discussed in more detail in an earlier post on this blog, the procedures to be followed in a PPP process were set out in legislation passed in 2013.  The Purple Line PPP was the first project to be managed under this new process, and the legislature gave its approval (also in 2013) for the state to begin the competitive bidding process to select a concessionaire for the project.

This approval by the legislature in 2013 to start the process could only be based, of course, on estimates of what the contract costs might be.  As I noted in the earlier blog post, the state issued a Request for Qualifications in November 2013 to identify interested bidders and a Request for Proposals in July 2014, and then received proposals from four bidders in November and December 2015.  Following a review and final negotiations, the state then announced the winning bidder on March 1, 2016.

The state legislature was then given 30 days to review the proposed contract (of close to 900 pages!), and was allowed within that 30-day window to vote non-approval, should it choose.  If no vote was held, then the contract was deemed approved, and it was.  That original contract, as noted in Section B above, provided for $1,971.9 million for construction and $5,589.9 million in total cost over the anticipated 35-year period:  for construction, operations, as well as the financing costs (interest basically).

The cost is now far higher, at $4,467.8 million for the construction and $9,847.3 million overall (including the July 2023 and March 2024 additions of $153.8 million and $415.0 million respectively).  Yet even though the total cost is $4,247.5 million higher – 76% more than was approved originally – the state legislature has never taken a vote on whether it approved of the additional payment obligations.  It has played no role – at least no formal role – in approving major increased expenditure commitments that future governors (and legislators) will be obliged to abide by.

All that was required by Maryland’s process was approval by the state’s Board of Public Works.  The Board is made up of three members – with one being the governor, one the State Treasurer, and one the State Comptroller – and only two votes are required for the expenditure commitments to pass.  With the governor having one vote, he only needs one other vote when such PPP contracts are being amended to bind the state to a financial commitment that appears to be unlimited.  It could be $4.3 billion and 76% higher than the original approval – as was the case here – or something ten times higher.  It does not appear that there is any limit where legislative approval would be required.  And the term of the obligation – 35 years here – could apparently also be extended to any number of years.

One would think that the state legislature should have a say in any such financial commitments.  Governor Hogan created major new financial obligations that will bind future governors and state legislators for 35 years, with no vote by the legislature on whether this was warranted.

E.  Conclusion

The Purple Line has been a fiasco.  It has been terribly mismanaged, where the cost of building it is now well more than twice what the original fixed price was supposed to be.  And its cost as of today (it could still go even higher) compared to what it cost to build a heavy rail line in Paris – all of it by underground tunnel – is more than ten times as much in terms of the capacity per mile provided.

The problems have not just been with the implementation of the PPP contract.  There were also obvious, and telling, issues with the studies done to forecast what ridership to expect and what the economic impacts would be if the project is built.  The PPP contract could also have been structured so that the potential concessionaires bidding on the contract would take seriously the ridership forecasts.  However, this would then have made it clear that the published ridership forecasts should not have been believed.

In addition, booking the payments that will need to be made during the 35 years of the Purple Line concession contract through a special trust account to be filled by the transfer of fare revenues from MARC, and then for the state legislature to cover those transfers through the annual state budget for MARC, is just a shell game.  The state budget will be covering the payments for the Purple Line.  There should be transparency on this being a state commitment that will bind future Maryland governors and legislatures with a major budgetary expenditure for 35 years.

All this points to a process that simply was not serious.  Expensive studies such as for the ridership forecasts and on the economic impact were not part of a process to determine whether a light rail line was the best use of scarce resources to serve the very real transit needs of these communities.  Rather, one can only see the process as work aimed at trying to find a way to justify a decision that had already been made.

As costs rose and the difficulties with the project became more and more clear, MTA staff did not then consider whether continuing with the project remained warranted (if it ever was).  Rather, they were praised by Governor Hogan for finding a way to push the project forward despite the far higher cost.

As the governor who made the decision in 2016 to have the Purple Line built, and then to sign the re-negotiated contract in 2022 at a far higher cost, Larry Hogan is in the end responsible for this.  It is certainly true that there were other Maryland officials and state legislators – including prior governors – who also pushed for the line to be built.  But Hogan is ultimately responsible for the key decision to proceed, and he should be held accountable.