The Government Debt to GDP Ratio is Falling

Fed Govt Debt as Share of GDP, 2006Q1 to 2014Q3

The US federal government debt to GDP ratio is falling.  A few years ago, conservative critics (such as Congressman Paul Ryan) argued that if drastic action were not taken immediately to slash government expenditures, consequent rapidly rising federal government debt would stifle growth and spiral ever upwards.  Liberals (such as Paul Krugman) argued that the federal deficit and debt were far less of a concern than these critics asserted:  With the recovery of the economy, both would soon start to fall.  And the detailed projections from the Congressional Budget Office backed this up, with projected falls in the debt to GDP ratio for at least a few years.  There would be a rise later if nothing further is done, in particular on medical costs, but the question at issue here is whether the debt to GDP ratio could fall in the near term without drastic cuts in government expenditures.  Conservatives asserted it would not be possible.

But these were projections and assertions.  The chart above shows the actual data.  With the release this morning by the Bureau of Economic Analysis of its first estimate of 2014 third quarter GDP (growth at a fairly solid 3.5% real rate), one can now see that there has been a downward turn in the debt to GDP ratio.  The ratio peaked at 72.8% of GDP in the first quarter of 2014, and dropped to 72.2% as of the third quarter.

The federal government debt figure used here is the debt held by the public.  There are also various trust funds (most notably the Social Security Trust Fund) that formally hold government debt in trust, but this reflects internal accounting within government.  The figures come from the US Treasury, with quarterly averages taken based on an average of the amounts outstanding each day of the quarter.  This average is then taken as a share of nominal GDP for the quarter (nominal GDP since debt is also a nominal concept).  And since nominal GDP reflects the flow of production over the course of the quarter, taking the daily average debt outstanding over the course of the quarter will better reflect the debt burden than simply taking debt as of the end of the quarter and dividing this by GDP (although this is commonly done by many).

There was an earlier downward dip in the public debt to GDP ratio in the third quarter of 2013, but this was due to special circumstances surrounding the delay by Congress to approve a rise in the statutory government debt ceiling.  Various accounting tricks were used to delay recognition of items that would add to the formally defined government debt in order to keep under the ceiling, which artificially suppressed the debt to GDP ratio in that quarter.  This carried over into the fourth quarter, with the Republicans forcing a shutdown of the federal government from October 1 by not approving a new budget.  The dispute was not resolved until October 16, when deals were reached to raise the debt ceiling and to approve a budget.  The debt ratio then returned to its previous path.

The fall in the debt ratio in 2014 is more significant.  Accounting tricks are not now being used due to debt ceiling disputes, and the fall reflects the continued fall in the fiscal deficit coupled with reasonably sound growth.  The deficit is estimated to have totaled $483 billion in fiscal 2014 (which just ended on September 30), or 2.8% of GDP.  This is sharply down from the $1.4 trillion (or 9.8% of GDP) of fiscal 2009, in the first year of the downturn.  The fiscal deficit has fallen primarily due to the recovery, but also due to cuts in federal government expenditures under Obama since 2010.  While not nearly as drastic as Congressman Ryan and other conservatives had insisted would be necessary, government spending has still fallen under Obama, in contrast to the increases allowed in previous downturns.

Note that the government expenditure cuts that were done do not represent what would have been the desirable path in deficit reduction:  As discussed in an earlier post on this blog, it would have been far better to follow a fiscal path similar to that followed by Reagan and others in earlier downturns, with government spending allowed to grow so that the economy could have more quickly returned to full employment.  Once full employment was reached, one would then consider fiscal cuts, if warranted, to address any debt concerns.

The path followed has thus been far from optimal.  But it has shown that the alarms raised by the conservative critics, that the debt to GDP ratio could not fall without drastic government cutbacks (far more severe than that seen under Obama), were simply wrong.

The Kansas Red State Experiment is Failing: Drastic Tax Cuts Have Not Led to Higher Employment Growth

State Employment Growth Relative to US, Kansas and others, Jan 2011 to Aug 2014

A.  Introduction

Republican Sam Brownback was elected governor of Kansas in 2010 and is now running for re-election.  An extreme Tea Party conservative, he has pursued a radically right-wing agenda in Kansas with little constraint from a Republican controlled state legislature (especially after he led a successful effort to purge relative moderates from his party by more extreme conservatives in the 2012 primaries).

Central to his program were drastic tax cuts enacted in 2012, with a further round of cuts in 2013.  They were one of the largest tax cuts ever enacted by a state in percentage terms, and were labeled by Brownback to be a “real live experiment” of the conservative vision of small government leading to fast growth.  The tax cuts would be like a “shot of adrenaline into the heart of the Kansas economy” he asserted, employment would boom, and the faster growth would lead to greater total tax revenues generated (despite the lower rates) due to a then larger economy.

But it has not happened.  Employment in Kansas has fallen relative to the rest of the US since Brownback took office.  This post will first describe in more detail the tax cut measures, and will then look at the impact on employment.

B.  The Brownback Tax Cuts

The tax cut measures have (so far) come in two major waves.  The first were passed in early 2012 and signed into law by Brownback in May 2012.  The second were passed and signed in mid 2013.  The first were the more important, and included:

a)  Cuts in personal income tax rates, especially for those in the higher brackets:

Income Bracket Old Rate New Rate % change
>$60,000 6.45% 4.90% -24.0%
$30,000 – $60,000 6.25% 4.90% -21.6%
<$30,000 3.50% 3.00% -14.3%

b)  Elimination of state business taxes for most companies in Kansas.  This covered over 200,000 firms in the state, and while purportedly aimed only to benefit small businesses, the definition included at least several of the subsidiaries of Koch Industries (owned by the multi-billionaire Koch brothers, which is based in Kansas and which has provided strong financial support to the campaigns of Brownback and other Tea Party favorites).  No other state has eliminated such taxes.

c)  And partially offsetting the personal income tax cuts, the state sales tax rate was raised.  Such a tax increase affects the poorest the most.

Tax rates were then cut further in 2013.  Among other measures, the rate for the top personal income tax bracket is being phased down from the 4.9% of the 2012 law to just 3.9% by 2018, a cut of over 20%.

The Institute on Taxation & Economic Policy along with the Center on Budget and Policy Priorities has calculated the impact of these tax changes by household income category.  They are hugely regressive.  The figure below (calculated from the ITEP and CBPP figures) shows the impact as a percentage of taxes previously due.  While the combined effect of the tax changes will lead to a 37% reduction in taxes due for the richest 1% in Kansas (whose average household income in 2010 was $1.025 million), the poorest 20% have seen their taxes go up by 14%:

Impact of Kansas 2012 and 2013 Tax Cuts by Income Category

And the regressive tax cuts came on top of a system that already taxes the poor more than the rich.  The poorest 20% will now be paying close to 11% of their income in state and local taxes in Kansas.  Those in the middle (between the 20th to the 80th percentiles) will be paying between 8 and 9%.  But the richest 1% will be paying less than 4%.

The tax cuts have not, however, led to an increase in government revenues.  As will be discussed below, employment has not gone up as a result of the cuts.  Rather, tax cuts have led to cuts in tax revenues received.  While most of us would find this not at all surprising, Brownback was advised by Arthur Laffer, famous for the so-called Laffer Curve, which posits that tax cuts will lead to such an increase in employment and income that tax revenues will rise despite the lower rates.  It has not happened.

A recent report from the Rockefeller Institute of Government found that state tax revenues in Kansas between the second quarter (April to June) of 2013 and the second quarter of 2014 fell by 22% in total, and fell by 43% for the state personal income tax only.  These reductions were larger than in any of the other 50 states.  The 2012 tax cuts went into effect as of January 1, 2013, and the taxes collected in the second quarter of that year will then mostly reflect what was due on incomes earned in 2012.  The reduction in revenues due and collected in the second quarter of 2014 (primarily on incomes earned in 2013) will then represent the first year impact of the 2012 law.

The reduction in tax revenues generated as a consequence of the sharp tax rate cuts should surprise few.  The non-partisan Legislative Research Department of the Kansas state legislature has estimated that, as a consequence of the tax cuts, state revenues will fall by an estimated $730 million in FY14, and by a cumulative $5.2 billion by FY18.  These are large amounts for a small state.  The predictable result has been sharp cuts in the government budget.  Much of this has been borne by education, which is close to inevitable simply because it constitutes such a large share of the state budget.  State funding for K to 12 education has been cut by over 15% during Brownback’s term in office, and he has proposed another 2% cut for FY2015.

Despite such expenditure cuts, the state budgetary situation is now precarious.  This has led to cuts in Kansas government bond ratings by Moody’s and S&P.  It will now be more expensive for Kansas to borrow for public investment and other needs.

C.  Impact on Kansas Employment

Governor Brownback claimed that the massive tax cuts would lead to a boom in Kansas employment.  The chart at the top of this post shows that relative to the United States as a whole, as well as relative to such Democratically controlled states as Colorado, California, Minnesota, and Massachusetts, Kansas has lagged since Brownback was inaugurated (in January 2011).  The data is from the Bureau of Labor Statistics.  The states chosen reflect two near-by states to Kansas with important agricultural sectors and with also Democratic control of the state political machinery (Democratic governors as well as Democratic majorities in both chambers of the state legislatures:  Colorado and Minnesota), and two normally Democratic states from the two coasts who are often charged as having especially high state tax rates (California and Massachusetts)

The chart shows what has happened to employment in each state expressed as a share of total US employment, normalized so that the January 2011 shares are all set to 100.  Thus it will show whether employment in the state grew at a faster, or a slower, pace than overall employment in the US.  The share will go up if employment in the state grows at a faster pace than in the US as a whole, and the share will go down if employment in the state grows more slowly.

Kansas has not performed well.  Its share in US employment has fallen, and more or less consistently fallen, since Brownback took office.  There is no indication that the massive tax cuts, passed into law in May 2012 and expected well before, have led to employers shifting to the state or expanding there.  In contrast, the Blue States of Colorado and California have done especially well, while the Blue States of Minnesota and Massachusetts have seen employment grow at roughly the same pace as the US as a whole.

D.  What This Does and Does Not Say

There is thus no evidence that the massive tax cuts Brownback was able to have enacted have led to the boom in employment he asserted would follow.  It was not a “shot of adrenaline into the heart”, as he asserted it would be.  But it is also important to be clear on what the evidence we have so far does not say:

a)  First, while there is no evidence that the tax cuts led to a boom in employment, there is also no clear evidence that the tax cuts led (at least so far) to major reductions in employment.  Rather, employment in Kansas has trended steadily downwards over this period relative to the rest of the country, with the tax cuts having little effect one way or the other.  State level employment depends on many things, and the state tax regime does not appear to be a terribly important one.  What matters more will likely be state structural issues, such as the mix of particular industries in the state (including agriculture), the age distribution of the population in the state, the mix of high skilled vs. low skilled workers in the state, and so on.

b)  While Kansas performed poorly relative to the other states depicted in the chart above, there were fifteen states that did even more poorly than Kansas over this period.  Overall US employment grew by 6.4% over this period as a whole (1.75% at an annualized rate), while employment in Kansas grew by only 3.7% (1.0% annualized).  But of the 50 states, employment growth was worst in Alaska, with growth of only 1.6% over the period (0.4% annualized).  As noted above, state specific structural issues will matter.

c)  Finally, one should recognize that the period so far has been short.  While Brownback can clearly no longer claim that there will be an immediate or even near-term positive impact on employment, he is (not surprisingly) now claiming that it will take more time.  One can of course not disprove this until more time has passed, but the question is how long does one need before one recognizes the failure.  But we do know that the tax cuts have devastated state finances, leading to the rating downgrades and to budget cuts that are slashing expenditures in important areas such as education.  There is good reason to expect that such cuts in education will have adverse impacts on employment in the longer term.  When the current generation of students graduate, a larger share will not have the level of skills required for good jobs, if any jobs.  Potential employers will shun a state where they cannot hire staff with the skills they need.  I would wager that the long term impact will be negative, not positive.

But what one can say now with confidence is that the evidence is clear that massive tax cuts of this Red State “experiment” have not led to a near term boom in jobs.  It is also clear that such tax cuts do lead to cuts, not increases, in tax revenues.  The experiment has failed to fulfill the claims originally set out for it.

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

Purple Line Costs vs BRT

A.  Introduction

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

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

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

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

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

B.  The Cost of the Purple Line

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

D.  A Cheaper and Better Alternative:  Free Bus Service

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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



Technical Note on Methodology, Data, and Assumptions Used

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

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

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

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

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

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

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

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

Specific figures used included:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


October 1, 2014:  Update

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

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

At One Time, You Could Work Your Way Through College – But Not Any More.

Earnings from Min Wage vs. University Costs, 1963-2013


At one time, not that long ago, a student could work at a minimum wage job over the summers and during holidays, and be able to cover the total cost (including room and board) of attending a four-year state university.  That is now far from possible.

With students now returning to school, it is perhaps a good time to look at what has happened to the affordability of college in recent decades for middle class families.  The chart above provides one indicator.  It compares what a student could earn in a summer job at the minimum wage, or in year-round work at the minimum wage while attending school (i.e. during summers, holidays, and part time during the academic term), as a ratio to what it would cost to attend a four-year state university.

The state university costs are for in-state tuition and required fees, plus the cost of on-campus room and board.  The figures are from the National Center for Education Statistics of the US Department of Education (with figures for 2013 calculated based on the 2012 to 2013 growth in the College Board estimates).  The university cost figures are for four-year, degree granting, state colleges and universities (i.e. they do not include two-year community colleges), and cover all such state schools.  The cost of attending the elite state schools (such as Berkeley, UVA, or the University of Michigan) would be more.  The years shown on the chart are for the beginning of the respective academic years (i.e. 2013 is for the 2013/14 academic year), and the minimum wage rate used is that which was in effect in July of that year.

The chart indicates that one could have covered the cost of attending a state university in the 1960s and 70s solely through minimum wage work.  Based on just a 17 week summer break, one would have earned enough to cover an average of 82% of the full cost of attending school.  An industrious student working full time over the summer and during vacation breaks (such as Christmas), plus 10 hours per week during the academic term, would have been able to cover the full cost and more – an average of 143% of the cost of school.  Hence summer work plus a bit more during vacations would have sufficed to cover the full cost of college.  In terms of dollar figures, the full cost of attending a state university in 1963/64 would have been $929, in the then current dollars.  A student could have earned $782 just from working at minimum wage over the summer, or $1,357 by working at minimum wage over the summer, during vacations, and 10 hours per week during the academic term.

These are, of course, just simple indicators.  One might have been able to earn more than the minimum wage, and/or worked a different number of hours.  But the point is that in the 1960s and 70s, when baby boomers such as myself were going to college, it was possible for the student alone, simply by working at the minimum wage, to have paid for the full cost of attending a four-year state university.

That began to change in the 1980s, as Reagan took office.  The change is indeed striking.  Affordability then began to fall, and it has fallen steadily since, as seen in the chart above.  By 1986, a student working even full time over the summer and during vacation breaks, and 10 hours a week during the academic term, no longer would have been able to cover the full cost of attending school.

The share of schooling costs that could be covered by work then continued to decline (with some bumps up when the minimum wage was sporadically changed) until the present day.  By 2013, summer work would only cover a quarter of the cost of schooling, while more comprehensive work over the entire year would only cover less than half.  In dollar terms, the average cost of attending a state university (for tuition, room, and board) was $18,037 per year in 2013.  But a student working over the summer at the minimum wage would have only been able to earn $4,930, or only a bit over a quarter of the cost of attending school.  Working full time over the summer and during vacations, plus 10 hours per week during the academic term, the student could have only earned $8,555, or less than half the cost of attending school.

As a consequence, students must now rely on their parents (when their parents can afford it), or a scarce number of scholarships (highly limited, especially for state schools), or on student loans.  Otherwise, they must give up on attending university.

The result has been an explosion in student loan debt outstanding.  As of June 30, 2014, student loan debt totaled an estimated $1,275 billion (based on Federal Reserve Board estimates), or five times the level outstanding in 2003 of $250 billion (the earliest figures I could find on a comparable basis; the amounts were so small earlier, that the Fed did not separately break them out).  Student loans have long been common in the US (I had them when I went to school in the early 1970s).  But the amounts outstanding then were relatively small, were at low interest rates, and were for most of us easily manageable.  It is different now.  Student loan debts have exploded in recent years, with a five-fold increase over just the past decade.

The declining affordability of college by this measure is of course a consequence of what has been happening to the two components of the measure.  One has been the unwillingness of Congress to allow the minimum wage to keep up with inflation.  As noted in an earlier post on this blog, the minimum wage in the US has stagnated over the last half century, and is indeed lower now (in real terms) than it was in 1950, when Harry Truman was president.  Real GDP per capita is 3.5 times higher now than it was in 1950, and real labor productivity has increased similarly.  These are not small increases.  I find it amazing (and shameful) that the real minimum wage is lower now than it was then.

For the period since 1963 (the earliest date in the chart), real GDP per capita and real labor productivity are both now 2.7 times higher than what they were then.  But the real minimum wage is close to 20% less now than it was in 1963.

The fall in the real minimum wage fall since the 1960s is half the story.  Note that the inflation measure used for determining the real minimum wage is the general consumer price index (the CPI).  This is the price index for the overall basket of goods and services a US household will purchase.  But the price index is an average over all the goods and services that households buy, and individual items can have price increases that are more than, or less than, this overall average.

In particular, the cost of attending a state university has increased by a good deal more than the overall CPI.  Based on the overall CPI, the real cost of attending a state university (for tuition, room, and board) is now 2.5 times what it was in 1963.  The cost of the tuition component alone is now 4.5 times higher.  The basic cause has been the cutbacks in state budgetary support for their colleges and universities, with tuition and other charges then increased to make up for it.

As a result, the minimum wage has fallen in real terms (based on the overall CPI) since the 1960s, at the same time that the real cost of attending school (relative to the overall CPI) has increased sharply.  The two factors together account for the steep fall in the share of state university costs that one can pay for by working at the minimum wage.  The curves in the chart at the top of this post show that path.

It is important to recognize that this declining affordability of attending state schools was not inevitable, but rather the result of policy choices.  The minimum wage has not been adjusted to reflect general inflation, even though real GDP per capita and labor productivity have both grown substantially.  And as was discussed in another post on this blog, there is no evidence that raising the minimum wage by the modest amounts now being discussed would lead to adverse effects on employment.

Government support for state colleges and universities has also been scaled back, leading to tuition and other cost increases substantially higher than that reflected in the general price index.  This has also been a policy choice.  And it is a policy choice that has prioritized the present generation (with tax cuts a prime example) over the coming generation, that is denying many of the coming generation the educational opportunities we ourselves had.

Transparency of Quality is Essential for a Well-Functioning Health Care System

New York State CABG Mortality, with distribution, 1989-2011

A.  Introduction

Prospective patients will try to assess the quality of the medical care provided by the doctors or hospitals where they might go, when deciding where to seek treatment.  They seek out recommendations from friends and family, they look at publicly available rankings such as those of US News and World Report, and they have their own past experience with some doctor or hospital.  More recently, more information has become available on the internet, allowing prospective patients to look up personal histories on medical providers (where they went to medical school, their age, what languages they speak), as well as to view consumer comments and ratings on dedicated medical websites as well as websites such as Yelp.  There may also be reputational ratings (where doctors are asked what other doctors they would recommend), such as those conducted by the Washingtonian magazine in the Washington, DC, area.

But such information is limited, possibly biased, and superficial.  Recommendations of friends and family, your own experience, and comments and ratings on sites such as Yelp, are really just anecdotal, based on a very limited number of cases.  Individuals will also not always know whether the care they received was in fact high quality or not (there may have been complications, but they will normally not know if they were avoidable).  Rankings in reports such as that of US News and World Report have been criticized for being based on a small set of statistics (limited to those that the publication can obtain) which might have limited relevance.  And reputational ratings can be self-reinforcing, as those being surveyed rate some doctor or hospital highly simply because they have been highly rated in the past.  They may well have no real basis for making an assessment.

Most fundamentally, this information does not focus on what one really wants to know:  Does the doctor or hospital provide good quality care that will cure the patient?  Information such as that above has little on whether the doctors or hospitals are in fact any good at what they do.  Rather, the information is mostly on inputs (where did the doctor go to medical school, for example), or on superficial factors (was the receptionist pleasant when one checked in).

As a result, one can find out more on the quality of a $500 television that one is looking to buy, than on the quality of a doctor who will perform a coronary artery bypass surgery on you.

But information on actual results of doctors and hospitals, in terms of success rates (was the condition cured) and mortality rates, the frequency of medical complications, and other such measures, in fact exist.  The problem is that most of this information, with some exceptions noted below, is kept secret from the public.  Especially limited is information on the performance of specific doctors.  But the information is collected.  There are mandatory reports filed with government and regulatory authorities (both at the federal and state levels in the US).  Insurance companies (including Medicare) will know for the population they cover whether the treatment actually worked or required additional attempts or changes in approach.  Insurance will also know whether there were complications that then had to be treated (with the resulting expenses then filed).  And they will know all this at the level of the individual doctor and medical facility, and for the well defined specific medical procedures which were performed.

The information therefore exists.  The problem is that it is not made publicly available.  The normal rationale provided for this secrecy is that the information is complex and can be difficult to interpret by someone other than a medical professional.  But that is a lame excuse.  The information could be released in a form which adjusts for such factors as the underlying riskiness of the particular cases a doctor has dealt with (there are standard statistical ways to do this), and with accompanying information on the degree of uncertainty (derived statistically) in the information being provided.  One would also expect that if such information were made publicly available, then specialized firms would develop who would take such information and assess it.  Based on their technical analysis, they would sell their findings to insurance companies and firms, as well as interested individuals, on which doctors and facilities performed the best for specific medical procedures.  Government entities interested in good quality care (such as Medicare, in the public interest and also because good quality care costs less in the end) could also assess and make such information available, for free.

The real reason such information on outcomes is in general not made publicly available is rather that the results can be embarrassing for the doctors and hospitals.  And more than simply embarrassing, there could be huge financial implications as well.  Patients would avoid the doctors and hospitals who had poor medical outcomes.  With close to $3 trillion now being spent each year on medical care in the US, this means there are huge vested interests in keeping this information secret from the public.

This is starting to change, however.  As noted above, there are exceptions as well as experiments underway to provide such information to the public.  But it has been fragmented, partial, and highly limited.  The limited information that has been provided so far has been primarily at the level of hospitals, although there have been some experiments with data also being provided on the performance of individual doctors in certain specialties.

From these trials and experiments, we know that widespread availability of such information in an easily accessible form could have profound impacts on the practice of American medicine.

B.  The Impact of Transparency – A New York Experiment

The oldest and longest lasting experiment has been in New York.  Starting with data from 1989 (made publicly available in 1990), the New York State Public Health Commissioner has released the risk-adjusted 30-day in-hospital mortality rates of those undergoing coronary artery bypass graft (CABG, or simply heart bypass) surgery, by specific hospital.  They started to release physician specific mortality rates (on a three-year rolling basis) from December 1992.  There have been a number of good descriptions of, and analyses of the impacts of, the New York program.  Sources I have used include the articles here, here, here, and here.  In addition, a good description is provided as the third chapter in the excellent book by Dr. Marty Makary, Unaccountable, a source I will make further use of below.  Dr. Marty Makary is a physician at The Johns Hopkins Hospital, specializing in pancreatic surgery.  In addition to his many medical research publications, Dr. Makary has undertaken research on how to improve the quality of medical care delivery.

The chart at the top of this post shows what happened to 30-day in-hospital mortality rates following heart bypass surgery since 1989, across hospitals in New York State performing this procedure.  Only hospitals doing 70 or more such surgeries in any given year are included in the chart.  This was to reduce the statistical noise arising from small samples (and there were only a few exclusions:  two hospitals were excluded in two of the 23 years of data, and only one or zero in all of the other years).  A total of 28 hospitals were covered in the 1989 set, with the number rising over time to 38 in 2011.

The data were drawn from the annual reports issued by the New York State Department of Health.  Reports for 1994 to 2011 (the most recent report issued) are available on their web site.  Reports for earlier years were provided to me by a helpful staff member (whom I would like to thank), and the figures for the first half of 1989 were published in a December 1990 article in the Journal of the American Medical Association.  All the mortality rates shown are risk-adjusted rates, as estimated by the New York Department of Health, which controls for the relative riskiness of the patients (compared to the others in New York State that year) that were treated in the facility.

The chart depicts a remarkable improvement in mortality rates once it became known that the figures would be gathered and made publicly available, with individual hospitals named.  The chart shows the fall over time of the average rate across the state (note this is not the median rate, but rather the mean), as well as the minimum and maximum rates across all hospitals with 70 or more CABG procedures in the year.  The ranges at the 90th and 10th percentiles are also shown.  Among the points to note:

1)  The average risk-adjusted mortality rate fell sharply in the early years, and since then has continued to improve.  Furthermore, the underlying improvement was in fact greater than what it appears to be in these figures.  The average mortality rates shown in the chart are for the mix of patients (by riskiness of their health status) in each given year.  But especially in the early years, when angioplasty and coronary stent procedures were developing and found to be suitable for lower risk patients, the pool of patients for whom coronary bypass surgery was needed became a riskier mix.  Taking this into account, while the overall average mortality rate fell by a very significant 21% between 1989 and 1992, once one accounts for the higher risk of the patients operated on in 1992, the fall in the cross year risk-adjusted mortality rate was an even larger 41% over just this three year period.  Technology for CABG procedures did not change over this period.  Transparency did.

2)  The improvement in the coronary artery bypass surgery mortality rate in New York is especially impressive as New York was starting from a rate which was already in 1989 better than the average across all US states.  And by 1992, the rate in New York was the best across all US states.

3)  What is perhaps even more interesting and important, not only did the average rate in New York improve, but also the dispersion in mortality rates across hospitals was dramatically reduced.  The maximum (worst) mortality rate dropped from almost 18% in the first half of 1989 to under 6% by 1992.  The minimum rate was 2.1% in 1989H1, and fell to zero in 9 of the 12 most recent years.  One sees this narrowing in dispersion also in the range between the 90th and 10th percentile bands.

Publication of the mortality results got a good deal of media attention in the early years, and led to pressure, especially on the poor performers, to improve.  Note that the information being gathered was not anything new.  State health authorities long had reports on death rates by hospitals.  What was new was to make this information publicly available, with hospitals named.

Hospitals with poor records then scrambled to improve.  A range of actions were taken.  Some might have seemed obvious, but even so, were not undertaken until the mortality rates by hospital were made publicly available.  For example, hospitals with poor records began to create cardiac specific teams of nurses and other staff, rather than draw on staff from a pool who could be assigned to a wide range of different medical conditions.  Such specialization allowed them to learn better what was needed in cardiac surgery, and to work better as teams.  Such a reorganization at Winthrop Hospital, which included bringing in a new Chief of Cardiac Surgery who led the effort, led to a drop in its mortality rate from 9.2% in 1989 (close to the worst in the state in that year) to 4.6% in 1990 and to 2.3% in 1991 (better than the state wide average that year of 3.1%).

Other issues were highly hospital specific.  For example, one hospital (St. Peter’s in Albany) saw that its mortality rates for pre-scheduled elective and even urgent CABG surgery cases were similar to those elsewhere in New York.  But it had especially poor rates for emergency cases, which raised its overall average.  After reviewing the data, its doctors concluded that they were not stabilizing sufficiently the emergency patients before the surgery.   After it corrected this, its mortality rates fell sharply.  They were among the highest in New York in 1991 and 1992 (at 6.6% and 5.8%), but the rates then fell to 2.5% in 1993 and 1.4% in 1994 (when the New York average rate was 2.5%).  Mortality in emergency cases fell from 26% in 1992 (11 of 42 cases) to 0% in 1993 (zero in 54 emergency cases).

Another hospital (Strong Memorial) also found that its mortality rates for routine elective cases were similar to the New York average, but very high for the emergency cases, bringing up its overall average.  The problem was that while they had a good adult cardiac surgeon, he was always fully booked with routine cases, and hence was not available when an emergency case came in.  They then used one of two doctors who were not trained in adult cardiac surgery to handle the emergencies (one was a vascular surgeon, and the other a specialist in pediatric cardiac surgery).  By hiring a new adult cardiac surgeon and then better balancing the schedule, the rates soon dropped to normal.

American health care has traditionally relied on state regulators, armed with reports on hospital and indeed surgeon specific practices and outcomes, to impose safety and good practice measures.  But there is no way a central regulator can know all that might be underlying the causes of poor outcomes, or what actions should be taken to remedy the problem.  They also will not focus on hospitals with relatively good, or even average, mortality rates, even though such institutions could often still improve.  By releasing the data to the public, hospitals with poor records will be under great pressure to improve, while even those with relatively good records will see the need to get better if they are to stay competitive.  And the actions taken will often be actions that no central regulator would have been able to see, much less require.

C.  Staff Surveys as Another Indicator of Quality

Outcome indicators, up to and including mortality rates, are one set of measures which could have a profound impact on the quality of health care delivery if made publicly available.  An additional type of measure has been developed by Dr. Marty Makary, tested with a number of hospitals, and is now routinely used in hospitals across the US.  But the results are then typically kept secret from the public.

Specifically, Dr. Makary developed a simple staff survey (see here and here, in addition to his book Unaccountable referenced above) with some key questions.  The survey goes to all staff in a hospital, and asks questions such as whether the respondent would feel comfortable having their own care performed in the hospital unit in which they work.

In the original test, the surveys were sent to all staff at 60 hospitals across the US.  They got a 77% response rate, which is quite good.  What is most interesting was the wide range they found in the results across the hospitals.  For example, on the question of whether the staff member would want their own care performed at the hospital unit in which they work, there were two hospitals where close to 100% of the staff said they would, but also one hospital in which only 16% said they would.  There was a fairly even spread between these two extremes, and in about half of the hospitals surveyed, less than half of the staff said they would want their own care performed there:

Makary Hospital Staff Survey - Care in Own Unit.003

This would be powerful information to have as a patient.  The insiders are really the ones who know best what quality of care is being provided.  If even they would not want their health care needs met at their hospital, one knows where one would want to avoid.

It is recognized that the original Makary survey was done with the promise that the identities of the individual hospitals would not be revealed.  Should such surveys be made publicly available, the staff responding might well be less negative.  But the identities of the individual staff members would still be kept confidential (with the data gathered by an independent third party, and anonymously over the web).  There would certainly still be some dispersion in results across hospitals, and one could take into account the possible biases when judging the results.  And if a hospital is rated poorly by its staff even when they know the results will be made public, one knows which hospitals to avoid.  One would expect such hospitals then to scramble to improve the quality of the care they provide.

D.  While a Number of Transparency Initiatives Are Underway, They Remain Fragmented and Partial

Patients have always sought information on the quality of the care they will need, and have made decisions on where to go based on what they can find out.  But the information that they have been able to obtain has been only partial, highly fragmented, and far from what they really need to know to make a wise decision.

People will also find measures that are easily observed, but not necessarily terribly important to the quality of the care they will receive.  For example, they may find out whether parking is free and convenient, but this should not normally be a driving factor for their decision.  More relevant, and obviously something they will know, will be geographic location:  Is the facility close to them, or further away?  But they will normally have little basis for determining whether it is worthwhile to go a facility that is further away.

There has been a substantial expansion in recent years in the amount of information one can find on providers.  While still limited, one can find out more now than before.  There is the New York experiment described above, which New York soon extended from hospitals to individual surgeons, and also to angioplasty and cardiac stent procedures.  New York has also brought together on one web site easy access to a wide range of health topic data sets.  These include data sets on outcomes and quality of care indicators (such as the most recent CABG mortality rates by hospital and by surgeon, for example) but also many others (such as the most common baby names chosen).

The Obama administration has also expanded substantially the public availability of information on hospital quality measures.  The Centers for Medicare and Medicaid Services (CMS) now makes available at its Medicare Hospital Compare site results at the hospital level, drawn primarily from the data they have for Medicare patients, on such outcome measures as mortality rates, complications, hospital readmission rates, and other indicators.  However, they are still partial, and instead of showing, for example, actual and historical figures by hospital for indicators such as the rate of complications or mortality, they simply show whether the rates are similar to the national norm, or better or worse by a statistically significant margin (at the 95% significance level).

With the clear positive impact of the New York experiment, other states have also begun to implement similar programs.  But they remain partial and fragmented, and do not provide the comprehensive picture a patient really needs if they are to make a wise choice.

In addition, many professional medical societies have begun to collect similar data from their members, and then calculate risk-adjusted measures.  However, they have then kept the individual results secret, with identifying information by hospital or physician not made available.  Individual hospitals and physicians could release them if they so chose, and some have.  But one can safely assume that only those with good results will release the information, while those with poor results will not.

The same is true for hospital staff surveys, such as the one described above pioneered by Dr. Makary.  Such surveys are now widely used.  Dr. Makary reports in Unaccountable (published in 2012) that approximately 1,500 hospitals were then undertaking such surveys.  The number is certainly higher now.  But the results are in general kept secret.  Some hospitals make them publicly available, but one can again safely assume that these will be the ones with the better results.  Without the others for comparison, it is difficult to judge how meaningful the individual figures are.

So the relevant data are often collected already.  It is only a matter of making them public.  There is not a question of feasibility in collecting such data, but rather a question of willingness to make them public.

E.  What a Transparent System of Information on Quality Should Include

As noted above, people will gather what information they can.  But what they can gather now is limited.  What is needed is hard data on actual outcomes, identified by hospital and by individual doctor.  As the New York experiment discussed above indicates, the result could have a profound impact on quality of care.

Specifically, there should be easy access to the following specific measures:

a)  Volume:  While not directly an outcome measure, it is now well established in the literature that a higher frequency of a doctor undertaking some specific medical procedure, or that is done by all the doctors at some hospital or medical facility, is positively associated with better outcomes.  A doctor that undertakes a procedure a hundred times a year, or more, will on average have better outcomes than one who does the procedure only a dozen times a year (i.e. once a month).  And volume can be easily measured.  The problem is in obtaining easy access to the information, and at the relevant level of detail (i.e. by individual doctor, and for the procedure actually being considered for the patient, not just of some standard benchmark procedure).

b)  Success rates:  While many of the outcome measures being used in various trials and experiments are negative measures (mortality rates; complication rates), a more useful starting point would be risk-adjusted success rates.  What percentage of the procedures undertaken by the individual doctor or at the medical facility for some condition actually leads to a cure of the condition?  How success is defined will vary by the medical issue, but standard ones are available.  If the risk-adjusted success rate is 80% for one doctor and 99% for another, the choice should be clear.  Yet I have never seen a trial or experiment where such success rates by medical facility, much less at the level of individual doctors, were made publicly available.

c)  Success rates without complications:  A more stringent measure would be not only that the procedure was a success, but that it was achieved without a noteworthy complication such as an infection.

d)  Complication rates:  Moving to negative measures, one wants to see minimized the complications associated with some procedure.  The medical profession has identified the complications often found as a result of some medical procedure, and significant complications will be reported.  They can also normally be identified from medical insurance records, as they require treatment.  As with mortality rates, these should be published on a risk-adjusted basis.

e)  Mortality rates:  The ultimate “complication” is mortality.  As discussed extensively above, these should be made available by medical procedure and by individual doctor on a risk-adjusted basis.  The 30 day mortality might be appropriate for most medical procedures, but for others the 60 day or 90 day rates might be more appropriate.  Medical societies can work out what makes most sense for a given procedure.  But everyone should then be required to use the same measure, to allow comparability.

f)  Bounceback rates:  Bounceback rates are the percentage of patients undergoing some procedure, who then need to be readmitted back to a hospital (the original one or some other) within some period following release, usually 90 days.  Readmission rates are regularly collected by hospitals, and they can also be risk adjusted when made publicly available.  They are a good indication that some problem developed.  Some rate of readmission might well be expected for certain procedures.  They are not risk free.  But one wants to see if the bounceback rates are especially high, or low, for the physician or medical facility being considered.

g)  Never events:  Never events are events that should never occur.  While a certain rate of complications will normally be expected, one should never see an operation done on the wrong side of the body, or sponges or medical instruments left in the body after the surgeon has sewn up.  Hospitals know these and keep track of them (as such never events often lead to expensive lawsuits), but not surprisingly want to keep them secret.

h)  Hospital Staff Surveys:  As discussed above, Dr. Marty Makary developed a survey that would go to all hospital staff, which asks a series of questions on the quality of care being provided at the facility.  While approximately 1,500 hospitals were already administering the survey in 2012 (when his book Unaccountable was published), they are voluntary and in general not made publicly available.  They should be.

While the surveys can cover a long list of questions, Dr. Makary recommends (Unaccountable, page 216) that the percentage of hospital staff responding “yes” to the following three questions, at least, should be made public:

-  “Would you have your operation at the hospital in which you work?”

-  “Do you feel comfortable speaking up when you have a safety concern?”

-  “Does the teamwork here promote doing what’s right for the patient?”

F.  Conclusion

There are of course many other measures of quality one could examine, and there has been some movement in recent years to making more available.  These include results from patient surveys (“were you content with your experience at the hospital?”, “were the rooms kept clean?”), as well as the percentage of cases where certain established medical best practices were followed (“was aspirin given within 24 hours of a suspected heart attack?”).

Such additional measures might well be useful in particular cases.  It will depend on the individual, their particular condition, and what specifically is important to them.  People should have a choice, and do the research they personally wish to do.

But until hard measures on actual outcomes, such as those described above, are made widely available, and on a comprehensive rather than partial and fragmented basis, it will not be possible to make a well informed and wise choice on which doctor and medical facility to go to.  Without this, there can be no effective competition across providers.  There will be little pressure on the poor quality providers either to improve their performance, or drop out and let providers who can deliver better quality care treat the patients.

The impact on the quality of health care services provided would be profound.