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

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

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

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

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

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

 

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

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

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

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

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

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

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

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

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

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

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

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Note on Sources:

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

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

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

The UK Parliamentary Election Results: A Big Victory for the Conservative Party, While Voters Shifted to the Left

UK Per Capita GDP 2008Q1 to 2015Q1 vs Great Depression

A.  Introduction

The recent Parliamentary election in the United Kingdom was without doubt a big victory for the Conservative Party and a loss for Labour.  The normally staid The Economist (which I try to read on a regular basis) sub-titled its Bagehot column this past week as “British voters have showed a crushing disdain for the Labour Party”, and in its first sentence termed the election a “calamitous defeat” for Labour.  Stronger language is available in other British publications, for those who are interested.

But while undoubtedly a defeat for Labour and a victory for the Conservatives (who gained a majority of the seats in Parliament, and hence will no longer need to rule in a coalition), one should not jump to what would seem to be the natural conclusion that voters shifted to the Right while abandoning the Left.  That did not happen.  Given the rules of the British electoral system, where Parliamentary seats are won by whomever gained most votes (not necessarily a majority) in each of the 650 constituencies (a “first past the post” system), plus the rise of significant third parties on both the Left and the Right, swings in voter support between Left and Right were quite different from swings in the number of seats won by the major parties.

This blog post will look at these election results, in comparison to the results from the most recent UK general election in May 2010.  That election brought a Conservative – Liberal Democrats coalition to power, replacing the previous Labour Government.  Some have argued (most strongly by the Conservative Party leaders themselves) that the recent election results mark a vindication of the economic program it launched soon after taking office, and a recognition of its success.  The first section below will examine very briefly whether that program can be termed a success, and the rest of the post will then look at the election results themselves.

B.  Growth Has Been Poor

Perhaps the best single measure of whether a government’s program succeeded or not is whether it led to good and sustained growth or not.  While there is of course much more to be concerned with, real incomes and living standards cannot increase overall unless there is growth.

The chart at the top of this post shows what happened in the recent downturn to real per capita GDP in the UK, measured relative to the peak level it had achieved before the downturn (in the first quarter of 2008).  It also shows the path followed by the economy during the Great Depression in the UK, from the peak reached in the first quarter of 1930. The recent data come from the UK Office of National Statistics, while the data on real GDP in the 1930s come from the data set (drawn from academic studies) released by the Bank of England and called “Three Centuries of Data” (a hot link is not possible, but just do a Google search to find it).

The rapid and steep decline in GDP at the start of the Great Depression, and then a delayed and initially slow recovery, had marked the worst previous period for the economy over at least the last century.  But the current recovery has been worse.

The downturn during the 2008 collapse initially traced very closely the path of the downturn during the first year of the Great Depression.  But the Labour Government in power until mid-2010 was able to turn this around after about a year with stabilization and stimulative measures, with the economy then starting to grow.  A Conservative led coalition (with the Liberal Democrats) then took power after the May 2010 general elections, and soon announced a sharp austerity program.  After one more quarter of growth (the new austerity measures began to be implemented in the fall of 2010), the economic recovery was brought to a halt.  Real per capita output was largely unchanged over the next two and a half years.

Policy then shifted, with an easing of the austerity measures.  Growth resumed in early 2013, to a modest rate averaging 1.9% a year (per capita) over the two years leading up to the recent election.  While better than no growth at all, as during the first two and a half years of the Conservative-led Government, such a modest growth rate for an economy coming out of the worst downturn since the Great Depression is poor.

Indeed, as the chart at the top of this post shows, the recovery was a good deal faster during the Great Depression.  At the same point in the downturn (after 28 quarters, or 7 years), real GDP per capita in the Great Depression was 7 1/2 % higher than it had been at its previous peak at the start of 1930.  In the current downturn, it is still 1% lower than it was at the start of 2008.

This is a terrible record.  While there is strong evidence in the political science literature that voters only pay attention to growth in just the year or so before a general election (rather than looking farther back to the full record of the administration), one cannot find evidence here that the austerity program of the Conservatives has been a great success. At most it says that voters can perhaps be fooled by timing the economic cycle to cut growth when first taking office to make it easy to have a “recovery” as the next election approaches.  But it is not clear that UK voters have in fact been fooled in this way, once one looks at whether voters indeed shifted to the Right or to the Left.

C.  Overall Election Results by Party

First the election results by party.  The figures are all taken from the BBC, and the comparisons are made relative to the results in the 2010 general election.  In terms of the number of seats gained or lost:

UK Parliament 2015 Election Results, Change in Number of Seats by Party

Labour lost 26 seats, and the Conservatives won 24, bringing the Conservative total to 331, or a majority in the 650 seat House of Commons in Parliament.  This was a major victory for the Conservatives and a loss for Labour.

The Liberal Democrats also lost, but by much more.  They had won 57 seats in the previous election but only 8 now, for a loss of 49.  They had joined as the junior partner in the Conservative-led coalition of the previous Parliament, and provided the key votes that allowed approval of the conservative agenda of austerity (as well as tax cuts, mostly benefiting the rich).  Voters who had supported the Liberal Democrats before clearly did not like what their party representatives had done, and the party was decimated.

But the biggest winner by far in terms of number of seats was the Scottish National Party (SNP), which gained 50 seats.  This was more than double what the Conservatives gained. The SNP advocates policies well to the left of Labour in terms of support for social programs (in addition to its support for a Scottish nation).  This hints at the need to start to break down the results to see what really happened.

The first step is to look at the swing by party in the share of votes cast.  Because of the UK “first past the post” system, plus the existence of significant smaller parties, swings in votes cast can differ significantly from swings in seats won.  The changes in the shares of the vote gained were:

UK Parliament 2015 Election Results, Change in Share of Vote by Party

Here the Labour Party actually gained, increasing their share of the UK vote by 1.5% points.  They gained in terms of their share of the vote, but still lost 26 seats.  The Conservatives also increased their share of the vote. but only by 0.8% points, about half of what Labour gained, even though this led to their gaining 24 seats in the new parliament. One cannot conclude from this that there was a big swing in sentiment away from Labour and towards the Conservatives.  Labour increased its share of the vote, and by more than the Conservatives did.  But with the UK first past the post system, what matters in terms of seats won is how these votes are spread across constituencies and what gains and losses are being made by third parties.

Among the remaining parties, the Liberal Democrats saw their share of the vote collapse by over 15% points, to a total in 2015 of less than 8% of the voters.  They were decimated, and their losses in seats are consistent with this.

The SNP, as noted above, won big in terms of seats gained, but at a national level their share of the vote rose by just 3% points.  But the SNP contested only seats in the 59 districts in Scotland (and took 56 of them), and there their share of the vote rose by 30% points.  The 50 seats the SNP gained came mostly from Labour (which lost 40 seats in the Scottish districts) and the Liberal Democrats (which lost 10 seats).  The Conservatives had one seat before in Scotland and one seat after, for no net loss, but they basically had nothing to lose there to start with.

The Green Party, with a focus on environmental and social issues, is also on the left. While they gained no new seats in parliament, they increased their share of the UK vote by close to 3% points.

On the right is UKIP (UK Independence Party), a radically right-wing populist party advocating exit from the EU and conservative social programs.  They increased their share of the vote (relative to the 2010 elections) by 9.5% points and gained one seat relative to their 2010 results (when they won zero seats; in by-elections since 2010, UKIP won two seats, so their one seat now is a reduction relative to what they had immediately before the 2015 elections were called).

The BNP (British National Party) is an even more extreme right wing, anti-immigrant and anti-EU, party.  It had close to 2% of the vote in 2010, but dropped to essentially zero now, with most of its supporters likely shifting to UKIP.  It held no seats before or after.

Finally, the other category is comprised of a fairly large number of mostly small parties. The more important among them are regional parties in Wales and Northern Ireland, whose primary focus is on regional issues.  They had no net gain or loss of seats, but they hold a not insignificant 21 seats in the Parliament.

 D.  Left, Center, or Right

Given this distribution across parties, how would the vote and seat count add up if one grouped the parties by ideology?  The Left is made up of Labour, the SNP, and the Greens; the Center is the Liberal Democrats; the Right is the Conservatives, TKIP, and BNP; and Other is everyone else.

The results in terms of share of the vote is:

UK Parliament 2015 Election Results, Change in Share of Vote by Left, Center, Right

The Left gained 7.4% points of the vote, while the Right gained 8.4%.  And the Center (the Liberal Democrats) lost over 15% points.  This is hardly a resounding trouncing of the Left, or a renunciation of their views.

There is then more of a consistent result in the number of seats won or lost:

UK Parliament 2015 Election Results, Change in Number of Seats by Left, Center, Right

The Left won 24 seats, the Right won 25, and the Center lost 49.  It is hard to argue the Left lost in this election.  It is more correct to say that the Center lost, with almost even shares then going to the Left and to the Right.

E.  Just Left or Right

Finally, it is arguable that the Liberal Democrats can no longer be considered a centrist party.  They have been coalition with the Conservatives in the most recent Parliament, and provided the critical votes they needed to implement their austerity program of cuts in government programs (while at the same time granting tax cuts mostly benefiting the rich). While I would not want to argue the point too strongly, for completeness it is of interest to look at how the results add up if one combines the Liberal Democrats with the right-leaning parties.

In terms of voting shares, there is now a clear shift from the Right to the Left:

UK Parliament 2015 Election Results, Change in Share of Vote by Left, Right

The Left gained 7.4% points of the vote, while the Right lost 6.7% points (while the Other category lost 0.7%).  This would then be a pretty sharp swing to the Left.

And in terms of seats won:

UK Parliament 2015 Election Results, Change in Number of Seats by Left, Right

The Left gained 24 and the Right lost 24.  Under a different voting system than that used in the UK, this would have been a major victory for the Left.

F.  Conclusion

There can be no dispute that the Conservative Party won big in this election, while Labour lost.  The Conservatives gained an absolute majority in the new Parliament, and no longer need to rule in coalition.

But this win was a result of the particular electoral rules used in the UK.  The rules are what they are, and those who win by those rules are those who form the new government, but one should not then jump to the conclusion that this win by the Conservatives reflects an endorsement by the voters of their economic program.  There was, rather, a big move to the Left among the electorate as a whole, to parties that strongly criticized the austerity policies of the Conservative-led government.

More fundamentally, the UK recovery from the 2008 downturn has been far too slow, with growth that is not only well below where the economy was at the same seven-year mark during the Great Depression (8% lower in real per capita terms), but also even still below where the economy was in 2008.  No one can dispute that this is a terrible record.

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

A.  Introduction

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

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

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

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

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

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

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

B.  Red Flags

Some obvious issues should have raised attention:

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

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

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

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

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

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

C.  Problems With the Report

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

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

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

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

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

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

There are therefore issues with the theoretical framework used.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are multiple issues:

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

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

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

But it gets worse.

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

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

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

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

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

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

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

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

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

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

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

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

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

D.  Conclusion

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

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

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

Why Wages Have Stagnated While GDP Has Grown: The Proximate Factors

Real GDP per Capita & Median Weekly Earnings, 1980-2013

A.  Introduction

A healthy debate appears to be developing in the run up to the 2016 elections, with politicians of all parties raising the issue of stagnant wages.  Republicans have charged that this is a recent development, and the fault of Obama, but that is certainly not the case.  As the diagram above shows, real median wages have been stagnant since at least 1980, despite real GDP per capita which is 78% higher now than then.  Real median wages are only 5% higher (and in fact unchanged from 1979).  In a normally developing economy, one would expect real GDP per capita and real wages to move together, growing at similar rates and certainly not diverging.  But that has not been the case in the US since at least the early 1980s.

Why has such a large wedge opened up between worker earnings and GDP per capita?  This blog post will look at the immediate factors that lead from one curve to the other.  This will all be data and arithmetic, but will allow one to decompose the separation into several key underlying factors.  A future blog post will look at policies that would address those factors.

B.  Moving from Growth in GDP per Capita to Stagnant Real Wages

The progression from GDP per capita to real wages, with intermediate steps shown, looks as follows:

Going from GDP per Capita to Median Wage, 1947 to 2013:14

The chart here goes back further, to 1947, to show the divergence in recent decades in a longer term perspective.  The data come from the Bureau of Economic Analysis (BEA) or the Bureau of Labor Statistics (BLS).  As one sees, the curves moved together until around the mid-1970s, after which they began to diverge.

1)  Real GDP per Capita

Starting at the top, real GDP per capita (the curve in blue) measures the progression, in real terms, of GDP per person in the US.  GDP captures the value of all goods and services produced in the economy.  Its price index, the GDP deflator, is a price index for all those goods and services.  Although there have been temporary dips with periodic recessions, real GDP per capita has in fact grown at a remarkably stable long term rate of about 1.9% per annum going back all the way to 1870.  The growth rate was in fact a bit higher, at 2.0%, from 1947 to 2014, as the 1947 starting point was somewhat below the long term trend.  With this growth, real GDP per person was 3.75 times higher in 2014 than what it was in 1947.

2)  Real GDP per FTE Worker

But wages are paid to individual workers, and the share of workers in the population can change over time.  The share has in fact grown significantly over the post-war period, and in particular since about the mid-1960s, principally due to women entering the labor force.  There will also be demographic effects leading to changes in the shares of the very young and of retirees.

With a growing share of the population in the labor force, real GDP per full time equivalent (FTE) worker (the measure of the labor force used by the BEA) will grow by less than it will per person in the population.  The path of real GDP per FTE worker (the curve in green in the chart above), will rise more slowly than the path for real GDP per capita.  The curves start to diverge in the mid-1960s, when large numbers of women began to enter the labor force.

It should also be noted that the divergence in the two paths will not necessarily continue forever.  Indeed, the paths have in fact grown broadly in parallel from around 1997 until 2008 (when GDP per capita dipped in the downturn that began in the last year of the Bush administration).  The number of women entering the labor force reached a peak as a share of the labor force around 1997, and a decade later the first of the baby boomers started to retire.

Thus while such demographic factors and labor force participation decisions led to a significant divergence in the two paths (between GDP per capita and GDP per FTE worker) from the mid-1960s to the late-1990s, the impact since then has been broadly neutral, and might in fact go the other way going forward.

3)  Average Real Wages using the GDP Deflator

Next, workers are paid wages, not units of GDP.  Wages and salaries made up roughly half of GDP in 1947, with most of the rest accounted for by profits to capital.  And it stayed in the narrow range of 49 to 51% of GDP continuously until 1974.  The share then fell to 48%, where it held until 1981, and then began to deteriorate much more sharply, to just 42% as of 2013 (the most recent year with this data).

If the share of wages in GDP had remained constant, then the growth of wages per FTE worker would have exactly matched the growth of GDP per FTE worker.  But with a declining share of wages in GDP (with a growing share of profits as the mirror image), the curve (shown in brown in the chart above) of wages per FTE worker will rise by less than the curve of real GDP per FTE worker.

4)  Average Real Wages using the Consumer Price Index

The curves so far have been measured in real terms based on the GDP deflator.  The GDP deflator is a price index that takes into account all goods and services produced in the economy, and the weights in the price index will be in accordance with the shares of each of the goods or services in the overall economy.  But to an individual, what matters is the prices of goods and services that he or she buys.  This is measured by the consumer price index (cpi), where the weights used are in accordance with the expenditures shares of households on each of the items.  These weights can be significantly different than the weights of the items in GDP, as GDP includes more than simply what households consume.

The curve in orange in the chart above is then the average real wage but with the cpi rather than the GDP deflator used to account for inflation.  From 1978 onwards, the average real wage based on the cpi grew by significantly less than the average real wage measured in terms of the GDP deflator.  That is, inflation as measured by the items that make up the cpi grew at a faster rate, from 1978 onwards, than inflation as measured by the items (and their weights) that go into the GDP deflator.  Up until 1978, the cpi and the GDP deflator grew at remarkably similar rates, so the two curves (brown and orange in the chart) follow each other closely up to that year.

What happened after 1978?  The prices of several items whose weight in the cpi is greater than their weight in the GDP deflator began to rise more rapidly than other prices.  Especially important was the rise in medical costs in recent decades, but also important was the rise in housing costs as well as energy (with energy increases already from 1974).

Thus wages expressed in terms of what households buy (the cpi) rose by less, from 1978 onwards, than when expressed in terms of what the economy produces overall (the GDP deflator).

5)  Median Real Wages using the Consumer Price Index

The final step is to note that average wages can be misleading when the distribution of wages becomes more skewed.  If the wages of a few relatively well off wage earners (lawyers, say) rise sharply, the average wage can go up even though the median wage (the wage at which 50% of the workers are earning more and 50% are earning less) has been flat.  And that median wage is what is shown as the red curve in the chart.

[Technical Note:  The median wage series used here is the median weekly earnings of full time workers, adjusted for inflation using the cpi.  The series unfortunately only starts in 1979, but is the only series on the median, as opposed to average, wage I could find that the BLS publishes which goes back even as far as that.  The source comes from the Current Population Survey, which is the same survey of households used to estimate the nation’s unemployment rate, among other statistics.]

Since 1980 (and indeed since 1979, when the series starts), the median real wage has been flat.  This is not a new phenomenon, that only began recently.  But it is a problem nonetheless, and more so because it has persisted over decades.

C.  The Astounding Deterioration in the Distribution of Income Since 1980

Aside from demographic effects (including the impact of women entering the labor force), and the differential impact of certain price increases (medical costs, as well as others), the reason median real wages have been flat since around 1980 despite an increase of real GDP per capita of close to 80% over this period, is distributional.  The share of wages in GDP has been reduced while the share of profits has increased, and the distribution within wages has favored the better off compared to the less well off (leading to a rise in the average wage even though the median wage has been flat).

That is, the US has a distribution problem.  Wages have lost relative to profits (and profits largely accrue to the rich and wealthy), and the wages of lower paid workers have fallen even while the wages of higher paid workers have risen.

There are therefore two reasons for the distribution of income at the household level to have deteriorated since 1980.  And one sees this in the data:

Piketty - Saez 1945 to 2012

This is an update of a chart presented in an earlier post, with data now available through 2012, and with the period from 1945 to 1980 included on the same chart as well.  The data comes from the World Top Incomes Database, which is maintained by Thomas Piketty, Emmanuel Saez, and others.  The data is drawn from individual income tax return filings, and thus the distribution is formally by tax reporting unit (which will normally be households).  The incomes reported are total taxable incomes, whether from wages or from capital.

Over the 33 years from 1947 to 1980, average reported taxable incomes rose in real terms (using the cpi price index to adjust for inflation) by 87%.  The incomes of the bottom 90%, the top 10%, and the top 0.01%, rose by almost exactly the same amount, while the incomes of the top 1% and top 0.1% also rose substantially (by 57% and 63% respectively).  It is amazing how close together all these figures are.

This changed dramatically from 1980.  As the chart above shows, the curves then started to diverge sharply.  Furthermore, the average reported income rose only by 24% over 1980 to 2012, even though real GDP per capita rose by 73% over this period.  The 24% average increase can be compared to the 28% increase over the same years in the average real wage (based on the cpi).  While from two totally different sources of data (income tax returns vs. the national income accounts of the BEA) and measuring somewhat different concepts, these are surprisingly close.

But while average real incomes per household rose by 24%, the bottom 90% saw their real incomes fall by 6%.  Instead, the rich gained tremendously:  by 80% for the top 10%, by 178% for the top 1%, by 312% for the top 0.1%, and by an astounding 431% for the top 0.01%.

The US really does have a distribution problem, and this deterioration in distribution largely explains why real median wages have stagnated since 1980, while real GDP per capita grew at a similar rate to what it had before.

D.  Summary

To summarize, in the post-war period from 1947 to about the mid-1970s, measures of real income per person grew substantially and at similar rates.  Since then, real GDP per capita continued to grow at about the same pace as it had before, but others fell back.  The median real wage has been stagnant.

One can attribute this to four effects, each of which has been broadly similar in terms of the magnitude of the impact:

a)  Real GDP per worker has grown by less than real GDP per capita, as the share of those working the population (primarily women) has grown, with this becoming important from around the mid-1960s.  However, there has been no further impact from this since around 1997 (i.e. the curves then moved in parallel).  It may be close to neutral going forward, but was an important factor in explaining the divergence in the period from the mid-1960s to the late-1990s.

b)  The average real wage (in terms of the GDP deflator) has grown by less than real GDP per worker, as the share of GDP going to wages has gone down while the share going to profits (the mirror image) has gone up, especially since about 1982.

c)  The average real wage measured in terms of the cpi has grown by less than the average real wage measured in terms of the GDP deflator, because of the rising relative price since 1978 of items important in the household consumption basket, including in particular medical costs, but also housing and energy.

d)  The median real wage has grown by less than the average real wage (and indeed has not grown at all since the data series began in 1979), because of increasing dispersion in wage earnings between the relatively highly paid and the rest.

The implication of all this is that if one wants to attack the problem of stagnant wages, one needs to address the sharp deterioration in distribution that has been observed since 1980, and secondly address issues like medical costs.  Medical costs have in fact stabilized under Obama, as was discussed in a recent post on this blog.  But while several of the measures passed as part of the Affordable Care Act (aka ObamaCare) have served to hold down costs, it is too early to say that the previous relentless upward pressure of medical costs has ended.  More needs to be done.

Future blog posts will discuss what policy measures could be taken to address the problem of stagnant real wages and the deterioration in the distribution of income, as well as what can be done to address medical costs.

Part Time Workers and the Affordable Care Act: A Proposal to Address the Real Issue

Part Time Workers as Share of Total Employed, Dec 2007 to Dec 2014

A.  Introduction

The Affordable Care Act (ACA, and also often referred to as ObamaCare) has been working well by any objective measure.  There are now more than 10 million additional Americans who have health insurance who could not get affordable health care before; the share of the uninsured in the US population is now a quarter less than what it was before the individual mandate of the Affordable Care Act went into effect; and this has been achieved at premium rates for the new plans that are reasonable and well less than opponents charged they would be.  Health care costs have also stabilized under Obama, both as a share of GDP and in terms of health prices relative to overall prices, in contrast to the relentless increases in both before.  And while some have criticized this, it is good that there are now minimum quality and coverage standards in health insurance plans.  Such standards are good in themselves.  And without such standards, purported health care “plans” which offer next to nothing (due, for example, to extremely high deductibles) and which can then cost next to nothing, would lead to a death spiral for genuine health care plans that cover costs when you are sick and need treatment.

Gains from the ACA are also reflected in the findings of a recently published report from The Commonwealth Fund.  The Commonwealth Fund has been organizing a periodic survey on health care coverage since 2001.  The most recent survey (for 2014) found that for the first time since the question was first asked in 2003, there was a reduction in the number of Americans avoiding (because of cost) health care services that they needed.  And for the first time since the question was first asked in 2005, the number reporting medical bill or debt problems also fell.  Personal financial distress due to medical problems has been reduced, due to greater access to health insurance and due to health insurance plans that now meet minimum standards.

Despite this (but not surprisingly given the position they staked out against the reform), the Republican Congress continues to vote to repeal, or at least weaken, the law.  The most recent vote was aimed at the provision in the Act which complements the individual mandate to purchase health insurance, with an employer mandate requiring firms with 100 full time equivalent employees or more from January 1 of this year (and with 50 or more from January 1, 2016) to offer health insurance to their full time employees or pay a fee.  The proposed Republican bill would change the definition of a full time worker from one who normally works 30 hours or more a week, to one who works 40 hours or more a week.

The supporters of the change charge that the prospect that employers (with 50 or 100 employees or more) will soon be required to offer health insurance to their full time employees has led firms to cut working hours of their employees, to shift them from full time to part time status, and hence avoid the employer mandate of the ACA.  As a Republican congressman from Texas said:  “We have heard story after story from every state in the union that employers are dropping workers’ hours from less than 39 hours a week to perhaps less than 29.”

This accusation is confused on several levels.  This post will first look at whether there is in fact any evidence that workers are being shifted from full time to part time status as a result of the ACA (or indeed for any other reason).  The answer is no, at least at the level of the overall economy.  Second, there has been a good deal of confusion in the discussion on what the issue really is with regard to part time workers, including by prominent congressmen such as Paul Ryan.  Either Ryan does not understand what the employer mandate is, or if he does, then he has deliberately mischaracterized it.

The public discussion has also avoided altogether the real issue.  It is not that firms with 50 workers or more would be required to offer health insurance to their employees (most do already), but that this insurance is only made available to their full time workers.  Part time workers get nothing, no matter what size firm they work at.  The final section of this blog post will discuss a way to resolve this equitably.

B.  What is the Evidence on Whether the ACA Has Increased the Ranks of Part Time Workers?

The opponents of ObamaCare assert that as a result of the employer mandate, firms have been shifting workers from full time to part time status.  E.g., instead of employing one worker for 40 hours, they are choosing to employ two workers for 20 hours each.  If true, the ratio of part time workers to the total employed will rise.

The chart at the top of this post shows this has not been the case.  It is based on data from the Bureau of Labor Statistics, from its Current Population Survey.  This monthly survey of households is used to determine the unemployment rate among other statistics.  The households surveyed are asked whether household members are employed full time or part time (if employed), and if part time, whether this is by choice (because they only want to work part time) or because they want a full time job but cannot find one.  The chart above shows the ratio of workers who are working part time not by choice but for economic reasons, to all workers employed.  Note that the BLS data defines a part time worker as one with fewer than 35 hours of work per week.  While this differs from the 30 hour standard in the ACA, as well as the 40 hour standard in the recently passed Republican legislation, the results in terms of the trends should be similar.  The BLS does not publish data with a different cutoff in terms of hours per week for what is considered part time work.

As in any economic downturn, the ratio rose rapidly in the economic collapse of the last year of the Bush administration.  Regular jobs were disappearing, with some of them shifting to part time status.  Indeed, the absolute number of part time jobs was increasing at the time, even as the total number of jobs was falling, thus leading to two reasons for the ratio to rise, and rise rapidly.

The ratio reached a peak soon after Obama took office, and began to fall about a year later.  Since then it has fallen at a fairly steady pace in terms of the trend.  There were sometimes relatively sharp month to month fluctuations in the data, but this can be on account of statistical noise.  The data comes from a limited sample of households, with only 5 to 6% or so of those employed on part time status (for economic reasons) for most of this period, so the statistical noise in a relative sense (month to month) will be large.  But the downward trend over time is clear, and at a similar downward pace for close to five years now.

What one does not see is any shift in this downward trend linked either to the signing of the Affordable Care Act in March 2010, or to the start of the individual health insurance mandate in January 2014, or to the anticipation of the start of the employer health insurance mandate in January 2015.  Note that since the classification of a worker as a full time or part time worker (and hence the classification of the firm as crossing the 100 or 50 full time worker standard) will be in a period of up to 12 months before the employer mandate goes into effect, one would have seen an impact in 2014 if the 2015 mandate mattered.  There is no indication of this.

The data cover the overall economy.  The figures refer to millions of workers as well as millions of employers.  The US is a large place.  Within such a large place, it will undoubtedly be possible to find particular cases where employers will say that they reduced worker hours to part time status so that they could avoid the health insurance employer mandate.  And one could indeed probably find a long list of firms making such statements.  It would be even easier to find a long list of firms and other entities where working hours were cut, whether or not there was any employer mandate pending.  In a dynamic economy, there will always be a large number of such cases (along with a large number of cases of firms going in the opposite direction, converting part time jobs to full time jobs).

Such anecdotal information, and even a long list of such anecdotes, is not evidence of an issue of substantial scale.  As seen above, there is no evidence of it in the overall numbers.  But one should still recognize that the issue could exist in particular cases.  The question, however, is what is the real issue here, and if there is one, how can it be addressed.

C.  What the Employer Health Insurance Mandate Says

For better or worse, the US health care insurance system is built around health plans normally provided to workers through their place of employment, as part of their overall wage compensation package.  The system began during World War II and has expanded since, supported through substantial tax advantages.  By now, health insurance provision is close to universal among large employers, but substantially less so among small private firms:

Share of Private Firms Offering Health Insurance – 2013
< 10 employees 28.0%
10 to 24 employees 55.3%
25 to 99 employees 77.2%
100 to 999 employees 93.4%
≥ 1000 employees 99.3%
< 50 employees 34.8%
≥ 50 employees 95.7%
All private employees 84.9%
Source:  MEPS, Tables I.A.2 and I.B.2 (2013)

Overall, 84.9% of private sector employees are in firms that offer health insurance as part of their wage packages.  And 96% of firms with more than just 50 employees offer health insurance.

The Affordable Care Act built on this and did not replace it.  Liberals (including myself) would have preferred moving to a system where Medicare would be extended to cover the entire population rather than just those over age 65.  Medicare is an efficient and well managed program, and as an earlier post in this blog discussed, its administrative expenses come to only 2.1% of the benefits paid.  In contrast, administrative costs (including profits) of private health insurance are seven times higher at 14.0% of benefits paid, and an even higher 18.6% of benefits paid in the privately administered Medicare Advantage plans.

But Obama agreed instead to support an approach first proposed by the conservative Heritage Foundation, which was then put forward by Republicans in Congress as their alternative to the health reforms proposed by the Clinton administration (coming out of the task force Hillary Clinton chaired), and which was later adopted in Massachusetts when Mitt Romney was governor.  These plans were built around keeping the existing employer-based provision of health insurance for most of those employed, but to complement this with markets where individuals could purchase health insurance directly if they did not have employer-based coverage, coupled with an individual mandate to buy such health insurance.  The individual mandate is necessary to counter what would otherwise be a resulting death spiral of health insurance plans if everyone is granted access (including those with pre-existing conditions) but only the sick then purchased health insurance (for a description and discussion, see this earlier Econ 101 blog post).

It was not unreasonable to believe that the Republicans would not oppose a plan whose origins lies in their own earlier proposals, but that was not to be.

As noted, the individual mandate is necessary to avoid death spirals in health insurance plans for individuals.  Complementing this, an employer mandate to offer health insurance to their employees is necessary to counter what could otherwise be a “race to the bottom”.  If certain firms did not support such health insurance for their employees, thus reducing the cost to them of their workers, they could undercut competitors who did provide good health insurance support.  It could lead to a race to the bottom.  While not yet widespread in the US, especially for larger firms (see the table above), there has been increasing competitive pressure in the US over the last couple of decades to cut such health insurance support.  An increasing number of employers have done so.

Thus the ACA includes an employer mandate to complement the individual mandate.  However, while the individual mandate went into effect on January 1, 2014, the employer mandate has been twice delayed, and has now (as of January 1, 2015) gone into effect for firms employing 100 of more full time equivalent employees, and will go into effect on January 1, 2016, for firms employing 50 or more full time equivalent employees.  It is this provision that the Republicans in Congress are now trying to subvert.

The charge by Paul Ryan and others has been that medium to small size firms have been cutting the hours of their employees to shift the workers from a full-time classification to a part-time one.  The aim, they say, has been to reduce the number of their full time workers to below 50 so as to avoid the employer mandate.  For example, in a recent opinion piece published in USA Today, Congressman Ryan wrote:  “The law requires employers with more than 50 full-time employees to give them health insurance.  But because the law defines “full time” as 30 hours or more, employers are keeping employees below that threshold to avoid the mandate entirely.”

However, that is not what the law says.  Precisely to avoid such an incentive, the boundaries on the size of a firm subject to the employer mandate is defined in terms of full time equivalent workers (whether 50 or 100).  That is, if a job is split from one full time worker to two half time workers, the number of full time equivalent workers is unchanged.  The two half time workers count as one full time worker for the purposes of the statute.  Cutting back on the number of hours of individual workers to make them part time will not change the status of the firm when the total hours of labor to produce whatever the firm is producing remains unchanged.  And it would be foolish for a firm to produce and sell less when the demand exists for such sales, simply to avoid this mandate.

There is, however, a critically important issue here which Ryan and his colleagues have not discussed.  While splitting jobs of full time workers into multiple part time jobs will not change the status of the firm on whether it is subject to the employer mandate, shifting workers from full time to part time status does affect whether the firm would be required to include health insurance as part of their wage compensation package.  Firms subject to the mandate must offer an affordable health insurance plan available to at least 95% of full time (not full time equivalent) workers, or pay a fee.  The fee (of up to $2,000 per year per worker, less 30 workers per firm) is designed to partially offset (and only very partially offset) the cost of health insurance that they are shifting to others.

But such health insurance typically only is provided to full time workers.  This is true even for giant corporations.  Hence a firm can avoid making health insurance available to its workers by shifting them from full time to part time status.  This has always been the case, and is indeed a problem.

The Affordable Care Act addresses the issue only partially and tangentially.  By including a definition of what constitutes full time work at 30 hours a week or more, the ACA reduces the incentive to shift workers from the traditional 40 hours per week for full time work, to just under 40 hours in order to avoid providing health insurance cover.  A firm would need to cut a normal worker’s hours to below 30 hours per week to avoid providing health insurance, and is unlikely to do that for its regular work force.  But by moving the dividing line up to 40 hours per week, as the Republican legislation passed on January 8 would do, one opens up a loophole for firms to reduce worker hours from 40 to say 39 per week (or 39 1/2 or even 39.99 I would suppose).  Firms would be able easily to avoid offering health insurance to what are in reality their regular, full time, workers; use this to undercut competitors who do offer such insurance; and thus spark a race to the bottom on health insurance coverage in those industries.

D.  Addressing the Problem of Health Insurance for Part Time Workers

As noted above, the ACA does not do much to address the problem of part time workers receiving nothing from their employers for the health insurance everyone needs.  Setting the floor at 30 hours per week helps by ensuring workers close to the traditional 40 hour workweek will receive an employer contribution to their health insurance, and avoids the incentive to shift workers from 40 hours per week to just a bit below.  But part time workers of less than 30 hours per week will still normally receive nothing from their employer to help cover their health insurance.  And it creates an incentive for employers to structure positions as two workers at 20 hours per week, say, than one at 40.  While whether or not the firm was subject to the employer mandate would not be affected (since it is expressed in terms of full time equivalent workers), whether or not the firms would need to provide anything in terms of health insurance would be affected.

But there is a way to address this, now that the individual health insurance marketplaces are operational under the ACA.  All firms could be required to contribute an amount for their part time workers proportional to the hours of such part time work to what full time work would be.  That is, if two workers are each working half time, the firm would contribute an amount of 50% (for each) of the cost of the employer contribution to the health insurance for one full time worker.  The total cost would be the same whether the firm employed one full time or two half time workers.  There would also then not be an incentive to split jobs from full time workers to multiple part time workers.

The employer contribution to the part time worker’s health insurance costs would then be paid, along with taxes such as for Social Security or Medicare, to the government in the name of the specific part time worker.  These funds would then be used as a partial pay down of the costs of that worker purchasing health insurance on the individual health insurance market exchanges set up under the ACA.  And while other splits could be considered, I would recommend that those funds would be split half and half between what the worker would need to pay on the exchange for his or her health plan, and what the government subsidy would provide.

A simple numerical example may help clarify this.  Using made up numbers, suppose the full monthly cost of a standard (Silver level) health insurance plan on the individual exchange where the worker resides is $400.  Assume also that at the current income level of this (part time) worker, the government subsidy for such insurance would be $200 per month, while the worker would pay $200 per month.  Now assume that firms would be required to pay proportional shares of what they provide to full time workers for their health insurance, and that this would come to $100 per month for this part time worker.  This would be split half and half between what the government subsidy would be and what the worker would pay, so under the new approach the government would provide $150, the worker would pay $150, and the funds coming from the firm would cover $100, summing to the $400 total cost.

A few specifics to note:  Many part time workers hold down multiple jobs.  They would receive for their “account” the total proportional amounts from all of their employers.  Many part time workers are also part of married couples.  There could be a household account into which all the sums were paid (for each family member), which could be used to purchase a family health plan on the exchanges.  In the event that the family was not purchasing insurance through the exchange (perhaps, for example, because the spouse worked at a firm providing family coverage), the amount paid by the firm for the part time worker would be returned to the firm (or canceled from the start).

And if the total amounts paid in from the full set of employers for that individual (or family) led to the government subsidy falling all the way to zero, any excess would be allocated to what the individual would pay for the insurance.  This could be common in cases where the family income of the part time worker was close to, or above, the income limit on which government subsidies are provided.

It is only with the advent of the individual health insurance exchanges that this method for covering part time workers became possible.  Previously, firms were not in a position to purchase half of an insurance policy for a half time worker.  But now they can contribute an amount equal to half the cost, with this then used to help purchase coverage on the individual marketplace exchanges.

Note also that with this reform, it would matter less whether full time work was defined as 30 hours per week or 40 hours per week or whatever.  I would recommend keeping the 30 hour per week boundary as it would be a factor in determining what the employer contribution would be.  But it would not be as critical as now, where the boundary determines whether 100% of the employer share of the health insurance cost is paid or 0% is paid.  There would be a smooth transition (a worker of 39 hours when 40 hours is defined as the standard would still receive 39/40 of the payment, and not zero), without a drop straight to zero.

There would also be no reason to limit this extension of the employer mandate only to firms with 50 (or 100) or more full time equivalent workers.  All firms should make such a contribution to covering the cost of their workers’ health insurance needs, just as they all make a contribution to Social Security and Medicare taxes.  Indeed firms of whatever size (although this will soon apply only to firms with less than 50 full time equivalent workers) that do not have any health insurance plan for their staff should participate.  The amounts paid could be set as a proportion to the cost of the medium Silver level plan available on the individual health insurance exchanges in their area.

Undoubtedly, there will be assertions by the Republicans that requiring such a contribution to health insurance costs for their part time workers will lead to an end to such jobs.  This would be similar to the arguments they have made that raising the minimum wage will lead to higher unemployment of lower paid workers, and arguments that were made earlier that paying Social Security taxes would lead to higher unemployment.  But as was discussed in an earlier blog post, there is no evidence that increases in the minimum wage in the magnitudes that have been discussed have led to such higher unemployment.  Ensuring firms contribute proportionally to the health insurance costs of their part time workers would not either.