Long-Term Structural Change in the US Economy: Manufacturing is Simply Following the Path of Agriculture

A.  Introduction

A major theme of Trump, both during his campaign and now as president, has been that jobs in manufacturing have been decimated as a direct consequence of the free trade agreements that started with NAFTA.  He repeated the assertion in his speech to Congress of February 28, where he complained that “we’ve lost more than one-fourth of our manufacturing jobs since NAFTA was approved”, but that because of him “Dying industries will come roaring back to life”.  He is confused.  But to be fair, there are those on the political left as well who are similarly confused.

All this reflects a sad lack of understanding of history.  Manufacturing jobs have indeed been declining in recent decades, and as the chart above shows, they have been declining as a share of total jobs in the economy since the 1940s.  Of all those employed, the share employed in manufacturing (including mining) fell by 7.6% points between 1994 (when NAFTA entered into effect) and 2015 (the most recent year in the sector data of the Bureau of Economic Analysis, used for consistency throughout this post), a period of 21 years. But the share employed in manufacturing fell by an even steeper 9.2% points in the 21 years before 1994.  The decline in manufacturing jobs (both as a share and in absolute number) is nothing new, and it is wrong to blame it on NAFTA.

It is also the case that manufacturing production has been growing steadily over this period.  Total manufacturing production (measured in real value-added terms) rose by 64% over the 21 years since NAFTA went into effect in 1994.  And this is also substantially higher than the 42% real growth in the 21 years prior to 1994.  Blaming NAFTA (and the other free trade agreements of recent decades) for a decline in manufacturing is absurd.  Manufacturing production has grown.

For those only interested in the assertion by Trump that NAFTA and the other free trade agreements have killed manufacturing in the US and with it the manufacturing jobs, one could stop here.  Manufacturing has actually grown strongly since NAFTA went into effect, and there are fewer manufacturing jobs now than before not because manufacturing has declined, but because workers in manufacturing are now more productive than ever before (with this a continuation of the pattern underway over at least the entire post-World War II period, and not something new).  But the full story is a bit more complex, as one also needs to examine why manufacturing production is at the level that it is.  For this, one needs to bring in the rest of the economy, in particular services. The rest of this blog post will address this broader issue,

Manufacturing jobs have nonetheless indeed declined.  To understand why, one needs to look at what has happened to productivity, not only in manufacturing but also in the other sectors of the economy (in particular in services).  And I would suggest that one could learn much by an examination of the similar factors behind the even steeper decline over the years in the share of jobs in agriculture.  It is not because of adverse effects of free trade.  The US is in fact the largest exporter of food products in the world.  Yet the share of workers employed in the agricultural sectors (including forestry and fishing) is now just 0.9% of the total.  It used to be higher:  4.3% in 1947 and 8.4% in 1929 (using the BEA data).  If one wants to go really far back, academics have estimated that agricultural employment accounted for 74% of all US employment in 1800, with this still at 56% in 1860.

Employment in agriculture has declined so much, from 74% of total employment in 1800 to 8.4% in 1929 to less than 1% today, because those employed in agriculture are far more productive today than they were before.  And while it leads to less employment in the sector, whether as a share of total employment or in absolute numbers, higher productivity is a good thing.  The US could hardly enjoy a modern standard of living if 74% of those employed still had to be working in agriculture in order to provide us food to eat. And while stretching the analysis back to 1800 is extreme, one can learn much by examining and understanding the factors behind the long-term trends in agricultural employment.  Manufacturing is following the same basic path.  And there is nothing wrong with that.  Indeed, that is exactly what one would hope for in order for the economy to grow and develop.

Furthermore, the effects of foreign trade on employment in the sectors, positive or negative, are minor compared to the long-term impacts of higher productivity.  In the post below we will look at what would have happened to employment if net trade would somehow be forced to zero by Trumpian policies.  The impact relative to the long term trends would be trivial.

This post will focus on the period since 1947, the earliest date for which the BEA has issued data on both sector outputs and employment.  The shares of agriculture as well as of manufacturing in both total employment and in output (with output measured in current prices) have both declined sharply over this period, but not because those sectors are producing less than before.  Indeed, their production in real terms are both far higher. Employment in those sectors has nevertheless declined in absolute numbers.  The reason is their high rates of productivity growth.  Importantly, productivity in those two sectors has grown at a faster pace than in the services sector (the rest of the economy).  As we will discuss, it is this differential rate of productivity growth (faster in agriculture and in manufacturing than in services) which explains the decline in the share employed in agriculture and manufacturing.

These structural changes, resulting ultimately from the differing rates of productivity growth in the sectors, can nonetheless be disruptive.  With fewer workers needed in a sector because of a high rate of productivity growth, while more workers are needed in those sectors where productivity is growing more slowly (although still positively and possibly strongly, just relatively less strongly), there is a need for workers to transfer from one sector to another.  This can be difficult, in particular for individuals who are older or who have fewer general skills.  But this was achieved before in the US as well as in other now-rich countries, as workers shifted out of agriculture and into manufacturing a century to two centuries ago.  Critically important was the development of the modern public school educational system, leading to almost universal education up through high school. The question the country faces now is whether the educational system can be similarly extended today to educate the workers needed for jobs in the modern services economy.

First, however, is the need to understand how the economy has reached the position it is now in, and the role of productivity growth in this.

B.  Sector Shares and Prices

As Chart 1 at the top of this post shows, employment in agriculture and in manufacturing have been falling steadily as a share of total employment since the 1940s, while jobs in services have risen.

[A note on the data:  The data here comes from the Bureau of Economic Analysis (BEA), which, as part of its National Income and Product Accounts (NIPA), estimates sector outputs as well as employment.  Employment is measured in full-time equivalent terms (so that two half-time workers, say, count as the equivalent of one full-time worker), which is important for measuring productivity growth.

And while the BEA provides figures on its web site for employment going all the way back to 1929, the figures for sector output on its web site only go back to 1947.  Thus while the chart at the top of this post goes back to 1929, all the analysis shown below will cover the period from 1947 only.  Note also that there is a break in the employment series in 1998, when the BEA redefined slightly how some of the detailed sectors would be categorized. They unfortunately did not then go back to re-do the categorizations in a consistent way in the years prior to that, but the changes are small enough not to matter greatly to this analysis.  And there were indeed similar breaks in the employment series in 1948 and again in 1987, but the changes there were so small (at the level of aggregation of the sectors used here) as not to be noticeable at all.

Also, for the purposes here the sector components of GDP have been aggregated to just three, with forestry and fishing included with agriculture, mining included with manufacturing, and construction included with services.  As a short hand, these sectors will at times be referred to simply as agriculture, manufacturing, and services.

Finally, the figures on sector outputs in real terms provided by the BEA data are calculated based on what are called “chain-weighted” indices of prices.  Chain-weighted indices are calculated based on moving shares of sector outputs (whatever the share is in any given period) rather than on fixed shares (i.e. the shares at the beginning or the end of the time period examined).  Chain-weighted indices are the best to use over extended periods, but are unfortunately not additive, where a sum (such as real GDP) will not necessarily equal exactly the sum of the estimates of the underlying sector figures (in real terms).  The issue is however not an important one for the questions being examined in this post.  While we will show the estimates in the charts for real GDP (based on a sum of the figures for the three sectors), there is no need to focus on it in the analysis.  Now back to the main text.]

The pattern in a chart of sector outputs as shares of GDP (measured in current prices by the value-added of each sector), is similar to that seen in Chart 1 above for the employment shares:

Agriculture is falling, and falling to an extremely small share of GDP (to less than 1% of GDP in 2015).  Manufacturing and mining is similarly falling from the mid-1950s, while services and construction is rising more or less steadily.  On the surface, all this appears to be similar to what was seen in Chart 1 for employment shares.  It also might look like the employment shares are simply following the shifts in output shares.

But there is a critical difference.  The shares of workers employed is a measure of numbers of workers (in full-time equivalent terms) as a share of the total.  That is, it is a measure in real terms.  But the shares of sector outputs in Chart 2 above is a measure of the shares in terms of current prices.  They do not tell us what is happening to sector outputs in real terms.

For sector outputs in real terms (based on the prices in the initial year, or 1947 here), one finds a very different chart:

Here, the output shares are not changing all that much.  There is only a small decline in agriculture (from 8% of the total in 1947 to 7% in 2015), some in manufacturing (from 28% to 22%), and then the mirror image of this in services (from 64% to 72%).  The changes in the shares were much greater in Chart 2 above for sector output shares in current prices.

Many might find the relatively modest shifts in the shares of sector outputs when measured in constant price terms to be surprising.  We were all taught in our introductory Economics 101 class of Engel Curve effects.  Ernst Engel was a German statistician who, in 1857, found that at the level of households, the share of expenditures on basic nourishment (food) fell the richer the household.  Poorer households spent a relatively higher share of their income on food, while better off households spent less.  One might then postulate that as a nation becomes richer, it will see a lower share of expenditures on food items, and hence that the share of agriculture will decline.

But there are several problems with this theory.  First, for various reasons it may not apply to changes over time as general income levels rise (including that consumption patterns might be driven mostly by what one observes other households to be consuming at the time; i.e. “keeping up with the Joneses” dominates).  Second, agricultural production spans a wide range of goods, from basic foodstuffs to luxury items such as steak.  The Engel Curve effects might mostly be appearing in the mix of food items purchased.

Third, and perhaps most importantly, the Engel Curve effects, if they exist, would affect production only in a closed economy where it was not possible to export or import agricultural items.  But one can in fact trade such agricultural goods internationally. Hence, even if domestic demand fell over time (due perhaps to Engel Curve effects, or for whatever reason), domestic producers could shift to exporting a higher share of their production.  There is therefore no basis for a presumption that the share of agricultural production in total output, in real terms, should be expected to fall over time due to demand effects.

The same holds true for manufacturing and mining.  Their production can be traded internationally as well.

If the shares of agriculture and manufacturing fell sharply over time in terms of current prices, but not in terms of constant prices (with services then the mirror image), the implication is that the relative prices of agriculture as well as manufacturing fell relative to the price of services.  This is indeed precisely what one sees:

These are the changes in the price indices published by the BEA, with all set to 1947 = 1.0.  Compared to the others, the change in agricultural prices over this 68 year period is relatively small.  The price of manufacturing and mining production rose by far more.  And while a significant part of this was due to the rise in the 1970s of the prices of mined products (in particular oil, with the two oil crises of the period, but also in the prices of coal and other mined commodities), it still holds true for manufacturing alone.  Even if one excludes the mining component, the price index rose by far more than that of agriculture.

But far greater was the change in the price of services.  It rose to an index value of 12.5 in 2015, versus an index value of just 1.6 for agriculture in that year.  And the price of services rose by double what the price of manufacturing and mining rose by (and even more for manufacturing alone).

With the price of services rising relative to the others, the share of services in GDP (in current prices) will then rise, and substantially so given the extent of the increase in its relative price, despite the modest change in its share in constant price terms.  Similarly, the fall in the shares of agriculture and of manufacturing (in current price terms) will follow directly from the fall in their prices (relative to the price of services), despite just a modest reduction in their shares in real terms.

The question then is why have we seen such a change in relative prices.  And this is where productivity enters.

C.  Growth in Output, Employment, and Productivity

First, it is useful to look at what happened to the growth in real sector outputs relative to 1947:

All sector outputs rose, and by substantial amounts.  While Trump has asserted that manufacturing is dying (due to free trade treaties), this is not the case at all.  Manufacturing (including mining) is now producing 5.3 times (in real terms) what it was producing in 1947.  Furthermore, manufacturing production was 64% higher in real terms in 2015 than it was in 1994, the year NAFTA went into effect.  This is far from a collapse.  The 64% increase over the 21 years between 1994 and 2015 was also higher than the 42% increase in manufacturing production of the preceding 21 year period of 1973 to 1994. There was of course much more going on than any free trade treaties, but to blame free trade treaties on a collapse in manufacturing is absurd.  There was no collapse.

Production in agriculture also rose, and while there was greater volatility (as one would expect due to the importance of weather), the increase in real output over the full period was in fact very similar to the increase seen for manufacturing.

But the biggest increase was for services.  Production of services was 7.6 times higher in 2015 than in 1947.

The second step is to look at employment, with workers measured here in full-time equivalent terms:

Despite the large increases in sector production over this period, employment in agriculture fell as did employment in manufacturing.  One unfortunately cannot say with precision by how much, given the break in the employment series in 1998.  However, there were drops in the absolute numbers employed in manufacturing both before and after the 1998 break in the series, while in agriculture there was a fall before 1998 (relative to 1947) and a fairly flat series after.  The change in the agriculture employment numbers in 1998 was relatively large for the sector, but since agricultural employment was such a small share of the total (only 1%), this does not make a big difference overall.

In contrast to the falls seen for agriculture and manufacturing, employment in the services sector grew substantially.  This is where the new jobs are arising, and this has been true for decades.  Indeed, services accounted for more than 100% of the new jobs over the period.

But one cannot attribute the decline in employment in agriculture and in manufacturing to the effects of international trade.  The points marked with a “+” in Chart 6 show what employment in the sectors would have been in 2015 (relative to 1947) if one had somehow forced net imports in the sectors to zero in 2015, with productivity remaining the same. There would have been an essentially zero change for agriculture (while the US is the world’s largest food exporter, it also imports a lot, including items like bananas which would be pretty stupid to try to produce here).  There would have been somewhat more of an impact on manufacturing, although employment in the sector would still have been well below what it had been decades ago.  And employment in services would have been a bit less. While most production in the services sector cannot be traded internationally, the sector includes businesses such as banking and other finance, movie making, professional services, and other areas where the US is in fact a strong exporter.  Overall, the US is a net exporter of services, and an abandonment of trade that forced all net imports (and hence net exports) to zero would lead to less employment in the sector.  But the impact would be relatively minor.

Labor productivity is then simply production per unit of labor.  Dividing one by the other leads to the following chart:

Productivity in agriculture grew at a strong pace, and by more than in either of the other two sectors over the period.  With higher productivity per worker, fewer workers will be needed to produce a given level of output.  Hence one can find that employment in agriculture declined over the decades, even though agricultural production rose strongly. Productivity in manufacturing similarly grew strongly, although not as strongly as in agriculture.

In contrast, productivity in the services sector grew at only a modest pace.  Most of the activities in services (including construction) are relatively labor intensive, and it is difficult to substitute machinery and new technology for the core work that they do.  Hence it is not surprising to find a slower pace of productivity growth in services.  But productivity in services still grew, at a positive 0.9% annual pace over the 1947 to 2015 period, as compared to a 2.8% annual pace for manufacturing and a 3.3% annual pace in agriculture.

Finally, and for those readers more technically inclined, one can convert this chart of productivity growth onto a logarithmic scale.  As some may recall from their high school math, a straight line path on a logarithmic scale implies a constant rate of growth.  One finds:

While one should not claim too much due to the break in the series in 1998, the path for productivity in agriculture on a logarithmic scale is remarkably flat over the full period (once one abstracts from the substantial year to year variation – short term fluctuations that one would expect from dependence on weather conditions).  That is, the chart indicates that productivity in agriculture grew at a similar pace in the early decades of the period, in the middle decades, and in the later decades.

In contrast, it appears that productivity in manufacturing grew at a certain pace in the early decades up to the early 1970s, that it then leveled off for about a decade until the early 1980s, and that it then moved to a rate of growth that was faster than it had been in the first few decades.  Furthermore, the pace of productivity growth in manufacturing following this turn in the early 1980s was then broadly similar to the pace seen in agriculture in this period (the paths are then parallel so the slope is the same).  The causes of the acceleration in the 1980s would require an analysis beyond the scope of this blog post. But it is likely that the corporate restructuring that became widespread in the 1980s would be a factor.  Some would also attribute the acceleration in productivity growth to the policies of the Reagan administration in those years.  However, one would also then need to note that the pace of productivity growth was similar in the 1990s, during the years of the Clinton administration, when conservatives complained that Clinton introduced regulations that undid many of the changes launched under Reagan.

Finally, and as noted before, the pace of productivity growth in services was substantially less than in the other sectors.  From the chart in logarithms, it appears the pace of productivity growth was relatively robust in the initial years, up to the mid-1960s.  While slower than the pace in manufacturing or in agriculture, it was not that much slower.  But from the mid-1960s, the pace of growth of productivity in services fell to a slower, albeit still positive, pace.  Furthermore, that pace appears to have been relatively steady since then.

One can summarize the results of this section with the following table:

Growth Rates:

1947 to 2015

Employment

Productivity

Output

Total (GDP)

1.5%

1.4%

2.9%

Agriculture

-0.7%

3.3%

2.6%

Manufacturing

-0.3%

2.8%

2.5%

Services

2.1%

0.9%

3.0%

The growth rate of output will be the simple sum of the growth rate of employment in a sector and the growth rate of its productivity (output per worker).  The figures here do indeed add up as they should.  They do not tell us what causes what, however, and that will be addressed next.

D.  Pulling It Together:  The Impact on Employment, Prices, and Sector Shares

Productivity is driven primarily by technological change.  While management skills and a willingness to invest to take advantage of what new technologies permit will matter over shorter periods, over the long term the primary driver will be technology.

And as seen in the chart above, technological progress, and the resulting growth in productivity, has proceeded at a different pace in the different sectors.  Productivity (real output per worker) has grown fastest over the last 68 years in agriculture (a pace of 3.3% a year), and fast as well in manufacturing (2.8% a year).  In contrast, the rate of growth of productivity in services, while positive, has been relatively modest (0.9% a year).

But as average incomes have grown, there has been an increased domestic demand in what the services sector produces, not only in absolute level but also as a share of rising incomes.  Since services largely cannot be traded internationally (with a few exceptions), the increased demand for services will need to be met by domestic production.  With overall production (GDP) matching overall incomes, and with demand for services growing faster than overall incomes, the growth of services (in real terms) will be greater than the growth of real GDP, and therefore also greater than growth in the rest of the economy (agriculture and manufacturing; see Chart 5).  The share of services in real GDP will then rise (Chart 3).

To produce this, the services sector needed more labor.  With productivity in the services sector growing at a slower pace (in relative terms) than that seen in agriculture and in manufacturing, the only way to obtain the labor input needed was to increase the share of workers in the economy employed in services (Chart 1).  And depending on the overall rate of labor growth as well as the size of the differences in the rates of productivity growth between the sectors, one could indeed find that the shift in workers out of agriculture and out of manufacturing would not only lead to a lower relative share of workers in those sectors, but also even to a lower absolute number of workers in those sectors.  And this is indeed precisely what happened, with the absolute number of workers in agriculture falling throughout the period, and falling in manufacturing since the late 1970s (Chart 6).

Finally, the differential rates of productivity growth account for the relative price changes seen between the sectors.  To be able to hire additional workers into services and out of agriculture and out of manufacturing, despite a lower rate of productivity growth in services, the price of services had to rise relative to agriculture as well as manufacturing. Services became more expensive to produce relative to the costs of agriculture or manufacturing production.  And this is precisely what is seen in Chart 4 above on prices.

To summarize, productivity growth allowed all sectors to grow.  With the higher incomes, there was a shift in demand towards services, which led it to grow at a faster pace than overall incomes (GDP).  But for this to be possible, particularly as its pace of productivity growth was slower than the pace in agriculture and in manufacturing, workers had to shift to services from the other sectors.  The effect was so great (due to the differing rates of growth of productivity) that employment in services rose to the point where services now employs close to 90% of all workers.

To be able to hire those workers, the price of services had to grow relative to the prices of the other sectors.  As a consequence, while there was only a modest shift in sector shares over time when measured in real terms (constant prices of 1947), there was a much larger shift in sector shares when measured in current prices.

The decline in the number of workers in manufacturing should not then be seen as surprising nor as a reflection of some defective policy.  Nor was it a consequence of free trade agreements.  Rather, it was the outcome one should expect from the relatively rapid pace of productivity growth in manufacturing, coupled with an economy that has grown over the decades with this leading to a shift in domestic demand towards services.  The resulting path for manufacturing was then the same basic path as had been followed by agriculture, although it has been underway longer in agriculture.  As a result, fewer than 1% of American workers are now employed in agriculture, with this possible because American agriculture is so highly productive.  One should expect, and indeed hope, that the same eventually becomes true for manufacturing as well.

Taxes to Pay for Highways: A Switch from the Tax on Gallons of Fuel Burned to a Tax on Miles Driven Would Be Stupid

Impact of Switching from Fuel Tax on Gallons Burned to Tax on Miles Driven

A.  Introduction

According to a recent report in the Washington Post, a significant and increasing number of state public officials and politicians are advocating for a change in the tax system the US uses to support highway building and maintenance.  The current system is based on a tax on gallons of fuel burned, and the proposed new system would be based on the number of miles a car is driven.  At least four East Coast states are proposing pilots on how this might be done, some West Coast states have already launched pilots, and states are applying for federal grants to consider the change.  There is indeed even a lobbying group based in Washington now advocating it:  The Mileage-Based User Fee Alliance.

There is no question that the current federal gas tax of 18.4 cents per gallon of gasoline is woefully inadequate.  It was last changed in 1993, 23 years ago, and has been kept constant in nominal terms ever since.  With general prices (based on the CPI) now 65% higher, 18.4 cents now will only buy 11.2 cents at the prices of 1993, a decline of close to 40%.  As a result, the Highway Trust Fund is terribly underfunded, and with all the politics involved in trying to find other sources of funding, our highways are in terrible shape. Basic maintenance is simply not being done.

An obvious solution would be simply to raise the gas tax back at least to where it was before in real terms.  Based on where the tax was when last set in 1993 and on the CPI for inflation since then, this would be 30.3 cents per gallon now, an increase of 11.9 cents from the current 18.4 cents per gallon.  Going back even further, the gasoline tax was set at 4 cents per gallon in 1959, to fund the construction of the then new Interstate Highway system (as well as for general highway maintenance).  Adjusting for inflation, that tax would be 32.7 cents per gallon now.  Also, looking at what the tax would need to be to fund adequately the Highway Trust Fund, a Congressional Budget Office report issued in 2014 estimated that a 10 to 15 cent increase (hence 28.4 cents to 33.4 cents per gallon) would be needed (based on projections through 2024).

These fuel tax figures are all similar.  Note also that while some are arguing that the Highway Trust Fund is underfunded because cars are now more fuel efficient than before, this is not the case.  Simply bringing the tax rate back in real terms to where it was before (30.3 cents based on the 1993 level or 32.7 cents based on the 1959 level) would bring the rate to within the 28.4 to 33.4 cents range that the CBO estimates is needed to fully fund the Highway Trust Fund.  The problem is not fuel efficiency, but rather the refusal to adjust the per gallon tax rate for inflation.

But Congress has refused to approve any such increase.  Anti-tax hardliners simply refuse to consider what they view as an increase in taxes, even though the measure would simply bring them back in real terms to where they were before.  And it is not even true that the general population is against an increase in the gas tax.  According to a poll sponsored by the Mineta Transportation Institute (a transportation think tank based at San Jose State University in California), 75% of those polled would support an immediate increase in the gas tax of 10 cents a gallon if the funds are dedicated to maintenance of our streets, roads, and highways (see the video clip embedded in the Washington Post article, starting at minute 3:00).

In the face of this refusal by Congress, some officials are advocating for a change in the tax, from a tax per gallon of fuel burned to a new tax per mile each car is driven.  While I do not see how this would address the opposition of the anti-tax politicians (this would indeed be a totally new tax, not an adjustment in the old tax to keep it from falling in real terms), there appears to be a belief among some that this would be accepted.

But even if such a new tax were viewed as politically possible, it would be an incredibly bad public policy move to replace the current tax on fuel burned with such a tax on miles driven.  It would in essence be a tax on fuel efficiency, with major distributional (as well as other) consequences, favoring those who buy gas guzzlers.  And as it would encourage the purchase of heavy gas guzzlers (relative to the policy now in place), it would also lead to more than proportional damage to our roads, meaning that road conditions would deteriorate further rather than improve.

This blog post will discuss why such consequences would follow.  To keep things simple, it will focus on the tax on gasoline (which I will sometimes simply referred to as gas, or as fuel).  There are similar, but separate taxes, on diesel and other fuels, and their levels should be adjusted proportionally with any adjustment for gasoline.  There is also the issue of the appropriate taxes to be paid by trucks and other heavy commercial vehicles.  That is an important, but separate, issue, and is not addressed here.

B.  The Proposed Switch Would Penalize Fuel Efficient Vehicles

The reports indicate that the policy being considered would impose a tax of perhaps 1.5 cents per mile driven in substitution for the current federal tax of 18.4 cents per gallon of gas burned (states have their own fuel taxes in addition, with these varying across states). For the calculations here I will take the 1.5 cent figure as the basis for the comparisons, even though no specific figure is as yet set.

First of all, it should be noted that at the current miles driven in the country and the average fuel economy of the stock of cars being driven, a tax of 1.5 cents per mile would raise substantially more in taxes than the current 18.4 cents per gallon of gas.  That is, at these rates, there would be a substantial tax increase.

Using figures for 2014, the average fuel efficiency (in miles per gallon) of the light duty fleet of motor vehicles in the US was 21.4 miles per gallon, and the average miles driven per driver was 13,476 miles.  At a tax of 1.5 cents per mile driven, the average driver would pay $202.14 (= $.015 x 13,476) in such taxes per year.  With an average fuel economy of 21.4 mpg, such a driver would burn 629.7 gallons per year, and at the current fuel tax of 18.4 cents per gallon, is now paying $115.87 (= $.184 x 629.7) in gas taxes per year. Hence the tax would rise by almost 75% ($202.14 / $115.87).  A 75% increase would be equivalent to raising the fuel tax from the current 18.4 cents to a rate of 32.1 cents per gallon.  While higher tax revenues are indeed needed, why a tax on miles driven would be acceptable to tax opponents while an increase in the tax per gallon of fuel burned is not, is not clear.

But the real reason to be opposed to a switch in the tax to miles driven is the impact it would have on incentives.  Taxes matter, and affect how people behave.  And a tax on miles driven would act, in comparison to the current tax on gallons of fuel burned, as a tax on fuel efficiency.

The chart at the top of this post shows how the tax paid would vary across cars of different fuel efficiencies.  It would be a simple linear relationship.  Assuming a switch from the current 18.4 cents per gallon of fuel burned to a new tax of 1.5 cents per mile driven, a driver of a highly fuel efficient car that gets 50 miles per gallon would see their tax increase by over 300%!  A driver of a car getting the average nation-wide fuel efficiency of 21.4 miles per gallon would see their tax increase by 75%, as noted above (and as reflected in the chart).  In contrast, someone driving a gas guzzler getting only 12 miles per gallon or less, would see their taxes in fact fall!  They would end up paying less under such a new system based on miles driven than they do now based on gallons of fuel burned.  Drivers of luxury sports cars or giant SUVs could well end up paying less than before, even with rates set such that taxes on average would rise by 75%.

Changing the tax structure in this way would, with all else equal, encourage drivers to switch from buying fuel efficient cars to cars that burn more gas.  There are, of course, many reasons why someone buys the car that they do, and fuel efficiency is only one.  But at the margin, changing the basis for the tax to support highway building and maintenance from a tax per gallon to a tax on miles driven would be an incentive to buy less fuel efficient cars.

C.  Other Problems

The change to a tax on miles driven from the tax on gallons of fuel burned would have a number of adverse effects:

a)  A Tax on Fuel Efficiency:  As noted above, this would become basically a tax on fuel efficiency.  More fuel efficient cars would pay higher taxes relative to what they do now, and there will be less of an incentive to buy more fuel efficient cars.  There would then be less of an incentive for car manufacturers to develop the technology to improve fuel efficiency.  This is what economists call a technological externality, and we all would suffer.

b)  Heavier Vehicles Cause Far More Damage to the Roads:  Heavier cars not only get poorer gas mileage, but also tear up the roads much more, leading to greater maintenance needs and expense.  Heavier vehicles also burn more fuel, but there is a critical difference.  As a general rule, vehicles burn fuel in proportion with their weight: A vehicle that weighs twice as much will burn approximately twice as much fuel.  Hence such a vehicle will pay twice as much in fuel taxes (when such taxes are in cents per gallon) per mile driven.

However, the heavier vehicle also cause more damage to the road over time, leading to greater maintenance needs.  And it will not simply be twice as much damage.  A careful early study found that the amount of damage from a heavier vehicle increases not in direct proportion to its weight, but rather approximately according to the fourth power of the ratio of the weights.  That is, a vehicle that weighs twice as much (for the same number of axles distributing the weight) will cause damage equal to 2 to the fourth power (=16) times as much as the lighter vehicle.  Hence if they were to pay taxes proportionate to the damage they do, a vehicle that is twice as heavy should pay 16 times more in taxes, not simply twice as much.

(Note that some now argue that the 2 to the fourth power figure found before might be an over-estimate, and that the relationship might be more like 2 to the third power.  But this would still imply that a vehicle that weighs twice as much does 8 times the damage (2 to the third power = 8).  The heavier vehicle still accounts for a grossly disproportionate share of damage to the roads.)

A tax that is set based on miles driven would tax heavy and light vehicles the same.  This is the opposite of what should be done:  Heavy vehicles cause far more damage to the roads than light vehicles do.  Encouraging heavy, fuel-thirsty, vehicles by switching from a tax per gallon of fuel burned to a tax per mile driven will lead to more road damage, and proportionately far more cost than what would be collected in highway taxes to pay for repair of that damage.

c)  Impact on Greenhouse Gases:  One also wants to promote fuel efficiency because of the impact on greenhouse gases, and hence global warming, from the burning of fuels. By basic chemistry, carbon dioxide (CO2) is a direct product of fuel that is burned.  The more fuel that is burned, the more CO2 will go up into the air and then trap heat. Economists have long argued that the most efficient way to address the issue of greenhouse gases being emitted would be to tax them in proportion to the damage they do.  A tax on gallons of fuel that are burned will do this, while a tax on miles driven (and hence independent of the fuel efficiency of the vehicle) will not.

An interesting question is what level of gasoline tax would do this.  That is, what would the level of fuel tax need to be, for that tax to match the damage being done through the associated emission of CO2.  The EPA has come up with estimates of what the social cost of such carbon emissions are (and see here for a somewhat more technical discussion of its estimates).  Unfortunately, given the uncertainties in any such calculations, as well as uncertainty on what the social discount rate should be (needed to discount costs arising in the future that follow from emitting greenhouse gases today), the cost range is quite broad. Hence the EPA presents figures for the social cost of emitting CO2 using expected values at alternative social discount rates of 2.5%, 3%, and 5%, as well as from a measure of the statistical distribution of one of them (the 95th percentile for the 3% discount rate, meaning there is only an estimated 5% chance that the cost will be higher than this).  The resulting costs per metric ton of CO2 emitted then range from a low of $11 for the expected value (the 50th percentile) at the 5% discount rate, $36 at the expected value for the 3% discount rate, and $56 for the expected value for the 2.5% discount rate, to $105 for the 95th percentile at a 3% discount rate (all for 2015).

With such range in social costs, one should be cautious in the interpretation of any one. But it may still be of interest to calculate how this would translate into a tax on gasoline burned by automobiles, to see if the resulting tax is “in the ballpark” of what our fuel taxes are or should be.  Every gallon of gasoline burned emits 19.64 pounds of CO2.  There are 2,204.62 pounds in a metric ton, so one gallon of gas burned emits 0.00891 metric tons of CO2.  At the middle social cost of $36 per metric ton of CO2 emitted (the expected value for the 3% social discount rate scenario), this implies that a fuel tax of 32.1 cents per gallon should be imposed.  This is surprisingly almost precisely the fuel tax figure that all the other calculations suggest is warranted.

d)  One Could Impose a Similar Tax on Electric Cars:  One of the arguments of the advocates of a switch from taxes on fuel burned to miles driven is that as cars have become more fuel efficient, they pay less (per mile driven) in fuel taxes.  This is true.  But as generally lighter vehicles (one of the main ways to improve fuel economy) they also cause proportionately far less road damage, as discussed above.

There is also an increasing share of electric, battery-powered, cars, which burn no fossil fuel at all.  At least they do not burn fossil fuels directly, as the electricity they need to recharge their batteries come from the power grid, where fossil fuels dominate.  But this is still close to a non-issue, as the share of electric cars among the vehicles on US roads is still tiny.  However, the share will grow over time (at least one hopes).  If the share does become significant, how will the cost of building and maintaining roads be covered and fairly shared?

The issue could then be addressed quite simply.  And one would want to do this in a way that rewards efficiency (as different electric cars have different efficiencies in the mileage they get for a given charge of electricity) rather than penalize it.  One could do this by installing on all electric cars a simple meter that keeps track of how much it receives in power charges (in kilowatt-hours) over say a year.  At an annual safety inspection or license renewal, one would then pay a tax based on that measure of power used over the year.  Such a meter would likely have a trivial cost, of perhaps a few dollars.

Note that the amounts involved to be collected would not be large.  According to the 2016 EPA Automobile Fuel Economy Guide (see page 5), all-electric cars being sold in the US have fuel efficiencies (in miles per gallon equivalent) of over 100 mpg, and as high as 124 mpg.  These are on the order of five times the 21.4 average mpg of the US auto stock, for which we calculated that the average tax to be paid would be $202.  Even ignoring that the electric cars will likely be driven for fewer miles per year than the average car (due to their shorter range), the tax per year commensurate with their fuel economy would be roughly $40.  This is not much.  It is also not unreasonable as electric cars are kept quite light (given the limits of battery technology) and hence do little road damage.

e)  There Are Even Worse Policies That Have Been Proposed:  As discussed above, there are many reasons why a switch from a tax on fuel burned to miles driven would be a bad policy change.  But it should be acknowledged that some have proposed even worse. One example is the idea that there should be a fixed annual tax per registered car that would fund what is needed for highway building and maintenance.  Some states in fact do this now.

The amounts involved are not huge.  As was calculated above, at the current federal gasoline tax of 18.4 cents per gallon, the driver of a car that gets the average mileage (of 21.4 mpg) for the average distance a year (of 13,476 miles) will pay $115.87 a year.  If the fuel tax were raised to 32.1 cents per gallon (or equivalently, if there were a tax of 1.5 cents per mile driven), the average tax paid would be still just $202.14 per year.  These are not huge amounts.  One could pay them as part of an annual license renewal.

But the tax structured in this way would then be the same for a driver who drives a fuel efficient car or a gas guzzler.  And it would be the same for a driver who drives only a few miles each year, or who drives far more than the average each year.  The driver of a heavy gas guzzler, or one who drives more miles each year than others, does more damage to the roads and should pay more to the fund that repairs such damage and develops new road capacity.  The tax should reflect the costs they are imposing on society, and a fixed annual fee does not.

f)  The Cost of Tax Collection Needs to be Recognized:  Finally, one needs to recognize that it will cost something to collect the taxes.  This cost will be especially high for a tax on miles driven.

The current system, of a tax on fuel burned, is efficient and costs next to nothing to collect.  It can be charged at the point where the gasoline and other fuels leaves in bulk from the refinery, as all of it will eventually be burned.  While the consumer ultimately pays for the tax when they pump their gas, the price being charged at the pump simply reflects the tax that had been charged at an earlier stage.

In contrast, a tax on miles driven would need to be worked out at the level of each individual car.  And if the tax is to include shares that are allocating to different states, the equipment will need to keep track of which states the car is being driven in.  As the Washington Post article on a possible tax on miles driven describes, experiments are underway on different ways this might be done.  All would require special equipment to be installed, with a GPS-based system commonly considered.

Such special equipment would have a cost, both up-front for the initial equipment and then recurrent if there is some regular reporting to the center (perhaps monthly) of miles driven.  No one knows right now what such a system might cost if it were in mass use, but one could easily imagine that a GPS tracking and reporting system might cost on the order of $100 up front, and then several dollars a month for reporting.  This would be a significant share of a tax collection that would generate an average of just $202 per driver each year.

There is also the concern that any type of GPS system would allow the overseers to spy on where the car was driven.  While this might well be too alarmist, and there would certainly be promises that this would not be done, some might not be comforted by such promises.

D.  Conclusion

While one should always consider whether given policies can be changed for the better, one needs also to recognize that often the changes proposed would make things worse rather than better.  Switching the primary source of funding for highway building and maintenance from a tax on fuel burned to a tax on miles driven is one example.  It would be a stupid move.

There is no doubt that the current federal tax on gasoline of 18.4 cents per gallon is too low.  The result is insufficient revenues for the Highway Trust Fund, and we end up with insufficient road capacity and roads that are terribly maintained.

What I was surprised by in the research for this blog post was finding that a wide range of signals all pointed to a similar figure for what the gasoline tax should be. Specifically:

  1. The 1959 gas tax of 4 cents per gallon in terms of current prices would be 32.7 cents per gallon;
  2. The 1993 gas tax of 18.4 cents per gallon in terms of current prices would be 30.3 cents per gallon;
  3. The proposal of a 1.5 cent tax per mile driven would be equivalent (given current average car mileage and the average miles driven per year) to 32.1 cents per gallon;
  4. The tax to offset the social cost of greenhouse gas emissions from burning fuel would be (at a 3% social discount rate) 32.1 cents per gallon.
  5. The Congressional Budget Office projected that the gasoline tax needed to fully fund the Highway Trust Fund would be in the range of 28.4 to 33.4 cents per gallon.

All these point in the same direction.  The tax on gasoline should be adjusted to between 30 and 33 cents per gallon, and then indexed for inflation.

The Rate of Return on Funds Paid Into Social Security Are Actually Quite Good

Social Security Real Rates of Return - Various Scenarios

 

A.  Introduction

The rate of return earned on what is paid into our Social Security accounts is actually quite good.  It is especially good when one takes into account that these are investments in safe assets, and thus that the proper comparison should be to the returns on other safe assets, not risky ones.  Yet critics of Social Security, mostly those who believe it should be shut down in its current form with some sort of savings plan invested through the financial markets (such as a 401(k) plan) substituted for it, often assert that the returns earned on the pension savings in Social Security are abysmally poor.

These critics argue that by “privatizing” Social Security, that is by shifting to individual plans invested through the financial markets, returns would be much higher and that thus our Social Security pensions would be “rescued”.  They assert that by privatizing Social Security investments, the system will be able to provide pensions that are either better than what we receive under the current system, or that similar pensions could be provided at lower contribution (Social Security tax) rates.

There are a number of problems with this.  They include that risks of poor financial returns (perhaps due, for example, to a financial collapse such as that suffered in 2008 in the last year of the Bush administration, when many Americans lost much or all of their retirement savings) would then be shifted on to individuals.  Individuals are not in a good position to take on such risks.  Individuals are also not financial professionals, nor normally in a good position to judge the competency of financial professionals who offer them services.  They also often underestimate the impact of high and compounding fees in depleting their savings over time.  For all these reasons, such an approach would serve as a bad substitute for the Social Security system such as we have now, which is designed to provide at least a minimum pension that people can rely on in their old age, with little risk.

But there is also a more fundamental problem with this approach.  It presumes that returns in the financial markets will in general be substantially higher than returns that one earns on what we pay into the Social Security system.  This blog post will show that this is simply not true.

The post looks at what the implicit rates of return are under several benchmark cases for individuals.  We pay into Social Security over our life time, and then draw down Social Security pensions in our old age.  The returns will vary for every individual, depending on their specific earnings profile (how much they earn in each year of their working career), their age, their marital situation, and other factors.  Hence there will be over 300 million different cases, one for each of the over 300 million Americans who are either paying into Social Security or are enjoying a Social Security pension now.  But by selecting a few benchmarks, and in particular extreme cases in the direction of where the returns will be relatively low, we can get a sense of the range of what the rates of return normally will be.

The chart at the top of this post shows several such cases.  The rest of this post will discuss each.

B.  Social Security Rates of Return Under Current Tax and Benefit Rates

The scenarios considered are all for an individual who is assumed to work from age 22 to age 65, who then retires at 66.  The individual is assumed to have reached age 65 in 2013 (the most recent year for which we have all the data required for the calculations), and hence reached age 62 in 2010 and was born in 1948.  The historical Social Security tax rates, the ceiling on wages subject to Social Security tax, the wage inflation factors used by Social Security to adjust for average wage growth, and the median earnings of workers by year, are all obtained from the comprehensive Annual Statistical Supplement to the Social Security Bulletin – 2014 (published April 2015).  Information on the parameters needed to calculate what the Social Security pension payments will be are also presented in detail in this Statistical Supplement, or in a more easy-to-use form for the specific case of someone reaching age 62 in 2010 in this publication of the Social Security Administration.  It is issued annually.

The Social Security pension for an individual is calculated by first taking the average annual earnings (as adjusted for average wage growth) over the 35 years of highest such earnings in a person’s working career.  For someone who always earned the median wage who reached age 62 in 2010, this would work out to $2,290 per month. The monthly pension (at full retirement age) would then be equal to 90% of the first $761, 32% of the earnings above this up to $4,586 per month, and then (if any is left, which would not be the case in this example of median earnings) 15% of the amount above $4,586.  Note the progressivity in these rates of 90% for the initial earnings, then 32%, and finally 15% for the highest earnings.  The monthly Social Security pension will then be the sum of these three components.  Since it is then adjusted for future inflation (as measured by the CPI), we do not need to make any further adjustments to determine the future pension payments in real terms.  The pensions will then be paid out from age 66 until the end of their life, which we take to be age 84, the current average life expectancy for someone who has reached the age of 65.

The historical series of payments made into the Social Security system through Social Security taxes (for Social Security Old-Age pensions only, and so excluding the taxes for Disability insurance and for Medicare) are then calculated by multiplying earnings by the tax rate (currently 10.6%, including the shares paid by both worker and employer).  The stream of payments are then put in terms of 2010 dollars using the historical CPI series from the Bureau of Labor Statistics.

We can thus calculate the real rates of return on Social Security pensions under various scenarios.  The first set of figures (lines A-1) in the chart above are for a worker whose earnings are equal to what median wages were throughout his or her working life.  (A table with the specific numbers on the rates of return is provided at the bottom of this post, for those who prefer a numerical presentation.)  The individual paid into the Social Security pension system when working, and will now draw a Social Security pension while in retirement.  One can calculate the real rate of return on this stream of payments in and then payments out, and in such a scenario for a single worker earning median wages throughout his or her career who retired at age 66 in 2014, the real rate of return works out to be 2.9%.  If the person is married, with a spouse receiving the standard spousal benefit, the real rate of return is 4.1%.

Such rates of return are pretty good, especially on what should be seen as a safe asset (provided the politicians do not kill the system).  Indeed, as discussed in an earlier post on this blog, the real rate of return (before taxes) on an investment in the S&P500 stock market index over the 50 year period 1962 to 2012, would have been just 2.9% per annum assuming fees on 401(k) type retirement accounts of 2.5% (which is typical once one aggregates the fees at all the various levels – see the discussion in section E.3 of this blog post).  But investing in the stock market, even in a broad based index such as the S&P500, is risky due to the volatility.  Retirement accounts in 401(k)’s are generally a mix of equity investments, fixed income securities (bonds of various maturities, CDs, and similar instruments), and cash.  Based on the recent average mix seen in 401(k)’s, and for the same 50 year period of 1962 to 2012, the average real rate of return achieved after the fees typically charged on such accounts would only have been 1.2%.  Social Security for a worker earning median wages is far better.

As noted above, there is a degree of progressivity in the system, as higher income earners will receive only a smaller boost in their pension (at the 15% rate) from the higher end of their earnings.  Thus the rates of return in Social Security for high income earners will be less.  The rates of return they will earn are shown on lines A-2 of the chart.  This extreme case is calculated for a worker who is assumed to have earned throughout his or her entire work life an amount equal to the maximum ceiling on wages subject to Social Security tax (which was $113,700 in 2013).  Note also that anyone earning even more than this will have the same rates of return, as they will not be paying any more into the Social Security system (it is capped at the wage ceiling subject to tax) and hence also not withdrawing any more (or less) in pension.

Such high income earners will nonetheless still see a positive real rate of return on their Social Security contributions, of 1.4% for a single earner and 2.8% if married receiving a spousal benefit.  That is, while there is some progressivity in the Social Security system, it is not such that the returns turn negative.  And the returns achieved are still better than what typical 401(k) retirement accounts earn.

One should also take into account that high income earners are living longer than low income earners.  Indeed, the increase in life expectancies have been substantial in the last 30 years for high income earners, but only modest for those in the bottom half of the earnings distribution.  While I do not have data on what the life expectancies are for a person whose earnings have been at the absolute top of the Social Security wage ceiling over the course of their careers, for the purposes here it was assumed their life expectancy (for someone who has reached age 65) would be increased to age 90 from the age of 84 for the overall population.

In such a scenario, the real rates of return for someone who paid into the Social Security system always at the wage ceiling over their entire life time and then drew a Social Security pension up to age 90 would be 2.2% if single and 3.4% if married with a standard spousal benefit.  These are far better than typical 401(k) returns, and indeed are quite good in comparison to an investment in any safe asset (once one takes into account fees).

C.  Social Security Rates of Return Assuming Higher Social Security Tax Rates

The rates of return calculated so far have been based on what the actual historical Social Security tax rates have been, and what the current benefit formula would determine for future pensions.  But as most know, at current tax and benefit rates the Social Security Trust Fund is projected to be depleted by about 2034 according to current estimates.  The reason is that life expectancies are now longer (which is a good thing), but inadequate adjustments have been made in Social Security tax rates to allow for pay-outs which will now need to cover longer lifetimes.  The problem has been gridlock in Washington, where an important faction of politicians opposed to Social Security are able to block any decision on how to pay for longer life expectancies.

There are a number of ways to ensure Social Security could be adequately funded.  One option, which I would recommend, would be simply to lift the ceiling on wages subject to Social Security tax (which was $113,700 in 2013, $118,500 in 2015, and will remain at $118,500 in 2016).  As discussed in section E.2 of this earlier blog post, it turns out that this alone should suffice to ensure the Social Security Trust Fund remains adequate for the foreseeable future.  The extra funding needed is an estimated 19.4% over what is collected now (based on calculations from an earlier post on this blog, but with data now a few years old), and it turns out that ending the wage ceiling would provide this.  At the ceiling on wages subject to Social Security tax of $113,700 in 2013, the share of workers earning at this ceiling or more was just 6.1%, but due to the skewed distribution of income in favor of the rich, untaxed wages in excess of the ceiling accounted for 17.3% of all wages paid.  That is, Social Security taxes were being paid on only 82.7% of all wages.  If the taxes were instead paid on the full 100%, Social Security would be collecting 21% more (= 100.0 / 82.7).

The extremely rich would then pay Social Security taxes at the same rate as most of the population, instead of something lower.  It should also be noted that it is the increase in life expectancy of those at the upper end of the income distribution which is driving the Social Security system into deficit at the current tax rates, as they are the ones living longer while those in the lower part of the income distribution are not.  Thus it is fair that those who will be drawing a Social Security pension for a longer period should be those who should be called on to pay more into the system.

To be highly conservative, however, for the rate of return calculations being discussed here I have assumed that the general Social Security tax rate will be increased by 19.4% on all wages below the ceiling, while the ceiling remains where it has been.  These calculations are for historical scenarios, where the purpose is to determine what the rates of return on payments into Social Security would have been had the tax rates been 19.4% higher on all, to provide for a fully funded system.  Finally, note that while these scenarios assume a higher Social Security tax rate historically, they also set the future pension benefits to be paid out to be the same as what they would be under the current benefit rates.  That is, the pay-out formulae would need to be changed to leave benefits the same despite the higher taxes being paid into the system.

The real rates of return would then be as shown in Panel B of the chart above.  While somewhat less than before, the real returns are still substantial, and still normally better than what is earned in a typical 401(k) plan.  The returns for someone earning at the median wage throughout their career will now be 2.4% if single and 3.6% if married (0.5% points less than before).  The returns for someone earning at or above the ceiling for wages subject to Social Security taxes would now be earning at the real rate of 0.8% if single and 2.2% if married for the age 84 life expectancy (0.6% points less than before), or 1.6% and 2.9% (for single and married) if the life expectancy of such high earners is in fact age 90 (also 0.6% points less, before round-off).

The real rates of return all remain positive, and generally good compared to what 401(k)’s typically earn.

D.  Conclusion

As noted above, the actual profile of Social Security taxes paid and pension received will vary by individual.  No two cases will be exactly alike.  But the calculations here indicate that for someone with median earnings, and still even in the extreme case of someone with very high earnings (where a degree of progressivity in the system will reduce the returns), the rates of return earned on what is paid into and then taken out of the Social Security system are actually quite good.  They generally are better than what is earned in a typical 401(k) account (after fees), and indeed often better than what would earn in a pure equity investment of the S&P500 index (and without the risk and volatility of such an investment).

Social Security is important and has become increasingly important.  Due to the end of many traditional defined benefit pension plans, with a forced switch to 401(k) plans or indeed often to nothing at all from the employer, Social Security now accounts (for those aged 65 or older) for a disturbingly high share on the incomes of many of the aged. Specifically, Social Security now accounts for half or more of total income for two-thirds of all those age 65 or older, and accounts for 100% of their income for one-quarter of them. And for the bottom 40% of this population, Social Security accounted for 90% or more of their total income for three-quarters of them, and 100% of their income for over half of them.

The problem is not in the Social Security system itself.  It is highly efficient, with an expense ratio in 2014 of just 0.4% of benefits paid.  Private 401(k) plans, with typical expenses of 2.5% of assets (not benefits) each year will have expenses over their life time that are 90 times as great as what Social Security costs to run.  And as seen in this post, the return on individual Social Security accounts are quite good.

The problem that Social Security faces is rather that with longer life expectancies (most importantly for those of higher income), the Social Security taxes being paid are no longer sufficient to cover the payouts to cover these longer lifetimes.  They need to be adjusted. There are several options, and my recommendation would be to start by ending the ceiling on wages subject to Social Security taxes.  This would suffice to solve the problem.  But one could go further.  As discussed in an earlier blog post (see Section E.2), not only should all wages be taxed equally, but one should extend this to taxing all forms of income equally (i.e. income from wealth as well as income from wages).  If one did this, one could then either cut the Social Security tax rate sharply, or raise the Social Security benefits that could be paid, or (and most likely) some combination of each.

But something needs to be done, or longer life spans will lead the Social Security Trust Fund to run out by around 2034.  The earlier this is resolved the better, both to ensure less of a shock when the change is finally made (as it could then be phased in over time) and for equity reasons (as it is those paying in now who are not adequately funding the system for what they will eventually drawdown).

 

============================================================

Annex:  Summary Table

Real Rates of Return from Social Security Old-Age Taxes and Benefits

A)  Social Security Scenarios – Current Rates

  1)  Earnings at Median Throughout Career

   a)  Single

2.9%

   b)  Married

4.1%

  2)  Earnings at Ceiling Throughout Career

   a)  Single

1.4%

   b)  Married

2.8%

  3)  Earnings at Ceiling, and Life Expectancy of 90

   a)  Single

2.2%

   b)  Married

3.4%

B)  Social Security with 19.4% higher tax rate

  1)  Earnings at Median Throughout Career

   a)  Single

2.4%

   b)  Married

3.6%

  2)  Earnings at Ceiling Throughout Career

   a)  Single

0.8%

   b)  Married

2.2%

  3)  Earnings at Ceiling, and Life Expectancy of 90

   a)  Single

1.6%

   b)  Married

2.9%

C)  Comparison to 401(k) Vehicles

  1)  S&P500 after typical fees

2.9%

  2)  Average 401(k) mix after typical fees

1.2%

More on the High Cost of the Purple Line: A Comparison to BRT on the Silver Spring to Bethesda Segment

Comparison of Purple Line to BRT Cost, Silver Spring to Bethesda

This is a quick post drawing on a report in today’s Washington Post on the implementation of bus rapid transit (BRT) in Montgomery County, Maryland.  The article notes that one of the early BRT routes planned in the county would run from Burtonsville to Silver Spring down US Highway 29, with an estimated capital cost of $200 million.

This would be a distance of 10.2 miles, so the cost would be $19.6 million per mile on average.  This BRT line is currently slated to stop in Silver Spring, but it would be straightforward to extend it along East-West Highway for a further 3.7 miles to Bethesda. Assuming the same average cost per mile, the capital cost of this addition would be $72 million.

The current plan is for the Purple Line light rail line to cover this same basic route, connecting Silver Spring to Bethesda.  As I have discussed in earlier blog posts, the Purple Line is incredibly expensive, even if one ignores (as the official cost estimates do) the environment costs of building and operating the line (including the value of parkland destroyed, which is implicitly being valued at zero, as well as the environmental costs from storm water run-off, habitat destruction, hazardous waste issues, higher greenhouse gas emissions, and more).  The current capital cost estimate, following the service and other cuts that Governor Hogan has imposed to bring down costs, is $2.25 billion.  This also does not include the costs that Montgomery County will cover directly for building the Bethesda station and well as the cost of a utilitarian path to be built adjacent to the train tracks.  The Purple Line would also cost more to operate per rider than the Montgomery County BRT routes are expected to cost, so there is no cost savings from lower operating costs.

The Purple Line would be 16.2 miles long in total.  Using just the $2.25 billion cost figure, this comes to $139 million per mile.  This is extremely high.  Indeed, the Columbia Pike streetcar line in Arlington County, which was recently cancelled due to its high cost, would have cost “only” $117 million per mile despite it being built through a high density urban corridor for most of its entire route.

The distance from Silver Spring to Bethesda on the Purple Line will be 4.4 miles if it is built. This is longer than the direct route by road since it will follow a more indirect path passing up and around the direct route.  Assuming the cost of this 4.4 miles is the same on average as for the rest of the Purple Line (it might be higher due to the need to build some major bridges, including over Rock Creek), the cost would come to $612 million.

The choice therefore is between spending $612 million to build this segment of the Purple Line from Silver Spring to Bethesda, or spending $72 million by extending the BRT.  The Purple Line cost is 8.5 times as much, and government could save $540 million ( = $612m – $72m) by terminating the Purple Line in Silver Spring and using BRT service instead.

As an earlier blog post argued, new thinking is necessary if we are to resolve the very real transportation issues we face in this region.  This is one more example of what could be done.  A half billion dollar savings is not small.

 

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.