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

A.  Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

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

B.  The Levelized Cost of Energy 

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

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

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

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

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

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

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

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

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

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

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

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

C.  The Market Prices for Solar Generated Power

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The cost estimates derived from the Lazard figures are then:

(all costs in 2018 prices)

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

Solar

Wind

Gas

Coal

Nuclear

low end

$30.56

$22.16

$31.33

$45.84

$85.57

mid-point

$32.85

$32.47

$43.93

$77.55

$114.99

high end

$35.15

$42.79

$56.54

$109.26

$144.41

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

Solar

Wind

Gas

Coal

Nuclear

low end

$4.00

$9.24

$24.38

$23.19

$23.87

mid-point

$4.66

$10.64

$26.51

$31.30

$25.11

high end

$5.33

$12.04

$28.64

$39.41

$26.35

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

Solar

Wind

Gas

Coal

Nuclear

low end

$0.00

$0.00

$23.16

$14.69

$9.63

mid-point

$0.00

$0.00

$25.23

$18.54

$9.63

high end

$0.00

$0.00

$27.31

$22.40

$9.63

A number of points follow from these cost estimates:

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

But also as noted before, local conditions vary and there will in fact be a range of costs for different types of plants.  The Lazard estimates indicate that a gas plant with costs at the low end of a reasonable range (estimated to be about $31 per MWHr) would be competitive with solar or wind plants at the mid-point of their cost range (about $32 to $33 per MWHr), and below the costs of a solar plant at the high end of its cost range ($35) and especially a wind plant at its high end of its costs ($43).  However, there are not likely to be many such cases:  Gas plants with a cost at their mid-point estimate would not be competitive, and even less so for gas plants with a cost near their high end estimate.

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

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

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

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

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

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

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

And the answer is that at these costs, which exclude the costs from the pollution generated, it would be cheaper to run that gas plant.  The LCOE costs for new solar power ranges from $31 to $35 per MWHr (panel A above), while the variable cost of operating a gas plant built to supply nighttime capacity ranges between $23 and $27 (panel C).  While the difference is not huge, it is still significant.

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

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

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

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

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

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

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

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

One then has:

D.  Cost of Greenhouse Gas Emissions:  $/MWHr

Solar

Wind

Gas

Coal

Nuclear

Tons of CO2 Emitted per MWHr

0.00

0.00

0.51

0.92

0.00

Cost at $20/ton CO2

$0.00

$0.00

$10.20

$18.40

$0.00

Cost at $40/ton CO2

$0.00

$0.00

$20.40

$36.80

$0.00

Cost at $60/ton CO2

$0.00

$0.00

$30.60

$55.20

$0.00

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

Solar

Wind

Gas

Coal

Nuclear

Cost at $20/ton CO2

$32.85

$32.47

$54.13

$95.95

$114.99

Cost at $40/ton CO2

$32.85

$32.47

$64.33

$114.35

$114.99

Cost at $60/ton CO2

$32.85

$32.47

$74.53

$132.75

$114.99

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

Solar

Wind

Gas

Coal

Nuclear

Cost at $20/ton CO2

$0.00

$0.00

$35.43

$36.94

$9.63

Cost at $40/ton CO2

$0.00

$0.00

$45.63

$55.34

$9.63

Cost at $60/ton CO2

$0.00

$0.00

$55.83

$73.74

$9.63

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

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

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

If gas as well as coal had to pay for the damages caused by their greenhouse gas emissions, then even at a cost of just $20 per ton of CO2 emitted they would not be competitive with building new solar or wind plants (whose LCOEs, in Panel E, are less).  At a cost of $40 or $60 per ton of CO2 emitted, they would be far from competitive, with costs of 40% to 130% higher.  There would be a strong incentive then to build new solar and wind plants to serve what they can (including just the day time markets), while existing gas plants (primarily) would in the near term be kept in reserve for service at night or at other times when solar and wind generation is not possible.

G.  Summary and Conclusion

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

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

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

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

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

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

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

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

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

The Survey of Establishments Say Employment is Rising, But the Survey of Households Say It Is Falling – Why?

A.  Introduction

Those who follow the monthly release of the Employment Situation report of the Bureau of Labor Statistics (with the most recent issue, for April, released on May 3) may have noticed something curious.  While the figures on total employment derived from the BLS survey of establishments reported strong growth, of an estimated 263,000 in April, the BLS survey of households (from which the rate of unemployment is estimated) reported that estimated employment fell by 103,000.  And while there is month-to-month volatility in the figures (they are survey estimates, after all), this has now been happening for several months in a row:  The establishment survey has been reporting strong growth in employment while the household survey has been reporting a fall.  The one exception was for February, where the current estimate from the establishment survey is that employment grew that month by a relatively modest 56,000 (higher than the initial estimate), while the household survey reported strong growth in employment that month of 255,000.

The chart above shows this graphically, with the figures presented in terms of their change relative to where they were in April 2017, two years ago.  For reasons we will discuss below, there is substantially greater volatility in the employment estimates derived from the household survey than one finds in the employment estimates derived from the establishment survey.  But even accounting for this, a significant gap appears to have opened up between the estimated growth in employment derived from the two sources.  Note also that the estimated labor force (derived from the household survey) has also been going down recently.  The unemployment rate came down to just 3.6% in the most recent month not because estimated employment rose – it in fact fell by 103,000 workers.  Rather, the measured unemployment rate came down because the labor force fell by even more (by 490,000 workers).

There are a number of reasons why the estimates from the two surveys differ, and this blog post will discuss what these are.  To start, and as the BLS tries to make clear, the concept of “employment” as estimated in the establishment survey is different from that as measured in the household survey.  They are measuring different, albeit close, things.  But there are other factors as well.

One can, however, work out estimates where the employment concepts are defined almost, but not quite, the same.  What is needed can be found in figures provided as part of the household survey.  We will look at those below and present the results in a chart similar to that above, but with employment figures from the household survey data adjusted (to the extent possible) to match the employment concept of the establishment survey.  But one finds that the gap that has opened up between the employment estimates of the two surveys remains, similar to that in the chart above.

There are residual differences in the two employment estimates.  And they follow a systematic pattern that appear to be correlated with the unemployment rate.  The final section below will look at this, and discuss what might be the cause.

The issues here are fairly technical ones, and this blog post may be of most interest to those interested in digging into the numbers and seeing what lies behind the headline figures that are the normal focus of news reports.  And while a consistent discrepancy appears to have opened up between the two estimates of employment growth, the underlying cause is not clear.  Nor are the implications for policy yet fully clear.  But the numbers may imply that we should be paying more attention to the much slower growth in the estimates of total employment derived from the household survey, than the figures from the establishment survey that we normally focus on.  We will find in coming months whether the inconsistency that has developed signals a change in the employment picture, or simply reflects unusual volatility in the underlying data.

B.  The BLS Surveys of Establishments, and of Households

The monthly BLS employment report is based on findings from two monthly surveys the BLS conducts, one of establishments and a second of households.  As described by the BLS in the Techincal Note that is released as part of each month’s report (and which we will draw upon here), they need both.  And while the surveys cover a good deal of material other than employment and related issues, we will focus here just on the elements relevant to the employment estimates.

The establishment survey covers primarily business establishments, but also includes government agencies, non-profits, and most other entities that employ workers for a wage.  However, the establishment survey does not include those employed in agriculture (for some reason, possibly some historical bureaucratic issue between agencies), as well as certain employment that can not be covered by a survey of establishments.  Thus they do not cover the self-employed (if they work in an unincorporated business), nor unpaid family workers.  Nor do they cover those employed directly by households (e.g. for childcare).

But for the business establishments, government agencies, and other entities that they do cover, they are thorough.  They survey more than 142,000 establishments each month, covering 689,000 individual worksites, and in all cover in this “sample” approximately one-third of all nonfarm employees.  This means they obtain direct figures each month on the employment of about 50 million workers (out of the approximately 150 million employed in the US), with this closer to a census than a normal sample survey.  But the extensive coverage is necessary in order to be able to arrive at statistically valid sample sizes at the detailed individual industries for which they provide figures.  And because of this giant sample size, the monthly employment figures cited publicly are normally taken from the establishment survey.

To arrive at unemployment rates and other figures, one must however survey households.  Businesses will know who they employ, but not who is unemployed.  And while the current sample size used of households is 60,000, this is far smaller relative to the sample size used for establishments (142,000) than it might appear.  A household will in general have just one or two workers, while a business establishment (or a government agency) could employ thousands.

Thus the much greater volatility seen in the employment estimates from the household survey should not be a surprise.  But they need the household survey to determine who is in the labor force.  They define this to be those adults of age 16 or older, who are either employed (even for just one hour, if paid) in the preceding week, or who, if not employed, were available for a job and were actively searching for one at some point in the four week period before the week of the survey.  Only in this way can the BLS determine the share of the labor force that is employed, and the share unemployed.  The survey of establishments by its nature cannot provide such information no matter what its sample size.

For this and other reasons, the definition of what is covered in “employment” between these two surveys will differ.  In particular:

a)  As discussed above, the establishment survey does not cover employment in the agricultural sector.  While they could, in principle, include agriculture, for some reason they do not.  The household survey does include those in agriculture.

b)  The establishment survey also does not include the self-employed (unless they are running an incorporated business).  They only survey businesses (or government agencies and non-profits), and hence cannot capture those who are self-employed.

c)  The establishment survey also does not capture unpaid family workers.  The household survey counts them as part of the labor force and employed if they worked in the family business 15 hours or more in the week preceding the survey.

d)  The establishment survey, since it does not cover households, cannot include private household workers (such as those providing childcare services).  The household survey does.

e)  Each of the above will lead to the count in the household survey of those employed being higher than what is counted in the establishment survey.  Working in the opposite direction, someone holding two or more jobs will be counted in the establishment survey two or more times (once for each job they hold).  The establishment being surveyed will only know who is working for them, and not whether they are also working elsewhere.  The household survey, however, will count such a worker as just one employed person.

f) The household survey also counts as employed those who are on unpaid leave (such as maternity leave).  The establishment survey does not (although it is not clear to me why they couldn’t – it would improve comparability if they would).

g)  The household survey also only includes those aged 16 or older as possibly in the labor force and employed.  The establishment survey covers all its workers, whatever their age.

There are therefore important differences between the two surveys as to who is covered in the figures provided for “total employment”.  And while the BLS tries to make this clear, the differences are often ignored in references by, for example, the news media.  One can, however, adjust for most, but not all, of these differences.  The data required are provided in the BLS monthly report (for recent months), or online (for the complete series).  But how to do so is not made obvious, as the data series required are scattered across several different tables in the report.

I will discuss in more detail in the next section below what I did to adjust the household survey figures to the employment concept as used in the establishment survey.  Adjustments could be made for each of the categories (a) through (e) in the list above, but was not possible for (f) and (g).  However, the latter are relatively small, with the residual difference following an interesting pattern that we will examine.

When those adjustments are made, the number of employed as estimated from the household survey, but reflecting (almost) the concept as estimated in the establishment survey, looks as follows:

 

While there are some differences between the estimates here and those in the chart at the top of this post of employment made using the household survey (as adjusted), the basic pattern remains.  While employment as estimated from the household survey (and excluding those in agriculture, the self-employed, unpaid family workers, household employees, and adjusted for multiple jobholders) is now growing, it was growing over the last half year at a much slower pace than what the establishment survey suggests.

C.  Adjustments Made to the Employment Estimates So They Will Reflect Similar Concepts

As noted above, adjustments were made to the employment figures to bring the two concepts of the different surveys into line with each other, to the extent possible.  While in principle one could have adjusted either, I chose to adjust the employment concept of the household survey to reflect the more narrow employment concept of the establishment survey.  This was because the underlying data needed to make the adjustments all came from the household survey, and it was better to keep the figures for the adjustments to be made all from the same source.

Adjustments could be made to reflect each of the issues listed above in (a) through (e), but not for (f) or (g).  But there were still some issues among the (a) through (e) adjustments.  Specifically:

1)  I sought to work out the series going back to January 1980, in order to capture several business cycles, but not all of the data required went back that far.  Specifically, the series on those holding multiple jobs started only in January 1994, and the series on household employees only started in January 2000.

2)  I also worked, to the extent possible, with the seasonally adjusted figures (for the establishment survey figures as well as those from the household survey).  However, the figures on unpaid family workers and of household employees were only available without seasonal adjustment.  I was therefore forced to use these.  But since the numbers in these categories are quite small relative to the overall number employed, one does not see a noticeable difference in the graphs.

One can then compare, as a ratio, the figures for total employment as adjusted from the household survey to those from the establishment survey.  The ratio will equal 1.0 when the figures are the same.  This was done in steps (depending on how far back one could go with the data), with the result:

 

The curve in black, which can go back all the way to 1980, shows the ratio when the employment figure in the household survey is adjusted by taking out those who are self-employed (in unincorporated businesses) and those employed in agriculture.  The curve in blue, from 1994 onwards, then adds in one job for each of those holding multiple jobs.  The assumption being made is that those with multiple jobs almost always have two jobs.  The establishment survey would count these as two employees (at two different establishments), while the household survey will only count these as one person (holding more than one job).  Therefore adding a count of one for each person holding multiple jobs will bring the employment concepts used in the two surveys into alignment (and on the basis used in the establishment survey).

Finally, the curve in red subtracts out unpaid family workers in non-agricultural sectors (as those in the agricultural sector will have already been taken out when total employees in agriculture were subtracted), plus subtracts out household employees.  Neither of these series are available in seasonally adjusted form, but they are small relative to total employment, so this makes little difference.

What is interesting is that even with all these adjustments, the ratio of the adjusted figures for employment from the household survey to those from the establishment survey follows a regular pattern.  The ratio is low when unemployment was low (as it was in 2000, at the end of the Clinton administration, and to a lesser extent now).  And it is high when unemployment was high, such as in mid-1980s during the Reagan administration (with a downturn that started in 1982) and again during the downturn of 2008/09 that began at the end of the Bush administration, with unemployment then peaking in 2010 before it started its steady recovery.

Keep in mind that the relative difference in the employment figures between the household survey (as adjusted) and the establishment survey are not large:  about 1% now and a peak of about 3% in 2009/10.  But there is a consistent difference.

Why?  In part there are still two categories of workers where we had no estimates available to adjust the figures from the household survey to align them with the employment concept of the establishment survey:  for those on unpaid leave (who are included as “employed” in the household survey but not in the establishment survey), and for those under age 16 who are working (who are not counted in the household survey but are counted as employees in the establishment survey).

These two categories of workers might account for the difference, but we do not know whether they will fully account for the difference as we have no estimates.  A more interesting question is whether these two categories might account for the correlation observed with unemployment.  We could speculate that during periods of high unemployment (such as 2009/10), those taking unpaid leave might be relatively high (thus bumping up the ratio), and that those under age 16 may find it particularly hard, relative to others, to find jobs when unemployment is high (as employers can easily higher older workers then, with this then also bumping up the ratio relative to times when overall unemployment is low).  But this would just be speculation, and indeed more like an ex-post rationalization of what is observed than an explanation.

Still, despite the statistical noise seen in the chart, the basic pattern is clear.  And that is of a ratio that goes up and down with unemployment.  But it is not large.  Based on the change in the ratio observed from May 2010 to April 2011 (using a 12 month average to smooth out the monthly fluctuations), to the average over May 2018 to April 2019, the monthly divergence in the employment growth figures would only be 23,000 workers.  That is, the unexplained residual difference in recent years between the growth in employment (as estimated by the household survey and as estimated by the establishment survey) would be about 23,000 jobs per month.

But the differences in the estimates for the monthly change in employment between the (adjusted) series from the household survey and that from the establishment survey are much more.  Between October 2018 and April 2019, employment in the adjusted household survey series grew by 65,000 per month on average.  In the establishment survey series the growth was 207,000 per month.  The difference (142,000) is much greater than the 23,000 that can be explained by whatever has been driving down the ratio between the two series since 2010 as unemployment has come down.  Or put another way, the 65,000 figure can be increased by 23,000 per month to 88,000 per month, from adding in the unexplained residual change we observe in the ratio between the two series in recent years.  That 88,000 increase in employment per month from the (adjusted) household survey figures is substantially less than the 207,000 per month figure found in the establishment survey.

D.  Conclusion

Due to the statistical noise in the employment estimates of the household series, one has to be extremely cautious in drawing any conclusions.  While a gap has opened up in the last half year between the growth in the employment estimates of the household survey and those of the establishment survey, it is still early to say whether that gap reflects something significant or not.

The gap is especially large if one just looks at the “employment” figures as published.  Employment as recorded in the household survey has fallen between December 2018 and now, and has been essentially flat since October.  But the total employment concepts between the two surveys differ, so such a direct comparison is not terribly meaningful.  However, if the figures from the household survey are adjusted (to the extent possible) to match the employment concept of the business survey, there is still a large difference.  Employment (under this concept) grew by 207,000 per month in the establishment survey, but by just 88,000 per month in the adjusted household survey figures.

Whether this difference is significant is not yet clear, due to the statistical noise in the household survey figures.  But it might be a sign that employment growth has been less than the headline figures from the establishment survey suggest.  We will see in coming months whether this pattern continues, or whether one series starts tracking the other more closely (and if so, which to which).

Allow the IRS to Fill In Our Tax Forms For Us – It Can and It Should

A.  Introduction

Having recently completed and filed this year’s income tax forms, it is timely to examine what impact the Republican tax bill, pushed quickly through Congress in December 2017 along largely party-line votes, has had on the taxes we pay and on the process by which we figure out what they are.  I will refer to the bill as the Trump/GOP tax bill as the new law reflected both what the Republican leadership in Congress wanted and what the Trump administration pushed for.

We already know well that the cuts went largely to the very well-off.  The chart above is one more confirmation of this.  It was calculated from figures in a recent report by the staff of the Joint Committee on Taxation of the US Congress, released on March 25, 2019 (report #JCX-10-19).  While those earning more than $1 million in 2019 will, on average, see their taxes cut by $64,428 per tax filing unit (i.e. generally households), those earning $10,000 or less will see a reduction of just $21.  And on the scale of the chart, it is indeed difficult to impossible even to see the bars depicting the reductions in taxes for those earning less than $50,000 or so.

The sharp bias in favor of the rich was discussed in a previous post on this blog, based there on estimates from a different group (the Tax Policy Center, a non-partisan think tank) but with similar results.  And while it is of course true that those who are richer will have more in taxes that can be cut (one could hardly cut $64,428 from a taxpayer earning less than $10,000), it is not simply the absolute amounts but also the share of taxes which were cut much further for the rich than for the poor.  According to the Joint Committee on Taxation report cited above, those earning $30,000 or less will only see their taxes cut by 0.5% of their incomes, while those earning between $0.5 million and $1.0 million will see a cut of 3.1%.  That is more than six times as much as a share of incomes.  That is perverse.

And the overall average reduction in individual income taxes will only be a bit less than 10% of the tax revenues being paid before.  This is in stark contrast to the more than 50% reduction in corporate income taxes that we have already observed in what was paid by corporations in 2018.

Furthermore, while taxes for households in some income category may have on average gone down, the numerous changes made to the tax code on the Trump/GOP bill meant that for many it did not.  Estimates provided in the Joint Committee on Taxation report cited above (see Table 2 of the report) indicate that for 2019 a bit less than two-thirds of tax filing units (households) will see a reduction in their taxes of $100 or more, but more than one-third will see either no significant change (less than $100) or a tax increase.  The impacts vary widely, even for those with the same income, depending on a household’s particular situation.

But the Trump/GOP tax bill promised not just a reduction in taxes, but also a reduction in tax complexity, by eliminating loopholes and from other such measures.  The claim was that most Americans would then be able to fill in their tax returns “on a postcard”.  But as is obvious to anyone who has filed their forms this year, it is hardly that.  This blog post will discuss why this is so and why filling in one’s tax returns remains such a headache.  The fundamental reason is simple:  The tax system is not less complex than before, but more.

There is, however, a way to address this, and not solely by ending the complexity (although that would in itself be desirable).  Even with the tax code as complicated as it now is (and more so after the Trump/GOP bill), the IRS could complete for each of us a draft of what our filing would look like based on the information that the IRS already collects.  Those draft forms would match what would be due for perhaps 80 to 85% of us (basically almost all of those who take the standard deduction).  For that 80 to 85% one would simply sign the forms and return them along with a payment if taxes are due or a request for a refund if a refund is due.  Most remaining taxpayers would also be able to use these initial draft forms from the IRS, but for them as the base for what they would need to file.  In their cases, additions or subtractions would be made to reflect items such as itemized deductions (mostly) and certain special tax factors (for some) where the information necessary to complete such calculations would not have been provided in the normal flow of reports to the IRS.  And a small number of filers might continue to fill in all their forms as now.  That small number would be no worse than now, while life would be much simpler for the 95% or more (perhaps 99% or more) who could use the pre-filled in forms from the IRS either in their entirety or as a base to start from.

The IRS receives most of the information required to do this already for each of us (and all that is required for most of us).  But what would be different is that instead of the IRS using such information to check what we filed after the fact, and then impose a fine (or worse) if we made a mistake, the IRS would now use that same information to fill in the forms for us.  We would then review and check them, and if necessary or advantageous to our situation we could then adjust them.  We will discuss how such a tax filing system could work below.

B.  Our Tax Forms are Now Even More Complex Than Before

Trump and the Republican leaders in Congress promised that with the Trump/GOP tax bill, the tax forms we would need to file could, for most of us, fit just on a postcard.  And Treasury Secretary Steven Mnuchin then asserted that the IRS (part of Treasury) did just that.  But this is simply nonsense, as anyone who has had to struggle with the new Form 1040s (or even just looked at them) could clearly see.

Specifically:

a)  Form 1040 is not a postcard, but a sheet of paper (front and back), to which one must attach up to six separate schedules.  This previously all fit on one sheet of paper, but now one has to complete and file up to seven just for the 1040 itself.

b)  Furthermore, there are no longer the forms 1040-EZ or 1040-A which were used by those with less complex tax situations.  Now everyone needs to work from a fully comprehensive Form 1040, and try to figure out what may or may not apply in their particular circumstances.

c)  The number of labeled lines on the old 1040 came to 79.  On the new forms (including the attached schedules) they come to 75.  But this is misleading, as what used to be counted as lines 1 through 6 on the old 1040 are now no longer counted (even though they are still there and are needed).  Including these, the total number of numbered lines comes to 81, or basically the same as before (and indeed more).

d)  Spreading out the old Form 1040 from one sheet of paper to seven does, however, lead to a good deal of extra white space.  This was likely done to give it (the first sheet) the “appearance” of a postcard.  But the forms would have been much easier to fill in, with less likelihood of error, if some of that white space had been used instead for sub-totals and other such entries so that all the steps needed to calculate one’s taxes were clear.

e)  Specifically, with the six new schedules, one has to carry over computations or totals from five of them (all but the last) to various lines on the 1040 itself.  But this was done, confusingly, in several different ways:  1)  The total from Schedule 4 was carried over to its own line (line 14) on the 1040.  It would have been best if all of them had been done this way, but they weren’t.  Instead, 2) The total from Schedule 2 was added to a number of other items on line 11 of the 1040, with the total of those separate items then shown on line 11.  And 3) The total from Schedule 1 was added to the sum of what is shown on the lines above it (lines1 through 5b of the 1040) and then recorded on line 6 of the 1040.

If this looks confusing, it is because it is.  I made numerous mistakes on this when completing my own returns (yes – I do these myself, as I really want to know how they are done).  I hope my final returns were done correctly.  And it is not simply me.  Early indications (as of early March) were that errors on this year’s tax forms were up by 200% over last year’s (i.e. they tripled).

f)  There is also the long-standing issue that the actual forms that one has to fill out are in fact substantially greater than those that one files, as one has to fill in numerous worksheets in order to calculate certain of the figures.  These worksheets should be considered part of the returns, and not hidden in the directions, in order to provide an honest picture of what is involved.  And they don’t fit on a postcard.

g)  But possibly what is most misleading about what is involved in filling out the returns is not simply what is on the 1040 itself, but also the need to include on the 1040 figures from numerous additional forms (for those that may apply).  Few if any of them are applicable to one’s particular tax situation, but to know whether they do or not one has to review each of those forms and make such a determination.  How does one know whether some form applies when there is a statement on the 1040 such as “Enter the amount, if any, from Form xxxx”?  The only way to know is to look up the form (fortunately now this can be done on the internet), read through it along with the directions, and then determine whether it may apply to you.  Furthermore, in at least a few cases one can only know if the form applies to your situation is by filling it in and then comparing the result found to some other item to see whether filing that particular form applies to you.

There are more than a few such forms.  By my count, one has just on the Form 1040 plus its Schedules 1 through 5 amounts that might need to be entered from Forms 8814, 4972, 8812, 8863, 4797, 8889, 2106, 3903, SE, 6251, 8962, 2441, 8863, 8880, 5695, 3800, 8801,1116, 4137, 8919, 5329, 5405, 8959, 8960, 965-A, 8962, 4136, 2439, and 8885.  Each of these forms may apply to certain taxpayers, but mostly only a tiny fraction of them.  But all taxpayers will need to know whether they might apply to their particular situation.  They can often guess that they probably won’t (and it likely would be a good guess, as most of these forms only apply to a tiny sliver of Americans), but the only way to know for sure is to check each one out.

Filling out one’s individual income tax forms has, sadly, never been easy.  But it has now become worse.  And while the new look of the Form 1040 appears to be a result of a political decision by the Trump administration (“make it look like it could fit on a postcard”), the IRS should mostly not be blamed for the complexity.  That complexity is a consequence of tax law, as written by Congress, which finds it politically advantageous to reward what might be a tiny number of supporters (and campaign contributors) with some special tax break.  And when Congress does this, the IRS must then design a new form to reflect that new law, and incorporate it into the Form 1040 and now the new attached schedules.  And then everyone, not simply the tiny number of tax filers to whom it might in fact apply, must then determine whether or not it applies to them.

There are, of course, also more fundamental causes of the complexity in the tax code, which must then be reflected in the forms.  The most important is the decision by our Congress to tax different forms of income differently, where wages earned will in general be taxed at the highest rates (up to 37%) while capital gains (including dividends on stocks held for more than 60 days) are taxed at rates of just 20% or less.  And there are a number of other forms of income that are taxed at various rates (including now, under the Trump/GOP tax bill, an effectively lower tax rate for certain company owners on the incomes they receive from their companies, as well as new special provisions of benefit to real estate developers).  As discussed in an earlier post on this blog, there is no good rationale, economic or moral, to justify this.  It leads to complex tax calculations as the different forms of income must each be identified and then taxed at rates that interact with each other.  And it leads to tremendous incentives to try to shift your type of income, when you are in a position to do so, from wages, say, to a type taxed at a lower rate (such as stock options that will later be taxed only at the long-term capital gains rate).

Given this complexity, it is no surprise that most Americans turn either to professional tax preparers (accountants and others) to fill in their tax forms for them, or to special tax preparation software such as TurboTax.  Based on statistics for the 2018 tax filing season (for 2017 taxes), 72.1 million tax filers hired professionals to prepare their tax forms, or 51% of the 141.5 million tax returns filed.  The cost varies by what needs to be filed, but even assuming an average fee of just $500 per return, this implies a total of over $36 billion is being paid by taxpayers for just this service.

Most of the remaining 49% of tax filers use tax preparation software for their returns (a bit over three-quarters of them).  But these are problematic as well.  There is also a cost (other than for extremely simple returns), but the software itself may not be that good.  A recent review by Consumer Reports found problems with each of the four major tax preparation software packages it tested (TurboTax, H&R Block, TaxSlayer, and TaxAct), and concluded they are not to be trusted.

And on top of this, there is the time the taxpayer must spend to organize all the records that will be needed in order to complete the tax returns – whether by a hired professional tax preparer, or by software, or by one’s own hand.  A 2010 report by a presidential commission examing options for tax reform estimated that Americans spend about 2.5 billion hours a year to do what is necessary to file their individual income tax returns, equivalent to $62.5 billion at an average time cost of $25 per hour.

Finally there are the headaches.  Figuring one’s taxes, even if a professional is hired to fill in the forms, is not something anyone wants to spend time on.

There is a better way.  With the information that is already provided to the IRS each year, the IRS could complete and provide to each of us a draft set of tax forms which would suffice (i.e. reflect exactly what our tax obligation is) for probably 80% or more of households.  And most of the remainder could use such draft forms as a base and then provide some simple additions or subtractions to arrive at what their tax obligation is.  The next section will discuss how this could be done.

C.  Have the IRS Prepare Draft Tax Returns for Each of Us

The IRS already receives, from employers, financial institutions, and others, information on the incomes provided to each of us during the tax year.  And these institutions then tell us each January what they provided to the IRS.  Employers tell us on W-2 forms what wages were paid to us, and financial institutions will tell us through various 1099 forms what was paid to us in interest, in dividends, in realized capital gains, in earnings from retirement plans, and from other such sources of returns on our investments.  Reports are also filed with the IRS for major transactions such as from the sale of a home or other real estate.

The IRS thus has very good information on our income each year.  Our family situation is also generally stable from year to year, although it can vary sometimes (such as when a child is born).  But basing an initial draft estimate on the household situation of the previous year will generally be correct, and can be amended when needed.  One could also easily set up an online system through which tax filers could notify the IRS when such events occur, to allow the IRS to incorporate those changes into the draft tax forms they next produce.

For most of those who take the standard deduction, the IRS could then fill in our tax forms exactly.  And most Americans take the standard deduction. Prior to the Trump/GOP tax bill, about 70% of tax filers did, and it is now estimated that with the changes resulting from the new tax bill, about 90% will.  Under the Trump/GOP tax bill, the basic standard deduction was doubled (while personal exemptions were eliminated, so not all those taking the standard deduction ended up better off).  And perhaps of equal importance, the deduction that could be taken on state and local taxes was capped at $10,000 while how much could be deducted on mortgage interest was also narrowed, so itemization was no longer advantageous for many (with these new limitations primarily affecting those living in states that vote for Democrats – not likely a coincidence).

The IRS could thus prepare filled in tax forms for each of us, based on information contained in what we had filed in earlier years and assuming the standard deduction is going to be taken.  But they would just be drafts.  They would be sent to us for our review, and if everything is fine (and for most of the 90% taking the standard deduction they would be) we would simply sign the forms and return them (along with a check if some additional tax is due, or information on where to deposit a refund if a tax refund is due).

But for the 10% where itemized deductions are advantageous, and for a few others who are in some special tax situation, one could either start with the draft forms and make additions or subtractions to reflect simple adjustments, or, if one wished, prepare a new set of forms reflecting one’s tax situation.  There would likely not be many of the latter, but it would be an option, and no worse than what is currently required of everyone.

For those making adjustments, the changes could simply be made at the end.  For example (and likely the most common such situation), suppose it was advantageous to take itemized deductions rather than the standard deduction.  One would fill in the regular Schedule A (as now), but then rather than recomputing all of the forms, one could subtract from the taxes due an amount based on what the excess was of the itemized deductions over the standard deduction, and one’s tax rate.  Suppose the excess of the itemized deductions over the standard deduction for the filer came to $1,000.  Then for the very rich (households earning over $600,000 a year after deductions), one would reduce the taxes due by 37%, or $370.  Those earning $400,000 to $600,000, in the 35% bracket, would subtract $350.  And so on down to the lower brackets, where those in the 12% bracket (those earning $19,050 to $77,400) would subtract $120 (and those earning less than $19,050 are unlikely to itemize).

[Side Note:  Why do the rich receive what is in effect a larger subsidy from the government than the poor do for what they itemize, such as for contributions to charities?  That is, why do the rich effectively pay just $630 for their contribution to a charity ($1,000 minus $370), while the poor pay $880 ($1,000 minus $120) for their contribution to possibly the exact same charity?  There really is no economic, much less moral, reason for this, but that is in fact how the US tax code is currently written.  As discussed in an earlier post on this blog, the government subsidy for such deductions could instead be set to be the same for all, at say a rate of 20% or so.  There is no reason why the rich should receive a bigger subsidy than the poor receive for the contributions they make.]

Another area where the information the IRS would not have complete information to compute taxes due would be where the tax filer had sold a capital asset which had been purchased before 2010.  The IRS only started in 2010 to require that financial institutions report the cost basis for assets sold, and this cost basis is needed to compute capital gains (or losses).  But as time passes, a smaller and smaller share of assets sold will have been purchased before 2010.  The most important, for most people, will likely be the cost of the home they bought if before 2010, but such a sale will happen only once (unless they owned multiple real estate assets in 2010).

But a simple adjustment could be made to reflect the cost basis of such assets, similar to the adjustment for itemized deductions.  The draft tax forms filled in by the IRS would leave as blank (zero) the cost basis of the assets sold in the year for which it did not have a figure reported.  The tax filer would then determine what the cost basis of all such assets should be (as they do now), add them up, and then subtract 20% of that total cost basis from the taxes due (for those in the 20% bracket for long term capital gains, as most people with capital gains are, or use 15% or 0% if those tax brackets apply in their particular cases).

There will still be a few tax filers with more complex situations where the IRS draft computations are not helpful, who will want to do their own forms.  This is fine – there would always be that option.  But such individuals would still be no worse off than what is required now.  And their number is likely to be very small.  While a guess, I would say that what the IRS could provide to tax filers would be fully sufficient and accurate for 80 to 85% of Americans, and that simple additions or subtractions to the draft forms (as described above) would work for most of the rest.  Probably less than 5% of filers would need to complete a full set of forms themselves, and possibly less than 1%.

D. Final Remarks

Such an approach would be new for the US.  But there is nothing revolutionary about it.  Indeed, it is common elsewhere in the world.  Much of Western Europe already follows such an approach or some variant of it, in particular all of the Scandinavian countries as well as Germany, Spain, and the UK, and also Japan.  Small countries, such as Chile and Estonia, have it, as do large ones.

It has also often been proposed for the US.  Indeed, President Reagan proposed it as part of his tax reduction and simplification bill in 1985, then candidate Barack Obama proposed it in 2007 in a speech on middle class tax fairness, a presidential commission in 2010 included it as one of the proposals in its report on simplifying the tax system, and numerous academics and others have also argued in its favor.

It would also likely save money at the IRS.  The IRS collects already most of the information needed.  But that information is not then sent back to us in fully or partially filled in tax forms, but rather is used by the IRS after we file to check to see whether we got anything wrong.  And if we did, we then face a fine or possibly worse.  Completing our tax returns should not be a game of “gotcha” with the IRS, but rather an effort to ensure we have them right.

Such a reform has, however, been staunchly opposed by narrow interests who benefit from the current frustrating system.  Intuit, the seller of TurboTax software, has been particularly aggressive through its congressional lobbying and campaign contributions in using Congress to block the IRS from pursuing this, as has H&R Block.  They of course realize that if tax filing were easy, with the IRS completing most or all of the forms for us, there would be no need to spend what comes to billions of dollars for software from Intuit and others.  But the morality of a business using its lobbying and campaign contributions to ensure life is made particularly burdensome for the citizenry, so that it can then sell a product to make it easier, is something to be questioned.

One can, however, understand the narrow commercial interests of Intuit and the tax software companies.  One can also, sadly, understand the opposition of a number of conservative political activists, with Grover Norquist the most prominent and in the lead. They have also aggressively lobbied Congress to block the IRS from making tax filing simpler.  They are ideologically opposed to taxes, and see the burden and difficulty in figuring out one’s taxes as a positive, not as a negative.  The hope is that with more people complaining about how difficult it is to fill in their tax forms, the more people will be in favor of cutting taxes.  While that view on how people see taxes might well be accurate, what many may not realize is that the tax cuts of recent decades have led to greater complexity and difficulty, not less.  With new loopholes for certain narrow interests, and with income taxed differently depending on the source of that income (with income from wealth taxed at a much lower rate than income from labor), the system has become more complex while generating less revenue overall.

But it is perverse that Congress should legislate in favor of making life more difficult.  The tax system is indeed necessary and crucial, as Reagan correctly noted in his 1985 speech on tax reform, but as he also noted in that speech, there is no need to make them difficult.  Most Americans, Reagan argued, should be able, and would be able under his proposals, to use what he called a “return-free” system, with the IRS working out the taxes due.

The system as proposed above would do this.  It would also be voluntary.  If one disagreed with the pre-filled in forms sent by the IRS, and could not make the simple adjustments (up or down) to the taxes due through the measures as discussed above, one could always fill in the entire set of forms oneself.  But for that small number of such cases this would just be the same as is now required for all.  Furthermore, if one really was concerned about the IRS filling in one’s forms for some reason (it is not clear what that might be), one could easily have a system of opting-out, where one would notify the IRS that one did not want the service.

The tax code itself should still be simplified.  There are many reforms that can and should be implemented, if there was the political will.  The 2010 presidential commission presented numerous options for what could be done.  But even with the current complex system, or rather especially because of the current complex system, there is no valid reason why figuring out and filing our taxes should be so difficult.  Let the IRS do it for us.

Taxes on Corporate Profits Have Continued to Collapse

 

The Bureau of Economic Analysis (BEA) released earlier today its second estimate of GDP growth in the fourth quarter ot 2018.  (Confusingly, it was officially called the “third” estimate, but was only the second as what would have been the first, due in January, was never done due to Trump shutting down most agencies of the federal government in December and January due to his border wall dispute.)  Most public attention was rightly focussed on the downward revision in the estimate of real GDP growth in the fourth quarter, from a 2.6% annual rate estimated last month, to 2.2% now.  And current estimates are that growth in the first quarter of 2019 will be substantially less than that.

But there is much more in the BEA figures than just GDP growth.  The second report of the BEA also includes initial estimates of corporate profits and the taxes they pay (as well as much else).  The purpose of this note is to update an earlier post on this blog that examined what happened to corporate profit tax revenues following the Trump / GOP tax cuts of late 2017.  That earlier post was based on figures for just the first half of 2018.

We now have figures for the full year, and they confirm what had earlier been found – corporate profit tax revenues have indeed plummeted.  As seen in the chart at the top of this post, corporate profit taxes were in the range of only $150 to $160 billion (at annual rates) in the four quarters of 2018.  This was less than half the $300 to $350 billion range in the years before 2018.  And there is no sign that this collapse in revenues was due to special circumstances of one quarter or another.  We see it in all four quarters.

The collapse shows through even more clearly when one examines what they were as a share of corporate profits:

 

The rate fell from a range of generally 15 to 16%, and sometimes 17%, in the earlier years, to just 7.0% in 2018.  And it was an unusually steady rate of 7.0% throughout the year.  Note that under the Trump / GOP tax bill, the standard rate for corporate profit tax was cut from 35% previously to a new headline rate of 21%.  But the actual rate paid turned out (on average over all firms) to come to just 7.0%, or only one-third as much.  The tax bill proponents claimed that while the headline rate was being cut, they would close loopholes so the amount collected would not go down.  But instead loopholes were not only kept, but expanded, and revenues collected fell by more than half.

If the average corporate profit tax rate paid in 2018 had been not 7.0%, but rather at the rate it was on average over the three prior fiscal years (FY2015 to 2017) of 15.5%, an extra $192.2 billion in revenues would have been collected.

There was also a reduction in personal income taxes collected.  While the proportional fall was less, a much higher share of federal income taxes are now borne by individuals than by corporations.  (They were more evenly balanced decades ago, when the corporate profit tax rates were much higher – they reached over 50% in terms of the amount actually collected in the early 1950s.)  Federal personal income tax as a share of personal income was 9.2% in 2018, and again quite steady at that rate over each of the four quarters.  Over the three prior fiscal years of FY2015 to 2017, this rate averaged 9.6%.  Had it remained at that 9.6%, an extra $77.3 billion would have been collected in 2018.

The total reduction in tax revenues from these two sources in 2018 was therefore $270 billion.  While it is admittedly simplistic to extrapolate this out over ten years, if one nevertheless does (assuming, conservatively, real growth of 1% a year and price growth of 2%, for a total growth of about 3% a year), the total revenue loss would sum to $3.1 trillion.  And if one adds to this, as one should, the extra interest expense on what would now be a higher public debt (and assuming an average interest rate for government borrowing of 2.6%), the total loss grows to $3.5 trillion.

This is huge.  To give a sense of the magnitude, an earlier post on this blog found that revenues equal to the original forecast loss under the Trump / GOP tax plan (summing to $1.5 trillion over the next decade, and then continuing) would suffice to ensure the Social Security Trust Fund would be fully funded forever.  As things are now, if nothing is done the Trust Fund will run out in about 2034.  And Republicans insist that the gap is so large that nothing can be done, and that the system will have to crash unless retired seniors accept a sharp reduction in what are already low benefits.

But with losses under the Trump / GOP tax bill of $3.1 trillion over ten years, less than half of those losses would suffice to ensure Social Security could survive at contracted benefit levels.  One cannot argue that we can afford such a huge tax cut, but cannot afford what is needed to ensure Social Security remains solvent.

In the nearer term, the tax cuts have led to a large growth in the fiscal deficit.  Even the US Treasury itself is currently forecasting that the federal budget deficit will reach $1.1 trillion in FY2019 (5.2% of GDP), up from $779 billion in FY2018.  It is unprecedented to have such high fiscal deficits at a time of full employment, other than during World War II.  Proper fiscal management would call for something closer to a balanced budget, or even a surplus, in those periods when the economy is at full employment, while deficits should be expected (and indeed called for) during times of economic downturns, when unemployment is high.  But instead we are doing the opposite.  This will put the economy in a precarious position when the next economic downturn comes.  And eventually it will, as it always has.

End Gerrymandering by Focussing on the Process, Not on the Outcomes

A.  Introduction

There is little that is as destructive to a democracy as gerrymandering.  As has been noted by many, with gerrymandering the politicians are choosing their voters rather than the voters choosing their political representatives.

The diagrams above, in schematic form, show how gerrymandering works.  Suppose one has a state or region with 50 precincts, with 60% that are fully “blue” and 40% that are fully “red”, and where 5 districts need to be drawn.  If the blue party controls the process, they can draw the district lines as in the middle diagram, and win all 5 (100%) of the districts, with just 60% of the voters.  If, in contrast, the red party controls the process for some reason, they could draw the district boundaries as in the diagram on the right.  They would then win 3 of the 5 districts (60%) even though they only account for 40% of the voters.  It works by what is called in the business “packing and cracking”:  With the red party controlling the process, they “pack” as many blue voters as possible into a small number of districts (two in the example here, each with 90% blue voters), and then “crack” the rest by scattering them around in the remaining districts, each as a minority (three districts here, each with 40% blue voters and 60% red).

Gerrymandering leads to cynicism among voters, with the well-founded view that their votes just do not matter.  Possibly even worse, gerrymandering leads to increased polarization, as candidates in districts with lines drawn to be safe for one party or the other do not need to worry about seeking to appeal to voters of the opposite party.  Rather, their main concern is that a more extreme candidate from their own party will not challenge them in a primary, where only those of their own party (and normally mostly just the more extreme voters in their party) will vote.  And this is exactly what we have seen, especially since 2010 when gerrymandering became more sophisticated, widespread, and egregious than ever before.

Gerrymandering has grown in recent decades both because computing power and data sources have grown increasingly sophisticated, and because a higher share of states have had a single political party able to control the process in full (i.e. with both legislative chambers, and the governor when a part of the process, all under a single party’s control).  And especially following the 2010 elections, this has favored the Republicans.  As a result, while there has been one Democratic-controlled state (Maryland) on common lists of the states with the most egregious gerrymandering, most of the states with extreme gerrymandering were Republican-controlled.  Thus, for example, Professor Samuel Wang of Princeton, founder of the Princeton Gerrymandering Project, has identified a list of the eight most egregiously gerrymandered states (by a set of criteria he has helped develop), where one (Maryland) was Democratic-controlled, while the remaining seven were Republican.  Or the Washington Post calculated across all states an average of the degree of compactness of congressional districts:  Of the 15 states with the least compact districts, only two (Maryland and Illinois) were liberal Democratic-controlled states.  And in terms of the “efficiency gap” measure (which I will discuss below), seven states were gerrymandered following the 2010 elections in such a way as to yield two or more congressional seats each in their favor.  All seven were Republican-controlled.

With gerrymandering increasingly common and extreme, a number of cases have gone to the Supreme Court to try to stop it.  However, the Supreme Court has failed as yet to issue a definitive ruling ending the practice.  Rather, it has so far skirted the issue by resolving cases on more narrow grounds, or by sending cases back to lower courts for further consideration.  This may soon change, as the Supreme Court has agreed to take up two cases (affecting lines drawn for congressional districts in North Carolina and in Maryland), with oral arguments scheduled for March 26, 2019.  But it remains to be seen if these cases will lead to a definitive ruling on the practice of partisan gerrymandering or not.

This is not because of a lack of concern by the court.  Even conservative Justice Samuel Alito has conceded that “gerrymandering is distasteful”.  But he, along with the other conservative justices on the court, have ruled against the court taking a position on the gerrymandering cases brought before it, in part, at least, out of the concern that they do not have a clear standard by which to judge whether any particular case of gerrymandering was constitutionally excessive.  This goes back to a 2004 case (Vieth v. Jubelirer) in which the four most conservative justices of the time, led by Justice Antonin Scalia, opined that there could not be such a standard, while the four liberal justices argued that there could.  Justice Anthony Kennedy, in the middle, issued a concurring opinion with the conservative justices there was not then an acceptable such standard before them, but that he would not preclude the possibility of such a standard being developed at some point in the future.

Following this 2004 decision, political scientists and other scholars have sought to come up with such a standard.  Many have been suggested, such as a set of three tests proposed by Professor Wang of Princeton, or measures that focus on the share of seats won to the share of the votes cast, and more.  Probably most attention in recent years has been given to the “efficiency gap” measure proposed by Professor Nicholas Stephanopoulos and Eric McGhee.  The efficiency gap is the gap between the two main parties in the “wasted votes” each party received in some past election in the state (as a share of total votes in the state), where a wasted vote is the sum of all the votes for a losing candidate of that party, plus the votes in excess of 50% when that party’s candidate won.  This provides a direct measure of the two basic tactics of gerrymandering, as described above, of “packing” as many voters of one party as possible in a small number of districts (where they might receive 80 or 90% of the votes, but with all those above 50% “wasted”), and “cracking” (by splitting up the remaining voters of that party into a large number of districts where they will each be in a minority and hence will lose, with those votes then also “wasted”).

But there are problems with each of these measures, including the widely touted efficiency gap measure.  It has often been the case in recent years, in our divided society, that like-minded voters live close to each other, and particular districts in the state then will, as a result, often see the winner of the district receive a very high share of the votes.  Thus, even with no overt gerrymandering, the efficiency gap as measured will appear large.  Furthermore, at the opposite end of this spectrum, the measure will be extremely sensitive if a few districts are close to 50/50.  A shift of just a few percentage points in the vote will then lead one party or the other to lose and hence will then see a big jump in their share of wasted votes (the 49% received by one party or the other).

There is, however, a far more fundamental problem.  And that is that this is simply the wrong question to ask.  With all due respect to Justice Kennedy, and recognizing also that I am an economist and not a lawyer, I do not understand why the focus here is on the voting outcome, rather than on the process by which the district lines were drawn.  The voting outcome is not the standard by which other aspects of voter rights are judged.  Rather, the focus is on whether the process followed was fair and unbiased, with the outcome then whatever it is.

I would argue that the same should apply when district lines are drawn.  Was the process followed fair and unbiased?  The way to ensure that would be to remove the politicians from the process (both directly and indirectly), and to follow instead an automatic procedure by which district lines are drawn in accord with a small number of basic principles.

The next section below will first discuss the basic point that the focus when judging fairness and lack of bias should not be on whether we can come up with some measure based on the vote outcomes, but rather on whether the process that was followed to draw the district lines was fair and unbiased or not.  The section following will then discuss a particular process that illustrates how this could be done.  It would be automatic, and would produce a fair and unbiased drawing of voting district lines that meets the basic principles on which such a map should be based (districts of similar population, compactness, contiguity, and, to the extent consistent with these, respect for the boundaries of existing political jurisdictions such as counties or municipalities).  And while I believe this particular process would be a good one, I would not exclude that others are possible.  The important point is that the courts should require the states to follow some such process, and from the example presented we see that this is indeed feasible.  It is not an impossible task.

The penultimate section of the post will then discuss a few points that arise with any such system, and their implications, and end with a brief section summarizing the key points.

B.  A Fair Voting System Should Be Judged Based on the Process, Not on the Outcomes

Voting rights are fundamental in any democracy.  But in judging whether some aspect of the voting system is proper, we do not try to determine whether or not (by some defined specific measure) the resulting outcomes were improperly skewed or not.

Thus, for example, we take as a basic right that our ballot may be cast in secret.  No government official, nor anyone else for that matter, can insist on seeing how we voted.  Suppose that some state passed a law saying a government-appointed official will look over the shoulder of each of us as we vote, to determine whether we did it “right” or not.  We would expect the courts to strike this down, as an inappropriate process that contravenes our basic voting rights.  We would not expect the courts to say that they should look at the subsequent voting outcomes, and try to come up with some specific measure which would show, with certainty, whether the resulting outcomes were excessively influenced or not.  That would of course be absurd.

As another absurd example, suppose some state passed a law granting those registered in one of the major political parties, but not those registered in the other, access to more early days of voting than the other.  This would be explicitly partisan, and one would assume that the courts would not insist on limiting their assessment to an examination of the later voting outcomes to see whether, by some proposed measure, the resulting outcomes were excessively affected.  The voting system, to be fair, should not lead to a partisan advantage for one party or the other.  But gerrymandering does precisely that.

Yet the courts have so far asked declined to issue a definitive ruling on partisan gerrymandering, and have asked instead whether there might be some measure to determine, in the voting outcomes, whether gerrymandering had led to an excessive partisan advantage for the party drawing the district lines.  And there have been open admissions by senior political figures that district borders were in fact drawn up to provide a partisan advantage.  Indeed, principals involved in the two cases now before the Supreme Court have openly said that partisan advantage was the objective.  In North Carolina, David Lewis, the Republican chair of the committee in the state legislature responsible for drawing up the district lines, said during the debate that “I think electing Republicans is better than electing Democrats. So I drew this map to help foster what I think is better for the country.”

And in the case of Maryland, the Democratic governor of the state in 2010 at the time the congressional district lines were drawn, Martin O’Malley, spoke out in 2018 in writing and in interviews openly acknowledging that he and the Democrats had drawn the district lines for partisan advantage.  But he also now said that this was wrong and that he hoped the Supreme Court would rule against what they had done.

But how to remove partisanship when district lines are drawn?  As long as politicians are directly involved, with their political futures (and those of their colleagues) dependent on the district lines, it is human nature that biases will enter.  And it does not matter whether the biases are conscious and openly expressed, or unconscious and denied.  Furthermore, although possibly diminished, such biases will still enter even with independent commissions drawing the district lines.  There will be some political process by which the commissioners are appointed, and those who are appointed, even if independent, will still be human and will have certain preferences.

The way to address this would rather be to define some automatic process which, given the data on where people live and the specific principles to follow, will be able to draw up district lines that are both fair (follow the stated principles) and unbiased (are not drawn up in order to provide partisan advantage to one party).  In the next section I will present a particular process that would do this.

C.  An Automatic Process to Draw District Lines that are Fair and Unbiased

The boundaries for fair and unbiased districts should be drawn in accord with the following set of principles (and no more):

a)  One Person – One Vote:  Each district should have a similar population;

b)  Contiguity:  Each district must be geographically contiguous.  That is, one continuous boundary line will encompass the entire district and nothing more;

c)  Compactness:  While remaining consistent with the above, districts should be as compact as possible under some specified measure of compactness.

And while not such a fundamental principle, a reasonable objective is also, to the extent possible consistent with the basic principles above, that the district boundaries drawn should follow the lines of existing political jurisdictions (such as of counties or municipalities).

There will still be a need for decisions to be made on the basic process to follow and then on a number of the parameters and specific rules required for any such process.  Individual states will need to make such decisions, and can do so in accordance with their traditions and with what makes sense for their particular state.  But once these “rules of the game” are fully specified, there should then be a requirement that they will remain locked in for some lengthy period (at least to beyond whenever the next decennial redistricting will be needed), so that games cannot be played with the rules in order to bias a redistricting that may soon be coming up.  This will be discussed further below.

Such specific decisions will need to be made in order to fully define the application of the basic principles presented above.  To start, for the one person – one vote principle the Supreme Court has ruled that a 10% margin in population between the largest and smallest districts is an acceptable standard.  And many states have indeed chosen to follow this standard.  However, a state could, if it wished, choose to use a tighter standard, such as a margin in the populations between the largest and smallest districts of no more than 8%, or perhaps 5% or whatever.  A choice needs to be made.

Similarly, a specific measure of compactness will need to be specified.  Mathematically there are several different measures that could be used, but a good one which is both intuitive and relatively easy to apply is that the sum of the lengths of all the perimeters of each of the districts in the state should be minimized.  Note that since the outside borders of the state itself are fixed, this sum can be limited just to the perimeters that are internal to the state.  In essence, since states are to be divided up into component districts (and exhaustively so), the perimeter lines that do this with the shortest total length will lead to districts that are compact.  There will not be wavy lines, nor lines leading to elongated districts, as such lines will sum to a greater total length than possible alternatives.

What, then, would be a specific process (or algorithm) which could be used to draw district lines?  I will recommend one here, which should work well and would be consistent with the basic principles for a fair and unbiased set of district boundaries.  But other processes are possible.  A state could choose some such alternative (but then should stick to it).  The important point is that one should define a fully specified, automatic, and neutral process to draw such district lines, rather than try to determine whether some set of lines, drawn based on the “judgment” of politicians or of others, was “excessively” gerrymandered based on the voting outcomes observed.

Finally, the example will be based on what would be done to draw congressional district lines in a state.  But one could follow a similar process for drawing other such district lines, such as for state legislative districts.

The process would follow a series of steps:

Step 1: The first step would be to define a set of sub-districts within each county in a state (parish in Louisiana) and municipality (in those states where municipalities hold similar governmental responsibilities as a county).  These sub-districts would likely be the districts for county boards or legislative councils in most of the states, and one might typically have a dozen or more of these in such jurisdictions.  When those districts are also being redrawn as part of the decennial redistricting process, then they should be drawn first (based on the principles set out here), before the congressional district lines are drawn.

Each state would define, as appropriate for the institutions of that specific state, the sub-districts that will be used for the purpose of drawing the congressional district lines.  And if no such sub-jurisdictions exist in certain counties of certain states, one could draw up such sub-districts, purely for the purposes of this redistricting exercise, by dividing such counties into compact (based on minimization of the sum of the perimeters), equal population, districts.  While the number of such sub-districts would be defined (as part of the rules set for the process) based on the population of the affected counties, a reasonable number might generally be around 12 or 15.

These sub-districts will then be used in Step 4 below to even out the congressional districts.

Step 2:  An initial division of each state into a set of tentative congressional districts would then be drawn based on minimizing the sum of the lengths of the perimeter lines for all the districts, and requiring that all of the districts in the state have exactly the same population.  Following the 2010 census, the average population in a congressional district across the US was 710,767, but the exact number will vary by state depending on how many congressional seats the state was allocated.

Step 3: This first set of district lines will not, in general, follow county and municipal lines.  In this step 3, the initial set of district lines would then be shifted to the county or municipal line which is geographically closest to it (as defined by minimizing the geographic area that would be shifted in going to that county or city line, in comparison to whatever the alternative jurisdiction would be).  If the populations in the resulting congressional districts are then all within the 10% margin for the populations (or whatever percent margin is chosen by the state) between the largest and the smallest districts, then one is finished and the map is final.

Step 4:  But in general, there may be one or more districts where the resulting population exceeds or falls short of the 10% limit.  One would then make use of the political subdivisions of the counties and municipalities defined in Step 1 to bring them into line.  A specific set of rules for that process would need to be specified.  One such set would be to first determine which congressional district, as then drawn, deviated most from what the mean population should be for the districts in that state.  Suppose that district had too large of a population.  One would then shift one of the political subdivisions in that district from it to whichever adjacent congressional district had the least population (of all adjacent districts).  And the specific political subdivision shifted would then be the one which would have the least adverse impact on the measure of compactness (the sum of perimeter lengths).  Note that the impact on the compactness measure could indeed be positive (i.e. it could make the resulting congressional districts more compact), if the political subdivision eligible to be shifted were in a bend in the county or city line.

If the resulting congressional districts were all now within the 10% population margin (or whatever margin the state had chosen as its standard), one would be finished.  But if this is not the case, then one would repeat Step 4 over and over as necessary, each time for whatever district was then most out of line with the 10% margin.

That is it.  The result would be contiguous and relatively compact congressional districts, each with a similar population (within the 10% margin, or whatever margin is decided upon), and following borders of counties and municipalities or of political sub-divisions within those entities.

This would of course all be done on the computer, and can be once the rules and parameters are all decided as there will no longer be a role for opinion nor an opportunity for political bias to enter.  And while the initial data entry will be significant (as one would need to have the populations and perimeter lengths of each of the political subdivisions, and those of the counties and municipalities that they add up to), such data are now available from standard sources.  Indeed, the data entry needed would be far less than what is typically required for the computer programs used by our politicians to draw up their gerrymandered maps.

D.  Further Remarks

A few more points:

a)  The Redistricting Process, Once Decided, Should be Locked In for a Long Period:  As was discussed above, states will need to make a series of decisions to define fully the specific process it chooses to follow.  As illustrated in the case discussed above, states will need to decide on matters such as what will be the maximum margin of the populations between the largest and smallest districts (no more than 10%, by Supreme Court decision, but it could be less).  And rules will need to be set on, also as in the case discussed above, what measure of compactness to use, or the criterion on which district should be chosen first to have a shift of a sub-district in order to even out the population differences, and so on.

Such decisions will have an impact on the final districts arrived at.  And some of those districts will favor Republicans and some will favor Democrats, just by random.  There would then be a problem if the redistricting were controlled by one party in the state, and that party (through consultants who specialize in this) tried out dozens if not hundreds of possible choices on the parameters to see which would turn out to be most advantageous to it.  While the impact would be far less than what we have now with the deliberate gerrymandering, there could still be some effect.

To stem this, one should require that once choices are made on the process to follow and on the rules and other parameters needed to implement that process, there could not then be a change in that process for the immediately upcoming decennial redistricting.  They would only apply to those following.  While this would not be possible for the very first application of the system, there will likely be a good deal of attention paid by the public to these issues initially so such an attempt to bias the system would be difficult.

As noted, this is not likely to be a major problem, and any such system will not introduce the major biases we have seen in the deliberately gerrymandered maps of numerous states following the 2010 census.  But by locking in any decisions made for a long period, where any random bias in favor of one party in a map might well be reversed following the next census, there will be less of a possibility to game the system by changing the rules, just before a redistricting is due, to favor one party.

b)  Independent Commissions Do Not Suffice  – They Still Need to Decide How to Draw the District Maps:  A reform that has been increasingly advocated by many in recent years is to take the redistricting process out of the hands of the politicians, and instead to appoint independent commissions to draw up the maps.  There are seven states currently with non-partisan or bipartisan, nominally independent, commissions that draw the lines for both congressional and state legislative districts, and a further six who do this for state legislative districts only.  Furthermore, several additional states will use such commissions starting with the redistricting that follows the 2020 census.  Finally, there is Iowa.  While technically not an independent commission, district lines in Iowa are drawn up by non-partisan legislative staff, with the state legislature then approving it or not on a straight up or down vote.  If not approved, the process starts over, and if not approved after three votes it goes to the Iowa Supreme Court.

While certainly a step in the right direction, a problem with such independent commissions is that the process by which members are appointed can be highly politicized.  And even if not overtly politicized, the members appointed will have personal views on who they favor, and it is difficult even with the best of intentions to ensure such views do not enter.

But more fundamentally, even a well-intentioned independent commission will need to make choices on what is, and what is not, a “good” district map.  While most states list certain objectives for the redistricting process in either their state constitutions or in legislation, these are typically vague, such as saying the maps should try to preserve “communities of interest”, but with no clarity on what this in practice means.  Thirty-eight states also call for “compactness”, but few specify what that really means.  Indeed, only two states (Colorado and Iowa) define a specific measure of compactness.  Both states say that compactness should be measured by the sum of the perimeter lines being minimized (the same measure I used in the process discussed above).  However, in the case of Iowa this is taken along with a second measure of compactness (the absolute value of the difference between the length and the width of a district), and it is not clear how these two criteria are to be judged against each other when they differ.  Furthermore, in all states, including Colorado and Iowa, the compactness objective is just one of many objectives, and how to judge tradeoffs between the diverse objectives is not specified.

Even a well-intentioned independent commission will need to have clear criteria to judge what is a good map and what is not.  But once these criteria are fully specified, there is then no need for further opinion to enter, and hence no need for an independent commission.

c)  Appropriate and Inappropriate Principles to Follow: As discussed above, the basic principles that should be followed are:  1) One person – One vote, 2) Contiguity, and 3) Compactness.  Plus, to the extent possible consistent with this, the lines of existing political jurisdictions of a state (such as counties and municipalities) should be respected.

But while most states do call for this (with one person – one vote required by Supreme Court decision, but decided only in 1964), they also call for their district maps to abide by a number of other objectives.  Examples include the preservation of “communities of interest”, as discussed above, where 21 states call for this for their state legislative districts and 16 for their congressional districts (where one should note that congressional districting is not relevant in 7 states as they have only one member of Congress).  Further examples of what are “required” or “allowed” to be considered include preservation of political subdivision lines (45 states); preservation of “district cores” (8 states); and protection of incumbents (8 states).  Interestingly, 10 states explicitly prohibit consideration of the protection of incumbents.  And various states include other factors to consider or not consider as well.

But many, indeed most, of these considerations are left vague.  What does it mean that “communities of interest” are to be preserved where possible?  Who defines what the relevant communities are?  What is the district “core” that is to be preserved?  And as discussed above, there is a similar issue with the stated objective of “compactness”, as while 38 states call for it, only Colorado and Iowa are clear on how it is defined (but then vague on what trade-offs are to be accepted against other objectives).

The result of such multiple objectives, mostly vaguely defined and with no guidance on trade-offs, is that it is easy to come up with the heavily gerrymandered maps we have seen and the resulting strong bias in favor of one political party over the other.  Any district can be rationalized in terms of at least one of the vague objectives (such as preserving a “community of interest”).  These are loopholes which allow the politicians to draw maps favorable to themselves, and should be eliminated.

d)  Racial Preferences: The US has a long history of using gerrymandering (as well as other measures) to effectively disenfranchise minority groups, in particular African-Americans.  This has been especially the case in the American South, under the Jim Crow laws that were in effect through to the 1960s.  The Voting Rights Act of 1965 aimed to change this.  It required states (in particular under amendments to Section 2 passed in 1982 when the Act was reauthorized) to ensure minority groups would be able to have an effective voice in their choice of political representatives, including, under certain circumstances, through the creation of congressional and other legislative districts where the previously disenfranchised minority group would be in the majority (“majority-minority districts”).

However, it has not worked out that way.  Indeed, the creation of majority-minority districts, with African-Americans packed into as small a number of districts as possible and with the rest then scattered across a large number of remaining districts, is precisely what one would do under classic gerrymandering (packing and cracking) designed to limit, not enable, the political influence of such groups.  With the passage of these amendments to the Voting Rights Act in 1982, and then a Supreme Court decision in 1986 which upheld this (Thornburg v. Gingles), Republicans realized in the redistricting following the 1990 census that they could then, in those states where they controlled the process, use this as a means to gerrymander districts to their political advantage.  Newt Gingrich, in particular, encouraged this strategy, and the resulting Republican gains in the South in 1992 and 1994 were an important factor in leading to the Republican take-over of the Congress following the 1994 elections (for the first time in 40 years), with Gingrich then becoming the House Speaker.

Note also that while the Supreme Court, in a 5-4 decision in 2013, essentially gutted a key section of the Voting Rights Act, the section they declared to be unconstitutional was Section 5.  This was the section that required pre-approval by federal authorities of changes in voting statutes in those jurisdictions of the country (mostly the states of the South) with a history of discrimination as defined in the statute.  Left in place was Section 2 of the Voting Rights Act, the section under which the gerrymandering of districts on racial lines has been justified.  It is perhaps not surprising that Republicans have welcomed keeping this Section 2 while protesting Section 5.

One should also recognize that this racial gerrymandering of districts in the South has not led to most African-Americans in the region being represented in Congress by African-Americans.  One can calculate from the raw data (reported here in Ballotpedia, based on US Census data), that as of 2015, 12 of the 71 congressional districts in the core South (Louisiana, Mississippi, Alabama, Georgia, South Carolina, North Carolina, Virginia, and Tennessee) had a majority of African-American residents.  These were all just a single district in each of the states, other than two in North Carolina and four in Georgia.  But the majority of African Americans in those states did not live in those twelve districts.  Of the 13.2 million African-Americans in those eight states, just 5.0 million lived in those twelve districts, while 8.2 million were scattered around the remaining districts.  By packing as many African-Americans as possible in a small number of districts, the Republican legislators were able to create a large number of safe districts for their own party, and the African-Americans in those districts effectively had little say in who was then elected.

The Voting Rights Act was an important measure forward, drafted in reaction to the Jim Crow laws that had effectively undermined the right to vote of African-Americans.  And defined relative to the Jim Crow system, it was progress.  However, relative to a system that draws up district lines in a fair and unbiased manner, it would be a step backwards.  A system where minorities are packed into a small number of districts, with the rest then scattered across most of the districts, is just standard gerrymandering designed to minimize, not to ensure, the political rights of the minority groups.

E.  Conclusion

Politicians drawing district lines to favor one party and to ensure their own re-election fundamentally undermines democracy.  Supreme Court justices have themselves called it “distasteful”.  However, to address gerrymandering the court has sought some measure which could be used to ascertain whether the resulting voting outcomes were biased to a degree that could be considered unconstitutional.

But this is not the right question.  One does not judge other aspects of whether the voting process is fair or not by whether the resulting outcomes were by some measure “excessively” affected or not.  It is not clear why such an approach, focused on vote outcomes, should apply to gerrymandering.  Rather, the focus should be on whether the process followed was fair and unbiased or not.

And one can certainly define a fair and unbiased process to draw district lines.  The key is that the process, once established, should be automatic and follow the agreed set of basic principles that define what the districts should be – that they should be of similar population, compact, contiguous, and where possible and consistent with these principles, follow the lines of existing political jurisdictions.

One such process was outlined above.  But there are other possibilities.  The key is that the courts should require, in the name of ensuring a fair vote, that states must decide on some such process and implement it.  And the citizenry should demand the same.