The World Bank Doing Business Report: Changes in Rank May Not Mean What They May Appear to Mean

A.  The Issue

The World Bank has been publishing its Doing Business report annually, from the first released in September 2003 (and titled Doing Business in 2004) until the one released in October 2019 (and titled Doing Business 2020).  It has always been a controversial report, criticized for a number of different reasons.  But it has also been one that gained a good deal of attention – from the news media, from investors, and from at least certain segments of the public.  And because it received a good deal of attention (indeed more than any other World Bank report), governments paid attention to what it said, especially about them.

It is not my intention here to review those criticisms.  They are numerous.  Rather, in this post I will present a different approach to how the results could have been presented, which would have been more informative and which might have quieted some (but not all) of the criticisms.

One feature of the report, which was also the most widely discussed aspect of the report, was its ranking of countries in terms of their (assessed) environment for doing business. Because the Doing Business reports were widely cited, countries paid attention to those rankings and by various methods sought to improve their rankings.  These were often positive and productive, with countries seeking to simplify business regulation so that businesses could operate more effectively.  While some criticized this as simplistic, it is difficult to see a rationale, for example, justifying that in Venezuela (in 2020), to start a business would require completing 20 different procedures normally requiring (given the times for review and other requirements) an estimated 230 days.  In contrast, in New Zealand, starting a business involves only one procedure and can be completed in half a day.  Of course, few new businesses in Venezuela actually take 230 days to get started.  Either they are simply ignored (with bribes then paid to the police to leave them alone despite their violation of the regulations), or bribes are paid to obtain the licenses without going through the complex processes.

Given the prominence of the report, some countries undertook to improve their rankings through less positive means.  Sometimes the system could be gamed in various ways (e.g. to enact some “reform”, but then to limit its application narrowly so as to fit the letter, but not the spirit, of what was being assessed).  Or sometimes pressure could be applied on those submitting the data to slant it in a favorable direction.

And sometimes a country would try to apply political pressure on World Bank management in order to secure more favorable treatment.  A recently released independent investigation (commissioned by the World Bank Board, and undertaken by the law firm WilmerHale), found that there was such pressure (or at least perceived such pressure) brought by China on Bank management to ensure its ranking in the 2018 report would not fall.  At the instigation of the president’s offiice, and then overseen by the then #2 at the World Bank (Kristalina Georgieva – now the head of the IMF), Bank staff were directed to re-examine the ratings that had been assigned to China, and indeed re-examine the methodology more broadly, to see whether with some set of changes China’s ranking would not fall.  According to the WilmerHale report, several different approaches were considered and tried until one was found which would lead to the desired outcome.  In the almost final draft of the report, just before its planned publication, China’s ranking would have fallen from #78 in 2017 to #85 in 2018 – a fall of 7 places.  But with the last minute changes overseen by Georgieva, China’s ranking in 2018 became #78 – the same as in 2017.  And that was what was then published.

B.  An Alternative Approach

This focus on rankings is misguided, although not surprising given how the results are presented.  A country may well have enacted significant measures improving its business environment, but if countries a bit below it in the previous year’s rankings did even more, then the country could see a fall in its ranking despite the reforms it had undertaken.  And that fall in ranking would then often be interpreted in the news media (as well as by others) as if the business environment had deteriorated.  In this example, it had not.  Rather, it just did not improve as much as others.  But this nuance could easily be missed, and often was.

A different presentation in the Doing Business reports could have addressed this, and might have made the report a bit less controversial.  Rankings, by their nature, are a zero-sum game, where a rise in the ranking of one country means a fall in the ranking of another.  While this is needed in a sports league, where a tiny difference in the seasonal won/loss record can determine the ranking and hence who goes to the playoffs, it is not the same for the business environment.  Investors want to know what is good and what is not so good in an absolute sense, and small differences in rankings are of no great consequence.  And while some might argue that countries are competing in a global market for investor interest, there are far more important issues for any investor than some small difference in rankings.  For example, China and Malta were ranked similarly for several years in the mid-2010s, but whether one was a bit above or a bit below the other would be basically irrelevant to an investor.

An alternative presentation of the findings, based on comparison to an absolute rather than relative scale, would have been more meaningful as well as less controversial.  Specifically, countries could have been compared not against each other in every given year to see if their rankings had moved up or down, but rather to what the set of scores were (and consequent rankings were) in some base year.  That is, one would see whether their business environment had gotten better or worse in terms of the base year set of scores and consequent rankings (what one could call that base year’s “ladder” of scores).  The Doing Business project had that data, reported the underlying scores, and used those scores to produce its rankings.  But there was no systematic presentation of how the business environments may have changed over time, with this then compared to what the rankings were in some base period.

There were, I should note, figures provided in the Doing Business reports on the one-year changes in scores for each individual country, but few paid much attention to them.  They were for one-year changes only, and did not show the more meaningful cumulative changes over a number of years.  Nor did they then show how such changes would affect the country’s position on an understandable scale, such as what the ratings were across countries in a base year.

In this new approach, the comparison would be to an absolute scale (the scores and consequent rankings in the base year), not a relative ranking in each future period.  The issue is analogous to different types of poverty measures.  Sometimes poverty in a country is measured as the bottom 10% of the population (ranked by income).  This is a useful measure for certain things (such as how to target various social programs), but will of course always show 10% of the population as being “poor” regardless of how effective the social programs may have been.  In contrast, one could have an absolute measure of poverty (for many years the World Bank used the measure of $1 per day of income per person), and one could then track how many people were moving out of poverty (or not) by this measure.  Similarly, with the relative ranking of the 190 countries covered in the Doing Business reports, one will always find a mean ranking of 95, regardless of what countries may have done to improve their business environment.  It is, obviously, simply a relative measure, and not terribly useful in conveying what has been happening to the business environment in any given country.  Yet everyone focuses on it.

Any comparisons over time of these scores must also be over periods where the methodological approach used (precisely what is measured, and what weights are assigned to those measures) has not changed.  Otherwise one is comparing apples to oranges, where changes in the scores may reflect the methodological changes and not necessarily changes in the business environment of the countries.  Such changes in the methodological specifics have been criticized by some, as such changes in methodology will, in itself, lead to changes in rankings.  But one should expect periodic changes in the methodological approach used, as experience is gained and more is learned.

C.  The Results

The 2016 to 2020 period was one where the methodological specifics used in the Doing Business reports did not change, and hence one can make a meaningful comparison of scores over this period.  The data needed are what they specifically call the “Doing Business Scores”, which are the absolute values for the indices used to measure the business environment.  They range from 100 (for the best possible) to 0 (for the worst).  These scores have (at least until now) been made publicly available in the online Doing Business database.  I used these to illustrate what could be done to present the Doing Business results based on an absolute, rather than relative, scale.

The results are presented in a table at the end of this post, and readers might want to take a brief look at that table now to see its structure.  The base year is 2016, and the calculations are based on the changes in the country’s Doing Business Scores over the period from 2016 to 2020.  No country scores 100, and the top-ranked country (New Zealand) had an overall score of 87.1 in 2016 and slightly less at 86.8 in 2020.  I re-normalized the scores to set the top score (New Zealand’s 87.1 in 2016) to 100, with the rest then scaled in proportion to that.

The scores are thus shown as a proportion to what the best overall score was in 2016.  And this was done not just for the 2016 scores but also for the 2020 scores.  Importantly, the 2020 scores are not taken as a proportion of the best score for any country in 2020, but rather as a proportion of what the best score was in 2016.  Those scores are shown in the last two columns of the table, first for 2016 (as a proportion of what the best score was in 2016 – New Zealand at 87.1), and then for 2020 (again as a proportion of what the best score was in 2016 – New Zealand’s 87.1).  Note that on this scaling, New Zealand in 2016 would be 100.00, while in 2020 its rating would fall very slightly to 99.66 (as the Doing Business Score for New Zealand fell slightly from 87.1 in 2016 to 86.8 in 2020, and 86.8 is 99.66% of the 2016 score of 87.1).

The first two columns in the table show the rankings of countries, as the Doing Business reports have them now, for 2016 and then for 2020.  The sequence in the table is according to the ranking in 2016.  The third (middle) column then shows where a country would have ranked in 2016, had they had then the business environment that they had in 2020.  These are shown as fractional “rankings”, where, for example, if a country’s score in 2020 was halfway between the scores of countries ranked #10 and #11 in 2016, then that country would have a “rank” of 10.5.  That is, that country would have ranked between those ranked #10 and #11 in 2016, had they had a business environment in 2016 that they in fact had in 2020.

This now provides an absolute measure of country performance over time.  It is a comparison to where they would have ranked in 2016 had they had then what their policies later were.  This will then provide a more accurate account of what has been happening in the business environment in the country.  Take the case of Germany, for example.  It ranked #16 in 2016 and then lower at #22 in 2020.  Many would interpret this as a business environment that had deteriorated over the period.  But that is in fact not the case.  The absolute score was 91.27 in 2016 and a slightly improved 91.50 in 2020.  The business environment became slightly better (as assessed) between those two years, and it would have moved up a bit to a “rank” of 15.3 if it had had in 2016 the business environment it had in 2020.  But because a number of countries below Germany in 2016 saw a larger improvement in their business environment by 2020 than that of Germany, the relative ranking of Germany fell to #22.

Some of the differences could be large.  El Salvador, for example, ranked #80 in 2016 and fell to #91 in the traditional (relative) ranking for 2020.  But its business environment in fact improved significantly over the period, where the business environment it had in 2020 would have placed it at a ranking of 70.0 in 2016.  That is, it would have seen an improvement by 10 positions between the two periods rather than a deterioration of 11.  But other countries moved around as well, changing the relative ranking even though in absolute terms the business environment in El Salvador became significantly better.

D.  The Politics

One can understand how politicians in some country could grow frustrated if, after a period where they pushed through substantial (and possibly politically costly) reforms that aimed to improve their business environment, they then found that their Doing Business ranking nonetheless declined.  One might try to explain that other countries were also changing, and that despite their improvement other countries improved even more and hence pushed them down in rank.  This can follow when the focus is on relative rankings.  But this is not likely to be very convincing, as what the politicians see reported in the news media is the relative rankings.

A calculation of how the ratings would have changed in absolute terms would not suffer from this problem.  Countries would be credited for what they accomplished, not based on how what they did compares to what other countries might have done.  If all countries improved, then one would see that reflected when an absolute scale is used.  But when the focus is on relative rankings, any move up in rank must be matched by someone else moving down.  It is a zero-sum game.

Such a change in approach might have made the Doing Business reports politically more palatable.  It might also have reduced the pressure from countries such as China.  Throughout the period of 2016 to 2020, China was taking actions which, in terms of the Doing Business measures, were leading to consistently higher absolute scores each year, including for 2018.  With an absolute scale, such as that proposed here, one would have seen those year-by-year improvements in China’s position relative to where it would have been in a base year ranking (2016 for example).  The improvements were relatively modest in 2017 and 2018, and then much more substantial in 2019 and 2020.  But despite that (modest) improvement in 2018, China’s relative ranking in that year would have fallen 7 places to #84 from the #78 rank in the published 2017 report.  China found this disconcerting, and the WilmerHale report describes how Bank staff were then pressured to come up with a way to keep China’s (relative) ranking no worse than the #78 position it had in 2017.  And they then did.

At this point, however, a move to a presentation in terms of an absolute scale is too late for the Doing Business project as it has operated up to now.  Following the release of the WilmerHale report, the World Bank announced on September 16 that it would no longer produce the Doing Business report at all.  Whatever it might do next, if anything, will likely be very different.

 

2016  Rank 2020 Rank Rank with 2020 policy but 2016 ladder 2016 fraction of 2016 best 2020 fraction of 2016 best
New Zealand 1 1 1.1 100.00 99.66
Singapore 2 2 1.4 97.47 98.97
Denmark 3 3 1.8 97.01 97.93
Hong Kong, China 4 4 1.8 96.79 97.93
United States 5 5 4.4 95.98 96.44
United Kingdom 6 8 5.3 95.64 95.87
Korea, Rep. 7 6 4.4 95.41 96.44
Norway 8 9 7.4 93.92 94.83
Sweden 9 10 7.8 93.69 94.14
Taiwan, China 10 15 10.5 93.34 92.88
Estonia 11 18 10.9 92.42 92.54
Australia 12 14 10.1 92.31 93.23
Finland 13 20 12.7 91.96 92.08
Canada 14 23 15.7 91.62 91.39
Ireland 15 24 15.7 91.62 91.39
Germany 16 22 15.3 91.27 91.50
Latvia 17 19 12.3 90.82 92.19
Iceland 18 26 19.0 90.70 90.70
Lithuania 19 11 9.0 90.70 93.69
Austria 20 27 20.5 90.47 90.36
Malaysia 21 12 9.3 90.24 93.57
Georgia 22 7 4.9 89.90 96.10
North Macedonia 23 17 10.8 89.44 92.65
Japan 24 29 22.8 88.98 89.55
Poland 25 40 27.0 88.29 87.72
Portugal 26 37 25.8 87.72 87.83
Switzerland 27 36 25.6 87.72 87.94
United Arab Emirates 28 16 10.5 87.60 92.88
Czech Republic 29 41 28.0 87.37 87.60
France 30 32 25.2 87.37 88.17
Mauritius 31 13 9.3 87.26 93.57
Spain 32 30 23.0 87.14 89.44
Netherlands 33 42 30.0 86.68 87.37
Slovak Republic 34 45 32.7 85.88 86.80
Slovenia 35 38 25.8 85.76 87.83
Russian Federation 36 28 22.2 85.07 89.78
Israel 37 34 25.4 83.81 88.06
Romania 38 55 36.7 83.47 84.16
Bulgaria 39 61 41.0 83.24 82.66
Belgium 40 46 33.7 83.12 86.11
Cyprus 41 52 36.6 82.66 84.27
Thailand 42 21 13.0 82.55 91.96
Italy 43 58 37.3 82.32 83.70
Mexico 44 60 40.0 82.20 83.12
Croatia 45 51 36.5 81.97 84.50
Moldova 46 48 35.5 81.97 85.42
Chile 47 59 38.5 81.75 83.35
Hungary 48 53 36.6 81.63 84.27
Kazakhstan 49 25 15.7 81.40 91.39
Montenegro 50 50 36.3 81.06 84.73
Serbia 51 44 32.5 80.37 86.91
Luxembourg 52 72 51.5 79.45 79.91
Armenia 53 47 35.3 79.33 85.53
Turkey 54 33 25.2 79.33 88.17
Colombia 55 65 50.8 79.10 80.48
Belarus 56 49 35.7 78.87 85.30
Puerto Rico 57 66 50.8 78.87 80.48
Costa Rica 58 74 52.0 77.73 79.45
Morocco 59 54 36.6 77.38 84.27
Peru 60 76 57.0 77.15 78.87
Rwanda 61 39 25.8 77.04 87.83
Greece 62 79 57.3 76.81 78.53
Bahrain 63 43 31.0 76.46 87.26
Qatar 64 77 57.0 76.35 78.87
Oman 65 68 51.0 76.12 80.37
Jamaica 66 71 51.4 76.00 80.02
South Africa 67 84 61.5 76.00 76.92
Botswana 68 87 67.0 75.20 76.00
Azerbaijan 69 35 25.4 75.09 88.06
Mongolia 70 80 57.9 74.97 77.84
Bhutan 71 89 67.3 74.51 75.77
Panama 72 86 63.0 74.28 76.46
Tunisia 73 78 57.0 74.17 78.87
Ukraine 74 64 50.7 73.71 80.60
Bosnia and Herzegovina 75 90 69.0 73.59 75.09
St. Lucia 76 93 75.7 72.90 73.13
Kosovo 77 56 36.8 72.33 84.04
Fiji 78 101 86.0 72.10 70.61
Vietnam 79 70 51.3 71.87 80.14
El Salvador 80 91 70.0 71.64 74.97
China 81 31 24.3 71.53 88.75
Malta 82 88 67.1 71.53 75.89
Indonesia 83 73 51.5 71.30 79.91
Guatemala 84 96 80.0 70.84 71.87
Uzbekistan 85 69 51.1 70.84 80.25
Dominica 86 111 92.0 70.61 69.46
San Marino 87 92 74.0 70.49 73.71
Trinidad and Tobago 88 105 90.0 70.49 70.38
Kyrgyz Republic 89 81 57.9 70.38 77.84
Tonga 90 103 88.0 70.38 70.49
Kuwait 91 83 59.0 69.69 77.38
Zambia 92 85 62.0 69.46 76.81
Samoa 93 98 83.0 69.12 71.30
Uruguay 94 102 86.0 69.12 70.61
Namibia 95 104 88.0 68.89 70.49
Nepal 96 94 76.7 68.54 72.56
Vanuatu 97 107 90.3 68.08 70.15
Antigua and Barbuda 98 113 92.7 67.97 69.23
Saudi Arabia 99 62 44.0 67.97 82.20
Sri Lanka 100 99 83.8 67.97 70.95
Seychelles 101 100 85.0 67.74 70.84
Philippines 102 95 79.0 66.82 72.10
Albania 103 82 58.0 66.70 77.73
Paraguay 104 124 100.5 66.70 67.85
Kenya 105 57 36.8 66.59 84.04
Dominican Republic 106 115 95.0 66.48 68.89
Barbados 107 128 106.0 66.25 66.48
Brunei Darussalam 108 67 50.8 66.02 80.48
Bahamas, The 109 119 95.3 65.56 68.77
Ecuador 110 129 107.0 65.56 66.25
St. Vincent and the Grenadines 111 130 111.0 65.56 65.56
Ghana 112 116 95.0 65.44 68.89
Eswatini 113 121 96.5 65.33 68.31
Argentina 114 126 101.0 65.10 67.74
Jordan 115 75 54.5 65.10 79.22
Uganda 116 117 95.0 64.98 68.89
Honduras 117 133 116.6 64.41 64.64
Papua New Guinea 118 120 95.7 64.29 68.66
Brazil 119 125 100.5 63.83 67.85
St. Kitts and Nevis 120 139 126.5 63.83 62.69
Belize 121 134 120.5 63.61 63.72
Iran, Islamic Rep. 122 127 101.6 63.61 67.16
Lesotho 123 122 96.8 63.38 68.20
Egypt, Arab Rep. 124 114 94.5 62.80 69.00
Lebanon 125 143 127.7 62.80 62.34
Solomon Islands 126 136 122.5 62.80 63.49
India 127 63 48.5 62.57 81.52
West Bank and Gaza 128 118 95.0 62.23 68.89
Nicaragua 129 142 127.3 62.11 62.46
Grenada 130 146 131.0 61.54 61.31
Cabo Verde 131 137 123.4 61.31 63.15
Palau 132 145 129.8 60.96 61.65
Cambodia 133 144 129.6 60.73 61.77
Mozambique 134 138 123.4 60.62 63.15
Maldives 135 147 131.3 60.16 61.19
Tajikistan 136 106 90.0 59.47 70.38
Guyana 137 135 120.5 58.55 63.72
Burkina Faso 138 151 136.5 58.21 59.01
Marshall Islands 139 153 137.3 58.09 58.44
Pakistan 140 108 90.5 57.86 70.03
Côte d’Ivoire 141 110 91.0 57.75 69.69
Mali 142 148 133.0 57.75 60.73
Bolivia 143 150 136.1 57.18 59.36
Malawi 144 109 90.7 57.06 69.92
Tanzania 145 140 127.0 57.06 62.57
Senegal 146 123 97.0 56.95 68.08
Benin 147 149 135.0 55.91 60.16
Nigeria 148 131 113.0 55.57 65.33
Lao PDR 149 154 137.7 55.34 58.32
Micronesia, Fed. Sts. 150 158 150.0 55.22 55.22
Zimbabwe 151 141 127.0 54.88 62.57
Sierra Leone 152 162 151.3 53.85 54.54
Togo 153 97 82.0 53.85 71.53
Suriname 154 163 151.3 53.27 54.54
Niger 155 132 113.5 53.16 65.21
Comoros 156 160 150.7 53.04 54.99
Gambia, The 157 155 142.0 53.04 57.75
Burundi 158 165 153.2 52.35 53.73
Sudan 159 171 160.5 52.24 51.44
Kiribati 160 164 153.0 52.12 53.85
Djibouti 161 112 92.0 50.86 69.46
Guinea 162 156 146.2 50.86 56.72
Algeria 163 157 147.3 50.75 55.80
Gabon 164 168 160.4 50.52 51.66
Ethiopia 165 159 150.3 50.29 55.11
Mauritania 166 152 136.9 50.29 58.67
São Tomé and Principe 167 169 160.4 50.29 51.66
Syrian Arab Republic 168 176 170.5 49.37 48.22
Iraq 169 172 160.6 49.25 51.32
Myanmar 170 166 153.2 48.34 53.73
Cameroon 171 167 157.2 48.11 52.93
Madagascar 172 161 151.1 48.11 54.76
Bangladesh 173 170 160.4 46.96 51.66
Guinea-Bissau 174 174 167.8 46.38 49.60
Equatorial Guinea 175 178 172.8 45.92 47.19
Liberia 176 175 167.8 45.92 49.60
Afghanistan 177 173 163.5 45.12 50.63
Timor-Leste 178 181 176.9 45.12 45.24
Congo, Rep. 179 180 176.7 44.66 45.35
Yemen, Rep. 180 187 188.0 44.09 36.51
Haiti 181 179 173.4 43.28 46.73
Angola 182 177 172.6 43.17 47.42
Chad 183 182 182.3 40.53 42.37
Congo, Dem. Rep. 184 183 182.6 39.49 41.56
Venezuela, RB 185 188 188.2 39.15 34.67
South Sudan 186 185 183.8 37.66 39.72
Libya 187 186 186.5 37.43 37.54
Central African Republic 188 184 182.9 36.74 40.87
Eritrea 189 189 188.9 24.00 24.80
Somalia 190 190 190.0 23.19 22.96

 

A Calculation of Covid-19 Case Rates for Those Vaccinated and Those Not Vaccinated

A.  Introduction

The vaccines available for the virus that causes Covid-19 are incredibly effective – far better than the vaccines for many diseases – but can only work if they are used.  Sadly, a substantial share of the US population, particularly those who identify as conservative and Republican, are declining the opportunity to be vaccinated.  While there are also those on the left who have indicated they will not be vaccinated against this terrible virus, a recent (June) Gallup survey found that while close to half (46%) of Republicans said they do not plan to accept the vaccinations, just 6% of Democrats said so.  A Washington Post – ABC survey in early July found almost exactly the same results, with 47% of Republicans saying they are not likely to get the vaccination, while again only 6% of Democrats said so.

It might help convince those who are reluctant to be vaccinated to see in hard numbers how effective the vaccines have turned out to be.  Part of the responsibility of the CDC is to collect and report data on disease incidence in the US as well as on the cause of all deaths in the country.  All hospitals, doctor’s offices, clinics, and other health centers in the US, are required to report these to the CDC, and the CDC in turn then consolidates the information and makes it available to the public and to researchers.  For Covid, the CDC has put together a separate online site providing extensive data on the spread of the disease in the country, calling it COVID Data Tracker, with the multiple individual data series updated daily.

For vaccinations against Covid, the CDC provides not only daily figures on the number of vaccinations given (down to the county level), but with this broken down by various demographic dimensions, including gender (male/female), age (9 different age groups), and ethnicity (7 groups).  Each of these are tracked daily, so one can also determine trends over time.  There is also daily tracking for each state (and perhaps county – I did not check) of the number of doses of each type of vaccine administered (Moderna, Pfizer, and Johnson & Johnson, and whether it was the first or second dose for Moderna and Pfizer), for key age groups (over age 65, over 18, and over 12).  Many of the charts the CDC provides are then picked up in the regular news media, so people can see daily the trends in Covid-19 cases, deaths, vaccinations, and other such important information.

But the CDC is not reporting what would be an extremely useful, and hopefully convincing, daily statistic along with these numbers.  And that would be not only the total number of new confirmed Covid cases that day, new hospitalizations due to Covid, and deaths due to Covid, but also how many of these are among those who have been vaccinated and among those who have not.  The simple figures could be provided, or, more usefully, expressed as a count per 100,000 in the relevant population (of vaccinated and unvaccinated).

The CDC is not doing this, and it is not clear why.  It may feel that the data it has is not of sufficiently high quality, but if so, one would think that a high priority would be to take whatever measures are necessary to upgrade that quality.  Indeed, it is difficult to think of anything that would be a higher priority than this.

This post will discuss what a chart with such a breakdown between vaccinated and unvaccinated may look like.  This chart (at the top of this post) is not based on directly reported data on Covid cases among the vaccinated and unvaccinated, as the CDC has not made whatever it has on this publicly available (at least among what I have been able to find – specialists in the field may have access to more of what the CDC has).  Rather, the chart presents what the rates would be, given the observed number of daily new Covid cases in the US, and assumptions on how effective the vaccines have been.

Given the high degree of effectiveness of these vaccines in preventing Covid cases, and even more so in preventing Covid deaths, why are so many people refusing to be vaccinated?  Sadly, identity politics has intervened, with many supporters of former president Trump appearing to take vaccination as a sign of disloyalty to a cause they believe in.  As we will see, there is a very strong negative correlation by state between the share of the population who have been vaccinated and the share who voted for Trump.

B.  Covid Case Rates Among Those Vaccinated and Those Unvaccinated 

Despite its extensive reports on Covid cases and vaccinations, one key dimension that the CDC does not report is the breakdown of the daily number of new Covid-19 cases, hospitalizations, and deaths, between those who have been vaccinated and those who have not.  Given the high degree of effectiveness of the vaccines (as observed in the clinical trials and in numerous studies since), one should expect huge disparities in such rates between these two groups.  And seeing such disparities daily in the news might convince at least a few, and hopefully many, of those hesitant to be vaccinated to accept that they should indeed be vaccinated.  It truly is a matter of survival.

So why doesn’t the CDC report this?  One would think that if the CDC does not obtain such reports already, that their highest priority would be to set up the system to ensure such numbers are gathered.  Anyone being tested for Covid-19 will certainly be asked if they have been vaccinated.  And the first question that will likely be asked of anyone entering a hospital for suspected Covid-19 (even before they are asked their name and medical insurance number) is whether they have been vaccinated.  Such information would then be recorded in (or certainly could be recorded in) the reports sent to the CDC on the number testing positive for Covid-19, and would certainly be available for anyone who has been hospitalized and then dies from Covid-19.

The CDC does appear to obtain at least partial information on the number of Covid-19 cases, hospitalizations, and deaths where vaccination status is known.  But they have evidently not sought to organize a more complete and reliable system.  In late May they issued a brief, non-technical, summary of data obtained on cases of Covid-19 among individuals who had been vaccinated (there were a total of 10,262, in reports from 46 states), covering the period between January 1 and April 30, 2021.  But they did not put this in any context, and while they noted that 101 million Americans had been fully vaccinated as of April 30, there were far fewer (essentially zero) as of January 1.  By itself, this report was basically useless.  And then the CDC announced that as of May 1, it would no longer even seek to collect comprehensive data on the vaccination status of confirmed cases of Covid-19, but rather seek this only for those hospitalized due to Covid-19 or who had died from it.

It is difficult to understand why the CDC would scale back the reporting of Covid-19 cases to it, rather than upgrade the quality and completeness of what is reported.  Furthermore, even with incomplete data on vaccination status of confirmed cases of Covid-19, as well as of hospitalizations and deaths from Covid-19, the CDC could still report the figures for those known to be vaccinated, those known not to be vaccinated, and those where vaccination status was not known.  Such a breakdown would still show the (likely huge) disparities in the rates between those known to be vaccinated and those not.  And gathering such data is critically important not only in determining the continued effectiveness (or not) of the vaccines overall against Covid-19 as mutations of the virus develop, but also the continued effectiveness (or not) of the several individual vaccines that have been approved (i.e. Moderna, Pfizer/BioNTech, and Johnson & Johnson, so far in the US).

In the absence of such real world data, the chart at the top of this post presents what the Covid-19 case rates would be under specific assumptions (based on the clinical trial results and more recent studies of efficacy among different groups) of what the effectiveness rates are.  Specifically, based on the clinical trial results as well as numerous studies undertaken for various special groups since, plus being conservative (given the new mutations that have developed and spread over this time period) and rounding down, I assumed effectiveness rates of:

a)  90% for those fully vaccinated (both shots) with either the Pfizer or Moderna vaccines (and with a lag of 14 days from the second shot).

b)  70% for those partially vaccinated (one-shot) with either the Pfizer or Moderna vaccines (and with a lag of 14 days from that shot).

c)  70% for those vaccinated with the one-shot Johnson & Johnson vaccine (again with a 14-day lag).

[Side note:  One will often see the terms “efficacy” and “effectiveness” used interchangeably in describing how well vaccines work.  Technically, efficacy refers to how well (how effective) the vaccines perform in clinical trials, while effectiveness is the term used for how well the vaccines perform in the real world.  For non-specialists, the distinction is not important.]

The chart at the top of this post shows what the respective number of daily newly confirmed Covid-19 rates would be for those fully vaccinated with the Pfizer or Moderna vaccines and for those not vaccinated with any vaccine (per 100,000 of population in each group) from December 27, 2020 (14 days after vaccinations began for the general public on December 13) to July 12, 2021.  To reduce clutter in the chart, I did not show the respective curves for those partially vaccinated or those vaccinated with the Johnson & Johnson vaccines, but those numbers were part of the calculations as the fact that some were vaccinated in this way will affect the position of the curves.  One has to solve a small algebra problem, as the data one has to work with are only the daily number of new cases, the number fully or partially vaccinated with one of the vaccines, and the assumed effectiveness rates (where effectiveness is defined relative to those not vaccinated).  The curves for the groups who had been partially vaccinated (as of a particular date) or who had had the Johnson & Johnson vaccine, would be in between the two curves shown (given the assumed 70% effectiveness rates for each).

The chart presents what the curves would be for daily new cases of Covid-19.  Also of interest are the number of those being hospitalized (indicating severe cases) and those dying from this disease.  In principle, one could prepare similar charts.  But here there is not as much data to go on to underlie the assumptions to be made on vaccine effectiveness.  It is however clear that, given how they function, the vaccines will likely be a good deal more effective in preventing serious cases of Covid-19 (those that require hospitalization) and of deaths than their effectiveness in preventing any type of case.  The reason is that exposures to the virus will be similar for those who are vaccinated and for those who are not:  The virus is floating in the air (due to a contagious person nearby) and it passes up the nose of some of those passing by.  But the difference then is that as the virus starts to replicate in the person’s lungs and body, the immune system of a vaccinated person will be primed and prepared to respond quickly, thus (in most cases) stopping the virus before it has replicated to levels that lead to a detectable illness.  But with 90% effectiveness, a detectable illness will still occur in 10% of such cases.

Most of these illnesses will then be mild, as the immune system in a vaccinated person is already acting to drive down the virus.  But some share of these will not be, possibly due to how the individual’s body had responded to the vaccine.  Still, since it will be some share (well less than 100%) of the 10%, the number will be small.  And it will likely be an even smaller share for those cases that turn out to be so severe that the patient dies.

With such small numbers, the effects will not be easily picked up in clinical trials.  For the clinical trial used to assess the efficacy of the Pfizer/BioNTech vaccine in the US, for example, one had 43,000 volunteers enrolled, of which half received the vaccine and half received a similar looking shot but which had just saline (salt water) in it.  Neither the patient nor the doctor overseeing the shots knew which it was – there was simply an identifying number to be revealed later.  The volunteers would then go about their lives as they had before.  Over time, some would then come down with Covid-19 (as Covid-19 was present in the country and spreading).  Those that did were treated as any other Covid-19 patient would be.  Once a certain number of cases arose (determined based on statistics, with 170 the trigger in the Pfizer/BioNTech trial), the identifying numbers were then, and only then, revealed.  When they were, they found in this trial that 162 of those cases were of individuals who had received just saline (i.e. no vaccine) while 8 had received the vaccine.  This thus showed 95% efficacy (as 8 is 5% of 162, the case burden among those not vaccinated).  Note this is for the effectiveness against getting a case of Covid-19, whether mild or severe.

For hospitalizations, the numbers will be far smaller.  In the Pfizer/BioNTech trial, only 10 of the 170 cases were “severe”, and of these 9 occurred in those who had the shot of saline while just one was in the vaccinated group.  This is far too small a sample to come up with a figure for how effective the virus is against severe cases requiring hospitalization, although it is clear that it helped.  And with no deaths at all among the 170, one can say even less.

There should, however, now be data on this in CDC files as close to half of the US population has been fully vaccinated.  The CDC has not reported on this.  But the Associated Press, working with experts, was able to find relevant data in the CDC files (possibly in files accessible to researchers with special software – I could not find them).  The AP reported that for the month of May, only 1,200 (1.1%) of the 107,000 patients who had been hospitalized for Covid had been vaccinated.  And only 150 (0.8%) of the 18,000 who had died from Covid in the month had been vaccinated.  Since one-third (34%) of the US population had been fully vaccinated as of May 1 (and 43% as of May 31), the shares of those vaccinated will not be small because the number who had been vaccinated were few.  Rather, the numbers give an indication that the vaccines are highly effective against severe cases of Covid developing, and even more effective against patients dying from the disease.  The numbers may well be imperfect, as the CDC has warned, but the impact of vaccination is still clear.

The figures are also consistent with public statements that have been made.  In late June, CDC Director Dr. Rochelle Walensky said that the vaccine is so effective the “nearly every death, especially among adults, due to Covid-19, is, at this point, entirely preventable.”  On July 1, Dr. Walensky said at a White House press briefing that “preliminary data” from certain states over the last six months suggested that “99.5 percent of deaths from COVID-19 in these states have occurred in unvaccinated people”.  On July 8, the White House coordinator on the coronavirus response, Jeff Zients, said “Virtually all Covid-19 hospitalizations and deaths in the United States are now occurring among unvaccinated individuals”.  And on July 12, Dr. Anthony Fauci said “99.5% of people who die of Covid are unvaccinated.  Only 0.5% of those who die are vaccinated” (with his source for these figures probably the same as Dr. Walensky’s).

I am sure all these statements are true.  But many would find them far more convincing if they would show us the actual numbers.  They could be partial, as noted above, with figures for those known to be vaccinated, known not to be vaccinated, and not known.  But the CDC should make public what it has.  And it would be even more convincing to show the numbers updated daily, to drive home the point, repeatedly, that vaccinations are highly effective in protecting us from suffering and possibly dying from this terrible disease.

C.  The Simple Dynamics of Pandemic Spread

As the chart indicates, the likelihood of becoming a victim of Covid-19 is far less for those vaccinated.  I would stress, however, that one should not jump to the conclusion that if by some miracle all of the US had been vaccinated as of December 27, that the path followed would have been the one shown in the chart for those fully vaccinated.  That would not be the case.  Rather, what the curves show is what the case rates would be for each such group where, as has in fact been the case, a substantial share of the population had not been vaccinated, and hence the virus continued to spread (primarily among the unvaccinated).  With a substantial number of people still infecting others with the virus, a certain number of vaccinated people will still catch the virus as the vaccines, while excellent at an effectiveness of 90% or even higher, are still not 100% effective.

The dynamics would be very different, and far better, if everyone (or even just most Americans) were fully vaccinated.  Indeed, under such a scenario the pandemic would soon end completely, and the line depicting cases of Covid-19 would not simply be low but at zero.

This is due to the mathematics of exponential spread of a virus in a pandemic.  The virus that causes Covid-19 cannot live on its own, but only in a living person.  When a person is exposed to the virus, with viral particles floating into their nose, the virus will take about a week to incubate.  The person is then infectious to others for about another week.  If an infected person spreads the virus on average to two others, then the number of cases will double every reproduction period (one and a half weeks on average for Covid-19).  This reproduction rate is called R0 (or R-naught, or R-zero) by epidemiologists, and refers to the reproduction rate in a setting where no measures are being taken to contain the spread of the virus (no vaccines, no masks, no social distancing).  For the original (pre-mutation) virus that causes Covid-19, the R0 was estimated to be between 2 and 3.  With the more recent – more easily spread – delta variant of the virus, it is believed the R0 is above 3.

A virus that then spreads from one person to three every reproduction period (every week and a half on average for Covid) means that if left unchecked, 100 cases to start (week 0) would grow to 8,100 cases by week 6 and to over 650,000 cases by week 12.  it is tripling every week and a half  However, if everyone has had a vaccine that is 90% effective, then the reproduction rate would be reduced from 3 to 0.3.  That means 100 cases to start would lead to only 30 cases in the next reproduction period and to less than one case by week 6, by which point it will have died out.

However, not everyone is vaccinated.  As of the day I am writing this (July 20), the share of the population fully vaccinated is 49%.  Using, for simplicity, a figure of 50%, and assuming the R0 for the mutations currently circulating in the US is 3, then the reproduction rate would fall not to 0.3 but only to 1.65 (the weighted average of half of the population at 0.3 and half still at 3).  With any reproduction rate above 1.0, the spread of the virus will grow, not diminish.  At a rate of 1.65, one will find by simple arithmetic that 100 cases initially (in week 0) would grow (if nothing else is done) to over 700 cases by week 6 and over 5,000 cases by week 12.  While far less than with a reproduction rate of 3, it is still growing.  And that is indeed what has been happening.

These numbers should be taken as illustrative as the modeling is simplistic.  True modeling would take much more into account.  Simple averages are assumed here, as well as no changes in other factors that affect the trends (in particular no change in the use of masks or social distancing).  Also, simple averages may not work that well for Covid-19, as it appears that some people will be far more contagious than others, plus it will depend on how such individuals behave.  A contagious person might spread the disease to dozens or even hundreds of others if they are in an enclosed hall, with a crowd of others who might be chanting or singing, such as at a political rally or a church service.

But the point here is that if 100% of the population were vaccinated, the curve in a chart showing the case rate among those vaccinated would not follow the curve in the chart at the top of this post.  Rather, it would very quickly drop to zero.  The reason why there are still cases among those vaccinated in the US is that, with only about half of the population vaccinated, the virus continues to spread among the unvaccinated.  It will then spread to a certain share of the vaccinated (about 10% of those who are exposed, for vaccines that are 90% effective).

D.  Why Are Some Not Accepting Vaccination?

Despite the high degree of safety and effectiveness of the vaccines, a significant share of Americans are still refusing to be vaccinated.  As noted at the top of this post, recent Gallup and Washington Post – ABC polls found the almost identical results that close to half (46 or 47%) of Republicans say they are unlikely to (or definitely will not) accept vaccination, while 6% of Democrats (in both polls) said that.

Why this opposition to vaccinations, particularly among Republicans?  It is always hard to discern motives and often the individuals themselves may not really know why they are opposed – they just are.  Surveys may provide an indication, but are limited as the survey questionnaire will typically provide a list of possible reasons and ask the person to check all that might be a factor for them.

For example, a survey conducted by Echelon Insights, a Republican firm, in mid-April asked those surveyed who had said they will not accept a vaccination, or were not sure, to choose from a list of possible reasons for why.  The top responses were (with the percentage saying yes, where one could choose multiple reasons):

a)  The vaccine was developed too quickly:  48%

b)  The vaccine was rushed for political reasons:  39%

c)  I don’t have enough information about side effects:  37%

d)  I don’t trust the information being published about the vaccine:  34%

e)  I’m taking enough measures to avoid Covid-19 without the vaccine:  30%

f)  I wouldn’t trust the vaccine until it’s been in use for at least a few years:  28%

g)  I don’t trust any vaccines:  26%

h)  I wouldn’t trust the vaccine until it’s been in use for at least several months:  20%

i)  I believe I’m personally unlikely to suffer serious long term effects if I contract the coronavirus:  17%

But it is not possible to say the extent to which these were in fact the primary reasons for their hesitancy (or direct opposition) to being vaccinated, and to what extent these were just convenient responses to provide to the person conducting the survey.

There are also more bizarre reasons given by some.  For example, a very recent Economist/YouGov poll (conducted between July 10 and 13) found that among those who say they will not be vaccinated, fully half (51%) said that the vaccinations are being used by the government to inject microchips into the population.  A common variant of this conspiracy theory is that Bill Gates will be using the microchips to monitor and/or control us.  It is hard to believe that half of those refusing to be vaccinated really believe this.  Rather, it appears they have decided they do not want to be vaccinated, and then they come up with various rationalizations.  Consistent with this, the Economist/YouGov poll also found that 85% of those who say they will not be vaccinated believe that the threat of the coronavirus has been exaggerated for political reasons (this despite over 600,000 Americans already having died from Covid).

This opposition has also been fed by such media groups as Fox News, with repeated segments that denigrate vaccination against this disease.  As one example, In early May, Tucker Carlson, the most watched political commentator on Fox News, told his audience that as of April 23, a CDC system had recorded that 3,362 people had died following their vaccination.  His report implied the vaccines caused those deaths, when this was not at all the case.  Numerous fact-checkers and commentators in the media almost immediately investigated and concluded that the Carlson allegations were false (see, for example, here, here, and here).  But the damage had been done.

Tucker Carlson took the figures from the Vaccine Adverse Events Recording System (VAERS), an on-line system set up by the CDC where anyone who had been vaccinated (and indeed anyone else) could make a report if they encountered some adverse event following their vaccination.  It is a voluntary system, open to anyone, and you may have noticed a description of how to use it in the papers you received when you were vaccinated (at least I received it as part of the instructions when I was vaccinated in Washington, DC).  Carlson’s report was that the VAERS showed 3,362 deaths between late December and April 23 (which he then extrapolated to 3,722 as of April 30).

But as the fact-checkers and commentators in the media immediately noted, just because a death was recorded by someone in the VAERS does not mean that the death was caused by the vaccine.  There are a certain number of deaths every year in the US, particularly among the elderly, and one should have taken that into account before jumping to a conclusion that a 3,362 figure (or 3,722 as of end-April) was abnormal and a consequence of the vaccinations they received.

It is straightforward to calculate what the expected number of deaths would be in a normal year for the number of individuals who were vaccinated between late 2020 and the April 30 date that Carlson focussed on.  About 2.9 million Americans died in 2019 (i.e. before any Covid cases or vaccinations), and simply assuming an average mortality rate of those vaccinated, the number that would be expected to die in this period in a normal year would be more than 100,000.  And since those being vaccinated over this period were disproportionately the elderly (as the elderly were prioritized in these early months), the far higher mortality rates of the elderly (compared to the entire population) would lead to a number several times higher.  Some of these deaths were then recorded by someone in the VAERS, but that does not mean the vaccinations caused them.  Indeed, there is no evidence so far that the vaccines have caused any deaths at all (although a very small number are being investigated).

[Technical note for those interested in the details of how this calculation was done:  I used the CDC numbers of those who had been fully vaccinated (as of end-December, 2020, and then daily through to April 30, 2021), the US population (332 million, from the Census Bureau), and the number of deaths in the US in 2019 (i.e. before Covid) of 2.9 million (from the CDC).  From this, one can easily calculate on a spreadsheet the number of person-days (through to April 30) of those vaccinated (i.e. starting at 120 days for those vaccinated as of December 31, and then counting down to zero for those vaccinated on April 30), and take the sum of this.  Dividing this total by the number of person-days in a year for the full US population (i.e. 332 million times 365) yields 3.7%.  Applying this share to the 2.9 million number of deaths in a pre-Covid year means that one would have expected 108,000 of those who had been vaccinated during this period to have died during this period for reasons that had nothing to do with Covid or the vaccines.  And the number dying of normal causes would likely be far higher than this 108,000, as that number is calculated assuming those being vaccinated during this period would have had the average mortality rate of the US as a whole.  But a disproportionate share of those vaccinated during this period were the elderly, as they were given priority, and the elderly will of course have naturally higher mortality rates than the population as a whole.  If one adjusted for the ages of those being vaccinated and then used age-specific mortality rates for these groups, the true number to expect would not be 108,000 but something far higher, and likely several times higher.]

It is not just the media, however.  A number of Republican politicians are saying the same.  And it is not only Republican politicians on the more extreme end of their party (such as Representative Marjorie Taylor Greene of Georgia, where Twitter has just suspended her account for 12 hours due to the misleading information she has posted on Covid-19 and the vaccines for it).  One also sees this among Republican office-holders who have been perceived as coming from the party’s establishment.  A prominent example is Senator Ron Johnson of Wisconsin.  In early May, for example, Senator Johnson also cited the VAERS as indicating “over 3,000” had died following their being vaccinated – implying causation.  And despite being called out on this by fact-checkers, Senator Johnson has continued to make these claims.  The fact-checkers at the Washington Post have recently given Senator Johnson their “highest” rating of four Pinocchios for his ongoing campaign of vaccine misinformation.

It is not clear, however, the extent to which vaccine hesitancy and/or outright opposition originated in the reports and statements of media figures such as Tucker Carlson or political figures such as Senator Ron Johnson, or whether the media and political figures found it advantageous to build on such perceptions and then spur along the concerns.  Which came first is not clear.

What is clear is that the issue of vaccination has become politicized, with vaccination being taken as a sign of political loyalties.  One saw the same politicization with the wearing of masks.  Comparing the share of state populations that have been vaccinated (fully or partially) to the share of the 2020 presidential vote in that state for Trump, one finds:

The correlation is incredibly high, with an R-squared of 0.77.  On average, the regression line indicates that for every additional percentage point in the vote share of Trump, the share of the population in the state that was fully or partially vaccinated (as of July 13) was 0.8 percentage points lower.  Furthermore, almost all of the states that voted for Biden have a higher share vaccinated than all of the states that voted for Trump (with the only significant exception being Georgia, and with a few states where the election was close – Arizona, Wisconsin, Michigan, and Nevada – having similar vaccination shares as some of the higher-end Trump states).

E.  Conclusion

The political division is stark, and it is not clear what might change this.  But with the vaccines so highly effective – against cases of Covid-19, more so against severe cases requiring hospitalization, and even more so against death – releasing hard numbers on what the rates have been among the vaccinated versus the unvaccinated may help.  The numbers currently available might be imperfect, and hence require releases with three categories (vaccinated, unvaccinated, and not known), but this would still tell the story.  If everyone saw each day that 995 out of 1,000 deaths had been among the unvaccinated, with only 5 among those who had been vaccinated (as Dr. Walensky’s 99.5% figure implies), self-interest in one’s own health might eventually win out.

This is obviously urgent.  The chart at the top of this post was based on case data downloaded on July 13 (for Covid-19 cases as of July 12).  As one can see in that chart, the daily number of new cases of Covid-19 has been rising over the last month.  It reached a trough around June 20 (using a 7-day moving average of the daily cases).  By July 12, the number of cases had more than doubled from this trough.  As I write this on July 20, it has increased by a further more than 50% since July 12, so it is now more than triple what it was on June 20.  It is spreading especially rapidly in the states with a relatively low rate of vaccination.

This increase has been due, in part, to new mutations (in particular the delta variant) that spread more rapidly than the original form of the virus.  This is what one would expect from standard evolutionary theory – mutations develop and those that spread more easily will soon dominate.  Adding to this is that social distancing and mask mandates have been sharply eased over the last month, leading many to act as if the virus is no longer a threat.  While that would be true if everyone were vaccinated, and is greatly reduced for those who have been vaccinated, the disease will continue to spread and the threat will remain real when half the population is not.

The Ridership Forecasts for the Baltimore-Washington SCMAGLEV Are Far Too High

The United States desperately needs better public transit.  While the lockdowns made necessary by the spread of the virus that causes Covid-19 led to sharp declines in transit use in 2020, with (so far) only a partial recovery, there will remain a need for transit to provide decent basic service in our metropolitan regions.  Lower-income workers are especially dependent on public transit, and many of them are, as we now see, the “essential workers” that society needs to function.  The Washington-Baltimore region is no exception.

Yet rather than focus on the basic nuts and bolts of ensuring quality services on our subways, buses, and trains, the State of Maryland is once again enamored with using the scarce resources available for public transit to build rail lines through our public parkland in order to serve a small elite.  The Purple Line light rail line was such a case.  Its dual rail lines will serve a narrow 16-mile corridor, passing through some of the richest zip codes in the nation, but destroying precious urban parkland.  As was discussed in an earlier post on this blog, with what will be spent on the Purple Line one could instead stop charging fares on the county-run bus services in the entirety of the two counties the Purple Line will pass through (Montgomery and Prince George’s), and at the same time double those bus services (i.e. double the lines, or double the service frequency, or some combination).

The administration of Governor Hogan of Maryland nonetheless pushed the Purple Line through, although construction has now been halted for close to a year due to cost overruns leading the primary construction contractor to withdraw.  Hogan’s administration is now promoting the building of a superconducting, magnetically-levitating, train (SCMAGLEV) between downtown Baltimore and downtown Washington, DC, with a stop at BWI Airport.  Over $35 million has already been spent, with a massive Draft Environmental Impact Statement (DEIS) produced.  As required by federal law, the DEIS has been made available for public comment, with comments due by May 24.

It is inevitable that such a project will lead to major, and permanent, environmental damage.  The SCMAGLEV would travel partially in tunnels underground, but also on elevated pylons parallel to the Baltimore-Washington Parkway (administered by the National Park Service).  The photos at the top of this post show what it would look like at one section of the parkway.  The question that needs to be addressed is whether any benefits will outweigh the costs (both environmental and other costs), and ridership is central to this.  If ridership is likely to be well less than that forecast, the whole case for the project collapses.  It will not cover its operating and maintenance costs, much less pay back even a portion of what will be spent to build it (up to $17 billion according to the DEIS, but likely to be far more based on experience with similar projects).  Nor would the purported economic benefits then follow.

I have copied below comments I submitted on the DEIS forecasts.  Readers may find them of interest as this project illustrates once again that despite millions of dollars being spent, the consulting firms producing such analyses can get some very basic things wrong.  The issue I focus on for the proposed SCMAGLEV is the ridership forecasts.  The SCMAGLEV project sponsors forecast that the SCMAGLEV will carry 24.9 million riders (one-way trips) in 2045.  The SCMAGLEV will require just 15 minutes to travel between downtown Baltimore and downtown Washington (with a stop at BWI), and is expected to charge a fare of $120 (roundtrip) on average and up to $160 at peak hours.  As one can already see from the fares, at best it would serve a narrow elite.

But there is already a high-speed train providing premier-level service between Baltimore and Washington – the Acela service of Amtrak.  It takes somewhat longer – 30 minutes currently – but its fare is also somewhat lower at $104 for a roundtrip, plus it operates from more convenient stations in Baltimore and Washington.  Importantly, it operates now, and we thus have a sound basis for forecasts of what its ridership might be in the future.

One can thus compare the forecast ridership on the proposed SCMAGLEV to the forecast for Acela ridership (also in the DEIS) in a scenario of no SCMAGLEV.  One would expect the forecasts to be broadly comparable.  One could allow that perhaps it might be somewhat higher on the SCMAGLEV, but probably less than twice as high and certainly less than three times as high.  But one can calculate from figures in the DEIS that the forecast SCMAGLEV ridership in 2045 would be 133 times higher than what they forecast Acela ridership would be in that year (in a scenario of no SCMAGLEV).  For those going just between downtown Baltimore and downtown Washington (i.e. excluding BWI travelers), the forecast SCMAGLEV ridership would be 154 times higher than what it would be on the comparable Acela.  This is absurd.

And it gets worse.  For reasons that are not clear, the base year figures for Acela ridership in the Baltimore-Washington market are more than eight times higher in the DEIS than figures that Amtrak itself has produced.  It is possible that the SCMAGLEV analysts included Acela riders who have boarded north of Baltimore (such as in Philadelphia or New York) and then traveled through to DC (or from DC would pass through Baltimore to ultimate destinations further north).  But such travelers should not be included, as the relevant travelers who might take the SCMAGLEV would only be those whose trips begin in either Baltimore or in Washington and end in the other metropolitan area.  The project sponsors have made no secret that they hope eventually to build a SCMAGLEV line the full distance between Washington and New York, but that would at a minimum be in the distant future.  It is not a source of riders included in their forecasts for a Baltimore to Washington SCMAGLEV.

The Amtrak forecasts of what it expects its Acela ridership would be, by market (including between Baltimore and Washington) and under various investment scenarios, come from its recent NEC FUTURE (for Northeast Corridor Future) study, for which it produced a Final Environmental Impact Statement.  Using Amtrak’s forecasts of what its Acela ridership would be in a scenario where major investments allowed the Acela to take just 20 minutes to go between Baltimore and Washington, the SCMAGLEV ridership forecasts were 727 times as high (in 2040).  That is complete nonsense.

My comment submitted on the DEIS, copied below, goes further into these results and discusses as well how the SCMAGLEV sponsors could have gotten their forecasts so absurdly wrong.  But the lesson here is that the consultants producing such forecasts are paid by project sponsors who wish to see the project built.  Thus they have little interest in even asking the question of why they have come up with an estimate that 24.9 million would take a SCMAGLEV in 2045 (requiring 15 minutes on the train itself to go between Baltimore and DC) while ridership on the Acela in that year (in a scenario where the Acela would require 5 minutes more, i.e. 20 minutes, and there is no SCMAGLEV) would be about just 34,000.

One saw similar issues with the Purple Line.  An examination of the ridership forecasts made for it found that in about half of the transit analysis zone pairs, the predicted ridership on all forms of public transit (buses, trains, and the Purple Line as well) was less than what they forecast it would be on the Purple Line only.  This is mathematically impossible.  And the fact that half were higher and half were lower suggests that the results they obtained were basically just random.  They also forecast that close to 20,000 would travel by the Purple Line into Bethesda each day but only about 10,000 would leave (which would lead to Bethesda’s population exploding, if true).  The source of this error was clear (they mixed up two formats for the trips – what is called the production/attraction format with origin/destination), but it mattered.  They concluded that the Purple Line had to be a rail line rather than a bus service in order to handle their predicted 20,000 riders each day on the segment to Bethesda.

It may not be surprising that private promoters of such projects would overlook such issues.  They may stand to gain (i.e. from the construction contracts, or from an increase in land values next to station sites), even though society as a whole loses.  Someone else (government) is paying.  But public officials in agencies such as the Maryland Department of Transportation should be looking at what is the best way to ensure quality and affordable transit services for the general public.  Problems develop once the officials see their role as promoters of some specific project.  They then seek to come up with a rationale to justify the project, and see their role as surmounting all the hurdles encountered along the way.  They are not asking whether this is the best use of scarce public resources to address our very real transit needs.

A high-speed magnetically-levitating train (with superconducting magnets, no less), may look attractive.  But officials should not assume such a shiny new toy will address our transit issues.

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May 22, 2021

Comment Submitted on the DEIS for SCMAGLEV

The Ridership Forecasts Are Far Too High

A.  Introduction

I am opposed to the construction of the proposed SCMAGLEV project between Baltimore and Washington, DC.  A key issue for any such system is whether ridership will be high enough to compensate for the environmental damage that is inevitable with such a project.  But the ridership forecasts presented in the DEIS are hugely flawed.  They are far too high and simply do not meet basic conditions of plausibility.  At more plausible ridership levels, the case for such a project collapses.  It will not cover its operating costs, much less pay back any of the investment (of up to $17 billion according to the DEIS, but based on experience likely to be far higher).  Nor will the purported positive economic benefits then follow.  But the damage to the environment will be permanent.

Specifically, there is rail service now between Baltimore and Washington, at three levels of service (the high-speed Acela service of Amtrak, the regular Amtrak Regional service, and MARC).  Ridership on the Acela service, as it is now and with what is expected with upgrades in future years, provides a benchmark that can be used.  While it could be argued that ridership on the proposed SCMAGLEV would be higher than ridership on the Acela trains, the question is how much higher.  I will discuss below in more detail the factors to take into account in making such a comparison, but briefly, the Acela service takes 30 minutes today to go between Baltimore and Washington, while the SCMAGLEV would take 15 minutes.  But given that it also takes time to get to the station and on the train, and then to the ultimate destination at the other end, the time savings would be well less than 50%.  The fare would also be higher on the SCMAGLEV (at an average, according to the DEIS, of $120 for a round-trip ticket but up to $160 at peak hours, versus an average of $104 on the Acela).  In addition, the stations the SCMAGLEV would use for travel between downtown Baltimore and downtown Washington are less conveniently located (with poorer connections to local transit) than the Acela uses.

Thus while it could be argued that the SCMAGLEV would attract more riders than the Acela, even this is not clear.  But being generous, one could allow that it might attract somewhat more riders.  The question is how many.  And this is where it becomes completely implausible.  Based on the ridership forecasts in the DEIS, for both the SCMAGLEV and for the Acela (in a scenario where the SCMAGLEV is not built), the SCMAGLEV in 2045 would carry 133 times what ridership would be on the Acela.  Excluding the BWI ridership on both, it would be 154 times higher.  There is no way to describe this other than that it is just nonsense.  And with other, likely more accurate, forecasts of what Acela ridership would be in the future (discussed below) the ratios become higher still.

Similarly, if the SCMAGLEV will be as attractive to MARC riders as the project sponsors forecast it will be, then most of those MARC riders would now be on the modestly less attractive Acela.  But they aren’t.  The Acela is 30 minutes faster than MARC (the SCMAGLEV would be 45 minutes faster), yet 28 times as many riders choose MARC over Acela between Baltimore and Washington.  I suspect the fare difference ($16 per day on MARC, vs. $104 on the Acela) plays an important role.  The model used could have been tested by calculating a forecast with their model of what Acela ridership would be under current conditions, with this then compared this to what the actual figures are.  Evidently this was not done.  Had they, their predicted Acela ridership would likely have been a high multiple of the actual and it would have been clear that their modeling framework has problems.

Why are the forecasts off by orders of magnitude?  Unfortunately, given what has been made available in the DEIS and with the accompanying papers on ridership, one cannot say for sure.  But from what has been made available, there are indications of where the modeling approach taken had issues.  I will discuss these below.

In the rest of this comment I will first discuss the use of Acela service and its ridership (both the actual now and as projected) as a basis for comparison to the ridership forecasts made for the SCMAGLEV.  They would be basically similar services, where a modest time saving on the SCMAGLEV (15 minutes now, but only 5 minutes in the future if further investments are made in the Acela service that would cut its Baltimore to DC time to just 20 minutes) is offset by a higher fare and less convenient station locations.  I will then discuss some reasons that might explain why the SCMAGLEV ridership forecasts are so hugely out-of-line with what plausible numbers might be.

B.  A Comparison of SCMAGLEV Ridership Forecasts to Those for Acela  

The DEIS provides ridership forecasts for the SCMAGLEV for both 2030 (several years after the DEIS says it would be opened, so ridership would then be stable after an initial ramping up) and for a horizon year of 2045.  I will focus here on the 2045 forecasts, and specifically on the alternative where the destination station in Baltimore is Camden Yards.  The DEIS also has forecasts for ridership in an alternative where the SCMAGLEV line would end in the less convenient Cherry Hill neighborhood of Baltimore, which is significantly further from downtown and with poorer connections to local transit options.  The Camden Yards station is more comparable to Penn Station – Baltimore, which the Acela (and Amtrak Regional trains and one of the MARC lines) use.  Penn Station – Baltimore has better local transit connections and would be more convenient for many potential riders, but this will of course depend on the particular circumstances of the rider – where he or she will be starting from and where their particular destination will be.  It will, in particular, be more convenient for riders coming from North and Northeast of Baltimore than Camden Yards would be.  And those from South and Southwest of Baltimore would be more likely to drive directly to the DC region than try to reach Camden Yards, or they would alight at BWI.

The DEIS also provides forecasts of what ridership would be on the existing train services between Baltimore and Washington:  the Acela services (operated by Amtrak), the regular Amtrak Regional trains, and the MARC commuter service operated by the State of Maryland.  Note also that the 2045 forecasts for the train services are for both a scenario where the SCMAGLEV is not built and then what they forecast the reduced ridership would be with a SCMAGLEV option.  For the purposes here, what is of interest is the scenario with no SCMAGLEV.

The SCMAGLEV would provide a premium service, requiring 15 minutes to go between downtown Baltimore and downtown Washington, DC.  Acela also provides a premium service and currently takes 30 minutes, while the regular Amtrak Regional trains take 40 to 45 minutes and MARC service takes 60 minutes.  But the fares differ substantially.  Using the DEIS figures (with all prices and fares expressed in base year 2018 dollars), the SCMAGLEV would charge an average fare of $120 for a round-trip (Baltimore-Washington), and up to $160 for a roundtrip at peak times.  The Acela also has a high fare for its also premium service, although not as high as SCMAGLEV, charging an average of $104 for a roundtrip (using the DEIS figures).  But Amtrak Regional trains charge only $34 for a similar roundtrip, and MARC only $16.

Acela service thus provides a reasonable basis for comparison to what SCMAGLEV would provide, with the great advantage that we know now what Acela ridership has actually been.  This provides a firm base for a forecast of what Acela ridership would be in a future year in a scenario where the SCMAGLEV is not built.  And while the ridership on the two would not be exactly the same, one should expect them to be in the same ballpark.

But they are far from that:

  DEIS Forecasts of SCMAGLEV vs. Acela Ridership, Annual Trips in 2045

Route

SCMAGLEV Trips

Acela Trips

Ratio

Baltimore – DC only

19,277,578

125,226

154 times as much

All, including BWI

24,938,652

187,887

133 times as much

Sources:  DEIS, Main Report Table 4.2-3; and Table D-4-48 of Appendix D.4 of the DEIS

Using estimates just from the DEIS, the project sponsor is forecasting that annual (one-way) trips on the SCMAGLEV in 2045 would be 133 times what they would be in that year on the Acela (in a scenario where the SCMAGLEV is not built).  And it would be 154 times as much for the Baltimore – Washington riders only.  This is nonsense.  One could have a reasonable debate if the SCMAGLEV figures were twice as high, and maybe even if they were three times as high.  But it is absurd that they would be 133 or 154 times as high.

And it gets worse.  The figures above are all taken from the DEIS.  But the base year Acela ridership figures in the DEIS (Appendix D.4, Table D.4-45) differ substantially from figures Amtrak itself has produced in its recent NEC FUTURE study.  This review of future investment options in Northeast Corridor (Washington to Boston) Amtrak service was concluded in July 2017.  As part of this it provided forecasts of what future Acela ridership would be under various alternatives, including one (its Alternative 3) where Acela trains would be substantially upgraded and require just 20 minutes for the trip between downtown Baltimore and downtown Washington, DC.  This would be quite similar to what SCMAGLEV service would be.

But for reasons that are not clear, the base year figures for Acela ridership between Baltimore and Washington differ substantially between what the SCMAGLEV DEIS has and what NEC FUTURE has.  The figure in the NEC FUTURE study (for a base year of 2013) puts the number of riders (one-way) between Baltimore and Washington (and not counting those who boarded north of Baltimore, at Philadelphia or New York for example, and then rode through to Washington, and similarly for those going from Washington to Baltimore) at just 17,595.  The DEIS for the SCMAGLEV put the similar Acela ridership (for a base year of 2017) at 147,831 (calculated from Table D.4-45, of Appendix D.4).  While the base years differ (2013 vs. 2017), the disparity cannot be explained by that.  It is far too large.  My guess would be that the DEIS counted all Acela travelers taking up seats between Baltimore and Washington, including those who alighted north of Baltimore (or whose destination from Washington was north of Baltimore), and not just those travelers traveling solely between Washington and Baltimore.  But the SCMAGLEV will be serving only the Baltimore-Washington market, with no interconnections with the train routes coming from north of Baltimore.

What was the source of the Acela ridership figure in the DEIS of 147,831 in 2017?  That is not clear.  Table D.4-45 of Appendix D.4 says that its source is Table 3-10 of the “SCMAGLEV Final Ridership Report”, dated November 8, 2018.  But that report, which is available along with the other DEIS reports (with a direct link at https://bwmaglev.info/index.php/component/jdownloads/?task=download.send&id=71&catid=6&m=0&Itemid=101), does not have a Table 3-10.  Significant portions of that report were redacted, but in its Table of Contents no reference is shown to a Table 3-10 (even though other redacted tables, such as Tables 5-2 and 6-3, are still referenced in the Table of Contents, but labeled as redacted).

One can only speculate on why there is no Table 3-10 in the Final Ridership Report.  Perhaps it was deleted when someone discovered that the figures reported there, which were then later used as part of the database for the ridership forecast models, were grossly out of line with the Amtrak figures.  The Amtrak figure for Acela ridership for Baltimore-Washington passengers of 17,595 (in 2013) is less than one-eighth of the figure on Acela ridership shown in the DEIS or 147,831 (in 2017).

It can be difficult for an outsider to know how many of those riding on the Acela between Washington and Baltimore are passengers going just between those two cities (as well as BWI).  Most of the passengers riding on that segment will be going on to (or coming from) cities further north.  One would need access to ticket sales data.  But it is reasonable to assume that Amtrak itself would know this, and therefore that the figures in the NEC FUTURE study would likely be accurate.  Furthermore, in the forecast horizon years, where Amtrak is trying to show what Acela (and other rail) ridership would grow to with alternative investment programs, it is reasonable to assume that Amtrak would provide relatively optimistic (i.e. higher) estimates, as higher estimates are more likely to convince Congress to provide the funding that would be required for such investments.

The Amtrak figures would in any case provide a suitable comparison to what SCMAGLEV’s future ridership might be.  The Amtrak forecasts are for 2040, so for the SCMAGLEV forecasts I interpolated to produce an estimate for 2040 assuming a constant rate of growth between the forecast SCMAGLEV ridership in 2030 and that for 2045.  Both the NEC FUTURE and SCMAGLEV figures include the stop at BWI.

    Forecasts of SCMAGLEV (DEIS) vs. Acela (NEC FUTURE) Ridership between Baltimore and Washington, Annual Trips in 2040 

Alternative

SCMAGLEV Trips

Acela Trips

Ratio

No Action

22,761,428

26,177

870 times as much

Alternative 1

22,761,428

26,779

850 times as much

Alternative 2

22,761,428

29,170

780 times as much

Alternative 3

22,761,428

31,291

727 times as much

Sources:  SCMAGLEV trips interpolated from figures on forecast ridership in 2030 and 2045 (Camden Yards) in Table 4.2-3 of DEIS.  Acela trips from NEC FUTURE Final EIS, Volume 2, Appendix B.08.

The Acela ridership figures are those estimated under various investment scenarios in the rail service in the Northeast Corridor.  NEC FUTURE examined a “No Action” scenario with just minimal investments, and then various alternative investment levels to produce increasingly capable services.  Alternative 3 (of which there were four sub-variants, but all addressing alternative investments between New York and Boston and thus not affecting directly the Washington-Baltimore route) would upgrade Acela service to the extent that it would go between Baltimore and Washington in just 20 minutes.  This would be very close to the 15 minutes for the SCMAGLEV.  Yet even with such a comparable service, the SCMAGLEV DEIS is forecasting that its service would carry 727 times as many riders as what Amtrak has forecast for its Acela service (in a scenario where there is no SCMAGLEV).  This is complete nonsense.

To be clear, I would stress again that the forecast future Acela ridership figures are a scenario under various possible investment programs by Amtrak.  The investment program in Alternative 3 would upgrade Acela service to a degree where the Baltimore – Washington trip (with a stop at BWI) would take just 20 minutes.  The NEC FUTURE study forecasts that in such a scenario the Baltimore-Washington ridership on Acela would total a bit over 31,000 trips in the year 2040.  In contrast, the DEIS for the SCMAGLEV forecasts that there would in that year be close to 23 million trips taken on the similar SCMAGLEV service, requiring 15 minutes to make such a trip.  Such a disparity makes no sense.

C.  How Could the Forecasts be so Wrong?

A well-known consulting firm, Louis Berger, prepared the ridership forecasts, and their “Final Ridership Report” dated November 8, 2018, referenced above, provides an overview on the approach they took.  Unfortunately, while I appreciate that the project sponsor provided a link to this report along with the rest of the DEIS (I had asked for this, having seen references to it in the DEIS), the report that was posted had significant sections redacted.  Due to those redactions, and possibly also limitations in what the full report itself might have included (such as summaries of the underlying data), it is impossible to say for sure why the forecasts of SCMAGLEV ridership were close to three orders of magnitude greater than what ridership has been and is expected to be on comparable Acela service.

Thus I can only speculate.  But there are several indications of what may have led the SCMAGLEV estimates to be so out of line with ridership on a service that is at least broadly comparable.  Specifically:

1)  As noted above, there were apparent problems in assembling existing data on rail ridership for the Baltimore-Washington market, in particular for the Acela.  The ridership numbers for the Acela in the DEIS were more than eight times higher in their base year (2017) than what Amtrak had in an only slightly earlier base year (2013).  The ridership numbers on Amtrak Regional trains (for Baltimore-Washington riders) were closer but still substantially different:  409,671 in Table D.4-45 of the DEIS (for 2017), vs. 172,151 in NEC FUTURE (for 2013).

Table D.4-45 states that its source for this data on rail ridership is a Table 3-10 in the Final Ridership Report of November 8, 2018.  But as noted previously, such a table is not there – it was either never there or it was redacted.  Thus it is impossible to determine why their figures differ so much from those of Amtrak.  But the differences for the Acela figures (more than a factor of eight) are huge, i.e. close to an order of magnitude by itself.  While it is impossible to say for sure, my guess (as noted above) is that the Acela ridership numbers in the DEIS included travelers whose trip began, or would end, in destinations north of Baltimore, who then traveled through Baltimore on their way to, or from, Washington, DC.  But such travelers are not part of the market the SCMAGLEV would serve.

2)  In modeling the choice those traveling between Baltimore and Washington would have between SCMAGLEV and alternatives, the analysts collapsed all the train options (Acela, Amtrak Regional, and MARC) into one.  See page 61 of the Ridership Report.  They create a weighted average for a single “train” alternative, and they note that since (in their figures) MARC ridership makes up almost 90% of the rail market, the weighted averages for travel time and the fare will be essentially that of MARC.

Thus they never looked at Acela as an alternative, with a service level not far from that of SCMAGLEV.  Nor do they even consider the question of why so many MARC riders (67.5% of MARC riders in 2045 if the Camden Yards option is chosen – see page D-56 of Appendix D-4 of the DEIS) are forecast to divert to the SCMAGLEV, but are not doing so now (nor in the future) to Acela.  According to Table D-45 of Appendix D.4 of the DEIS, in their data for their 2017 base year, there are 28 times as many MARC riders as on Acela between downtown Baltimore and downtown Washington, and 20 times as many with those going to and from the BWI stop included.  Evidently, they do not find the Acela option attractive.  Why should they then find the SCMAGLEV train attractive?

3)  The answer as to why MARC riders have not chosen to ride on the Acela almost certainly has something to do with the difference in the fares.  A round-trip on MARC costs $16 a day.  A round trip on Acela costs, according to the DEIS, an average of $104 a day.  That is not a small difference.  For someone commuting 5 days a week and 50 weeks a year (or 250 days a year), the annual cost on MARC would be $4,000 but $26,000 a year on the Acela.  And it would be an even higher $30,000 a year on the SCMAGLEV (based on an average fare of $120 for a round trip), and $40,000 a year ($160 a day) at peak hours (which would cover the times commuters would normally use).  Even for those moderately well off, $40,000 a year for commuting would be a significant expense, and not an attractive alternative to MARC with its cost of just one-tenth of this.

If such costs were properly taken into account in the forecasting model, why did it nonetheless predict that most MARC riders would switch to the SCMAGLEV?  This is not fully clear as the model details were not presented in the redacted report, but note that the modelers assigned high dollar amounts for the time value of money ($31.00 to $46.50 for commuters and other non-business travel, and $50.60 to $75.80 for business travel – see page 53 of the Ridership Report).  However, even at such high values, the numbers do not appear to be consistent.  Taking a SCMAGLEV (15 minute trip) rather than MARC (60 minutes) would save 45 minutes each way or 1 1/2 hours a day.  Only at the very high end value of time for business travelers (of $75.80 per hour, or $113.70 for 1 1/2 hours) would this value of time offset the fare difference of $104 (using the average SCMAGLEV fare of $120 minus the MARC fare of $16).  And even that would not suffice for travelers at peak hours (with its SCMAGLEV fare of $160).

But there is also a more basic problem.  It is wrong to assume that travelers on MARC treat their 60 minutes on the train as all wasted time.  They can read, do some work, check their emails, get some sleep, or plan their day.  The presumption that they would pay amounts similar to what some might on average earn in an hour based on their annual salaries is simply incorrect.  And as noted above, if it were correct, then one would see many more riders on the Acela than one does (and similarly riders on the Amtrak Regional trains, that require about 40 minutes for the Washington to Baltimore trip, with an average fare of $34 for a round trip).

There is a similar issue for those who drive.  Those who drive do not place a value on the time spent in their cars equal to what they would earn in an hourly equivalent of their regular salary.  They may well want to avoid traffic jams, which are stressful and frustrating for other reasons, but numerous studies have found that a simple value-of-time calculation based on annual salaries does not explain why so many commuters choose to drive.

4)  Data for the forecasting model also came in part from two personal surveys.  One was an in-person survey of travelers encountered on MARC, at either the MARC BWI Station or onboard Penn Line trains, or at BWI airport.  The other was an online internet survey, where they unfortunately redacted out how they chose possible respondents.

But such surveys are unreliable, with answers that depend critically on how the questions are phrased.  The Final Ridership report does not include the questionnaire itself (most such reports would), so one cannot know what bias there might have been in how the questions were worded.  As an example (and admittedly an exaggerated example, to make the point) were the MARC riders simply asked whether they would prefer a much faster, 15 minute, trip?  Or were they asked whether they would pay an extra $104 per day ($144 at peak hours) to ride a service that would save them 45 minutes each way on the train?

But even such willingness to pay questions are notoriously unreliable.  An appropriate follow-up question to a MARC rider saying they would be willing to pay up to an extra $144 a day to ride a SCMAGLEV, would be why are they evidently not now riding the Acela (at an extra $88 a day) for a ride just 15 minutes longer than what it would be on the SCMAGLEV.

One therefore has to be careful in interpreting and using the results from such a survey in forecasting how travelers would behave.  If current choices (e.g. using the MARC rather than the Acela) do not reflect the responses provided, one should be concerned.

5)  Finally, the particular mathematical form used to model the choices the future travelers would make can make a big difference to the findings.  The Final Ridership Report briefly explains (page 53) that it used a multinomial logit model as the basis for its modeling.  Logit functions assign a continuous probability (starting from 0 and rising to 100%) of some event occurring.  In this model, the event is that a traveler going from one travel zone to another will choose to travel via the SCMAGLEV, or not.  The likelihood of choosing to travel via the SCMAGLEV will be depicted as an S-shaped function, starting at zero and then smoothly rising (following the S-shape) until it reaches 100%, depending on, among other factors, what the travel time savings might be.

The results that such a model will predict will depend critically, of course, on the particular parameters chosen.  But the heavily redacted Final Ridership Report does not show what those parameters were nor how they were chosen or possibly estimated, nor even the complete set of variables used in that function.  The report says little (in what remains after the redactions) beyond that they used that functional form.

A feature of such logit models is that while the choices are discrete (one either will ride the SCMAGLEV or will not), it allows for “fuzziness” around the turning points, that recognize that between individuals, even if they confront a similar combination of variables (a combination of cost, travel time, and other measured attributes), some will simply prefer to drive while some will prefer to take the train.  That is how people are.  But then, while a higher share might prefer to take a train (or the SCMAGLEV) when travel times fall (by close to 45 minutes with the SCMAGLEV when compared to their single “train” option that is 90% MARC, and by variable amounts for those who drive depending on the travel zone pairs), how much higher that share will be will depend on the parameters they selected for their logit.

With certain parameters, the responses can be sensitive to even small reductions in travel times, and the predicted resulting shifts then large.  But are those parameters reasonable?  As noted previously, a test would have been whether the model, with the parameters chosen, would have predicted accurately the number of riders actually observed on the Acela trains in the base year.  But it does not appear such a test was done.  At least no such results were reported to test whether the model was validated or not.

Thus there are a number of possible reasons why the forecast ridership on the SCMAGLEV differs so much from what one currently observes for ridership on the Acela, and from what one might reasonably expect Acela ridership to be in the future.  It is not possible to say whether these are indeed the reasons why the SCMAGLEV forecasts are so incredibly out of line with what one observes for the Acela.  There may be, and indeed likely are, other reasons as well.  But due to issues such as those outlined here, one can understand the possible factors behind SCMAGLEV ridership forecasts that deviate so markedly from plausibility.

D.  Conclusion

The ridership forecasts for the SCMAGLEV are vastly over-estimated.  Predicted ridership on the SCMAGLEV is a minimum of two, and up to three, orders of magnitude higher than what has been observed on, and can reasonably be forecast for, the Acela.  One should not be getting predicted ridership that is more than 100 times what one observes on a comparable, existing (and thus knowable), service.

With ridership on the proposed system far less than what the project sponsors have forecast, the case for building the SCMAGLEV collapses.  Operational and maintenance costs would not be covered, much less any possibility of paying back a portion of the billions of dollars spent to build it, nor will the purported economic benefits follow.

However, the harm to the environment will have been done.  Even if the system is then shut down (due to the forecast ridership never materializing), it will not be possible to reverse much of that environmental damage.

The US very much needs to improve its public transit.  It is far too difficult, with resulting harm both to the economy and to the population, to move around in the Baltimore-Washington region.  But fixing this will require a focus on the basic nuts and bolts of operating, maintaining, and investing in the transit systems we have, including the trains and buses.  This might not look as attractive as a magnetically levitating train, but will be of benefit.  And it will be of benefit to the general public – in particular to those who rely on public transit – and not just to a narrow elite that can afford $120 fares.  Money for public transit is scarce.  It should not be wasted on shiny new toys.