Toll Index November 2019 – stalled

Starting in July 2018 the BAG – Bundesamt für Güterverkehr introduced yet another policy change which affected how lorries pay tolls within the MAUT system as well as the data that come out of this process which are used for computing the Toll Index. The change expanded the network of roads in which toll is due by adding all bundesstraßen to it.

While in the long run this is bound to make the Toll Index more accurate in these past twelve months it made it useless for nowcasting. Moreover the BAG had difficulty producing the numbers timely for about year. Since July 2019 each month is now comparable to the value of the same month in 2018. Of course we have a missing value for 2018 since it is not comparable to 2017 due to the policy change.

The Toll Index was first proposed in IZA DP5522 which was published in the Journal of Forecasting. It has been widely covered in national and international media (selection):

The German statistical office, in cooperation with the Bundesamt für Güterverkehr,  has taken the MAUT data in its portfolio of data products and their efforts can be found here. The Destatis document describing the data is here and here is their publication calendar for 2019.

UK elections 2019 – Odds, Polls, Google buzz

Based on Google Trends data  the Conservative party will beat the Labour party by about 7 percentage points in terms of the popular vote while the LibDems will trail the Conservatives by about 35 points.

The pollsters have the Conservatives leading Labour by anywhere between 6 and 15 percentage points in 14 polls in December with the average prediction at 9.5 pct points.

The bookies have the odds at 1/33 for a Conservative victory and 2/5 for an overall Conservative majority.

In Google search the footprints of Labour, Conservative and LibDems in the last seven days average to 42, 30 and 20 points respectively.

Labour always leads the Conservatives in Google buzz most likely due to demographics

 

We can still use the elections of 2015 and 2017 to take out party composition fixed effects from the Google data. When we do so we project that Labour will fall 5.4 to 8.5 percentage points behind the Conservatives in the popular vote while the LibDems will trail by 33.3 to 36.4 points.

Toll Index October 2019

Starting in July 2018 the BAG – Bundesamt für Güterverkeht introduced yet another policy change which affected how lorries pay tolls within the MAUT system as well as the data the come out of this process which is used for computing the Toll Index. The change expanded the network of roads in which toll is due by adding all bundesstraßen to it.

While in the long run this is bound to make the Toll Index more accurate in these past twelve months it made it useless for nowcasting. Even the BAG had difficulty producing the numbers timely. September 2019 is now comparable to September 2018 values. Of course we have a missing value for September 2018 since it is not comparable to “September 2017” due to the policy change.

The Toll Index was first proposed in IZA DP5522 which ws published in the Journal of Forecasting. It has been widely covered in national and international media (selection):

The German statistical office, in cooperation with the Bundesamt für Güterverkehr,  has taken the MAUT data in its portfolio of data products and their efforts can be found here. The Destatis document describing the data is here and here is their publication calendar for 2019.

Taxing times

Taxing times, by Michael Gold – The Economist Intelligence Unit

Contains a reference to my data tax idea, some quotes of mine and is an interesting read in its own right.

I have written about the Data Tax in:

#BREXIT: Vox Populi

Why the parliament expresses the “will of the people” better than any of its MPs including its PM and better than the brexit referendum

In 1907 Francis Galton published a paper in Nature titled Vox Populi. In it he discusses how the collective judgement of the crowd is better than that of the average individual (a fact which goes back to a simple mathematical identity, also known as diversity prediction theorem, which states that the squared error of the average of all predictions is smaller than or equal to the average squared error of all prediction because from the latter we need to subtract the diversity of the individual predictions to get the former). In what we would, today, call policy recommendations of his paper, Galton states:

The result is, I think, more creditable to the trustworthiness of a democratic judgement than might have been expected.

On the other hand, consider as Marquis de Condorset might have done in the 18th century, three voters Peter, Paul and Mary ordering three different alternatives, let’s say: Remain, Deal Brexit and No Deal Brexit. Here is how their preferences might be:

Peter: Remain, Deal Brexit, No Deal Brexit

Paul: Deal Brexit, No Deal Brexit, Remain

Mary: No Deal Brexit, Remain, Deal Brexit

All three are opinionated “patriots” who want “the best” for the UK. Peter wants to remain but if not feasible he wants an amicable divorce. Paul wants a divorce but if possible an amicable one and Mary wants no compromises: either leave “proud” or don’t try it at all.

It is irrelevant who is “right” or “wrong” but we can assume whatever contributes to the opinion of each individual does so consistently. In other words they might change their minds but each time you ask them they have an ordering which puts the three options in order. Their personality, gender, hormonal levels, socioeconomic status, childhood etc all might play a role. Now let’s examine which option has majority. Remain is preferred over Deal Brexit by a 2/3 majority (Peter and Mary), Deal Brexit is preferred over No Deal Brexit by a 2/3 majority (Peter and Paul), No Deal Brexit is preferred over Remain by a 2/3 majority (Paul and Mary). So the “democratic process” picks Remain over Deal, Deal over No Deal (hence Remain is better than No Deal) but it also prefers No Deal over Remain. So it prefers Remain over No Deal and No Deal over Remain!

So what gives? The mess the UK is in has some simple mathematics which if paid attention to could help bring emotions down and make some very simple thoughts which might lead to some very reasonable decisions. A referendum is too crude a tool to use to make important decisions especially with a turnout of 72% and a Leave to Remain outcome of 51.89% to 48.11%. Marquis de Condorcet will tell you that when the Leavers say that the referendum is “the will of the people” they are just bullshitting the public opinion. If you posed seemingly equivalent formualtions of the same question you could very well get contradictory outcomes such as leaving is better than remaining and remaining is better than leaving. On the other hand Galton will tell you that the parliament as a collective has a better chance at figuring things out than any MP (or citizen) including the PM.

Toll Index September 2019: inbound -1.8% | outbound -4.3%

For the first time after the Great Recession we are measuring a September-to-September drop in German border crossing lorries: -1.8% for inbound and -4.3% for outbound.

Starting in July 2018 the BAG – Bundesamt für Güterverkeht introduced yet another policy change which affected how lorries pay tolls within the MAUT system as well as the data the come out of this process which is used for computing the Toll Index. The change expanded the network of roads in which toll is due by adding all bundesstraßen to it.

While in the long run this is bound to make the Toll Index more accurate in these past twelve months it made it useless for nowcasting. Even the BAG had difficulty producing the numbers timely. September 2019 is now comparable to September 2018 values. Of course we have a missing value for September 2018 since it is not comparable to “September 2017” due to the policy change.

The Toll Index was first proposed in IZA DP5522 which ws published in the Journal of Forecasting. It has been widely covered in national and international media (selection):

The German statistical office, in cooperation with the Bundesamt für Güterverkehr,  has taken the MAUT data in its portfolio of data products and their efforts can be found here. The Destatis document describing the data is here and here is their publication calendar for 2019.

Toll Index August 2019

Starting in July 2018 the BAG – Bundesamt für Güterverkeht introduced yet another policy change which affected how lorries pay tolls within the MAUT system as well as the data the come out of this process which is used for computing the Toll Index. The change expanded the network of roads in which toll is due by adding all bundesstraßen to it.

While in the long run this is bound to make the Toll Index more accurate in these past twelve months it made it useless for nowcasting. Even the BAG had difficulty producing the numbers timely. August 2019 is now comparable to August 2018 values. Of course we have a missing value for August 2018 since it is not comparable to “August 2017” due to the policy change.

The Toll Index was first proposed in IZA DP5522 which ws published in the Journal of Forecasting. It has been widely covered in national and international media (selection):

The German statistical office, in cooperation with the Bundesamt für Güterverkehr,  has taken the MAUT data in its portfolio of data products and their efforts can be found here. The Destatis document describing the data is here and here is their publication calendar for 2019.