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Category: Data
Toll Index March 2020 – sizing COVID-19
How big is the impact of the COVID-19 pandemic on the economy? Here is another measurement to begin to fathom that extend of the damage.
The Toll Index for the month of March, based on fresh data from the German Bundesamt für Güterverkehr, shows a whopping 7.8% drop in inbound border crossing lorries and 10% drop for outbound ones compared to March of 2019 and controlling for number of working days.
The first and biggest drop since 2009 and the story is still developing.

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. After July 2019 we can report year on year changes for each month (with a missing value in 2018 for all months from July to December and a missing value in 2019 for all months from January to June.
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):
- Focus Magazin,
- Tim Harford – The undercover economist,
- Financial Times,
- MoneyWeek,
- WirtschaftsWoche,
- CNN International,
- DRS3 Swiss public radio,
- Deutsche Welle.
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.
An internet picture of labor under COVID-19
COVID-19 pandemic

Unemployment

Videconferencing




Working in teams online, collaborating



Traffic congestions moves from the road to the internet


Coronavirus, telecommuting and the labor market
Before the coronavirus pandemic nobody wrote the words “social” and “distancing” in the same Google search query. Now there is a Google topic called “social distancing” and starting in March 9 as much as about 20% of all search queries which contained the word “social” also contained the word “distancing” in the US. Similarly with the words “rules” and “lockdown”.
In fact chances are that as you are reading this your are in some form of lock-down (make sure you know your regional rules) and most certainly your are practicing some form of telecommuting. I studied the regularity by which Germans log Google search queries containing the word “stau” (traffic jam) in a paper published at PLOS ONE. They type it together with a source of traffic jam information (e.g. radio or tv station or a website), or together with a highway number revealing their itinerary in some way. Here is what this looked like the last seven days on an hourly basis now (blue) and the same time interval three weeks ago:

On the average this is about a threefold reduction in such searches (more on the peaks) which correlates well with the fact that driving on German highways has been much more pleasant of late.
Similarly there is a 57% reduction in flights world wide! If we could find a way to fly less without affecting productivity we might even save the planet.

On the other hand Google search interest in telecommuting has surged world wide as you can see in the comparative graph below, which shows ninety days of searches on the topic of “telecommuting” now (in blue) and a year ago. There is as much as an eight-fold surge as one can see by just eyeballing the graph.

As a result “traffic jams” moved from the road to the internet, a fact which has led Netflix and Youtube to lower video quality in Europe in order to not overload infrastructure. I think in the weeks ahead as more and more people join the ranks of home-office (not every socioeconomic entity was able to respond as quickly to the coronavirus shock) we might see the investment gap exposed, especially in Germany.
First signs show the coronavirus pandemic already impacting the labor market worldwide. In the graph below we see 90 days of searches on the topic of “unemployment benefits” world wide now (in blue) and a year ago.

In the US from March 14 to March 21 there has been a surge of 1064% in Initial Unemployment claims (that’s one thousand and sixty four percent)!

Those who want to make the argument that less driving and less flying will benefit the planet as we are doing home office ought to factor in that video-conferencing and all digital tech is based on large farms of servers running 24/7. It would be interesting to estimate the numbers so here is an interesting research question: what is the net environmental benefit (say in terms of CO2 emissions), per unit of welfare produced, from reducing traveling while increasing compute-center electricity consumption to offset that reduction?
Toll Index February 2020

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. After July 2019 we can report year on year changes for each month (with a missing value in 2018 for all months from July to December and a missing value in 2019 for all months from January to June.
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):
- Focus Magazin,
- Tim Harford – The undercover economist,
- Financial Times,
- MoneyWeek,
- WirtschaftsWoche,
- CNN International,
- DRS3 Swiss public radio,
- Deutsche Welle.
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.
Python & Stata Workshop – German Stata Conference – Frankfurt | 4-5 June 2020
In the case of natural languages you swear in your mother tongue, write papers in English and when in Rome it helps to speak a little bit of Italian. Being a polyglot promotes communication, understanding and expression but it also sometimes increase the probability of confusion. One thing is for certain: in a globalized world for most of us our mother tongue will not suffice.
In the case of programming languages it is very much the same. The workshop is meant for those whose mother tongue is Stata but want to explore the added value of learning python or the reverse.
Besides an introduction to Python the workshop will demonstrate how to use the Stata SFI api to embed python code in a stata program and pass data between stata and python. Examples of when such an embedding is advantageous will be discussed and demonstrated. These include: text mining (python regular expressions), web scraping (programming a web browser in python), using web APIs to get data (e.g. Google Trends, Yahoo finance etc), speeding up with python multiprocessing (e.g. parallelize a for loop), unsupervised learning (e.g. python implementation of Luvain clustering algorithm) etc.
If you want to join here is the conference web page with a registration link and if you do register and have any extra wishes tweet them to me and I will do my best to include them.
The course will be a series of live demonstrations using Jupyter notebooks and the course material will be shared with all participants. For active participation you will need a Laptop (hopefully we will have local wifi) with Stata16 and Anaconda3 (with Python 3.7 or so). If you want to run the Stata Jupyter notebooks you need to have installed the Stata Kernel for Jupyter (alternative you copy paste the code from Jupyter notebooks to Stata16.
PS: Two modules written for the course use the Stata16 sfi to import (some of the) functionality of python modules to Stata. If you have Stata 16 try:
- Stata command to get stock prices from Yahoo finance
. ssc install stockquote, replace and then run it as follows:
. stockquote AAPL, start_date(2020-01-01) end_date(2020-01-30)
to get 30 days worth of Apple stock price information. The module wraps itself around Python’s yfinance module and uses the following stata/python classes: sfi.Macro and sfi.Data, sfi. Datetime.
- Stata command to find communities in weighted networks:
. ssc install louvain
. man louvain
On the help page follow the example by clicking on the commands. You will cluster a weighted graph of all numbers from 1 to 10 where two numbers are connected iff they are not coprime. When they are connected the weight is their gcd minus one. It wraps around the python modules python-louvain and uses stata frames and the stata sfi classes: sfi.Data, sfi.Macro and sfi.Frame.
Toll Index January 2020
Annual January to January changes of inbound or outbound lorries (after accounting for working day differences) are rarely non-positive. The drop of 2.1% for inbound and 1.8% for outbound traffic in the first month of 2020 should therefore be seen as a rare and hence significant fact.

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. After July 2019 we can report year on year changes for each month (with a missing value in 2018 for all months from July to December and a missing value in 2019 for all months from January to June.
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):
- Focus Magazin,
- Tim Harford – The undercover economist,
- Financial Times,
- MoneyWeek,
- WirtschaftsWoche,
- CNN International,
- DRS3 Swiss public radio,
- Deutsche Welle.
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.