The October value of the Toll Index reads at 100.7 continuing a multi-month positive trend. It is up 1.3% on Steptember 2024 and up 6.1% on October 2023.
The Border Crossing Toll Index is adjusted for number of weekdays, time, month and MAUT-policy-regime fixed effects. For the newly defined Toll Index see here. The smoothing used is what I call the eye-balling smoothing.
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 September value of the Border Crossing Toll Index (just out) adjusted for number of weekdays, time, month and MAUT-policy-regime fixed effects (Figure 1) came at 100.6 continuing an improving trend of several months as can easily be seen in Figure 2 (smoothed). For the newly defined Toll Index see here.
Additionally Figure 3 shows the IZA/Fable SWIPE consumption index (raw in blue and adjusted for inflation in red). While neither goods transportation nor consumption is particularly strong they are both on a clear positive trend indicating that the Fall assessment of the German government which expects a .2% contraction for the year might be overly and unnecessarily pessimistic.
We have just released a new consumption index for Germany just in time as macro-economic conditions are taking center stage again: global supply chain issues, geopolitical turmoil, economic slow-down in much of the world economy (e.g. China stimulus package, USA interest rate cuts, ECB rate cuts, or a mixed German business cycle and a government forecast of contraction in 2024 etc), climate phenomena, right wing populism or lesser but potentially impactful disruptions such as the advent of generative AI.
Final consumption amounts to over 70% of German GDP, whereas household final consumption (more likely to be in our data than, say, government final expenditures) amounts to over 50% of the German GDP. This demonstrates the importance of a consumption indicator for Germany and beyond. Consumption and the Labor Market intermediated by economic growth are interdependent like yin and yang.
Our indicator is informed by daily ingested data. The preliminary value for a given month will be released around the 20th of the month. Incoming data then updates the index daily until it is finalized 2-3 days after the end of the month.
All changes are reflected in the live graph of the index below.
The IZA/Fable Data Consumption Indicator for Germany is based on consumer and small business transactions data from FableData, a leading provider of anonymized, pan-European spending data. It is and IDSC product, introduced in a joint paper of mine with Fable coauthors A.B. Martinez and F.S. Cereda.
The embedded graph above is live in the sense that it is continuously updated in a programmatic manner and it is interactive in the sense that you can zoom in and out of it. Notice also that blue annotations mark important shocks and time regimes useful for understanding the data. In mid-2025 our indicator will cover France and the UK in addition.
If you want to embed it in your website feel free to visit https://fable.radar.iza.org and copy an embed code snippet (top right). We’d be happy to hear how you are using it.
I have been presenting the Toll Index in “raw form” putting off having to do the work to account for structural changes in the MAUT rules (e.g. including trucks of lower tonnage, adding more highways etc). In July of 2024 yet another change took place so I decided to do the econometric work to produce an index without the effect of the various MAUT reforms (see here).
So the newly defined Toll Index is based on border crossing lorries (inbound and outbound) divided by the number of working days in each month. Then we regress the result on:
time
reform dummies
reform dummies interacted with time
month-of-year fixed effects
quarter-of-year fixed effects
Taking the residuals after removing the predicted effects of 2, 3, 4 and 5 above and scaling so that the value is 100 in January of 2007 gives us the Toll Index.
In the figure below we jointly plot the Toll Index and the seasonal and calendar adjusted Index of Industrial Production.
The correlation is visible to the naked eye. In the graph we report this month’s value, the overall mean, the previous month and the same month a year ago. Comparing with the mean tells us how unexpected the new value is (the z-score shows the eccentricity of our current state), comparing with the previous month gives the monthly derivative and comparing with the same month last year gives us the annual derivative. In this chart we are mildly below the mean.
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.
Citation: “Nowcasting business cycles using toll data.” Journal of Forecasting 32:4 (2013): 299–306(with K. F. Zimmermann).
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 this introduced a discontinuity. The BAG even 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):
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.
Citation: “Nowcasting business cycles using toll data.” Journal of Forecasting 32:4 (2013): 299–306(with K. F. Zimmermann).
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 this introduced a discontinuity. The BAG even 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):
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.
Citation: “Nowcasting business cycles using toll data.” Journal of Forecasting 32:4 (2013): 299–306(with K. F. Zimmermann).
