Lockdown Strategies, Mobility Patterns and COVID-19

We develop a multiple-events model and exploit within and between country variation in the timing, type and level of intensity of various public policies to study their dynamic effects on the daily incidence of COVID-19 and on population mobility patterns across 135 countries. We remove concurrent policy bias by taking into account the contemporaneous presence of multiple interventions. The main result of the paper is that cancelling public events and imposing restrictions on private gatherings followed by school closures have quantitatively the most pronounced effects on reducing the daily incidence of COVID-19. They are followed by workplace as well as stay-at-home requirements, whose statistical significance and levels of effect are not as pronounced. Instead, we find no effects for international travel controls, public transport closures and restrictions on movements across cities and regions. We establish that these findings are mediated by their effect on population mobility patterns in a manner consistent with time-use and epidemiological factors.

Keywords: COVID-19, public policies, non-pharmaceutical interventions, mul- tiple events, mobility

JEL codes: I12, I18, G14

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Toll Index April 2020 | 24% drop!

corrected: 20200518-15:50hrs

A whopping 24% drop in incoming lorries (-22% for outbound) compared to April of 2019 (controlling for number of working days). Larger drop than in the great recession.

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):

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 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):

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.