Brexit. A Eurovision Negotiating Contest. How are the contestants doing? To read the UK press you’d be forgiven for being ... completely misinformed. We’re lurching into a crisis on a scale not seen since at least 1956 & probably 1945. Here’s what’s really going on. A thread./1.
⁃They tell you precisely what 10 Downing Street currently wants those readers, which include other journalists, to hear. /3.
(a) The EU negotiates as a single entity on changes of membership, whether accession of new member states or departure of old member states, & on .../10.
Every outcome, other than staying in the customs union & single market, badly hammers the UK. By definition, an abject negotiating failure. /27.
More from Brexit
End of week 2 thread on post Brexit food trade
There is continued growing unease. The main picture remains one of depressed/tentative trade (c50% down y-o-y) and some high profile logistics business have taken the rational step to stop and regroup.
The big worry here is that ‘not-trading’becomes a habit. We can’t/won’t carry on at half the volumes of before, but as volumes claw back we may only reach something like 80% of previous volumes and that is a disaster for a food industry already battered by a recession.
Lots of focus has been on the idea of EU businesses stopping serving the UK. Worries about how we feed ourselves has trumped worry about our exporters at every stage. Even though it is the collapse of our export businesses that is (and has always been) the greater threat.
To reassure the mainland British shopper that feels like less of a risk. UK is a large market of wealthy consumers, and UK gov has shown it will do anything (however unfair) to ensure stuff gets in - even letting supermarkets have access to the fast track lane to Dover.
I am not as close to this but it feels like shortage on the shelves is more of a genuine immediate threat for the island of Ireland. The types of innovative solutions we have discussed this week can help but will they come in quick enough?
There is continued growing unease. The main picture remains one of depressed/tentative trade (c50% down y-o-y) and some high profile logistics business have taken the rational step to stop and regroup.
The big worry here is that ‘not-trading’becomes a habit. We can’t/won’t carry on at half the volumes of before, but as volumes claw back we may only reach something like 80% of previous volumes and that is a disaster for a food industry already battered by a recession.
Lots of focus has been on the idea of EU businesses stopping serving the UK. Worries about how we feed ourselves has trumped worry about our exporters at every stage. Even though it is the collapse of our export businesses that is (and has always been) the greater threat.
To reassure the mainland British shopper that feels like less of a risk. UK is a large market of wealthy consumers, and UK gov has shown it will do anything (however unfair) to ensure stuff gets in - even letting supermarkets have access to the fast track lane to Dover.
NEW. \U0001f6a8\U0001f6a8\U0001f69b\U0001f1ec\U0001f1e7\U0001f644\U0001f69b\U0001f69a\U0001f6a8\U0001f6a8 clear signs govt is preparing for coming #brexit turbulence - consulting on new fast-track scheme for food lorries returning to Europe /1
— Peter Foster (@pmdfoster) January 13, 2021
https://t.co/QqDKxBUu3f
I am not as close to this but it feels like shortage on the shelves is more of a genuine immediate threat for the island of Ireland. The types of innovative solutions we have discussed this week can help but will they come in quick enough?
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Module 1
Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...
... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit https://t.co/EZt0agsdlV
This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!
In Module 1 of this Nano course, we will learn about :
# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)
# Using Google Colab
Intro link is here on YT: https://t.co/MqMSDBaQri
Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb
You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want
# Importing Libraries
Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.
==========================
Module 1
Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...
... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit https://t.co/EZt0agsdlV
This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!
In Module 1 of this Nano course, we will learn about :
# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)
# Using Google Colab
Intro link is here on YT: https://t.co/MqMSDBaQri
Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb
You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want
# Importing Libraries
Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.

1/OK, data mystery time.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.
4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.

4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.