a column by Neil Cummins for VOX: CEPR’s Policy Portal
Within countries, the driving force behind the 20th century’s dramatic drop in inequality were the declines in the wealth shares of the top 1%.
Based on 60 million death and probate records covering a period of 100 years, this column argues that in the case of Britain the distributional gains from the Great Equalisation were exclusively confined to the top 30% of the wealth distribution. This left the nation’s social and political fabric vulnerable to the protest vote of many in 2016 to leave the EU, following the austerity induced by the financial crisis.
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Showing posts with label wealth_distribution. Show all posts
Showing posts with label wealth_distribution. Show all posts
Wednesday, 27 February 2019
Wednesday, 14 March 2018
If you’re so smart, why aren’t you rich? Turns out it’s just chance
an article by Emerging Technology from the arXiv [MIT Technology Review via 3 Quarks Daily]
The most successful people are not the most talented, just the luckiest, a new computer model of wealth creation confirms. Taking that into account can maximize return on many kinds of investment.
The distribution of wealth follows a well-known pattern sometimes called an 80:20 rule: 80 percent of the wealth is owned by 20 percent of the people. Indeed, a report last year concluded that just eight men had a total wealth equivalent to that of the world’s poorest 3.8 billion people.
This seems to occur in all societies at all scales. It is a well-studied pattern called a power law that crops up in a wide range of social phenomena. But the distribution of wealth is among the most controversial because of the issues it raises about fairness and merit. Why should so few people have so much wealth?
The conventional answer is that we live in a meritocracy in which people are rewarded for their talent, intelligence, effort, and so on. Over time, many people think, this translates into the wealth distribution that we observe, although a healthy dose of luck can play a role.
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Hazel’s comment
You will need a modicum of maths skills to understand parts of the argument but that luck plays a large role in the acquisition of wealth is something we can all understand, and most of us resent because we are not the lucky ones.
The most successful people are not the most talented, just the luckiest, a new computer model of wealth creation confirms. Taking that into account can maximize return on many kinds of investment.
The distribution of wealth follows a well-known pattern sometimes called an 80:20 rule: 80 percent of the wealth is owned by 20 percent of the people. Indeed, a report last year concluded that just eight men had a total wealth equivalent to that of the world’s poorest 3.8 billion people.
This seems to occur in all societies at all scales. It is a well-studied pattern called a power law that crops up in a wide range of social phenomena. But the distribution of wealth is among the most controversial because of the issues it raises about fairness and merit. Why should so few people have so much wealth?
The conventional answer is that we live in a meritocracy in which people are rewarded for their talent, intelligence, effort, and so on. Over time, many people think, this translates into the wealth distribution that we observe, although a healthy dose of luck can play a role.
Continue reading
Hazel’s comment
You will need a modicum of maths skills to understand parts of the argument but that luck plays a large role in the acquisition of wealth is something we can all understand, and most of us resent because we are not the lucky ones.
Wednesday, 7 March 2018
Should the Office for Budget Responsibility also forecast inequality?
a post by Adam Corlett for the Resolution Foundation blog
The strengths and weaknesses of economic forecasting are under scrutiny, perhaps like never before. How might GDP perform under different Brexit policies compared to a world with no Brexit? Is unemployment now likely to rise or fall? What will public borrowing in 2022 be? Whatever your politics, such modelling and forecasting is indispensable – so long as its limitations are appreciated. But whose job is it to forecast inequality?
We have just released our own projections for household disposable incomes. These are not based on new modelling of economic fundamentals but, rather, combine the latest official forecasts from the Office for Budget Responsibility (OBR) with the default tax and benefit system that will exist unless policy changes. Crucially, this allows us to explore not just aggregate figures like average income but how different groups might fare and the outlook for income inequality. It is a projection not just for growth but for the distribution of growth.
For example, while in our projection average household incomes (after housing costs) rise by a fairly weak 3 per cent between 2016-17 and 2020-21 after accounting for inflation, this hides an increase of 4 per cent for the richest third of the population and growth of zero for the poorest third. As shown below, we therefore project an increase in inequality after 2016-17 – maybe even to record highs on some measures.
Continue reading
The strengths and weaknesses of economic forecasting are under scrutiny, perhaps like never before. How might GDP perform under different Brexit policies compared to a world with no Brexit? Is unemployment now likely to rise or fall? What will public borrowing in 2022 be? Whatever your politics, such modelling and forecasting is indispensable – so long as its limitations are appreciated. But whose job is it to forecast inequality?
We have just released our own projections for household disposable incomes. These are not based on new modelling of economic fundamentals but, rather, combine the latest official forecasts from the Office for Budget Responsibility (OBR) with the default tax and benefit system that will exist unless policy changes. Crucially, this allows us to explore not just aggregate figures like average income but how different groups might fare and the outlook for income inequality. It is a projection not just for growth but for the distribution of growth.
For example, while in our projection average household incomes (after housing costs) rise by a fairly weak 3 per cent between 2016-17 and 2020-21 after accounting for inflation, this hides an increase of 4 per cent for the richest third of the population and growth of zero for the poorest third. As shown below, we therefore project an increase in inequality after 2016-17 – maybe even to record highs on some measures.
Continue reading
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